A rapid parameter configuration system and control method for a temperature controller

Abstract

This invention relates to the technical field of temperature control systems, specifically to a rapid parameter configuration system and control method for a temperature controller. The control method includes the following steps: S1, entering a parameter switching preparation stage; S2, executing guided control; S3, calculating a switching readiness index to characterize the correlation between the two; S4, determining whether the switching readiness index meets a preset switching access condition; S5, if the determination result is met, terminating the preparation stage; if the switching access condition is not met within a preset timeout period, terminating the preparation stage and maintaining the first parameter group; by guiding control to bring the system operating condition close to the optimal switching state before executing parameter switching, the parameter quiescent conflict caused by the mismatch between the control law and the physical state is eliminated, thereby preventing the accumulation of trace amounts of condensate caused by small temperature fluctuations and the resulting series of long-term cumulative damages.

Description

Technical Field

[0001] This invention relates to the technical field of temperature control systems, and in particular to a rapid configuration system and control method for temperature controller parameters. Background Technology

[0002] Modern refrigeration systems, such as large cold storage facilities and pharmaceutical storage warehouses, typically have multiple operating modes to adapt to different cargo storage needs or operational processes, such as "conventional refrigeration mode," "quick-freezing mode," and "thawing mode." Each operating mode corresponds to a set of specifically optimized PID temperature control parameters. Different parameter sets have significant differences in core parameters such as proportional coefficient, integral time, and derivative time to meet the different requirements of each mode for cooling rate, steady-state accuracy, and disturbance rejection capability. Current temperature controller parameter configuration methods generally adopt a hard-switching strategy, that is, upon receiving a mode switching command, the temperature controller's PID control parameters are instantaneously replaced from the parameter set corresponding to the current operating mode (e.g., parameter set A) to the parameter set corresponding to the target operating mode (e.g., parameter set B). This switching process is completed at the software level at millisecond speeds, but it completely ignores the fact that the refrigeration system is a physical system with huge thermal inertia. Its internal physical states (such as evaporator coil temperature, air temperature distribution inside the warehouse, cargo center temperature, airflow distribution, etc.) cannot change abruptly with parameter switching. At the moment of switching, the actual physical state of the system remains at the end of the previous mode's operation, which is significantly different from the optimal control condition preset by the target mode. Applying a set of control parameters designed for the steady-state condition of the target mode (such as the large proportional coefficient and short integral time required by the quick-freezing mode) directly to a system whose physical state is still at the end of the previous mode will inevitably lead to a serious mismatch between the control law and the actual physical state.

[0003] The mismatch between the aforementioned control law and the physical state can trigger a parameter quiescent conflict phenomenon that has long been overlooked in this field. This phenomenon manifests as follows: for several minutes to a dozen minutes after mode switching, the temperature inside the storage chamber does not smoothly transition to the target temperature. Instead, it experiences one or more short-lived, minute-amplitude temperature overshoots or undershoots (e.g., within 0.5°C). Because these minute fluctuations are typically below the preset standard alarm threshold of the temperature control system, they neither trigger alarm signals nor are they easily detected by operators in regular operating data, thus remaining in a quiescent state for an extended period.

[0004] In existing technologies, in application scenarios with frequent mode switching, such as the daily multiple cargo inflows and outflows and mode adjustments during peak logistics seasons, these periodic, minute temperature fluctuations repeatedly occur in specific critical areas within cold storage facilities, such as the blind spots of the remote air supply from the fans, the heat exchange interfaces near the storage doors, and the air stagnation areas between stacked goods. These temperature fluctuations cause localized air in these areas to repeatedly cross the dew point temperature boundary, resulting in the periodic condensation of moisture in the air onto the surfaces of cargo packaging and metal structural components, forming tiny water films that are difficult to detect with the naked eye. Over time, this continuous accumulation of minute amounts of condensate can trigger a series of serious secondary damages, including but not limited to: corrosion of metal building structures and refrigeration equipment; immersion and damage to cargo packaging leading to stack collapse; the creation of a humid microenvironment necessary for mold growth, thus threatening food and drug safety; and a significant increase in the frequency of evaporator defrosting and energy consumption due to frequent frost formation. These cumulative damages constitute substantial economic losses and safety hazards. Therefore, there is an urgent need for an intelligent temperature controller parameter configuration control method that can proactively guide the system's physical operating conditions to gradually approach the optimal state required by the target mode when switching operating modes, thereby eliminating parameter quiescent conflicts and their long-term cumulative damage from the root cause. Summary of the Invention

[0005] To address the technical problems existing in the background art, this invention proposes a rapid configuration system and control method for temperature controller parameters, the specific solution of which is as follows: A rapid configuration control method for thermostat parameters, applied to a temperature control system with at least two operating modes, wherein the operating modes include a first mode and a second mode, the first mode and the second mode respectively corresponding to a first parameter group and a second parameter group, comprising the following steps: S1. In response to receiving a switching instruction from the first mode to the second mode, enter the parameter switching preparation stage; S2. During the preparation phase, guidance control is executed to drive the real-time system operating conditions of the temperature control system toward the preset optimal switching state characterized by smooth switching. S3. During the preparation phase, a switching readiness index, which characterizes the correlation between the real-time system operating conditions and the optimal switching state, is calculated in parallel and periodically. S4. Determine whether the handover readiness index meets the preset handover admission conditions; S5. If the judgment result is satisfied, the preparation stage is terminated and the control parameters are switched from the first parameter group to the second parameter group; if the switching admission condition is not satisfied within the preset timeout period, the preparation stage is terminated and the first parameter group is maintained.

[0006] Furthermore, prior to S1, the process also includes: pre-constructing and storing the optimal switching state; Obtain a multi-dimensional system operating condition vector sample dataset collected at the moment when the switching timing is confirmed to be optimal during multiple smooth mode switching processes; Calculate the feature mean vector and feature covariance matrix of the multidimensional system operating condition vector sample dataset, and store the mathematical model jointly defined by the feature mean vector and feature covariance matrix as the optimal switching state.

[0007] Furthermore, in S1, the parameter switching preparation phase begins, including: Before entering the preparation stage, the received switching command is first validated. The validation includes confirming that the first mode and the second mode are different operating modes and that the second parameter group has been pre-configured and stored in the temperature control system. After the validity verification is passed, the preparation phase begins, where the current system operating status information is recorded, and the mode switching interface of the temperature control system is locked to prevent any new mode switching commands from being responded to during the preparation phase. The switching command is generated manually by the operator through the human-machine interface, or automatically by the temperature control system according to the preset operation scheduling plan; and when entering the parameter switching preparation stage, a switching preparation start notification is generated and sent to the log recording module of the temperature control system or the upper monitoring system.

[0008] Furthermore, in S2, the execution of guided control is based on the feature mean vector as the guiding target, a built-in simplified transfer function model, predicting the system operating condition vector trajectory within a future preset time period under multiple candidate control action sequences, evaluating each candidate control action sequence according to a preset cost function, selecting the candidate control action sequence that minimizes the cost function, and outputting the first control action in the candidate control action sequence to drive the temperature control system.

[0009] Furthermore, in S3, a switching readiness index is calculated to characterize the degree of correlation between the two, including: Collect the system's multidimensional operating condition vector at the current moment. Using the feature mean vector and the feature covariance matrix, the working condition correlation norm is calculated: ; in, Represents the feature mean vector. Represents the characteristic covariance matrix. Represents the transpose of a vector. The expression represents the inversion of a matrix, and the norm of the correlation degree of the working conditions. This refers to the handover readiness indicator; The physical quantities included in the multidimensional system operating condition vector include at least: evaporator coil temperature, return air temperature or silo temperature, rate of change of return air temperature or silo temperature, compressor operating power or operating frequency, and expansion valve opening; the system multidimensional operating condition vector collected at the current moment... This is obtained by synchronously assembling the sampled values ​​of the sensors corresponding to each physical quantity in the temperature control system at the same time.

[0010] Furthermore, in S4, determining whether the handover readiness indicator meets the preset handover admission conditions includes: The current calculated value of the handover readiness index is compared with a preset handover admission threshold. The handover admission condition is specifically that the value of the handover readiness index is less than the handover admission threshold. The handover admission threshold is a preset constant, and its value represents the critical value of the handover readiness index when the real-time system condition and the optimal handover state enter the preset core association area.

[0011] Furthermore, in S5, if the judgment result is satisfied, the guidance control executed in the preparation stage is terminated, the control action based on the guidance control output is stopped, the control parameters of the temperature control system are replaced from the first parameter group to the second parameter group, and the second parameter group is used as the control parameter to enter the stable operation control state in the second mode. If the switching access condition is not met within the preset timeout period, the timing starts from the start of the preparation phase. When the timing reaches the preset timeout period and the switching readiness indicator still does not meet the switching access condition, the guidance control is terminated, the control actions based on the guidance control output are stopped, the control parameters of the temperature control system are maintained at the first parameter group, and a switching failure record is generated. The switching failure record includes at least the timestamp of the timeout, the real-time system operating condition value at the timeout, and the switching readiness indicator value at the timeout.

[0012] Furthermore, the preset timeout period is set within the range of 10 minutes to 60 minutes.

[0013] A rapid parameter configuration system for a temperature controller is applied to a temperature control system with at least two operating modes, wherein the operating modes include a first mode and a second mode, the first mode and the second mode respectively corresponding to a first parameter group and a second parameter group, including: The instruction response module is used to respond to receiving a switching instruction from the first mode to the second mode and enter the parameter switching preparation stage; The guidance control module is used to perform guidance control during the preparation phase to drive the real-time system operating conditions of the temperature control system to approach the preset optimal switching state characterized by smooth switching. The indicator calculation module is used to calculate, in parallel and periodically, a handover readiness indicator that characterizes the correlation between the real-time system operating conditions and the optimal handover state during the preparation phase. The switching decision and execution module is used to determine whether the switching readiness index meets the preset switching admission conditions. If the determination result is met, the preparation phase is terminated and the control parameters are switched from the first parameter group to the second parameter group. If the switching admission conditions are not met within the preset timeout period, the preparation phase is terminated and the first parameter group is maintained.

[0014] Compared with the prior art, the present invention can achieve at least the following beneficial effects: 1. This invention introduces a preparation stage and guidance control before parameter switching, actively driving the real-time operating condition of the system to approach the preset optimal switching state, rather than replacing parameters instantaneously after receiving the switching command as in the prior art. After the system operating condition is gradually adjusted to a physical state that matches the optimal operating condition required by the target mode through guidance control, parameter switching is then performed. This fundamentally avoids the mismatch between the control law and the physical state, thereby completely eliminating the slight temperature overshoot or undershoot phenomenon that occurs within a few minutes to a dozen minutes after mode switching. Because the parameter quiescent conflict phenomenon is eliminated at the source, periodic micro-temperature fluctuations no longer occur during mode switching. Critical areas such as the far end of the cold storage fan, near the door, and between stacked goods no longer form micro-water films due to repeated temperature crossings of the dew point temperature boundary. Therefore, it can effectively block a series of secondary damage chains caused by the accumulation of trace amounts of condensate, including preventing corrosion of metal building structures and refrigeration equipment, protecting the cardboard packaging of goods from immersion damage, inhibiting mold growth to ensure food and drug safety, and reducing the frequency of evaporator defrosting and energy consumption caused by frequent frost, thereby significantly reducing economic losses and safety hazards. By calculating the switching readiness index between the real-time system operating condition and the optimal switching state in parallel and periodically, and determining whether the preset switching admission conditions are met, the precise quantitative judgment of the switching timing is achieved. This mechanism makes the triggering time of parameter switching depend entirely on whether the actual physical state of the system meets the standard for safe switching, rather than a fixed waiting time or human experience judgment. It can adaptively determine the best switching time under different load conditions and different environmental conditions, ensuring the smoothness and consistency of each switching. A timeout protection mechanism is built into the preparation phase. If the system fails to converge to the optimal switching state within the preset timeout period due to abnormal factors such as equipment failure or an open door, the preparation phase is automatically terminated and the system safely reverts to the first mode, preventing the system from being in an uncertain transition state for a long time. This mechanism ensures that the method has deterministic and safe system behavior under any abnormal conditions, meeting the high reliability requirements of industrial control systems.

[0015] 2. This invention introduces a parameter switching preparation stage during mode switching. First, guided control is executed to actively drive the system's real-time operating condition to approach the preset optimal switching state. Simultaneously, a switching readiness index, which characterizes the correlation between the current operating condition and the optimal switching state, is calculated in parallel. Parameter replacement is only performed when the index meets the preset switching access conditions. This eliminates the parameter quiescent conflict caused by the mismatch between the control law and the physical state at its source, avoids the generation of minor temperature overshoot or undershoot after mode switching, and effectively prevents a series of long-term cumulative damages caused by the repeated condensation of small water films in local areas of the cold storage due to periodic temperature fluctuations, such as metal structure corrosion, damage to cargo packaging, mold growth, and increased defrosting energy consumption. This significantly reduces economic losses and safety hazards. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of 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. Wherein: Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This is a system principle block diagram of the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] Example 1, please refer to Figure 1 The present invention provides a method for rapid configuration and control of thermostat parameters, which is applied to a temperature control system with at least two operating modes, wherein the operating modes include a first mode and a second mode, and the first mode and the second mode correspond to a first parameter group and a second parameter group, respectively.

[0020] It should be noted that the temperature control system used in this solution refers to a temperature control system with at least two operating modes. Typical application scenarios include refrigeration systems in large cold storage facilities and pharmaceutical storage facilities that require switching temperature control strategies according to different goods or operational processes. The first mode and the second mode are two different operating modes of the temperature control system, such as "conventional refrigeration mode" and "quick-freezing mode." Each mode corresponds to a set of pre-tuned control parameters, namely the first parameter set and the second parameter set. The first parameter set and the second parameter set are usually PID control parameter sets, each containing at least three core parameters: proportional coefficient, integral time, and derivative time.

[0021] Includes the following steps: Before S1, the process also includes: pre-constructing and storing the optimal switching state; Obtain a multi-dimensional system operating condition vector sample dataset collected at the moment when the switching timing is confirmed to be optimal during multiple smooth mode switching processes; Calculate the feature mean vector and feature covariance matrix of the multidimensional system operating condition vector sample dataset, and store the mathematical model jointly defined by the feature mean vector μ and the feature covariance matrix Σ as the optimal switching state.

[0022] It should be noted that the optimal switching state is pre-built and stored before S1 to provide a clear and quantifiable target benchmark for subsequent guidance control and switching readiness index calculations. The moment confirmed as the optimal switching time refers to the instant during which the system's actual operation is most stable and without adverse phenomena such as temperature overshoot or undershoot during multiple smooth mode switching processes in history. The criteria for determining the optimal switching time can be: the system return air temperature change rate is continuously lower than a first threshold, and the compressor operating frequency fluctuation amplitude is continuously lower than a second threshold, indicating that the system has entered a thermodynamic quasi-steady state. The specific values ​​of the first and second thresholds can be determined according to the actual scale of the refrigeration system and the control accuracy requirements. For example, the range of the first threshold can be 0.1°C / min to 0.5°C / min, and the range of the second threshold can be ±1% to ±3% of the compressor's rated frequency. Statistical analysis is performed on the collected multi-dimensional system operating condition vector sample dataset to extract the common statistical characteristics of these optimal switching moments. Among them, the calculated characteristic mean vector μ represents the center position of the system condition vector at all optimal switching moments, that is, the center value of the multidimensional physical quantity in the ideal switching state; the characteristic covariance matrix Σ describes the correlation and coupling relationship between different physical quantities at each optimal switching moment. For example, when the load is large, the inherent law that a lower compressor power and a higher return air temperature will occur simultaneously.

[0023] S1. In response to receiving a switching instruction to switch from the first mode to the second mode, enter the parameter switching preparation stage.

[0024] In an optional embodiment, in S1, the parameter switching preparation stage is entered, including: Before entering the preparation stage, the received switching command is first validated. The validation includes confirming that the first mode and the second mode are different operating modes and that the second parameter group has been pre-configured and stored in the temperature control system. After the validity verification is passed, the preparation phase begins, where the current system operating status information is recorded, and the mode switching interface of the temperature control system is locked to prevent any new mode switching commands from being responded to during the preparation phase. The switching command is generated manually by the operator through the human-machine interface, or automatically by the temperature control system according to the preset operation scheduling plan; and when entering the parameter switching preparation stage, a switching preparation start notification is generated and sent to the log recording module of the temperature control system or the upper monitoring system.

[0025] It should be noted that the purpose of validating the switching command is to prevent invalid switching due to misoperation or system configuration errors. Confirming that the first and second modes are different operating modes avoids meaningless switching operations on the same mode; confirming that the second parameter group is pre-configured and stored in the temperature control system prevents switching failures or system malfunctions due to missing target parameter groups. Recording the current system operating status information provides a traceability basis for troubleshooting potential switching anomalies. The recorded information includes at least the current operating mode identifier, the parameter values ​​of the currently applied first parameter group, and the timestamp of the recording time. Locking the temperature control system's mode switching interface ensures that the system will not be interfered with by new mode switching commands during the preparation phase, preventing multiple switching processes from executing concurrently and causing control logic chaos.

[0026] It should be noted that the switching command can originate from the operator manually triggering it through the human-machine interface of the temperature control system based on on-site operational needs. For example, a cold storage manager manually initiates the switch from refrigeration mode to quick-freezing mode after completing the handling of goods. Alternatively, it can be automatically triggered by the temperature control system according to a preset operational schedule. For instance, in a pharmaceutical storage system, the system automatically switches time nodes based on a pre-set temperature strategy according to the drug storage schedule. Generating a switchover preparation start notification and sending it to the log recording module or the upper-level monitoring system allows operators or the remote monitoring center to be aware in real time that the system has entered the mode switchover preparation state, facilitating production scheduling and status monitoring.

[0027] S2. During the preparation phase, guidance control is executed to drive the real-time system operating conditions of the temperature control system toward the preset optimal switching state characterized by smooth switching.

[0028] It should be noted that the core function of the aforementioned guided control is to smoothly guide the real-time physical condition of the temperature control system from the current first mode operating state to an ideal state suitable for parameter switching through an active transition control strategy after the mode switching command is issued and before the actual switching of control parameters. Unlike the "hard switching" method of instantaneous parameter switching in the prior art, the guided control executed in this step is essentially a dynamic optimization process with a target and constraints. Its purpose is to avoid the slight temperature fluctuations caused by the mismatch between the control law and the physical state during parameter switching.

[0029] In an optional embodiment, in S2, the execution of guided control is to use the feature mean vector as the guiding target, predict the system operating condition vector trajectory within a future preset time period under multiple candidate control action sequences based on a built-in simplified transfer function model, evaluate each candidate control action sequence according to a preset cost function, select the candidate control action sequence that minimizes the cost function, and output the first control action in the candidate control action sequence to drive the temperature control system.

[0030] It should be noted that the guided control uses the characteristic mean vector μ as the guiding target because μ, as the mathematical center of the optimal switching state, represents the statistical average position of the system's operating conditions at all historical best switching moments. When the system's real-time operating condition vector approaches μ, it means that the system's physical quantities and their coupling relationships have entered an ideal region that has been historically verified as safe for switching. This provides the guided control with a clear and quantifiable final target.

[0031] It should be noted that predicting the system operating condition vector trajectory over a future preset time period under multiple candidate control action sequences aims to pre-evaluate the potential effects of different control strategies before applying control actions. A candidate control action sequence refers to a series of possible combinations of control outputs. For example, in the current control cycle, different compressor frequency increments can be selected (increase by 5%, remain unchanged, decrease by 5%, etc.), each leading to a different future operating condition trajectory. By predicting the operating condition evolution over a short period (i.e., the preset time period, typically several to dozens of control cycles) based on a simplified transfer function model, the controller is guided to anticipate the long-term effects of different actions, rather than reacting solely based on current deviations.

[0032] It should be noted that selecting the candidate control action sequence that minimizes the cost function and outputting the first control action in that sequence to drive the temperature control system embodies the control concept of rolling optimization. In each control cycle, the guided controller re-predicts the future trajectory and solves for the optimal action sequence based on the latest system operating information, but always executes only the first control action corresponding to the current cycle. This process is repeated in the next control cycle, thus achieving closed-loop rolling optimization of "prediction, decision-making, execution, and update." This mechanism enables the guided control to continuously adjust dynamically according to the actual system response, possessing robustness against model errors and external disturbances.

[0033] It should be noted that during the preparation phase of the guided control, the actual control actions applied to the refrigeration equipment by the temperature control system are the control actions output by the guided controller, rather than the PID control output based on the first parameter group. In other words, during the preparation phase, the original PID control loop corresponding to the first parameter group is temporarily replaced or bypassed by the guided controller, and the system directly responds to control signals such as compressor frequency commands generated by the guided controller. This mechanism ensures that the guided control can independently drive the system operating condition according to the preset optimization target, without being disturbed by the original PID control strategy. Only after the guided control phase ends (i.e., entering the switching or rollback step S5) does the system revert to the PID control mode based on the corresponding parameter group.

[0034] In an optional embodiment, the preset cost function The definition of is: ; in, The feature mean vector, For the predicted system operating condition vector, To control the amount of change in motion, To represent the square of the Euclidean norm, and These are preset weighting coefficients; It should be noted that the preset cost function It consists of two weighted sums, and its design embodies the idea of ​​multi-objective optimization. The first term It measures the predicted system operating condition vector. With guiding objectives The degree of deviation between them drives the system to converge to the optimal switching state as quickly as possible; the second item This measures the amplitude of changes in control actions. This factor suppresses drastic fluctuations in control output, preventing frequent compressor starts and stops or large frequency jumps due to excessive pursuit of rapid convergence, thereby protecting equipment and maintaining system stability. Weighting coefficient and Used to adjust the relative importance between convergence rate and control stationarity: when When the setting is large, the guidance control focuses more on quickly reaching the switching conditions; when When the setting is large, the guided control places greater emphasis on the smoothness of the transition process. The specific value of the weighting coefficient can usually be determined through on-site debugging or simulation analysis based on the actual thermal inertia of the system and the equipment protection requirements.

[0035] It should be noted that the weighting coefficients and The value of is usually set based on the thermal inertia of the refrigeration system and the actual control requirements: and The ratio typically ranges from 1:1 to 10:1. When the system has high thermal inertia and requires a fast response, it can be increased. The relative weights are adjusted to accelerate the convergence speed of the operating condition to the optimal switching state; when the system has high requirements for equipment protection and needs to avoid frequent compressor operation, the relative weights can be increased. The relative weights are used to enhance the smoothness of the control output.

[0036] The simplified transfer function model is a first-order inertial element or a second-order underdamped element, with the compressor frequency command as input and the rate of change of return air temperature or warehouse temperature as output.

[0037] It should be noted that the simplified transfer function model is an engineering approximation of the key dynamic characteristics of the refrigeration system. In actual refrigeration systems, changes in compressor frequency cause changes in refrigeration capacity, which in turn affect the rate of temperature change within the cold storage. This process inherently possesses inertial and hysteresis characteristics. Using a first-order inertial element or a second-order underdamped element as a simplified model can capture the core dynamic behavior of the refrigeration system with relatively low computational complexity while ensuring real-time control. The first-order inertial element is suitable for scenarios where the system dynamics are primarily characterized by a single time constant, such as in small cold storage facilities or where operating conditions change slowly. The second-order underdamped element is suitable for scenarios where the system exhibits significant oscillation characteristics or multi-capacity coupling, such as in large cold storage facilities where there is a significant heat exchange delay between the evaporator and the air inside the storage. The simplified transfer function model takes the compressor frequency command as input and outputs the return air temperature or the rate of temperature change within the storage facility. This input-output relationship directly corresponds to the physical essence of adjusting the compressor frequency to control the cooling rate in refrigeration control.

[0038] It should be noted that when the simplified transfer function model uses a first-order inertial element, its transfer function expression is: Where s is a complex variable in the Laplace transform, K is the system gain, and K represents the steady-state rate of change of return air temperature caused by a unit change in compressor frequency. The time constant represents the magnitude of the inertia in the system's thermal response. The value range is typically from 60 seconds to 600 seconds, with the specific value depending on the cold storage volume, cargo loading capacity, and insulation performance: the larger the cold storage volume and the greater the cargo loading capacity, the greater the system's thermal inertia, and the corresponding time constant. The larger the value, the better. When the simplified transfer function model uses a second-order underdamped element, its transfer function expression is: Where s is the complex variable in the Laplace transform, and K is the system gain. For the damping ratio, The undamped natural frequency. The damping ratio is... The value of K is typically between 0.3 and 0.8. Within this range, the system response exhibits both a relatively fast rise rate and minimal overshoot, effectively simulating the oscillating characteristics of large refrigeration systems caused by the delayed heat exchange between the evaporator and the air inside the refrigeration unit. The K values ​​in the aforementioned transfer function model... Or K, , These parameters can be obtained by fitting actual system operating data through system identification methods, or by theoretical estimation based on the design parameters of the refrigeration system.

[0039] S3. During the preparation phase, a switching readiness index, which characterizes the correlation between the real-time system operating conditions and the optimal switching state, is calculated in parallel and periodically.

[0040] It should be noted that the guidance control described above is executed in parallel during the preparation phase. Parallel execution means that within each control cycle, the temperature control system, on the one hand, outputs control actions through guidance control to drive the system operating condition towards the optimal switching state; on the other hand, it simultaneously collects the current system operating condition information and calculates the switching readiness index. These two operations do not block each other and proceed simultaneously. This parallel mechanism ensures that while actively guiding the evolution of the operating condition, the system can monitor the proximity between the operating condition and the target in real time and without interruption, providing a continuous data foundation for subsequent switching timing judgments. Periodic execution means that the above-mentioned data collection and calculation operations are repeated once every fixed control cycle (e.g., every 1 second or every few seconds), thereby forming a continuous tracking of the system operating condition change trend.

[0041] In an optional embodiment, in S3, a handover readiness metric for characterizing the correlation between the two is calculated, including: Collect the system's multidimensional operating condition vector at the current moment. Using the feature mean vector and the feature covariance matrix, the working condition correlation norm is calculated: ; in, Represents the feature mean vector. Represents the characteristic covariance matrix. Represents the transpose of a vector. The expression represents the inversion of a matrix, and the norm of the correlation degree of the working conditions. This refers to the handover readiness indicator; It should be noted that the aforementioned working condition correlation norm Its mathematical essence is a mismatch quantification index based on Mahalanobis distance. This norm is not a simple geometric distance, but a standardized deviation measure that considers the inherent coupling relationship between various operating condition variables. (Eigenvalue covariance matrix) inverse matrix In the calculation formula, it plays a role in "decorrelated" and "standardization": it incorporates the dimensional differences and statistical correlations between different physical quantities into the calculation, ensuring that the contribution of each dimension to the norm is corrected for correlation. For example, under full-load conditions in a cold storage facility, lower compressor power is usually accompanied by higher return air temperature, a normal correlation determined by the system load characteristics. If a simple Euclidean distance is used for calculation, this "low power-high temperature" combination would be considered to deviate significantly from the optimal switching state; however, after using the norm calculation method, due to the covariance matrix... Having "learned" this correlation pattern, the combination will be correctly identified as having a high correlation with the optimal switching state (i.e., a small norm value). Due to this characteristic, the correlation norm of the operating conditions can effectively overcome the problem of absolute physical threshold drift caused by external factors such as different cold storage loads and seasonal changes, eliminating the need to set separate switching thresholds for different operating conditions, thus ensuring the consistency and robustness of switching decisions.

[0042] It should be noted that the aforementioned working condition correlation norm The smaller the value, the higher the correlation between the current system condition and the optimal switching state, meaning the system's current physical state is closer to the ideal state suitable for smooth parameter switching based on historical experience. This index compresses the complex matching relationship between the multi-dimensional operating condition vector and the optimal switching state model into a single scalar value, making the subsequent switching admission condition judgment in S4 simpler and more certain.

[0043] The physical quantities included in the multidimensional system operating condition vector include at least: evaporator coil temperature, return air temperature or silo temperature, rate of change of return air temperature or silo temperature, compressor operating power or operating frequency, and expansion valve opening; the system multidimensional operating condition vector collected at the current moment... This is obtained by synchronously assembling the sampled values ​​of the sensors corresponding to each physical quantity in the temperature control system at the same time.

[0044] It should be noted that the multidimensional system operating condition vector A vector is a multidimensional data set formed by arranging the synchronously sampled values ​​of multiple key physical quantities of the temperature control system at a certain time t. It is called "multidimensional" because this vector contains multiple different types of physical quantities, each constituting one dimension of the vector. The evaporator coil temperature refers to the surface temperature of the evaporator heat exchange coil in the refrigeration system, which directly reflects the evaporation state of the refrigerant and the system's refrigeration output capacity. The return air temperature or cold storage temperature refers to the air temperature returning from inside the cold storage to the evaporator or the air temperature at a representative location inside the cold storage; this temperature is a core indicator for measuring the overall thermal state inside the cold storage. The rate of change of the return air temperature or cold storage temperature refers to the rate of change of the above temperatures per unit time; this rate of change characterizes whether the system is currently in a state of rapid cooling, slow cooling, or trending towards stability. The compressor operating power or operating frequency refers to the actual output power or operating frequency of the compressor driving the refrigeration cycle; this parameter directly determines the system's refrigeration output. The expansion valve opening degree refers to the degree to which the throttling device in the refrigeration system is open; this opening degree affects the refrigerant flow rate and evaporation pressure. The above physical quantities are selected to form a multidimensional system operating condition vector because they jointly characterize the complete thermodynamic state of the refrigeration system from different perspectives. A single physical quantity cannot fully reflect whether the system is ready to accept parameter switching.

[0045] It should be noted that the system multi-dimensional operating condition vector collected at the current moment is... This synchronization is achieved by synchronously grouping the sampled values ​​of the sensors corresponding to various physical quantities in the temperature control system at the same time. Synchronous grouping means that the system simultaneously initiates data acquisition requests to various sensing units, such as the evaporator coil temperature sensor, return air temperature sensor, compressor power sensor, and expansion valve opening sensor, at the same sampling moment. It obtains the instantaneous sampled values ​​of each sensor at that moment and then combines these sampled values ​​into a multi-dimensional vector according to a predefined order. This synchronous acquisition method ensures the temporal consistency of the physical quantity values ​​in the vector, avoiding misjudgments of the operating condition caused by asynchronous sampling times of the sensors. In practical implementation, the real-time clock signal of the temperature control system can be used as the synchronization trigger source, and the synchronization of data acquisition from each sensor can be ensured through hardware interrupts or software timer mechanisms.

[0046] S4. Determine whether the handover readiness index meets the preset handover admission conditions.

[0047] It should be noted that the aforementioned judgment step is executed synchronously and periodically during the preparation phase, in conjunction with the guidance control of S2 and the indicator calculation of S3. Within each control cycle, after the system completes the calculation of the switching readiness indicators, it immediately enters the judgment logic for this step to determine whether the current threshold for parameter switching has been met. This synchronous judgment mechanism ensures that the system can trigger the switching action as soon as the operating conditions meet the switching conditions, avoiding missing the optimal switching opportunity due to judgment lag.

[0048] In an optional embodiment, in step S4, determining whether the handover readiness indicator meets preset handover admission conditions includes: The current calculated value of the handover readiness index is compared with a preset handover admission threshold. The handover admission condition is specifically that the value of the handover readiness index is less than the handover admission threshold. The handover admission threshold is a preset constant, and its value represents the critical value of the handover readiness index when the real-time system condition and the optimal handover state enter the preset core association area.

[0049] It should be noted that the preset switching threshold is a pre-defined constant. This constant represents the critical boundary between the real-time system operating condition and the optimal switching state, indicating that they have entered a sufficiently close core correlation region where parameter switching can be safely performed. When the value of the switching readiness index is less than this threshold, it means that the deviation between the current system operating condition and the optimal switching state has been reduced to an acceptable small range, and the various physical quantities of the system and their coupling relationships are highly consistent with the ideal state that has been historically verified as safe for switching. At this point, when parameter switching is performed, the control law preset by the new parameter set will be highly matched with the actual physical state of the current system, thereby effectively avoiding minor temperature fluctuations caused by parameter-state mismatch.

[0050] It should be noted that the specific value of the handover threshold can usually be set based on the actual debugging experience of the system and the control accuracy requirements. In a preferred embodiment, since the handover readiness index adopts the operating condition correlation norm based on Mahalanobis distance, this norm has a clear probabilistic interpretation in a statistical sense: when the data follows a multivariate normal distribution, the square of the Mahalanobis distance approximately follows a chi-square distribution. Therefore, the value of the handover threshold can be theoretically set with reference to the quantiles of the chi-square distribution. For example, when the value is 1.0, it roughly corresponds to the core region of the optimal handover state distribution (within about one standard deviation), and this value can provide good handover timing judgment in most application scenarios. Of course, for application scenarios with higher control accuracy requirements, the threshold can be set smaller (e.g., 0.5) to require the system operating condition to more strictly approach the center of the optimal handover state; for scenarios that allow a certain tolerance, the threshold can be appropriately relaxed (e.g., 1.5) to achieve a balance between handover speed and handover smoothness.

[0051] It should be noted that the specific method for determining the handover admission threshold includes the following steps: First, in the offline stage, collect or simulate system multi-dimensional operating condition vector samples at multiple successful smooth handover moments under different load conditions to form an optimal handover state sample set; Second, perform a multivariate normality test on the optimal handover state sample set. If the test passes, it is confirmed that the sample set approximately follows a multivariate normal distribution; Third, calculate the operating condition correlation norm of each sample in the sample set, i.e., the Mahalanobis distance, to form a Mahalanobis distance sample distribution; Fourth, based on the actual application's requirements for balancing handover smoothness and handover speed, select a quantile of the Mahalanobis distance sample distribution as the handover admission threshold; wherein, selecting a lower quantile (such as the 30th to 50th quantile) can obtain stricter admission conditions, suitable for scenarios with extremely high requirements for handover smoothness; selecting a higher quantile (such as the 50th to 80th quantile) can obtain relatively relaxed admission conditions, suitable for scenarios that allow small, stable fluctuations but require faster handover completion. When sufficient samples cannot be obtained for the above statistics, the theoretical quantiles of the chi-square distribution can also be used to set the threshold: based on the dimension (i.e., degrees of freedom) of the multidimensional system condition vector and the desired confidence level, the corresponding theoretical Mahalanobis distance threshold is obtained by querying the chi-square distribution table as the switching admission threshold.

[0052] It should be noted that this step uses a fixed switching threshold, rather than setting different thresholds for different operating conditions (such as no-load and full-load). This technical advantage is due to the inherent adaptive characteristics of the operating condition correlation norm used in S3. Since the operating condition correlation norm has eliminated the influence of differences in the correlation between physical quantities under different operating conditions through the feature covariance matrix Σ during the calculation process, this norm has a unified measurement standard for the "proximity" under different load conditions. Therefore, the same fixed switching threshold can be used to determine the switching timing under different operating conditions, without the need to set up complex operating condition identification logic and threshold switching mechanisms, thereby simplifying system implementation and improving the reliability of judgment.

[0053] S5. If the judgment result is satisfied, the preparation stage is terminated and the control parameters are switched from the first parameter group to the second parameter group; if the switching admission condition is not satisfied within the preset timeout period, the preparation stage is terminated and the first parameter group is maintained.

[0054] It should be noted that step S5 is the final decision-making and execution stage of the parameter switching preparation phase. Based on the judgment result of S4, two mutually exclusive execution branches are entered: a successful switching branch when the condition is met, and a safe rollback branch when a timeout occurs. These two branches together constitute the complete closed-loop control logic of this method, ensuring that the system can enter a deterministic and safe operating state under any circumstances.

[0055] It should be noted that during a successful branch switch, terminating the guidance control executed in the preparation phase and stopping control actions based on the guidance control output aims to reclaim system control from the guidance controller during the transition period. In the guidance control phase described in S2, the actual control output of the temperature control system is generated by the guidance controller, not based on the PID control of the first parameter group. If the guidance control is not terminated synchronously after the switching conditions are met, the output of the guidance control may conflict with the PID control output of the second parameter group that is about to be connected, leading to the superposition or abrupt change of system control commands. Therefore, completely stopping the guidance control before switching the control parameters to the second parameter group ensures a clear and orderly handover of control.

[0056] In an optional embodiment, in S5, if the judgment result is satisfied, the guidance control executed in the preparation stage is terminated, the control action based on the guidance control output is stopped, the control parameters of the temperature control system are replaced from the first parameter group to the second parameter group, and the second parameter group is used as the control parameter to enter the stable operation control state in the second mode. It should be noted that replacing the control parameters of the temperature control system entirely from the first parameter group to the second parameter group means that all control parameters used for PID calculations within the temperature controller, including at least the proportional coefficient, integral time, and derivative time, are updated to the preset values ​​corresponding to the second mode all at once within the same control cycle. Using a "whole replacement" rather than "item-by-item replacement" method is to avoid the mixing of old and new parameters during the transition process, preventing unpredictable control behavior due to parameter mismatch. After the replacement is completed, the system enters a stable operating control state in the second mode using the second parameter group as the control parameters. At this point, the temperature control system has completely left the first mode and the preparation stage, and begins normal temperature regulation according to the target temperature and control strategy of the second mode.

[0057] If the switching access condition is not met within the preset timeout period, the timing starts from the start of the preparation phase. When the timing reaches the preset timeout period and the switching readiness indicator still does not meet the switching access condition, the guidance control is terminated, the control actions based on the guidance control output are stopped, the control parameters of the temperature control system are maintained at the first parameter group, and a switching failure record is generated. The switching failure record includes at least the timestamp of the timeout, the real-time system operating condition value at the timeout, and the switching readiness indicator value at the timeout.

[0058] The preset timeout period is set within the range of 10 minutes to 60 minutes.

[0059] It should be noted that in the timeout rollback branch, the preset timeout period is the time limit for determining whether the switchover has failed. This timeout period starts counting from the start of the preparation phase, not from the issuance of the switchover command or the initiation of the guidance control. Its purpose is to set an overall time limit for the entire preparation phase. This time limit is set based on the maximum expected time required for the temperature control system to transition from the typical operating conditions of the first mode to the optimal switching state. In engineering practice, this time is closely related to the thermal inertia of the refrigeration system; the greater the thermal inertia, the slower the system operating conditions change, and the longer the maximum transition time required.

[0060] It should be noted that the preset timeout period is set within a range of 10 to 60 minutes. This range is derived from practical operating experience of refrigeration systems of different sizes and types. For small cold storage facilities or refrigeration systems with low thermal inertia, the system operating condition can usually be guided to near the optimal switching state within a short time (e.g., 10 to 20 minutes). Therefore, a shorter timeout period can be set to avoid unnecessary waiting. For large cold storage facilities, pharmaceutical storage facilities, or systems with significant thermal inertia, the cooling or heating process itself requires a longer time, and a significant change in operating condition may take 30 minutes or even longer. Therefore, a longer timeout period (e.g., 40 to 60 minutes) is required to give the guidance control sufficient time to drive the system operating condition to gradually approach the target. If the switching access conditions are not met after 60 minutes, it usually means that there are abnormal factors (such as the cold storage door not being closed, refrigeration equipment failure, abnormal increase in external heat load, etc.) that are hindering the normal evolution of the system operating condition towards the optimal switching state. In this case, continuing to wait is meaningless, and the timeout rollback mechanism should be triggered.

[0061] It should be noted that in the timeout rollback branch, terminating the pilot control and ceasing control actions based on the pilot control output serves the same purpose as in the successful switchover branch, i.e., reclaiming control from the pilot controller. Maintaining the control parameters of the temperature control system at the first parameter set means that the system returns to its operating state before the switchover command was issued, continuing to adjust according to the temperature control target of the first mode. This ensures that the system will not fall into a dangerous state of no control or chaotic control due to switchover failure. This safety rollback mechanism is a fundamental principle in industrial control system design, ensuring that this method exhibits deterministic and safe system behavior under any abnormal circumstances.

[0062] It's important to note that the purpose of generating switchover failure records is to provide comprehensive data support for subsequent troubleshooting and system maintenance. The record contains three pieces of information, each with diagnostic value: the timestamp at the timeout determines the specific time the abnormal event occurred, facilitating correlation analysis with other information such as operation logs and video surveillance; the real-time system operating condition values ​​at the timeout record the actual state of each physical quantity at the end of the preparation phase, helping to identify which physical quantities failed to reach the expected range, thus pinpointing the root cause of the anomaly (e.g., a consistently high return air temperature change rate may indicate continuous cooling leakage); and the switchover readiness index value at the timeout quantifies the degree of deviation between the system's final state and the optimal switchover state. The magnitude and trend of this value reflect the effectiveness of guidance and control throughout the preparation phase and the severity of the abnormal factors. The record's identifier, "Operating Condition Correlation Timeout Failed to Reach Admission Threshold," clearly indicates the type of failure, facilitating rapid retrieval and classification statistics from a large volume of system logs. By analyzing the accumulated data from multiple switchover failure records, maintenance personnel can identify recurring anomalies in system operation and then take targeted maintenance measures.

[0063] Example 2, please refer to Figure 2 This invention provides a rapid parameter configuration system for a temperature controller, applicable to a temperature control system with at least two operating modes. The operating modes include a first mode and a second mode, which correspond to a first parameter group and a second parameter group, respectively. The instruction response module is used to respond to receiving a switching instruction from the first mode to the second mode and enter the parameter switching preparation stage; The guidance control module is used to perform guidance control during the preparation phase to drive the real-time system operating conditions of the temperature control system to approach the preset optimal switching state characterized by smooth switching. The indicator calculation module is used to calculate, in parallel and periodically, a handover readiness indicator that characterizes the correlation between the real-time system operating conditions and the optimal handover state during the preparation phase. The switching decision and execution module is used to determine whether the switching readiness index meets the preset switching admission conditions. If the determination result is met, the preparation phase is terminated and the control parameters are switched from the first parameter group to the second parameter group. If the switching admission conditions are not met within the preset timeout period, the preparation phase is terminated and the first parameter group is maintained.

[0064] In summary, this invention application eliminates parameter quiescent conflicts and their long-term cumulative damage by adding a parameter switching preparation stage to guide the control drive system to match the optimal switching state before performing parameter replacement.

[0065] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0066] In the embodiments provided by this invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0067] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0069] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0070] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for rapid configuration and control of thermostat parameters, applied to a temperature control system having at least two operating modes, wherein the operating modes include a first mode and a second mode, the first mode and the second mode respectively corresponding to a first parameter group and a second parameter group, characterized in that, Includes the following steps: S1. In response to receiving a switching instruction from the first mode to the second mode, enter the parameter switching preparation stage; S2. During the preparation phase, guidance control is executed to drive the real-time system operating conditions of the temperature control system toward the preset optimal switching state characterized by smooth switching. S3. During the preparation phase, a switching readiness index, which characterizes the correlation between the real-time system operating conditions and the optimal switching state, is calculated in parallel and periodically. S4. Determine whether the handover readiness index meets the preset handover admission conditions; S5. If the judgment result is satisfactory, the preparation stage is terminated, and the control parameters are switched from the first parameter group to the second parameter group. If the switching admission conditions are not met within the preset timeout period, the preparation phase is terminated and the first parameter group is maintained.

2. The rapid configuration and control method for temperature controller parameters as described in claim 1, characterized in that: Before S1, the process also includes: pre-constructing and storing the optimal switching state; Obtain a multi-dimensional system operating condition vector sample dataset collected at the moment when the switching timing is confirmed to be optimal during multiple smooth mode switching processes; Calculate the feature mean vector and feature covariance matrix of the multidimensional system operating condition vector sample dataset, and store the mathematical model jointly defined by the feature mean vector and feature covariance matrix as the optimal switching state.

3. The rapid configuration control method for temperature controller parameters as described in claim 1, characterized in that: In S1, the parameter switching preparation phase begins, including: Before entering the preparation stage, the received switching command is first validated. The validation includes confirming that the first mode and the second mode are different operating modes and that the second parameter group has been pre-configured and stored in the temperature control system. After the validity verification is passed, the preparation phase begins, where the current system operating status information is recorded, and the mode switching interface of the temperature control system is locked to prevent any new mode switching commands from being responded to during the preparation phase. The switching command is generated manually by the operator through the human-machine interface, or automatically by the temperature control system according to the preset operation scheduling plan; and when entering the parameter switching preparation stage, a switching preparation start notification is generated and sent to the log recording module of the temperature control system or the upper monitoring system.

4. The rapid configuration control method for temperature controller parameters as described in claim 2, characterized in that: In S2, the execution of guided control is based on the feature mean vector as the guiding target, a built-in simplified transfer function model, predicting the system operating condition vector trajectory within a future preset time period under multiple candidate control action sequences, evaluating each candidate control action sequence according to a preset cost function, selecting the candidate control action sequence that minimizes the cost function, and outputting the first control action in the candidate control action sequence to drive the temperature control system.

5. The rapid configuration control method for temperature controller parameters as described in claim 2, characterized in that: In S3, the handover readiness index used to characterize the correlation between the two is calculated, including: Collect the system's multidimensional operating condition vector at the current moment, and calculate the operating condition correlation norm using the feature mean vector and the feature covariance matrix; The physical quantities included in the multidimensional system operating condition vector include at least: evaporator coil temperature, return air temperature or silo temperature, rate of change of return air temperature or silo temperature, compressor operating power or operating frequency, and expansion valve opening. The system multidimensional operating condition vector at the current moment is obtained by synchronously grouping the sampled values ​​of the sensors corresponding to each physical quantity in the temperature control system at the same moment.

6. The rapid configuration control method for temperature controller parameters as described in claim 1, characterized in that: In S4, determining whether the handover readiness index meets the preset handover admission conditions includes: The current calculated value of the handover readiness index is compared with a preset handover admission threshold. The handover admission condition is specifically that the value of the handover readiness index is less than the handover admission threshold. The handover admission threshold is a preset constant, and its value represents the critical value of the handover readiness index when the real-time system condition and the optimal handover state enter the preset core association area.

7. The rapid configuration control method for temperature controller parameters as described in claim 6, characterized in that: In S5, if the judgment result is satisfied, the guidance control executed in the preparation stage is terminated, the control action based on the guidance control output is stopped, the control parameters of the temperature control system are replaced from the first parameter group to the second parameter group, and the second parameter group is used as the control parameter to enter the stable operation control state in the second mode. If the switching access condition is not met within the preset timeout period, the timing starts from the start of the preparation phase. When the timing reaches the preset timeout period and the switching readiness indicator still does not meet the switching access condition, the guidance control is terminated, the control actions based on the guidance control output are stopped, the control parameters of the temperature control system are maintained at the first parameter group, and a switching failure record is generated. The switching failure record includes at least the timestamp of the timeout, the real-time system operating condition value at the timeout, and the switching readiness indicator value at the timeout.

8. The rapid configuration control method for temperature controller parameters as described in claim 7, characterized in that: The preset timeout period is set within the range of 10 minutes to 60 minutes.

9. A rapid configuration system for thermostat parameters, employing the rapid configuration control method for thermostat parameters as described in any one of claims 1-8, applied to a temperature control system having at least two operating modes, wherein the operating modes include a first mode and a second mode, the first mode and the second mode respectively corresponding to a first parameter group and a second parameter group, characterized in that, include: The instruction response module is used to respond to receiving a switching instruction from the first mode to the second mode and enter the parameter switching preparation stage; The guidance control module is used to perform guidance control during the preparation phase to drive the real-time system operating conditions of the temperature control system to approach the preset optimal switching state characterized by smooth switching. The indicator calculation module is used to calculate, in parallel and periodically, a handover readiness indicator that characterizes the correlation between the real-time system operating conditions and the optimal handover state during the preparation phase. The switching decision and execution module is used to determine whether the switching readiness index meets the preset switching admission conditions. If the determination result is met, the preparation stage is terminated and the control parameters are switched from the first parameter group to the second parameter group. If the switching admission conditions are not met within the preset timeout period, the preparation phase is terminated and the first parameter group is maintained.

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