Elevator and building energy system fusion energy-saving management and control method based on Internet of Things

Abstract

The invention discloses an elevator and building energy system fusion energy-saving management and control method based on the Internet of Things, and relates to the technical field of energy management. In order to solve the problems that traditional elevator operation management and building energy system control are mutually independent and lack of an effective cooperation mechanism, so that energy waste is serious, the operation states of an elevator and a building energy system are difficult to comprehensively master in real time, and an energy-saving strategy cannot be accurately formulated. Elevator operation and building energy system data are collected in real time through the sensor network, the remote supervision center deeply integrates, analyzes and excavates a correlation mode and a time sequence relation between operation characteristics of the elevator and the building energy system data, an operation characteristic analysis model is constructed, an accurate energy-saving strategy is formulated, energy consumption is remarkably reduced, and the energy utilization efficiency is improved; the energy-saving effect is quantitatively evaluated, a closed-loop optimization mechanism is formed, and the energy-saving management and control effect is continuously improved; by establishing a fault prediction model, equipment faults are predicted in advance, a maintenance scheme is generated, and stable operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to an energy-saving management and control method for integrating elevator and building energy systems based on the Internet of Things. Background Art

[0002] Elevators, as crucial vertical transportation tools in buildings, along with other building energy system equipment like lighting and air conditioning, consume significant amounts of electricity during daily operation. Traditionally, elevator operation management and building energy system control are independent of each other, lacking effective coordination mechanisms, leading to significant energy waste. Furthermore, existing technologies struggle to comprehensively monitor the operating status of elevators and building energy systems in real time, making it impossible to accurately formulate energy-saving strategies. Furthermore, frequent equipment failures and the lack of effective predictive methods not only impact building operations but also indirectly increase energy consumption. Summary of the Invention

[0003] The purpose of the present invention is to provide an energy-saving management and control method for the integration of elevator and building energy systems based on the Internet of Things, build a sensor network, integrate elevator and building energy systems, formulate efficient energy-saving strategies, and significantly reduce energy consumption. At the same time, by establishing an equipment failure prediction model, equipment failures can be prevented in advance, and energy waste and operation interruptions caused by failures can be reduced, so as to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The IoT-based integrated energy-saving management and control method for elevators and building energy systems includes:

[0006] The sensor network collects elevator operation status data and building energy system data in real time, and establishes a data exchange channel between the sensor network and the remote monitoring center based on a wireless communication link;

[0007] The remote monitoring center integrates and analyzes elevator operating status data and building energy system data based on the data interaction channel, and formulates energy-saving management and control strategies based on the operating characteristics of the elevator and building energy systems;

[0008] Based on the energy-saving control strategy, the elevator operating parameters and building energy system equipment are regulated, and the execution data is fed back in real time to evaluate the implementation effect of the energy-saving control strategy.

[0009] Furthermore, the sensor network specifically includes:

[0010] A weight sensor is installed in the elevator car to monitor the elevator load data in real time, and a speed sensor and start-stop counter are installed in the elevator machine room to obtain the elevator running speed and start-stop frequency data in real time;

[0011] Integrate elevator load data, elevator operating speed, and start and stop times data based on time series to generate elevator operating status data;

[0012] Install current and voltage sensors in the building's power distribution box to measure the building's power consumption data in real time. Install brightness sensors in each lighting area of ​​the building and temperature sensors at each air-conditioning outlet to monitor environmental parameter data in real time.

[0013] Integrate the real-time collected power consumption data and environmental parameter data based on time series to generate a building energy system dataset;

[0014] At the same time, a sensor network is constructed based on the network communication link using weight sensors, speed sensors, start-stop counters, current and voltage sensors, brightness sensors, and temperature sensors, to establish a connection between the elevator operation status data and the building energy system data set and the wireless communication link.

[0015] Furthermore, the remote monitoring center also includes building a dedicated database to store the received elevator operation status data and building energy system data in the dedicated database, and the data in the dedicated database is labeled and classified according to the sensor category of the elevator operation status data and the building energy system data.

[0016] Furthermore, the remote monitoring center integrates and analyzes elevator operating status data and building energy system data, including:

[0017] dividing the elevator operation status data and the building energy system data into at least one time window based on the time series of the elevator operation status data and the building energy system data;

[0018] Extract the elevator usage frequency data from the elevator operation status data in each time window, and at the same time, obtain the energy consumption data of the building in the corresponding time window;

[0019] Obtain the combination of elevator usage frequency characteristics and building energy consumption characteristics that frequently appear in different time windows, and identify the correlation pattern between elevator usage frequency and building energy consumption in this time period;

[0020] Obtain historical elevator operating status data and historical building energy system data, obtain the changing trend of elevator usage frequency and the changing characteristics of building energy consumption, and determine the chronological relationship between the time series of elevator usage frequency changes and the time series of building energy consumption changes;

[0021] Based on the correlation pattern and time series relationship between elevator usage frequency and building energy consumption, the elevator operation status data and building energy system data are integrated to form a comprehensive elevator and building energy system operation data set;

[0022] Key features are extracted from the elevator and building energy system operation data sets, target key features related to the formulation of energy-saving management and control strategies are screened out, and an operation characteristic analysis model for the integration of elevator and building energy systems is constructed.

[0023] Furthermore, an operational characteristic analysis model integrating elevators and building energy systems is constructed, specifically including:

[0024] Read the target key features of the elevator and building energy system operation data set, and determine the operating characteristics of the elevator and building energy system under different working conditions based on the reading results;

[0025] Describe the operating status of the elevator and building energy system based on the operating characteristics under different working conditions. At the same time, determine the operating status of the elevator and building energy system based on the description results, and judge the degree to which the current operating status deviates from the ideal energy-saving state;

[0026] Based on the evaluation results of the operating status, determine the key goals and key optimization nodes of energy-saving management and control, determine the starting point and direction of energy-saving strategy formulation, and search for the corresponding energy-saving strategy model in the pre-built energy-saving strategy knowledge base;

[0027] The energy-saving strategy knowledge base includes various energy-saving strategy models for different operating scenarios of elevators and building energy systems. When multiple energy-saving strategy models match key objectives and key optimization nodes, a priority ranking strategy based on energy-saving potential and implementation cost is adopted to select the most feasible model.

[0028] Furthermore, judging the extent to which the current operating state deviates from the ideal energy-saving state also includes classifying the elevator operating state into three categories according to energy consumption: high efficiency, normal, and low efficiency, and grading each area of ​​the building energy system according to the degree of energy waste.

[0029] Furthermore, formulating energy-saving management and control strategies also includes: extracting corresponding strategy implementation points and operating methods based on the selected energy-saving strategy model, formulating corresponding energy-saving management and control execution plans, and connecting the execution plans with actual elevator and building energy system control equipment, and regulating elevator operating parameters and building energy system equipment based on the energy-saving management and control strategies.

[0030] Furthermore, based on the energy-saving control strategy, the elevator operating parameters and building energy system equipment are regulated, including:

[0031] Calculate the degree of energy-saving effect achieved after implementing the energy-saving management and control strategy based on the pre-set energy-saving effect evaluation indicators, and compare the degree of energy-saving effect achieved with the pre-set energy-saving threshold;

[0032] If the energy-saving effect does not reach the preset energy-saving threshold, go back to the previous step, readjust the strategy implementation points and operation methods, adjust the execution plan, and repeat this process until the preset energy-saving threshold is reached;

[0033] If the energy-saving effect is higher than the preset energy-saving threshold, the elevator operating parameters and building energy system equipment will be adjusted based on the energy-saving management and control strategy.

[0034] Furthermore, the effectiveness of the implementation of energy-saving control strategies is evaluated, including:

[0035] Obtain energy consumption data for each region after implementing the energy-saving control strategy, and draw a trend curve of energy consumption data based on the target value of energy consumption data;

[0036] Compare the trend curve of energy consumption data with the standard trend curve of energy consumption data based on the preset energy-saving target, and judge the implementation effect of the energy-saving control strategy based on the comparison results;

[0037] If the trend curve of actual energy consumption data deviates significantly from the standard trend curve, and the energy saving effect does not reach the preset target, the reasons for not reaching the preset target will be explored and improvement measures will be formulated;

[0038] If the two curves show basically the same trend and the actual energy consumption data fluctuates within the preset energy-saving target range, it indicates that the implementation of the energy-saving management and control strategy is effective. Analyze the energy-saving contribution of each region and determine the areas with outstanding energy-saving results and weak areas.

[0039] Furthermore, it also includes:

[0040] Obtain and analyze historical operating data of elevator and building energy system control equipment to establish equipment failure prediction models;

[0041] Predicting the probability of failure of control equipment based on the equipment failure prediction model, and analyzing the performance change trend and common failure modes of the equipment based on the equipment type of the control equipment;

[0042] Combined with the probability of failure, a corresponding equipment failure maintenance plan is generated based on the performance change trend and common failure modes of the equipment.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] Through the sensor network, real-time data on elevator operation and building energy systems are collected. The remote monitoring center conducts in-depth integration and analysis to discover the correlation patterns and temporal relationships between the operating characteristics of the two, build an operating characteristic analysis model, formulate precise energy-saving strategies, significantly reduce energy consumption, and improve energy utilization efficiency. At the same time, the energy-saving effects are quantitatively evaluated to form a closed-loop optimization mechanism to continuously improve the energy-saving management and control effects. By analyzing the historical operating data of the control equipment, a fault prediction model is established to predict equipment failures in advance, generate maintenance plans, reduce the risk of equipment failures and maintenance costs, and ensure stable system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the energy-saving management and control method for integrating elevators and building energy systems based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] To address the technical issues of traditional elevator operation management and building energy system control being independent of each other and lacking effective coordination mechanisms, resulting in serious energy waste, difficulty in fully and comprehensively understanding the operating status of elevators and building energy systems in real time, inability to accurately formulate energy-saving strategies, and lack of effective equipment failure prediction methods, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0048] The IoT-based integrated energy-saving management and control method for elevators and building energy systems includes:

[0049] Based on the sensor network, the system collects elevator operation status data (such as operating speed, number of starts and stops, load, etc.) and building energy system data (such as power consumption, lighting brightness, air conditioning temperature, etc.) in real time, and establishes a data exchange channel between the sensor network and the remote monitoring center based on the wireless communication link;

[0050] The remote monitoring center integrates and analyzes elevator operating status data and building energy system data based on the data interaction channel, and formulates energy-saving management and control strategies based on the operating characteristics of the elevator and building energy systems;

[0051] In this embodiment, the remote monitoring center further includes constructing a dedicated database to store the received elevator operation status data and building energy system data in the dedicated database. The data in the dedicated database is tagged and classified according to the sensor type of the elevator operation status data and the building energy system data, and mapped into a fixed-dimensional vector space. Through efficient vector calculations, large amounts of data can be accurately and quickly classified and stored in a tagged manner. The data location can be quickly located based on the tags, reducing system retrieval time.

[0052] Based on the energy-saving control strategy, the elevator operating parameters and building energy system equipment (such as lighting systems, air conditioning systems, etc.) are regulated and controlled, and the execution data is fed back in real time, such as the actual changes in elevator operating parameters and real-time changes in building energy consumption, to evaluate the implementation effect of the energy-saving control strategy for subsequent optimization.

[0053] Obtain and analyze historical operating data of elevator and building energy system control equipment to establish equipment failure prediction models;

[0054] Predicting the probability of failure of control equipment based on the equipment failure prediction model, and analyzing the performance change trend and common failure modes of the equipment based on the equipment type of the control equipment;

[0055] Combined with the probability of failure, a corresponding equipment failure maintenance plan is generated based on the performance change trend and common failure modes of the equipment.

[0056] In this embodiment, by constructing a sensor network to comprehensively collect elevator and building energy system data, using the operation characteristic analysis model to accurately identify the correlation pattern and time series relationship, formulate targeted energy-saving strategies, and significantly reduce energy consumption, the remote monitoring center integrates and analyzes the data, can dynamically adjust the energy-saving strategy in real time, and quantitatively evaluate the energy-saving effect. When the energy-saving effect does not reach the preset threshold, the key points of the strategy and the execution plan can be quickly retroactively adjusted until the energy-saving target is achieved, forming a closed-loop intelligent management and control system, continuously improving the level of energy-saving management and control, establishing a fault prediction model based on the historical operation data of the equipment, predicting the probability of control equipment failure in advance, and generating maintenance plans based on the performance change trend of the equipment and common failure modes, avoiding sudden equipment failures that lead to increased energy consumption and operation interruptions, reducing equipment maintenance costs, and extending the service life of the equipment.

[0057] In this embodiment, the sensor network specifically includes:

[0058] A weight sensor is installed in the elevator car to monitor the elevator load data in real time, and a speed sensor and start-stop counter are installed in the elevator machine room to obtain the elevator running speed and start-stop frequency data in real time;

[0059] Integrate elevator load data, elevator operating speed, and start and stop times data based on time series to generate elevator operating status data;

[0060] Install current and voltage sensors in the building's power distribution box to measure the building's power consumption data in real time. Install brightness sensors in each lighting area of ​​the building and temperature sensors at each air-conditioning outlet to monitor environmental parameter data in real time.

[0061] Integrate the real-time collected power consumption data and environmental parameter data based on time series to generate a building energy system dataset;

[0062] At the same time, a sensor network is constructed based on the network communication link, including weight sensors, speed sensors, start-stop counters, current and voltage sensors, brightness sensors, and temperature sensors. The elevator operation status data and building energy system data sets are connected to the wireless communication link to ensure that these data can be transmitted.

[0063] In this embodiment, the remote monitoring center integrates and analyzes elevator operating status data and building energy system data, specifically including:

[0064] dividing the elevator operation status data and the building energy system data into at least one time window based on the time series of the elevator operation status data and the building energy system data;

[0065] Extract the elevator usage frequency data (a quantitative indicator of the number of elevator starts and stops within the time period) from the elevator operation status data in each time window. At the same time, obtain the building's energy consumption data (total building power consumption, lighting power consumption in each area, and air conditioning system power consumption, etc.) within the corresponding time window.

[0066] The system captures frequently occurring combinations of elevator usage frequency and building energy consumption characteristics within different time windows, identifying correlation patterns between elevator usage frequency and building energy consumption within those time periods. For example, it was found that between 9:00 AM and 10:00 AM on weekdays, when the number of elevator starts and stops reached a certain threshold, office area lighting power consumption and air conditioning system power consumption both exhibited specific growth patterns.

[0067] Obtain historical elevator operating status data and historical building energy system data to identify trends in elevator usage frequency and characteristics of building energy consumption. This allows for the sequential relationship between the time series of elevator usage frequency and building energy consumption. For example, after multiple consecutive full-load runs, the power consumption of the air conditioning system will increase significantly over the next 30 minutes. This allows for the identification of more complex and time-dependent correlation patterns.

[0068] Based on the correlation pattern and time series relationship between elevator usage frequency and building energy consumption, the elevator operation status data and building energy system data are integrated to form a comprehensive elevator and building energy system operation data set;

[0069] Extract key features from the elevator and building energy system operation data set, including determining the peak operation period of the elevator through data such as the average operating speed of the elevator, the load change rate, and the frequency of use in different time periods; obtaining the estimated value of the occupant density in each area of ​​the building (which can be obtained indirectly through access control system data, the number of Wi-Fi connected devices, etc.), environmental parameters such as ambient temperature and humidity, and corresponding energy consumption data to determine the peak period of building energy consumption; screen out the target key features related to the formulation of energy-saving management and control strategies, and construct an operation characteristic analysis model that integrates the elevator and building energy systems.

[0070] Obtain an estimate of the occupant density in each area of ​​the building. This can be obtained indirectly through methods such as access control system data and the number of Wi-Fi connected devices. Specifically, the following methods are used:

[0071] Based on access control system data: The access control system records the time and identity of people entering and exiting various areas. The number of access card swipes at each area during different time periods is counted as preliminary data on the number of people entering and exiting the area. Combined with the area's area, the fluctuation in the number of people entering and exiting per unit area is calculated. For example, if a 100-square-meter office area has 50 access card swipes between 9:00 and 10:00 a.m., the formula (number of card swipes divided by area) provides a preliminary estimate of 0.5 people entering and exiting per square meter during that period. Further analysis of the fluctuations in access numbers across different weekdays and time periods allows for a model to be developed that models the temporal variation of access numbers, thereby predicting the area's occupancy density during different time periods. Furthermore, by combining historical data on energy consumption at different occupancy densities in the area, energy consumption patterns related to occupancy density are inferred, providing a reference for energy management.

[0072] Based on the number of connected Wi-Fi devices: Multiple Wi-Fi signal receiving points are deployed within the building to receive Wi-Fi signals from mobile devices in real time. By extracting features from the received Wi-Fi signals, such as signal strength and MAC address, different mobile devices can be identified, tracked, and counted. For example, the signal strength attenuation formula (e.g., p = p0 - 10nlog10(d / d0), where p is the received power, p0 is the reference power, n is the path loss exponent, d is the transmission distance, and d0 is the reference distance) is used along with changes in signal strength and differences in the location of receiving points to determine the movement trajectory of mobile devices, enabling accurate device identification and counting. By counting the number of connected Wi-Fi devices per unit area, occupancy density can be indirectly estimated. For example, in a 50-square-meter conference room, if Wi-Fi signals detect 30 simultaneously connected devices, and assuming that each person carries an average of one Wi-Fi device, the occupancy density in the area can be estimated to be 0.6 people per square meter. By integrating and analyzing data on the number of Wi-Fi connected devices in different areas and at different times, and combining it with the functional attributes of the area (such as conference rooms, offices, corridors, etc.), a correlation model between occupancy density and the number of Wi-Fi connected devices is established to more accurately estimate the occupancy density in each area of ​​the building. At the same time, the occupancy density data is combined with environmental parameters such as power consumption, lighting brightness, air conditioning temperature, and corresponding energy consumption data in the building energy system to determine the peak period of building energy consumption. For example, when holding a meeting in a large conference room with a large number of people, the frequency and power of lighting, air conditioning and other equipment may increase significantly, causing energy consumption to reach a peak. Through data analysis, the specific relationship between energy consumption and occupancy density in such scenarios is clarified, providing a basis for energy conservation management and control.

[0073] In this embodiment, an operational characteristic analysis model integrating elevator and building energy systems is constructed, specifically including:

[0074] Read the target key features of the elevator and building energy system operation data set, and determine the operating characteristics of the elevator and building energy system under different working conditions based on the reading results;

[0075] Describe the operating status of the elevator and building energy system based on the operating characteristics under different working conditions. At the same time, determine the operating status of the elevator and building energy system based on the description results, and judge the degree to which the current operating status deviates from the ideal energy-saving state;

[0076] Elevator operation status is divided into three categories according to energy consumption: high efficiency, normal, and low efficiency. Each area of ​​the building energy system is divided into different levels according to the degree of energy waste.

[0077] Determine the key objectives and key optimization nodes for energy-saving management and control based on the evaluation results of the operating status. For example, if the evaluation finds that elevators frequently run empty during off-peak hours, resulting in high energy consumption, the energy-saving goal is set to reduce the energy consumption of elevator empty operation during off-peak hours, and the key optimization node is to optimize the elevator scheduling strategy. If the air-conditioning system in a certain area of ​​the building still maintains high cooling power when the temperature is suitable, the energy-saving goal is to reasonably adjust the cooling power of the air-conditioning in that area. The key optimization node is to accurately control the air-conditioning operating parameters according to the ambient temperature and personnel needs. Determine the starting point and direction of energy-saving strategy formulation, and search for the corresponding energy-saving strategy model in the pre-built energy-saving strategy knowledge base.

[0078] The energy-saving strategy knowledge base includes various energy-saving strategy models for different operating scenarios of elevators and building energy systems. When multiple energy-saving strategy models match key objectives and key optimization nodes, a priority ranking strategy based on energy-saving potential and implementation cost is used to select the most feasible model;

[0079] Based on the selected energy-saving strategy model, the corresponding strategy implementation points and operation methods are extracted. For example, for the elevator time-sharing operation strategy model, the key points such as the switching time of the elevator operation mode and the operating speed adjustment range in different time periods are extracted; for the building energy zoning control strategy model, the operation methods such as the control threshold and control time interval of the energy equipment in each area are extracted, and the corresponding energy-saving control execution plan is formulated. For example, it is clearly stated that between 1:00 and 5:00 in the morning on weekdays, the elevators in non-critical areas will be switched to low-speed sleep mode, and the lighting brightness in the area will be reduced to 30% at the same time; during the lunch break period of office area personnel, the air-conditioning temperature setting value will be increased by 2 degrees Celsius and other specific operation instructions will be given. The execution plan is connected with the actual elevator and building energy system control equipment, and the elevator operation parameters and building energy system equipment are regulated based on the energy-saving control strategy to ensure that the strategy can be effectively implemented.

[0080] In this embodiment, through time series analysis and feature extraction, the correlation pattern between elevator usage frequency and building energy consumption was accurately identified. Based on the operational characteristics analysis model, the elevator and building energy systems were classified into efficient, normal, and inefficient operating states, achieving optimal resource allocation and utilization. By reducing elevator no-load operation and optimizing the energy consumption of equipment such as air conditioning systems, the building's operating costs were significantly reduced. At the same time, equipment was precisely controlled based on environmental parameters and personnel needs, improving environmental comfort. The construction of an intelligent energy-saving strategy knowledge base and multi-scenario strategy selection enhanced management efficiency, and the real-time monitoring and early warning mechanism improved system reliability. Overall, it effectively reduced energy waste, promoted green buildings and sustainable development, and brought significant economic and environmental benefits.

[0081] In this embodiment, the elevator operating parameters and building energy system equipment are regulated based on the energy-saving control strategy, which also includes:

[0082] Calculate the degree of energy-saving effect achieved after implementing the energy-saving management and control strategy based on the pre-set energy-saving effect evaluation indicators, and compare the degree of energy-saving effect achieved with the pre-set energy-saving threshold;

[0083] If the energy-saving effect does not reach the preset energy-saving threshold, go back to the previous step, readjust the strategy implementation points and operation methods, adjust the execution plan, and repeat this process until the preset energy-saving threshold is reached;

[0084] If the energy-saving effect is higher than the preset energy-saving threshold, the elevator operating parameters and building energy system equipment will be adjusted based on the energy-saving control strategy;

[0085] In this embodiment, the reduction rate of total building energy consumption per unit time and the reduction rate of energy consumption per unit elevator transportation volume are used as evaluation indicators. If the energy-saving effect is lower than the preset target, for example, the preset target is to reduce the total building energy consumption by 15%, and the currently calculated energy consumption reduction rate is only 10%, then the process goes back to the previous step, re-examines the key points and operation methods of the strategy implementation, and adjusts the execution plan, such as further optimizing the elevator sleep time and increasing the lighting brightness adjustment range, and repeats this process until the energy-saving target is achieved. When the energy-saving effect is higher than the preset target, the system operation status is continuously monitored. On the premise of ensuring the energy-saving effect, it is explored whether there is room for further optimization, such as fine-tuning the air-conditioning temperature setting value, pursuing higher energy-saving benefits without affecting the comfort level, until a stable and efficient energy-saving management and control strategy system is formed.

[0086] In this embodiment, the implementation effect of the energy-saving control strategy is evaluated, specifically including:

[0087] Obtain energy consumption data for each region after implementing the energy-saving control strategy, and draw a trend curve of energy consumption data based on the target value of energy consumption data;

[0088] Compare the trend curve of energy consumption data with the standard trend curve of energy consumption data based on the preset energy-saving target, and judge the implementation effect of the energy-saving control strategy based on the comparison results;

[0089] If the trend curve of actual energy consumption data deviates significantly from the standard trend curve, and the energy saving effect does not reach the preset target, the reasons for not reaching the preset target will be explored and improvement measures will be formulated;

[0090] If the two curves show basically the same trend and the actual energy consumption data fluctuates within the preset energy-saving target range, it indicates that the implementation of the energy-saving management and control strategy is effective. Analyze the energy-saving contribution of each region and determine the areas with outstanding energy-saving results and weak areas.

[0091] In this embodiment, based on areas with outstanding energy-saving achievements and weak areas, successful experiences are summarized for promotion in other areas. Furthermore, the causes of weak areas are thoroughly investigated, such as whether there are problems such as aging equipment that has not been updated in a timely manner or inadequate policy implementation. From the perspective of equipment operation, the elevator and building energy system control equipment are checked for faults or performance degradation that lead to increased energy waste. For example, severe wear of elevator motors increases energy consumption, and refrigerant leakage in air conditioning systems affects cooling efficiency and increases electricity consumption. From the perspective of policy implementation, any deviations in execution are investigated, such as the lighting system failing to reduce brightness during specific hours as planned, or the elevator scheduling policy not being effectively implemented, resulting in high elevator idle rates.

[0092] In this embodiment, for equipment issues, timely equipment maintenance or upgrade plans are arranged, such as replacing aging elevator motors or repairing air conditioning system leaks. For implementation deviations, the execution supervision mechanism is strengthened, the responsible parties are clearly defined, and the energy-saving management and control strategy is accurately implemented. The implementation points and operating methods of the energy-saving management and control strategy are readjusted, and the energy-saving effect is recalculated and compared with the preset energy-saving threshold. This cycle is repeated until the trend curve of the energy consumption data meets the preset energy-saving target requirements. This forms a continuously optimized closed-loop evaluation loop for the implementation effect of the energy-saving management and control strategy, continuously improving the integrated energy-saving management and control level of elevator and building energy systems.

[0093] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An energy-saving management and control method based on the integration of elevator and building energy systems based on the Internet of Things is characterized by: include: The sensor network collects elevator operation status data and building energy system data in real time, and establishes a data exchange channel between the sensor network and the remote monitoring center based on a wireless communication link; The remote monitoring center integrates and analyzes elevator operating status data and building energy system data based on the data interaction channel, and formulates energy-saving management and control strategies based on the operating characteristics of the elevator and building energy systems; Based on the energy-saving management and control strategy, the elevator and building energy system control equipment are regulated and controlled, and the execution data is fed back in real time to evaluate the implementation effect of the energy-saving management and control strategy.

2. The method for energy-saving management and control of elevator and building energy system integration based on the Internet of Things according to claim 1, characterized in that: The sensor network specifically includes: A weight sensor is installed in the elevator car to monitor the elevator load data in real time, and a speed sensor and start-stop counter are installed in the elevator machine room to obtain the elevator running speed and start-stop frequency data in real time; Integrate elevator load data, elevator operating speed, and start and stop times data based on time series to generate elevator operating status data; Install current and voltage sensors in the building's power distribution box to measure the building's power consumption data in real time. Install brightness sensors in each lighting area of ​​the building and temperature sensors at each air-conditioning outlet to monitor environmental parameter data in real time. Integrate the real-time collected power consumption data and environmental parameter data based on time series to generate a building energy system dataset; At the same time, a sensor network is constructed based on the network communication link using weight sensors, speed sensors, start-stop counters, current and voltage sensors, brightness sensors, and temperature sensors, to establish a connection between the elevator operation status data and the building energy system data set and the wireless communication link.

3. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 2, characterized in that: The remote monitoring center also includes building a dedicated database to store the received elevator operation status data and building energy system data in the dedicated database. The data in the dedicated database is labeled and classified according to the sensor category of the elevator operation status data and the building energy system data.

4. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 3, characterized in that: The remote monitoring center integrates and analyzes elevator operating status data and building energy system data, including: dividing the elevator operation status data and the building energy system data into at least one time window based on the time series of the elevator operation status data and the building energy system data; Extract the elevator usage frequency data from the elevator operation status data in each time window, and at the same time, obtain the energy consumption data of the building in the corresponding time window; Obtain the combination of elevator usage frequency characteristics and building energy consumption characteristics that frequently appear in different time windows, and identify the correlation pattern between elevator usage frequency and building energy consumption in this time period; Obtain historical elevator operating status data and historical building energy system data, obtain the changing trend of elevator usage frequency and the changing characteristics of building energy consumption, and determine the chronological relationship between the time series of elevator usage frequency changes and the time series of building energy consumption changes; Based on the correlation pattern and time series relationship between elevator usage frequency and building energy consumption, the elevator operation status data and building energy system data are integrated to form a comprehensive elevator and building energy system operation data set; Key features are extracted from the elevator and building energy system operation data sets, target key features related to the formulation of energy-saving management and control strategies are screened out, and an operation characteristic analysis model for the integration of elevator and building energy systems is constructed.

5. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 4, characterized in that: Construct an operational characteristic analysis model for the integration of elevators and building energy systems, specifically including: Read the target key features of the elevator and building energy system operation data set, and determine the operating characteristics of the elevator and building energy system under different working conditions based on the reading results; Describe the operating status of the elevator and building energy system based on the operating characteristics under different working conditions. At the same time, determine the operating status of the elevator and building energy system based on the description results, and judge the degree to which the current operating status deviates from the ideal energy-saving state; Based on the evaluation results of the operating status, determine the key goals and key optimization nodes of energy-saving management and control, determine the starting point and direction of energy-saving strategy formulation, and search for the corresponding energy-saving strategy model in the pre-built energy-saving strategy knowledge base; The energy-saving strategy knowledge base includes various energy-saving strategy models for different operating scenarios of elevators and building energy systems. When multiple energy-saving strategy models match key objectives and key optimization nodes, a priority ranking strategy based on energy-saving potential and implementation cost is adopted to select the most feasible model.

6. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 5, characterized in that: Judging the extent to which the current operating status deviates from the ideal energy-saving state also includes classifying the elevator operating status into three categories: high efficiency, normal, and low efficiency according to energy consumption, and grading each area of ​​the building energy system according to the degree of energy waste.

7. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 6, characterized in that: Formulating an energy-saving management and control strategy also includes: extracting the corresponding strategy implementation points and operating methods based on the selected energy-saving strategy model, formulating a corresponding energy-saving management and control execution plan, and connecting the execution plan with the actual elevator and building energy system control equipment, and regulating the elevator operating parameters and building energy system equipment based on the energy-saving management and control strategy.

8. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 7, characterized in that: Regulate elevator operating parameters and building energy system equipment based on energy-saving management and control strategies, including: Calculate the degree of energy-saving effect achieved after implementing the energy-saving management and control strategy based on the pre-set energy-saving effect evaluation indicators, and compare the degree of energy-saving effect achieved with the pre-set energy-saving threshold; If the energy-saving effect does not reach the preset energy-saving threshold, go back to the previous step, readjust the strategy implementation points and operation methods, adjust the execution plan, and repeat this process until the preset energy-saving threshold is reached; If the energy-saving effect is higher than the preset energy-saving threshold, the elevator operating parameters and building energy system equipment will be adjusted based on the energy-saving management and control strategy.

9. The method for energy-saving management and control of elevator and building energy system integration based on Internet of Things according to claim 8, characterized in that: Evaluate the effectiveness of energy conservation management and control strategies, including: Obtain energy consumption data for each region after implementing the energy-saving control strategy, and draw a trend curve of energy consumption data based on the target value of energy consumption data; Compare the trend curve of energy consumption data with the standard trend curve of energy consumption data based on the preset energy-saving target, and judge the implementation effect of the energy-saving control strategy based on the comparison results; If the trend curve of actual energy consumption data deviates significantly from the standard trend curve, and the energy saving effect does not reach the preset target, the reasons for not reaching the preset target will be explored and improvement measures will be formulated; If the two curves show basically the same trend and the actual energy consumption data fluctuates within the preset energy-saving target range, it indicates that the implementation of the energy-saving management and control strategy is effective. Analyze the energy-saving contribution of each region and determine the areas with outstanding energy-saving results and weak areas.

10. The method for energy-saving management and control based on the integration of elevator and building energy system based on Internet of Things according to claim 9, characterized in that: Also includes: Obtain and analyze historical operating data of elevator and building energy system control equipment to establish equipment failure prediction models; Predicting the probability of failure of control equipment based on the equipment failure prediction model, and analyzing the performance change trend and common failure modes of the equipment based on the equipment type of the control equipment; Combined with the probability of failure, a corresponding equipment failure maintenance plan is generated based on the performance change trend and common failure modes of the equipment.

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