A method and system for continuous temperature detection of molten steel in carbon steel converters

Abstract

This application relates to the technical field of thermophysical performance testing, and more particularly to a method for continuous temperature detection of molten steel in a carbon steel converter. The method includes acquiring waste heat parameters of the converter; adjusting the operating state of a thermoelectric power generation unit in real time based on the waste heat parameters, controlling a bismuth telluride-based thermoelectric module to convert waste heat into electrical energy; dynamically distributing electrical energy to a temperature detection unit and a cooling management unit through a multi-channel intelligent power distribution cabinet; controlling a paraffin-based phase change material to absorb heat through the cooling management unit; continuously detecting the molten steel temperature using a non-contact infrared thermometer; and dynamically adjusting the power consumption of the temperature detection unit and the cooling management unit based on an energy management strategy, achieving adaptive adjustment of sampling frequency and cooling intensity through a rule engine. This application significantly reduces dependence on external energy sources, reduces energy consumption and carbon footprint, achieves green and continuous temperature detection, and improves the reliability of the system in unstable power supply environments.

Description

Technical Field

[0001] This application relates to the technical field of thermophysical property testing, and in particular to a method and system for continuous temperature detection of molten steel in a carbon steel converter. Background Technology

[0002] In the carbon steel converter steelmaking process, accurate and continuous monitoring of molten steel temperature is crucial for controlling the steelmaking process, improving product quality, and optimizing energy utilization. Continuous temperature monitoring provides real-time feedback on temperature changes within the furnace, offering data support to operators, ensuring the stability and efficiency of the steelmaking process, thereby reducing energy waste and improving steel quality. With the development of automation technology, temperature monitoring systems have become an indispensable part of modern converter steelmaking.

[0003] In related technologies, common continuous temperature detection systems typically employ non-contact infrared temperature sensors combined with active cooling devices such as water-cooling or air-cooling systems to protect the sensors and ensure normal operation in high-temperature environments. These systems usually rely on an external power supply to drive the sensor, cooling pump, and data transmission unit, while maintaining the sensor temperature within its operating parameter range through a fixed cooling loop. This design achieves real-time temperature monitoring to some extent, but requires stable infrastructure support.

[0004] However, the aforementioned technologies have significant drawbacks: the systems are highly dependent on external power and cooling supplies, resulting in high energy consumption, increased operating costs, and potential interruptions in unstable power environments, affecting the continuity of detection. Furthermore, the high energy consumption and resulting carbon footprint contradict the principles of green manufacturing, limiting their application in sustainable production. Therefore, a low-energy, self-powered temperature detection method is urgently needed to address this issue. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this application provides a method and system for continuous temperature detection of molten steel in a carbon steel converter.

[0006] In a first aspect, this application provides a method for continuous temperature detection of molten steel in a carbon steel converter, comprising the following steps:

[0007] The waste heat parameters of the converter are obtained, including the thermal gradient data of the outer wall of the converter collected in real time by a distributed temperature sensor array, and a standardized waste heat parameter dataset containing average heat flux density, temperature fluctuation frequency and regional heat distribution characteristic values.

[0008] Based on the waste heat parameters, the working state of the thermoelectric power generation unit is adjusted in real time by a PID controller, and the bismuth telluride-based thermoelectric module is controlled to convert waste heat into electrical energy. When the heat flux density is high, the conversion efficiency is increased, and when the heat flux density is low, the mode is switched to energy saving.

[0009] The power is dynamically distributed to the temperature detection unit and the cooling management unit through a multi-channel intelligent power distribution cabinet. The distribution strategy is dynamically adjusted based on real-time power generation and unit priority, and priority is given to ensuring the power supply to the temperature detection unit.

[0010] The paraffin-based phase change material absorbs heat through a cooling management unit. When the ambient temperature exceeds a set threshold, the temperature control valve is activated to accelerate heat transfer and achieve passive cooling.

[0011] The temperature of molten steel is continuously detected using a non-contact infrared thermometer. A dual-probe redundancy design and an anti-interference compensation algorithm are employed to ensure detection accuracy.

[0012] The power consumption of the temperature detection unit and the cooling management unit is dynamically adjusted based on the energy management strategy. This includes adaptive adjustment of the sampling frequency and cooling intensity through a rule engine based on real-time power generation, unit power consumption priority, and system status flags.

[0013] By adopting the above technical solutions, thermal gradient data is accurately acquired through a distributed temperature sensing array. Combined with PID intelligent control of thermoelectric power generation and dynamic power distribution technology, passive cooling with phase change materials and dual-probe redundant anti-interference detection are used. Adaptive energy consumption management is achieved based on a rule engine, which significantly reduces external energy dependence and carbon footprint, improves the reliability of the system in unstable power environments, and realizes green and continuous steel temperature detection.

[0014] In one possible implementation, the step of obtaining the waste heat parameters of the converter includes:

[0015] A distributed sensor array consisting of at least 24 pairs of K-type thermocouples is arranged in a grid pattern in the high-temperature region of the outer wall of the converter.

[0016] The mineral-insulated metal sheath structure ensures stable operation of the sensor in a high-temperature environment of 1100°C.

[0017] Cold junction compensation and 24-bit high-precision analog-to-digital conversion are performed through a multi-channel data acquisition module;

[0018] A three-dimensional thermal gradient distribution map is generated based on a spatial interpolation algorithm, and characteristic parameters such as maximum heat flux density, regional temperature difference, and temperature change rate are extracted.

[0019] By adopting the above technical solutions, high-temperature stability is ensured through the grid-like distribution of multiple thermocouples and mineral insulation sheaths. Multi-channel acquisition, compensation, conversion, and spatial interpolation three-dimensional thermal gradient analysis are used to improve the accuracy and comprehensiveness of thermal gradient measurements, optimize power generation efficiency, and provide accurate real-time data support for energy management.

[0020] In one possible implementation, the step of dynamically adjusting the power consumption of the temperature detection unit and the cooling management unit based on the energy management strategy includes:

[0021] Real-time monitoring of the operating mode status of the temperature detection unit and the heat load data of the cooling management unit;

[0022] The power supply voltage ratio is dynamically adjusted by a programmable digital potentiometer, and the power supply voltage of the temperature detection unit is reduced from 12V to 5V when it is in the sampling interval.

[0023] When the phase change material heat storage capacity of the cooling management unit is lower than the threshold, its power supply ratio is increased;

[0024] A hysteresis control algorithm is used to avoid frequent switching of critical states, and a minimum guaranteed power threshold is set to ensure the operation of core functions.

[0025] By adopting the above technical solutions, through real-time monitoring of working status and heat load, using programmable potentiometer dynamic voltage regulation and hysteresis control to prevent frequent switching, and setting a minimum power threshold to ensure core functions, the system effectively reduces ineffective energy consumption, extends its service life, and ensures continuous detection capability and accurate power distribution under varying operating conditions.

[0026] One possible implementation also includes:

[0027] Excess electrical energy is stored in an energy storage unit that combines lithium-ion batteries and supercapacitors.

[0028] Constant current-constant voltage charging and pulse discharge control are achieved through a bidirectional DC-DC converter;

[0029] Based on the ARIMA model, predict the energy consumption demand for the next 15 minutes and generate the optimal charging and discharging strategy;

[0030] Multiple safety mechanisms are implemented, including overcharge and over-discharge protection, temperature monitoring, and isolation protection.

[0031] By adopting the above technical solutions, through hybrid energy storage configuration and bidirectional conversion intelligent charging and discharging control, combined with time series energy consumption prediction and multiple safety protection mechanisms, energy utilization efficiency is significantly improved, the system's resilience to operating condition fluctuations is enhanced, the risk of production interruption is reduced, and operational safety and reliability are improved.

[0032] One possible implementation also includes:

[0033] Steel temperature data were acquired at a frequency of 100Hz using an InGaAs detector infrared thermometer array.

[0034] The moving window difference algorithm is used to calculate the rate of temperature change, and digital filtering is introduced to eliminate abnormal fluctuations.

[0035] When the rate of change remains below 2°C / s for 10 seconds, the sampling frequency is gradually reduced to 1Hz and the data reconstruction algorithm is activated.

[0036] When the rate of change instantaneously exceeds 5°C / s, the sampling frequency is increased to 50Hz within 100ms and the backup sensor channel is activated.

[0037] By adopting the above technical solution, high-frequency infrared temperature acquisition and moving window differential calculation are used, and digital filtering is employed to eliminate noise interference. This enables intelligent adjustment of the sampling frequency, effectively reducing the energy consumption of invalid sampling, extending the system's battery life, accelerating the temperature response speed, and maintaining high-precision measurement performance.

[0038] One possible implementation also includes:

[0039] The ambient light intensity in the 400-1100nm wavelength band was monitored using a multispectral light sensor array.

[0040] It employs industrial-grade silicon photodiodes equipped with self-cleaning optical windows and temperature compensation circuitry;

[0041] When the average light intensity for 5 consecutive minutes is greater than 200 W / m² and the thermoelectric power generation is insufficient, the solar power supply is connected through a soft start method.

[0042] The solar auxiliary power supply will be delayed if the light intensity remains below 50W / m² for 10 consecutive minutes.

[0043] By adopting the above technical solutions, and through multispectral full-band illumination monitoring and industrial phototube self-cleaning compensation technology, intelligent start-stop control of solar energy is achieved, which enhances the system's energy security capability, extends the continuous operation time under severe weather conditions, improves environmental adaptability, and optimizes the efficiency of multi-energy synergistic utilization.

[0044] One possible implementation also includes:

[0045] Record system energy consumption data and converter operating status signals in 1-minute intervals;

[0046] A time series analysis method was used to establish an energy consumption prediction model with multiple influencing factors.

[0047] The model parameters are updated every 24 hours using a sliding window mechanism and an adaptive weighting algorithm.

[0048] Based on the forecast results, the charging and discharging plans of the energy storage units are adjusted in advance, charging to 90% capacity during periods of low energy consumption and maintaining a discharged state during peak periods.

[0049] By adopting the above technical solutions, and through multi-factor time series prediction and sliding window adaptive weighting algorithm, intelligent scheduling of energy storage systems is achieved, which significantly reduces dependence on external energy, improves energy utilization efficiency, enhances equipment operation stability, and improves the accuracy of scheduling decisions.

[0050] Secondly, this application provides a continuous temperature detection system for molten steel in a carbon steel converter, comprising:

[0051] The thermoelectric power generation unit, composed of bismuth telluride-based thermoelectric modules, is directly attached to the high-temperature area of ​​the outer wall of the converter and is configured to convert waste heat into electrical energy.

[0052] The temperature detection unit includes symmetrically arranged non-contact infrared temperature probes, equipped with a water-cooled sheath and an anti-interference compensation module, and is configured to continuously detect the temperature of molten steel.

[0053] The cooling management unit, comprising a paraffin-based phase change material capsule assembly and a heat pipe heat transfer system, is configured to achieve passive cooling through latent heat absorption.

[0054] The energy management unit adopts an industrial-grade embedded controller with a built-in multi-channel analog input / output module, and is configured to dynamically adjust power consumption based on real-time power generation and system status.

[0055] The distribution unit includes an intelligent power distribution cabinet and a multi-channel power management module, configured to dynamically distribute power according to priority.

[0056] The units are connected by a star topology, and the system also includes an energy storage unit that combines lithium iron phosphate batteries and supercapacitors, as well as a multispectral illumination monitoring unit.

[0057] By adopting the above technical solutions, through the wall-mounted structure of thermoelectric modules and the symmetrical water-cooled infrared detection design, combined with phase change material heat pipe cooling and embedded intelligent management, and by using multi-channel dynamic power distribution and hybrid energy storage multi-spectral synergy, the system integration is improved, maintenance costs are reduced, overall energy consumption is reduced, continuous operation time is extended, and environmental adaptability is enhanced.

[0058] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory is used to store computer program code, and the processor is used to execute the computer program code stored in the memory to implement the methods in the first aspect and any one of the first aspects, or in the second aspect and any possible implementation of the second aspect.

[0059] Fourthly, this application provides a computer-readable storage medium storing a computer program or instructions that, when executed, implement the methods described in the first aspect and any one thereof, or the second aspect and any possible implementation thereof. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a method for continuous temperature detection of molten steel in a carbon steel converter, as provided in an embodiment of this application.

[0061] Figure 2 This is a schematic diagram of a continuous temperature detection system for molten steel in a carbon steel converter, provided as an embodiment of this application.

[0062] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0063] The technical solutions in this application will now be described with reference to all the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0064] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, in the description of the embodiments of this application, "plural" or "multiple" refers to two or more than two.

[0065] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0066] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two.

[0067] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "one embodiment," "some embodiments," "another embodiment," "other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0068] This application provides a method for continuous temperature detection of molten steel in a carbon steel converter, executed by an electronic device. This electronic device can be a standalone physical electronic device, a cluster of multiple physical electronic devices, a distributed system, or a cloud electronic device providing cloud computing services. This application does not impose any limitations on this method. Figure 1 As shown, the method includes:

[0069] S1. Obtain the waste heat parameters of the converter.

[0070] Specifically, thermal gradient data is collected in real time through an array of distributed temperature sensors installed on the outer wall of the converter. The temperature sensors are high-temperature resistant thermocouples with a measurement range covering 300°C to 800°C, which can accurately reflect the waste heat distribution.

[0071] The sensor array is arranged at a uniform spacing in the high-temperature area of ​​the converter and is connected to the data acquisition module via shielded cables. The module filters and amplifies the raw signal to eliminate electromagnetic interference in the industrial environment.

[0072] Furthermore, the collected thermal gradient data is transmitted via industrial Ethernet to the data processing unit of the central control host to generate a standardized waste heat parameter dataset. This dataset includes average heat flux density, temperature fluctuation frequency, and regional heat distribution characteristics. This hardware architecture ensures the real-time and reliable acquisition of parameters, providing accurate input for subsequent energy conversion.

[0073] Furthermore, during the sensor deployment phase, positioning is optimized using thermal simulation models to avoid measurement blind spots. The data processing unit employs multi-source information fusion technology to correlate temperature data with converter operating conditions such as oxygen blowing time and molten steel capacity, thereby dynamically correcting waste heat parameters. This enhances the systematic nature and contextual adaptability of the parameters, improves data quality, and reduces the risk of misjudgments caused by localized thermal anomalies.

[0074] In summary, this step enables quantitative management of waste heat resources, providing a stable input benchmark for the self-powered system. It transforms waste heat, traditionally considered a disturbance factor, into a controllable parameter. Through systematic collection and processing, it lays the technical foundation for energy self-sufficiency, significantly reduces dependence on the external power grid, and meets the requirements of green manufacturing.

[0075] In some embodiments, in order to optimize power generation efficiency and ensure the stability of energy supply, S1 further includes the following steps:

[0076] S101. The thermal gradient of the converter wall is monitored in real time by a temperature sensor, and waste heat parameter data is generated.

[0077] Specifically, the monitoring strategy mainly adopts a high-temperature resistant distributed thermocouple sensor array, which consists of at least 24 pairs of K-type thermocouples, which are evenly arranged in a grid pattern in specific high-temperature areas such as the corresponding position of the molten pool on the outer wall of the converter.

[0078] Furthermore, the thermocouple sensor array adopts a mineral-insulated metal sheath structure, with a maximum temperature resistance of 1100°C, and is fixed to the furnace wall surface with high-temperature ceramic adhesive. Each thermocouple sensor is connected to a multi-channel data acquisition module, which has a dedicated cold junction compensation circuit for thermocouples and a 24-bit high-precision analog-to-digital converter.

[0079] Furthermore, the raw temperature data collected is transmitted to the central control unit, where a specific thermal gradient calculation module generates a three-dimensional thermal gradient distribution map based on a spatial interpolation algorithm, and extracts key feature parameters, including maximum heat flux density, regional temperature difference, and temperature change rate, thereby forming a standardized waste heat parameter dataset.

[0080] Furthermore, the traditional single temperature measurement point is improved into a distributed sensor network, capturing the non-uniform characteristics of the thermal field through multi-point spatial measurements. Simultaneously, during the sensor deployment phase, computational fluid dynamics simulations are used to identify key areas with drastic changes in thermal gradients, optimizing sensor distribution density to ensure that measurement data comprehensively reflects waste heat characteristics.

[0081] In summary, through hardware architecture and data processing methods, a refined characterization of the converter waste heat field was achieved, elevating thermal gradient measurement from traditional single-point qualitative assessment to multi-dimensional quantitative analysis. This provides precise input parameters for the cogeneration unit, improving power generation efficiency. Simultaneously, it significantly reduces energy supply fluctuations caused by inaccurate parameters, laying a solid foundation for the energy autonomy of the entire system.

[0082] Based on this, by monitoring the thermal gradient in real time and accurately obtaining waste heat parameters, power generation efficiency was optimized, ensuring the stability of energy supply and further reducing external energy demand.

[0083] In this embodiment, the method further includes the following steps:

[0084] S2. Based on waste heat parameters, control the thermoelectric power generation unit to convert waste heat into electrical energy.

[0085] Specifically, the thermoelectric power generation unit consists of multiple thermoelectric modules that operate based on the first thermoelectric effect. These modules are made of bismuth telluride-based composite materials and are directly attached to the high-temperature region of the converter's outer wall.

[0086] Each thermoelectric module is connected to a DC-DC converter, and its output voltage and current are adjusted in real time by the central control host through a PID controller.

[0087] Its control strategy is based on the waste heat parameters generated by S1. Specifically, when the heat flux density is high, the conversion efficiency is increased to maximize power generation; when the heat flux density is low, the energy-saving mode is switched to prevent energy backflow.

[0088] The output of the thermal power generation unit is connected to a smart meter to monitor the power generation in real time and feed it back to the central control host, forming a closed-loop control.

[0089] Furthermore, waste heat is transformed from an environmental burden into useful energy. The control system employs an adaptive algorithm to dynamically adjust the operating point of the thermoelectric module based on changes in the thermal gradient, avoiding efficiency drops or equipment damage caused by sudden temperature changes.

[0090] In summary, this step achieves on-site energy conversion and refined management, directly coupling power generation control with waste heat parameters, breaking through the limitations of traditional external power supply, not only reducing the carbon footprint, but also improving the system's energy autonomy under unstable operating conditions.

[0091] S3. Distribute electrical energy to the temperature detection unit and the cooling management unit.

[0092] Specifically, the power distribution strategy employs a multi-channel intelligent power distribution cabinet, the core of which is a programmable logic controller (PLC). After receiving instructions from the central control host, the PLC switches the power distribution path via solid-state relays. Priority is given to ensuring the operation of the temperature detection unit, with the remaining energy allocated to the cooling management unit.

[0093] The distribution cabinet integrates current and voltage sensors to monitor the load status of each branch in real time. If an overload or short circuit is detected, a protection mechanism is immediately triggered to cut off the circuit. The power distribution strategy is dynamically adjusted based on real-time power generation and unit priority, and the energy flow is displayed through a human-machine interface (HMI).

[0094] Furthermore, the power distribution process features a redundant design and automatic switching between primary and backup power supplies to ensure uninterrupted power supply to critical units.

[0095] In summary, through intelligent allocation, the system optimizes energy utilization efficiency, realizes dynamic scheduling of energy on demand, avoids energy waste, and significantly improves the overall energy efficiency and operational stability of the system.

[0096] S4. Passive cooling is achieved by controlling the heat absorption of the phase change material through the cooling management unit.

[0097] Specifically, the cooling management unit consists of a phase change material capsule assembly, a heat pipe heat transfer system, and a temperature control valve. The phase change material in the capsule assembly is a paraffin-based composite material with a melting point of 60-80°C, encapsulated within an aluminum capsule and positioned in the sensor's hotspot area. The heat pipe heat transfer system efficiently conducts excess heat to the phase change material capsule assembly. When the ambient temperature exceeds a set threshold, the temperature control valve opens to accelerate heat transfer.

[0098] Furthermore, the cooling management unit has a built-in temperature sensor to monitor the capsule status in real time and synchronously feed the data back to the central control host, which can then be used to trigger active cooling backup, such as starting the fan.

[0099] Furthermore, phase change materials achieve zero-energy cooling through latent heat absorption, reducing the power consumption of traditional active cooling. Simultaneously, the material encapsulation design enhances thermal cycling durability and extends service life.

[0100] In summary, this step significantly reduces cooling energy consumption through passive cooling technology, deeply integrates phase change thermal management with the system thermal environment, improves cooling efficiency and economy, and provides a guarantee for the long-term continuous operation of the system.

[0101] S5. Continuous detection of molten steel temperature is performed through the temperature detection unit.

[0102] Specifically, the temperature detection unit uses a non-contact infrared thermometer. Its optical probe is installed in a water-cooled jacket near the converter's observation port, and the jacket is circulated with cooling water to prevent overheating. The infrared thermometer collects 10-20 data points per second, which are transmitted via fiber optic cable to a signal processor for noise filtering and environmental compensation, such as dust interference correction. Finally, the data is uploaded to the central control unit, which generates a temperature trend curve and issues an alarm for exceeding limits.

[0103] Furthermore, the temperature detection unit employs a dual-probe redundancy design, automatically switching to a backup probe when one fails. Moreover, the probe layout avoids turbulent areas within the furnace, thereby improving measurement accuracy.

[0104] In summary, this step achieves high-precision and high-reliability continuous temperature monitoring, combining anti-interference design and redundancy mechanisms to ensure data quality and provide a reliable basis for process control.

[0105] S6. Dynamically adjust the power consumption of the temperature detection unit and the cooling management unit based on the energy management strategy to maintain system operation.

[0106] Specifically, the energy management strategy is executed by the rule engine built into the central control host. Its engine inputs include real-time power generation, unit power consumption priority, and system status flags.

[0107] Furthermore, the dynamic adjustment strategy includes, for example, reducing the sampling frequency of the temperature detection unit to the lowest feasible value and suspending the auxiliary fan of the cooling management unit when power generation is insufficient; and increasing the sampling frequency and activating additional cooling when power generation is excessive. All operations are executed by sending data to each unit controller via the Modbus protocol, and status change logs are used for offline strategy optimization.

[0108] Furthermore, the strategy engine uses machine learning based on historical data to continuously optimize itself and improve adjustment accuracy. Simultaneously, the system supports remote strategy updates to adapt to different operating conditions.

[0109] In summary, by dynamically adjusting power consumption, a precise balance between energy supply and demand is achieved, and energy management and operation strategies are intelligently integrated, significantly improving the system's adaptability and endurance in resource-constrained environments.

[0110] Based on this, by utilizing converter waste heat for self-powered operation and passive cooling, the dependence on external energy sources is significantly reduced, energy consumption and carbon footprint are decreased, green and continuous temperature monitoring is achieved, and the system reliability is improved in unstable power supply environments.

[0111] In some embodiments, S6 further includes the following steps:

[0112] S601. Adjust the power distribution ratio according to the working status and cooling requirements of the temperature detection unit to reduce idle power consumption.

[0113] Specifically, the central control unit has a built-in power management module that monitors the working mode of the temperature detection unit in real time, including standby, sampling, and data transmission status, while also monitoring the real-time heat load data of the cooling management unit.

[0114] The specific hardware components of this module include a current sensor embedded within the temperature detection unit to monitor the operating current, and a heat flow sensor positioned at the heat exchange interface of the cooling management unit. This sensor is connected to the data acquisition card of the central control unit via a high-precision analog-to-digital converter.

[0115] Furthermore, the power management module dynamically adjusts the voltage ratio allocated to each unit according to a preset priority strategy, such as prioritizing temperature detection over auxiliary cooling, using a programmable digital potentiometer. Specifically, when the temperature detection unit is in the sampling interval, its supply voltage is automatically reduced from the rated 12V to the maintenance voltage of 5V; when the phase change material heat storage capacity of the cooling management unit is below the threshold, its supply ratio is increased to enhance the cooling effect.

[0116] Furthermore, the system can automatically optimize energy allocation based on real-time operating conditions and achieve precise power supply under different operating modes by establishing a state-power correspondence model. In particular, the system introduces a hysteresis control algorithm to avoid energy consumption fluctuations caused by frequent switching near critical states, while setting a minimum guaranteed power threshold to ensure that critical functions are not affected.

[0117] In summary, the system achieves refined energy consumption management, which is more energy-efficient than the traditional fixed distribution method. It coordinates and optimizes the traditional independent temperature detection and cooling system power supply control, and through the organic combination of real-time status perception and dynamic power adjustment, it not only avoids energy waste but also ensures the continuous and stable operation of the system's core functions.

[0118] Based on this, by dynamically adjusting power consumption, energy waste is avoided, system lifespan is extended, and continuous detection capability under varying operating conditions is ensured.

[0119] In some embodiments, the method further includes the following steps:

[0120] S7. Store excess electrical energy in an energy storage unit and release it during peak demand periods to supplement power supply.

[0121] Specifically, the central control unit has a built-in intelligent energy storage management system, which includes a bidirectional DC-DC converter, an energy storage unit, a battery management system (BMS), and a load forecasting module. The energy storage unit uses a hybrid configuration of lithium-ion batteries and supercapacitors.

[0122] In practice, an energy distribution node is set at the output of the thermoelectric power generation unit, and the difference between the power generation and the power consumption is monitored in real time by current and voltage sensors. When the power generation is excessive, the bidirectional DC-DC converter charges the energy storage unit with the excess power in a constant current-constant voltage mode; when the system power consumption demand exceeds the power generation capacity, the BMS controls the energy storage unit to supplement the power supply in a pulse discharge mode.

[0123] Furthermore, the energy storage unit adopts a modular design, with each module containing four sets of lithium iron phosphate batteries connected in series and one set of supercapacitor modules, and maintaining the consistency of each cell through an active balancing circuit.

[0124] Furthermore, the load forecasting module establishes an ARIMA model based on historical operating data to predict energy consumption demand within the next 15 minutes and generate the optimal charging and discharging strategy.

[0125] Furthermore, by introducing a hybrid energy storage system, the energy supply and demand imbalances across different time scales can be coordinated. Supercapacitors can handle instantaneous power fluctuations, while lithium batteries can provide energy buffering over longer periods; the two work collaboratively through a multi-objective optimization control algorithm. Moreover, by proactively adjusting the energy storage state through load forecasting, supply and demand imbalances can be avoided. In addition, the system incorporates multiple protection mechanisms, including overcharge and over-discharge protection, temperature monitoring, and isolation protection, to ensure the safe operation of the energy storage system.

[0126] In summary, intelligent energy storage management improves system energy utilization and enhances peak power support capabilities. Combining hybrid energy storage technology with load forecasting enables the spatiotemporal shift of energy, effectively resolving the power supply instability caused by fluctuations in converter operating conditions.

[0127] Based on this, by storing and releasing electrical energy, energy supply and demand are balanced, system adaptability is improved, the risk of production interruption is reduced, and more stable temperature monitoring is supported.

[0128] In some embodiments, the method further includes the following steps:

[0129] S8. Obtain the temperature change rate data of molten steel.

[0130] Specifically, the acquisition process involves real-time collection of molten steel temperature data using a high-precision infrared temperature sensor array. The sensors employ InGaAs detectors with a temperature measurement range of 800-1800°C. Two temperature probes are symmetrically arranged on either side of the converter's observation port, each sampling at a 100Hz frequency to acquire raw temperature data. The acquired data is transmitted via shielded twisted-pair cable to a signal conditioning module, where wavelet noise reduction and temperature drift compensation are performed.

[0131] Specifically, the central control unit has a built-in rate of change calculation module, which uses a moving window differential algorithm to calculate the temperature change rate (unit: °C / s) in real time with a 1-second time window. Digital filtering technology is incorporated into the calculation process to eliminate abnormal fluctuations and ensure the stability and reliability of the rate of change data.

[0132] Furthermore, an asymmetric calibration method is employed during probe placement, allowing for cross-verification of measurement data from two probes to improve the accuracy of rate of change calculations. In addition, the system incorporates an adaptive filtering algorithm that automatically adjusts filtering parameters based on furnace operating conditions, ensuring reliable rate of change data is obtained at different production stages.

[0133] S9. Dynamically adjust the sampling frequency of the temperature detection unit based on the rate of change data.

[0134] Specifically, the dynamic adjustment system is implemented by a frequency control module, which is integrated into the central control unit and includes a frequency adjustment circuit composed of an FPGA processor and digital potentiometers. The system presets multiple sampling frequency levels, such as 1Hz, 5Hz, 10Hz, 20Hz, and 50Hz, and sets two thresholds: a low-change zone when the rate of change is below 2°C / s and a high-change zone when the rate of change is above 5°C / s.

[0135] The control module receives the rate of change data in real time and determines the optimal sampling frequency using a fuzzy logic algorithm. Its frequency switching command is transmitted to the main control chip of the temperature sensor via a digital I / O module, enabling stepless adjustment of the sampling frequency. The entire adjustment process employs a smooth transition algorithm to avoid data discontinuity caused by sudden frequency changes.

[0136] Furthermore, by establishing a correspondence model between the rate of change and the sampling frequency, the strategy is continuously optimized and adjusted based on real-time data feedback.

[0137] S901. When the rate of change is lower than the threshold, reduce the sampling frequency.

[0138] Specifically, when the temperature change rate is detected to be below 2°C / s for 10 seconds, the system automatically reduces the sampling frequency from the current value to 1Hz.

[0139] The temperature reduction process employs a step-by-step approach, adjusting the level every 5 seconds to avoid abrupt changes that could affect system stability. In low sampling frequency mode, the system simultaneously activates a data reconstruction algorithm, using interpolation to maintain the continuity of the temperature curve. Simultaneously, a power optimization mode is activated, disabling some sensor auxiliary circuits to reduce the power consumption of the temperature measurement unit.

[0140] S902. When the rate of change is higher than the threshold, increase the sampling frequency.

[0141] Specifically, when the detected temperature change rate instantaneously exceeds 5°C / s, the system increases the sampling frequency to a maximum of 50Hz within 100ms. Oversampling is employed during this increase, sampling at 100Hz for 3 seconds before stabilizing to 50Hz to ensure the capture of details of rapid temperature changes. Simultaneously, a data augmentation mode is activated, enabling backup sensor channels and increasing the measurement dimensions. The system automatically records detailed data during periods of high change rate, providing support for subsequent process analysis.

[0142] In summary, by adjusting the intelligent sampling frequency, the system reduces energy consumption when the molten steel temperature is stable and ensures measurement accuracy when the temperature changes drastically. By introducing the temperature change rate as a control parameter into the sampling frequency adjustment system, a novel adaptive measurement mode is established, solving the technical challenge of balancing energy consumption and accuracy in traditional fixed-frequency sampling. While ensuring detection accuracy, it significantly reduces energy consumption from invalid sampling, extends system endurance, and enhances the intelligence and economy of energy utilization.

[0143] In some embodiments, the method further includes the following steps:

[0144] S10. Monitor ambient light intensity data.

[0145] Specifically, this step is achieved by a multispectral light sensor array installed on the top of the converter workshop. The array contains three independently measuring sensor units, which measure the light intensity of different wavelengths: visible light 400-700nm, near-infrared 700-1100nm, and full spectrum 300-1100nm.

[0146] The sensors utilize industrial-grade silicon photodiodes, equipped with self-cleaning optical windows and temperature compensation circuits. The sensors are distributed at a 120-degree angle and connected to the data acquisition unit via an RS-485 bus, collecting data once per second and transmitting it to the environmental monitoring module of the central control unit.

[0147] Furthermore, the data processing unit employs a moving average algorithm to eliminate instantaneous interference and generates a light intensity trend curve, while simultaneously recording the light intensity distribution characteristics over different time periods, providing data support for subsequent energy dispatching. All sensors are equipped with dustproof and waterproof housings and automatic calibration functions to ensure long-term stable operation in the harsh environment of the steel industry.

[0148] Furthermore, multi-band measurements allow for the acquisition of more comprehensive ambient light information, while incorporating self-cleaning and temperature compensation functions into the sensor design can eliminate factors that may affect measurement accuracy in advance. In addition, the system introduces a reference sensor comparison mechanism, periodically calibrating online a reference sensor installed under standard lighting conditions to ensure the accuracy and reliability of the measurement data.

[0149] S11. Based on the light intensity data, control the start or stop of the solar auxiliary power supply unit to supplement the power supply of the thermoelectric power generation unit.

[0150] Specifically, the solar-assisted power supply unit consists of high-efficiency monocrystalline silicon photovoltaic panels, a maximum power point tracking (MPPT) controller, and a grid-connected inverter. The photovoltaic panels are installed at the optimal tilt angle on the workshop roof.

[0151] Specifically, this start-stop control strategy calculates available solar power in real time based on the light intensity-to-power conversion module. When the average solar irradiance is detected to be greater than 200W / m² for 5 consecutive minutes and the thermoelectric power generation is insufficient, solar power supply is automatically activated. The startup process uses a soft-start method, gradually connecting the load via solid-state relays to avoid grid impact. The stop threshold is set to a solar irradiance that is below 50W / m² for 10 consecutive minutes, employing a delayed disconnection mechanism to prevent frequent start-stop cycles.

[0152] Furthermore, solar energy is organically combined with traditional thermal power generation to form a multi-energy complementary system, and the energy structure is automatically adjusted according to real-time sunlight conditions. In particular, a power prediction algorithm can be introduced to predict the solar power generation potential several hours in advance based on historical sunlight data and weather forecasts, providing decision support for energy dispatch. Its control strategy adopts fuzzy logic control, comprehensively considering multiple factors such as sunlight intensity, thermal power generation, and system load to achieve intelligent energy management.

[0153] In summary, by introducing a solar-assisted power supply system, a new multi-energy collaborative power supply model has been created. Integrating ambient light monitoring into the energy management system of the converter temperature detection system overcomes the limitations of traditional single-energy supply and reduces reliance on a single waste heat source. The use of multi-band light measurement technology improves the accuracy and reliability of environmental monitoring. An intelligent light intensity-power conversion control strategy has been established, enabling seamless switching and optimized ratios between different energy sources. This results in increased system energy self-sufficiency and improved operational stability, especially during cloudy / rainy weather and nighttime operation periods, ensuring continuous and stable operation.

[0154] In some embodiments, the method further includes the following steps:

[0155] S12. Obtain historical energy consumption data and generate a prediction model.

[0156] Specifically, this step is achieved through an energy consumption monitoring unit deployed in the central control host, which includes a high-precision power metering chip, a data storage module, and a model training processor.

[0157] In practice, the system continuously records the following data every minute: the operating current and voltage of the temperature detection unit, the power consumption curve of the cooling management unit, the ambient temperature sensor readings, and converter operating status signals, such as oxygen blowing and steel tapping.

[0158] Furthermore, the generation module of this prediction model employs time series analysis to establish an energy consumption prediction model based on multiple influencing factors, including production plans, seasonal variations, and equipment status. During model training, a sliding window mechanism is introduced, using a 72-hour time window and an adaptive weighted algorithm to process historical data, automatically updating model parameters every 24 hours.

[0159] In summary, by analyzing historical data to identify energy consumption patterns in advance, the predictive model can automatically optimize and adjust over time. In particular, the system can introduce a transfer learning mechanism, using operational data from other similar converters as an auxiliary training set to improve the model's generalization ability.

[0160] S13. Adjust the charging and discharging schedule of the energy storage unit in advance based on the prediction model to match the expected energy consumption demand.

[0161] Specifically, the charge and discharge planning optimization system consists of a planning generation module and an execution control unit. The planning generation module runs a prediction model every 4 hours, outputting the energy demand curve and power generation prediction curve for the next 24 hours.

[0162] Furthermore, this optimization algorithm aims to minimize external energy dependence, taking into account factors such as the charging and discharging efficiency and cycle life limitations of the energy storage unit, to generate an optimal charging and discharging schedule. Its execution control unit communicates with the energy storage management system via a CAN bus to adjust the charging and discharging strategy in real time.

[0163] Specific controls include: charging to 90% capacity in advance during periods of low expected energy consumption, such as maintenance periods; maintaining a discharged state during periods of high expected energy consumption, such as oxygen blowing for intensified smelting; and using energy storage for power supply during periods of high electricity prices.

[0164] In summary, a new energy management model has been achieved through intelligent prediction and optimized scheduling. Firstly, a historical data-driven prediction model has been introduced into the energy management of the converter temperature monitoring system, realizing a shift from passive response to proactive prediction. Secondly, a multi-factor coupled energy consumption prediction algorithm has been established, improving prediction accuracy and practicality. Furthermore, a model-based predictive control-based charging and discharging optimization strategy has been developed, achieving efficient utilization of the energy storage unit. This scheme reduces the system's dependence on external energy sources, improves energy utilization efficiency, and enhances equipment operational stability.

[0165] The continuous temperature detection system for molten steel in a carbon steel converter provided in the embodiments of this application is described below. The continuous temperature detection system for molten steel in a carbon steel converter described below can be referred to in correspondence with the continuous temperature detection method for molten steel in a carbon steel converter described above.

[0166] refer to Figure 2 The continuous temperature monitoring system for molten steel in a carbon steel converter includes:

[0167] Thermoelectric power generation unit 1 is configured to convert converter waste heat into electrical energy.

[0168] Among them, the thermoelectric power generation unit 1 is composed of multiple thermoelectric modules, which are directly attached to the high-temperature area of ​​the outer wall of the converter. It uses bismuth telluride-based thermoelectric material and is in close contact with the furnace wall through thermally conductive silicone grease. Its output end is connected to the distribution unit 5.

[0169] Temperature detection unit 2 is configured to continuously detect the temperature of molten steel.

[0170] The temperature detection unit 2 includes two non-contact infrared temperature probes, which are symmetrically arranged on both sides of the converter observation hole. The probes are equipped with water-cooled protective sleeves. The power input terminal is connected to the distribution unit 5, and the data output terminal is connected to the energy management unit 4.

[0171] Cooling management unit 3 includes a phase change material configured to absorb heat for passive cooling.

[0172] The cooling management unit 3 consists of a phase change material capsule assembly and a heat pipe heat transfer system. The phase change material is a paraffin-based composite material, encapsulated in an aluminum capsule and placed in the sensor hot spot area, and contacts the device through a thermally conductive interface material.

[0173] Energy management unit 4 is configured to dynamically adjust the power consumption of temperature detection unit 2 and cooling management unit 3 based on energy management strategy.

[0174] Among them, the energy management unit 4 adopts an industrial-grade embedded controller, which has a built-in multi-channel analog input module and digital output module, and is connected to each unit through a cable.

[0175] Distribution unit 5 is configured to distribute electrical energy to temperature detection unit 2 and cooling management unit 3.

[0176] The distribution unit 5 includes an intelligent power distribution cabinet and a multi-channel power management module. Its input end is connected to the thermoelectric power generation unit 1 and an optional external power source, and its output end is connected to the temperature detection unit 2 and the cooling management unit 3, respectively.

[0177] In summary, the system integrates self-powered and passive cooling mechanisms, and solves the problems of high energy consumption and external dependence through waste heat recovery and intelligent energy consumption regulation, achieving low-carbon, low-cost continuous operation and enhancing sustainability.

[0178] In addition, the system also includes an energy storage unit, which uses a hybrid configuration of lithium iron phosphate battery packs and supercapacitors and is connected to the distribution unit 5 via a bidirectional converter.

[0179] An environmental monitoring unit, which includes a light sensor and a temperature sensor, is installed on the top of the workshop, and its output signal is connected to the energy management unit 4.

[0180] Furthermore, each unit is installed in a centralized cabinet and connected in a star topology. The electrical energy generated by the thermoelectric power generation unit 1 is preferentially supplied to the temperature detection unit 2 via the distribution unit 5, and the remaining energy is distributed to the cooling management unit 3 and the energy storage unit.

[0181] The energy management unit 4 monitors the system power consumption in real time and achieves dynamic power management by adjusting the output voltage of the distribution unit 5.

[0182] Cooling management unit 3 absorbs the heat generated during equipment operation through phase change material, and activates auxiliary heat dissipation device when the temperature exceeds the set value.

[0183] Based on this, the system adopts a modular design, allowing each unit to be independently replaced and maintained, adapting to the high-temperature and high-humidity environment of the steel industry. Through waste heat recovery and intelligent energy management, the system achieves continuous and stable temperature monitoring, significantly reducing dependence on external energy and operating costs.

[0184] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0185] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with the embodiments of this application. Processor 301 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0186] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0187] The memory 303 may be a ROM (Read-Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM (Electrically Erasable Programmable Read-Only Memory), a CD-ROM (Compact Disc Read-Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0188] The memory 303 is used to store application code that executes the scheme of the embodiments of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0189] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments described in this application.

[0190] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described method for continuous temperature detection of molten steel in a carbon steel converter.

[0191] Since the embodiments of the computer-readable storage medium portion correspond to the embodiments of the method portion, please refer to the description of the embodiments of the method portion for the embodiments of the computer-readable storage medium portion.

[0192] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0193] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for continuous temperature detection of molten steel in a carbon steel converter, characterized in that, Includes the following steps: The waste heat parameters of the converter are obtained, including the thermal gradient data of the outer wall of the converter collected in real time by a distributed temperature sensor array, and a standardized waste heat parameter dataset containing average heat flux density, temperature fluctuation frequency and regional heat distribution characteristic values. The steps for obtaining the waste heat parameters of the converter specifically include: forming a distributed sensor array of at least 24 pairs of K-type thermocouples and arranging them in a grid pattern in the high-temperature region of the converter's outer wall; using a mineral-insulated metal sheath structure to ensure stable operation of the sensors in a high-temperature environment of 1100°C; performing cold junction compensation and 24-bit high-precision analog-to-digital conversion through a multi-channel data acquisition module; generating a three-dimensional thermal gradient distribution map based on a spatial interpolation algorithm, and extracting characteristic parameters such as maximum heat flux density, regional temperature difference, and temperature change rate. Based on the waste heat parameters, the working state of the thermoelectric power generation unit is adjusted in real time by a PID controller, and the bismuth telluride-based thermoelectric module is controlled to convert waste heat into electrical energy. When the heat flux density is high, the conversion efficiency is increased, and when the heat flux density is low, the mode is switched to energy saving. The power is dynamically distributed to the temperature detection unit and the cooling management unit through a multi-channel intelligent power distribution cabinet. The distribution strategy is dynamically adjusted based on real-time power generation and unit priority, and priority is given to ensuring the power supply to the temperature detection unit. The paraffin-based phase change material absorbs heat through a cooling management unit. When the ambient temperature exceeds a set threshold, the temperature control valve is activated to accelerate heat transfer and achieve passive cooling. The temperature of molten steel is continuously detected using a non-contact infrared thermometer. A dual-probe redundancy design and an anti-interference compensation algorithm are employed to ensure detection accuracy. The power consumption of the temperature detection unit and the cooling management unit is dynamically adjusted based on the energy management strategy, including adaptive adjustment of sampling frequency and cooling intensity through a rule engine based on real-time power generation, unit power consumption priority and system status flags. The steps for dynamically adjusting power consumption specifically include: real-time monitoring of the operating mode status of the temperature detection unit and the heat load data of the cooling management unit; dynamically adjusting the power supply voltage ratio through a programmable digital potentiometer, reducing the power supply voltage of the temperature detection unit from 12V to 5V when it is in the sampling interval; increasing the power supply ratio of the cooling management unit when the phase change material heat storage capacity is below the threshold; using a hysteresis control algorithm to avoid frequent switching of critical states, and setting a minimum guaranteed power threshold to ensure the operation of core functions. The method further includes: acquiring molten steel temperature data at a frequency of 100Hz using an InGaAs detector infrared temperature measurement array; calculating the temperature change rate using a moving window differential algorithm and introducing digital filtering to eliminate abnormal fluctuations; when the change rate is below 2°C / s for 10 seconds, gradually reducing the sampling frequency to 1Hz and starting the data reconstruction algorithm; and when the change rate instantaneously exceeds 5°C / s, increasing the sampling frequency to 50Hz within 100ms and activating the backup sensor channel.

2. The method according to claim 1, characterized in that, Also includes: Excess electrical energy is stored in an energy storage unit that combines lithium-ion batteries and supercapacitors. Constant current-constant voltage charging and pulse discharge control are achieved through a bidirectional DC-DC converter; Based on the ARIMA model, predict the energy consumption demand for the next 15 minutes and generate the optimal charging and discharging strategy; Multiple safety mechanisms are implemented, including overcharge and over-discharge protection, temperature monitoring, and isolation protection.

3. The method according to claim 1, characterized in that, Also includes: The ambient light intensity in the 400-1100nm wavelength band was monitored using a multispectral light sensor array. It employs industrial-grade silicon photodiodes equipped with self-cleaning optical windows and temperature compensation circuitry; When the average light intensity for 5 consecutive minutes is greater than 200 W / m² and the thermoelectric power generation is insufficient, the solar power supply is connected through a soft start method. The solar auxiliary power supply will be delayed if the light intensity remains below 50W / m² for 10 consecutive minutes.

4. The method according to claim 1, characterized in that, Also includes: Record system energy consumption data and converter operating status signals in 1-minute intervals; A time series analysis method was used to establish an energy consumption prediction model with multiple influencing factors. The model parameters are updated every 24 hours using a sliding window mechanism and an adaptive weighting algorithm. Based on the forecast results, the charging and discharging plans of the energy storage units are adjusted in advance, charging to 90% capacity during periods of low energy consumption and maintaining a discharged state during peak periods.

5. An electronic device, characterized in that, include: One or more processors; One or more memory units; And one or more computer programs, wherein the one or more computer programs are stored in the one or more memories, the one or more computer programs including instructions that, when executed by the one or more processors, cause the electronic device to perform the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The storage medium stores a program or instructions that, when executed, implement the method as described in any one of claims 1 to 4.

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