Elevator energy-saving optimization method based on big data analysis

Abstract

The invention discloses an elevator energy-saving optimization method based on big data analysis, and particularly relates to the technical field of elevator operation control, and the method comprises the following steps: S1, obtaining physical information, management information and environment information in the daily operation process of an elevator; s2, acquiring stable operation signals of the physical information, the management information and the environment information; s3, collecting stable operation signals at different times to construct a data set; and S4, analyzing the data set to obtain a resource management coefficient, further analyzing the resource management coefficient, and generating different levels of management signals, including high-level and intermediate-level management signals. According to the method, remarkable energy conservation is achieved through full-process optimization, and energy consumption can be effectively reduced in a single-ladder scene and a multi-ladder scene; and meanwhile, the energy-saving conversion efficiency is improved, the energy-saving effect of strategy adjustment is more remarkable, long-term stable energy saving is guaranteed through a closed-loop mechanism, and energy waste in elevator operation is practically reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator operation control, and more specifically, to an elevator energy-saving optimization method based on big data analysis. Background Art

[0002] In recent years, with the continuous development of the economy and the rapid growth of urban public construction industries, the demand for elevators, as an indispensable means of vertical transportation in buildings, has continued to increase. However, elevators consume a huge amount of electricity, so it is very necessary to study elevator energy-saving technologies.

[0003] Most existing systems only collect basic data such as the number of elevator operations and fault codes, ignoring the correlation between load rate, start-stop acceleration and ambient pedestrian flow. This results in a lack of comprehensiveness in energy consumption analysis. The judgment of stable elevator operation relies on the experience of operation and maintenance personnel. When collecting data, interference factors such as transmission delays and equipment failures are not considered and are directly used for analysis, resulting in low elevator operation efficiency and serious energy waste.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an elevator energy-saving optimization method based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An elevator energy-saving optimization method based on big data analysis includes the following steps:

[0008] Step S1: Acquire physical information, management information, and environmental information during daily elevator operation;

[0009] Step S2: Collecting operation stability signals of physical information, management information and environmental information;

[0010] Step S3: collecting stable operation signals at different times to construct a data set;

[0011] Step S4: Analyze the data set to obtain resource management coefficients, further analyze the resource management coefficients to generate different levels of management signals, including high-level and intermediate management signals;

[0012] Step S5: Adjust the elevator operation according to the obtained management signal.

[0013] In a preferred embodiment, in step S1, the physical information includes the real-time running speed of the elevator, the start and stop acceleration, and the load weight and load rate in the car;

[0014] Management information includes rated load, rated speed and fault handling;

[0015] Environmental information includes external environment data of elevator operation, real-time passenger flow on each floor of the building, and the nature of building use.

[0016] In a preferred embodiment, in step S2, the stable state parameters of the physical information are collected in real time, and the stability index of the management information is extracted based on the elevator management system database, the corresponding environmental information is extracted, and the corresponding stability signal is generated.

[0017] In a preferred embodiment, in step S3, a data collection interval is set, and a time length corresponding to the data collection interval is obtained;

[0018] Dividing the data set interval into a first data set interval and a second data set interval equally;

[0019] Obtaining a time length during which the delay value in the first data set interval is greater than the data set delay threshold; obtaining a time length during which the delay value in the second data set interval is greater than the data set delay threshold;

[0020] Calculate the delay coefficient, the expression is: Among them, YC, c, y1, and y2 are respectively the delay coefficient, the time length corresponding to the data set interval, the time length when the delay value in the first data set interval is greater than the data set delay threshold, and the time length when the delay value in the second data set interval is greater than the data set delay threshold.

[0021] In a preferred embodiment, a delay coefficient threshold is set, and if the delay coefficient is less than or equal to the delay coefficient threshold, a data set is constructed;

[0022] If the delay coefficient is greater than the delay coefficient threshold, the current stable signal is discarded and the data set is reconstructed.

[0023] In a preferred embodiment, in step S4, a weighted algorithm is used to comprehensively analyze the data set and assign weights to stable signals of physical, management, and environmental information;

[0024] Set the resource management coefficient threshold. If the resource management coefficient is greater than or equal to the resource management coefficient threshold, the resource management status is determined to be excellent and a high-level management signal is generated, indicating that the elevator operation resource matching degree is high and no major adjustment is required.

[0025] If the resource management coefficient is less than the resource management coefficient threshold, it is determined that the resource management status is good but there is room for optimization, and an intermediate management signal is generated, indicating that some operating parameters need to be adjusted.

[0026] In a preferred embodiment, in step S5, the current core operating parameters and dispatching strategy of the elevator are maintained for the high-level management signal, and only minor adjustments are made to maintain stability;

[0027] Maintain the current partition operation mode, continue the established standby sleep time, optimize the door opening and closing time of the door machine, and record the current operation status as a benchmark for subsequent optimization.

[0028] In a preferred embodiment, in step S5, for the intermediate management signal, if the intermediate signal is triggered due to a large load rate fluctuation, the operating speed is dynamically adjusted;

[0029] If the signal is triggered due to uneven distribution of passenger flow, the elevator service area will be re-divided;

[0030] If stability is affected by deviations in maintenance records, arrange maintenance plans in advance to ensure reliable operation of the elevator during peak hours.

[0031] The technical effects and advantages of the elevator energy-saving optimization method based on big data analysis of the present invention are as follows:

[0032] 1. By collecting physical, management, and environmental information, a correlation model between energy consumption and multiple factors is established, breaking through the limitations of traditional single data, converting stable operation into quantifiable threshold indicators, and improving the accuracy of status assessment through multi-dimensional signal linkage verification.

[0033] 2. Abnormal data is eliminated through delay coefficient calculation to ensure the reliability of data sets. High-level signals maintain the stability of core parameters and reduce energy consumption fluctuations caused by unnecessary adjustments. Intermediate signals are optimized in a targeted manner to improve the energy-saving conversion rate of parameter adjustments, which has a good energy-saving and environmental protection effect. By recording the baseline status and iteratively adjusting the strategy, the system can adapt to the operating characteristics of different time periods and scenarios to achieve long-term and stable energy-saving effects.

[0034] The present invention achieves significant energy saving through full-process optimization, and can effectively reduce energy consumption in both single-elevator and multi-elevator scenarios; at the same time, it improves the energy-saving conversion efficiency, making the energy-saving effect of strategy adjustment more significant, and ensures long-term and stable energy saving through a closed-loop mechanism, effectively reducing energy waste in elevator operation, and achieving good energy-saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of an elevator energy-saving optimization method based on big data analysis according to the present invention. DETAILED DESCRIPTION

[0036] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0037] Figure 1 The present invention provides an elevator energy-saving optimization method based on big data analysis, which includes the following steps:

[0038] Step S1: Acquire physical information, management information, and environmental information during daily elevator operation;

[0039] Step S2: Collecting operation stability signals of physical information, management information and environmental information;

[0040] Step S3: collecting stable operation signals at different times to construct a data set;

[0041] Step S4: Analyze the data set to obtain resource management coefficients, further analyze the resource management coefficients to generate different levels of management signals, including high-level and intermediate management signals;

[0042] Step S5: Adjust the elevator operation according to the obtained management signal.

[0043] In step S1, the physical information includes the real-time running speed of the elevator, the start and stop acceleration, and the load weight and load rate in the car;

[0044] Management information includes rated load, rated speed and fault handling;

[0045] Environmental information includes external environment data of elevator operation, real-time passenger flow on each floor of the building, and the nature of building use.

[0046] Focusing on core parameters such as real-time operating speed and load rate in physical information can directly reflect the key factors affecting elevator energy consumption; the rated load capacity, fault handling status, etc. in management information provide a basis for adapting elevator performance and operating scenarios; the flow of people, building properties, etc. in environmental information can be used to correlate the relationship between external demand and elevator energy consumption. The integration of these three types of information avoids the limitations of a single data dimension, provides comprehensive, multi-dimensional data support for subsequent energy-saving analysis, and ensures that the analysis results are more in line with actual operating needs. Only information that is strongly related to energy-saving optimization is collected (such as eliminating irrelevant elevator appearance parameters) to reduce data redundancy and the computational cost of subsequent data processing. At the same time, it ensures that the data focuses on the key points of energy consumption optimization, making the analysis process more efficient and the goals clearer.

[0047] In step S2, the stable state parameters of the physical information are collected in real time, and the stability index of the management information is extracted based on the elevator management system database, the corresponding environmental information is extracted, and the corresponding stability signal is generated.

[0048] By collecting stability thresholds from physical data in real time, such as speed fluctuation ≤ ±0.1 m / s, extracting stability indicators from management data, such as maintenance interval deviation ≤ ±2 days, and generating stability signals from environmental data, such as passenger flow rate change ≤ ±10%, this system transforms abstract stability states into quantifiable indicators, shifting the assessment of elevator operating status from subjective judgment to objective data support, providing a unified, standardized benchmark for subsequent analysis. The stability signals from physical, management, and environmental data corroborate each other. A surge in passenger flow during meeting hours in environmental data can be linked to an increase in load rate in physical data, verifying the signal's authenticity, avoiding analysis bias caused by misjudgment of a single signal, and improving the accuracy of elevator operating status perception.

[0049] In step S3, the data collection interval is set and the time length corresponding to the data collection interval is obtained;

[0050] Dividing the data set interval into a first data set interval and a second data set interval equally;

[0051] Obtaining a time length during which the delay value in the first data set interval is greater than the data set delay threshold; obtaining a time length during which the delay value in the second data set interval is greater than the data set delay threshold;

[0052] Calculate the delay coefficient, the expression is: Among them, YC, c, y1, and y2 are respectively the delay coefficient, the time length corresponding to the data set interval, the time length when the delay value in the first data set interval is greater than the data set delay threshold, and the time length when the delay value in the second data set interval is greater than the data set delay threshold.

[0053] Set a delay coefficient threshold. If the delay coefficient is less than or equal to the delay coefficient threshold, then construct a data set.

[0054] If the delay coefficient is greater than the delay coefficient threshold, the current stable signal is discarded and the data set is reconstructed.

[0055] By calculating the delay coefficient and combining the length of the data interval with the time when the delay exceeds the threshold, abnormal data sets caused by signal transmission delays and collection failures are eliminated to ensure that the data included in the analysis has temporal continuity and stability. When the delay coefficient exceeds the threshold, the data is discarded to avoid misjudgment of energy consumption correlation patterns caused by delayed data, providing a high-quality data foundation for subsequent resource management coefficient calculations. Dividing the data set interval into the first and second data set intervals, the delays of different sub-intervals can be compared to accurately locate the abnormal data period. At the same time, combined with the time dimension, such as the division between peak and non-peak, the data set can reflect the operating characteristics of different time periods, provide segmented data support for targeted energy-saving strategies, and avoid the average data masking the performance consumption issues of the time period.

[0056] In step S4, a weighted algorithm is used to comprehensively analyze the data set and assign weights to the stable signals of physical, management, and environmental information;

[0057] Set the resource management coefficient threshold. If the resource management coefficient is greater than or equal to the resource management coefficient threshold, the resource management status is determined to be excellent and a high-level management signal is generated, indicating that the elevator operation resource matching degree is high and no major adjustment is required.

[0058] If the resource management coefficient is less than the resource management coefficient threshold, it is determined that the resource management status is good but there is room for optimization, and an intermediate management signal is generated, indicating that some operating parameters need to be adjusted.

[0059] Weights are assigned to stable signals from physical, management, and environmental information, allowing resource management coefficient calculations to focus on factors with the greatest impact on energy conservation, avoiding interference from irrelevant factors. Load stability significantly impacts energy consumption, and its weighting improves the coefficient's accuracy in reflecting actual energy consumption. Resource management coefficient thresholds are used to distinguish between advanced and intermediate management signals. Advanced signals require no significant adjustments, reducing energy consumption fluctuations caused by unnecessary parameter changes. Intermediate signals clearly indicate room for optimization, providing guidance for targeted adjustments, making energy-saving strategies more aligned with actual needs and improving optimization efficiency.

[0060] In step S5, for the high-level management signal, the current core operating parameters and dispatching strategy of the elevator are maintained, and only minor adjustments are made to maintain stability;

[0061] Maintain the current partition operation mode, continue the established standby sleep time, optimize the door opening and closing time of the door machine, and record the current operation status as a benchmark for subsequent optimization.

[0062] In step S5, for the intermediate management signal, if the intermediate signal is triggered due to large fluctuations in the load rate, the operating speed is dynamically adjusted;

[0063] If the signal is triggered due to uneven distribution of passenger flow, the elevator service area will be re-divided;

[0064] If stability is affected by deviations in maintenance records, arrange maintenance plans in advance to ensure reliable operation of the elevator during peak hours.

[0065] Maintaining core parameters can avoid energy consumption fluctuations caused by frequent adjustments. By optimizing only details like the door operator's opening and closing times, we can achieve minimal energy savings while ensuring stable operation. This, combined with recording baseline conditions for comparison purposes in subsequent optimizations, creates a closed loop of continuous improvement. Adjusting operating speeds to account for load fluctuations directly reduces ineffective energy consumption during light loads. Re-dividing service areas to account for uneven passenger flow can reduce cross-zone idle travel. Preemptive scheduling for maintenance discrepancies can avoid high-energy-consumption emergency operations caused by failures. These three targeted measures make energy-saving optimization more precise and effective.

[0066] Working principle:

[0067] Build a foundation for energy-saving analysis through multi-dimensional data collection. Using equipment such as elevator sensors and building monitoring systems, we simultaneously collect physical information (real-time speed, load factor, etc.), management information (rated parameters, maintenance records, etc.), and environmental information (traffic flow, building characteristics, etc.). This creates a comprehensive data pool covering the elevator's own performance, operating status, and external requirements, providing comprehensive input for subsequent analysis.

[0068] Secondly, accurate perception of operating status is achieved through quantitative signal processing. The collected raw data is converted into quantifiable and stable signals, such as setting thresholds for speed fluctuations and passenger flow rate changes. At the same time, multi-dimensional signal linkage verification ensures that the assessment of elevator operating status is transformed from subjective judgment to objective data support, thereby improving the accuracy of status perception.

[0069] Next, data quality screening is performed to ensure analytical reliability. Using a delay coefficient calculation model, a quantitative assessment of signal delays across different time intervals is performed. Abnormal data exceeding a delay threshold is eliminated, reducing the proportion of invalid data and ensuring the continuity and stability of the data set entering the analysis phase, providing a high-quality data foundation for subsequent decision-making.

[0070] Then, refined management decisions are made through the generation of hierarchical signals. A weighted algorithm analyzes the data set, calculates the resource management coefficient, and compares it with a preset threshold to generate a high-level or medium-level management signal. High-level signals correspond to states with high resource matching.

[0071] Finally, continuous energy savings are achieved through targeted adjustments and closed-loop iterations. For high-level signals, core parameters are kept stable with only minor adjustments to avoid energy consumption fluctuations. For mid-level signals, differentiated optimization is implemented based on specific incentives. By recording baseline states and iterating strategy parameters, the system adapts to different scenarios, ultimately achieving overall energy savings in multiple elevator scenarios.

[0072] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments 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 or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in 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 device. The 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, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0074] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or 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 can 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.

[0075] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided in this 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 merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0077] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0079] If the functions are implemented in the form of software function modules and sold or used as independent products, they 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment 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 (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0080] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0081] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An elevator energy-saving optimization method based on big data analysis, characterized in that: The steps include: Step S1: Acquire physical information, management information, and environmental information during daily elevator operation; Step S2: Collecting operation stability signals of physical information, management information and environmental information; Step S3: collecting stable operation signals at different times to construct a data set; Step S4: Analyze the data set to obtain resource management coefficients, further analyze the resource management coefficients to generate different levels of management signals, including high-level and intermediate management signals; Step S5: Adjust the elevator operation according to the obtained management signal.

2. The elevator energy-saving optimization method based on big data analysis according to claim 1, characterized in that: In step S1, the physical information includes the real-time running speed of the elevator, the start and stop acceleration, and the load weight and load rate in the car; Management information includes rated load, rated speed and fault handling; Environmental information includes external environment data of elevator operation, real-time passenger flow on each floor of the building, and the nature of building use.

3. The elevator energy-saving optimization method based on big data analysis according to claim 2, characterized in that: In step S2, the stable state parameters of the physical information are collected in real time, and the stability index of the management information is extracted based on the elevator management system database, the corresponding environmental information is extracted, and the corresponding stability signal is generated.

4. The elevator energy-saving optimization method based on big data analysis according to claim 3, characterized in that: In step S3, the data collection interval is set and the time length corresponding to the data collection interval is obtained; Dividing the data set interval into a first data set interval and a second data set interval equally; Obtaining a time length during which the delay value in the first data set interval is greater than the data set delay threshold; obtaining a time length during which the delay value in the second data set interval is greater than the data set delay threshold; Calculate the delay coefficient, the expression is: Among them, YC, c, y1, and y2 are respectively the delay coefficient, the time length corresponding to the data set interval, the time length when the delay value in the first data set interval is greater than the data set delay threshold, and the time length when the delay value in the second data set interval is greater than the data set delay threshold.

5. The elevator energy-saving optimization method based on big data analysis according to claim 4, characterized in that: Set a delay coefficient threshold. If the delay coefficient is less than or equal to the delay coefficient threshold, then construct a data set. If the delay coefficient is greater than the delay coefficient threshold, the current stable signal is discarded and the data set is reconstructed.

6. The elevator energy-saving optimization method based on big data analysis according to claim 5, characterized in that: In step S4, a weighted algorithm is used to comprehensively analyze the data set and assign weights to the stable signals of physical, management, and environmental information; Set the resource management coefficient threshold. If the resource management coefficient is greater than or equal to the resource management coefficient threshold, the resource management status is determined to be excellent and a high-level management signal is generated, indicating that the elevator operation resource matching degree is high and no major adjustment is required. If the resource management coefficient is less than the resource management coefficient threshold, it is determined that the resource management status is good but there is room for optimization, and an intermediate management signal is generated, indicating that some operating parameters need to be adjusted.

7. The elevator energy-saving optimization method based on big data analysis according to claim 6, characterized in that: In step S5, for the high-level management signal, the current core operating parameters and dispatching strategy of the elevator are maintained, and only minor adjustments are made to maintain stability; Maintain the current partition operation mode, continue the established standby sleep time, optimize the door opening and closing time of the door machine, and record the current operation status as a benchmark for subsequent optimization.

8. The elevator energy-saving optimization method based on big data analysis according to claim 7, characterized in that: In step S5, for the intermediate management signal, if the intermediate signal is triggered due to large fluctuations in the load rate, the operating speed is dynamically adjusted; If the signal is triggered due to uneven distribution of passenger flow, the elevator service area will be re-divided; If stability is affected by deviations in maintenance records, arrange maintenance plans in advance to ensure reliable operation of the elevator during peak hours.

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