An optimized control method and system for a high-pressure constant-temperature intelligent environmental control device.

Abstract

This invention discloses an optimized control method and system for a high-pressure constant-temperature intelligent environmental control device, relating to the field of temperature control device technology. The method includes: S1: collecting real-time data from the environmental control device system, constructing a multi-dimensional parameter set and calculating intermediate parameters to generate a dynamic thermal potential energy balance factor; S2: based on the dynamic thermal potential energy balance factor and the multi-dimensional parameter set, dynamically determining the target operating mode and generating an equipment control strategy, wherein the target operating mode includes: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode, and heating mode; S3: according to the equipment control strategy, controlling the environmental control device system to perform linkage control to achieve temperature control. Through this intelligent control method, the temperature control accuracy and environmental adaptability are significantly improved, while simultaneously optimizing system energy efficiency and reducing overall costs.

Description

Technical Field

[0001] This invention relates to the field of temperature control device technology, specifically to an optimized control method and system for a high-pressure constant temperature intelligent environmental control device. Background Technology

[0002] Existing temperature control devices suitable for high-pressure air supply scenarios (air pressure ≥ 4000Pa) typically suffer from several technical drawbacks: Firstly, conventional temperature control devices generally employ a single compressor combined with frequency converter regulation, resulting in high equipment costs, limited long-term operational reliability, and insufficient accuracy in controlling the outlet air temperature under high-pressure conditions. Secondly, when the high-pressure air supply fan is operating, the fresh air experiences a temperature rise of approximately 8℃-15℃ after compression. A system layout using a single evaporator is insufficient to achieve efficient and stable temperature control across a wide range of ambient temperatures throughout the year. In high-temperature environments, cooling capacity is often insufficient; in low-temperature environments, overcooling is likely, and it is difficult to fully utilize the fan's own temperature rise to reduce energy consumption. Furthermore, auxiliary heating control methods used in low-temperature environments are often crude, with limited real-time and precise heating power adjustment, easily causing outlet air temperature fluctuations and making it difficult to achieve precise matching and energy-saving synergy with dynamically changing fan temperature rises. Summary of the Invention

[0003] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide an optimized control method and system for a high-pressure constant-temperature intelligent environmental control device to solve the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an optimized control method for a high-pressure constant-temperature intelligent environmental control device, comprising: S1: Collect real-time data from the environmental control system, construct a multi-dimensional parameter set and calculate intermediate parameters to generate a dynamic balance factor of thermal potential energy. S2: Based on the dynamic balance factor of thermal potential energy and the multi-dimensional parameter set, the target operating mode is dynamically determined and the equipment control strategy is generated. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. S3: Based on the equipment control strategy, control the environmental control system to perform linkage control to achieve temperature control.

[0005] The present invention is further configured such that the environmental control device system includes: a blower, a first compressor, a second compressor, a first evaporator, a second evaporator, a refrigerant regulating valve, and a heating device.

[0006] The present invention is further configured such that S1 includes: a multidimensional parameter set construction step, an intermediate feature extraction step, and a thermal potential energy dynamic balance factor calculation step.

[0007] The present invention is further configured such that the multidimensional parameter set construction step includes: The environmental parameters, operating parameters, and equipment status parameters of the environmental control device system are collected using multi-dimensional sensors. The environmental parameters include ambient temperature and ambient humidity. The operating parameters include outlet air temperature, supply air volume, supply air static pressure, refrigerant pressure of the first evaporator, refrigerant pressure of the second evaporator, air pressure difference before and after the evaporator, electric heater current, and feedback of electric regulating valve opening. The status parameters include the operating status of the first compressor and the second compressor. Based on environmental parameters, operating parameters, and equipment status parameters, abnormal data is eliminated through range checks and mutation checks to obtain preliminary processed signals. The pre-processed signals are classified and filtered. Specifically, a first-order hysteresis filtering algorithm is used for temperature signals, a moving average filtering algorithm is used for pressure, flow and current signals, and an amplitude limiting filtering or moving average filtering algorithm is used for humidity signals and valve position feedback signals. Based on the preset calibration parameters, the filtered signal values ​​are converted into corresponding physical quantities, forming a multi-dimensional parameter set.

[0008] The present invention is further configured such that the intermediate feature extraction step includes: Based on the ambient temperature in a multidimensional parameter set, and combined with historical ambient temperature data within a preset time window, trend fitting calculation is performed to obtain the rate of change of ambient temperature. Based on the first and second evaporator refrigerant pressures in the multidimensional parameter set, the corresponding first and second evaporation temperatures are determined by querying a preset refrigerant property relationship table according to the evaporator refrigerant pressure. Based on the air pressure difference before and after the evaporator in a multi-dimensional parameter set, combined with the cumulative operating time of the evaporator and the preset design pressure difference, the evaporator efficiency coefficient is calculated. Based on the electric heater current in a multi-dimensional parameter set, the actual heating power is calculated according to the electric heater current and the preset rated voltage. The heater response coefficient is calculated by comparing the deviation between the preset standard heating power and the actual heating power. Based on the operating status of the first and second compressors in the multidimensional parameter set, and combined with the compressor's historical operating records, a weighted average is performed to obtain the compressor's historical load rate.

[0009] The present invention is further configured such that the step of calculating the dynamic equilibrium factor of thermal potential energy includes: Based on the ambient temperature from a multi-dimensional parameter set, combined with the preset target outlet air temperature and the fan design temperature rise, the basic thermal potential energy is generated through a nonlinear mapping function. Based on the basic thermal potential energy, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate, a dynamic correction thermal potential energy is generated through weighted correction. Based on historical ambient temperature data, a weighted calculation is performed using the rate of change of ambient temperature and historical statistical patterns to obtain the predicted trend value. Based on the dynamically corrected thermal potential energy, the rate of change of ambient temperature, and the predicted trend value, a dynamic balance factor of thermal potential energy is generated by superimposing compensation and limiting.

[0010] The present invention is further configured such that S2 includes: Based on the dynamic balance factor of thermal potential energy, the temperature boundary values ​​corresponding to each target operation mode are dynamically adjusted to form a flexible boundary. Based on the comparison between the current ambient temperature and the flexible boundary in the multidimensional parameter set, and combined with the specific numerical range of the dynamic equilibrium factor of thermal potential energy, the target operation mode is determined according to the preset decision rules.

[0011] The present invention is further configured such that S2 further includes: Extract the first evaporation temperature, the second evaporation temperature, the rate of change of ambient temperature, and the historical load rate of the compressor from the intermediate parameters; The proportional-integral-derivative control parameters of the refrigeration and heating circuits are adaptively tuned based on the dynamic balance factor of thermal potential energy. Based on the dynamic balance factor of thermal potential energy, the first evaporation temperature, the second evaporation temperature and the rate of change of ambient temperature, the anti-frost predictive parameters are generated by combining conditions. Based on the dynamic balance factor of thermal potential energy and the historical load rate of the compressor, the compressor start-up priority is generated by weighted calculation. By integrating the target operating mode, proportional-integral-derivative control parameters, anti-frost predictive parameters, and compressor start priority, an equipment control strategy is constructed.

[0012] The present invention is further configured such that S3 includes: Based on the equipment control strategy, perform safety interlock verification and command priority arbitration; For equipment control commands that have passed verification, the blower, compressor, refrigerant regulating valve and heating device are controlled to work together in accordance with the preset start-stop sequence and rate limit; Based on the tuned proportional-integral-derivative control parameters and the real-time acquired multi-dimensional parameter set of outlet air temperature, closed-loop regulation is performed to maintain the target outlet air temperature.

[0013] The present invention also provides an optimized control system for a high-pressure constant-temperature intelligent environmental control device, the system comprising: Intelligent sensing and feature calculation module: collects real-time data from the environmental control system, constructs a multi-dimensional parameter set and calculates intermediate parameters, and generates a dynamic balance factor for thermal potential energy; Intelligent decision-making and strategy generation module: Based on the dynamic balance factor of thermal potential energy and multi-dimensional parameter set, dynamically determine the target operating mode and generate equipment control strategy. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. Safety execution and linkage control module: Based on the equipment control strategy, it controls the environmental control device system to perform linkage control to achieve temperature control.

[0014] This invention provides an optimized control method and system for a high-pressure constant-temperature intelligent environmental control device. The method comprises: S1: collecting real-time data from the environmental control device system, constructing a multi-dimensional parameter set, calculating intermediate parameters, and generating a dynamic thermal potential energy balance factor; S2: dynamically determining the target operating mode based on the dynamic thermal potential energy balance factor and the multi-dimensional parameter set, and generating an equipment control strategy. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode, and heating mode; S3: controlling the environmental control device system according to the equipment control strategy to achieve temperature control. The resulting beneficial effects include: Significantly improved control precision and intelligence: By introducing a comprehensive situational awareness parameter—the dynamic balance factor of thermal potential energy—dynamic mode decision-making and parameter adaptive tuning are achieved, enabling high-precision closed-loop control of the outlet air temperature. This overcomes the limitations of traditional single temperature threshold control, proactively responding to changes in the environment and system state, ensuring the outlet air temperature remains stable at the set target value, such as 15℃, with fluctuations controlled within ±0.5℃.

[0015] Enhanced environmental adaptability and energy efficiency optimization: By utilizing a dynamic balance factor of thermal potential energy to dynamically adjust the flexible boundary of mode switching and intelligently integrating the fan compressor temperature rise, the system can automatically select the optimal operating mode within a wide ambient temperature range of -15℃ to 45℃. Under suitable operating conditions, it can fully utilize natural temperature rise to achieve "zero energy consumption" constant temperature. Under conditions requiring heating or cooling, it achieves efficient operation by precisely matching equipment capabilities, thereby significantly expanding the application range and reducing overall operating energy consumption.

[0016] System reliability and maintainability are improved: Predictive strategies based on dynamic thermal energy balance factors and equipment health status, such as anti-frost prediction and equipment balancing, are adopted, upgrading the control logic from passive response to proactive prevention, reducing the risk of abnormal equipment operation and sudden failures. Simultaneously, the use of highly reliable fixed-frequency compressors and mature actuators, combined with modular and logically clear intelligent control software, reduces the system failure rate and makes condition monitoring and maintenance more convenient.

[0017] Significant overall cost advantages: While ensuring and improving control performance, the expensive variable frequency compressor solution is replaced by a control algorithm based on the dynamic balance factor of thermal potential energy, thereby reducing hardware purchase costs. At the same time, energy efficiency optimization and equipment lifespan management under all operating conditions further reduce the long-term operation and maintenance costs of the system.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating an optimized control method for a high-pressure constant-temperature intelligent environmental control device is shown as an exemplary embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an optimized control system for a high-pressure constant temperature intelligent environmental control device, which is an exemplary embodiment of the present invention. Detailed Implementation

[0020] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0021] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0022] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention. Example 1

[0023] An optimized control method for a high-pressure constant-temperature intelligent environmental control device, such as... Figure 1 As shown, it includes: S1: Collect real-time data from the environmental control system, construct a multi-dimensional parameter set and calculate intermediate parameters to generate a dynamic balance factor of thermal potential energy. S2: Based on the dynamic balance factor of thermal potential energy and the multi-dimensional parameter set, the target operating mode is dynamically determined and the equipment control strategy is generated. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. S3: Based on the equipment control strategy, control the environmental control system to perform linkage control to achieve temperature control.

[0024] The present invention is further configured such that the environmental control system includes: a blower, a first compressor, a second compressor, a first evaporator, a second evaporator, a refrigerant regulating valve, and a heating device. Specifically, the system configuration includes: a blower, a first compressor, a second compressor, a first evaporator, a second evaporator, a refrigerant regulating valve, and an electric heating device; wherein the rated air pressure of the blower is 6500 Pascals, the two compressors are specifically fixed-frequency compressors with a rated power of 3 kW, the heat exchange area of ​​the first evaporator is 8 square meters, the heat exchange area of ​​the second evaporator is 6 square meters, the refrigerant regulating valve is an electrically adjustable two-way valve with an adjustment range of 0 to 100%, and the electric heating device adopts a thyristor power control module with a power range of 0 to 5 kW. In addition, the environmental control system is equipped with a sensor network containing various sensors for collecting multi-dimensional parameter sets. These include: an ambient temperature and humidity sensor installed at the air inlet of the first evaporator, an outlet air temperature sensor installed at the air outlet of the second evaporator, air volume and air pressure sensors installed on the air supply duct, a pressure sensor for measuring refrigerant return gas pressure, a differential pressure sensor for measuring the air-side resistance of the evaporator, a current sensor for monitoring the heater operating current, and a valve position sensor for feedback on valve opening. The system's control core is a programmable logic controller (PLC), which executes various calculations, decisions, and command outputs in the optimized control method. The compressor circuit is equipped with high and low pressure protection devices. All of the above components are connected through air ducts, refrigeration pipes, and electrical wiring, forming a complete high-pressure constant-temperature, four-season air-cooled environmental control system.

[0025] The present invention is further configured such that S1 includes: a multi-dimensional parameter set construction step, an intermediate feature extraction step, and a thermal potential energy dynamic balance factor calculation step. Specifically, the multi-dimensional parameter set construction step is used to synchronously collect raw physical signals from multiple dimensions, such as environment, equipment, and operating status, through a sensor network deployed in the environmental control device system; and to perform standardization processing on these raw signals, including validity verification, classification filtering, and engineering value conversion, to finally generate a parameter set with a unified format, reliable data, and clear physical meaning. The intermediate feature extraction step is used to further refine and calculate key feature quantities that can directly reflect the core operating status and performance trend of the system from the standardized multi-dimensional parameter set, i.e., intermediate features, specifically including: ambient temperature change rate, first evaporation temperature and second evaporation temperature, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate; the role of extracting intermediate features is to perform deeper processing on the raw monitoring data, and to generate intermediate parameters with clear engineering guidance significance through specific algorithms and models such as trend fitting, property lookup, performance degradation calculation, deviation analysis, and historical statistics. The calculation steps for the dynamic balance factor of thermal potential energy are used to aggregate multiple intermediate characteristic parameters into a single, comprehensive situation indicator—the dynamic balance factor of thermal potential energy—through a multi-level fusion model. Specifically, it first assesses the basic energy demand of the system under ideal conditions, then corrects for the impact of the current actual performance of the equipment and historical loads, and finally incorporates short-term predictions of future environmental changes. Through such multi-level calculation and fusion, the calculated dynamic balance factor of thermal potential energy can dynamically and comprehensively quantify the energy situation faced by the system in reaching and maintaining the target temperature, thereby transforming the complex multivariate coupling problem into a unified quantitative signal.

[0026] The present invention is further configured such that the multidimensional parameter set construction step includes: The environmental parameters, operating parameters, and equipment status parameters of the environmental control device system are collected using multi-dimensional sensors. The environmental parameters include ambient temperature and ambient humidity. The operating parameters include outlet air temperature, supply air volume, supply air static pressure, refrigerant pressure of the first evaporator, refrigerant pressure of the second evaporator, air pressure difference before and after the evaporator, electric heater current, and feedback of electric regulating valve opening. The status parameters include the operating status of the first compressor and the second compressor. Based on environmental parameters, operating parameters, and equipment status parameters, abnormal data is eliminated through range checks and mutation checks to obtain preliminary processed signals. The pre-processed signals are classified and filtered. Specifically, a first-order hysteresis filtering algorithm is used for temperature signals, a moving average filtering algorithm is used for pressure, flow and current signals, and an amplitude limiting filtering or moving average filtering algorithm is used for humidity signals and valve position feedback signals. Based on preset calibration parameters, the filtered signal values ​​are converted into corresponding physical quantities, forming a multi-dimensional parameter set. Specifically, through a sensor network deployed within the environmental control system, multi-dimensional raw signals of the environment, equipment, and operating status are simultaneously collected. Environmental parameters include ambient temperature and humidity; operating parameters include outlet air temperature, supply air volume, supply air static pressure, refrigerant pressure between the first and second evaporators, air pressure difference across the evaporators, electric heater current, and feedback on the opening of the electric regulating valve; and status parameters include the operating status of the first and second compressors. The collected raw signals undergo two stages of data cleaning to ensure data reliability. Step 1: Abnormal data removal. Range checking and abrupt change checking methods are used. Range checking determines if the signal value is within the sensor's preset reasonable range. Abrupt change checking determines if the difference between the current sampled value and the previous valid value exceeds the maximum change threshold set based on physical probability. For example, a temperature change exceeding 5°C within 1 second is considered abnormal. Data points that fail the check are considered invalid and replaced by the previous valid sampled value or a safe default value. Step 2: Classified digital filtering is applied to the pre-processed signals that pass the verification to suppress random interference. For temperature signals with high inertia, a first-order hysteresis filtering algorithm is used. The principle is to calculate a weighted average of the current sampled value and the previous filtered output value to smooth random fluctuations and retain the true trend of change. For pressure, flow, and current signals, a moving average filtering algorithm is used, which calculates the arithmetic mean of multiple recent sampled values ​​in a fixed-length data queue. For humidity and valve position feedback signals, amplitude limiting filtering or moving average filtering can be used. Amplitude limiting filtering filters out pulse interference by determining whether the change in adjacent sampled values ​​exceeds the allowable step size. Finally, the filtered digital signal is converted into a physical quantity with standard engineering units, completing the construction of a multi-dimensional parameter set. The conversion process is specifically calculated based on the pre-calibrated conversion coefficients and offsets of each sensor. For example, the filtered ambient temperature digital value, after being multiplied by a specific conversion coefficient and then linearly calculated with the offset, yields a temperature value in °C. The pre-calibrated conversion coefficients are specific values ​​obtained through a standard process of factory calibration or field calibration of the sensors. For example, a typical ambient temperature sensor has a conversion coefficient of 0.01 °C per digital unit and an offset of -20 °C. All parameters are processed in this way, ultimately generating a unified and reliable dataset containing items such as ambient temperature, outlet air temperature, system pressure, current, and equipment status.

[0027] The present invention is further configured such that the intermediate feature extraction step includes: Based on the ambient temperature in a multidimensional parameter set, and combined with historical ambient temperature data within a preset time window, trend fitting calculation is performed to obtain the rate of change of ambient temperature. Based on the first and second evaporator refrigerant pressures in the multidimensional parameter set, the corresponding first and second evaporation temperatures are determined by querying a preset refrigerant property relationship table according to the evaporator refrigerant pressure. Based on the air pressure difference before and after the evaporator in a multi-dimensional parameter set, combined with the cumulative operating time of the evaporator and the preset design pressure difference, the evaporator efficiency coefficient is calculated. Based on the electric heater current in a multi-dimensional parameter set, the actual heating power is calculated according to the electric heater current and the preset rated voltage. The heater response coefficient is calculated by comparing the deviation between the preset standard heating power and the actual heating power. Based on the operating status of the first and second compressors in the multidimensional parameter set, and combined with the compressor's historical operating records, a weighted average is performed to obtain the compressor's historical load rate. Specifically, the intermediate features include: ambient temperature change rate, first evaporation temperature and second evaporation temperature, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate. The ambient temperature change rate is calculated based on the ambient temperature data in the multidimensional parameter set. The system acquires continuous historical ambient temperature sampling points within a preset time window, applies linear regression fitting to these historical ambient temperature sampling point data, and calculates an optimal fitting trend line. The slope of this trend line, after unit conversion, yields the temperature change rate in degrees Celsius per minute, which is used to quantify the recent trend of ambient temperature change. The corresponding evaporation temperature is determined based on the refrigerant pressure of the first and second evaporators. The system reads the current pressure value, queries a pre-stored refrigerant saturation property relationship table, locates the interval of the current pressure value in the table, and then calculates the precise first and second evaporation temperatures through linear interpolation. These two evaporation temperature values ​​reflect the real-time operating status of the corresponding evaporators. When calculating the evaporator efficiency coefficient, the system first obtains the actual pressure difference between the inlet and outlet of the evaporator on the air side, i.e., the air pressure difference before and after the evaporator, from a multi-dimensional parameter set. Then, this measured air pressure difference before and after the evaporator is compared with the original design pressure difference of the evaporator to obtain the pressure difference ratio. At the same time, combined with the cumulative operating time since the last maintenance, it is input into an empirical performance degradation model to calculate the evaporator efficiency coefficient, which is between 0 and 1. This evaporator efficiency coefficient quantifies the degree of degradation of heat exchange performance relative to the design level. The aforementioned empirical performance degradation model can be constructed based on the pressure difference ratio and operating time through linear weighting, piecewise functions, or table lookup. For example, a common construction method is to multiply the pressure difference ratio by a time-related degradation factor, where the degradation factor decreases linearly with the increase of operating time. The construction method is existing technology and will not be elaborated here. The system for calculating the heater response coefficient evaluates it by comparing the deviation between the command and the actual power. Specifically, the system calculates the actual power based on the heater's operating current from the collected multi-dimensional parameter set and a pre-set system rated voltage (e.g., 380 volts). Within a set statistical time window, it continuously compares the pre-set standard heating power command issued by the controller with the calculated actual power, calculates the average deviation between the two, and compares and normalizes this deviation with the heater's maximum rated power to ultimately generate a heater response coefficient reflecting the control tracking accuracy. The calculation of the compressor's historical load rate uses an exponentially weighted moving average algorithm. The system continuously records the operating status time series of each compressor (i.e., the first and second compressors), assigning different weights to data at different times during calculation. The specific weighting scheme is as follows: recent data has a higher weight, while historical data weights decrease exponentially.Subsequently, by averaging the weighted state sequence, a continuous value that smoothly reflects the recent trend of workload changes is obtained, which is the compressor's historical load rate. The calculation of all the above intermediate parameters is based on physical quantities in a multidimensional parameter set, employing standard data processing methods including linear regression, table lookup interpolation, and exponential weighted averaging to generate a set of intermediate feature parameters for advanced decision-making.

[0028] The present invention is further configured such that the step of calculating the dynamic equilibrium factor of thermal potential energy includes: Based on the ambient temperature from a multi-dimensional parameter set, combined with the preset target outlet air temperature and the fan design temperature rise, the basic thermal potential energy is generated through a nonlinear mapping function. Based on the basic thermal potential energy, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate, a dynamic correction thermal potential energy is generated through weighted correction. Based on historical ambient temperature data, a weighted calculation is performed using the rate of change of ambient temperature and historical statistical patterns to obtain the predicted trend value. Based on dynamically corrected thermal potential energy, the rate of change of ambient temperature, and predicted trend values, a dynamic thermal potential energy balance factor is generated through superimposed compensation and limiting. Specifically, a multi-level fusion model is set up to aggregate multiple intermediate features into a unified dynamic thermal potential energy balance factor that quantifies the overall thermal balance of the system. The dynamic thermal potential energy balance factor is a dimensionless scalar with a value between -1 and +1. A positive value indicates that the system needs to provide cooling to eliminate excess heat, and the larger the value, the more urgent the cooling demand. A negative value indicates that the system needs to supplement heat, and the smaller the value, the more urgent the heating demand. A value close to 0 indicates that the system is close to an ideal thermal balance state. The calculation process is completed in three progressive levels. The first stage calculates the basic thermal potential energy used to quantify the theoretical energy demand. Based on the ambient temperature in the multi-dimensional parameter set, combined with the preset target outlet air temperature (e.g., a pre-calibrated constant temperature of 15℃) and the fan design temperature rise (e.g., a calibrated 10℃), the theoretical uninterrupted outlet air temperature is first calculated. Then, the temperature difference between the uninterrupted outlet air temperature and the target temperature is calculated. Subsequently, the temperature difference is input into a nonlinear mapping function, such as the hyperbolic tangent function. The role of the nonlinear mapping function here is to smoothly map any temperature difference to the interval between -1 and +1, outputting a basic thermal potential energy representing the direction and intensity of the basic energy demand. For example, when the ambient temperature is 25℃, the target temperature is 15℃, and the fan temperature rise is 10℃, the theoretical temperature difference is +20℃, and after nonlinear mapping, a basic thermal potential energy of approximately +0.76 may be obtained. The second level calculates the dynamically corrected thermal potential energy used to quantify the actual system capability. Based on the base thermal potential energy, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate, a weighted correction is used to generate the dynamically corrected thermal potential energy. The specific weights are pre-set through experimental calibration based on the importance of each device in the system. For example, when evaluating the overall system performance, the weight of the evaporator efficiency coefficient can be set to 0.6, and the weight of the heater response coefficient to 0.4. The calculation process first evaluates the overall system performance based on the device performance coefficients by weighted averaging the evaporator efficiency coefficient and heater response coefficient. Then, based on the compressor historical load rate, the compressor historical load rate is linearly mapped to the [-1, 1] interval. For example, the formula is: Load Inertia Factor = (1 - Compressor Historical Load Rate) × 2 - 1. When the compressor historical load rate is 0 (long-term shutdown), the factor is +1, indicating "very clean"; when the load rate is 1 (long-term full load), the factor is -1, indicating "very fatigued," generating a load inertia factor representing the system's "fatigue" or "clean" state. Finally, the basic thermal potential energy is multiplied by a correction factor related to the overall performance. This correction factor is determined based on the system's overall performance through a pre-defined functional relationship. This functional relationship can be obtained through experimental calibration, and its trend is: the correction factor is smaller when the system's overall performance is high, and larger when the system's overall performance is low.For example, a linear function relationship might be: Correction coefficient = 1.2 - 0.3 × System overall efficiency, plus a load inertia factor, to obtain a dynamically corrected thermal potential energy that better reflects the current actual capacity of the equipment. Continuing the previous example, the base thermal potential energy is +0.76. Considering good equipment performance and a history of light loads, the dynamically corrected thermal potential energy might be +0.68 after correction. The third level: Finally, a dynamic balance factor for thermal potential energy is generated to incorporate trend prediction. Based on historical ambient temperature data, a weighted calculation is performed using the rate of change of ambient temperature and historical statistical patterns to obtain the predicted trend value. Based on the dynamically corrected thermal potential energy, the rate of change of ambient temperature, and the predicted trend value, the final dynamic balance factor for thermal potential energy is generated through superposition compensation and limiting. Specifically: the rate of change of ambient temperature and the historical predicted trend are combined according to preset weights to generate a predicted trend compensation value; this compensation value is added to the dynamically corrected thermal potential energy; finally, the result is limited to a range of -1 to +1. Continuing the previous example, the dynamically corrected thermal potential energy is +0.68. Adding a positive compensation value of approximately 0.085 due to the slow increase in ambient temperature, we get +0.765. After limiting, the final dynamic balance factor of the thermal potential energy is +0.77. This explicit numerical signal will directly drive the control system to enter the corresponding mode and adopt matched control parameters. The entire calculation process utilizes standard mathematical operations such as function mapping, weighted averaging, and limiting.

[0029] The present invention is further configured such that S2 includes: Based on the dynamic balance factor of thermal potential energy, the temperature boundary values ​​corresponding to each target operation mode are dynamically adjusted to form a flexible boundary. Based on the comparison between the current ambient temperature and the flexible boundary in the multidimensional parameter set, and combined with the specific numerical range of the dynamic equilibrium factor of thermal potential energy, the target operating mode is determined according to the preset decision rules. Specifically, firstly, the preset default mode settings are retrieved. The default operating mode is divided according to the fixed boundary of ambient temperature: when the ambient temperature is between 0℃ and 5℃, mode three, i.e., natural temperature rise mode, is adopted, only the blower is run, and the compression temperature rise is used to achieve constant temperature without energy consumption; when the ambient temperature is between 5℃ and 30℃, mode one, i.e., single-stage cooling mode, is adopted, and the second compressor and the corresponding evaporator are started for cooling regulation; when the ambient temperature is higher than 30℃, mode two, i.e., dual-stage cooling mode, is adopted, and dual compressors and dual evaporators are started for synergistic enhanced cooling; when the ambient temperature is lower than 0℃, mode four, i.e., heating mode, is adopted, and the blower and electric heating device are started for auxiliary heating. Based on the previously calculated dynamic balance factor of thermal potential energy, the default temperature boundary value of the above modes is dynamically adjusted to form a flexible boundary. For example, the base value of the upper boundary of the natural temperature rise mode is 5℃, and its dynamic adjustment formula can be set as: 5 + 1.5 * dynamic balance factor of thermal potential energy. The adjustment logic is as follows: when the dynamic balance factor of thermal potential energy is positive, indicating that the system has a cooling demand, the exit threshold of the energy-saving mode is appropriately increased to prompt the system to enter the cooling mode in advance; when the factor is negative, the opposite is true to expand the applicable range of the energy-saving mode. Subsequently, the system performs two-factor decision-making. In the decision-making process, the current ambient temperature is compared with the dynamically calculated flexible boundary, and the specific numerical range of the dynamic balance factor of thermal potential energy is determined. The final mode is determined according to the preset decision rules. For example, if the ambient temperature is higher than the dynamic flexible upper boundary of the natural temperature rise mode, and the value of the dynamic balance factor of thermal potential energy is in the range of 0.1 to 0.6, then the target operating mode is determined to be the single-stage cooling mode. The decision is made based on this rule: assuming the current ambient temperature is 8℃, higher than the flexible boundary of 5.9℃, and the dynamic balance factor of thermal potential energy is +0.58, falling within the range of 0.1 to 0.6, the system ultimately determines to enter single-stage cooling mode. This process allows mode switching to respond to the system's real-time energy supply and demand situation. Traditional fixed boundary control might still maintain energy-saving mode at 8℃; however, this method, based on dynamic energy situation judgment, switches to cooling earlier, making mode switching more precise and faster, achieving more accurate constant temperature control and energy efficiency optimization.

[0030] The present invention is further configured such that S2 further includes: Extract the first evaporation temperature, the second evaporation temperature, the rate of change of ambient temperature, and the historical load rate of the compressor from the intermediate parameters; The proportional-integral-derivative control parameters of the refrigeration and heating circuits are adaptively tuned based on the dynamic balance factor of thermal potential energy. Based on the dynamic balance factor of thermal potential energy, the first evaporation temperature, the second evaporation temperature and the rate of change of ambient temperature, the anti-frost predictive parameters are generated by combining conditions. Based on the dynamic balance factor of thermal potential energy and the historical load rate of the compressor, the compressor start-up priority is generated by weighted calculation. The system integrates the target operating mode, proportional-integral-derivative control parameters, anti-frost predictive parameters, and compressor start priority to construct an equipment control strategy. Specifically, the equipment strategy is generated by extracting the first evaporation temperature, second evaporation temperature, ambient temperature change rate, and historical compressor load rate from intermediate parameters. Multiple sub-strategy generation calculations are then performed, ultimately integrating them into a target-clear and parameter-complete equipment control strategy. The specific implementation process is as follows: First, the control loop parameters are adaptively tuned based on the current thermal potential energy dynamic balance factor. For both refrigeration and heating loops, the system dynamically updates the proportional, integral, and derivative parameters using preset adjustment rules. For example, when the thermal potential energy dynamic balance factor is +0.58, the proportional coefficient of the refrigeration loop is increased and the integral time is shortened according to the rules to make the controller respond more quickly and adapt to moderate refrigeration demands. Second, based on the thermal potential energy dynamic balance factor, evaporation temperature, and ambient temperature change rate, anti-frost predictive parameters are generated through combined condition judgments. The system has preset judgment logic; for example, when the second evaporation temperature is below 3°C, the thermal potential energy dynamic balance factor is negative, and the ambient temperature is decreasing, a high-level warning is triggered. Assuming the current second evaporation temperature is 7°C, the dynamic balance factor of thermal potential energy is +0.58, and the environment is slowly heating up at a rate of 0.1°C per minute, all conditions are not met. The system generates anti-frost parameters at a risk-free level, indicating that no protective intervention is required. Next, based on the dynamic balance factor of thermal potential energy and the compressor's historical load rate, a compressor start-up priority is generated through weighted calculation. The system compares the historical loads of each compressor and prioritizes starting the equipment with the lighter cumulative load to achieve balanced wear. For example, if the first compressor's historical load rate is 0.8 and the second compressor's is 0.3, a start-up priority instruction is generated, explicitly specifying that the second compressor should be started first in single-stage refrigeration mode. Finally, the system integrates and packages the determined target operating mode instruction, the calibrated proportional-integral-derivative control parameters, the anti-frost predictive parameters, and the compressor start-up priority to form a complete equipment control strategy. This strategy package clearly answers key execution-level questions such as operating mode, control parameter settings, special protection requirements, and equipment scheduling sequence, achieving a reliable conversion from intelligent decision-making to executable instructions.

[0031] The present invention is further configured such that S3 includes: Based on the equipment control strategy, perform safety interlock verification and command priority arbitration; For equipment control commands that have passed verification, the blower, compressor, refrigerant regulating valve and heating device are controlled to work together in accordance with the preset start-stop sequence and rate limit; Based on the tuned proportional-integral-derivative control parameters and the real-time acquired multi-dimensional parameter set of outlet air temperature, closed-loop regulation is executed to maintain the target outlet air temperature. Specifically, this step is the final execution layer of the control method, responsible for safely, reliably, and accurately translating the equipment control strategy into coordinated actions and closed-loop regulation of the field equipment. First, safety interlock verification and command priority arbitration are performed, and the control system forcibly verifies key safety signals, such as confirming that the refrigeration system pressure is within the normal operating range, the supply air static pressure is higher than the minimum safety threshold, and there are no electrical faults or emergency stop signals. After confirming that all hard interlock conditions are safe, commands are arbitrated to ensure that high-priority commands such as emergency stop can immediately interrupt normal operation. For commands that pass verification, the system controls the coordinated action of equipment according to the preset start-stop sequence and rate limits. Taking the start of single-stage cooling mode as an example: the system first sends a start command to the blower inverter and, at a rate limit of no more than 5% per second, smoothly increases the blower speed to the rated value within ten seconds. After a 15-second delay to allow the air duct to stabilize, the second compressor is started. Simultaneously, the electric regulating valve of the second evaporator is controlled to open from the fully closed position to the preset initial opening at an opening change rate of no more than 5% per second. This process ensures that each device starts and stops in an orderly manner, with smooth action and avoids shocks. Finally, based on the calibrated proportional-integral-derivative control parameters and the real-time acquired outlet air temperature, closed-loop regulation is executed to maintain the target outlet air temperature. The closed-loop controller loads parameters that have been adaptively calibrated according to the dynamic balance factor of thermal potential energy, with 15℃ as the setpoint and the real-time collected and filtered outlet air temperature as the feedback value, and periodically calculates the temperature deviation. Based on the deviation and the calibrated parameters, the controller obtains the control output through proportional, integral, and derivative calculations, driving the electric regulating valve to adjust the refrigerant flow. For example, when the initial outlet air temperature is high, the controller outputs a larger opening command to enhance cooling; as the temperature drops and approaches the set value, the output decreases, and the valve opening adjusts back. Through dynamic adjustment, the outlet air temperature eventually stabilizes near the target value, achieving high-precision constant temperature control. The entire process completes a closed loop from strategy command to safe, coordinated, and precise execution. Example 2

[0032] Please see Figure 2 The exemplary high-pressure constant temperature intelligent environmental control device optimization control system includes: Intelligent sensing and feature calculation module: collects real-time data from the environmental control system, constructs a multi-dimensional parameter set and calculates intermediate parameters, and generates a dynamic balance factor for thermal potential energy; Intelligent decision-making and strategy generation module: Based on the dynamic balance factor of thermal potential energy and multi-dimensional parameter set, dynamically determine the target operating mode and generate equipment control strategy. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. Safety execution and linkage control module: Based on the equipment control strategy, it controls the environmental control device system to perform linkage control to achieve temperature control.

[0033] It should be noted that the optimized control system for a high-pressure constant-temperature intelligent environmental control device provided in the above embodiments and the optimized control method for a high-pressure constant-temperature intelligent environmental control device provided in the above embodiments belong to the same concept. The specific methods of operation of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the optimized control system for a high-pressure constant-temperature intelligent environmental control device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0034] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high-pressure constant-temperature intelligent environment control device optimization control method, characterized in that, include: S1: Collect real-time data from the environmental control system, construct a multi-dimensional parameter set and calculate intermediate parameters to generate a dynamic balance factor of thermal potential energy. S2: Based on the dynamic balance factor of thermal potential energy and the multi-dimensional parameter set, the target operating mode is dynamically determined and the equipment control strategy is generated. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. S3: Based on the equipment control strategy, control the environmental control system to perform linkage control to achieve temperature control.

2. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 1, characterized in that, The environmental control system includes: a blower, a first compressor, a second compressor, a first evaporator, a second evaporator, a refrigerant regulating valve, and a heating device.

3. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 1, characterized in that, The S1 includes: a multidimensional parameter set construction step, an intermediate feature extraction step, and a thermal potential energy dynamic balance factor calculation step.

4. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 3, characterized in that, The steps for constructing the multidimensional parameter set include: The environmental parameters, operating parameters, and equipment status parameters of the environmental control device system are collected using multi-dimensional sensors. The environmental parameters include ambient temperature and ambient humidity. The operating parameters include outlet air temperature, supply air volume, supply air static pressure, refrigerant pressure of the first evaporator, refrigerant pressure of the second evaporator, air pressure difference before and after the evaporator, electric heater current, and feedback of electric regulating valve opening. The status parameters include the operating status of the first compressor and the second compressor. Based on environmental parameters, operating parameters, and equipment status parameters, abnormal data is eliminated through range checks and mutation checks to obtain preliminary processed signals. The pre-processed signals are classified and filtered. Specifically, a first-order hysteresis filtering algorithm is used for temperature signals, a moving average filtering algorithm is used for pressure, flow and current signals, and an amplitude limiting filtering or moving average filtering algorithm is used for humidity signals and valve position feedback signals. Based on the preset calibration parameters, the filtered signal values ​​are converted into corresponding physical quantities, forming a multi-dimensional parameter set.

5. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 4, characterized in that, The intermediate feature extraction step includes: Based on the ambient temperature in a multidimensional parameter set, and combined with historical ambient temperature data within a preset time window, trend fitting calculation is performed to obtain the rate of change of ambient temperature. Based on the first and second evaporator refrigerant pressures in the multidimensional parameter set, the corresponding first and second evaporation temperatures are determined by querying a preset refrigerant property relationship table according to the evaporator refrigerant pressure. Based on the air pressure difference before and after the evaporator in a multi-dimensional parameter set, combined with the cumulative operating time of the evaporator and the preset design pressure difference, the evaporator efficiency coefficient is calculated. Based on the electric heater current in a multi-dimensional parameter set, the actual heating power is calculated according to the electric heater current and the preset rated voltage. The heater response coefficient is calculated by comparing the deviation between the preset standard heating power and the actual heating power. Based on the operating status of the first and second compressors in the multidimensional parameter set, and combined with the compressor's historical operating records, a weighted average is performed to obtain the compressor's historical load rate.

6. The optimal control method of a high-pressure constant-temperature intelligent environment control device according to claim 5, characterized in that, The calculation steps for the dynamic equilibrium factor of thermal potential energy include: Based on the ambient temperature from a multi-dimensional parameter set, combined with the preset target outlet air temperature and the fan design temperature rise, the basic thermal potential energy is generated through a nonlinear mapping function. Based on the basic thermal potential energy, evaporator efficiency coefficient, heater response coefficient, and compressor historical load rate, a dynamic correction thermal potential energy is generated through weighted correction. Based on historical ambient temperature data, a weighted calculation is performed using the rate of change of ambient temperature and historical statistical patterns to obtain the predicted trend value. Based on the dynamically corrected thermal potential energy, the rate of change of ambient temperature, and the predicted trend value, a dynamic balance factor of thermal potential energy is generated by superimposing compensation and limiting.

7. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 1, characterized in that, S2 includes: Based on the dynamic balance factor of thermal potential energy, the temperature boundary values ​​corresponding to each target operation mode are dynamically adjusted to form a flexible boundary. Based on the comparison between the current ambient temperature and the flexible boundary in the multidimensional parameter set, and combined with the specific numerical range of the dynamic equilibrium factor of thermal potential energy, the target operation mode is determined according to the preset decision rules.

8. The optimal control method of a high-pressure constant-temperature intelligent environment control device according to claim 7, characterized in that, S2 further includes: Extract the first evaporation temperature, the second evaporation temperature, the rate of change of ambient temperature, and the historical load rate of the compressor from the intermediate parameters; The proportional-integral-derivative control parameters of the refrigeration and heating circuits are adaptively tuned based on the dynamic balance factor of thermal potential energy. Based on the dynamic balance factor of thermal potential energy, the first evaporation temperature, the second evaporation temperature and the rate of change of ambient temperature, the anti-frost predictive parameters are generated by combining conditions. Based on the dynamic balance factor of thermal potential energy and the historical load rate of the compressor, the compressor start-up priority is generated by weighted calculation. By integrating the target operating mode, proportional-integral-derivative control parameters, anti-frost predictive parameters, and compressor start priority, an equipment control strategy is constructed.

9. The optimal control method of the high-pressure constant-temperature intelligent environment control device according to claim 1, characterized in that, S3 includes: Based on the equipment control strategy, perform safety interlock verification and command priority arbitration; For equipment control commands that have passed verification, the blower, compressor, refrigerant regulating valve and heating device are controlled to work together in accordance with the preset start-stop sequence and rate limit; Based on the tuned proportional-integral-derivative control parameters and the real-time acquired multi-dimensional parameter set of outlet air temperature, closed-loop regulation is performed to maintain the target outlet air temperature.

10. A high-pressure constant-temperature intelligent environmental control device optimization control system, used to implement the high-pressure constant-temperature intelligent environmental control device optimization control method according to any one of claims 1-9, characterized in that, include: Intelligent sensing and feature calculation module: collects real-time data from the environmental control system, constructs a multi-dimensional parameter set and calculates intermediate parameters, and generates a dynamic balance factor for thermal potential energy; Intelligent decision-making and strategy generation module: Based on the dynamic balance factor of thermal potential energy and multi-dimensional parameter set, dynamically determine the target operating mode and generate equipment control strategy. The target operating modes include: natural temperature rise mode, single-stage refrigeration mode, two-stage refrigeration mode and heating mode. Safety execution and linkage control module: Based on the equipment control strategy, it controls the environmental control device system to perform linkage control to achieve temperature control.

Images