Elevator passenger capacity obtaining method and device, storage medium and electronic equipment

Abstract

The invention provides an elevator passenger capacity obtaining method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring electric quantity consumption data of the elevator; constructing an electric quantity consumption time sequence according to the electric quantity consumption data; a time sequence deep learning model is used for processing the electricity consumption time sequence to obtain the change condition of the number of passengers in the elevator; and the real-time passenger number in the elevator is obtained according to the initial passenger number of the elevator and the passenger number change condition of the elevator. By means of the elevator passenger capacity obtaining method, the real-time number of passengers in the elevator can be accurately estimated.

Description

Technical Field

[0001] The present application belongs to the field of elevators and relates to a method for obtaining the passenger capacity of an elevator, and in particular to a method, device, storage medium and electronic equipment for obtaining the passenger capacity of an elevator. Background Art

[0002] With the rapid development of modern cities and the increase in the number of high-rise buildings, elevators have become an indispensable means of vertical transportation in places such as shopping malls, office buildings and residential buildings. The efficient operation and scientific management of elevators not only directly affect the travel experience of passengers, but also are related to the operating efficiency and energy consumption level of the entire building. In the prior art, elevator management mainly relies on traditional regular scheduling strategies and simple statistical analysis of passenger load conditions. However, this method is difficult to accurately predict the real-time passenger flow distribution of elevators, resulting in frequent elevator overloads or unreasonable scheduling during peak hours, and may cause waste of elevator resources during non-peak hours, restricting the overall efficiency optimization of the elevator system.

[0003] In order to achieve efficient management of elevators, it is crucial to accurately obtain elevator passenger capacity data. By real-time monitoring and collecting elevator passenger capacity information, passenger flow dynamics can be accurately analyzed and elevator scheduling strategies can be optimized. However, existing methods for obtaining elevator passenger capacity data still have some shortcomings. Summary of the invention

[0004] The embodiments of the present application provide a method, device, storage medium and electronic device for obtaining the passenger capacity of an elevator, which are used to accurately obtain the number of passengers in an elevator.

[0005] In a first aspect, an embodiment of the present application provides a method for obtaining the passenger capacity of an elevator based on power changes, the method comprising: obtaining the power consumption data of the elevator; constructing a power consumption time series based on the power consumption data; processing the power consumption time series using a time series deep learning model to obtain changes in the number of elevator passengers; and obtaining the real-time number of passengers in the elevator based on the initial number of passengers in the elevator and the changes in the number of passengers in the elevator.

[0006] In an implementation of the first aspect, obtaining the power consumption data of the elevator includes: obtaining the power consumption data of the elevator from a motor control system of the elevator or an energy management system of a building.

[0007] In an implementation of the first aspect, constructing a power consumption time series according to the power consumption data includes: obtaining power difference values ​​between adjacent time points of the power consumption data; and constructing the power consumption time series according to the power difference values.

[0008] In an implementation of the first aspect, the output of the time series deep learning model includes the change in the number of passengers and the number of floors passed within the corresponding time period.

[0009] In an implementation of the first aspect, the method further includes: obtaining passenger riding capacity based on the real-time number of passengers in the elevator and the number of floors passed.

[0010] In an implementation of the first aspect, the loss function used in the training of the time series deep learning model is:

[0011]

[0012] Where N represents the total length of the time series, and P i They represent the predicted value and actual value of the change in the number of passengers in the i-th time period, and H i They respectively represent the predicted value and actual value of the number of floors passed in the i-th time period.

[0013] In an implementation of the first aspect, the temporal deep learning model is a long short-term memory network model.

[0014] In a second aspect, an embodiment of the present application provides an elevator passenger capacity acquisition device based on power consumption changes, the device comprising: a data acquisition module for acquiring the power consumption data of the elevator; a time series acquisition module for constructing a power consumption time series based on the power consumption data; a quantity change acquisition module for processing the power consumption time series using a time series deep learning model to obtain changes in the number of elevator passengers; a real-time passenger number acquisition module for acquiring the real-time number of passengers in the elevator based on the initial number of passengers in the elevator and the changes in the number of passengers in the elevator.

[0015] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in any one of the first aspects of the embodiment of the present application is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing any of the methods described in the first aspect of the embodiment of the present application when the computer program is called.

[0017] As described above, the elevator passenger capacity acquisition method, device, storage medium and electronic device provided in the embodiments of the present application have the following beneficial effects:

[0018] The elevator passenger capacity acquisition method provided in the embodiment of the present application infers the dynamic change of the number of elevator passengers based on the elevator power consumption data, and then can obtain the real-time number of passengers in the elevator. In this way, the increase or decrease of the number of passengers in the elevator can be accurately estimated without the need for external sensors or visual equipment, and the elevator scheduling optimization can be assisted.

[0019] In the embodiment of the present application, the power data is used instead of the detection results of external sensors such as weight sensors and door switch sensors to infer the elevator passenger flow, which greatly simplifies the system design and maintenance work. Compared with the traditional elevator passenger flow detection solution that relies on multiple sensors, the embodiment of the present application effectively avoids the errors and inaccurate data caused by sensor failures, environmental changes (such as temperature, humidity), etc., and significantly reduces maintenance costs.

[0020] The elevator passenger capacity acquisition method provided in the embodiment of the present application does not need to use cameras, sensors and other equipment to monitor the number of passengers in the elevator, reducing the risk of privacy leakage. The method does not need to add additional hardware equipment or monitoring facilities to the elevator system, and has the advantages of easy implementation and low cost. The method can be applied to existing building management systems and can be widely used in various types of buildings, such as office buildings, shopping malls, residential buildings, etc., and has extremely high scalability.

[0021] In the embodiment of the present application, the changes in the power of the elevator can be continuously monitored, and the changes in the passenger flow in the elevator can be inferred in real time by combining the time series deep learning model, so as to optimize the operation and scheduling of the elevator. During peak hours, the solution provided by the embodiment of the present application can quickly identify the passenger load of the elevator, reasonably allocate elevator resources, and reduce the waiting time of passengers.

[0022] In the embodiment of the present application, by introducing a deep learning timing model, the electricity consumption change pattern of the elevator can be dynamically analyzed, and the increase or decrease of passengers and the change of passenger flow can be accurately calculated, so as to perform intelligent scheduling on the allocation of elevator resources, reduce the number of times the elevator runs without load, and reduce unnecessary energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Shown is a flow chart of a method for obtaining elevator passenger capacity provided in an embodiment of the present application.

[0024] Figure 2 Shown is a flow chart for obtaining a time series of power consumption in an embodiment of the present application.

[0025] Figure 3 Shown is an architectural diagram of a time series deep learning model in an embodiment of the present application.

[0026] Figure 4 Shown is a flow chart of adjusting the elevator dispatching strategy in an embodiment of the present application.

[0027] Figure 5 Shown is a schematic diagram of the structure of an elevator passenger capacity acquisition device provided in an embodiment of the present application.

[0028] Figure 6 The color display is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0029] Component number description

[0030] 500 Elevator passenger capacity acquisition device

[0031] 501 Data Acquisition Module

[0032] 502 Time Series Acquisition Module

[0033] 503 Quantity change acquisition module

[0034] 504 Real-time passenger number acquisition module

[0035] 600 Electronic equipment

[0036] 601 Processor

[0037] 602 Memory

[0038] 6021 Operating System

[0039] 6022 Applications

[0040] 603 Network Interface

[0041] 604 bus system

[0042] 605 User Interface

[0043] Steps S11 to S14

[0044] Steps S21 to S22

[0045] Steps S41 to S46 DETAILED DESCRIPTION

[0046] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0047] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present application in a schematic manner, and therefore the illustrations only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0048] In the embodiments of the present application, the words "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0049] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0050] Elevators are commonly used to achieve vertical transportation in buildings and are widely used in residential buildings, office buildings, shopping malls, hospitals and other places. The basic structure of an elevator includes a car, counterweight, guide rails, traction machine, control system and safety system. The operation of an elevator usually drives the steel rope through the traction machine, so that the car and counterweight move up and down along the guide rails, thereby achieving efficient transportation of people or goods. The motor control system is the core part of the elevator drive and operation. It is responsible for controlling the start and stop of the elevator, speed adjustment and positioning accuracy, ensuring the smooth, safe and efficient operation of the elevator.

[0051] In order to achieve efficient management of elevators, it is crucial to accurately obtain elevator passenger capacity data. In some technical solutions, visual monitoring systems or passenger sensing devices, such as installing cameras or weight sensors, are used to monitor the use of elevators. However, the installation and maintenance costs of visual monitoring systems and sensing devices are high, especially for large buildings. The deployment of multiple monitoring devices may greatly increase construction and maintenance costs. In addition, these solutions require the collection of visual information or weight detection in the elevator, which will cause concerns about user privacy leakage, especially in the context of increasingly stringent data protection requirements. In addition, non-visual sensing devices are easily affected by environmental factors (such as temperature, humidity, improper maintenance, etc.), resulting in equipment failure or inaccurate data. Complex sensor networks will also increase the difficulty of system failure and maintenance.

[0052] At least to address the above-mentioned problem, an embodiment of the present application provides a method for obtaining elevator passenger capacity. Figure 1 The flowchart of the method for obtaining the elevator passenger capacity provided by the embodiment of the present application is shown. Figure 1 As shown, the method for obtaining the elevator passenger capacity provided in the embodiment of the present application includes the following steps S11 to S14.

[0053] S11, obtaining power consumption data of the elevator. For example, the power consumption data of the elevator can be obtained from a motor control system of the elevator or an energy management system of the building.

[0054] S12, construct a power consumption time series based on the power consumption data. Specifically, during the operation of the elevator, its power consumption changes dynamically with the load, floor spacing, and acceleration and deceleration state. In step S12, the power consumption of the elevator at a specific time interval (for example, 1 minute interval) can be collected to form a time series input vector.

[0055] For example, within a certain period of time, if the power data per minute is e(t), the input vector at that moment can be expressed as:

[0056] E=[e(t),e(t+1),e(t+2),…,e(t+n)]

[0057] Wherein, e(t) represents the power consumption in the tth minute, and n is the duration of the observation window, and its value is, for example, several minutes.

[0058] In some implementations, the collection interval of power consumption data can be set according to actual needs, or the power consumption data can be combined with other auxiliary features to form a richer input vector. The time series deep learning model can obtain a more accurate change in elevator passenger capacity by processing the input vector.

[0059] S13, using the time series deep learning model to process the power consumption time series to obtain the change of the elevator passenger capacity. The time series deep learning model can learn the relationship between the power consumption time series and the change of passenger capacity. After the training is completed, the time series deep learning model can infer the change of the elevator passenger capacity under the power input of several consecutive minutes.

[0060] In some implementations, the time series deep learning model is, for example, an LSTM (Long Short-Term Memory) model, which can effectively process high-frequency time series data, capture short-term and long-term dependencies, and is suitable for modeling complex nonlinear relationships between power consumption time series and passenger flow changes. It should be understood that the present application does not limit the type of time series deep learning models. For example, in some embodiments, other time series models may also be used, such as Transformer-based models, RNN (Recurrent Neural Network) models, etc., to process long time series and enhance the model's ability to capture complex elevator power change patterns.

[0061] Exemplarily, after being given the power consumption time series of the past 5 minutes, the time series deep learning model can output the change in the elevator passenger capacity within this time period. For example, if 3 people get on the elevator and 2 people get off the elevator, the final output change in the elevator passenger capacity is 1 person.

[0062] S14, obtaining the real-time number of passengers in the elevator according to the initial number of passengers in the elevator and the change in the passenger capacity of the elevator.

[0063] Specifically, during the operation of an elevator, its power consumption will fluctuate with changes in factors such as passenger capacity, number of floors traveled, and speed. Based on the real-time power data of the elevator, a correlation model between power consumption and elevator passenger capacity can be established, thereby realizing the inference of elevator passenger flow without the need for additional hardware equipment. This method can avoid the high cost and privacy leakage problems in related technologies, and can be applied to various types of buildings, with broad application prospects.

[0064] See also Figure 2 In some implementations, constructing a power consumption time series based on power consumption data includes the following steps S21 and S22.

[0065] S21, obtaining the power difference of the power consumption data at adjacent time points, as shown below:

[0066] ΔE i =E i -E i-1

[0067] where ΔEi is the difference in power between adjacent time points, E i and E i-1 are the power consumption at two adjacent time points.

[0068] S22, constructing a power consumption time series according to the power difference.

[0069] In some implementations, the output of the time series deep learning model includes the change in the number of passengers and the number of floors passed within the corresponding time period.

[0070] For example, Figure 3 It is a schematic diagram of the structure of a time series deep learning model. Figure 3 As shown in the figure, the time series deep learning model includes an input layer, a hidden layer (LSTM layer), a fully connected layer, and an output layer. The input layer is used to input the power consumption time series. The hidden layer includes multiple layers of LSTM units to capture time series dependencies and feature extraction. The number of layers and neurons of the LSTM unit can be adjusted according to actual needs. The fully connected layer is used to integrate feature information and feature conversion and output mapping. The output layer is used to output the change in the number of passengers in a specific time period and the number of floors passed.

[0071] For example, if the input vector X = [0.005, 0.007, 0.007, 0.008, 0.008, 0.009, 0.010, …], the model output predicts a change in the number of passengers of +3, indicating that the number of passengers in the elevator increased by 3 during the time period. The number of floors passed is 2, indicating that the elevator passed 2 floors during the time period.

[0072] In some implementations, the elevator passenger capacity acquisition method provided in the embodiment of the present application may further include: obtaining the passenger capacity according to the real-time number of passengers in the elevator and the number of floors passed. The passenger capacity refers to the product of the number of passengers and the number of floors passed by the elevator, which can be used to assist in the optimization scheduling of the elevator. For example, if the elevator carries 4 passengers in a journey, runs from the 2nd floor to the 8th floor, and passes through 6 floors during the journey, the capacity is 4×6=24.

[0073] For example, the riding capacity data can be used as input to the scheduling optimization algorithm to reduce the elevator load pressure through real-time scheduling during peak hours or in specific scenarios.

[0074] In some implementations, the historical power data and the corresponding actual values ​​of passenger flow changes and the number of floors passed can be used to train the time series deep learning model. Through iterative training, the time series deep learning model can accurately learn the mapping relationship between high-frequency power changes and passenger flow changes.

[0075] For example, the training data set may include information such as the sequence of power changes every m seconds, actual passenger flow changes, and the number of floors passed, where the value of m is, for example, 10. Historical passenger flow changes can be obtained through short-term installed sensors, and the data of floors passed can be recorded by the elevator control system. It should be understood that these data are only used in the model training phase, and the number of elevator passengers can be obtained without using sensors after the training is completed.

[0076] For example, the loss function used in the training of the time series deep learning model can be:

[0077]

[0078] Where N represents the total length of the time series, and P i They represent the predicted value and actual value of the change in the number of passengers in the i-th time period, and H i They respectively represent the predicted value and actual value of the number of floors passed in the i-th time period.

[0079] For example, the Adam optimizer can be used to update the parameters of the time series deep learning model to accelerate convergence. The Adam optimizer dynamically adjusts the learning rate of each parameter by adaptively estimating the first-order and second-order gradients, and can find the optimal value of the loss function faster without significantly increasing the computational and memory overhead, thereby reducing training time and improving the stability of the model.

[0080] In some implementations, obtaining the real-time number of passengers in the elevator according to the initial number of passengers in the elevator and the change in the number of passengers in the elevator includes: accumulating the change values ​​of the number of passengers in the elevator in consecutive time periods to obtain the real-time number of passengers in the elevator, as shown in the following formula:

[0081]

[0082] Where P(t) represents the real-time number of passengers in the elevator, P0 is the initial number of passengers, is the passenger flow change value in the i-th time period, which can be obtained through the output of the time series deep learning model. The initial number of passengers refers to the number of passengers in the elevator when the elevator passenger capacity acquisition method starts to execute, which can be set according to the actual situation.

[0083] In some implementations, the number of floors passed by the elevator can be obtained by accumulating the number of floors passed in each time period, as shown in the following formula:

[0084]

[0085] in Indicates the total number of floors that the elevator passes through. It represents the number of floors passed in the i-th time period, which can be obtained through the output of the time series deep learning model.

[0086] The following will introduce the elevator passenger capacity acquisition method provided by the embodiment of the present application through a specific example. It should be noted that the content in this example does not limit the elevator passenger capacity acquisition method provided by the embodiment of the present application, and in actual application, it can be adjusted based on this example according to needs. Figure 4 The flowchart of the method for obtaining the elevator passenger capacity in this example is shown. Figure 4 As shown, the method for obtaining the elevator passenger capacity includes the following steps.

[0087] S41, collecting the power consumption data of the elevator. For example, the power monitoring function of the elevator motor control system can be used to collect the power consumption data of the elevator in real time. The collection frequency can be set to once every 10 seconds, that is, the cumulative power consumption of the elevator is recorded once every 10 seconds. The power consumption data can be obtained through the standard interface of the elevator control system, or it can be extracted from the energy management system of the building. For example, Table 1 shows an example table of the collected power consumption data of the elevator.

[0088] Table 1. Example of power consumption data

[0089] time Cumulative power consumption (kWh) 08:00:00 500.000 08:00:10 500.005 08:00:20 500.012 08:00:30 500.020 08:00:40 500.028 08:00:50 500.037 08:01:00 500.047 … …

[0090] S42, constructing a power consumption time series according to the power consumption data. For example, the power consumption change every 10 seconds can be obtained by calculating the power difference between adjacent time points, as shown in Table 2.

[0091] Table 2. Example of cumulative power consumption data

[0092] Time interval Cumulative power consumption (kWh) 08:00:00-08:00:10 500.005-500.000＝0.005 08:00:10-08:00:20 500.012-500.005＝0.007 08:00:20-08:00:30 500.020-500.012＝0.008 08:00:30-08:00:40 500.028-500.020＝0.008 08:00:40-08:00:50 500.037-500.028＝0.009 08:00:50-08:01:00 500.047-500.037＝0.010 … …

[0093] The change in the power consumption of the elevator is constructed into a power consumption time series X = [0.005, 0.007, 0.007, 0.008, 0.008, 0.009, 0.010, …]. The input vector X can be used as the input of the time series deep learning model to predict the change in the number of elevator passengers.

[0094] S43, constructing and training a time series deep learning model, which is, for example, an LSTM model, whose input is a time series of power consumption, and whose output is a change in the number of elevator passengers and the number of floors passed.

[0095] S44, inputting the power consumption time series into the time series deep learning model to obtain the changes in the number of elevator passengers and the number of floors passed.

[0096] For example, if the initial number of passengers P0=2, in the four time periods, the passenger flow change value ΔP and the number of floors passed ΔH output by the time series deep learning model are shown in Table 3.

[0097]

[0098] S45, calculating the passenger capacity according to the change in the number of elevator passengers and the number of floors passed. For example, in a certain journey, the elevator carries 4 passengers and passes 5 floors, then the capacity is: L = 4 × 5 = 20.

[0099] S46, adjusting the elevator dispatching strategy according to the passenger capacity. For example, when the passenger capacity is high, the spare elevators may be dispatched first or unnecessary stops may be reduced to avoid overloaded elevator operation.

[0100] In summary, the embodiment of the present application provides a method for obtaining the passenger capacity of an elevator based on power changes. By using a time series deep learning model to analyze the power data of the elevator every minute during operation, the change in the number of passengers in the elevator is predicted in real time. This method does not require additional hardware equipment and is applicable to various elevator systems with power monitoring functions. In addition, this method can improve the intelligence of elevator scheduling, reduce energy consumption, and reduce the cost and complexity of the monitoring system.

[0101] The protection scope of the elevator passenger capacity acquisition method provided in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0102] The embodiment of the present application also provides an elevator passenger capacity acquisition device, which can implement the elevator passenger capacity acquisition method described in the present application, but the implementation device of the elevator passenger capacity acquisition method described in the present application includes but is not limited to the structure of the elevator passenger capacity acquisition device listed in the present embodiment, and all structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0103] Figure 5 is a schematic block diagram of an elevator passenger capacity acquisition device provided in an embodiment of the present application. Figure 5 As shown, the elevator passenger capacity acquisition device 500 includes a data acquisition module 501, a time series acquisition module 502, a quantity change acquisition module 503 and a real-time passenger quantity acquisition module 504.

[0104] The data acquisition module 501 is used to acquire the power consumption data of the elevator. The time series acquisition module 502 is used to construct the power consumption time series according to the power consumption data. The quantity change acquisition module 503 is used to process the power consumption time series using the time series deep learning model to obtain the change in the number of elevator passengers. The real-time passenger number acquisition module 504 is used to obtain the real-time number of passengers in the elevator according to the initial number of elevator passengers and the change in the number of elevator passengers.

[0105] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0106] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the elevator passenger capacity acquisition method provided by the embodiment of the present application is implemented. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0107] An embodiment of the present application also provides an electronic device. Figure 6 is a schematic block diagram of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 includes: at least one processor 601, a memory 602, at least one network interface 603 and a user interface 605. The various components in the electronic device 600 are coupled together through a bus system 604. It can be understood that the bus system 604 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 604 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 604 is not shown in FIG. Figure 6 In the specification, various buses are labeled as bus systems.

[0108] The user interface 605 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0109] It is understood that the memory 602 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of exemplary but not limiting explanation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present application is intended to include but is not limited to these and any other suitable categories of memory.

[0110] The memory 602 in the embodiment of the present application is used to store various categories of data to support the operation of the electronic device 600. Examples of these data include: any executable program for operating on the electronic device 600, such as an operating system 6021 and an application 6022; the operating system 6021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 6022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for obtaining the elevator passenger capacity provided in the embodiment of the present application may be included in the application 6022.

[0111] The method disclosed in the above embodiment of the present application can be applied to the processor 601, or implemented by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or an instruction in the form of software. The above processor 601 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 601 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor 601 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0112] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0113] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0114] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0119] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0120] In summary, the present application provides an elevator passenger capacity acquisition method, device, storage medium and electronic device. The elevator passenger capacity acquisition method provided in the embodiment of the present application infers the dynamic change of the number of elevator passengers based on the power consumption data of the elevator, and then can obtain the real-time number of passengers in the elevator. In this way, the increase or decrease of the number of passengers in the elevator can be accurately estimated without the need for external sensors or visual equipment, and the elevator scheduling optimization can be assisted. In the embodiment of the present application, the power data is used instead of the detection results of external sensors such as weight sensors and door switch sensors to infer the elevator passenger flow, which greatly simplifies the system design and maintenance work. Compared with the traditional elevator passenger flow detection scheme that relies on multiple sensors, the embodiment of the present application effectively avoids the errors and inaccurate data caused by sensor failures, environmental changes (such as temperature, humidity), etc., and significantly reduces the maintenance cost. The elevator passenger capacity acquisition method provided in the embodiment of the present application does not need to use cameras, sensors and other equipment to monitor the number of passengers in the elevator, reducing the risk of privacy leakage. The method does not need to add additional hardware equipment or monitoring facilities to the elevator system, and has the advantages of easy implementation and low cost. This method can be applied to existing building management systems and can be widely used in various types of buildings, such as office buildings, shopping malls, residences, etc., with extremely high scalability. In the embodiment of the present application, the power changes of the elevator can be continuously monitored, and the passenger flow changes in the elevator can be inferred in real time in combination with the time series deep learning model, thereby optimizing the operation scheduling of the elevator. During peak hours, the solution provided in the embodiment of the present application can quickly identify the passenger load of the elevator, reasonably allocate elevator resources, and reduce passenger waiting time. In the embodiment of the present application, by introducing a deep learning time series model, the law of changes in the power of the elevator can be dynamically analyzed, and the increase and decrease of passengers and passenger flow changes can be accurately calculated, so that intelligent scheduling can be performed on the allocation of elevator resources, reducing the number of times the elevator runs empty and reducing unnecessary energy consumption. Therefore, the present application effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0121] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for obtaining elevator passenger capacity based on power changes, characterized in that: The method comprises: Get the power consumption data of the elevator; Constructing a power consumption time series according to the power consumption data; Using a time series deep learning model to process the power consumption time series to obtain changes in the number of elevator passengers; The real-time number of passengers in the elevator is obtained according to the initial number of passengers in the elevator and the change in the number of passengers in the elevator.

2. The method for obtaining elevator passenger capacity based on power changes according to claim 1, characterized in that: Acquiring the power consumption data of the elevator includes: acquiring the power consumption data of the elevator from a motor control system of the elevator or an energy management system of a building.

3. The method for obtaining elevator passenger capacity based on power changes according to claim 1, characterized in that: Constructing a power consumption time series according to the power consumption data includes: Obtaining the power consumption data difference between adjacent time points; The power consumption time series is constructed according to the power difference.

4. The method for obtaining elevator passenger capacity based on power changes according to claim 1, characterized in that: The output of the time series deep learning model includes the change in the number of passengers and the number of floors passed within the corresponding time period.

5. The method for obtaining the elevator passenger capacity based on the change of electric quantity according to claim 4 is characterized in that: The method further comprises: The passenger capacity is obtained according to the real-time number of passengers in the elevator and the number of floors passed.

6. The method for obtaining elevator passenger capacity based on power changes according to claim 4, characterized in that: The loss function used in the training of the time series deep learning model is: Where N represents the total length of the time series, and P i They represent the predicted value and actual value of the change in the number of passengers in the i-th time period, and H i They respectively represent the predicted value and actual value of the number of floors passed in the i-th time period.

7. The method for obtaining elevator passenger capacity based on power changes according to claim 1, characterized in that: The time series deep learning model is a long short-term memory network model.

8. A device for obtaining the passenger capacity of an elevator based on the change of electric quantity, characterized in that: The device comprises: A data acquisition module, used to acquire the power consumption data of the elevator; A time series acquisition module, used to construct a power consumption time series according to the power consumption data; A quantity change acquisition module is used to process the power consumption time series using a time series deep learning model to obtain changes in the number of elevator passengers; The real-time passenger number acquisition module is used to acquire the real-time passenger number in the elevator according to the initial passenger number of the elevator and the change of the passenger number of the elevator.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and executes the method according to any one of claims 1 to 7 when calling the computer program.

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