Method of elevator energy consumption optimization control system

Abstract

The invention discloses a method of an elevator energy consumption optimization control system, which relates to the technical field of elevator energy consumption optimization, and comprises the following steps: in the elevator running process, acquiring elevator running information of a floor planned to stop in the running process in real time, and preprocessing the acquired elevator running data to obtain a pre-processed elevator running data; and a basis is provided for analyzing the use mode and the operation efficiency of the elevator. According to the invention, through intelligent division of near-distance stopping, middle-distance stopping and long-distance stopping, the elevator system realizes fine speed and acceleration adjustment aiming at different running distances, energy consumption and transportation efficiency are effectively balanced, and the power peak demand and electric power waste are reduced. The instantaneous current impact is reduced by short-distance stopping, and the mechanical wear is relieved; and the acceleration is optimized by long-distance parking, so that excessive consumption under high load is prevented, and the temperature rise of the equipment is avoided. On the whole, intelligent control reduces equipment aging and heat dissipation requirements, the service life of the elevator is remarkably prolonged, and the long-term maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator energy consumption optimization, and in particular to a method for controlling an elevator energy consumption optimization system. Background Art

[0002] Energy consumption optimization of the elevator energy consumption optimization control system refers to the real-time monitoring of the elevator's operating status (such as load capacity, floor stop frequency, acceleration and deceleration modes, etc.) through intelligent control algorithms and sensing technology, analyzing the energy consumption characteristics of the elevator under different operating modes, and automatically adjusting the operating strategy, such as optimizing the acceleration and deceleration curves during start and stop, reasonably arranging the elevator's route and number of stops, or reducing no-load operation during non-peak hours, etc., thereby effectively reducing the elevator's energy consumption in different usage scenarios, improving overall energy efficiency, and reducing unnecessary energy waste.

[0003] The prior art has the following deficiencies: In the prior art, elevators usually ascend or descend at a fixed speed, but this method significantly increases energy consumption when running between different floor distances, especially when frequently stopping at close-distance floors or running long distances. For frequent stops at close-distance floors, the elevator needs to start and stop frequently, and the acceleration and deceleration at a fixed speed generates frequent power peak demands, increasing instantaneous current, resulting in power waste and increased equipment wear. When stopping at distant floors, although frequent starts and stops are reduced, fixed-speed operation prolongs the high-energy-consuming operation time, especially under heavy load conditions, resulting in significant energy loss and accelerated equipment aging. At the same time, the system temperature rises, increasing the energy consumption of the heat dissipation equipment.

[0004] 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 constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the control system of elevator energy consumption. By intelligently dividing short, medium and long-distance stops, the elevator system realizes refined speed and acceleration adjustment for different running distances, effectively balancing energy consumption and transportation efficiency, and reducing power peak demand and electricity waste. Short-distance stops reduce instantaneous current shocks and alleviate mechanical wear; long-distance stops optimize acceleration, prevent excessive consumption under high loads, and avoid equipment temperature increases. Overall, intelligent control reduces equipment aging and heat dissipation requirements, significantly extends the life of the elevator, and reduces long-term maintenance costs to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for optimizing the control system of energy consumption of an elevator, comprising the following steps: During the operation of the elevator, the elevator operation information of the planned stop floors is obtained in real time, and the obtained elevator operation data is pre-processed to provide a basis for analyzing the use mode and operation efficiency of the elevator; Extract features from pre-processed elevator operation data, and use the trained machine learning model to intelligently evaluate changes in elevator stop floors based on the extracted features, identifying potential patterns and trends in elevator operation; According to the evaluation results of the machine learning model, the elevator's stop floors are divided into short-distance stops, medium-distance stops, and long-distance stops; For mid-distance stops, the elevator speed is reset based on the elevator load data; For short-distance and long-distance stops, the acceleration and deceleration are adjusted intelligently according to the load data and the elevator running speed reset for medium-distance stops.

[0007] Preferably, feature extraction is performed on the preprocessed elevator operation data, and the extracted features include the distribution density between elevator call requests on different floors and the offset distance of the elevator from the current floor to the farthest target floor. After acquisition, the distribution density between elevator call requests on different floors and the offset distance of the elevator from the current floor to the farthest target floor are analyzed under the detection window, and an elevator call distribution sparse index and a high-level offset index are generated respectively. The elevator call distribution sparse index and the high-level offset index are input into a pre-learned machine learning model, and an operation span coefficient is generated by the machine learning model. The preprocessed elevator operation route is intelligently evaluated by the operation span coefficient.

[0008] Preferably, within the detection window, the distribution density of elevator call requests on different floors is analyzed to generate an elevator call distribution sparse index, and the specific steps are as follows: In the detection window, all floors with elevator call requests are collected and sorted in chronological order to form an elevator call request floor sequence. ,in, , where It is The number of floors requested by the elevator call, is the total number of elevator call requests; Floor sequence for elevator call request For each pair of elevator request floors, the distance between floors is calculated and stored in the distance weight matrix between floors. The calculation expression is as follows: , where is the distance weight between floors, representing the distance weight matrix between floors The element of and The distance weight between elevator calls is It is The number of floors requested by the elevator call, is the distance weight factor; Based on the weight of the distance between floors , construct the discrete increment matrix To reflect the sparse distribution of elevator call requests, the calculation expression is as follows: , where It is and The discrete increment between elevator calls is is the elevator call distance impact index, is the time gap impact index, is a decimal number that prevents division by zero; The discrete increment matrix All discrete increment values ​​in The cumulative sum is used to generate the call distribution sparse index. The calculation expression is as follows: , where is the call distribution sparsity index.

[0009] Preferably, under the detection window, the offset distance of the elevator from the current floor to the farthest target floor is analyzed to generate a high-rise offset index, and the specific steps are as follows: In the detection window, first, set the starting floor of the elevator and mark the starting floor of the elevator as , all the target floors of the elevator in this operation are grouped into a set, the set is ,in, It is the first target floor in this elevator operation. is the total number of target floors in this elevator operation, generating the offset distance. The calculation expression is as follows: , where It indicates the farthest target floor in this run. It indicates the offset distance; In order to improve the recognition of the offset distance in long-distance operation, a polynomial nonlinear transformation is introduced to convert the offset distance Convert to a higher-order offset and define the offset distance after nonlinear conversion. The calculation expression is as follows: , where is the offset distance after nonlinear transformation, is the nonlinear adjustment coefficient, is the coefficient of the quadratic offset term, used to control The impact of the item, Is the coefficient of the 1.5-power offset term, used to adjust The influence of the term further increases the nonlinear weight of long distances; In actual operation, the load and time factors will affect the energy consumption of the elevator. The load and time factors are introduced as correction factors to calculate the corrected offset distance. The calculation expression is as follows: , where is the corrected offset distance, It is load-bearing. is the time factor, is the load influence coefficient, is the smoothing factor, is the time influence coefficient is a decimal number that prevents division by zero; Further enhance the recognition of long distance runs, corrected offset distance Applying exponential amplification, the high-level offset index generated is calculated as follows: , where is the high-level deviation index, is the exponential magnification factor, is the smoothing factor of the denominator, is the adjustment factor for the offset distance.

[0010] Preferably, the running span coefficient generated after analyzing the elevator running route under the detection window is compared and analyzed with the preset running span coefficient reference range, and the obtained elevator running data is divided. The specific division steps are as follows: If the operating span coefficient is within the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the operating span coefficient is less than the minimum value of the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the running span coefficient is greater than the maximum value of the running span coefficient reference range, the elevator running route is divided into long-distance stops.

[0011] Preferably, for short-distance and long-distance stops, the specific steps of intelligently adjusting the acceleration and deceleration according to the load data and the elevator running speed reset for the medium-distance stop are as follows: First, according to the real-time load of the elevator and mid-distance stops reset target running speed , to determine the reasonable target speed of the elevator at different stopping distances, the expression is as follows: , where is the target speed of the elevator. is the weight coefficient, is the maximum load capacity of the elevator; Target speed based on elevator operation As well as the short-distance and long-distance docking requirements, calculate the acceleration and deceleration. The calculation expression is as follows:

[0012] , where is the acceleration, is the default acceleration, is the current stop distance, is the maximum stopping distance of the elevator design. is the deceleration, is the default deceleration; In order to improve the effect of intelligent control, according to the sparse index of elevator call distribution and high-level deviation index , adjust the elevator status update frequency, adjust the node update interval, and the calculation expression is as follows: , where is the state update frequency, is the default update frequency of the elevator system, is the call distribution sparseness index Adjustment factor, is the high-level deviation index Adjustment factor; According to the current acceleration of the elevator and deceleration The actual acceleration and deceleration should be adjusted according to the target speed of the elevator. Make timely fine-tuning with the stroke characteristics, the expression is as follows: , , where is the actual acceleration, is the current elevator running speed, is the actual deceleration, It is the feedback adjustment coefficient used to adjust the elevator acceleration. is the feedback adjustment coefficient of deceleration; Finally, execute the acceleration and deceleration control obtained in the above steps, and monitor the elevator operation status in real time to ensure that the control effect reaches the expected result.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention realizes refined speed adjustment for different running distances by intelligently dividing the elevator's short-distance, medium-distance and long-distance stops. For medium-distance stops, the system dynamically adjusts the elevator's running speed according to the load data to ensure a balance between energy consumption and transportation efficiency. Too fast a speed will bring unnecessary energy consumption, while too slow a speed will affect efficiency. The reset speed can reduce power demand while ensuring passenger comfort, thereby optimizing energy efficiency. For short-distance stops, the adjustment of elevator acceleration and deceleration reduces the instantaneous current impact during frequent stops, reduces power peak demand, and reduces electricity waste. At the same time, long-distance stops avoid excessive consumption of equipment by adjusting appropriate acceleration and deceleration. Under the intelligent analysis and control of the system, all types of stop distances can achieve the best balance between speed and energy, reducing the overall energy consumption of the elevator.

[0014] The present invention effectively alleviates the problems of frequent starting and stopping and long-term high-load operation of elevators at different stopping distances through intelligent speed and acceleration control, and significantly reduces equipment wear. For close-distance stops, the system reduces the load on the motor and transmission device by reducing instantaneous high-current shocks, avoiding mechanical wear caused by frequent acceleration and emergency stops. For long-distance stops, intelligent control avoids excessive operation at high speed for a long time, especially under heavy load conditions, to prevent the equipment from overheating and increasing wear. The system also reduces unnecessary energy loss by optimizing the heat dissipation equipment. These optimization strategies not only reduce the speed of equipment aging, but also reduce the additional load requirements of the heat dissipation equipment, extend the operating life of the elevator, and reduce long-term maintenance and replacement costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced 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.

[0016] Figure 1 The present invention is a method flow chart of an elevator energy consumption optimization control system. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of 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 the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0018] The present invention provides Figure 1A method for optimizing an elevator energy consumption control system is shown, comprising the following steps: During the operation of the elevator, the elevator operation information of the planned stop floors is obtained in real time, and the obtained elevator operation data is pre-processed to provide a basis for analyzing the use mode and operation efficiency of the elevator; When the elevator starts running, it obtains the elevator operation information of the planned stop floors in real time, including the elevator call requests pressed by passengers on each floor and the target floors selected in the elevator. By obtaining this information, the system can fully understand the route of the elevator's operation and provide the necessary data basis for subsequent analysis and optimization.

[0019] Extract features from pre-processed elevator operation data, and use the trained machine learning model to intelligently evaluate changes in elevator stop floors based on the extracted features, identifying potential patterns and trends in elevator operation; Extract features from the pre-processed elevator operation data. The extracted features include the distribution density of elevator call requests on different floors and the offset distance of the elevator from the current floor to the farthest target floor. After acquisition, analyze the distribution density of elevator call requests on different floors and the offset distance of the elevator from the current floor to the farthest target floor under the detection window, and generate an elevator call distribution sparse index and a high-level offset index respectively. Input the elevator call distribution sparse index and the high-level offset index into a pre-learned machine learning model, generate an operation span coefficient through the machine learning model, and perform intelligent evaluation on the pre-processed elevator operation route through the operation span coefficient; The sparse distribution density of elevator calls between different floors usually indicates that the elevator is in a long-distance and large-span operation state. Sparse elevator call request distribution means that the floor distance between each elevator call request is large, and the elevator needs to cross multiple intermediate floors without frequent stops. This distribution often occurs in high-rise buildings. For example, during peak hours in office buildings, the elevator may need to directly cross multiple floors from the lower floors to stop at a designated floor on the higher floors, or during non-peak hours, the passengers' destination floors are more dispersed, resulting in the elevator starting from the first floor and going to a farther target floor. Densely distributed elevator call requests usually occur in areas where passengers get on and off in large numbers. In this case, the elevator stops at adjacent or close floors many times.

[0020] Under the detection window, the distribution density of elevator call requests on different floors is analyzed to generate an elevator call distribution sparse index. The specific steps are as follows: In the detection window, all floors with elevator call requests are collected and sorted in chronological order to form an elevator call request floor sequence. ,in, , where It is The number of floors requested by the elevator call, is the total number of elevator call requests; Floor sequence for elevator call request For each pair of elevator request floors, the distance between floors is calculated and stored in the distance weight matrix between floors. The calculation expression is as follows: , where is the distance weight between floors, representing the distance weight matrix between floors The element of and The distance weight between elevator calls is It is The number of floors requested by the elevator call, is the distance weight factor; The inter-floor distance weight refers to the distance between each pair of elevator request floors. It is weighted by power transformation (i.e., raised to a certain index) so that requests with longer inter-floor distances have a greater impact on the sparse index. In the formula, this weight is expressed by Indicates that is the absolute distance between floors, raised to The purpose of this design is to emphasize elevator calls with larger spacing, because large-span requests usually indicate that the elevator may be in a long-distance operation state. Giving them a higher weight can more accurately reflect the characteristics of the elevator's large-span operation.

[0021] Distance weight factor is a hyperparameter that adjusts the influence of the inter-floor distance in calculating the sparsity index. A value (usually greater than 1) can amplify the weight of long-distance requests, making them contribute more significantly to the sparse index. The value of will aggravate the impact of long-distance elevator calls on the sparse index, making the long-distance operation mode more prominent in the sparse index. In this way, the distance weight factor plays a key regulatory role in the index calculation and controls the recognition sensitivity of the long-distance operation state of the elevator.

[0022] It means that for the range from 1 to All possible pairs of integers ,satisfy In these cases, The first elevator request and the All combinations of elevator call requests must be calculated or considered.

[0023] Based on the weight of the distance between floors , construct the discrete increment matrix To reflect the sparse distribution of elevator call requests, the calculation expression is as follows: , where It is and The discrete increment between elevator calls is is the elevator request distance impact index, which adjusts the contribution of long distance to sparsity. is the time gap impact index, increasing the time span has an impact on the sparse index, It is a decimal number that prevents division by zero, and is usually close to 0; Elevator call request distance impact index Indicates the degree of influence of the distance between floors on the sparseness when calculating the sparse index of elevator call distribution. In the formula, it is calculated by weighting the distance between floors. Promote to power to amplify or weaken the effect of different floor spacing on the sparse index. The value of will significantly increase the contribution of farther floor spacing to the sparse index, thereby more prominently identifying the long-distance running characteristics; while the lower A value of will weaken the impact of the distance between floors, making the sparsity index pay more attention to short-distance elevator calls. The role of the elevator call request distance impact index here is to highlight the weight of the floor distance on sparsity, making long-distance elevator calls more influential in the sparsity calculation, thereby more accurately reflecting the operating span of the elevator.

[0024] Time gap impact index Indicates the degree of influence of the time interval between different elevator requests on the sparseness when calculating the elevator call distribution sparse index. A power transformation is performed to adjust the effect of the time interval. The value will weaken the role of time interval in the sparse index and highlight the spatial sparsity; while the lower The value will amplify the effect of time gap, making the distribution sparse index pay more attention to the concentration or dispersion in time. In this formula, the role of the time gap impact index is to adjust the timing density of elevator calls, help identify those large-span operation requirements caused by elevator calls occurring relatively close, and thus optimize elevator scheduling.

[0025] The discrete increment matrix All discrete increment values ​​in The cumulative sum is used to generate the call distribution sparse index. The calculation expression is as follows: , where is the call distribution sparseness index; Under the detection window, the larger the sparse distribution index performance value generated by analyzing the distribution density of elevator call requests between different floors, it usually indicates that the elevator is running in a long-distance and large-span state. This index determines the span of the elevator operation by analyzing the distribution density of elevator call requests between different floors under the monitoring window. When the distribution of elevator call requests is sparse, that is, the index performance value is large, it means that the floor distance between each elevator call request is far, and the elevator needs to cross multiple floors in this operation without frequent stops, which is typical of long-distance operation characteristics. When the sparse distribution index of the elevator is small, that is, the distribution density is high, it means that the elevator mainly stops between adjacent or close floors, which is usually not a large-span operation.

[0026] The rapid increase in the offset distance from the current floor to the farthest target floor usually indicates that the elevator is in a long-distance and large-span operation state. The offset distance reflects the extent of the floor span of the elevator during this operation. The rapidly increasing offset distance means that the elevator needs to cross a large number of intermediate floors on the way from the starting floor to the target floor. This situation usually occurs when the floors are unevenly distributed or the passenger demand is mainly concentrated on floors that are far apart, such as running directly from the low floor to the high floor area. Long-distance operation not only requires the elevator to continue to operate at high speed to optimize time efficiency, but also brings high energy consumption due to large-span stops. The rapid increase in the offset distance forces the elevator to undergo a series of actions such as acceleration, stable high speed, and deceleration in a short period of time, which increases the load and power consumption of the motor. Therefore, the rapid increase in the offset distance indicates a special mode of elevator operation. At this time, the elevator is in a long-distance and large-span operation state, and the speed and energy consumption need to be optimized to improve operation efficiency and reduce overall power consumption.

[0027] In the detection window, the offset distance of the elevator from the current floor to the farthest target floor is analyzed to generate a high-rise offset index. The specific steps are as follows: In the detection window, first, set the starting floor of the elevator, that is, the floor where the elevator is currently located, and mark the starting floor of the elevator as , all the target floors of the elevator in this operation are grouped into a set, the set is ,in, It is the first target floor in this elevator operation. is the total number of target floors in this elevator operation, generating the offset distance. The calculation expression is as follows: , where It indicates the farthest target floor in this run. It indicates the offset distance and is the starting floor The farthest target floor The absolute value of the floor difference between them; It is the last floor in the target floor set, that is, the last target floor selected by the passenger in the elevator.

[0028] The target floor set in the elevator is determined by the target floors selected by passengers after boarding the elevator on different floors, and is not arranged in the order in which the elevator runs. For example, if a passenger gets on the elevator on the 1st floor and selects Another passenger was Upstairs elevator selected The target floor set may be recorded as , rather than in the order in which the elevators run arrangement.

[0029] Indicates the target floor set The maximum value among is the target floor farthest from the starting floor in this run. It is the floor with the largest value selected from all target floors.

[0030] In order to improve the recognition of the offset distance in long-distance operation, a polynomial nonlinear transformation is introduced to convert the offset distance Convert to a higher-order offset and define the offset distance after nonlinear conversion. The calculation expression is as follows: , where is the offset distance after nonlinear transformation, Is a nonlinear adjustment coefficient used to control the offset distance The exponential amplification degree of is the coefficient of the quadratic offset term, used to control The influence of the term is mainly used to strengthen the square effect of the offset distance. Is the coefficient of the 1.5-power offset term, used to adjust The influence of the term further increases the nonlinear weight of long distances; Nonlinear adjustment coefficient Is used to adjust the offset distance The exponential amplification effect of The item makes the offset distance present a nonlinear amplification effect when the offset distance is different. A value of 0 will significantly increase the contribution of the offset distance to the result, thus increasing its importance when running over long distances, and having less effect when running over short distances. The role of is to amplify the influence of the offset distance nonlinearly, so that when the elevator runs over a long distance, this item can more obviously reflect the effect of the long distance on the system.

[0031] The coefficient of the quadratic offset term control The weight of the term, which is a square term, is mainly used to further enhance long-distance recognition. Since the square term can amplify the impact of the distance value more than the first power or 1.5 power, the contribution of this term to the result will increase significantly in long-distance runs. The role of is to adjust the influence of this square term, so that when the elevator runs long distances across multiple floors, the effect of the offset distance can be amplified, further improving the recognition of long-distance operation.

[0032] The coefficient of the 1.5-power offset term adjust The weight of the term, which is between the first and second powers, provides an intermediate degree of nonlinear amplification. Compared to the sharp increase of the square term, the 1.5 power increase is more gradual, allowing the offset distance to have a moderate impact on the results when running at medium or long distances. Therefore, The role of is to provide a relatively smooth increment to ensure that the system's perception of long-distance operation is not too extreme, but to increase the contribution of the offset distance to the result in a moderate way, thereby ensuring the stability and sensitivity of long-distance recognition.

[0033] In actual operation, the load (total weight or number of passengers) and time factor (estimated running time) will affect the energy consumption of the elevator. The load and time factors are introduced as correction factors to calculate the corrected offset distance. The calculation expression is as follows: , where is the corrected offset distance, It is load-bearing. is the time factor (estimated running time), is the load influence coefficient, which is used to adjust the influence of load on the offset distance. It is a smoothing factor, which acts on the denominator of the load correction term to avoid fluctuations in the result caused by a too small denominator when the load is small. is the time impact coefficient, adjusting the time factor (estimated running time) The impact of the offset distance, It is a decimal number that prevents division by zero, and is usually close to 0; Load influence factor It is a parameter used to adjust the effect of elevator load on offset distance correction. During elevator operation, load directly affects the power demand and energy consumption of the motor, especially when running over long distances and large spans, heavy load will significantly increase the load on the system. By introducing the load influence coefficient , you can control the weight of the load on the offset distance correction. When the value is large, the load will be offset to the corrected distance. As the contribution of load increases, the system will pay more attention to the role of load in long-distance operation, thus reflecting the higher energy consumption demand under heavy load conditions.

[0034] Time influence coefficient It is a parameter that adjusts the effect of the expected elevator running time on the offset distance correction. When the elevator is running for a long time, it needs to maintain the motor power, which will gradually increase the energy consumption, especially when running over long distances. Ensure that expected run time has appropriate weighting on offset distance correction, so that when run time is longer, the system increases the corrected offset distance value, emphasizing the contribution of long-term operation to elevator energy consumption. The existence of helps the system more accurately reflect the actual impact of time factors on elevator energy efficiency in scenarios of long-distance and long-time operation.

[0035] Further enhance the recognition of long distance runs, corrected offset distance Applying exponential amplification, the high-level offset index generated is calculated as follows: , where is the high-level deviation index, is an exponential amplification factor used to enhance the impact of the severity of the offset. It is the smoothing factor of the denominator to avoid the exponent being too large due to a too small denominator. It is the adjustment factor of the offset distance, which is used to control the amplification effect of long distances; In the detection window, the larger the high-rise offset index performance value generated after analyzing the offset distance of the elevator from the current floor to the farthest target floor, the longer the elevator is in a long-distance and long-span operation state. The high-rise offset index evaluates the span of this operation by calculating the offset distance of the elevator from the current floor to the farthest target floor in the monitoring window. When the index value is large, it means that the elevator needs to cross a larger floor interval and run directly from the lower floor to the upper floor (or vice versa), which is a typical long-distance operation state, usually accompanied by a long period of continuous high-speed operation, higher energy consumption and equipment load. When the high-rise offset index value is small, it means that the distance between the floors where the elevator stops is short, which belongs to the medium-short distance operation scenario.

[0036] The machine learning model is not limited here, and can achieve the sparse index of elevator call distribution and high-level deviation index Generate the operating span coefficient after comprehensive analysis In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Running span factor The generation formula is as follows: , where , are the call distribution sparseness index and high-level deviation index The preset scaling factor of , Both are greater than 0.

[0037] It can be seen from the calculation expression of the operating span coefficient that, under the detection window, the larger the elevator call distribution sparse index performance value generated after analyzing the distribution density between elevator call requests on different floors, the larger the high-rise offset index performance value generated after analyzing the offset distance of the elevator from the current floor to the farthest target floor. The larger the operating span coefficient performance value generated after analyzing the elevator operation route under the detection window, the larger the elevator is in a long-distance and large-span operation state. Otherwise, it means that the distance between the floors where the elevator stops is short, which belongs to a medium-short distance operation scenario.

[0038] According to the evaluation results of the machine learning model, the elevator's stop floors are divided into short-distance stops, medium-distance stops, and long-distance stops; The operation span coefficient generated after analyzing the elevator operation route under the detection window is compared and analyzed with the preset reference range of the operation span coefficient, and the obtained elevator operation data is divided. The specific division steps are as follows: If the operating span coefficient is within the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the operating span coefficient is less than the minimum value of the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the operating span coefficient is greater than the maximum value of the operating span coefficient reference range, the elevator operation route is divided into long-distance stops; Close-distance stops refer to stops at floors that are adjacent or one or two floors apart, medium-distance stops refer to stops with a moderate distance between floors, and long-distance stops refer to stops with a large distance between floors.

[0039] For mid-distance stops, the elevator speed is reset based on the elevator load data; For mid-distance floor stops, a new operating speed is set based on load data and operating efficiency. Mid-distance operation requires a balance between speed and energy consumption: too fast a speed will increase energy consumption, while too slow a speed will affect transportation efficiency. By optimizing the speed setting, unnecessary energy consumption can be reduced while ensuring passenger comfort.

[0040] For short-distance and long-distance stops, the acceleration and deceleration are adjusted intelligently according to the load data and the elevator running speed reset for medium-distance stops; For short-distance and long-distance stops, the specific steps for intelligently adjusting acceleration and deceleration based on load data and the reset elevator running speed for medium-distance stops are as follows: First, according to the real-time load of the elevator and mid-distance stops reset target running speed , to determine the reasonable target speed of the elevator at different stopping distances, the expression is as follows: , where is the target speed of the elevator. is the weight coefficient, which is used to balance the speed changes under different loads. is the maximum load capacity of the elevator; The heavier the load, the slower the elevator should run to reduce the burden on the motor. When the load is lighter, the elevator can maintain a higher speed to improve operating efficiency.

[0041] Target speed based on elevator operation As well as the short-distance and long-distance docking requirements, calculate the acceleration and deceleration. The calculation expression is as follows:

[0042] , where is the acceleration, is the default acceleration, is the current stop distance (the distance between short or long floors), is the maximum stopping distance of the elevator design. is the deceleration, is the default deceleration; When the stopping distance is short (such as short-distance stopping), the peak power demand and energy consumption can be reduced by reducing the acceleration and deceleration. For long-distance stopping, the acceleration and deceleration can be appropriately increased to speed up the journey and shorten the running time.

[0043] The default acceleration and deceleration are usually determined by the design specifications and safety standards of the elevator, and are usually set as maximum acceleration and maximum deceleration in the elevator's control system to ensure safety and ride comfort. The specific values ​​can be obtained from the elevator's model parameters, which are usually preset by the manufacturer when the elevator leaves the factory and indicated in the operating manual or technical specifications. For example, the default acceleration of a typical passenger elevator is usually between 0.5-1.0 m / s², and the deceleration range is similar to avoid discomfort to passengers when the elevator starts or stops. At the same time, these default values ​​must comply with relevant elevator safety standards (such as ISO8100) and local regulations to ensure safety and meet technical requirements.

[0044] In order to improve the effect of intelligent control, according to the sparse index of elevator call distribution and high-level deviation index , adjust the elevator status update frequency, adjust the node update interval, and the calculation expression is as follows: , where It is the status update frequency, that is, the frequency at which the system adjusts and optimizes the elevator operating parameters (such as speed, acceleration, etc.) during operation. It is the default update frequency of the elevator system. It is generally the basic frequency set by the manufacturer or management system when the elevator is installed and set up to ensure that the system has sufficient response speed under normal circumstances. is the call distribution sparseness index Adjustment coefficient, used to control the sparse index of elevator call distribution The degree of impact on the frequency of status updates, is the high-level deviation index Adjustment factor used to control the high-level deviation index The degree of impact on the frequency of status updates; According to the current acceleration of the elevator and deceleration The actual acceleration and deceleration should be adjusted according to the target speed of the elevator. Make timely fine-tuning with the stroke characteristics, the expression is as follows: , , where is the actual acceleration, is the current elevator running speed, is the actual deceleration, It is the feedback adjustment coefficient used to adjust the elevator acceleration, reflecting the influence of the difference between the actual speed and the target speed of the elevator on the acceleration adjustment. It is the feedback adjustment coefficient of deceleration, which controls the adjustment effect of the difference between the current speed and the target speed of the elevator on the deceleration; When there is a large gap between the actual speed of the elevator and the target speed, the feedback adjustment is amplified to ensure that the elevator quickly approaches the target speed with appropriate acceleration or deceleration, thereby improving the running smoothness and efficiency.

[0045] Finally, the acceleration and deceleration control obtained by executing the above steps is carried out, and the elevator operation status is monitored in real time to ensure that the control effect reaches the expected effect. If the power peak is found to exceed the set range or the operation is unstable during the execution process, the system automatically adjusts various parameters to achieve dynamic optimization. The key parameters monitored include power consumption, operating time and equipment temperature, so as to ensure the efficiency and safety of the elevator in different parking scenarios.

[0046] The present invention realizes refined speed adjustment for different running distances by intelligently dividing the elevator's short-distance, medium-distance and long-distance stops. For medium-distance stops, the system dynamically adjusts the elevator's running speed according to the load data to ensure a balance between energy consumption and transportation efficiency. Too fast a speed will bring unnecessary energy consumption, while too slow a speed will affect efficiency. The reset speed can reduce power demand while ensuring passenger comfort, thereby optimizing energy efficiency. For short-distance stops, the adjustment of elevator acceleration and deceleration reduces the instantaneous current impact during frequent stops, reduces power peak demand, and reduces electricity waste. At the same time, long-distance stops avoid excessive consumption of equipment by adjusting appropriate acceleration and deceleration. Under the intelligent analysis and control of the system, all types of stop distances can achieve the best balance between speed and energy, reducing the overall energy consumption of the elevator.

[0047] The present invention effectively alleviates the problems of frequent starting and stopping and long-term high-load operation of elevators at different stopping distances through intelligent speed and acceleration control, and significantly reduces equipment wear. For close-distance stops, the system reduces the load on the motor and transmission device by reducing instantaneous high-current shocks, avoiding mechanical wear caused by frequent acceleration and emergency stops. For long-distance stops, intelligent control avoids excessive operation at high speed for a long time, especially under heavy load conditions, to prevent the equipment from overheating and increasing wear. The system also reduces unnecessary energy loss by optimizing the heat dissipation equipment. These optimization strategies not only reduce the speed of equipment aging, but also reduce the additional load requirements of the heat dissipation equipment, extend the operating life of the elevator, and reduce long-term maintenance and replacement costs.

[0048] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the control system of elevator energy consumption, characterized in that: The following steps are involved: During the operation of the elevator, the elevator operation information of the planned stop floors is obtained in real time, and the obtained elevator operation data is pre-processed to provide a basis for analyzing the use mode and operation efficiency of the elevator; Extract features from pre-processed elevator operation data, and use the trained machine learning model to intelligently evaluate changes in elevator stop floors based on the extracted features, identifying potential patterns and trends in elevator operation; According to the evaluation results of the machine learning model, the elevator's stop floors are divided into short-distance stops, medium-distance stops, and long-distance stops; For mid-distance stops, the elevator speed is reset based on the elevator load data; For short-distance and long-distance stops, the acceleration and deceleration are adjusted intelligently according to the load data and the elevator running speed reset for medium-distance stops.

2. The method of an elevator energy consumption optimization control system according to claim 1, characterized in that: The preprocessed elevator operation data is subjected to feature extraction. The extracted features include the distribution density of elevator call requests at different floors and the offset distance of the elevator from the current floor to the farthest target floor. After acquisition, the distribution density of elevator call requests at different floors and the offset distance of the elevator from the current floor to the farthest target floor are analyzed under the detection window, and then a call distribution sparse index and a high-level offset index are generated respectively. The call distribution sparse index and the high-level offset index are input into a pre-learned machine learning model, and an operation span coefficient is generated by the machine learning model. The preprocessed elevator operation route is intelligently evaluated by the operation span coefficient.

3. The method of an elevator energy consumption optimization control system according to claim 2, characterized in that: Under the detection window, the distribution density of elevator call requests on different floors is analyzed to generate an elevator call distribution sparse index. The specific steps are as follows: In the detection window, all floors with elevator call requests are collected and sorted in chronological order to form an elevator call request floor sequence. ,in, , where It is The number of floors requested by the elevator call, is the total number of elevator call requests; Floor sequence for elevator call request For each pair of elevator request floors, the distance between floors is calculated and stored in the distance weight matrix between floors. The calculation expression is as follows: , where is the distance weight between floors, representing the distance weight matrix between floors The element of and The distance weight between elevator calls is It is The number of floors requested by the elevator call, is the distance weight factor; Based on the weight of the distance between floors , construct the discrete increment matrix To reflect the sparse distribution of elevator call requests, the calculation expression is as follows: , where It is and The discrete increment between elevator calls is is the elevator call distance impact index, is the time gap impact index, is a decimal number that prevents division by zero; The discrete increment matrix All discrete increment values ​​in The cumulative sum is used to generate the call distribution sparse index. The calculation expression is as follows: , where is the call distribution sparsity index.

4. The method of an elevator energy consumption optimization control system according to claim 2, characterized in that: In the detection window, the offset distance of the elevator from the current floor to the farthest target floor is analyzed to generate a high-rise offset index. The specific steps are as follows: In the detection window, first, set the starting floor of the elevator and mark the starting floor of the elevator as , all the target floors of the elevator in this operation are grouped into a set, the set is ,in, It is the first target floor in this elevator operation. is the total number of target floors in this elevator operation, generating the offset distance. The calculation expression is as follows: , where It indicates the farthest target floor in this run. It indicates the offset distance; In order to improve the recognition of the offset distance in long-distance operation, a polynomial nonlinear transformation is introduced to convert the offset distance Convert to a higher-order offset and define the offset distance after nonlinear conversion. The calculation expression is as follows: , where is the offset distance after nonlinear transformation, is the nonlinear adjustment coefficient, is the coefficient of the quadratic offset term, used to control The impact of the item, Is the coefficient of the 1.5-power offset term, used to adjust The influence of the term further increases the nonlinear weight of long distances; In actual operation, the load and time factors will affect the energy consumption of the elevator. The load and time factors are introduced as correction factors to calculate the corrected offset distance. The calculation expression is as follows: , where is the corrected offset distance, It is load-bearing. is the time factor, is the load influence coefficient, is the smoothing factor, is the time influence coefficient is a decimal number that prevents division by zero; Further enhance the recognition of long distance runs, corrected offset distance Applying exponential amplification, the high-level offset index generated is calculated as follows: , where is the high-level deviation index, is the exponential magnification factor, is the smoothing factor of the denominator, is the adjustment factor for the offset distance.

5. The method of an elevator energy consumption optimization control system according to claim 2, characterized in that: The operation span coefficient generated after analyzing the elevator operation route under the detection window is compared and analyzed with the preset reference range of the operation span coefficient, and the obtained elevator operation data is divided. The specific division steps are as follows: If the operating span coefficient is within the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the operating span coefficient is less than the minimum value of the operating span coefficient reference range, the elevator operation route is divided into medium-distance stops; If the running span coefficient is greater than the maximum value of the running span coefficient reference range, the elevator running route is divided into long-distance stops.

6. The method of an elevator energy consumption optimization control system according to claim 5, characterized in that: For short-distance and long-distance stops, the specific steps for intelligently adjusting acceleration and deceleration based on load data and the reset elevator running speed for medium-distance stops are as follows: First, according to the real-time load of the elevator and mid-distance stops reset target running speed , to determine the reasonable target speed of the elevator at different stopping distances, the expression is as follows: , where is the target speed of the elevator. is the weight coefficient, is the maximum load capacity of the elevator; Target speed based on elevator operation As well as the short-distance and long-distance docking requirements, calculate the acceleration and deceleration. The calculation expression is as follows: In the formula, is the acceleration, is the default acceleration, is the current stop distance, is the maximum stopping distance of the elevator design. is the deceleration, is the default deceleration; In order to improve the effect of intelligent control, according to the sparse index of elevator call distribution and high-level deviation index , adjust the elevator status update frequency, adjust the node update interval, and the calculation expression is as follows: , where is the state update frequency, is the default update frequency of the elevator system, is the call distribution sparseness index Adjustment factor, is the high-level deviation index Adjustment factor; According to the current acceleration of the elevator and deceleration The actual acceleration and deceleration should be adjusted according to the target speed of the elevator. Make timely fine-tuning with the stroke characteristics, the expression is as follows: , , where is the actual acceleration, is the current elevator running speed, is the actual deceleration, It is the feedback adjustment coefficient used to adjust the elevator acceleration. is the feedback adjustment coefficient of deceleration; Finally, execute the acceleration and deceleration control obtained in the above steps, and monitor the elevator operation status in real time to ensure that the control effect reaches the expected result.

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