Intelligent building energy consumption dynamic regulation and control and energy recovery device

Abstract

The invention relates to the technical field of intelligent buildings, and discloses an intelligent building energy consumption dynamic regulation and control and energy recovery device which comprises a multi-source sensing module, a dynamic optimization decision unit, an energy recovery subsystem, a self-adaptive distribution controller and a closed-loop feedback actuator. According to the invention, the intelligent level of building energy consumption management and the comprehensive efficiency of energy recovery are improved, the building operation cost and carbon footprint are effectively reduced, and the goal of realizing a green low-carbon building is assisted. The device has technical advancement and practicability, is suitable for various large public buildings and commercial complexes, and has wide popularization and application prospects and remarkable social and economic benefits.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent buildings, in particular to an intelligent building energy consumption dynamic regulation and energy recovery device. BACKGROUND

[0002] With the continuous acceleration of urbanization, building energy consumption has become an important part of global energy consumption, especially large public buildings and commercial complexes, whose total energy consumption accounts for an increasing proportion of urban energy consumption year by year. Traditional building energy consumption management relies on static control strategies, lacking real-time response capability to dynamic environmental changes and energy demand, resulting in low energy utilization efficiency, obvious peak-valley load difference, and thus increasing the pressure on the power grid and operating costs. At the same time, a large amount of low-grade waste heat in buildings, such as elevator braking potential energy, air conditioning condensing waste heat, and power grid transient surge energy, is often not effectively recovered and utilized, resulting in energy waste and environmental burden.

[0003] In the prior art, although some intelligent building systems have introduced energy consumption monitoring and automatic control functions, they mainly focus on the recovery of a single energy form, lack a comprehensive recovery system across energy types, and are difficult to achieve optimal regulation and control of building overall energy consumption. In addition, existing energy consumption regulation algorithms are mostly based on rules or simple prediction models, which are difficult to adapt to complex and variable building operating environments and price fluctuations, resulting in delayed response of regulation strategies, and difficulty in balancing comfort and energy saving effect. The energy recovery system and the building energy consumption regulation system are usually deployed independently, lack effective coordination mechanisms, and fail to fully utilize renewable energy.

[0004] In addition, the distribution of renewable energy after energy recovery mostly adopts fixed priority or simple scheduling, lacks intelligent and dynamic adjustment capabilities, and cannot flexibly distribute according to real-time load demand, energy storage state, and power grid conditions, affecting the economy and utilization rate of energy. Existing systems also have deficiencies in data security and transaction transparency, making it difficult to meet the needs of modern intelligent buildings for energy asset management and carbon emission accounting. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the deficiencies in the prior art, the present application provides an intelligent building energy consumption dynamic regulation and energy recovery device to solve the above problems.

[0007] (II) Technical solutions

[0008] To achieve the above purpose, the present application provides the following technical solutions: an intelligent building energy consumption dynamic regulation and energy recovery device, comprising:

[0009] a multi-source perception module for real-time acquisition of building internal environment parameters, equipment operating state and external power grid load signals;

[0010] The dynamic optimization decision-making unit has a built-in energy consumption prediction model based on reinforcement learning. It generates equipment control instructions based on data from multi-source sensing modules, historical building energy consumption patterns, and real-time electricity price signals.

[0011] The energy recovery subsystem includes a thermoelectric converter, a waste heat recovery heat exchanger, and a flywheel energy storage device, which respectively capture elevator braking energy, air conditioning condensation waste heat, and transient surge energy of the power distribution system.

[0012] The adaptive distribution controller dynamically distributes the renewable energy output from the energy recovery subsystem to building electrical loads or energy storage devices according to priority.

[0013] The closed-loop feedback actuator receives instructions from the dynamic optimization decision unit to adjust the air conditioning compressor frequency, lighting brightness, and elevator start-stop curves, while simultaneously receiving the energy supply status of the energy recovery subsystem to achieve compensation control.

[0014] As a preferred technical solution of the present invention, the thermoelectric converter is integrated into the elevator drive system. It converts the downward potential energy of the car into DC power through a piezoelectric material matrix. Its output end is connected to a bidirectional DC / AC inverter to realize grid-connected or off-grid switching with the building microgrid.

[0015] As a preferred embodiment of the present invention, the waste heat recovery heat exchanger adopts a microchannel phase change heat storage structure, including:

[0016] Spiral copper pipe assembly, embedded in the air conditioner condenser exhaust path;

[0017] A phase change material capsule layer encapsulates a copper tube assembly and is filled with a paraffin / carbon nanotube composite phase change material.

[0018] The temperature-triggered valve automatically opens when the capsule layer temperature is ≥50℃, releasing heat energy to the domestic hot water system.

[0019] As a preferred technical solution of the present invention, the energy consumption prediction model of the dynamic optimization decision unit integrates a temporal convolutional network (TCN) and a Q-learning algorithm, specifically including:

[0020] The TCN module handles long-term dependencies in historical energy consumption data;

[0021] The Q-learning agent iteratively optimizes control strategies in the state space {grid load peak and valley, recovered energy reserve rate, indoor comfort deviation} based on real-time electricity price fluctuations and the supply of recovered energy.

[0022] As a preferred embodiment of the present invention, the cost function for indoor comfort deviation is defined as follows:

[0023] C t =α·(T) act -T set )2 +β·∫P grid ·Δt+γ·max(0,S bat -S max ),

[0024] Where α, β, and γ are weighting coefficients, Tact / Tset are actual / set temperatures, Pgrid is the electricity purchased from the grid, and Sbat is the over-limit value of the energy storage device.

[0025] As a preferred embodiment of the present invention, the adaptive allocation controller executes a three-level energy allocation strategy:

[0026] Primary allocation: Renewable energy is prioritized for supplying constant temperature equipment with a real-time power output greater than 1kW;

[0027] Secondary allocation: Residual energy is injected into a hybrid lithium battery / supercapacitor energy storage device;

[0028] Three-tier allocation: When the SOC of the energy storage device is ≥90%, it feeds back power to the grid and activates the blockchain billing contract.

[0029] As a preferred technical solution of the present invention, the method for dynamic control of energy consumption in intelligent buildings includes:

[0030] Step S1: Continuously acquire building energy consumption data streams through multi-source sensing modules;

[0031] Step S2: The dynamic optimization decision unit updates the equipment control instruction set every 10 minutes;

[0032] Step S3: The energy recovery subsystem captures discrete waste energy and converts it into stable renewable electricity;

[0033] Step S4: The adaptive allocation controller dynamically switches the regenerative energy output path according to the instruction set;

[0034] Step S5: The closed-loop feedback actuator synchronously adjusts the equipment power and recovers energy compensation.

[0035] As a preferred embodiment of the present invention, the generation of the control instruction set in step S2 includes the following constraints:

[0036] The air conditioner's set temperature adjustment range is ≤ ±2℃ / cycle;

[0037] The illumination intensity decay gradient maintains an illuminance of ≥300 lux;

[0038] Elevator group control algorithms avoid starting and stopping during peak electricity price periods.

[0039] As a preferred technical solution of the present invention, the output path switching logic in step S4 includes:

[0040] When the power grid is in peak hours and the recovered energy power is greater than 3kW, the main power supply to the air conditioner is cut off and switched to the direct supply mode of recovered energy.

[0041] When a fault signal is detected in the energy storage device, the backup hydrogen fuel cell power supply link is activated.

[0042] As a preferred embodiment of the present invention, a building energy management system is provided, with the following additions:

[0043] A visual human-machine interface dynamically displays the energy flow topology and carbon emission reduction of each subsystem;

[0044] The blockchain auditing module records renewable energy transaction data and generates tamper-proof evidence.

[0045] Compared with existing technologies, this invention provides a device for dynamic regulation and energy recovery of intelligent building energy consumption, which has the following beneficial effects:

[0046] The "Intelligent Building Energy Consumption Dynamic Control and Energy Recovery Device" of this invention achieves dynamic and precise control of building energy consumption and efficient recovery and utilization of various forms of energy through multi-source sensing, multi-dimensional data fusion, and intelligent algorithm optimization. First, the device integrates elevator potential energy thermoelectric conversion, air conditioning condensation waste heat phase change heat storage, and flywheel energy storage for transient surge energy in the power distribution system, forming a multi-source recovery system covering typical low-grade energy in buildings. This significantly improves the comprehensiveness and efficiency of energy recovery, avoiding the limitations and energy waste of traditional single-recovery devices. Second, based on a dynamic optimization decision model combining temporal convolutional networks and reinforcement learning, it can accurately predict building energy consumption trends in real time, intelligently adjust the operating status of key equipment such as air conditioning, lighting, and elevators, effectively balance user comfort and grid load, reduce peak-valley differences, reduce grid dependence during peak hours, and improve the economic efficiency and environmental friendliness of energy use.

[0047] Furthermore, the innovative three-tiered energy allocation strategy and adaptive control mechanism ensure that recovered renewable energy prioritizes the stable power supply needs of temperature-controlled equipment, while surplus energy is rationally stored or fed back to the grid, achieving efficient energy recycling and maximizing economic value. The introduction of blockchain technology provides a secure, transparent, and tamper-proof means of recording energy trading and usage data, enhancing the system's credibility and traceability, and facilitating subsequent energy asset management and carbon emission accounting. The design of closed-loop feedback actuators and backup energy links ensures the stability and security of system operation, enabling intelligent control and energy recovery to operate continuously and efficiently under complex and changing building conditions.

[0048] In summary, this invention not only improves the intelligence level of building energy management and the overall efficiency of energy recovery, but also effectively reduces building operating costs and carbon footprint, contributing to the achievement of green and low-carbon building goals. This device combines technological advancement with practicality, is suitable for various large public buildings and commercial complexes, and has broad application prospects and significant socio-economic benefits. Attached Figure Description

[0049] Fig. 1 This is a schematic diagram of the structure of the present invention;

[0050] Fig. 2 This is a schematic diagram of the structure of the present invention.

[0051] in:

[0052] 101. Multi-source sensing module; 102. Dynamic optimization decision-making unit; 103. Energy recovery subsystem; 104. Adaptive distribution controller; 105. Lithium battery / supercapacitor hybrid energy storage device; 106. Closed-loop feedback actuator;

[0053] 103a. Thermoelectric converter; 103b. Waste heat recovery heat exchanger; 103c. Flywheel energy storage device;

[0054] 103b-1, Spiral copper tube assembly; 103b-2, Phase change material capsule layer; 103b-3, Temperature-triggered valve;

[0055] 201. Visualized human-computer interface; 202. Blockchain audit module. Detailed Implementation

[0056] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other. 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.

[0057] It should be noted that if the embodiments of the invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0058] Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the invention.

[0059] Please see Figs. 1-2

[0060] Example 1: Device Structure and Working Principle

[0061] This embodiment provides an intelligent building energy consumption dynamic control and energy recovery device, suitable for commercial complexes with a building area of ​​5,000-20,000 square meters. It integrates multi-source sensing, dynamic optimization decision-making, energy recovery, distribution control, and closed-loop feedback functions. The hardware structure and workflow of the device are as follows:

[0062] 1. Device Composition

[0063] Multi-source sensing module 101

[0064] The multi-source sensing module includes a temperature and humidity sensor (model: DHT22, accuracy ±0.5℃ / ±2%RH), a light intensity sensor (model: TSL2561, range 0-40000lux), a power monitoring unit (model: PZEM-004T, accuracy ±1%), and a power grid load interface (supporting Modbus protocol). The sensors are deployed in the building's air conditioning room, elevator shaft, office area, and main power distribution room to collect environmental parameters (temperature, humidity, light intensity), equipment operating power (air conditioning, lighting, elevators), and external power grid load signals (peak and off-peak electricity prices, transient fluctuations) in real time. Data is transmitted to the dynamic optimization decision-making unit via a LoRa wireless network at 1-second intervals.

[0065] Dynamic optimization decision-making unit 102

[0066] The dynamic optimization decision unit uses an embedded industrial computer (Raspberry Pi 4, 8GB RAM) to run a customized energy consumption prediction model. The model integrates a Temporal Convolutional Network (TCN) and a Q-learning reinforcement learning algorithm, as specifically implemented below:

[0067] The TCN module processes 30 days of historical energy consumption data (sampling frequency: 10 minutes / time), extracts the time-dependent characteristics of building energy consumption, and predicts the energy demand curve for the next 24 hours.

[0068] The Q-learning agent takes the state space {grid load peak and valley, recovered energy storage rate, indoor comfort deviation} as input and the action space {air conditioning frequency adjustment, lighting brightness adjustment, elevator start / stop optimization} as input. It iteratively optimizes equipment control commands through an ε-greedy strategy. The cost function is defined as:

[0069] C t =α·(T) act -T set ) 2 +β·∫P grid ·Δt+γ·max(0,S bat -S max ), where the weighting coefficients α = 0.4, β = 0.5, and γ = 0.1; T act T represents the actual temperature. set Set the temperature (default 22℃); P grid Electricity purchased for the power grid; S bat For the energy storage device's state of charge (SOC), S max The SOC limit is 90%. The model updates the control instructions every 10 minutes.

[0070] Energy recovery subsystem 103

[0071] The energy recovery subsystem comprises the following three core components:

[0072] Thermoelectric converter 103a: Integrated into the elevator drive system (suitable for permanent magnet synchronous motors, power range 15-30kW). Utilizing piezoelectric ceramic material (PZT-5H, piezoelectric coefficient d33 = 600pC / N), it converts the downward potential energy of the elevator car into direct current. The output connects to a bidirectional DC / AC inverter (efficiency ≥95%), supporting grid-connected or off-grid operation with a building microgrid. The measured recovery efficiency is approximately 40% of the downward energy of the elevator car.

[0073] Waste heat recovery heat exchanger 103b: Deployed in the exhaust path of the central air conditioning condenser, it uses microchannel spiral copper tubes (2mm diameter, 0.5mm wall thickness) wrapped with paraffin / carbon nanotube composite phase change material (phase change temperature 48℃, latent heat 200kJ / kg). When the heat exchanger temperature ≥50℃, a temperature-triggered valve (solenoid valve, response time <0.5 seconds) opens, transferring heat energy to the domestic hot water system (500L storage tank). The measured heat recovery rate reaches 65%.

[0074] Flywheel energy storage device 103c: Captures transient surge energy in power distribution systems, employing a carbon fiber flywheel (maximum speed 30,000 rpm, energy storage capacity 1 kWh). The flywheel reduces energy loss through magnetic levitation bearings (friction coefficient < 0.001), and is connected to a bidirectional converter to achieve energy storage and release.

[0075] (4) Adaptive allocation controller 104

[0076] The adaptive energy distribution controller, based on a PLC (model: Siemens S7-1200), implements a three-level energy distribution strategy:

[0077] Primary allocation: Renewable energy is prioritized for temperature control equipment with a real-time power of >1kW (such as precision air conditioners, with a power range of 5-20kW).

[0078] Secondary allocation: Remaining energy is injected into a lithium battery / supercapacitor hybrid energy storage device (capacity 10kWh, supercapacitors account for 20%).

[0079] Three-tier allocation: When the energy storage device's SOC is ≥ 90%, it feeds power back to the grid through a bidirectional meter (accuracy ±0.5%) and activates the blockchain billing contract (based on an Ethereum smart contract, recording transaction hashes). The allocation cycle is 1 minute, and the switching latency is < 100ms.

[0080] (5) Closed-loop feedback actuator 106

[0081] The closed-loop feedback actuator includes a frequency converter (for air conditioning compressors, frequency range 10-60Hz), a dimmer (for LED lighting, brightness range 10-100%), and an elevator group control module (supporting coordinated scheduling of 8 elevators). The actuator receives instructions from the dynamic optimization decision unit and synchronously monitors the power supply status of the energy recovery subsystem to ensure dynamic matching between equipment operating power and recovered energy.

[0082] (6) Energy storage device 105

[0083] The energy storage device uses a combination of lithium batteries (lithium iron phosphate, cycle life > 2000 cycles) and supercapacitors (rated voltage 2.7V, capacity 3000F), supporting fast charging and discharging (maximum current 200A). The energy storage device is connected to the building microgrid via a bidirectional converter, and the state of charge (SOC) is uploaded to the distribution controller in real time.

[0084] 2. Work Process

[0085] Step S1: Data Acquisition

[0086] The multi-source sensing module collects data on building temperature (range 18-30℃), humidity (range 30-70%), light intensity (range 100-1000 lux), equipment power (range 0-100kW), and grid load signal (peak-hour electricity price 0.9 yuan / kWh, off-peak price 0.3 yuan / kWh) at a frequency of 1 second. The data is then aggregated to the dynamic optimization decision unit via a LoRa gateway.

[0087] Step S2: Dynamic Optimization Decision

[0088] The dynamic optimization decision unit runs a prediction and optimization process every 10 minutes.

[0089] The TCN model predicts energy demand for the next hour based on historical data (error < 5%).

[0090] The Q-learning agent outputs a set of control instructions based on real-time electricity prices, recovered energy, and comfort deviations (such as increasing the air conditioning set temperature by 1°C, reducing lighting brightness by 10%, and operating the elevator during peak hours). Constraints include:

[0091] Air conditioning temperature adjustment range ≤ ±2℃ / cycle;

[0092] Lighting intensity ≥300lux;

[0093] Elevator operating intervals are ≤5 minutes.

[0094] Step S3: Energy Recovery

[0095] When the elevator descends, the thermoelectric converter converts potential energy into electrical energy (average output power 2kW / unit).

[0096] The waste heat from air conditioning condensation is converted into hot water through a heat exchanger (average daily heat recovery of 50 MJ).

[0097] The flywheel energy storage device absorbs transient surges (single energy storage of 0.1-0.5 kWh). The recovered energy is fed into the microgrid bus.

[0098] Step S4: Energy Allocation

[0099] The adaptive distribution controller dynamically switches energy output paths based on load demand.

[0100] During peak hours (electricity price > 0.8 yuan / kWh) and when the recovered energy power is > 3kW, the main circuit of the air conditioner is cut off and switched to direct supply of recovered energy.

[0101] When the SOC of the energy storage device is less than 30%, recycled energy is injected first.

[0102] When SOC ≥ 90%, excess electricity is sold back to the grid (approximately 5 kWh per day).

[0103] If the energy storage device fails, the backup hydrogen fuel cell (5kW power) will start automatically.

[0104] Step S5: Closed-loop feedback

[0105] The closed-loop feedback actuator adjusts the equipment's operating status according to instructions and monitors fluctuations in the recovered energy supply in real time. If the energy supply is insufficient (e.g., recovered power < 10% of load demand), the actuator automatically compensates for grid power to ensure comfort deviation < ±1℃.

[0106] 3. Implementation Results

[0107] In a real-world test of a commercial building (10,000 square meters in area, with an average daily energy consumption of 2,000 kWh):

[0108] The energy recovery subsystem recovers an average of 50 kWh of electrical energy and 60 MJ of thermal energy per day, with a comprehensive recovery efficiency of 55%.

[0109] The dynamic optimization decision-making unit reduces peak electricity consumption by 30%, resulting in a 20% saving in electricity costs (approximately 500 yuan per day).

[0110] The adaptive distribution controller enables the self-consumption rate of recovered energy to reach 85%, and the amount of electricity sold back to the grid accounts for 10% of the total recovered energy.

[0111] Indoor comfort deviation is controlled within ±0.8℃, illuminance is always ≥300 lux, and user satisfaction is >90%.

[0112] Example 2: Energy Management System Integration

[0113] Based on the device of Embodiment 1, a visual human-machine interface and a blockchain audit module are further integrated to form a complete building energy management system:

[0114] Visualized human-machine interface: It adopts a 15-inch touch screen (1920x1080 resolution) to display energy flow topology (real-time power flow diagram), carbon emission reduction (calculated based on the grid emission factor of 0.6kg CO2 / kWh) and equipment operating status.

[0115] Blockchain audit module: Based on the Ethereum platform, it records renewable energy transaction data (hash storage, block generation time <15 seconds) to ensure that the electricity sold back is transparent and traceable.

[0116] This system adds energy trading functionality to the existing implementation, making it suitable for buildings participating in demand-side response or carbon trading markets.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0118] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0119] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A smart building energy consumption dynamic control and energy recovery device, characterized in that, include: The multi-source sensing module (101) collects in real time the building's internal environmental parameters (temperature, humidity, light intensity), equipment operating status (air conditioning, lighting, elevator power consumption) and external power grid load signals; The dynamic optimization decision unit (102) has a built-in energy consumption prediction model based on reinforcement learning, which generates equipment control instructions based on data from multi-source sensing modules, historical building energy consumption patterns and real-time electricity price signals. The energy recovery subsystem (103) includes a thermoelectric converter (103a), a waste heat recovery heat exchanger (103b), and a flywheel energy storage device (103c), which respectively capture elevator braking energy, air conditioning condensation waste heat, and transient surge power energy of the power distribution system; An adaptive distribution controller (104) dynamically distributes the renewable energy output from the energy recovery subsystem to building electrical loads or energy storage devices (105) according to priority. The closed-loop feedback actuator (106) receives instructions from the dynamic optimization decision unit to adjust the air conditioning compressor frequency, lighting brightness and elevator start-stop curve, and simultaneously receives the energy supply status of the energy recovery subsystem to achieve compensation control.

2. The intelligent building energy consumption dynamic control and energy recovery device according to claim 1, characterized in that: The thermoelectric converter (103a) is integrated into the elevator drive system. It converts the downward potential energy of the car into DC power through a piezoelectric material matrix. Its output is connected to a bidirectional DC / AC inverter (103a-1) to achieve grid-connected or off-grid switching with the building microgrid.

3. The intelligent building energy consumption dynamic control and energy recovery device according to claim 1, characterized in that: The waste heat recovery heat exchanger (103b) adopts a microchannel phase change heat storage structure, including: Spiral copper tube assembly (103b-1), embedded in the air conditioner condenser exhaust path; A phase change material capsule layer (103b-2) is used to encapsulate a copper tube assembly and fill it with a paraffin / carbon nanotube composite phase change material. The temperature-triggered valve (103b-3) automatically opens when the capsule layer temperature is ≥50℃, releasing heat energy to the domestic hot water system.

4. The intelligent building energy consumption dynamic control and energy recovery device according to claim 1, characterized in that: The energy consumption prediction model of the dynamic optimization decision unit (102) integrates a temporal convolutional network (TCN) and a Q-learning algorithm, specifically including: The TCN module (102-1) handles long-term dependencies in historical energy consumption data; The Q-learning agent (102-2) iteratively optimizes the control strategy in the state space {grid load peak and valley, recycled energy reserve rate, indoor comfort deviation} based on real-time electricity price fluctuations and the supply of recycled energy.

5. The intelligent building energy consumption dynamic control and energy recovery device according to claim 1, characterized in that: The cost function for indoor comfort deviation is defined as follows: C t =α·(T act -T set ) 2 +β·∫P grid ·Δt+γ·max(0,S bat -S max ), Where α, β, γ are weighting coefficients, and T act / T set For actual / set temperature, P grid S purchases electricity for the power grid bat This indicates that the energy storage device has exceeded its limit.

6. The intelligent building energy consumption dynamic control and energy recovery device according to claim 1, characterized in that: The adaptive allocation controller (104) executes a three-level energy allocation strategy: Primary allocation: Renewable energy is prioritized for supplying constant temperature equipment with a real-time power output greater than 1kW; Secondary allocation: Residual energy is injected into a lithium battery / supercapacitor hybrid energy storage device (105); Three-tier allocation: When the SOC of the energy storage device is ≥90%, it feeds back power to the grid and activates the blockchain billing contract.

7. A method for dynamic control of energy consumption in intelligent buildings based on the device according to any one of claims 1-6, characterized in that, include: Step S1: Continuously acquire building energy consumption data streams through multi-source sensing modules; Step S2: The dynamic optimization decision unit updates the equipment control instruction set every 10 minutes; Step S3: The energy recovery subsystem captures discrete waste energy and converts it into stable renewable electricity; Step S4: The adaptive allocation controller dynamically switches the regenerative energy output path according to the instruction set; Step S5: The closed-loop feedback actuator synchronously adjusts the equipment power and recovers energy compensation.

8. The method for dynamic control of energy consumption in intelligent buildings according to claim 7, characterized in that: The generation of the control instruction set in step S2 includes the following constraints: The air conditioner's set temperature adjustment range is ≤ ±2℃ / cycle; The illumination intensity decay gradient maintains an illuminance of ≥300 lux; Elevator group control algorithms avoid starting and stopping during peak electricity price periods.

9. The method for dynamic control of energy consumption in intelligent buildings according to claim 7, characterized in that: The output path switching logic in step S4 includes: When the power grid is in peak hours and the recovered energy power is greater than 3kW, the main power supply to the air conditioner is cut off and switched to the direct supply mode of recovered energy. When a fault signal is detected in the energy storage device, the backup hydrogen fuel cell power supply link is activated.

10. A building energy management system, characterized in that... Integrate the apparatus according to any one of claims 1-6, and add: A visual human-machine interface (201) dynamically displays the energy flow topology and carbon emission reduction of each subsystem; The blockchain audit module (202) records renewable energy transaction data and generates tamper-proof evidence.

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