A dynamic adjusting method and system for a traditional Chinese medicine cooking temperature

Abstract

This application provides a method and system for dynamically adjusting the temperature of traditional Chinese medicine (TCM) decoction. The method involves real-time acquisition of temperature and steam inlet pressure parameters for each zone of the decoction pot, followed by regional weighting to generate temperature distribution deviation parameters. These parameters are then dynamically coupled with the steam pressure to form a composite parameter reflecting heat exchange efficiency. A multi-loop control strategy is employed to dynamically adjust the opening degree of the steam valve groups in the corresponding zones based on these composite parameters, with valve priority set according to the spatial location of each zone. An innovative pressure fluctuation compensation mechanism is introduced, using an iterative algorithm to apply physical boundary constraints to the opening parameters, ultimately converging the temperature distribution unevenness of each zone to a preset threshold. A closed-loop control system is formed through valve collaborative control and a dynamic compensation algorithm. The technical solution provided in this application achieves rapid and balanced temperature control of the decoction pot zones, effectively improving the thermal consistency and extraction stability of medicinal components during the TCM decoction process.

Description

Technical Field

[0001] This application relates to the field of cooking temperature technology, and in particular to a method and system for dynamically adjusting the cooking temperature of traditional Chinese medicine. Background Technology

[0002] In large-scale traditional Chinese medicine (TCM) production, the uniformity of cooking temperature directly affects the extraction efficiency of active ingredients and the quality of the finished product. Due to the large capacity of the decoction pot, the lag in steam heat transfer, and the significant differences in the physical properties of different medicinal materials, traditional fixed-parameter temperature control methods struggle to prevent temperature imbalances in certain areas of the pot, leading to uneven heating of the herbs. There is an urgent need for an intelligent temperature control technology that can sense temperature changes in multiple areas in real time, dynamically respond to steam pressure disturbances, and coordinate the operation of multiple actuators to meet the industry's high standards for temperature uniformity.

[0003] Current advanced solutions employ microwave resonant cavity dielectric property feedback control technology. This system embeds a microwave sensor array within the cooking tank. By detecting the dielectric constant of the medicinal materials, it inverts their moisture content and effective component concentration in real time, and generates dynamic temperature control commands based on a preset dielectric-temperature correlation model. For example, when a sudden increase in the dielectric loss factor is detected (indicating a large release of cell fluid), a cooling program is automatically triggered to prevent localized overheating. Simultaneously, leveraging the penetrating power of microwaves, a multi-modal field strength distribution optimization algorithm compensates for hotspot effects caused by uneven accumulation of medicinal materials within the tank. Theoretically, this system can achieve indirect sensing of the microscopic state of the medicinal materials through high-frequency sampling and dielectric spectrum feature extraction.

[0004] The core problem with microwave feedback schemes lies in the significant nonlinearity and time-varying nature of the mapping relationship between dielectric parameters and the actual thermal state of medicinal materials. Moisture migration and chemical ionization within the medicinal materials alter the dielectric response characteristics, and existing models, based solely on static calibration data, cannot adapt to the dynamic changes in the porous structure of the medicinal materials during cooking (such as fiber softening and starch gelatinization). This leads to decoupling between the dielectric signal and the actual temperature field, especially in the critical phase transition region (such as the initial boiling stage), resulting in misjudgments. Furthermore, multiple reflections from the microwave field and the metal container, as well as signal attenuation under high humidity conditions, result in high measurement errors for dielectric parameters, requiring frequent manual calibration and severely limiting control accuracy and reliability. Summary of the Invention

[0005] This application provides a method and system for dynamically adjusting the temperature of traditional Chinese medicine cooking, in order to solve the problems of uneven cooking caused by the lack of zone temperature monitoring and insufficient accuracy of multi-valve coordinated control in the prior art.

[0006] Firstly, this application provides a method for dynamically adjusting the temperature of traditional Chinese medicine steaming, including:

[0007] The temperature parameters of each zone on the side wall of the decoction pot are obtained, and the steam supply pressure parameters at the steam pipe inlet are collected simultaneously.

[0008] The temperature parameters are subjected to regional weighting to generate deviation parameters that characterize the unevenness of temperature distribution in each zone. The steam supply pressure parameters are then dynamically coupled with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency.

[0009] Based on the composite deviation parameters, the opening combination of the regulating valve group in the steam pipeline is dynamically adjusted through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a section of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding section.

[0010] During the adjustment process, the composite deviation parameter is correlated with the current change of the steam supply pressure parameter to generate valve opening correction parameters that include pressure fluctuation compensation terms. The valve opening correction parameters are then subjected to physical constraint boundary processing through an iterative correction algorithm to generate a valve opening parameter set.

[0011] Based on the set of valve opening parameters, each regulating unit of the regulating valve group is controlled in a coordinated manner, so that the temperature distribution unevenness of each zone converges to a preset threshold range.

[0012] Optionally, the composite deviation parameter is correlated with the current change in the steam supply pressure parameter to generate a valve opening correction parameter including a pressure fluctuation compensation term. Then, the valve opening correction parameter is subjected to physical constraint boundary processing using an iterative correction algorithm to generate a valve opening parameter set, including:

[0013] The current change in steam supply pressure parameters is calculated in real time, where the current change is the difference between the pressure parameters at the current moment and the average pressure parameters at the previous time period.

[0014] Based on the composite deviation parameter and the current change, an initial correlation value is determined, and a pressure fluctuation compensation item is generated based on the initial correlation value and a preset compensation coefficient.

[0015] The pressure fluctuation compensation term is superimposed on the composite deviation parameter to obtain the unconstrained valve opening correction parameter;

[0016] The unconstrained valve opening correction parameter is input into the iterative corrector, and in each iteration it is determined whether the unconstrained valve opening correction parameter exceeds the physical opening boundary of the corresponding regulating unit. If it exceeds the boundary, the excess part is reduced proportionally to the boundary value corresponding to the physical opening boundary, and the reduction amount is redistributed to the opening correction parameters of the remaining regulating units according to the priority weight of adjacent partitions.

[0017] The iterative correction process is repeated until the valve opening correction parameters of all control units meet the physical opening boundary constraints, in order to generate a set of valve opening parameters.

[0018] Optionally, the temperature parameters are subjected to regional weighting to generate deviation parameters characterizing the unevenness of temperature distribution in each zone, and the steam supply pressure parameters are dynamically coupled with the deviation parameters to generate composite deviation parameters reflecting the actual heat exchange efficiency, including:

[0019] Determine the weighting coefficients of each section of the side wall of the decoction pot, wherein the weighting coefficients are inversely proportional to the distance between the geometric position of each section on the side wall of the pot and the center of the bottom of the pot.

[0020] Based on the calculation results of the temperature parameters and the weighting coefficients, a deviation parameter characterizing the unevenness of temperature distribution in each zone is generated.

[0021] Calculate the ratio of the steam supply pressure parameter to the standard pressure parameter, and determine the initial composite deviation parameter based on the deviation parameter of the ratio and the temperature distribution unevenness.

[0022] The initial composite deviation parameter is input into the dynamic coupler, and the gain of the initial composite deviation parameter is adjusted according to the fluctuation range of the steam supply pressure parameter within a preset time window to generate a composite deviation parameter that reflects the actual heat exchange efficiency.

[0023] Optionally, based on the composite deviation parameter, the opening combination of the regulating valve group in the steam pipeline is dynamically adjusted through a multi-loop control strategy, including:

[0024] The regulating valve group in the steam pipeline is divided into multiple independently controlled regulating sub-units. Each regulating sub-unit corresponds to a section of the decoction pot, and priority weights are assigned to each regulating sub-unit based on the spatial location characteristics of each section.

[0025] Based on the composite deviation parameter, the initial adjustment amount of the opening of each adjustment subunit is calculated, wherein the initial adjustment amount of the opening is proportional to the product of the priority weight of the corresponding partition and the composite deviation parameter.

[0026] The current change in steam supply pressure parameters is acquired in real time. The current change is used as a correction factor to dynamically correct the initial adjustment of the opening of each regulating subunit, thereby generating the intermediate adjustment of each subunit.

[0027] For the intermediate adjustment amount of multiple adjustment subunits, a superposition effect analysis is performed. If the intermediate adjustment amount of the adjustment subunits corresponding to adjacent partitions are in opposite directions, a superposition correction coefficient is generated based on the spatial adjacent distance between the partitions, and the intermediate adjustment amount is compensated in reverse based on the superposition correction coefficient.

[0028] Based on the intermediate adjustment amount after compensation of all regulating subunits, and combined with the preset physical constraint boundary of valve opening, the opening combination command of each regulating subunit is generated through a coordinated allocation algorithm, so as to adjust the opening combination of the regulating valve group in the steam pipeline through the opening combination command.

[0029] Optionally, based on the valve opening parameter set, each regulating unit of the regulating valve group is coordinated to reduce the temperature distribution unevenness of each zone to a preset threshold range, including:

[0030] Extract the target opening value of each regulating unit from the set of valve opening parameters, and determine the execution order of the regulating units based on the priority weight of each partition;

[0031] The temperature parameters of each zone are acquired in real time, and the current temperature distribution unevenness is calculated. If the temperature distribution unevenness does not reach the preset threshold, the priority weight of the corresponding adjustment unit is dynamically adjusted according to the temperature change direction of each zone to determine the priority order of multiple adjustment units.

[0032] For the adjustment units of adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, an adjacent compensation coefficient is generated based on the difference value, and the opening values ​​of the two adjustment units are compensated in reverse based on the compensation coefficient, so that the temperature change trends of adjacent zones inhibit each other.

[0033] The compensated target opening value is input into the valve action controller, and the opening adjustment of each adjustment unit is executed in sequence according to the priority order. After each execution, the temperature distribution unevenness is detected in real time. If the rate of decrease of unevenness is lower than the preset value, the priority weight of the currently unexecuted adjustment unit is forcibly increased, and the execution order is redistributed until the difference between the temperature parameter and the average temperature of all zones is less than the preset threshold, and the temperature distribution unevenness is kept stable within the preset range.

[0034] Optionally, based on the compensated intermediate adjustment amounts of all regulating subunits and combined with the preset physical constraint boundaries of valve opening, an opening combination command for each regulating subunit is generated through a coordinated allocation algorithm, including:

[0035] The upper and lower limits of adjustment for each regulating subunit are determined according to the preset physical constraint boundary of valve opening. The upper and lower limits of adjustment are set according to the maximum and minimum allowable opening of the regulating valve group in the steam pipeline.

[0036] Calculate the superposition result of the intermediate adjustment amount after compensation and the current opening value for each adjustment subunit. If the superposition result exceeds the corresponding adjustment upper limit, the excess part is marked as a positive over-limit; if it is lower than the corresponding adjustment lower limit, the insufficient part is marked as a negative over-limit.

[0037] For all adjustment sub-units with positive or negative over-limits, the over-limits are processed sequentially according to the spatial priority of their respective partitions to obtain the processed superposition result.

[0038] The over-limit processing procedure is repeated until the superposition result of all adjustment sub-units is between the upper and lower adjustment limits;

[0039] The superposition result after the last loop processing is converted into the target opening value of each adjustment subunit. Based on the difference between the target opening value and the current opening value of each adjustment subunit, the corresponding opening change step size is generated. The opening change step size execution sequence of each adjustment subunit is arranged according to the priority order to form the opening combination instruction of each adjustment subunit.

[0040] Optionally, for adjustment units in adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, and an adjacent compensation coefficient is generated based on the difference value, including:

[0041] If the target opening value of the first adjustment subunit is positively increased relative to the current opening value, and the target opening value of the second adjustment subunit is negatively decreased relative to the current opening value, then the ratio of the absolute difference of the target opening change of the two adjacent adjustment subunits to the smaller of the two values ​​is calculated to obtain the opening change ratio difference.

[0042] If the difference in the opening degree change ratio is greater than a preset first difference threshold, the ratio is multiplied by a preset benchmark compensation factor to generate a first type of adjacent compensation coefficient; if the difference in the opening degree change ratio is less than or equal to the first difference threshold, the reciprocal of the ratio is multiplied by the benchmark compensation factor to generate a second type of adjacent compensation coefficient.

[0043] Based on the spatial adjacent distance between the first adjustment subunit and the second adjustment subunit, the first or second type of adjacent compensation coefficient is corrected by distance attenuation to obtain the adjacent compensation coefficient.

[0044] Secondly, this application provides a dynamic adjustment system for the cooking temperature of traditional Chinese medicine, comprising:

[0045] The acquisition module is used to acquire the temperature parameters of each zone on the side wall of the decoction pot, and simultaneously collect the steam supply pressure parameters at the steam pipe inlet.

[0046] The generation module is used to perform regional weighting processing on the temperature parameters to generate deviation parameters that characterize the unevenness of temperature distribution in each zone, and to dynamically couple the steam supply pressure parameters with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency.

[0047] The adjustment module is used to dynamically adjust the opening combination of the regulating valve group in the steam pipeline based on the composite deviation parameter through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a partition of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding partition.

[0048] The generation module is also used to perform correlation calculation between the composite deviation parameter and the current change of the steam supply pressure parameter during the adjustment process, generate valve opening correction parameters including pressure fluctuation compensation terms, and perform physical constraint boundary processing on the valve opening correction parameters through an iterative correction algorithm to generate a set of valve opening parameters.

[0049] The processing module is used to coordinately control each regulating unit of the regulating valve group according to the valve opening parameter set, so that the temperature distribution unevenness of each zone converges to a preset threshold range.

[0050] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for dynamically adjusting the temperature of traditional Chinese medicine steaming as described in the first aspect above.

[0051] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a method for dynamically adjusting the temperature of traditional Chinese medicine steaming as described in the first aspect.

[0052] In this embodiment, a multi-dimensional dynamic sensing system is constructed by synchronously acquiring temperature parameters of each zone of the decoction pot and pressure parameters of the steam pipe inlet. This enables real-time mapping of the thermal field state and energy input, providing a data foundation for global disturbance suppression. Temperature parameters are weighted regionally to generate distribution imbalance deviation parameters, which are then dynamically coupled with steam pressure parameters to generate composite deviation parameters. A nonlinear correlation model of the pressure-temperature field is established to address the energy imbalance problem caused by valve action competition. Based on the composite deviation parameters, multi-loop valve group opening adjustments are implemented, and adjustment priorities are allocated according to the spatial location characteristics of each zone to achieve heat conduction compensation and boundary heat flow counterbalancing suppression, reducing local overshoot / undershoot amplitude. The composite deviation parameters are correlated with steam pressure changes to generate correction parameters with compensation terms. Physical constraint boundary processing prevents valves from exceeding limits, ensuring that the adjustment process conforms to the equipment's mechanical characteristics. An iterative correction algorithm is used to generate the final valve opening parameter set, causing the temperature distribution imbalance to converge to a preset threshold, achieving synergistic optimization of steady-state accuracy and dynamic response.

[0053] Furthermore, the pressure fluctuation compensation and valve opening correction process specifically includes: real-time calculation of steam pressure changes, generation of pressure fluctuation compensation terms based on composite deviation parameters, physical opening boundary detection and over-limit reduction of unconstrained correction parameters using an iterative corrector, and redistribution of the reduction amount according to the priority of adjacent zones. Its technical effect is: through a dynamic compensation mechanism under pressure change scenarios, it eliminates the chain interference of steam supply fluctuations on the temperature field; simultaneously, by utilizing boundary constraints and weight redistribution strategies, it maintains multi-region thermal balance while ensuring valve operation safety, avoiding secondary imbalances caused by single-point over-adjustment.

[0054] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart of a method for dynamically adjusting the temperature of steaming and boiling traditional Chinese medicine provided in this application is shown;

[0057] Figure 2 This application provides a schematic diagram of a shampoo with dynamically adjustable steaming temperature for traditional Chinese medicine.

[0058] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0061] Researchers have discovered systemic defects in current traditional Chinese medicine (TCM) cooking equipment during zoned temperature control, including sudden changes in steam pressure causing disordered operation of multiple valves, lag in temperature field gradient compensation due to superimposed heat conduction, and dynamic mismatch in heat exchange efficiency caused by nonlinear changes in material heat capacity. Based on this, a dynamic adjustment method for TCM cooking temperature is proposed. This method achieves balanced heat flow control across multiple zones through dynamic coupling modeling of steam pressure and temperature fields, a multi-loop collaborative control algorithm with pressure fluctuation compensation, and an iterative valve correction mechanism based on physical constraints. This suppresses chain-like temperature field imbalances caused by steam disturbances and adapts to changes in the dynamic thermal characteristics of materials, ensuring that the temperature distribution unevenness stably converges to a preset threshold range. The technical solution of this application is applicable to TCM decoction equipment, multi-zone steam thermal processing systems, and other scenarios.

[0062] The entire R&D process demonstrates how multi-dimensional parameter coupling and dynamic collaborative control mechanisms can significantly improve the thermal uniformity and anti-interference capability of the traditional Chinese medicine decoction process. By synchronously collecting steam pressure and zone temperature parameters in real time, a dynamically coupled composite deviation parameter is constructed to accurately characterize the nonlinear correlation between heat exchange efficiency and temperature field uniformity. Based on a multi-loop control strategy and spatial priority allocation rules, the opening degree of the regulating valve group is dynamically adapted to the thermal demand of each zone. Combined with pressure fluctuation compensation terms and physical constraint processing, valve overshoot and energy offset caused by sudden changes in steam pressure are effectively mitigated. Valve collaborative control parameters generated through iterative correction algorithms drive the temperature distribution unevenness of each zone to converge rapidly to the process requirement range under complex heat conduction delay and multivariate coupling conditions, while suppressing mechanical losses caused by frequent valve operations. This technology breaks through the dependence of traditional zone temperature control systems on steady-state conditions, simultaneously enhancing temperature field uniformity and optimizing energy distribution under dynamic pressure fluctuations and heat load changes, providing a highly adaptive and robust dynamic equilibrium control paradigm for steam heating systems.

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] Figure 1 A flowchart illustrating a method for dynamically adjusting the cooking temperature of traditional Chinese medicine, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes:

[0065] 101. Obtain the temperature parameters of each zone on the side wall of the decoction pot, and simultaneously collect the steam supply pressure parameters at the steam pipe inlet.

[0066] Zoned temperature parameters refer to the real-time temperature data collected in each zone after the side wall of the decoction pot is divided into multiple independent zones (such as top, middle, and bottom), used to reflect the local heating status. Steam supply pressure parameters refer to the steam pressure data at the inlet of the steam pipe, characterizing the energy input intensity of the supply system.

[0067] In this embodiment, a distributed temperature sensor network (e.g., two PT100 thermocouples per section) is first deployed on the side wall of the decoction pot to collect the temperature of each section at a frequency of 10Hz. Simultaneously, steam inlet pressure data is acquired through a pressure transmitter. The temperature and pressure data are stored in the same time series database using a timestamp alignment algorithm (e.g., based on the NTP protocol). Sensor noise is eliminated by sliding window filtering, and time-synchronized temperature and pressure parameters are output.

[0068] For example, the side wall of a decoction pot in a pharmaceutical factory is evenly divided into four regions: upper region A, lower region B, left region C, and right region D. Thermocouple temperature sensors T_A, T_B, T_C, and T_D are installed at representative locations in regions A, B, C, and D, respectively. Simultaneously, a pressure sensor P_in is installed at the inlet of the steam pipe connected to the decoction pot jacket. The control system is set to collect data once per second. At a certain moment t = 1 second, the system synchronously reads and records: T_A = 95℃, T_B = 98℃, T_C = 94℃, T_D = 96℃, and P_in = 0.3MPa. These data {T_A, T_B, T_C, T_D} constitute the current temperature parameter set, and P_in constitutes the steam supply pressure parameter.

[0069] 102. Perform regional weighting processing on the temperature parameters to generate deviation parameters that characterize the unevenness of temperature distribution in each zone, and dynamically couple the steam supply pressure parameters with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency.

[0070] Regional weighting refers to assigning weight coefficients to each zone based on its thermal conductivity characteristics (such as area and material thermal conductivity) to calculate a weighted average of the temperature deviation. The deviation parameter is a scalar value generated through weighting, quantifying the overall degree of temperature difference between zones. Dynamic coupling refers to modeling the nonlinear relationship between steam pressure and temperature deviation (such as a pressure-temperature transfer function) to generate composite parameters.

[0071] In this embodiment of the application, the temperature parameters obtained in step 101 are used to perform regional weighting processing on the temperature parameters to calculate the weighted average temperature:

[0072] T_avg_weighted=(w_AT_A+w_BT_B+w_CT_C+w_DT_D) / (w_A+w_B+w_C+w_D)

[0073] Where w_i is the weight of each zone. Next, based on the weighted processing results, the absolute deviation between the temperature of each zone and the weighted average is calculated, and the maximum value or weighted sum is taken as the deviation parameter D = func({|T_i-T_avg_weighted|}). Then, the deviation parameter D is dynamically coupled with the steam supply pressure parameter P_in obtained in step 101, and a composite deviation parameter CD = f(D, P_in) is generated through a preset function. The preset function can be set according to the requirements, for example, CD = αD + β·|P in -P ideal| where temperature deviation D and pressure deviation are linearly superimposed, and their weights are adjusted by coefficients α and β. P_in is the real-time monitored steam pressure, reflecting the current state of the system. ideal The optimal target pressure designed for the system is used to compare whether the actual pressure is reasonable.

[0074] Continuing with the example in step 101, assume the four regions have equal weights (w_A = w_B = w_C = w_D = 1), and the weighted average temperature T_avg = (95 + 98 + 94 + 96) / 4 = 95.75℃. Calculate the deviation parameter D, for example, by taking the square root of the sum of the squares of the deviations of each region from the average (the standard deviation is calculated similarly): D = sqrt(((95 - 95.75)^2 + (98 - 95.75)^2 + (94 - 95.75)^2 + (96 - 95.75)^2) / 4) ≈ 1.48℃. Simultaneously, obtain the pressure P_in = 0.3MPa. Assume the ideal pressure set for the system is 0.32MPa, and that pressure fluctuations will affect efficiency. The dynamic coupling processing function is set as CD = D + k * |P_in - P_ideal|, where k is a coefficient (e.g., k = 5). The composite deviation parameter CD = 1.48 + 5 * |0.3 - 0.32| = 1.48 + 5 * 0.02 = 1.58. This value of 1.58 will serve as the primary basis for the next control strategy.

[0075] 103. Based on the composite deviation parameter, the opening combination of the regulating valve group in the steam pipeline is dynamically adjusted through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a partition of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding partition.

[0076] Multi-loop control strategy refers to assigning an independent control loop to each zone and achieving global optimization through a coordination algorithm. Spatial location priority refers to allocating valve adjustment priority based on the distance between the zone and the steam inlet (e.g., bottom priority) and thermal inertia (e.g., high response delay in the middle).

[0077] In this embodiment, the system receives the composite deviation parameter CD generated in step 102. Based on the composite deviation function parameter, the multi-loop control strategy (each regulating unit corresponds to a zone of the decoction pot) begins to calculate the opening adjustment amount required for each regulating unit (valve). During the calculation, spatial location characteristics are considered for priority allocation: for example, the system identifies that zone B has the highest temperature (98℃), zone C has the lowest temperature (94℃), and zone C is far from the steam inlet, so its response may be slower. The control strategy prioritizes calculating the increase in valve opening in zone C and gives it a larger weight, while simultaneously calculating the decrease in valve closing in zone B. The opening combination of the regulating valve group in the steam pipeline is dynamically adjusted according to the opening adjustment amount.

[0078] Continuing with the example from step 102, the composite deviation parameter CD = 1.58 is received from step 102. The system employs multi-loop PID control, with each zone (A, B, C, D) corresponding to a PID controller and a regulating valve (V_A, V_B, V_C, V_D). The controller detects that T_C (94℃) is significantly lower than T_B (98℃) and below the average, while T_B is significantly higher than the average. The composite deviation CD > 0 indicates that adjustment is needed. Assuming zone C (left side) is farthest from the steam inlet and has a higher priority, the PID controller calculates: V_A opening increases by 2%, V_B opening decreases by 5%, V_C opening (high priority) increases by 8%, and V_D opening increases by 1%. This {+2%, -5%, +8%, +1%} is the initial opening adjustment amount, which will be applied to the current valve opening.

[0079] 104. During the adjustment process, the composite deviation parameter is correlated with the current change of the steam supply pressure parameter to generate valve opening correction parameters that include pressure fluctuation compensation terms. The valve opening correction parameters are then subjected to physical constraint boundary processing through an iterative correction algorithm to generate a valve opening parameter set.

[0080] The pressure fluctuation compensation term refers to the correction amount generated based on the rate of pressure change, used to offset the interference of sudden pressure changes on valve control. Physical constraint boundary treatment refers to limiting the valve opening correction amount within the mechanical limit range (e.g., 0-100%) through iterative algorithms (such as the projected gradient method).

[0081] In this embodiment, the system calculates the change in current steam pressure ΔP. Then, it correlates this change in current steam pressure ΔP with the composite deviation parameter CD to generate a pressure fluctuation compensation term. For example, if the pressure drops (ΔP < 0) and CD is large (indicating uneven temperature and reduced efficiency), the pressure fluctuation compensation term will require a general increase in valve opening. The pressure fluctuation compensation term is added to the opening adjustment calculated in step 103 to obtain valve opening correction parameters. Next, an iterative correction algorithm is applied, considering the mutual influence between valve adjustments or total steam flow limitations. Finally, physical constraint boundary processing is performed on the corrected target opening: checking whether the target opening of each valve is within the range [0%, 100%]; if it is below 0%, it is set to 0%; if it is above 100%, it is set to 100%. After processing, the final, executable valve opening parameter set {V_A_final, V_B_final, V_C_final, V_D_final} is obtained.

[0082] Continuing with the example from step 103, assuming the steam pressure in the previous cycle was 0.31 MPa and the current steam pressure P_in = 0.3 MPa, then the pressure change ΔP = 0.3 - 0.31 = -0.01 MPa. The system detects a pressure drop, and the composite deviation CD = 1.58 is not zero. After correlation calculation, a positive pressure fluctuation compensation term is generated, assumed to increase the opening of all valves by 1%.

[0083] Combining the opening adjustment amounts from step 103: V_A adjustment = +2% + 1% = +3%; V_B adjustment = -5% + 1% = -4%; V_C adjustment = +8% + 1% = +9%; V_D adjustment = +1% + 1% = +2%. Assume the current valve openings are {V_A = 50%, V_B = 60%, V_C = 45%, V_D = 55%}.

[0084] The corrected target valve opening is then {50+3=53%, 60-4=56%, 45+9=54%, 55+2=57%}. Boundary checks: all values ​​are between 0% and 100%. Therefore, the final valve opening parameter set is {V_A=53%, V_B=56%, V_C=54%, V_D=57%}.

[0085] 105. Based on the set of valve opening parameters, each regulating unit of the regulating valve group is controlled in a coordinated manner so that the temperature distribution unevenness of each zone converges to a preset threshold range.

[0086] Coordinated control refers to coordinating the actions of various valves through distributed control protocols (such as DMPC) to avoid global oscillations caused by local adjustments.

[0087] In this embodiment, based on the valve opening parameter set {V_A_final, V_B_final, V_C_final, V_D_final} generated in step 104, a corresponding control signal (such as a 4-20mA current signal or digital communication command) is generated. This control signal is simultaneously sent to each regulating unit (actuator of V_A, V_B, V_C, V_D) in the regulating valve group. The actuator drives the valve to precisely adjust to the opening specified by the command, ensuring accurate distribution of steam flow into each zone. The entire process (steps 101-105) is continuously cyclically executed, with each cycle adjusting based on the latest measurement data. This gradually reduces the temperature distribution unevenness (such as deviation parameter D) in each zone of the drive system, eventually stabilizing within a preset threshold range, achieving uniform heating.

[0088] Continuing with the example in step 104, the system converts the final valve opening parameter set {V_A = 53%, V_B = 56%, V_C = 54%, V_D = 57%} into specific control signals. For example, if a 4-20mA signal is used, where 0% corresponds to 4mA and 100% corresponds to 20mA, then the signal sent to V_A is 4 + (53 / 100) * 16 = 12.48mA, the signal sent to V_B is 4 + (56 / 100) * 16 = 12.96mA, and so on to V_C (12.64mA) and V_D (13.12mA). These control signals drive each valve to adjust to the new opening degree. After a preset time period (the next control period t = 2s), the system executes step 101 again and measures the new temperatures as {T_A = 95.5℃, T_B = 97℃, T_C = 95℃, T_D = 96.5℃}. At this point, the calculated deviation parameter D' will be smaller than the previous 1.48℃. The system will continue to execute steps 102-105, repeatedly adjusting until all temperatures are within the range of [96℃, 97℃], satisfying the preset threshold of unevenness less than or equal to 1℃.

[0089] This solution constitutes a closed-loop, intelligent temperature uniformity control system for the decoction pot. By collecting temperature and steam source pressure data from each zone in real time and synchronously, and combining regional weighting, dynamic coupling, and multi-loop control strategies that consider spatial characteristics, the system can accurately assess the unevenness of temperature distribution and actual heat exchange efficiency. Furthermore, by introducing pressure fluctuation compensation and physical boundary constraint processing, precise, feasible, and coordinated valve opening commands are generated and executed. The entire process is continuously iterated and optimized, ultimately making the temperature distribution of each zone on the side wall of the decoction pot tend to be uniform and stabilized within a preset range of minimal differences. This significantly improves heating uniformity, ensures uniform heating of the medicinal liquid, enhances heat exchange efficiency and decoction quality, and simultaneously achieves automated and refined process management.

[0090] In some embodiments, the composite deviation parameter is correlated with the current change in the steam supply pressure parameter to generate a valve opening correction parameter that includes a pressure fluctuation compensation term. Then, an iterative correction algorithm is used to apply physical constraint boundary processing to the valve opening correction parameter to generate a valve opening parameter set, including:

[0091] 201. Calculate the current change in steam supply pressure parameters in real time, wherein the current change is the difference between the pressure parameters at the current moment and the average pressure parameters of the previous period.

[0092] The current change refers to the difference between the current steam pressure value and the average pressure parameter of the previous period. The average pressure parameter of the previous period is obtained by taking the moving average of the pressure data within the historical time window, which is used to eliminate instantaneous noise and reflect the overall trend of pressure changes.

[0093] In this embodiment, a high-precision pressure sensor (sampling frequency 10Hz) is deployed at the steam pipeline inlet. Raw pressure data is continuously collected through a sliding time window mechanism (default 30 seconds, which can be dynamically adjusted according to system inertia). High-frequency noise is filtered out by a Kalman filter (e.g., the original sequence [2.48, 2.52, 2.47] is filtered to 2.49MPa). Then, the weighted average pressure parameter of the previous 30 seconds is calculated using the sliding window mechanism (the weight decays over time, e.g., the weight distribution within the window is [0.1, 0.2, ..., 1.0], and the average pressure parameter P_avg of the previous period is calculated to be 2.51MPa). The difference between the current steam supply pressure parameter P_now (e.g., 2.53MPa) and the average pressure parameter P_avg of the previous period is calculated in real time, i.e., the current change ΔP = +0.02MPa. The current change ΔP is used for subsequent compensation calculations.

[0094] 202. Determine the initial correlation value based on the composite deviation parameter and the current change, and generate a pressure fluctuation compensation item based on the initial correlation value and a preset compensation coefficient;

[0095] The initial correlation value is the intermediate value obtained by weighting the composite deviation parameter (CD) and the current change (ΔP), used to quantify the comprehensive impact of pressure fluctuations on system control. The compensation coefficient is a preset adjustment weight coefficient used to control the magnitude of the compensation term, avoiding over-compensation or under-compensation. The pressure fluctuation compensation term is the correction amount generated by multiplying the initial correlation value and the compensation coefficient; its function is to dynamically adjust the valve opening to offset the temperature deviation caused by pressure fluctuations.

[0096] In this embodiment, based on the linear combination of the composite deviation parameter CD and the current change ΔP (formula: V_initial=α×CD+β×ΔP, α=0.7, β=0.3), the initial correlation value V_initial=1.103 is calculated; further, through a pre-trained LSTM model (inputting the current change ΔP sequence of the past 5 minutes, outputting the dynamic compensation coefficient K=0.8), the pressure fluctuation compensation term C=0.8×0.184=0.1472 is generated, and C is written to shared memory for subsequent use.

[0097] 203. The pressure fluctuation compensation term is superimposed on the composite deviation parameter to obtain the unconstrained valve opening correction parameter;

[0098] Unconstrained valve opening correction parameters refer to the initial adjustment amount that does not take into account the physical limitations of the valve. Its value may exceed the actual operable opening range of the valve (e.g., exceeding 100% or falling below 0%).

[0099] In this embodiment, the pressure fluctuation compensation term C = 1.0% is directly superimposed on the initial valve adjustment amount corresponding to the original composite deviation parameter (e.g., V_C adjustment changes from +8% to +9%) to obtain unconstrained valve opening correction parameters. For example, if the current valve opening correction parameters are {V_A = 50%, V_B = 60%, V_C = 45%, V_D = 55%}, the superimposed valve opening correction parameters are {53%, 56%, 54%, 57%}. At this stage, valve physical limitations are not considered and further correction is required.

[0100] 204. Input the unconstrained valve opening correction parameter into the iterative corrector, and in each iteration determine whether the unconstrained valve opening correction parameter exceeds the physical opening boundary of the corresponding regulating unit. If it exceeds, reduce the excess part proportionally to the boundary value corresponding to the physical opening boundary, and redistribute the reduction amount to the opening correction parameters of the remaining regulating units according to the priority weight of adjacent partitions.

[0101] Physical opening boundaries refer to the minimum and maximum physical limits (0% to 100%) of valve opening. Priority weights refer to the weight values ​​assigned based on the spatial location or functional importance of the valve within its zone (e.g., zones closer to the steam inlet have higher weights). An iterative corrector is a loop approximation algorithm that successively reduces the correction amount for the out-of-limit portion and distributes the reduction amount to other valves that do not exceed the limits according to their weights until all correction parameters satisfy the constraints.

[0102] In this embodiment, the unconstrained valve opening correction parameter is input into the iterative corrector. If the unconstrained valve opening correction parameter is detected to exceed the physical opening boundary of the corresponding adjustment unit (e.g., valve opening correction parameter V_C = 54%, exceeding the upper limit of 50%), the excess portion is allocated according to the priority weight of adjacent partitions. For example, if the valve opening correction parameter V_C exceeds the limit by 4%, it is redistributed according to the weight of other valves that do not exceed the limit, V_B:V_D = 3:1: V_B increases by 3% (56% → 59%), V_D increases by 1% (57% → 58%), and V_C is corrected to 50%. During the iteration process, the parameters are continuously detected and adjusted until all valve opening correction parameters meet the preset constraint [0%, 100%] boundary.

[0103] 205. Repeatedly execute the iterative correction process until the valve opening correction parameters of all regulating units meet the physical opening boundary constraints, so as to generate a set of valve opening parameters.

[0104] The valve opening parameter set refers to a set of final executable valve opening commands after iterative correction, with all parameters located within the physical opening boundary (0% to 100%). The generation of this set must ensure that the correction amount of each partition, while satisfying the constraints, is as close as possible to the theoretical optimal value, in order to achieve rapid convergence and stability of temperature control.

[0105] In this embodiment, after multiple iterative corrections, the valve opening correction parameters converge to {V_A = 53%, V_B = 59%, V_C = 50%, V_D = 58%}, and all valve opening correction parameters are within the physical boundaries. The system outputs the final valve opening correction parameters, generating a valve opening parameter set; and converts it into a 4-20mA control signal (e.g., 12.44mA corresponding to V_B = 59%), driving the valve actuator to complete the adjustment, reducing the temperature distribution unevenness from 1.48℃ to 0.8℃, and achieving stable control under dynamic pressure fluctuations.

[0106] Here is a specific example:

[0107] In a heating network control scenario, step 204 receives the unconstrained valve opening correction parameters output from step 203 (e.g., valve C's valve opening correction parameter is 54%, but its mechanical upper limit is 50%). The iterative corrector first marks the over-limit node (Δ = +4%), and based on the network topology, allocates the over-limit amount to neighboring valves B (weight 0.6) and D (weight 0.5) according to priority. After allocation, valve B's valve opening correction parameter rises to 61.18% (over-limit 1.18%), which then triggers cross-zone impedance allocation: the residual over-limit amount is allocated to non-associated valves according to impedance ratio (valve A: 0.8Ω, valve E: 1.2Ω), and finally valve A's valve opening correction parameter is corrected to 53.47%. The global optimization model further balances the flow deviation, constrains valve B to the upper limit of 60% through quadratic programming, and fine-tunes other valves, outputting a feasible solution (valve C hard limit 50%, valve B: 59.8%, total flow deviation from 12.3m). 3 / h decreased to 3.8m 3 The entire process took 48ms ( / h).

[0108] This solution achieves precise control of pipeline flow while ensuring equipment safety through multi-level collaborative optimization and dynamic constraint mechanisms. The system prioritizes local allocation at nodes near over-limit valves, quickly mitigating most out-of-bounds risks. Residual deviations are balanced across regions based on the pipeline's physical impedance characteristics, combined with a global optimization model to ensure mathematical optimality of flow conservation and target opening. The entire process possesses millisecond-level real-time response capabilities, adapting to topology changes and dynamic priority adjustments, while continuously improving the adaptability of the allocation strategy through historical data feedback. This solution completely eliminates mechanical out-of-bounds risks while significantly reducing system flow deviation and pressure fluctuations, balancing actuator smoothness and control stability, providing a highly robust solution for real-time control of complex fluid networks.

[0109] In some embodiments, the temperature parameters are subjected to regional weighting to generate deviation parameters characterizing the unevenness of temperature distribution in each zone. The steam supply pressure parameters are then dynamically coupled with the deviation parameters to generate a composite deviation parameter reflecting the actual heat exchange efficiency, including:

[0110] 301. Determine the weighting coefficient of each section of the side wall of the decoction pot, wherein the weighting coefficient is inversely proportional to the distance between the geometric position of each section on the side wall of the pot and the center of the bottom of the pot.

[0111] The weighting coefficient refers to the weighting factor assigned to each zone on the side wall of the decoction pot. Its value is determined by an inverse proportional relationship between the geometric center of the zone and the center of the pot bottom. The center of the pot bottom is the core area for heat conduction; zones closer to the center receive more direct heat and thus have a higher weight in the overall temperature balance. This inverse proportional allocation is achieved through a mathematical formula, specifically by the weight decreasing hyperbolically as distance increases. This ensures that temperature fluctuations in the near-end zones dominate the weighted calculation, thereby accurately reflecting the thermal state of key areas.

[0112] In this embodiment, based on the three-dimensional modeling and thermodynamic characteristic analysis of the decoction pot, the pot body model is first constructed by laser scanning to determine the three-dimensional coordinates of the center of each partition, and the Euclidean distance di=\sqrt{x_i^2+y_i^2+z_i^2} from the center of the bottom of the pot is calculated. The initial weights are assigned using the inverse proportional function wi=1 / 1+k·di (k is the experimentally calibrated attenuation coefficient). After normalization, the weight coefficient wi′ is obtained to ensure that the partitions farther from the bottom of the pot (such as the top) have higher weights due to the higher risk of heat loss. The weight data is stored in a real-time database for subsequent use.

[0113] 302. Based on the calculation results of the temperature parameters and the weighting coefficients, generate deviation parameters that characterize the unevenness of temperature distribution in each zone;

[0114] The temperature distribution unevenness deviation parameter is a quantitative index generated from weighted temperature data, used to characterize the degree of temperature dispersion in each zone.

[0115] In this embodiment, the weighted average temperature Tavg=∑wi′Ti is calculated based on the weighting coefficient wi′, and the temperature distribution unevenness deviation parameter D is generated by the weighted standard deviation algorithm D=sqrt{∑wi′(Ti-Tavg)^2} to quantify the temperature dispersion of each zone. This parameter is transmitted to the dynamic coupling module in real time through the message queue.

[0116] 303. Calculate the ratio of the steam supply pressure parameter to the standard pressure parameter, and determine the initial composite deviation parameter based on the ratio and the deviation parameter of the temperature distribution unevenness.

[0117] The initial composite deviation parameter is a comprehensive control quantity that integrates steam pressure state and temperature deviation. It is generated by multiplying the pressure ratio (actual pressure / standard pressure) and the temperature deviation parameter. The pressure ratio is used to quantify the degree to which the steam supply intensity deviates from the ideal operating conditions. When the actual pressure is lower than the standard value, this ratio will amplify the effect of the temperature deviation; conversely, it will suppress its effect.

[0118] In this embodiment, the steam pressure parameter and temperature deviation are dynamically integrated: the logarithmic ratio of real-time pressure Pin to standard pressure Pstd is calculated as R = log10(Pin / Pstd)R, amplifying the sensitivity of low-pressure conditions; the initial composite deviation parameter (α, β are experimentally calibrated weighting coefficients) is generated through the linear formula CDinitial = αD + β|R|, realizing dual-dimensional coordinated control of temperature and pressure.

[0119] 304. Input the initial composite deviation parameter into the dynamic coupler, and adjust the gain of the initial composite deviation parameter according to the fluctuation range of the steam supply pressure parameter within the preset time window to generate a composite deviation parameter that reflects the actual heat exchange efficiency.

[0120] The dynamic coupler is a parameter adjustment module with time-domain adaptive capability. It dynamically adjusts the gain coefficient of the initial composite deviation parameter by analyzing the pressure fluctuation amplitude (standard deviation) and fluctuation frequency within a preset time window (such as 30 seconds).

[0121] In this embodiment, the initial composite deviation parameter is input into the dynamic coupler. The input includes the initial composite deviation parameter (CD_initial) and the pressure sequence within the time window (sampling at 10Hz, 300 data points in total). Based on the LSTM neural network, the historical pressure fluctuation amplitude and frequency characteristics (input is the pressure sequence within the time window) are analyzed to extract key features such as pressure change rate and range. The dynamic gain coefficient K∈[0.5,2.0] is output. The final composite deviation parameter is generated by the formula CDfinal=cl ip(K·CDinitial,0,5), which drives the multi-loop PID controller to dynamically adjust the valve opening of each zone, prioritizing compensation for low temperature or high pressure fluctuation areas.

[0122] Here is a specific example:

[0123] A pharmaceutical factory's 1.5-meter diameter decoction pot is divided into 6 sector-shaped zones, equipped with PT1000 high-precision temperature sensors (±0.1℃). The initial temperature distribution is [96℃, 97℃, 95℃, 98℃, 94℃, 96℃], and the steam inlet pressure is 0.32MPa (standard pressure 0.35MPa). First, based on 3D modeling, the distance from each zone to the center of the pot bottom is calculated. The farthest zone, 3 (0.8 meters), has a weight of 0.12, and the closest zone, 1 (0.2 meters), has a weight of 0.18. Then, the weighted average temperature is calculated to be 95.8℃, generating a temperature deviation parameter of 1.62℃. Using the pressure logarithmic ratio (R = log(0.32 / 0.35) = -0.093), an initial composite deviation parameter of 1.15 is generated. A dynamic coupler detects a sudden pressure drop from 0.34MPa to 0.30. MPa (rate of change -0.013MPa / s), LSTM model output gain coefficient 1.4, generating final composite deviation parameter 1.61, driving the control system to prioritize increasing the valve opening in the low temperature zone (zone 5) by 12% and decreasing the valve opening in the high temperature zone (zone 4) by 8%; after 3 control cycles (6 seconds), the temperature distribution converged to [95.5℃, 96.8℃], the maximum temperature difference was reduced from 4℃ to 1.3℃, the steam flow fluctuation was reduced from ±15% to ±7%, the system energy efficiency was improved by 22%, and fully automated control was achieved.

[0124] This solution achieves precise temperature control and system optimization of the decoction pot through spatial weight allocation, multi-parameter dynamic coupling, and intelligent gain adjustment. It significantly reduces temperature differences between zones, improving heating uniformity; greatly enhances dynamic response speed, rapidly adapting to steam pressure fluctuations and ensuring stable heat exchange efficiency; effectively reduces energy waste and improves resource utilization; significantly enhances system robustness, maintaining temperature stability under complex operating conditions; and fully automated control reduces manual intervention, ensuring production continuity and consistency. This solution provides a high-precision, high-reliability, and high-energy-efficiency intelligent temperature control solution for the modern production of traditional Chinese medicine, promoting the intelligent and refined development of pharmaceutical processes.

[0125] In some embodiments, based on the composite deviation parameter, the opening combination of the regulating valve group in the steam pipeline is dynamically adjusted through a multi-loop control strategy, including:

[0126] 401. Divide the regulating valve group in the steam pipeline into multiple independently controlled regulating sub-units. Each regulating sub-unit corresponds to a section of the decoction pot, and assign priority weights to each regulating sub-unit based on the spatial location characteristics of each section.

[0127] The regulating subunit refers to a steam valve assembly with an independent drive circuit; the priority weight is a 0-1 normalized parameter reflecting the importance of zoned temperature control; spatial location characteristics include dimensions such as vertical distance from the bottom of the pot, angle of heat source radiation, and number of adjacent zones.

[0128] In this embodiment, the system acquires the topology of the decoction pot through 3D point cloud scanning and establishes a fluid-structure interaction simulation model. After discretizing the steam pipeline into a finite element mesh, the system calculates the thermal influence factor of each fluid unit. Based on the spectral clustering algorithm, units with similar thermodynamic properties are aggregated into independent regulating sub-units, and each sub-unit corresponds to the main temperature control channel of a single partition. Priority weights are assigned to each regulating sub-unit, and the entropy weight method is used to quantify indicators such as thermal inertia, response delay, and fault sensitivity. The weight coefficients are dynamically updated in conjunction with an online learning mechanism. When a partition experiences a continuous temperature deviation, the system automatically increases its priority weight to enhance the control intensity. Finally, the system achieves encrypted synchronization of the weight matrix and maintenance of global state consistency through a distributed database.

[0129] 402. Calculate the initial adjustment amount of the opening of each adjustment subunit based on the composite deviation parameter, wherein the initial adjustment amount of the opening is proportional to the product of the priority weight of the corresponding partition and the composite deviation parameter.

[0130] The initial adjustment amount of the opening refers to the theoretical adjustment value without considering pressure fluctuations; the composite deviation parameter CD includes the temperature field imbalance and pressure coupling effect; the product factor k1 = 0.15 is an empirical coefficient calibrated through 300 sets of experiments.

[0131] In this embodiment, spatiotemporal distribution data of temperature, pressure, and flow velocity are collected by a multi-channel sensor array. Wavelet transform is used to separate the steady-state components and transient interference in the signal. A three-dimensional temperature gradient field model is constructed to calculate the spatial imbalance of each zone. An LSTM network is used to predict the trend of latent heat of vapor phase change and to perform convolution operation with real-time pressure fluctuations to generate dynamic compensation terms. The composite deviation parameters and priority weights are subjected to Kronecker product operation to amplify the control signals of key zones. At the same time, the historical adjustment effect is statistically analyzed through a sliding time window, triggering a Bayesian optimization algorithm to combine with the dynamic compensation terms to correct the product factor gain, thereby obtaining the initial adjustment sequence of the opening.

[0132] 403. Real-time acquisition of the current change in steam supply pressure parameters, using the current change as a correction factor to dynamically correct the initial adjustment of the opening of each regulating subunit, and generating the intermediate adjustment of each subunit.

[0133] Steam supply pressure variation refers to the dynamic pressure fluctuation characteristics of the pipeline network captured by high-frequency pressure sensors. The correction factor is a nonlinear compensation parameter combining the pressure change rate and amplitude, used to offset the effects of external disturbances. Spatial domain compensation refers to a technique for regionally differentiated correction of adjustment amounts based on the pipeline network topology.

[0134] This application embodiment eliminates pressure signal noise and extracts the true fluctuation trend through Kalman filtering. Combining the pipeline network topology and fluid dynamics model, it establishes the spatial propagation equation of pressure disturbance in the pipeline system, predicting the dynamic influence weight (interference coefficient matrix) of each sub-unit under different disturbance sources. The composite deviation parameter and interference coefficient are comprehensively calculated to generate the basic adjustment amount for each sub-unit. Furthermore, an equivalent time delay correction term is introduced, and the adjustment amount is time-shifted based on fluid inertial characteristics to ensure that the action sequence is synchronized with disturbance propagation. For high-pressure sensitive areas, an exponential gain strategy is used to dynamically enhance the adjustment amplitude, while a nonlinear limiting mechanism constrains overshoot risk. To avoid action conflicts between adjacent sub-units, a phase shift is applied to the adjustment amount based on the equivalent delay difference, causing upstream and downstream control commands to be executed at off-peak times in the time domain, suppressing fluid impact oscillations. A final intermediate adjustment amount, containing both dynamic compensation and the physical coupling characteristics of disturbance propagation, is generated as the core input for subsequent superposition effect analysis and global optimization.

[0135] 404. Perform superposition effect analysis on the intermediate adjustment amount of multiple adjustment subunits. If the intermediate adjustment amount of the adjustment subunits corresponding to adjacent partitions are in opposite directions, generate a superposition correction coefficient based on the spatial adjacent distance between the partitions, and perform reverse compensation on the intermediate adjustment amount based on the superposition correction coefficient.

[0136] The superposition effect refers to the steam flow field coupling interference phenomenon caused by the coordinated regulation of multiple sub-units. The fluid dynamic coupling strength refers to the mutual influence coefficient between sub-units calculated based on the Navier-Stokes equations. Timing misalignment compensation refers to the technique of decoupling conflicting regulation commands through a timestamp offset strategy.

[0137] This application utilizes a pipeline spatiotemporal coupling simulator to analyze the superposition effect of pressure wave propagation, pipe wall deformation, and steam inertia. Specifically targeting conflict scenarios where adjacent sub-units have opposite adjustment directions (e.g., pressurization in sub-unit A and depressurization in adjacent sub-unit B), it identifies the risk of stress concentration in boundary areas caused by bidirectional pressure tearing. Based on spatial topology and preset thresholds, if adjacent sub-units have mutually exclusive adjustment directions, a spatial distance less than the material fatigue threshold, and a historical overshoot probability exceeding the limit, a superposition correction coefficient is generated based on the reciprocal of the adjacent distance (the closer the distance, the higher the correction strength), triggering a high-priority reverse compensation mechanism to reverse compensate for intermediate adjustments. The reverse compensation mechanism's correction process includes dynamic weight redistribution (reducing the weights of the conflicting adjustments proportionally to the distance) and boundary buffer compensation (injecting small reverse commands to neutralize sudden pressure changes), while simultaneously integrating existing correction factors (weight decay, phase smoothing) to achieve multi-strategy synergy. If the residual conflict still exceeds the limit after correction, the correction coefficient is iteratively optimized through gradient descent until the pressure fluctuation converges to a safe range. The final output of the conflict-free instruction set integrates spatial correction parameters and conflict heatmaps, which not only eliminates the risk of overlapping mutually exclusive instructions in adjacent partitions, but also provides boundary gradient constraints for the global optimization in step 405.

[0138] 405. Based on the intermediate adjustment amount after compensation of all regulating subunits, and combined with the preset physical constraint boundary of valve opening, generate the opening combination command of each regulating subunit through the coordination allocation algorithm, so as to adjust the opening combination of the regulating valve group in the steam pipeline through the opening combination command.

[0139] Valve opening physical constraints refer to hardware limitations such as mechanical stroke limits, opening and closing rate thresholds, and cumulative fatigue life.

[0140] This application first models the physical boundaries (opening threshold, action rate limit), cross-subunit flow conservation equation, and mechanical interference rules of the pipeline valve group as a dynamic constraint space, defining a hardware-compatible feasible region for command generation. Based on the feasible region, a multi-objective game-gradient search hybrid algorithm is used to solve for the optimal combination of opening degrees of each subunit, outputting physically executable opening degree combination commands for each regulating subunit. To mitigate action risks, the opening degree combination commands are verified through pipeline transient simulation: if pressure oscillation or mechanical resonance is detected (such as excessive difference in opening degrees between adjacent valves), a rolling time-domain optimization segmented correction command sequence is triggered to ensure the dynamic stability of the opening degree combination. The opening degree combination commands are calibrated through closed-loop feedback: after being sent to the subunit controller, actual opening degree data is collected in real time; if execution deviation occurs (such as valve response delay), the allocation weights and constraint boundaries of subsequent commands are dynamically corrected to achieve dynamic coordination and global feasibility of cross-subunit opening degree combinations of regulating valve groups in the pipeline.

[0141] Here is a specific example:

[0142] A city's water supply network experienced a sudden drop in terminal pressure to 0.18 MPa (below the safety threshold of 0.2 MPa) due to peak water usage. The system implemented dynamic control throughout the entire process, from steps 401 to 405. First, step 401 located the abnormal area and quantified the demand: the terminal pressure needed to be increased to 0.25 MPa within 10 minutes, corresponding to a 15% increase in total flow. Then, step 402 decomposed and generated theoretical correction values, requiring pump stations A and B to increase their speed by 5% and 8% respectively, pressure regulating valve D to increase its opening by 12%, and valve group F to decrease its closing by 8%. In step 403, the system performed saturation compensation to address the mechanical aging problem of valve group D (its actual maximum opening was only 90%), reducing its opening correction to 10.8%, and added 2 seconds of inertial compensation to pump station B to prevent water hammer. Step 404 output the compensated commands, including adjustments to the speed of pump stations A and B and corrections to the opening of valve groups D and F. Finally, in step 405, a multi-constraint coordination algorithm is used to resolve the conflict between the valve group D / F opening difference limit (≤15%) and the grid power limit (≤20%): dynamically increasing the output of pump station C by 3%, reducing the valve group F closing amplitude to 6%, and inserting a two-stage opening command for valve group D to avoid the risk of transient pressure difference. After execution, the terminal pressure stabilizes to 0.24 MPa within 8 minutes, the total energy consumption increase is controlled at 18.7%, and the mechanical interference risk is eliminated, achieving safe, efficient, and adaptive global control.

[0143] This solution significantly improves the system's response speed and robustness to sudden operating conditions through a closed-loop control mechanism involving dynamic anomaly detection, compensation correction, and global coordination across multiple constraints. It achieves efficient collaboration of resources across sub-units while ensuring physical feasibility. Based on multi-objective optimization algorithms and transient simulation verification, it effectively balances conflicting requirements such as energy consumption limitations, equipment safety, and control precision, suppressing hardware damage risks and the propagation of pressure fluctuations. The closed-loop feedback mechanism can calibrate execution deviations in real time and adaptively adjust instruction weights to ensure the strict achievement of core objectives under complex constraints. Furthermore, the solution possesses good scalability, supporting dynamic addition and removal of sub-units and topology reconfiguration, providing a standardized and adaptive technical framework for the flexible control of large-scale systems.

[0144] In some embodiments, each regulating unit of the regulating valve group is coordinated and controlled according to the valve opening parameter set, so that the temperature distribution unevenness of each zone converges to a preset threshold range, including:

[0145] 501. Extract the target opening value of each regulating unit from the valve opening parameter set, and determine the execution order of the regulating units based on the priority weight of each partition;

[0146] The valve opening parameter set refers to a structured data set of the current and target opening values ​​of all regulating units (valve), including information such as position code, regulating magnitude, and direction of action. Priority weight refers to a dynamically allocated control weight coefficient based on the functional attributes of a zone (such as core area, peripheral area) or thermal load sensitivity (such as equipment heat dissipation area, densely populated area); a larger value indicates a higher control priority.

[0147] In this embodiment, the system first extracts the target opening value of each adjustment unit from the valve opening parameter set using preset valve identification rules (such as spatial topology coding), and then weights and sorts the execution order of the adjustment units based on a partition priority weight matrix (such as core area weight = 0.6, ordinary area weight = 0.3). A weighted priority sorting algorithm (WPSA) is used to generate a comprehensive priority index by multiplying the weight by the opening adjustment magnitude (absolute value), and then arranging them in descending order to form an execution queue. For example, if a core area valve has a weight of 0.6 and needs to adjust its opening by 10%, its comprehensive index is 6; if an ordinary area valve has a weight of 0.3 but needs to adjust by 15%, its index is 4.5. Therefore, the core area valve is executed first.

[0148] 502. Real-time acquisition of temperature parameters of each zone, calculation of the current temperature distribution unevenness, and if the temperature distribution unevenness does not reach the preset threshold, dynamic adjustment of the priority weight of the corresponding adjustment unit according to the temperature change direction of each zone to determine the priority order of multiple adjustment units.

[0149] Temperature distribution unevenness refers to the root mean square difference between the temperature of each zone and the system's set average temperature, used to quantify the uniformity of the temperature field. The direction of temperature change refers to the trend of deviation of the zone temperature from the set value (e.g., continuous increase or decrease).

[0150] In this embodiment, the system collects temperature parameters of each zone in real time using distributed temperature sensors and calculates the temperature distribution unevenness every 5 seconds (formula: σ=√(∑(Ti-Tavg)^2 / N)). If the temperature distribution unevenness exceeds a preset threshold (e.g., σ>1.5℃), dynamic weight adjustment is triggered: for zones with aggravated temperature deviation (e.g., temperatures continuously rising and exceeding the set value), the priority weight of the corresponding adjustment unit of the zone is increased by 20%-50% (weight coefficient = original weight × 1.2~1.5); conversely, the weight of zones with temperatures approaching stability is reduced by 10%. The updated weight matrix is ​​re-input into the WPSA algorithm in step 501 to generate a new execution order queue, forming a feedback loop.

[0151] 503. For the adjustment units of adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, an adjacent compensation coefficient is generated based on the difference value, and the opening values ​​of the two adjustment units are compensated in reverse based on the compensation coefficient, so that the temperature change trends of adjacent zones inhibit each other.

[0152] The difference in valve opening percentage refers to the ratio of the target opening adjustment ranges of valves in adjacent zones (e.g., if valve A's opening is +12% and valve B's is -8%, then the difference ratio is 12:8 = 1.5). The adjacent compensation coefficient is a correction factor generated based on the difference ratio, used to offset the temperature field interference effect caused by the reverse adjustment of adjacent valves.

[0153] In this embodiment, the system identifies pairs of regulating units for adjacent zone valves (e.g., valves in room A and corridor B). If the target opening values ​​of the two valves are adjusted in opposite directions (e.g., A needs to be opened wider, and B needs to be closed narrower), the system calculates the absolute difference ratio (ΔA / ΔB) of the target opening values ​​of the regulating units. The difference ratio is mapped to the (0,1) interval using a compensation coefficient generation algorithm (formula: K = 1 / (1 + e^(-ΔA / ΔB))) to generate an adjacent compensation coefficient. Based on this adjacent compensation coefficient, the target opening values ​​of the regulating units for the two adjacent zone valves are corrected in the opposite direction: the target opening of valve A is adjusted to ΔA × (1 - K), and the target opening of valve B is adjusted to ΔB × (1 + K). For example, if A originally needs +15% and B needs -10%, and the difference ratio of 1.5 generates K = 0.6, then A is ultimately adjusted to 6% and B to -16%, thereby suppressing the spread of temperature fluctuations caused by conflicting valve actions in adjacent areas.

[0154] 504. Input the compensated target opening value into the valve action controller, and execute the opening adjustment of each adjustment unit in sequence according to the priority order. After each execution, the temperature distribution unevenness is detected in real time. If the rate of decrease of unevenness is lower than the preset value, the priority weight of the currently unexecuted adjustment unit is forcibly increased, and the execution order is redistributed until the difference between the temperature parameter and the average temperature of all zones is less than the preset threshold, and the temperature distribution unevenness is kept stable within the preset range.

[0155] The rate of decrease in unevenness refers to the change in the unevenness of temperature distribution per unit time (e.g., a decrease of 0.3℃ per minute). Forced weight boosting refers to temporarily increasing the weight of low-priority valves through override control logic when the control efficiency is insufficient.

[0156] In this embodiment, the compensated target opening value is sent to the valve controller via the Modbus protocol, and the opening adjustments of each regulating unit are executed sequentially according to the updated priority queue. After each valve action, the system monitors the rate of decrease in temperature distribution unevenness at a 2-second interval. If the rate of decrease is lower than a preset threshold (e.g., <0.1℃ / min) for three consecutive cycles, a forced weight increase is triggered: the weight of the unexecuted valve is multiplied by a dynamic coefficient (coefficient = 1 + (target rate - actual rate) / target rate), and the execution order queue is regenerated. For example, when the rate of a priority 5 valve is insufficient due to execution delay, its weight is increased from 0.3 to 0.45, thereby enabling it to be executed earlier in the next round of regulation, until the difference between all zone temperature parameters and the average temperature is less than ±0.5℃ and the difference stabilizes within 0.8℃.

[0157] Here is a specific example:

[0158] The air conditioning system of a commercial complex needs to reduce the temperature distribution imbalance in Zone A (core shops), Zone B (corridor), and Zone C (equipment room) from 2.1℃ to 0.8℃ within 15 minutes. The system first generates an initial control queue based on the high priority weight (0.7) of Zone A, prioritizing the valve opening of Zone A by +12%. Real-time monitoring reveals that the rapid cooling in Zone A causes a temperature rise in Zone B, triggering a dynamic weight adjustment mechanism. The weight of Zone B is increased from 0.2 to 0.4, and the system identifies the reverse valve actions in Zones A and B (A opens wider, B closes less). An adjacent compensation coefficient is generated by calculating the difference ratio using a compensation coefficient algorithm. Based on this coefficient, the opening of Zone A is adjusted to +5.4%, and Zone B to -12.4%. After execution, due to insufficient rate of temperature distribution imbalance reduction, the weight of Zone C is forcibly increased to 0.3. After three queue iterations, the deviation of all zone temperature parameters from the average temperature is less than ±0.5℃, and the imbalance stabilizes below 0.75℃, allowing the system to enter steady-state operation.

[0159] This solution significantly improves the balance and stability of multi-zone temperature control through the synergistic effect of dynamic priority allocation, adjacent area compensation mechanism, and closed-loop feedback control. Based on real-time data, valve action priorities are dynamically adjusted to ensure rapid response to critical areas. The adjacent compensation algorithm effectively suppresses local temperature fluctuations caused by reverse valve actions, reducing inter-zone interference. The closed-loop feedback mechanism monitors control efficiency in real time and dynamically corrects the execution strategy to overcome control bottlenecks. Ultimately, the temperature distribution imbalance is rapidly converged to the preset range, the deviation between the temperature of each zone and the target value is continuously reduced, and the system maintains long-term uniformity in steady state. Simultaneously, it reduces equipment wear caused by frequent valve actions, forming a highly adaptive and robust global temperature collaborative control system.

[0160] In some embodiments, based on the compensated intermediate adjustment amounts of all regulating subunits and combined with preset physical constraints on valve opening, a coordinated allocation algorithm is used to generate opening combination commands for each regulating subunit, including:

[0161] 601. Determine the upper and lower adjustment limits for each regulating subunit according to the preset physical constraint boundary of valve opening. The upper and lower adjustment limits are set according to the maximum and minimum allowable opening of the regulating valve group in the steam pipeline.

[0162] The physical constraint boundary of valve opening refers to the maximum and minimum allowable opening values ​​of the control valve assembly in the steam pipeline, which are limited by the valve's mechanical structure or safety specifications. The upper / lower adjustment limit refers to the dynamic adjustment range set according to the valve's maximum allowable opening (e.g., 100%) and minimum allowable opening (e.g., 0%), used to limit the valve's actuation range.

[0163] In this embodiment, the system predefines an upper adjustment limit (e.g., +15%) and a lower adjustment limit (e.g., -10%) for each regulating subunit (e.g., valve in zone A) based on the maximum and minimum allowable opening values ​​of the regulating valve assembly in the steam pipeline, as defined by the valve's mechanical structure or safety specifications. For example, if the valve's current opening is 50%, its upper adjustment limit is 65% (50% + 15%) and its lower limit is 40% (50% - 10%). This step ensures that all subsequent adjustment operations are within the mechanical safety range.

[0164] 602. Calculate the superposition result of the intermediate adjustment amount after compensation and the current opening value of each adjustment subunit. If the superposition result exceeds the corresponding adjustment upper limit, the excess part is marked as positive over-limit; if it is lower than the corresponding adjustment lower limit, the insufficient part is marked as negative over-limit.

[0165] The compensated intermediate adjustment amount refers to the preliminary valve adjustment recommendation value calculated by the adjacent area compensation algorithm. Positive over-limit refers to the portion exceeding the upper adjustment limit after the compensated intermediate adjustment amount and the current opening degree are added together. Negative over-limit refers to the portion below the lower adjustment limit after the addition.

[0166] In this embodiment, the system directly superimposes the compensated intermediate adjustment amount of each adjustment subunit (e.g., +12% for valve in area A) with the current opening value (e.g., 50%) to obtain a superimposed result, which is then compared with the adjustment upper limit (65%) and lower limit (40%) in step 601. If the superimposed result (62%) does not exceed the limit, it is directly retained; if the superimposed result exceeds the limit (e.g., the superimposed result is 70%), a positive over-limit amount (70% - 65% = 5%) is recorded; if the superimposed result is lower than the lower limit (e.g., the superimposed result is 35%), a negative over-limit amount (35% - 40% = -5%) is recorded. This step marks all adjustment requirements that may violate physical constraints.

[0167] 603. For all adjustment sub-units with positive or negative over-limits, the over-limits are processed sequentially according to the spatial priority of their respective partitions to obtain the processed superposition result.

[0168] Spatial location priority refers to the execution order defined based on the functional importance of the zoning (e.g., the core shop area A has a higher priority than the corridor area B) or the strength of thermal coupling (e.g., adjacent areas have a higher priority).

[0169] In this embodiment, all adjustment subunits with positive or negative over-limit values ​​are processed sequentially according to their spatial priority within their respective partitions (e.g., A > B > C). For example, if A has a positive over-limit of 5% and B has a negative over-limit of -3%, the positive over-limit value of A is processed first: the adjustment amount in A is reduced from +12% to +10% using a linear reduction algorithm (ensuring that 65% of the superposition result is within limits), and the reduced 2% is weighted and distributed to the adjacent B region (e.g., the adjustment amount in B region changes from -8% to -10%) to maintain overall thermal balance, resulting in the processed superposition result. This step eliminates over-limit values ​​while ensuring the needs of priority regions are met through a dynamic redistribution strategy.

[0170] 604. Repeat the over-limit processing procedure until the superposition result of all adjustment sub-units is between the upper and lower adjustment limits;

[0171] Loop execution refers to iteratively processing the excess until the superposition result of all adjustment subunits satisfies the physical constraints.

[0172] In this embodiment, if there are still excess limits after the initial excess amount processing (e.g., new excess limits are generated in area C due to adjustment allocation), the system enters a loop process: recalculate the excess limits based on the updated adjustment amounts, and redistribute them according to the aforementioned priority order. For example, if area C still exceeds the limit by -2% after the first adjustment, then in the second loop, the negative excess amount of area C is allocated to lower priority areas (e.g., area D in the equipment room) according to the aforementioned priority order, until the adjustment amounts of all areas are within the constraint range.

[0173] 605. Convert the superposition result after the last loop processing into the target opening value of each adjustment subunit. Based on the difference between the target opening value and the current opening value of each adjustment subunit, generate the corresponding opening change step size. Arrange the opening change step size execution sequence of each adjustment subunit according to the priority order to form the opening combination instruction of each adjustment subunit.

[0174] The target opening value refers to the valve opening target value that ultimately satisfies the physical constraints. The opening change step size refers to the difference between the current opening and the target opening, used to generate step-by-step execution instructions.

[0175] In this embodiment, the superposition result after the last loop processing (e.g., 62% in area A, 45% in area B, and 38% in area C) is converted into the target opening value of each regulating subunit. The difference between the target opening value and the current opening value of each regulating subunit is calculated as the corresponding step size (e.g., if area A needs to increase from 50% to 62%, the step size is +12%). The opening change step size execution sequence of each regulating subunit is generated according to the priority order (area A > area B > area C): valves in area A are executed first with +12%, valves in area B with -5%, and valves in area C with -2%. This step ensures that high-priority areas respond first, avoiding system oscillations caused by multiple valves operating simultaneously.

[0176] Here is a specific example:

[0177] A commercial complex needs to equalize the temperature of Zone A (core shops), Zone B (corridor), and Zone C (equipment room) within 15 minutes. The system initially calculates that Zone A needs an opening of +18% (intermediate adjustment after compensation), but this is limited by an adjustment cap of +15% (step 601), resulting in a 3% over-limit after aggregation (step 602). The over-limit in Zone A is handled according to priority (step 603): the adjustment amount in Zone A is reduced to +15%, and the 3% over-limit is allocated to Zone B according to thermal coupling weights (the original adjustment amount -5% becomes -8%). Loop detection reveals that the new adjustment amount of -8% in Zone B results in a negative over-limit of -2% (step 604), so the over-limit in Zone B is again allocated to Zone C according to priority (the adjustment amount changes from 0% to -2%). Finally, the execution sequence is generated (step 605): Zone A +15%, Zone B -8%, Zone C -2%, all valve adjustments are within physical constraints, and the temperature imbalance decreases from 2.1℃ to 0.7℃.

[0178] This solution addresses the physical conflicts and execution efficiency issues in multi-zone valve collaborative control through dynamic constraint boundary setting, priority-driven over-limit allocation, and iterative cyclic mechanisms. While ensuring equipment safety, it prioritizes meeting the control needs of critical zones, while maintaining global thermal balance through over-limit redistribution to prevent frequent valve oscillations. Ultimately, it achieves rapid convergence and precise execution of opening commands, significantly improving temperature uniformity, reducing equipment wear, and making it suitable for robust control in complex building environments.

[0179] In some embodiments, for adjustment units in adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, and an adjacent compensation coefficient is generated based on the difference value, including:

[0180] 701. If the target opening value of the first regulating subunit is positively increased relative to the current opening value, and the target opening value of the second regulating subunit is negatively decreased relative to the current opening value, then the ratio of the absolute difference of the target opening change of the two adjacent regulating subunits to the smaller of the two values ​​is calculated to obtain the opening change ratio difference.

[0181] Positive increment refers to the adjustment amount by which the target opening value is higher than the current opening value (e.g., current opening 50% → target opening 55%, increment +5%). Negative decrease refers to the adjustment amount by which the target opening value is lower than the current opening value (e.g., current opening 50% → target opening 45%, decrease -5%). Absolute difference refers to the absolute value of the difference between two values ​​(e.g., the absolute difference between +5% and -5% is 10%). Opening change ratio difference refers to the ratio of the absolute difference of the target opening change of two adjacent control sub-units to the smaller of the two values, used to quantify the degree of adjustment conflict.

[0182] In this embodiment, it is assumed that the target opening changes of two adjacent regulating subunits A (positive increment) and B (negative reduction) are +8% and -6%, respectively. First, the absolute difference between the two adjacent regulating subunits is calculated: |8% - (-6%)| = 14%. Taking the smaller absolute value (6%) of the target opening changes of regulating subunits A (positive increment) and B (negative reduction) as the denominator, the opening change ratio difference is calculated as 14% / 6% ≈ 2.33. This opening change ratio difference reflects the conflict intensity when the adjustment amounts of regulating subunits A (positive increment) and B (negative reduction) are reversed; the larger the ratio difference, the higher the compensation requirement.

[0183] 702. If the difference in the opening degree change ratio is greater than a preset first difference threshold, the ratio is multiplied by a preset benchmark compensation factor to generate a first type of adjacent compensation coefficient; if the difference in the opening degree change ratio is less than or equal to the first difference threshold, the reciprocal of the ratio is multiplied by the benchmark compensation factor to generate a second type of adjacent compensation coefficient.

[0184] The first difference threshold refers to a preset proportional difference critical value (e.g., 1.5), used to distinguish compensation strategies. The baseline compensation factor refers to the initial compensation intensity coefficient (e.g., 0.8), calibrated based on historical data or experiments. The first type of adjacent compensation coefficient refers to the positive compensation coefficient (directly enhancing compensation) when there is high difference. The second type of adjacent compensation coefficient refers to the negative compensation coefficient (suppressing over-adjustment) when there is low difference.

[0185] In this embodiment, if the difference in the opening degree change ratio calculated in step 701 is 2.33 (> the preset first difference threshold of 1.5), then the difference in the opening degree change ratio of 2.33 is multiplied by the benchmark compensation factor of 0.8 to obtain the first type of compensation coefficient 2.33×0.8≈1.86. If the difference in the opening degree change ratio is 1.2 (≤ the preset first difference threshold of 1.5), then the reciprocal 1 / 1.2≈0.83 is calculated, and then multiplied by the benchmark compensation factor to obtain the second type of compensation coefficient 0.83×0.8≈0.66. This step dynamically selects the compensation strategy through the difference threshold: strengthening compensation when there is high conflict and weakening compensation when there is low conflict.

[0186] 703. Based on the spatial adjacent distance between the first adjustment subunit and the second adjustment subunit, the first or second type of adjacent compensation coefficient is corrected by distance attenuation to obtain the adjacent compensation coefficient.

[0187] Spatial adjacency distance refers to the physical distance between two partitions (e.g., A and B are 2 meters apart, and B and C are 5 meters apart). Distance attenuation correction refers to attenuating the compensation coefficient based on the spatial distance; the greater the distance, the stronger the attenuation.

[0188] In this embodiment, an exponential decay function is used, and the distance decay factor α is calculated based on the spatial adjacent distance d between the first and second adjustment subunits. The formula is α = e^(-k·d), where d represents the spatial distance between the subunits, and k is a preset decay coefficient (e.g., 0.3) used to control the decay rate. The compensation coefficient obtained in step 702 (e.g., a first-type compensation coefficient of 1.8 or a second-type compensation coefficient of 0.66) is multiplied by the decay factor to obtain the corrected adjacent compensation coefficient. For example, if the distance between A and B is 2 meters, the decay factor α is e^(-0.3×2) = 0.55, then the compensation coefficient 1.86×0.55≈1.02 in step 702; if the distance is 5 meters, the decay factor α is e^(-0.3×5) = 0.22, and the compensation coefficient is reduced to 1.86×0.22≈0.41.

[0189] Here is a specific example:

[0190] In a multi-zone control system of a certain regional heating system, Zone A needs to increase the target valve opening by 15% due to a sudden increase in heat load, while the adjacent Zone B needs to decrease it by 10% to meet the temperature target, and Zone C needs to increase it by 8% in the same direction as Zone A. The system first identifies the differences in the status of each zone in step 701, and then generates compensation coefficients in step 702: Zone A and Zone B are adjusted in opposite directions, triggering a first-type compensation coefficient of 1.8, and Zone A and Zone C are adjusted in the same direction, triggering a second-type compensation coefficient of 0.5. Step 703 further corrects the compensation intensity based on spatial distance: Zone A and Zone B are 4 meters apart, and the distance attenuation factor is calculated to be 0.30 using the exponential attenuation formula, reducing the compensation coefficient to 0.54 (1.8 × 0.30); Zone A and Zone C are only 1 meter apart, with an attenuation factor of 0.74, resulting in a corrected coefficient of 0.37 (0.5 × 0.74). When the final adjustment was implemented, the actual reduction in Zone B was suppressed to 4.6% (54% of the original 10%), while the actual opening in Zone C was increased to 11% due to synergistic enhancement.

[0191] This solution effectively addresses the dynamic control challenges of multi-zone heating systems through a comprehensive linkage mechanism encompassing state identification, compensation coefficient generation, and spatial attenuation correction. Based on attenuation correction according to the physical distance between zones, the system can accurately match the spatial characteristics of heat transfer, avoiding energy redundancy caused by ineffective compensation in distant areas. Simultaneously, by classifying and handling reverse conflicts and unidirectional collaborative relationships through a directional compensation strategy, it not only suppresses the overcompensation risk caused by reverse regulation but also enhances the linkage efficiency of adjacent unidirectional zones, significantly improving system response consistency. Furthermore, the dynamic compensation mechanism constrained by the pipeline topology can adaptively adapt to changes in zone distance or layout, reducing energy loss from frequent valve adjustments while ensuring heating temperature stability. Ultimately, it achieves high-precision, low-interference, and high-efficiency operation of multi-objective control in complex heating network environments, balancing system robustness and overall energy efficiency optimization.

[0192] Figure 2 This application provides a schematic diagram of the structure of a dynamic temperature control system for steaming and boiling traditional Chinese medicine, as shown in the embodiment of the present application. Figure 2 As shown, the system includes:

[0193] The acquisition module 21 is used to acquire the temperature parameters of each zone on the side wall of the decoction pot, and simultaneously collect the steam supply pressure parameters at the steam pipe inlet.

[0194] The generation module 22 is used to perform regional weighting processing on the temperature parameters to generate deviation parameters that characterize the unevenness of temperature distribution in each zone, and to dynamically couple the steam supply pressure parameters with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency.

[0195] The adjustment module 23 is used to dynamically adjust the opening combination of the regulating valve group in the steam pipeline based on the composite deviation parameter through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a partition of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding partition.

[0196] The generation module 22 is also used to perform correlation calculation between the composite deviation parameter and the current change of the steam supply pressure parameter during the adjustment process, generate valve opening correction parameters including pressure fluctuation compensation terms, and perform physical constraint boundary processing on the valve opening correction parameters through an iterative correction algorithm to generate a valve opening parameter set.

[0197] Processing module 24 is used to coordinately control each regulating unit of the regulating valve group according to the valve opening parameter set, so that the temperature distribution unevenness of each zone converges to a preset threshold range. Figure 2 The aforementioned dynamic temperature control system for steaming and boiling traditional Chinese medicine can perform... Figure 1The implementation principle and technical effects of the dynamic adjustment method for the cooking temperature of traditional Chinese medicine described in the above embodiment will not be repeated here. The specific operation methods of each module and unit in the dynamic adjustment system for the cooking temperature of traditional Chinese medicine in the above embodiment have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0198] In one possible design, Figure 2 The dynamic temperature control system for steaming and boiling traditional Chinese medicine, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0199] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0200] The processing component 32 is used for the above Figure 1 The embodiment describes a method for dynamically adjusting the temperature of traditional Chinese medicine steaming and boiling.

[0201] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0202] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0203] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0204] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0205] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0206] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0207] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a method for dynamically adjusting the cooking temperature of traditional Chinese medicine.

[0208] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0209] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0210] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for dynamically adjusting the cooking temperature of traditional Chinese medicine, characterized in that, include: The temperature parameters of each zone on the side wall of the decoction pot are obtained, and the steam supply pressure parameters at the steam pipe inlet are collected simultaneously. The temperature parameters are subjected to regional weighting to generate deviation parameters that characterize the unevenness of temperature distribution in each zone. The steam supply pressure parameters are then dynamically coupled with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency. Based on the composite deviation parameters, the opening combination of the regulating valve group in the steam pipeline is dynamically adjusted through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a section of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding section. During the adjustment process, the composite deviation parameter is correlated with the current change of the steam supply pressure parameter to generate valve opening correction parameters that include pressure fluctuation compensation terms. The valve opening correction parameters are then subjected to physical constraint boundary processing through an iterative correction algorithm to generate a valve opening parameter set. Based on the set of valve opening parameters, each regulating unit of the regulating valve group is controlled in a coordinated manner so that the temperature distribution unevenness of each zone converges to a preset threshold range. The method of dynamically adjusting the opening combination of regulating valve groups in the steam pipeline based on the composite deviation parameter through a multi-loop control strategy includes: The regulating valve group in the steam pipeline is divided into multiple independently controlled regulating sub-units. Each regulating sub-unit corresponds to a section of the decoction pot, and priority weights are assigned to each regulating sub-unit based on the spatial location characteristics of each section. Based on the composite deviation parameter, the initial adjustment amount of the opening of each adjustment subunit is calculated, wherein the initial adjustment amount of the opening is proportional to the product of the priority weight of the corresponding partition and the composite deviation parameter. The current change in steam supply pressure parameters is acquired in real time. The current change is used as a correction factor to dynamically correct the initial adjustment of the opening of each regulating subunit, thereby generating the intermediate adjustment of each subunit. For the intermediate adjustment amount of multiple adjustment subunits, a superposition effect analysis is performed. If the intermediate adjustment amount of the adjustment subunits corresponding to adjacent partitions are in opposite directions, a superposition correction coefficient is generated based on the spatial adjacent distance between the partitions, and the intermediate adjustment amount is compensated in reverse based on the superposition correction coefficient. Based on the intermediate adjustment amount after compensation of all regulating subunits, and combined with the preset physical constraint boundary of valve opening, the opening combination command of each regulating subunit is generated through a coordinated allocation algorithm, so as to adjust the opening combination of the regulating valve group in the steam pipeline through the opening combination command. The process involves correlating the composite deviation parameter with the current change in the steam supply pressure parameter to generate valve opening correction parameters that include a pressure fluctuation compensation term. Then, an iterative correction algorithm is used to apply physical constraint boundary processing to these valve opening correction parameters, generating a set of valve opening parameters, including: The current change in steam supply pressure parameters is calculated in real time, where the current change is the difference between the pressure parameters at the current moment and the average pressure parameters at the previous time period. Based on the composite deviation parameter and the current change, an initial correlation value is determined, and a pressure fluctuation compensation item is generated based on the initial correlation value and a preset compensation coefficient. The pressure fluctuation compensation term is superimposed on the composite deviation parameter to obtain the unconstrained valve opening correction parameter; The unconstrained valve opening correction parameter is input into the iterative corrector, and in each iteration it is determined whether the unconstrained valve opening correction parameter exceeds the physical opening boundary of the corresponding regulating unit. If it exceeds the boundary, the excess part is reduced proportionally to the boundary value corresponding to the physical opening boundary, and the reduction amount is redistributed to the opening correction parameters of the remaining regulating units according to the priority weight of adjacent partitions. The iterative correction process is repeated until the valve opening correction parameters of all control units meet the physical opening boundary constraints, in order to generate a set of valve opening parameters.

2. The method according to claim 1, characterized in that, The temperature parameters are subjected to regional weighting to generate deviation parameters characterizing the unevenness of temperature distribution in each zone. The steam supply pressure parameters are then dynamically coupled with these deviation parameters to generate a composite deviation parameter reflecting the actual heat exchange efficiency, including: Determine the weighting coefficients of each section of the side wall of the decoction pot, wherein the weighting coefficients are inversely proportional to the distance between the geometric position of each section on the side wall of the pot and the center of the bottom of the pot. Based on the calculation results of the temperature parameters and the weighting coefficients, a deviation parameter characterizing the unevenness of temperature distribution in each zone is generated. Calculate the ratio of the steam supply pressure parameter to the standard pressure parameter, and determine the initial composite deviation parameter based on the deviation parameter of the ratio and the temperature distribution unevenness. The initial composite deviation parameter is input into the dynamic coupler, and the gain of the initial composite deviation parameter is adjusted according to the fluctuation range of the steam supply pressure parameter within a preset time window to generate a composite deviation parameter that reflects the actual heat exchange efficiency.

3. The method according to claim 1, characterized in that, Based on the valve opening parameter set, each regulating unit of the regulating valve group is coordinated and controlled to bring the temperature distribution unevenness of each zone to a preset threshold range, including: Extract the target opening value of each regulating unit from the set of valve opening parameters, and determine the execution order of the regulating units based on the priority weight of each partition; The temperature parameters of each zone are acquired in real time, and the current temperature distribution unevenness is calculated. If the temperature distribution unevenness does not reach the preset threshold, the priority weight of the corresponding adjustment unit is dynamically adjusted according to the temperature change direction of each zone to determine the priority order of multiple adjustment units. For the adjustment units of adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, an adjacent compensation coefficient is generated based on the difference value, and the opening values ​​of the two adjustment units are compensated in reverse based on the compensation coefficient, so that the temperature change trends of adjacent zones inhibit each other. The compensated target opening value is input into the valve action controller, and the opening adjustment of each adjustment unit is executed in sequence according to the priority order. After each execution, the temperature distribution unevenness is detected in real time. If the rate of decrease of unevenness is lower than the preset value, the priority weight of the currently unexecuted adjustment unit is forcibly increased, and the execution order is redistributed until the difference between the temperature parameter and the average temperature of all zones is less than the preset threshold, and the temperature distribution unevenness is kept stable within the preset range.

4. The method according to claim 1, characterized in that, Based on the compensated intermediate adjustment amounts of all regulating subunits, and combined with the preset physical constraint boundaries of valve opening, a coordinated allocation algorithm generates opening combination commands for each regulating subunit, including: The upper and lower limits of adjustment for each regulating subunit are determined according to the preset physical constraint boundary of valve opening. The upper and lower limits of adjustment are set according to the maximum and minimum allowable opening of the regulating valve group in the steam pipeline. Calculate the superposition result of the intermediate adjustment amount after compensation and the current opening value for each adjustment subunit. If the superposition result exceeds the corresponding adjustment upper limit, the excess part is marked as a positive over-limit; if it is lower than the corresponding adjustment lower limit, the insufficient part is marked as a negative over-limit. For all adjustment sub-units with positive or negative over-limits, the over-limits are processed sequentially according to the spatial priority of their respective partitions to obtain the processed superposition result. The over-limit processing procedure is repeated until the superposition result of all adjustment sub-units is between the upper and lower adjustment limits; The superposition result after the last loop processing is converted into the target opening value of each adjustment subunit. Based on the difference between the target opening value and the current opening value of each adjustment subunit, the corresponding opening change step size is generated. The opening change step size execution sequence of each adjustment subunit is arranged according to the priority order to form the opening combination instruction of each adjustment subunit.

5. The method according to claim 3, characterized in that, For adjustment units in adjacent zones, if their target opening values ​​are adjusted in opposite directions, the difference in the opening change ratio between the two is calculated, and an adjacent compensation coefficient is generated based on the difference value, including: If the target opening value of the first adjustment subunit is positively increased relative to the current opening value, and the target opening value of the second adjustment subunit is negatively decreased relative to the current opening value, then the ratio of the absolute difference of the target opening change of the two adjacent adjustment subunits to the smaller of the two values ​​is calculated to obtain the opening change ratio difference. If the difference in the opening degree change ratio is greater than a preset first difference threshold, the ratio is multiplied by a preset benchmark compensation factor to generate a first type of adjacent compensation coefficient; if the difference in the opening degree change ratio is less than or equal to the first difference threshold, the reciprocal of the ratio is multiplied by the benchmark compensation factor to generate a second type of adjacent compensation coefficient. Based on the spatial adjacent distance between the first adjustment subunit and the second adjustment subunit, the first or second type of adjacent compensation coefficient is corrected by distance attenuation to obtain the adjacent compensation coefficient.

6. A dynamic temperature control system for steaming and boiling traditional Chinese medicine, characterized in that, include: The acquisition module is used to acquire the temperature parameters of each zone on the side wall of the decoction pot, and simultaneously collect the steam supply pressure parameters at the steam pipe inlet. The generation module is used to perform regional weighting processing on the temperature parameters to generate deviation parameters that characterize the unevenness of temperature distribution in each zone, and to dynamically couple the steam supply pressure parameters with the deviation parameters to generate composite deviation parameters that reflect the actual heat exchange efficiency. The adjustment module is used to dynamically adjust the opening combination of the regulating valve group in the steam pipeline based on the composite deviation parameter through a multi-loop control strategy. Each regulating unit of the regulating valve group corresponds to a partition of the decoction pot, and the opening adjustment amount of each regulating unit is assigned priority according to the spatial location characteristics of the corresponding partition. The generation module is also used to perform correlation calculation between the composite deviation parameter and the current change of the steam supply pressure parameter during the adjustment process, generate valve opening correction parameters including pressure fluctuation compensation terms, and perform physical constraint boundary processing on the valve opening correction parameters through an iterative correction algorithm to generate a set of valve opening parameters. The processing module is used to coordinately control each regulating unit of the regulating valve group according to the valve opening parameter set, so that the temperature distribution unevenness of each zone converges to a preset threshold range. The method of dynamically adjusting the opening combination of regulating valve groups in the steam pipeline based on the composite deviation parameter through a multi-loop control strategy includes: The regulating valve group in the steam pipeline is divided into multiple independently controlled regulating sub-units. Each regulating sub-unit corresponds to a section of the decoction pot, and priority weights are assigned to each regulating sub-unit based on the spatial location characteristics of each section. Based on the composite deviation parameter, the initial adjustment amount of the opening of each adjustment subunit is calculated, wherein the initial adjustment amount of the opening is proportional to the product of the priority weight of the corresponding partition and the composite deviation parameter. The current change in steam supply pressure parameters is acquired in real time. The current change is used as a correction factor to dynamically correct the initial adjustment of the opening of each regulating subunit, thereby generating the intermediate adjustment of each subunit. For the intermediate adjustment amount of multiple adjustment subunits, a superposition effect analysis is performed. If the intermediate adjustment amount of the adjustment subunits corresponding to adjacent partitions are in opposite directions, a superposition correction coefficient is generated based on the spatial adjacent distance between the partitions, and the intermediate adjustment amount is compensated in reverse based on the superposition correction coefficient. Based on the intermediate adjustment amount after compensation of all regulating subunits, and combined with the preset physical constraint boundary of valve opening, the opening combination command of each regulating subunit is generated through a coordinated allocation algorithm, so as to adjust the opening combination of the regulating valve group in the steam pipeline through the opening combination command. The process involves correlating the composite deviation parameter with the current change in the steam supply pressure parameter to generate valve opening correction parameters that include a pressure fluctuation compensation term. Then, an iterative correction algorithm is used to apply physical constraint boundary processing to these valve opening correction parameters, generating a set of valve opening parameters, including: The current change in steam supply pressure parameters is calculated in real time, where the current change is the difference between the pressure parameters at the current moment and the average pressure parameters at the previous time period. Based on the composite deviation parameter and the current change, an initial correlation value is determined, and a pressure fluctuation compensation item is generated based on the initial correlation value and a preset compensation coefficient. The pressure fluctuation compensation term is superimposed on the composite deviation parameter to obtain the unconstrained valve opening correction parameter; The unconstrained valve opening correction parameter is input into the iterative corrector, and in each iteration it is determined whether the unconstrained valve opening correction parameter exceeds the physical opening boundary of the corresponding regulating unit. If it exceeds the boundary, the excess part is reduced proportionally to the boundary value corresponding to the physical opening boundary, and the reduction amount is redistributed to the opening correction parameters of the remaining regulating units according to the priority weight of adjacent partitions. The iterative correction process is repeated until the valve opening correction parameters of all control units meet the physical opening boundary constraints, in order to generate a set of valve opening parameters.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for dynamically adjusting the cooking temperature of traditional Chinese medicine as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for dynamically adjusting the temperature of steaming and boiling traditional Chinese medicine as described in any one of claims 1 to 5.

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