An intelligent temperature control device for prefabricated component maintenance of assembled subway station

Abstract

This invention discloses an intelligent temperature control device for the curing of prefabricated components in prefabricated subway stations. It includes an ultra-low power wireless sensing module, an edge AI intelligent decision-making module, a multi-device collaborative execution module, and a cloud-based energy and carbon management module, forming a closed-loop control system of sensing-decision-execution-traceability, specifically adapted to the curing scenario of prefabricated components in prefabricated subway stations. This invention achieves three-stage adaptive and precise temperature control based on the heat of hydration law, with temperature fluctuations ≤±1℃, significantly reducing the risk of component cracking. Equipped with an edge AI intelligent decision-making module, this invention enables offline autonomy and unattended operation throughout the entire curing process, reducing labor costs. This invention integrates engineering-level data traceability and cloud-based linkage functions, meeting the requirements for full-process traceability of quality, energy consumption, and carbon emissions in rail transit engineering.

Description

Technical Field

[0001] This invention belongs to the field of prefabricated component maintenance and control technology, specifically relating to the design of an intelligent temperature control device for the maintenance of prefabricated components in prefabricated subway stations. Background Technology

[0002] In the construction of prefabricated subway stations, the main structure uses a large number of large-volume, high-strength precast concrete components. The quality of curing directly determines the strength, durability, appearance quality and crack control level of the components, and is the core link to ensure the safety and service life of the project.

[0003] Existing precast component curing temperature control devices are mostly centralized wired temperature control systems, consisting of a PLC controller, a wired temperature sensor, and a single actuator (steam valve / electric heater), which generally have the following drawbacks: (1) The temperature control logic is disconnected from the heat of hydration of concrete. Fixed parameters are often used for closed-loop control. It is impossible to dynamically match the heating, constant temperature and cooling strategies according to the heat of hydration release rate of the component. Problems such as excessively rapid heating, excessively steep cooling and excessive internal and external temperature difference are likely to occur, resulting in component cracking and uneven strength.

[0004] (2) The equipment control method is crude, only realizing single power / opening adjustment, without combining temperature and humidity, environmental compensation, and energy consumption status for multi-dimensional coordinated linkage. Steam, heating, fan and other equipment operate inefficiently for a long time, resulting in serious energy waste, which does not meet the dual carbon target and green and low-carbon construction requirements.

[0005] (3) Traditional temperature control devices have complex wiring and complicated deployment. They mostly use wired sensors and centralized controllers, which are difficult to install and disassemble on site and have poor expandability. They are difficult to adapt to the fine synchronous control of multiple maintenance units and multiple component areas.

[0006] (4) The device relies on manual parameter setting and inspection and control, lacks local intelligent decision-making and offline autonomous capabilities, has high management costs and slow response, and cannot achieve unattended operation and full-process automatic control, thus limiting maintenance efficiency and component turnover speed.

[0007] (5) The general temperature control device is not designed specifically for the curing scenario of precast components, lacks the function of quantitative accounting of energy consumption and carbon emissions, and the data is not traceable, which cannot meet the needs of quality control and low-carbon performance verification of rail transit engineering.

[0008] The aforementioned problems severely restrict the construction quality, energy efficiency, and intelligence level of prefabricated components for prefabricated subway stations, and there is an urgent need for a dedicated intelligent temperature control device that is adaptable to hydration heat, has precise temperature control, low energy consumption, and is traceable in terms of energy and carbon. Summary of the Invention

[0009] The purpose of this invention is to solve the problems of existing precast component curing temperature control devices, such as mismatch with the heat of hydration law, single control method, complex deployment, reliance on manual labor, and lack of traceability of energy and carbon data. This invention proposes an intelligent temperature control device for the curing of precast components in prefabricated subway stations.

[0010] The technical solution of this invention is as follows: an intelligent temperature control device for the curing of prefabricated components of prefabricated subway stations, comprising an ultra-low power wireless sensing module, an edge AI intelligent decision-making module, a multi-device collaborative execution module, and a cloud-based energy and carbon management module. The edge AI intelligent decision-making module is communicatively connected to the ultra-low power wireless sensing module, the multi-device collaborative execution module, and the cloud-based energy and carbon management module. The ultra-low power wireless sensing module is used to collect real-time data on the core temperature of the prefabricated components of the prefabricated subway station, the temperature and humidity of the curing space, the outdoor environment, and energy consumption. The edge AI intelligent decision-making module is used to construct a 12-dimensional input vector based on the data collected by the ultra-low power wireless sensing module, and to process the 12-dimensional input vector based on a lightweight CNN feature extraction algorithm, a fuzzy PID self-tuning algorithm, and a hydration heat matching fusion model to obtain a 6-dimensional collaborative control command. The multi-device collaborative execution module is used to perform phased collaborative adjustment of the temperature of the prefabricated components of the prefabricated subway station according to the 6-dimensional collaborative control command. The cloud-based energy and carbon management module is used to centrally monitor, calculate energy and carbon, trace data, and remotely manage the temperature control process of the prefabricated components curing of the prefabricated subway station.

[0011] Furthermore, the ultra-low power wireless sensing module includes a sensing and acquisition unit, an ultra-low power main control unit, a wireless communication unit, and a power management unit. The sensing and acquisition unit includes a core-embedded temperature acquisition circuit, a curing space temperature and humidity acquisition circuit, an outdoor environment temperature and humidity compensation acquisition circuit, and an energy consumption acquisition circuit, used to collect in real time the core temperature, curing space temperature and humidity, outdoor environment, and energy consumption of the prefabricated components of the assembled subway station. The wireless communication unit is used for wireless data transmission with the edge AI intelligent decision-making module. The ultra-low power main control unit is used to provide control signals for the sensing and acquisition unit and the wireless communication unit. The power management unit is used to provide adaptive power for the sensing and acquisition unit, the ultra-low power main control unit, and the wireless communication unit.

[0012] Furthermore, the 12-dimensional input vector includes the core temperature of the component. T c Temperature of the maintenance space T s Humidity of the maintenance space H s Outdoor temperature T o Outdoor humidity H o Temperature difference between inside and outside Δ T = Tc - T s Hydration heat stage marking S Current energy consumption P Equipment operating status D Target constant temperature T set Allowable deviation Δ T set and maintenance time t .

[0013] Furthermore, the lightweight CNN feature extraction algorithm includes the following steps: S1. Perform a one-dimensional convolution on the normalized 12-dimensional input vector to obtain the convolution output features. : in Represents one-dimensional convolution. This represents the normalized 12-dimensional input vector. K Indicates the kernel size. S Indicates the convolution stride. P Indicates the fill length.

[0014] S2, Convolution Output Features Perform max pooling to obtain the pooled output features. : in This indicates max pooling.

[0015] S3, Pooling output characteristics Perform fully connected fusion to obtain the hydration heat feature vector. F : in W Represents the weight matrix. b This represents the bias vector.

[0016] Furthermore, the hydration heat matching fusion model is based on the hydration heat feature vector. F A theoretical hydration heat temperature curve was constructed, and the temperature deviation of the core of the actual component was calculated. : in The theoretical curve of hydration heat temperature is shown in t The theoretical heat of hydration at time _____ Indicates the initial temperature of the concrete. Indicates the peak temperature of hydration heat. Represents the natural constant. This represents the hydration heat rate coefficient.

[0017] Furthermore, the fuzzy PID self-tuning algorithm is based on the actual core temperature deviation of the component. The core temperature control quantity is calculated using PID control formulas based on deviations in humidity, ambient temperature, energy consumption, and equipment operating status within the curing space. Humidity control quantity Environmental compensation control quantity Energy consumption optimization control quantity and equipment safety control quantity The final fusion control quantity is obtained through weighted fusion. U : in Representing dimensions x The PID output control quantity, Representing dimensions x The proportionality coefficient, Representing dimensions x The integral coefficient, Representing dimensions x The differential coefficients, Representing dimensions x Deviation, dimension x This can be any one of the following: the actual core temperature of the component, the humidity of the curing space, the ambient temperature, the real-time energy consumption, and the equipment operating status. This indicates a deviation in humidity within the maintenance space. Indicates the deviation of ambient temperature. Indicates energy consumption deviation. Indicates deviation in equipment operating status. This indicates the real-time humidity of the maintenance space. This indicates the target humidity setting value for maintenance. This indicates the actual internal and external temperature difference of the component. This indicates the real-time energy consumption of the maintenance system. This indicates the upper limit setting value for the energy consumption of the maintenance system. Indicates the real-time operating status of the equipment. This indicates the threshold for the device's safety status.

[0018] Furthermore, the 6-dimensional coordinated control commands include steam proportional control valve opening commands. Power command for graded electric heaters Variable frequency circulating fan speed command Precision humidification device spray volume command Hydration heat temperature control stage instructions Energy Carbon Optimization Power Limiting Directive : in , All of these are mapping coefficients calibrated experimentally. This represents the limiting function, where 0 and 100 represent the minimum and maximum value boundaries of the device instruction.

[0019] The multi-device collaborative execution module includes a steam proportional control valve, a staged electric heater, a variable frequency circulating fan, and a precision humidification device. The steam proportional control valve opening command... Used to control the power command of the steam proportional regulating valve and the staged electric heater. Used to control the speed commands of the staged electric heater and the variable frequency circulating fan. Used to control the variable frequency circulating fan and precisely control the spray volume of the humidification device. Used to control precise humidification devices, hydration heat temperature control stage commands. Used to control the temperature control stage logic, and energy-optimized power limiting instructions. Used to control the global power limit.

[0020] Furthermore, the specific method for phased and coordinated temperature regulation of prefabricated components in prefabricated subway stations is as follows: When the hydration heat temperature control stage command During the heating phase, with a heating rate of 1~3℃ / h as the control target, the steam proportional control valve is opened first, and the opening degree is determined by the steam proportional control valve opening command. Given: When the steam opening reaches 70% but the heating rate has not yet been reached, the staged electric heater will automatically start, and a power command from the staged electric heater will be output. Given: The variable frequency circulating fan starts synchronously, with its speed increasing linearly with the heat source output to ensure rapid and uniform heat diffusion; the precision humidification device is activated in advance to maintain humidity ≥90%RH, preventing early water loss and cracking of the concrete; when the core temperature rise rate >3℃ / h, the AI ​​intelligent decision module automatically reduces power by 30%~50%, and the opening of the steam proportional regulating valve is adjusted synchronously with the power of the staged electric heater; power limiting commands are used throughout the process to optimize energy and carbon emissions. Implement power limiting to avoid overload and unnecessary energy consumption.

[0021] When the hydration heat temperature control stage command When the system is in a constant temperature range, with temperature fluctuations ≤ ±1℃ and humidity 90%~98% RH as control targets, the steam proportional regulating valve is slightly adjusted as the main means of constant temperature regulation to avoid large-scale switching. When the ambient temperature drops by >2℃, the staged electric heater is controlled to automatically supplement heat by 10%~20% to achieve environmental compensation. The variable frequency circulating fan is controlled to maintain stable operation at medium and low speeds to ensure uniform temperature and humidity in the maintenance space. The precision humidification device is controlled to automatically pulse spray according to humidity deviations to prevent overshoot. When the system approaches the target temperature, the total power of the system is automatically reduced to 60%, entering the energy-saving constant temperature mode.

[0022] When the hydration heat temperature control stage command During the cooling phase, a gradual cooling strategy is implemented with a cooling rate of 0.5~1℃ / h as the control target. The opening of the steam proportional regulating valve is gradually reduced, and the steam supply is reduced first without being directly shut off. The staged electric heater is controlled to dynamically compensate for the internal and external temperature difference. When the internal and external temperature difference is >15℃, the cooling is paused and the temperature is kept constant for 1~2 hours. The variable frequency circulating fan is controlled to increase its speed to promote uniform heat dissipation and avoid excessively rapid local temperature drop. The precision humidification device is kept in operation to maintain high humidity curing and prevent cracking of the concrete surface. When the curing termination temperature is reached, all equipment is shut down in sequence to complete the curing process.

[0023] Furthermore, carbon accounting includes calculating carbon emissions from electricity consumption. Steam carbon emissions Total carbon emissions from maintenance Carbon emission intensity per unit of precast component Carbon emission reduction and energy saving and carbon reduction rate : in This indicates the total measured power consumption during the maintenance phase. Indicates the carbon emission factor of the regional power grid. This indicates the measured steam consumption during the maintenance phase. Indicates the carbon emission factor of industrial steam. N Indicates the number of maintenance components. This indicates the carbon emissions from traditional maintenance methods.

[0024] Furthermore, remote management includes remote connection and security authentication, remote parameter distribution and parsing, breakpoint resume and command retransmission, remote control and real-time intervention, real-time data upload and display, anomaly alarm and remote handling, and historical data tracing and export.

[0025] The beneficial effects of this invention are: (1) This invention proposes for the first time a hydration heat matching fusion model that integrates the hydration heat law, instead of using a general temperature control algorithm. Through three-stage coordinated adjustment, it fully matches the hydration heat characteristics of precast components, with constant temperature fluctuation ≤ ±1℃, reducing the risk of cracking by more than 60%.

[0026] (2) This invention adopts multi-device collaborative control to replace the extensive operation of a single device, and combined with time-sharing peak shaving strategy, it saves 20% to 40% more energy than traditional devices. At the same time, it has built-in energy carbon accounting function, realizing that energy consumption and carbon emissions can be measured and verified.

[0027] (3) The ultra-low power wireless sensing module in this invention adopts an ultra-low power wireless main control unit + long-lasting battery. The device is free of wiring and can be used immediately after installation. The deployment efficiency is improved by 80%, and it is suitable for various scenarios such as curing sheds and curing kilns.

[0028] (4) The present invention realizes local real-time decision-making and offline operation through the edge AI intelligent decision-making module, which is not affected by network fluctuations, does not require manual inspection and parameter adjustment, reduces labor costs by 60%, and significantly improves maintenance efficiency.

[0029] (5) This invention can realize full-process data storage, curves can be exported, carbon emissions can be calculated, forming a three-in-one traceability system of quality-energy consumption-carbon emissions, which is fully compatible with the requirements of subway station engineering acceptance and low-carbon management.

[0030] (6) This invention can be quickly adapted to various concrete precast component curing scenarios such as integrated pipe corridors, precast box girders, and prefabricated buildings, and has large-scale promotion value. Attached Figure Description

[0031] Figure 1 The diagram shown is a structural block diagram of an intelligent temperature control device for the curing of prefabricated components in prefabricated subway stations, provided by an embodiment of the present invention. Detailed Implementation

[0032] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0033] This invention provides an intelligent temperature control device for the curing of prefabricated components in prefabricated subway stations, such as... Figure 1 As shown, it includes an ultra-low power wireless sensing module, an edge AI intelligent decision-making module, a multi-device collaborative execution module, and a cloud-based energy and carbon management module. The edge AI intelligent decision-making module is communicatively connected to the ultra-low power wireless sensing module, the multi-device collaborative execution module, and the cloud-based energy and carbon management module, respectively.

[0034] Among them, the ultra-low power wireless sensing module is used to collect real-time data on the core temperature, curing space temperature and humidity, outdoor environment, and energy consumption of prefabricated components of prefabricated subway stations; the edge AI intelligent decision-making module is used to construct a 12-dimensional input vector based on the data collected by the ultra-low power wireless sensing module, and to process the 12-dimensional input vector based on a lightweight CNN feature extraction algorithm, a fuzzy PID self-tuning algorithm, and a hydration heat matching fusion model to obtain a 6-dimensional collaborative control command; the multi-device collaborative execution module is used to perform phased collaborative adjustment of the temperature of prefabricated components of prefabricated subway stations according to the 6-dimensional collaborative control command; and the cloud-based energy and carbon management module is used to centrally monitor, calculate energy and carbon, trace data, and remotely manage the temperature control process of prefabricated components curing in prefabricated subway stations.

[0035] In this embodiment of the invention, the ultra-low power wireless sensing module adopts a fully wireless, integrated, time-sharing power supply hardware architecture, including a sensing and acquisition unit, an ultra-low power main control unit, a wireless communication unit, and a power management unit.

[0036] The sensing and acquisition unit includes a core-embedded temperature acquisition circuit, a curing space temperature and humidity acquisition circuit, an outdoor environment temperature and humidity compensation acquisition circuit, and an energy consumption acquisition circuit. These circuits are used to collect real-time data on the core temperature, curing space temperature and humidity, outdoor environment, and energy consumption of the prefabricated components of the assembled subway station. Each acquisition circuit is independently controlled and powered by the main control GPIO, and is completely powered off during non-acquisition periods.

[0037] In this embodiment of the invention, the embedded temperature acquisition circuit uses a PT1000 thin-film platinum resistance thermometer as the sensing element, and is equipped with a 100μA precision constant current source, a zero-drift instrumentation amplifier, and a 16-bit Σ-Δ ADC to form the acquisition front end. The constant current source provides excitation to the PT1000, converting temperature changes into voltage signals, which are then amplified by the instrumentation amplifier and sent to the ADC for analog-to-digital conversion. The circuit is equipped with a TVS diode and a current-limiting resistor to achieve ESD protection, meeting the IP67 protection level, with a measurement range of -20℃ to 120℃, a temperature measurement accuracy of ±0.1℃, and a response time ≤1s.

[0038] In this embodiment of the invention, the temperature and humidity acquisition circuit of the maintenance space adopts a digital temperature and humidity chip, which is connected to the main controller via an I2C interface. The bus is equipped with a 4.7kΩ pull-up resistor and an RC filter circuit. The sensor power supply is controlled by an NMOS load switch, and the power is cut off immediately after the acquisition is completed, with a sleep current of approximately 0. The temperature range is 0℃~80℃ with an accuracy of ±0.2℃, and the humidity range is 0~100% RH with an accuracy of ±1% RH, meeting the IP65 protection requirements.

[0039] In this embodiment of the invention, the outdoor ambient temperature and humidity compensation acquisition circuit adopts a wide temperature range digital temperature and humidity chip. The input front end is connected in series with a GDT gas discharge tube, a varistor, and a common mode inductor to form a lightning protection and anti-interference circuit, which can resist electromagnetic interference and surge impact in the outdoor industrial environment. The sensor has a range of -30℃ to 120℃ and a temperature measurement accuracy of ±0.3℃. The data is sent to the main control via a UART interface for ambient temperature and humidity compensation calculation.

[0040] In this embodiment of the invention, the energy consumption acquisition circuit consists of a voltage divider network, a manganese-copper shunt, and a 0.5-level dedicated metering chip, supporting voltage acquisition from 0 to 500V and current acquisition from 0 to 100A. The metering chip has a built-in high-precision ADC and energy calculation core, and outputs active power and cumulative energy consumption data to the main controller through the SPI interface. The metering accuracy reaches 0.5 level, and it enters low-power sleep mode when there is no acquisition task, with a current of <0.5μA.

[0041] The wireless communication unit is used for wireless data transmission with the edge AI intelligent decision-making module. It adopts a Sub-1GHz unlicensed frequency band RF chip, supports 433MHz / 470MHz frequency bands, GFSK modulation, and TDMA time-division multiple access communication mechanism. The RF front-end includes a π-type impedance matching network, a 50Ω antenna interface, and ESD protection circuitry. The wireless communication unit is independently powered by the main control GPIO, only powering on during data transmission and reception, and immediately powering off after transmission and reception are completed. Its transmit current is <25mA, receive current is <8mA, sleep current is ≤0.3μA, air transmission time is <10ms, and duty cycle is <0.1%. The wireless communication unit uses wireless time-division uploading to avoid multi-node conflicts and achieve stable low-power data transmission.

[0042] The ultra-low-power main control unit provides control signals to the sensing and wireless communication units. It employs an ARM Cortex-M0+ core ultra-low-power MCU, operating at 1.8~3.6V, and features multiple low-power modes. In this embodiment, the ultra-low-power main control unit includes a clock circuit, a reset and protection circuit, a storage circuit, and an interface circuit. The clock circuit uses a 32.768kHz low-speed crystal oscillator for RTC timed wake-up and incorporates an 8MHz high-speed crystal oscillator for data processing. The reset and protection circuit integrates power-on reset (POR) and low-voltage detection (LVD) with a threshold of 2.0V to prevent undervoltage operation. The storage circuit incorporates Flash and RAM for storing programs, calibration parameters, and acquired data. The interface circuit provides I2C, SPI, UART, and multiple GPIOs, supports peripheral clock gating, and disables the clock when not in use to reduce dynamic power consumption. The MCU supports 1s-cycle RTC timed wake-up, quickly completing the acquisition, packaging, and transmission process after wake-up, and then enters a deep sleep mode with a sleep current <0.5μA.

[0043] The power management unit (PMU) uses a 3.6V lithium thionyl chloride (LiTHC) battery, along with a low-power power management circuit, to provide power to the sensing and acquisition unit, ultra-low-power main control unit, and wireless communication unit. In this embodiment, the PMU includes a reverse connection protection circuit and a voltage regulator circuit. The reverse connection protection circuit uses a series Schottky diode to prevent damage to components from reversed battery polarity, while a parallel TVS diode absorbs transient high voltage. The voltage regulator circuit uses an ultra-low-power LDO with a quiescent current of <0.5μA, providing a stable 3.3V output. The output is automatically cut off when the battery voltage drops below 2.0V to protect the battery from severe damage. The PMU's voltage divider resistor network feeds the battery voltage into the ADC for real-time power monitoring. The PMU's average operating current is <1μA, and with a 3.8Ah LiTHC battery, it theoretically has a battery life exceeding 5 years, meeting the patented design requirements of ≥3 years battery life and complete wiring elimination.

[0044] In this embodiment of the invention, the edge AI intelligent decision-making module adopts an ARM Cortex-A7 dual-core processor, 512MB DDR3 + 8GB Flash, supports local offline computing, and controls latency <500ms.

[0045] In this embodiment of the invention, the 12-dimensional input vector includes the core temperature of the component. T c Temperature of the maintenance space T s Humidity of the maintenance space H s Outdoor temperature T o Outdoor humidity H o Temperature difference between inside and outside ΔT = T c - T s Hydration heat stage marking S Current energy consumption P Equipment operating status D Target constant temperature T set Allowable deviation Δ T set and maintenance time t .

[0046] In this embodiment of the invention, the lightweight CNN feature extraction algorithm uses ReLU as the activation function and includes the following steps S1~S3: S1. Perform a one-dimensional convolution on the normalized 12-dimensional input vector to obtain the convolution output features. : in Represents one-dimensional convolution. This represents the normalized 12-dimensional input vector. K Indicates the kernel size. S Indicates the convolution stride. P Indicates the fill length.

[0047] In this embodiment of the invention, a normalization algorithm is used to normalize the parameters in the 12-dimensional input vector to the [0,1] interval and eliminate dimensions, ensuring the stability of subsequent model inference. The formula for the normalization algorithm is: in Represents the normalized i-th i One input parameter, Indicates the first i One original acquired parameter, This indicates the minimum value of the parameter's range. This indicates the maximum range of the parameter.

[0048] S2, Convolution Output Features Perform max pooling to obtain the pooled output features. : in This indicates max pooling.

[0049] S3, Pooling output characteristics Perform fully connected fusion to obtain the hydration heat feature vector. F : in W Represents the weight matrix. b This represents the bias vector.

[0050] In this embodiment of the invention, the hydration heat matching fusion model is based on the hydration heat feature vector. F A theoretical hydration heat temperature curve was constructed, and the temperature deviation of the core of the actual component was calculated. : in The theoretical curve of hydration heat temperature is shown in t The theoretical heat of hydration at time _____ Indicates the initial temperature of the concrete. Indicates the peak temperature of hydration heat. Represents the natural constant. This represents the hydration heat rate coefficient.

[0051] In this embodiment of the invention, the fuzzy PID self-tuning algorithm is based on the actual core temperature deviation of the component. The core temperature control quantity is calculated using PID control formulas based on deviations in humidity, ambient temperature, energy consumption, and equipment operating status within the curing space. Humidity control quantity Environmental compensation control quantity Energy consumption optimization control quantity and equipment safety control quantity The final fusion control quantity is obtained through weighted fusion. U : in Representing dimensions x The PID output control quantity, Representing dimensions x The proportionality coefficient, Representing dimensions x The integral coefficient, Representing dimensions x The differential coefficients, Representing dimensions x Deviation, dimension xThis can be any one of the following: the actual core temperature of the component, the humidity of the curing space, the ambient temperature, the real-time energy consumption, and the equipment operating status. This indicates a deviation in humidity within the maintenance space. Indicates the deviation of ambient temperature. Indicates energy consumption deviation. Indicates deviation in equipment operating status. This indicates the real-time humidity of the maintenance space. This indicates the target humidity setting value for maintenance. This indicates the actual internal and external temperature difference of the component. This indicates the real-time energy consumption of the maintenance system. This indicates the upper limit setting value for the energy consumption of the maintenance system. Indicates the real-time operating status of the equipment. This indicates the threshold for the device's safety status.

[0052] The parameter self-calibration formula for the fuzzy PID self-tuning algorithm is as follows: in , and These represent the initial proportional coefficient, initial integral coefficient, and initial derivative coefficient, respectively. , and These represent the fuzzy inference corrections for the proportional coefficient, integral coefficient, and differential coefficient, respectively.

[0053] In this embodiment of the invention, the 6-dimensional collaborative control command includes a steam proportional regulating valve opening command. Power command for graded electric heaters Variable frequency circulating fan speed command Precision humidification device spray volume command Hydration heat temperature control stage instructions Energy Carbon Optimization Power Limiting Directive : in , All of these are mapping coefficients calibrated experimentally. This represents the limiting function, ensuring the command remains within the effective range of the equipment. 0 and 100 represent the minimum and maximum value boundaries of the equipment command, respectively. (Steam proportional control valve opening command) The value range is 0%~100%, and the power command for the graded electric heater is... The value range is 0~10kW, and the variable frequency circulating fan speed command is... The value range is 500~3000r / min, which is the spray volume command of the precision humidification device. The value range is 0~5L / h, and the energy carbon optimization power limiting command The value range is 0%~100%, and the hydration heat temperature control stage command is... A value of 0 indicates the heating phase, a value of 1 indicates the isothermal phase, and a value of 2 indicates the cooling phase. This is the hydration heat temperature control stage instruction. In the final fusion control quantity U Switch phases when a preset threshold is triggered.

[0054] In this embodiment of the invention, the rule for determining the heating stage is as follows: and The rules for determining the constant temperature range are as follows: The rule for determining the cooling phase is as follows: and .

[0055] The multi-device collaborative execution module includes a steam proportional control valve, a staged electric heater, a variable frequency circulating fan, and a precision humidification device. The steam proportional control valve opening command... Used to control the power command of the steam proportional regulating valve and the staged electric heater. Used to control the speed commands of the staged electric heater and the variable frequency circulating fan. Used to control the variable frequency circulating fan and precisely control the spray volume of the humidification device. Used to control precise humidification devices, hydration heat temperature control stage commands. Used to control the temperature control stage logic, and energy-optimized power limiting instructions. Used to control the global power limit.

[0056] In this embodiment of the invention, the power limiting instruction is based on energy and carbon optimization. A global power limit is imposed on all heat source equipment to achieve peak shaving and energy saving. Steam is used as the primary heat source for temperature control, and electric heating is used as an environmental compensation heat source to avoid overloading of a single heat source. The speed of the variable frequency circulating fan is adjusted synchronously with the heat source output to ensure a uniform temperature field and prevent local overheating. The humidification amount is adjusted in real time according to the ambient humidity to maintain 90%~98%RH without overshooting or underhumidification. The equipment operating status is transmitted back to the edge AI intelligent decision-making module in real time to form a complete closed-loop control.

[0057] In this embodiment of the invention, the specific method for phased and coordinated temperature regulation of prefabricated components for prefabricated subway stations is as follows: When the hydration heat temperature control stage command During the heating phase, with a heating rate of 1~3℃ / h as the control target, the steam proportional control valve is opened first, and the opening degree is determined by the steam proportional control valve opening command. Given: When the steam opening reaches 70% but the heating rate has not yet been reached, the staged electric heater will automatically start, and a power command from the staged electric heater will be output. Given: The variable frequency circulating fan starts synchronously, with its speed increasing linearly with the heat source output to ensure rapid and uniform heat diffusion; the precision humidification device is activated in advance to maintain humidity ≥90% RH, preventing early water loss and cracking of the concrete; when the core temperature rise rate >3℃ / h, the AI ​​intelligent decision module automatically reduces power by 30%~50%, and the opening of the steam proportional regulating valve is adjusted synchronously with the power of the staged electric heater; power limiting commands are implemented throughout the process to optimize energy and carbon emissions. Implement power limiting to avoid overload and unnecessary energy consumption.

[0058] When the hydration heat temperature control stage command When the system is in a constant temperature range, with temperature fluctuations ≤ ±1℃ and humidity 90%~98% RH as control targets, the steam proportional regulating valve is slightly adjusted as the main means of constant temperature regulation to avoid large-scale switching. When the ambient temperature drops by >2℃, the staged electric heater is controlled to automatically supplement heat by 10%~20% to achieve environmental compensation. The variable frequency circulating fan is controlled to maintain stable operation at medium and low speeds to ensure uniform temperature and humidity in the maintenance space. The precision humidification device is controlled to automatically pulse spray according to humidity deviations to prevent overshoot. When the system approaches the target temperature, the total power of the system is automatically reduced to 60%, entering the energy-saving constant temperature mode.

[0059] When the hydration heat temperature control stage command During the cooling phase, a gradual cooling strategy is implemented with a cooling rate of 0.5~1℃ / h as the control target. The opening of the steam proportional regulating valve is gradually reduced, and the steam supply is reduced first without being directly shut off. The staged electric heater is controlled to dynamically compensate for the internal and external temperature difference. When the internal and external temperature difference is >15℃, the cooling is paused and the temperature is kept constant for 1~2 hours. The variable frequency circulating fan is controlled to increase its speed to promote uniform heat dissipation and avoid excessively rapid local temperature drop. The precision humidification device is kept in operation to maintain high humidity curing and prevent cracking of the concrete surface. When the curing termination temperature is reached, all equipment is shut down in sequence to complete the curing process.

[0060] In this embodiment of the invention, multiple devices are started at different times with intervals of ≥5 minutes to avoid grid impact. When the ambient temperature is >30℃, the heat source power automatically drops to 30% to reduce ineffective energy consumption. Steam and heating are not at full load simultaneously; steam, an energy-saving heat source, is used first, with electric heating only used for compensation. The output is adjusted in real time based on energy consumption data to achieve optimal energy consumption per unit component.

[0061] In this embodiment of the invention, upper and lower limit hard protections are set for steam valve opening, heating power, and fan speed to prevent over-limit operation. In case of sensor failure, it automatically switches to a safe, constant output without interrupting maintenance. In case of equipment overload / abnormality, it immediately triggers a limiting or shutdown mechanism and uploads alarm information.

[0062] In this embodiment of the invention, centralized monitoring supports access for ≥100 devices, and data refresh time is ≤10 seconds.

[0063] In this embodiment of the invention, carbon accounting includes calculating the carbon emissions from electricity consumption. Steam carbon emissions Total carbon emissions from maintenance Carbon emission intensity per unit of precast component Carbon emission reduction and energy saving and carbon reduction rate : in This indicates the total measured power consumption during the maintenance phase. Indicates the carbon emission factor of the regional power grid. This indicates the measured steam consumption during the maintenance phase. Indicates the carbon emission factor of industrial steam. N Indicates the number of maintenance components. This indicates the carbon emissions from traditional maintenance methods.

[0064] In this embodiment of the invention, the time-series database for data traceability is stored for ≥3 years, and supports curve export, alarm records, and parameter logs.

[0065] In this embodiment of the invention, remote management includes remote connection and security authentication, remote parameter distribution and parsing, breakpoint resume and command retransmission, remote control and real-time intervention, real-time data upload and display, anomaly alarm and remote handling, and historical data tracing and export.

[0066] Among them, remote connection and security authentication: the edge AI intelligent decision-making module establishes an encrypted connection with the cloud energy and carbon management module through 4G / 5G, adopts dual authentication of AES-128 encryption + device unique identification code, and establishes a 10-second heartbeat link to monitor online status.

[0067] Remote parameter distribution and parsing: The cloud-based energy and carbon management module inputs maintenance parameters and encrypts and distributes them. The edge AI intelligent decision-making module decrypts and verifies the parameters and updates the local parameters. If the verification fails, an alarm is uploaded and execution is refused.

[0068] Resuming interrupted transmission and resending instructions: When the network is interrupted, the edge AI intelligent decision-making module automatically caches data and instructions. After the network is restored, the interrupted transmission and instructions are resumed and resent in chronological order to ensure that data and instructions are not lost.

[0069] Remote control and real-time intervention: The cloud-based energy and carbon management module supports remote adjustment of equipment output, switching of control modes, and remote start-up and shutdown of maintenance processes. All commands are subject to authorization verification to prevent misoperation.

[0070] Real-time data upload and display: The edge AI intelligent decision-making module encrypts and uploads data every 10 seconds, and the cloud-based energy and carbon management module refreshes the operating conditions in real time and generates temperature, humidity, energy consumption, and carbon emission curves, which can be viewed online.

[0071] Anomaly Alarms and Remote Handling: The cloud-based energy and carbon management module monitors alarms such as over-temperature, over-humidity, sensor failure, and equipment malfunction in real time, provides local automatic protection, and pushes notifications from the cloud-based energy and carbon management module, supporting remote reset and fault confirmation.

[0072] Historical data traceability and export: The cloud-based energy and carbon management module stores ≥3 years of historical data, supports conditional queries, and exports temperature control reports, energy consumption reports, and carbon emission reports to meet the requirements of project acceptance and low-carbon verification.

[0073] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An intelligent temperature control device for the curing of prefabricated components in prefabricated subway stations, characterized in that, It includes an ultra-low power wireless sensing module, an edge AI intelligent decision-making module, a multi-device collaborative execution module, and a cloud-based energy and carbon management module. The edge AI intelligent decision-making module is communicatively connected to the ultra-low power wireless sensing module, the multi-device collaborative execution module, and the cloud-based energy and carbon management module, respectively. The ultra-low power wireless sensing module is used to collect data on the core temperature, curing space temperature and humidity, outdoor environment, and energy consumption of prefabricated components in prefabricated subway stations in real time. The edge AI intelligent decision-making module is used to construct a 12-dimensional input vector based on the data collected by the ultra-low power wireless sensing module, and to process the 12-dimensional input vector based on the lightweight CNN feature extraction algorithm, the fuzzy PID self-tuning algorithm and the hydration heat matching fusion model to obtain a 6-dimensional collaborative control command. The multi-device collaborative execution module is used to coordinately adjust the temperature of prefabricated components of the assembled subway station in stages according to the 6-dimensional collaborative control instructions. The cloud-based energy and carbon management module is used for centralized monitoring, energy and carbon accounting, data traceability, and remote control of the temperature control process for the curing of prefabricated components in prefabricated subway stations.

2. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 1, characterized in that, The ultra-low power wireless sensing module includes a sensing and acquisition unit, an ultra-low power main control unit, a wireless communication unit, and a power management unit. The sensing and acquisition unit includes a core embedded temperature acquisition circuit, a curing space temperature and humidity acquisition circuit, an outdoor environment temperature and humidity compensation acquisition circuit, and an energy consumption acquisition circuit, which are used to collect the core temperature, curing space temperature and humidity, outdoor environment, and energy consumption of the prefabricated components of the prefabricated subway station in real time. The wireless communication unit is used to transmit wireless data with the edge AI intelligent decision-making module. The ultra-low power main control unit is used to provide control signals for the sensing and acquisition unit and the wireless communication unit; The power management unit is used to provide adaptive power for the sensing and acquisition unit, the ultra-low power main control unit, and the wireless communication unit.

3. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 1, characterized in that, The 12-dimensional input vector includes the core temperature of the component. T c Temperature of the maintenance space T s Humidity of the maintenance space H s Outdoor temperature T o Outdoor humidity H o Temperature difference between inside and outside Δ T = T c - T s Hydration heat stage marking S Current energy consumption P Equipment operating status D Target constant temperature T set Allowable deviation Δ T set and maintenance time t .

4. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 3, characterized in that, The lightweight CNN feature extraction algorithm includes the following steps: S1. Perform a one-dimensional convolution on the normalized 12-dimensional input vector to obtain the convolution output features. : in Represents one-dimensional convolution. This represents the normalized 12-dimensional input vector. K Indicates the kernel size. S Indicates the convolution stride. P Indicates the fill length; S2, Convolution Output Features Perform max pooling to obtain the pooled output features. : in This indicates max pooling; S3, Pooling output characteristics Perform fully connected fusion to obtain the hydration heat feature vector. F : in W Represents the weight matrix. b This represents the bias vector.

5. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 4, characterized in that, The hydration heat matching and fusion model is based on hydration heat feature vectors. F A theoretical hydration heat temperature curve was constructed, and the temperature deviation of the core of the actual component was calculated. : in The theoretical curve of hydration heat temperature is shown in t The theoretical heat of hydration at time _____ Indicates the initial temperature of the concrete. Indicates the peak temperature of hydration heat. Represents the natural constant. This represents the hydration heat rate coefficient.

6. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 5, characterized in that, The fuzzy PID self-tuning algorithm is based on the actual core temperature deviation of the component. The core temperature control quantity is calculated using PID control formulas based on deviations in humidity, ambient temperature, energy consumption, and equipment operating status within the curing space. Humidity control quantity Environmental compensation control quantity Energy consumption optimization control quantity and equipment safety control quantity The final fusion control quantity is obtained through weighted fusion. U : in Representing dimensions x The PID output control quantity, Representing dimensions x The proportionality coefficient, Representing dimensions x The integral coefficient, Representing dimensions x The differential coefficients, Representing dimensions x Deviation, dimension x This can be any one of the following: the actual core temperature of the component, the humidity of the curing space, the ambient temperature, the real-time energy consumption, and the equipment operating status. This indicates a deviation in humidity within the maintenance space. Indicates the deviation of ambient temperature. Indicates energy consumption deviation. Indicates deviation in equipment operating status. This indicates the real-time humidity of the maintenance space. This indicates the target humidity setting value for maintenance. This indicates the actual internal and external temperature difference of the component. This indicates the real-time energy consumption of the maintenance system. This indicates the upper limit setting value for the energy consumption of the maintenance system. Indicates the real-time operating status of the equipment. This indicates the threshold for the device's safety status.

7. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 1, characterized in that, The 6-dimensional coordinated control commands include steam proportional control valve opening commands. Power command for graded electric heaters Variable frequency circulating fan speed command Precision humidification device spray volume command Hydration heat temperature control stage instructions Energy Carbon Optimization Power Limiting Directive : in , All of these are mapping coefficients calibrated experimentally. This represents the limiting function, where 0 and 100 represent the minimum and maximum value boundaries of the device command. The multi-device collaborative execution module includes a steam proportional control valve, a staged electric heater, a variable frequency circulating fan, and a precision humidification device. The steam proportional control valve opening command... The power command for controlling the steam proportional regulating valve, the staged electric heater The variable frequency circulating fan speed command is used to control the staged electric heater. The spray volume command for the precision humidification device is used to control the variable frequency circulating fan. The hydration heat temperature control stage command is used to control a precision humidification device. The energy-optimized power limiting command is used to control the temperature control stage logic. Used to control the global power limit.

8. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 7, characterized in that, The specific method for phased and coordinated temperature regulation of prefabricated components in prefabricated subway stations is as follows: When the hydration heat temperature control stage command During the heating phase, with a heating rate of 1~3℃ / h as the control target, the steam proportional control valve is opened first, and the opening degree is determined by the steam proportional control valve opening command. Given: When the steam opening reaches 70% but the heating rate has not yet been reached, the staged electric heater will automatically start, and a power command from the staged electric heater will be output. Given: The variable frequency circulating fan starts synchronously, with its speed increasing linearly with the heat source output to ensure rapid and uniform heat diffusion; the precision humidification device is activated in advance to maintain humidity ≥90%RH, preventing early water loss and cracking of the concrete; when the core temperature rise rate >3℃ / h, the AI ​​intelligent decision module automatically reduces power by 30%~50%, and the opening of the steam proportional regulating valve is adjusted synchronously with the power of the staged electric heater; power limiting commands are used throughout the process to optimize energy and carbon emissions. Implement power limiting to avoid overload and unnecessary energy consumption; When the hydration heat temperature control stage command When the system is in a constant temperature range, with temperature fluctuations ≤ ±1℃ and humidity 90%~98% RH as control targets, the steam proportional regulating valve is slightly adjusted as the main means of constant temperature regulation to avoid large-scale on / off actions. When the ambient temperature drops by >2℃, the staged electric heater is controlled to automatically supplement heat by 10%~20% to achieve environmental compensation. The variable frequency circulating fan is controlled to maintain stable operation at medium and low speeds to ensure uniform temperature and humidity in the maintenance space. The precision humidification device is controlled to automatically pulse spray according to humidity deviations to prevent overshoot. When the system approaches the target temperature, the total power of the system is automatically reduced to 60%, entering the energy-saving constant temperature mode. When the hydration heat temperature control stage command During the cooling phase, a gradual cooling strategy is implemented with a cooling rate of 0.5~1℃ / h as the control target. The opening of the steam proportional regulating valve is gradually reduced, and the steam supply is reduced first without being directly shut off. The staged electric heater is controlled to dynamically compensate for the internal and external temperature difference. When the internal and external temperature difference is >15℃, the cooling is paused and the temperature is kept constant for 1~2 hours. The variable frequency circulating fan is controlled to increase its speed to promote uniform heat dissipation and avoid excessively rapid local temperature drop. The precision humidification device is kept in operation to maintain high humidity curing and prevent cracking of the concrete surface. When the curing termination temperature is reached, all equipment is shut down in sequence to complete the curing process.

9. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 1, characterized in that, The energy and carbon accounting includes calculating carbon emissions from electricity consumption. Steam carbon emissions Total carbon emissions from maintenance Carbon emission intensity per unit of precast component Carbon emission reduction and energy saving and carbon reduction rate : in This indicates the total measured power consumption during the maintenance phase. Indicates the carbon emission factor of the regional power grid. This indicates the measured steam consumption during the maintenance phase. Indicates the carbon emission factor of industrial steam. N Indicates the number of maintenance components. This indicates the carbon emissions from traditional maintenance methods.

10. The intelligent temperature control device for curing prefabricated components of prefabricated subway stations according to claim 1, characterized in that, The remote management and control includes remote connection and security authentication, remote parameter distribution and parsing, breakpoint resume and command retransmission, remote control and real-time intervention, real-time data upload and display, anomaly alarm and remote handling, and historical data tracing and export.

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