AI-driven building electromechanical equipment energy efficiency-service life cooperative control system

Abstract

The invention discloses an AI-driven building electromechanical equipment energy efficiency-life cooperative control system. The system comprises a data acquisition module, a data transmission module, a data processing module, a control decision module and an execution module. The data acquisition module acquires multi-dimensional data through multiple types of sensors and transmits the multi-dimensional data to the data processing module in a wired and wireless manner; an equipment operation prediction model and an energy consumption-life collaborative optimization model are arranged in the data processing module, and collaborative analysis is realized through data interaction between the equipment operation prediction model and the energy consumption-life collaborative optimization model; the control decision module generates a dynamic control instruction based on the analysis result, and the execution module executes equipment regulation and control; through double-model cooperation and double-target optimization, the problems that a traditional system is low in energy efficiency and large in equipment loss are solved, the energy consumption is reduced by 25%-35%, the equipment failure rate is reduced by 25% or above, and the method is suitable for various building electromechanical equipment control scenes.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent control of building electromechanical equipment, and relates to an AI-driven energy efficiency-life cycle collaborative control system for building electromechanical equipment, which is applicable to the control of air conditioning, lighting, fresh air and other electromechanical equipment in various buildings such as office buildings, commercial complexes, hotels, and hospitals. Background Technology

[0002] Existing building electromechanical equipment control systems generally suffer from the following technical defects: 1. Single objective dimension: Focusing only on energy consumption optimization or equipment lifespan protection, lacking a synergistic mechanism between the two, resulting in the contradiction of "energy saving at the expense of lifespan" or "high energy consumption to ensure lifespan". For example, CN201710206912.8 only achieves energy consumption control through a single equipment frequency converter, without considering the degradation of equipment lifespan. 2. Lack of model collaboration: The prediction model and the optimization model operate independently, failing to form a closed loop of "prediction-optimization-feedback". The prediction results are out of touch with the actual control requirements, and the model parameters are vague. For example, CN202510672373.1 uses a digital twin model, but does not clearly define the model interaction path and core parameters. 3. Insufficient scene adaptation: The model has weak generalization ability and is only applicable to specific building scenarios (e.g., CN202311219279.8 is only applicable to subways), and cannot adapt to the equipment operation characteristics of buildings with different functions; 4. Vague technical solutions: The scope of data collection, data processing flow, and control logic are not clearly quantified, and the core algorithm lacks specific implementation details.

[0003] The aforementioned technical defects lead to poor control performance and poor feasibility of the control system, and also make it difficult for those skilled in the art to reproduce the problem.

[0004] Therefore, in order to solve the above-mentioned technical problems, the technical solution of this application is proposed. Summary of the Invention

[0005] I. Technical problems to be solved The purpose of this invention is to provide an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, addressing the technical problems of existing building electromechanical equipment control systems, such as "single target dimension, lack of model collaboration, insufficient scenario adaptation, and vague technical solutions." Specifically, it includes: 1. How to clearly define the specific scope, accuracy, and frequency of data collection to provide reliable input for model analysis; 2. How to design the specific structure, interaction mechanism, and parameter settings of the "prediction-optimization" dual model to achieve synergistic optimization of energy efficiency and lifespan; 3. How to quantify the data processing flow, control strategies, and model optimization methods to ensure that those skilled in the art can reproduce the results; 4. How to make the system applicable to various building electromechanical equipment control scenarios through scenario adaptation mechanisms.

[0006] II. Technical Solution An AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment includes a data acquisition module, a data transmission module, a data processing module, a control decision module, an execution module, and an optional human-machine interaction module. The specific implementation methods of each module are as follows: (a) Data Acquisition Module Core functions: Accurately collect three types of data: environment, equipment operation, and personnel, providing a foundation for subsequent analysis and clarifying "which data to acquire" and the collection standards.

[0007] Table 1 (Details of Collected Data) Hardware configuration: Temperature / humidity sensor: Employs SHT30 digital sensor, I2C communication interface, 3.3V power supply; Light sensor: BH1750FVI sensor, detection range 1-65535 lux, response time <12ms; Current / voltage sensors: ACS712 (range 0-50A) and INA226 (range 0-380V) are used respectively, supporting SPI communication; Personnel Presence Sensor: Employs the FMCW-24G millimeter-wave radar sensor, which is highly resistant to interference and suitable for complex environments.

[0008] (ii) Data transmission module Core Functions: Ensure real-time and stable data transmission, clearly defining transmission methods and safeguard mechanisms. Transmission method details (quantization parameters): Wired transmission: Building Ethernet using TCP / IP protocol, transmission rate 100Mbps, transmission latency ≤20ms, suitable for equipment operation data with large data volume (such as medical equipment current / voltage); transmission cable uses Category 5e unshielded twisted pair, supports PoE power supply, transmission distance ≤100m; Wireless transmission: ZigBee (protocol IEEE802.15.4) transmission distance ≤100m, number of nodes ≤64, power consumption ≤10mA, suitable for low power sensors (such as temperature / humidity sensors); Wi-Fi (protocol IEEE802.11n) transmission rate 300Mbps, transmission distance ≤50m, suitable for people presence sensors and light sensors. Data integrity guarantee: The CRC-32 check algorithm is used during transmission. If the check fails, the data will be automatically retransmitted (retransmission count ≤ 3 times) to ensure that the data loss rate is ≤ 0.1%.

[0009] (III) Data Processing Module Core Functionality: Enables "Detailed Analysis and Model Optimization," incorporating a built-in device operation prediction model (based on DNN) and an energy consumption-lifetime co-optimization model (based on the deep reinforcement learning PPO algorithm). These two models interact in real-time via a CAN bus (communication rate 500kbps). The specific process is as follows: Data preprocessing: Spatiotemporal alignment: The NTP protocol is used to achieve timestamp synchronization (accuracy ±1ms), unifying all sensor data to a millisecond-level time axis; spatial alignment is achieved through a preset "sensor location - device control area" mapping table (e.g., "3rd floor 2nd zone temperature sensor → 3rd floor 2nd zone air conditioner"). Noise removal: Kalman filtering algorithm is used, with the parameters set as follows: State equation: X(k) = A×X(k-1) + B×U(k) + W(k) (A is the state matrix, B is the control matrix, and W is the process noise); Observation equation: Z(k) = H × X(k) + V(k) (H is the observation matrix, V is the observation noise); Specific parameters: A=[[0.99, 0], [0, 0.99]], H=[[1, 0], [0, 1]], process noise covariance Q=diag([1e-4, 1e-4]), observation noise covariance R=diag([1e-3, 1e-3]); Filtering window: Set to 5 sampling points for slowly changing data such as temperature and humidity, and set to 3 sampling points for rapidly changing data such as current and voltage; Outlier removal: The isolated forest algorithm is used, with 100 trees, a sample subset size of 256, and an outlier threshold of “sample outlier score > 0.8” (the outlier score is calculated based on the average path length of the sample in the decision tree). Invalid data such as sensor malfunctions (e.g., current sudden change to the upper limit of the range) and data transmission errors (e.g., humidity > 100%RH) are removed.

[0010] Feature extraction: CNN spatial feature extraction: The network structure is "input layer → convolutional layer 1 → pooling layer 1 → convolutional layer 2 → pooling layer 2 → fully connected layer"; Input: Spatial dimension × sensor data matrix (e.g., temperature data of a 5×5 area); Convolutional layer 1: 32 3×3 convolutional kernels, ReLU activation function, stride 1; Pooling layer 1: 2×2 max pooling, step size 2; Convolutional layer 2: 64 3×3 convolutional kernels, ReLU activation function, stride 1; Pooling layer 2: 2×2 max pooling, step size 2; Fully connected layer: Outputs a 64-dimensional spatial feature vector (representing the spatial relationships of the regional environment, such as temperature conduction between adjacent rooms); LSTM temporal feature extraction: The network structure is "input layer → LSTM layer → Dropout layer → fully connected layer"; Input: Device operation data time series (length 60, i.e., 1 hour of sampled data); LSTM layer: 128 hidden units, activation function tanh, forget gate threshold 0.8; Dropout layer: Dropout rate 0.2 (to prevent overfitting); Fully connected layer: Outputs a 64-dimensional time feature vector (representing the operating sequence of the device, such as the diurnal variation of air conditioning load); Attention mechanism fusion: The scaled dot product attention mechanism is adopted, and the formula is: Attention(Q, K, V) = softmax(Q×K^T / d_k)×V; Q (query vector) = temporal feature vector, K (key vector) = spatial feature vector, V (value vector) = fusion feature candidate vector; d_k=64 (feature dimension), during peak hours (e.g., office building hours 9:00-18:00), the feature weight of personnel data is increased by 50%; Output: 128-dimensional fused feature vector (fusing spatiotemporal features of environment, equipment, and personnel data).

[0011] Model calculation: Equipment operation prediction model (DNN): Network structure: Input layer (128-dimensional fused feature vector) → Hidden layer 1 (256 neurons, ReLU activation) → Hidden layer 2 (128 neurons, ReLU activation) → Output layer (6 neurons, linear activation). Output parameters: Equipment operating parameters for the next 1-3 hours (current, voltage, power, load factor, probability of failure, and predicted energy consumption). Training method: Adam optimizer (learning rate 0.001), batch size 32, number of iterations 500, loss function is mean squared error (MSE). Training dataset: Contains 100,000 historical operational data points from different seasons and load scenarios (covering air conditioning, lighting, medical equipment, etc.). Accuracy Guarantee: 5-fold cross-validation to adjust parameters ensures prediction error ≤3% (error calculation method: |predicted value - actual value| / actual value × 100%). Energy consumption-lifetime co-optimization model (PPO algorithm): State space: 128-dimensional fused feature vector + 3-dimensional target parameters (preset energy consumption threshold, remaining equipment lifespan, personnel comfort threshold), totaling 131 dimensions; Action space: discrete actions (equipment start-up and shutdown) + continuous actions (power adjustment step size 0.5kW, operation mode switching), action constraint is "power adjustment range = 30%-100% of equipment rated power"; Reward function: Energy consumption deviation = (actual energy consumption prediction value - optimal energy consumption threshold) / optimal energy consumption threshold (the optimal energy consumption threshold is preset based on the building type, such as the optimal energy consumption of office building air conditioners is 20kWh / h / unit). Equipment loss factor = (cumulative running time / design life) × 0.6 + (average load rate / rated load rate) × 0.4 (design life is taken from the equipment's factory parameters, such as an air conditioner with a design life of 10 years). The reward function R = α × (1 - energy consumption deviation) + β × (1 - equipment loss coefficient) (α = 0.55, β = 0.45, which can be adjusted through the human-computer interaction module, α + β = 1). Optimization objective: Output optimal control parameters (such as air conditioner compressor frequency of 35Hz and lighting brightness of 500 lux) to achieve Pareto optimality with "lowest energy consumption and minimum loss".

[0012] Model optimization: Transfer learning: Suitable for deployment in new scenarios. The source domain data consists of 100,000 operational data points from an already deployed scenario (such as an office building), and the target domain data consists of 10,000 initial data points from a new scenario (such as a hospital). A fine-tuning approach is used, freezing the parameters of the first two layers of the DNN and training only the last two layers and the output layer. After transfer learning, the model's convergence speed is improved by 60%. Online learning: An incremental learning algorithm (SGD optimizer, learning rate 0.0001) is used. The model weight is updated once every 1,000 valid data points are collected. The update strategy is "historical weights × 0.8 + new data training weights × 0.2" to avoid model drift. Data update mechanism: On the 5th of each month, data from the past 6 months is automatically filtered, retaining valid data with a "prediction error ≤ 3%" (approximately 800,000 records), and removing abnormal and expired data; data is backed up to local storage (1TB capacity) via FTP server, and the dual model parameters are updated simultaneously to ensure long-term operational accuracy.

[0013] (iv) Control Decision Module Core Function: Based on model analysis results and preset targets, generate quantitative control instructions, and clarify the control strategy and logic. Hardware configuration: STM32F407 chip based on ARM Cortex-M4 core, 168MHz main frequency, 1MB storage capacity, supports CAN and UART communication interfaces, instruction generation delay ≤10ms; Preset target quantification: Energy efficiency targets: 25%-35% reduction in energy consumption for office buildings / shopping malls / hotels, and 20%-30% reduction in energy consumption for medical buildings / airports; Lifespan targets: Equipment failure rate ≤5%, remaining lifespan ≥ 80% of design life, maintenance cycle extended by 30%; Comfort targets: Indoor temperature 22-26℃, humidity 40%-60%, light intensity 300-1000 lux (office area); The specific implementation of the three optimization strategies: Equipment load balancing strategy: A greedy algorithm is used to establish a multi-device load distribution model. The objective function is min(max(load rate_i)), and the constraint is 60%≤load rate_i≤80% (normal state). For example, when three air conditioners are controlled in a coordinated manner, the load rates are allocated to 70%, 65%, and 75% respectively according to the room temperature requirements to avoid overloading of a single device. Time cycle optimization strategy: Preset peak and off-peak electricity price periods (e.g., peak hours in Chengdu are 9:00-12:00 and 17:00-22:00, and off-peak hours are 0:00-7:00). During peak hours, the equipment load rate is reduced to 60%-70%, and the air conditioner set temperature is increased by 1-2℃; during off-peak hours, the load rate is increased to 75%-80%, and the air conditioner set temperature is decreased by 1-2℃. User behavior adaptation strategy: Set thresholds based on personnel presence data. When the number of personnel is ≥5, the load rate is ≥70%. When the number of personnel is 0 for 30 minutes, the load rate drops to 30%-50% (air conditioning) / turns off (lighting). When the frequency of personnel presence status switching is ≥5 times / hour, α is adjusted to 0.6 (increase comfort weight). Lifespan protection control logic: Remaining life calculation: Remaining life = Design life - Cumulative operating time × (1 + Average load rate × 0.3); For example, if the air conditioner has a design life of 10 years (87,600 hours), a cumulative operating time of 20,000 hours, and an average load rate of 70%, then the remaining life = 87,600 - 20,000 × (1 + 0.7 × 0.3) = 63,400 hours (approximately 7.25 years); Maintenance warning trigger: When the remaining lifespan is ≤ 10% of the maintenance cycle (e.g., if the maintenance cycle is 1 year, the remaining lifespan is ≤ 36.5 days), the equipment load rate will be automatically adjusted to 50%-60%, and a maintenance reminder (including equipment number and maintenance item) will be pushed through the human-machine interaction module. Post-maintenance recovery: After the administrator inputs the "maintenance completed" command through the human-computer interaction module, the system will restore the load rate to 60%-80% within 5 minutes.

[0014] (v) Execution Module Core functions: Precisely execute control commands, clearly defining connection methods and execution components. Specific implementation of the connection method: Hard-wired connections: RS485 bus (Modbus-RTU communication protocol, 9600bps baud rate) connects the air conditioning unit and the distribution box switch, using shielded twisted-pair cable (transmission distance ≤1200m); relay control (model OMRONG2R-1) connects high-power equipment (such as air conditioning compressor), with a control voltage of 220VAC and a response time ≤5ms; Wireless communication connectivity: The ZigBee module (model CC2530) connects to smart lighting devices and supports group control (each group ≤ 16 lights); the Wi-Fi module (model ESP8266) connects to split air conditioners and water heaters, using the MQTT protocol to transmit control commands, with a command execution confirmation time ≤ 20ms. Component configuration: The frequency converter is a Schneider ATV312 model (power range 0.75-75kW) used for power regulation of the air conditioning compressor; the intelligent controller is a Siemens S7-1200 model used for multi-device collaborative control; the relay module is a PLC expansion module that supports 16-channel switch output.

[0015] (vi) Human-computer interaction module Hardware configuration: 10.1-inch touchscreen (1280×800 resolution, brightness ≥500cd / m²), user interface developed based on Linux system, response time ≤1ms; Functionality implementation: Data visualization: Line charts display real-time energy consumption / equipment operating parameters (updated every 1 second), pie charts display the energy consumption percentage of each device, and red warning lights indicate faulty devices (flashing every 2 times per second). Parameter settings: Supports manual input of energy consumption threshold (range 10-100kWh / h), adjustment of α / β weight (step size 0.05), and setting of maintenance cycle (range 1-12 months); Log query: Stores control command records, equipment fault records, and model update records for the past year, and supports retrieval by time / equipment type.

[0016] (vii) External Interface The data processing module is equipped with RS485 and Ethernet interfaces, supporting communication with regional energy management platforms (such as the State Grid Smart Energy Platform). The communication protocol adopts IEC61850. The data upload frequency is 5 minutes / time, and the uploaded content includes real-time energy consumption, equipment operating status, and optimized control results. After receiving global optimization instructions (such as regional power restriction instructions) issued by the platform, the module adjusts the control strategy (such as reducing the equipment load rate by 10%) within 30 seconds.

[0017] III. Working Principle This invention operates based on a closed-loop process of "data acquisition - collaborative analysis - dynamic decision-making - precise execution - closed-loop feedback," with the specific steps as follows: Data acquisition and transmission: Each sensor collects environmental, equipment, and personnel data at a preset frequency, which is then aggregated to the data processing module via wired / wireless transmission modules. During transmission, CRC-32 verification is performed to ensure integrity (data loss rate ≤0.1%). Data preprocessing and feature fusion: The data processing module performs spatiotemporal alignment, noise removal, and outlier removal on the raw data, and then extracts a 128-dimensional fused feature vector through a CNN+LSTM+attention mechanism; Dual-model collaborative analysis: The feature vector input to the equipment operation prediction model is integrated to output the predicted values ​​of equipment operation parameters for the next 1-3 hours (error ≤3%); the prediction results and the preset target are jointly input into the energy consumption-lifetime collaborative optimization model, and the optimal control parameters are calculated through the PPO algorithm; Dynamic decision-making and execution: The control decision module receives the optimal control parameters, combines three optimization strategies to generate quantitative control commands (such as "air conditioner compressor frequency 35Hz" and "lighting brightness 500 lux"), and adjusts the equipment operating status through the execution module; Closed-loop feedback optimization: The execution module feeds back the actual operating data of the equipment (such as actual power and temperature) to the data processing module. The data processing module updates the model weights every 1,000 data points through online learning; the model is iterated once a month to ensure that the prediction error is ≤3%; at the same time, the human-computer interaction module displays the execution results and supports managers to manually adjust the parameters.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. An AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, which achieves a 25%-35% reduction in energy consumption and a more than 25% reduction in equipment failure rate through dual-model collaboration and three optimization strategies. Compared with existing technologies (such as CN202510672373.1, which improves energy efficiency by 15%-30%), the optimization effect is improved by 10%-15%. 2. In this invention, the data collection details, algorithm parameters, model structure, and control logic are clearly defined, and all core parameters are quantified (such as the Kalman filter Q / R matrix, the number of CNN convolution kernels, and the reward function α / β values). 3. In this invention, transfer learning is used to adapt to different buildings such as office buildings, hotels, hospitals, and airports, thus overcoming the limitations of existing technologies in terms of application scenarios. 4. In this invention, the data transmission loss rate is ≤0.1%, the model prediction error is ≤3%, and the control command response time is ≤30ms, ensuring the long-term stable operation of the system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments, experimental examples, and comparative examples will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is an overall architectural diagram of the present invention; Figure 2 This is a diagram illustrating the analysis steps of the data processing module in this invention; Figure 3 This is an architecture diagram of the control decision module in this invention; Figure 4 This is an architectural diagram of the execution module and electromechanical equipment in this invention; Figure 5 This is an architecture diagram of the data processing module in this invention; Figure 6 This is an architecture diagram of the control decision module in this invention; Figure 7 This is a diagram of the architecture of the energy consumption-lifetime co-optimization model in this invention; Figure 8 This is an architecture diagram showing how the data processing module in this invention connects to the regional energy management platform and the control decision module via an external interface. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings, embodiments, experimental examples, and comparative examples. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0024] I. Implementation Examples Example 1 This invention discloses an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the specific implementation method of this embodiment is as follows: Central Air Conditioning Control System for Office Buildings in Chengdu High-tech Zone I. Project Background An office building in Chengdu High-tech Zone has a floor area of ​​15,000 m² and is equipped with 10 multi-split central air conditioning units (rated power 15kW / unit, design life 10 years). Under traditional control, the average annual energy consumption is 130 kWh / m² and the equipment failure rate is 12%.

[0025] II. System Deployment Data collection: Data to be collected: outdoor temperature, indoor room temperature / humidity, air conditioner operating current / voltage / power, cumulative running time, cumulative number of starts and stops, and the presence status of people in the conference room; Hardware configuration: Two SHT30 temperature / humidity sensors and one BH1750 light sensor are installed in each office area. An ACS712 current sensor and an INA226 voltage sensor are installed in each air conditioner power supply line. One FMCW-24G personnel presence sensor is installed in each of the five meeting rooms. Data collection frequency: Temperature / humidity 10 seconds / time, current / voltage 1 second / time, personnel presence status 3 seconds / time.

[0026] Data transmission: The sensors in the office area use Ethernet transmission (Category 5e unshielded twisted pair cable), while the outdoor sensors use ZigBee wireless transmission (CC2530 module). The transmission delay is ≤30ms and the data loss rate is 0.05%.

[0027] Data processing: Data preprocessing: Kalman filter Q=diag([1e-4, 1e-4]), R=diag([1e-3, 1e-3]), isolated forest anomaly score threshold 0.8, and removal of current surge data (>30A) during air conditioner startup. Feature extraction: The CNN uses 2 layers of convolution (32 3×3 convolution kernels → 64 3×3 convolution kernels), 128 LSTM hidden units, and the attention mechanism assigns a weight of 0.6 to the personnel data from 9:00 to 18:00. Model calculation: The equipment operation prediction model has a prediction cycle of 2 hours, inputs 128-dimensional fused features, and outputs current (prediction error 2.8%), power (prediction error 2.5%), and energy consumption (prediction error 2.2%); the energy consumption-lifetime co-optimization model has α=0.55, β=0.45, and the optimal energy consumption threshold is 20kWh / h / unit; Model optimization: The transfer learning source domain is 50,000 air conditioning operation data from another office building in Chengdu. The weights are updated every 1,000 data points in online learning (SGD learning rate 0.0001).

[0028] Control Decisions: Preset targets: Energy consumption reduced by 30%, equipment failure rate ≤5%, indoor temperature 22-26℃; Control strategy: Weekdays 9:00-18:00 (high population density): Load balancing strategy allocates 10 air conditioners to a load rate of 65%-75%, and user behavior adaptation strategy sets the temperature to 25℃ when "number of people ≥ 3". 18:00-9:00 the next day (unmanned): Time cycle optimization strategy (valley load rate 75%-80%, peak load rate 60%-65%), load rate reduced to 50%, temperature set at 26℃; Lifespan guarantee: An air conditioner has accumulated 15,000 hours of operation, with a designed lifespan of 87,600 hours. The remaining lifespan is 87,600 - 15,000 × (1 + 0.7 × 0.3) = 72,600 hours. The maintenance cycle is 1 year (8,760 hours). The remaining lifespan is greater than 10% of the maintenance cycle (876 hours), maintaining the normal load rate.

[0029] Execution module: The air conditioning unit is connected to the control decision module via an RS485 bus (Modbus-RTU protocol), and the indoor unit is connected via a ZigBee module. The control command response time is ≤20ms.

[0030] III. Implementation Results After one year of operation, the air conditioning energy consumption was reduced to 90 kWh / m² (a reduction of 30.7%), the equipment failure rate was reduced to 8% (a reduction of 33.3%), the indoor temperature fluctuation was ≤ ±1℃, and the average model prediction error was 2.3%, meeting the ≤3% requirement of claim 7.

[0031] Example 2 This invention discloses an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the specific implementation method of this embodiment is: multi-device collaborative control in Chongqing Jiefangbei commercial complex. I. Project Background A commercial complex in Jiefangbei, Chongqing, has a building area of ​​80,000 m², including a shopping mall (50,000 m²), restaurants (20,000 m²), and a cinema (10,000 m²). It is equipped with 30 air conditioners (rated power 20kW / unit) and 1,000 lighting fixtures (rated power 36W / lamp). Under traditional control, the annual energy consumption of lighting and air conditioning is 110 kWh / m², and the annual equipment maintenance cost is 200,000 yuan.

[0032] II. System Deployment Data collection: Data to be collected: temperature / humidity / light intensity in shopping malls / restaurants / cinemas, current / voltage / power / cumulative running time of air conditioning / lighting, number of people, and equipment fault signals; Hardware configuration: One BH1750 light sensor is installed every 50m² in the shopping mall, one FMCW-24G people presence sensor is installed every 10m² in the food and beverage area, and ACS712 / INA226 sensors are installed in the air conditioning / lighting circuits. Data collection frequency: light intensity 5 seconds / time, number of people 3 seconds / time, equipment operation data 1 second / time.

[0033] Data processing: Feature extraction: CNN-LSTM models were constructed for air conditioning and lighting respectively. The air conditioning model has 128 LSTM hidden units, and the lighting model has 64 LSTM hidden units. Model calculation: The equipment operation prediction model predicts the energy consumption of air conditioning (2-hour cycle) and lighting (1-hour cycle) respectively. The energy consumption-lifetime co-optimization model sets β=0.5 (priority lifetime) for the catering area and α=0.6 (priority energy efficiency) for the shopping mall area. Reward function calculation: Shopping mall area energy consumption deviation = (predicted energy consumption - 15kWh / h / unit) / 15kWh / h / unit, equipment loss coefficient = (cumulative running time / 87600) × 0.6 + (load rate / 100%) × 0.4.

[0034] Control Decisions: Shopping mall area: When the illuminance is ≥1500 lux, turn off 50% of the lighting fixtures (load rate reduced to 50%), and use the air conditioning load balancing strategy to distribute the load rate of 30 air conditioners to 60%-70%; For the dining area: when there are ≥10 people, the air conditioning load rate is increased to 75%, α=0.5, β=0.5; when there are <3 people, the load rate is reduced to 60%, β=0.55.

[0035] Execution module: Lighting fixtures are controlled in groups (10 fixtures per group) via ZigBee modules (CC2530), and air conditioners are connected via RS485 bus. The control command execution confirmation time is ≤15ms.

[0036] III. Implementation Results After 8 months of operation, the overall energy consumption dropped to 75 kWh / m² (a reduction of 31.8%), equipment maintenance costs dropped to 140,000 yuan (a reduction of 30%), customer comfort ratings improved by 15%, air conditioning prediction error was 2.1%, and lighting prediction error was 1.8%.

[0037] Example 3 This invention discloses an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the specific implementation method of this embodiment is: control of electromechanical equipment in hotel rooms in Xi'an Qujiang New District. I. Project Background A four-star hotel in Xi'an Qujiang New District has 300 guest rooms. Under traditional control, the average annual energy consumption of each guest room is 90 kWh / m², and the water heater failure rate is 15%.

[0038] II. System Deployment Data Acquisition: Each guest room is equipped with an SHT30 temperature / humidity sensor and an FMCW-24G occupancy sensor, and the power supply line for the water heater (rated power 5kW / unit) is equipped with an ACS712 current sensor; the data collected includes guest room temperature / humidity, occupancy status, and water heater current / power / cumulative running time.

[0039] Data processing: Equipment operation prediction model: Input features include occupancy rate, historical water consumption, and ambient temperature. It uses 64 LSTM hidden units to predict the water consumption of the water heater in the next hour (prediction error 2.4%). Energy consumption-lifetime co-optimization model: The "anti-dry burning" threshold is set to current ≥10A (at which point the power is automatically reduced to 50%), α=0.5, β=0.5.

[0040] Control Decisions: When guests check in (signal detected by the presence sensor): the water heater power is increased to 80% (load rate 80%). When guests leave for ≥30 minutes: power is reduced to 30% (load rate 30%). Lifespan Guarantee: The water heater is designed for an 8-year (69,120 hours) lifespan, with a cumulative operating time of 20,000 hours. The remaining lifespan is 69,120 - 20,000 × (1 + 0.8 × 0.3) = 69,120 - 24,800 = 44,320 hours. The maintenance cycle is 180 days (4,320 hours). When the remaining lifespan is ≤ 18 days (10% maintenance cycle), the load rate should be adjusted to 60%.

[0041] Results: After six months of operation, guest room energy consumption dropped to 62 kWh / m² (a 31.1% reduction), water heater failure rate dropped to 9% (a 40% decrease), and guest satisfaction increased by 12%.

[0042] Example 4 This invention discloses an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the specific implementation method of this embodiment is as follows: Kunming Medical University Affiliated Hospital Medical Electromechanical Equipment Control I. Project Background The inpatient building of the Affiliated Hospital of Kunming Medical University has a building area of ​​20,000 m² and includes medical electromechanical equipment such as ventilators, infusion pumps, and air conditioners. Under traditional control, the air conditioning energy consumption is 120 kWh / m², and the equipment maintenance response time is ≥2 hours.

[0043] II. System Deployment Data acquisition: SHT30 temperature / humidity sensors (accuracy ±0.2℃) are installed in the wards, high-precision current / voltage sensors (accuracy ±0.5%) are installed on the power supply lines of medical equipment, and FMCW-24G personnel presence sensors are installed in the corridors; the data collected include ward temperature and humidity, medical equipment operating parameters (current / voltage / power), and personnel presence status.

[0044] Data processing: Equipment operation prediction model: Set a "fault warning threshold" (trigger warning when current fluctuation is ≥5%), 128 LSTM hidden units, predict the risk of medical equipment failure (prediction accuracy ≥92%). Energy consumption-lifetime co-optimization model: α=0.4, β=0.6 (prioritizing equipment stability), and adding medical equipment priority parameters to the state space (ICU equipment has the highest priority).

[0045] Control Decisions: ICU ward: Air conditioning is maintained at 24℃ 24 hours a day (70% load rate), unaffected by peak and off-peak electricity prices; General wards: When patients leave for ≥1 hour, the air conditioning temperature should be set to 26℃ (load rate 60%). Maintenance reminder: When the remaining lifespan of the equipment is ≤15 days (maintenance cycle is 150 days, 10% is 15 days), a maintenance reminder will be automatically pushed to the mobile phone of medical staff, and the maintenance response time will be shortened to 30 minutes.

[0046] Implementation results: After 10 months of operation, the air conditioning energy consumption was reduced to 82 kWh / m² (a reduction of 31.7%), the equipment maintenance response time was shortened to 30 minutes, and the failure rate of medical equipment was reduced to 6%.

[0047] Example 5 This invention discloses an AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the specific implementation method of this embodiment is as follows: Control of electromechanical equipment in Chengdu Tianfu International Airport terminal building. Project Background: Chengdu Tianfu International Airport Terminal 2 has a building area of ​​40,000 square meters. 2It involves electromechanical equipment such as air conditioning, lighting, and baggage conveyor belts, with an average annual energy consumption of 140 kWh / m² under traditional control. 2 The baggage conveyor belt failure rate is 10%. System Deployment: Data collection: One BH1750 light sensor is installed every 100m² in the terminal hall; an ACS712 current / voltage sensor is installed on the baggage conveyor motor (rated power 15kW / unit); and an FMCW-24G personnel presence sensor is installed at the boarding gate. The collected data items include light intensity, baggage conveyor operating parameters, number of people, and flight takeoff and landing times. Data processing: Equipment operation prediction model: Input features include flight schedules, historical load data, and personnel numbers; Control strategy: During peak flight hours (8:00-12:00, 14:00-18:00), the baggage conveyor load rate is increased to 80%, and reduced to 50% during off-peak hours. Lighting is automatically adjusted based on light intensity (some lights are turned off when the light intensity is ≥1500 lux). Implementation results: After one and a half years of operation, the overall energy consumption dropped to 95 kWh / m³. 2 (Reduced by 32.1%), baggage conveyor failure rate decreased to 6% (reduced by 40%), and operating costs decreased by 28%.

[0048] II. Experimental Examples Experiment Example 1: Coordinated Control of Air Conditioning on Different Floors of an Office Building in Wuhou District, Chengdu Test subjects: 10th floor (high floor, high outdoor wind speed) and 3rd floor (low floor, densely populated area) of an office building in Wuhou District, Chengdu, each floor 2000m. 2 All are equipped with the same model of multi-split air conditioner (Gree GMV-H120WL / A).

[0049] System deployment differences: 10 layers: Temperature sensors are encrypted and installed (every 50m). 2 Install 1 unit), set the β value of the energy consumption-lifespan co-optimization model to 0.5 (high-rise buildings have large wind loads, so priority should be given to ensuring the lifespan of the equipment). Layer 3: The sampling frequency of the personnel presence sensor is increased to 1 time / 2 seconds, and the model α value is set to 0.6 (for densely populated areas, energy consumption is optimized first).

[0050] Test period: June 1-30, 2024 (Chengdu summer high temperature period, average daily outdoor temperature 28-35℃).

[0051] Table 2 (Key test data for Experiment Example 1) Conclusion: Adjusting model weights to account for environmental differences on different floors can achieve optimal energy consumption while ensuring lifespan, keeping the failure rate of high floors below 1% and improving the prediction accuracy of low floors to over 98%.

[0052] Experiment Example 2: Control of Dedicated Electromechanical Equipment in the Operating Room of Fucheng District Hospital, Mianyang Test subjects: Operating rooms on the 2nd floor of a tertiary hospital in Fucheng District, Mianyang (5 rooms in total, 80m² per room) 2 This involves equipment such as air conditioners (cleanliness level 10,000), shadowless lamps, and medical constant temperature chambers.

[0053] System special settings: The data acquisition module adds a differential pressure sensor (to detect the pressure difference between the operating room and the corridor, range 5-10 Pa) and a cleanliness sensor (to detect particulate matter concentration, ≤3.5 particles / L). The energy consumption-lifespan co-optimization model is set to "medical priority lock": the β value is increased to 0.7 during the surgical period (8:00-18:00) (equipment failure may affect the surgery, so lifespan is prioritized), and the α value is increased to 0.6 during non-surgical periods.

[0054] Test period: March 1-31, 2024 (Spring, average daily number of surgeries: 8).

[0055] Key test data: Daily energy consumption of operating room air conditioning: 78 kWh / m 2 (Traditional control is 110kWh / m³) 2 (a decrease of 29.1%). Failure rate of shadowless lamp: 0.3% (1.5% with traditional control, a reduction of 80%); Differential pressure / cleanliness compliance rate: 100% (traditional control is 92%, an improvement of 8.7%).

[0056] Conclusion: By adding dedicated sensors and prioritization mechanisms to meet the specific needs of medical scenarios, energy consumption and lifespan can be optimized simultaneously while meeting medical standards, resulting in a failure rate reduction far exceeding that of ordinary building scenarios.

[0057] Experiment Example 3: Seasonal Control of Hotel Rooms in Guanshanhu District, Guiyang Test subjects: 30 guest rooms on the 15th floor of a five-star hotel in Guanshanhu District, Guiyang (each room is 35m²) 2 This involves air conditioning, water heaters, and fresh air systems. Guiyang experiences mostly cloudy and rainy winters (average daily temperature 5-12℃) and cool summers (average daily temperature 20-26℃).

[0058] System seasonal adaptation: Winter (December-February): The water heater preheating time is set to 2 hours before moving in, and the energy consumption-lifespan co-optimization model α value is 0.5 (hot water demand is high in winter, balancing energy consumption and water heater lifespan). Summer (June-August): The ratio of fresh air to air conditioning is adjusted to 1:3, and the model α value is 0.6 (for cool summers, fresh air is used first to save energy).

[0059] Testing period: December 2023 - August 2024 (covering both winter and summer seasons, a total of 9 months).

[0060] Table 3 (Key Test Data for Experiment Example 3) Conclusion: By adjusting equipment parameters and model weights according to the season, energy consumption is reduced by 30% in winter and 35% in summer, while equipment lifespan is extended by 16%-22%, verifying the system's ability to adapt to seasonal scenarios.

[0061] III. Comparative Examples (Benchmarking against existing technologies) Comparative Example 1: Comparison with existing technology 1 (CN201710206912.8, frequency converter control only) Test conditions: 3rd floor (2000m) office building in Wuhou District, Chengdu, the same as in Experiment 1. 2 (The same model of Gree air conditioner) adopts the existing technology 1 "inverter + contactor" control method, which only optimizes energy consumption by adjusting the frequency, without considering the equipment life and personnel behavior.

[0062] Test period: June 1, 2024 - June 30, 2024 (same as Experiment 1, excluding environmental interference).

[0063] Table 4 (Comparative data for Comparative Example 1) Reasons for the difference: Existing technology 1 only adjusts the load through the frequency converter, lacking human behavior perception (such as high-frequency operation when no one is present) and life protection mechanism (high-frequency operation leads to increased wear of the compressor); This invention achieves a balance of "energy consumption-lifespan-comfort" through dual-model collaboration and human data feedback, with significant advantages.

[0064] Comparative Example 2: Benchmarking against existing technology 4 (CN202510672373.1, digital twin + fuzzy PID) Test conditions: The same operating room (80m²) of Fucheng District Hospital in Mianyang as in Experiment Example 2. 2 The existing technology 4 uses a "digital twin model + fuzzy PID controller", but does not specify the model coordination mechanism and PID parameters (according to existing technical literature, the PID parameters are set to the default values: proportional coefficient 2.0, integral time 10s, derivative time 2s).

[0065] Test period: March 1, 2024 - March 31, 2024 (same as Experiment 2).

[0066] Table 5 (Comparative data for Comparative Example 2) Reasons for the difference: The digital twin model of the existing technology 4 and the fuzzy PID controller do not have a clear data interaction path (the literature does not record how to convert the twin simulation results into PID control parameters), and the default PID parameters cannot adapt to the high precision requirements of medical scenarios; the present invention, through a clear link of "equipment operation prediction model → energy consumption-lifetime co-optimization model → control command", combined with scenario-based parameter adjustment, has prediction accuracy and control effect far exceeding the existing technology.

[0067] Comparative Example 3: Compared to traditional fixed control (no AI algorithm, parameters are set manually) Test conditions: Same as Experiment 3, a hotel room in Guanshanhu District, Guiyang (1 room, 35m²) 2 The system uses traditional fixed controls: the air conditioner is set to 24℃ and always on, the water heater heats for 24 hours, and the fresh air ratio is fixed at 1:1 with no adaptive adjustment.

[0068] Test period: December 2023 - August 2024 (same as Experiment 3).

[0069] Table 6 (Comparative data for Comparative Example 3) Reasons for the difference: Traditional control cannot respond to the occupancy status of guests (such as high load operation even when guests are away) and seasonal changes (such as high power heating even in summer), resulting in energy waste and excessive wear and tear on equipment; This invention completely solves the "rigidity defect" of traditional control by using real-time sensor perception and dynamic adjustment of AI model.

[0070] IV. Summary of Experiments and Comparisons Compared with existing technologies 1 (frequency converter control) and 4 (digital twin), this invention reduces energy consumption by 15%-38%, reduces failure rate by 76%-100%, reduces prediction error by 69.9%, and can be adapted to specific scenarios such as medical care and hotels. Its technical effect far exceeds that of existing technologies. All experimental examples are based on real areas in Sichuan (Chengdu, Mianyang) and surrounding areas (Guiyang), including specific equipment models, test cycles, environmental parameters and quantitative data, and can be reproduced by those skilled in the art; Through experimental examples of "different floors / seasons / scenarios", the main application scenarios of building electromechanical equipment are covered; through comparative examples of "benchmarking existing technologies + traditional control", the technical boundaries and advantages of the present invention are clarified.

[0071] The above description is only a preferred embodiment, experimental example, and comparative example of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment, characterized in that, It includes a data acquisition module, a data transmission module, a data processing module, a control decision module, and an execution module; The data acquisition module includes a temperature sensor, a humidity sensor, a light sensor, a current sensor, and a voltage sensor, used to collect data on the building's internal and external environment and the operation data of electromechanical equipment. The data transmission module uses a combination of wired and wireless transmission to transmit the data collected by the data acquisition module to the data processing module. The data processing module has a built-in equipment operation prediction model and an energy consumption-lifetime co-optimization model. The equipment operation prediction model outputs prediction results to the energy consumption-lifetime co-optimization model, and the two work together to output analysis results to the control decision module. The control decision module generates control commands based on the analysis results and preset energy efficiency-lifetime targets; The execution module is connected to each electromechanical device, receives control commands, and executes operations.

2. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The data acquisition module also includes a personnel presence sensor; the personnel presence sensor is installed in each room and public area of ​​the building to detect the presence status and number of people in the area, and the detection data is transmitted to the data processing module through the data transmission module.

3. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The analysis process of the data processing module includes the following steps: S1. Data preprocessing: The collected data is spatiotemporally aligned, noise is removed by Kalman filtering, and outliers are removed by the isolated forest algorithm; S2. Feature Extraction: CNN is used to extract spatial correlation features of environmental data, LSTM is used to extract time series features of device operation data, and attention mechanism is combined to generate a fused feature vector; S3. Model Calculation: Input the fused feature vector into the device operation prediction model to predict the device's operating status in the next 1-3 hours; input the prediction results into the energy consumption-lifetime co-optimization model to calculate the optimal control parameters; S4. Model Optimization: Adapt to new scenarios through transfer learning and update model weights using online learning.

4. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The control decision module incorporates three optimization strategies: a device load balancing strategy, a time cycle optimization strategy, and a user behavior adaptation strategy. The device load balancing strategy distributes the operating load of multiple devices to keep the load rate of a single device between 60% and 80%. The time cycle optimization strategy adjusts the device operating mode according to peak and off-peak electricity price periods. The user behavior adaptation strategy adjusts the device operating parameters based on personnel presence data.

5. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The connection methods between the execution module and the electromechanical equipment include hard-wired connection and wireless communication connection; the hard-wired connection adopts RS485 bus or relay control, which is suitable for distribution box switches and air conditioning main units; the wireless communication connection adopts ZigBee or Wi-Fi technology, which is suitable for intelligent lighting equipment and split air conditioners.

6. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: It also includes a human-computer interaction module; the human-computer interaction module includes a display screen and an operating interface, and is installed in the building monitoring room; The display screen is used to show the data collected by the data acquisition module, the operating status of the equipment, and the execution of control commands; the operation interface is used to input control parameters and adjust optimization targets.

7. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The data processing module updates the historical data monthly, retaining valid data for the past 6 months and removing abnormal and expired data. At the same time, it updates the parameters of the equipment operation prediction model and the energy consumption-lifetime co-optimization model based on the newly collected data to ensure that the model prediction error is ≤3%.

8. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: When generating control commands, the control decision module first obtains the cumulative running time and preset maintenance cycle of the electromechanical equipment; when the remaining lifespan of the equipment is ≤ 10% of the maintenance cycle, the equipment load rate is adjusted to 50%-60% until the equipment completes maintenance.

9. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The energy consumption-lifetime co-optimization model includes a state space and an action space. The state space includes real-time environmental parameters, equipment operating parameters, and personnel behavior parameters. The action space includes equipment start-up and shutdown, power adjustment, and operation mode switching. The reward function of the energy consumption-lifetime co-optimization model is set as R=α×(1-energy consumption deviation)+β×(1-equipment loss coefficient), where α=0.55, β=0.45, and α+β=1.

10. The AI-driven energy efficiency-lifespan collaborative control system for building electromechanical equipment according to claim 1, characterized in that: The data processing module is also equipped with an external interface for connecting to the regional energy management platform. The data processing module uploads the analysis results to the regional energy management platform, and at the same time receives global optimization instructions issued by the regional energy management platform and transmits them to the control decision module.

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