Intelligent temperature control system for capsule production process

Abstract

The invention relates to the technical field of process control, and discloses an intelligent temperature control system for a capsule production process, which comprises a disturbance probe generator, a dynamic response receiver and a core logic processing unit, and the core logic processing unit is configured to judge the thermodynamic trend of the system based on the phase drift and amplitude change of the response signal, and output a discontinuous pulse type control instruction only when the trend meets the judgment condition. By analyzing the dynamic response characteristics of the system to the weak disturbance, prospective judgment and intervention on the imbalance trend are realized, continuous energy consumption generated for counteracting the small disturbance is avoided, and the energy efficiency and the system stability of process control are further improved.

Description

Technical Field

[0001] This invention relates to an intelligent temperature control system for capsule manufacturing processes, belonging to the field of process control technology. Background Technology

[0002] The current proportional-integral-derivative (PID) control is a long-standing and widely used technical solution. It continuously and dynamically adjusts the heating or cooling power by frequently sampling the temperature of the controlled temperature zone and comparing it with the set value, in order to accurately maintain the temperature within the target range.

[0003] However, in real industrial production environments, the controlled temperature zone is inevitably subject to small, random, and high-frequency disturbances from the outside, such as airflow disturbances in the workshop or changes in heat generated by equipment. In order to cope with these fluctuations that have little impact on the quality of the final product, the PID control method, due to its inherent lag compensation principle, must perform a large number of dense, small-amplitude power adjustment actions. This operating mode reveals an inherent compromise between control accuracy and energy efficiency in existing technologies. That is, the pursuit of higher apparent control accuracy is often accompanied by huge and continuous energy consumption by the system to offset invalid disturbances.

[0004] At a deeper level, the root cause of this compromise lies in the insufficient utilization of information dimensions in existing control methods. They rely solely on temperature, a static scalar, as feedback, ignoring the dynamic characteristics such as phase and amplitude contained in the response signal that can predict the future state of the system. This results in a passive, lagging compensation rather than proactive, forward-looking guidance. Specifically, existing technologies suffer from the following shortcomings: 1. Numerous ineffective adjustment actions occur when dealing with random disturbances, leading to unnecessary energy waste; 2. They fail to extract predictive dynamic characteristics from the feedback signal, limiting the possibility of developing control methods towards lower energy consumption and higher robustness. Therefore, how to design a novel process control system that can effectively distinguish between random disturbances and actual system imbalance trends, and maintain process stability with minimal energy intervention based on a forward-looking judgment of the system's dynamic response characteristics, becomes the technical problem this invention aims to solve. Summary of the Invention

[0005] This invention provides an intelligent temperature control system for capsule manufacturing processes. Its main purpose is to solve the problems of existing temperature control methods, such as continuous energy consumption to offset minor random disturbances and the inability to predict system imbalance trends in advance.

[0006] To achieve the above objectives, the present invention provides a temperature intelligent control system for capsule manufacturing process, comprising:

[0007] The disturbance probe generator is configured to apply a periodic, weak energy disturbance with a reference frequency to the heat transfer medium within a controlled temperature zone.

[0008] A dynamic response receiver is configured to acquire the response signal of the heat transfer medium to energy disturbances.

[0009] The core logic processing unit, connected to the disturbance probe generator and the dynamic response receiver, is configured to: continuously measure the phase drift and amplitude change of the response signal relative to the energy disturbance; and when the phase drift and amplitude change together satisfy a judgment condition, issue a discontinuous pulse-type control command to a temperature control actuator in the process.

[0010] Furthermore, after the core logic processing unit issues a control command to the temperature control actuator, it is further configured to: continuously monitor the actual characteristics of the phase drift and amplitude change of the subsequent response signal within a second set time window; compare the actual characteristics with an expected response characteristic associated with the control command stored in a memory; and, based on the comparison result, determine whether there is a fault in the working link composed of the disturbance probe generator, the dynamic response receiver, and the temperature control actuator.

[0011] Preferably, the dynamic response receiver includes at least two microphone units arranged back-to-back; and the core logic processing unit is configured to receive the response signal processed by a differential amplifier circuit.

[0012] Preferably, the disturbance probe generator and at least two microphone units are encapsulated together in a miniature cavity made of acoustic damping material.

[0013] Preferably, the core logic processing unit is further configured to: when the system starts up, determine a resonant frequency with the largest amplitude by scanning a frequency range and comparing the amplitude of the response signal at each frequency, and set the resonant frequency as the reference frequency.

[0014] Preferably, the determination condition is: the absolute value of the phase drift continuously exceeds a phase threshold within a first set time length, and the amplitude value of the acquired response signal shows a monotonically changing trend within this time length.

[0015] Preferably, the core logic processing unit is further configured to: measure the time width feature of each pulse in the response signal; generate a dynamic calibration coefficient based on the time width feature; and use the dynamic calibration coefficient to adjust a decision threshold applied to the judgment condition.

[0016] Preferably, the core logic processing unit adjusts the decision threshold using the following relationship: in, The adjusted decision threshold; This serves as a baseline decision threshold; The measured pulse's time width characteristic; As a reference time width; and It is a constant obtained through calibration.

[0017] Preferably, the system also includes an independent temperature sensor for measuring the absolute temperature of the controlled temperature zone; and the core logic processing unit is further configured to: before issuing a control command to the temperature control actuator, perform a safety arbitration verification between the control command and the absolute temperature value measured by the independent temperature sensor, and execute the control command only if the verification passes.

[0018] Preferably, the safety arbitration verification includes: when the control command is a heating command, verifying whether the absolute temperature value is lower than a safe upper temperature limit stored in a memory; and when the control command is a cooling command, verifying whether the absolute temperature value is higher than a safe lower temperature limit stored in a memory.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. By applying weak periodic disturbances to the controlled temperature zone through a disturbance probe generator and collecting response signals carrying thermodynamic state information of the entire temperature zone by a dynamic response receiver, the traditional temperature control method of relying on lag temperature values ​​for compensation is changed. The core logic processing unit no longer directly responds to the final temperature change, but identifies the inherent trend of system energy flow by continuously measuring the phase drift and amplitude change of the response signal. Only when it is judged that an imbalance trend is about to form will it issue discontinuous pulse control commands to the temperature control actuator. This control method avoids the continuous energy consumption generated to offset small random disturbances, and transforms process control from continuous energy compensation to intermittent intervention based on trend prediction.

[0021] 2. By setting up a miniature cavity made of acoustic damping material outside the disturbance probe generator and dynamic response receiver, and by including two back-to-back microphone units in the dynamic response receiver, and then having the core logic processing unit receive the response signal processed by the differential amplifier circuit, this synergy of multiple technical features enables the system to physically separate the effective response signal generated by the probe from the far-field noise from the external environment at the source of information acquisition. Furthermore, the core logic processing unit also inverts the humidity change of the heat transfer medium by measuring the time width characteristics of each pulse in the response signal, and uses this to generate dynamic calibration coefficients to adjust the decision threshold. This combination of physical isolation and logical self-calibration enables the system to stably acquire the response signal for core decision-making in industrial environments containing various interferences.

[0022] 3. By adding an independent temperature sensor and having the core logic processing unit perform safety arbitration verification before issuing control commands, a decision-making safety boundary based on different physical principles is established for the system, preventing the main control logic from outputting obviously unreasonable commands due to abnormalities. Furthermore, after issuing control commands, the core logic processing unit compares the actual characteristics of subsequent response signals with the stored expected response characteristics. This method of reusing control commands as a health check of the system itself enables the system to proactively determine whether there are faults in its core operating links. This pre-arbitration and post-verification mechanism works in synergy, giving the system the ability to distinguish between external process deviations and internal component failures, thereby improving the reliability and maintainability of the entire control system in actual production applications. Attached Figure Description

[0023] Figure 1 This invention provides a system architecture and workflow diagram for an intelligent temperature control system for capsule manufacturing processes.

[0024] Figure 2 This is a graph showing the relationship between the core logic processing unit of this invention and the dynamic adjustment of the decision threshold based on the pulse width characteristics.

[0025] Figure 3 This is a schematic diagram of the data flow and control logic inside the core logic processing unit of the present invention.

[0026] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention in any way.

[0028] This invention provides a temperature intelligent control system for a capsule manufacturing process. The system architecture includes a disturbance probe generator, a dynamic response receiver, and a core logic processing unit. The basic workflow is as follows: the disturbance probe generator applies a periodic, weak energy disturbance with a reference frequency to the heat transfer medium within the controlled temperature zone. The dynamic response receiver simultaneously acquires the response signal generated by the heat transfer medium to this energy disturbance and transmits the signal to the core logic processing unit. Based on the analysis of the dynamic characteristics of the response signal, the core logic processing unit determines the system's thermodynamic trend and only outputs discontinuous pulse-type control commands to the process's temperature control actuator when the trend meets preset judgment conditions. In industrial environments such as capsule manufacturing processes where high temperature stability is required, existing technologies, in order to cope with small, random external disturbances such as airflow or equipment-associated heat, inherently lack... The principle of hysteresis compensation necessitates extensive and intensive power regulation, stemming from the fact that the control system relies solely on the lagging temperature scalar as feedback. To address this high-energy-consumption compensation mode, this solution employs a disturbance probe generator and a dynamic response receiver to form an online detection link for the thermodynamic state of the controlled temperature region. The disturbance probe generator can be specifically implemented as a piezoelectric ceramic actuator, operating at a reference frequency to apply a negligible periodic disturbance to the heat transfer medium. The dynamic response receiver, on the other hand, can be implemented as an electret microphone to pick up the response signal carrying information about the thermodynamic state of the entire temperature region. Thus, by actively applying weak disturbances and analyzing the system's dynamic response characteristics, a forward-looking judgment of the system's imbalance trend can be achieved, expanding the dimension of information acquisition from static temperature values ​​to dynamic characteristics such as phase and amplitude that can predict the future state of the system.

[0029] To extract predictive dynamic features from the response signal, the core logic processing unit is configured to execute a specific set of signal analysis and decision-making logic. Instead of directly responding to the final temperature change, it continuously measures the phase drift and amplitude change of the response signal relative to energy disturbances. The core logic processing unit has a preset trend-based imbalance judgment condition, which is specified as follows: the absolute value of the phase drift continuously exceeds a phase threshold for a first predetermined time period, and the amplitude value of the acquired response signal exhibits monotonic changes during this time period. Only when both the phase drift and amplitude change satisfy this judgment condition does the core logic processing unit determine that an irreversible imbalance trend is about to form, and accordingly issues discontinuous pulse-like control commands to the temperature control actuator of the process. This intermittent intervention based on trend prediction aims to avoid interfering with minor imbalances. The system suffers from continuous energy consumption due to random disturbances. To ensure that the disturbance and response analysis mechanism can obtain effective signal feedback in different physical environments, the determination of the reference frequency is designed as an adaptive calibration procedure. Specifically, the core logic processing unit is configured to determine the resonant frequency with the largest amplitude by scanning a preset frequency range, such as 0.05Hz to 0.5Hz, when the system starts up. In this calibration process, the core logic processing unit controls the disturbance probe generator to output energy disturbances at multiple frequency points in the frequency range in sequence, and simultaneously compares the amplitude of the response signal collected by the dynamic response receiver at each frequency. Finally, the frequency that generates the maximum response signal amplitude is automatically set as the reference frequency for the subsequent stable operation phase. This procedure enables the system to adapt to the physical characteristics of specific devices and operate at the optimal signal-to-noise ratio without manual tuning.

[0030] Given the continuous broadband mechanical vibrations and environmental noise present in industrial environments such as capsule manufacturing workshops, whose energy is sufficient to overwhelm the target response signal, a specific hardware structure and signal processing interface are employed to separate the effective signal generated at the physical source of signal acquisition from the environmental noise. Specifically, the dynamic response receiver includes at least two back-to-back microphone units, and these two microphone units and the disturbance probe generator are encapsulated together in a miniature cavity made of acoustic damping material. The signal acquired by the two microphone units is processed by a differential amplifier circuit before being sent to the core logic processing unit. Due to the near-field signal generated by the disturbance probe generator... When the signal reaches the two back-to-back microphones, it is out of phase, while the ambient noise from the far field reaches the two microphones in phase. Therefore, the result of differential amplification is that the target signal is enhanced by in-phase superposition, while the common-mode ambient noise is canceled out. This combination of physical isolation and differential processing enables the system to stably acquire a high signal-to-noise ratio response signal for core decision-making in industrial environments containing various interferences. Furthermore, to address the measurement uncertainties caused by changes in the physical properties of the heat transfer medium itself, such as fluctuations in air humidity, the system also integrates an intrinsic adaptive calibration logic. The core logic processing unit is further configured to measure the time width characteristics of each pulse in the response signal. This time-width characteristic can be quantified as the time difference between the rising edge of a pulse signal exceeding a certain threshold and the falling edge falling back to the same threshold. It can characterize the acoustic damping effect caused by changes in air humidity. Based on this time-width characteristic, the core logic processing unit generates a dynamic calibration coefficient and uses this coefficient to adjust the decision threshold applied to the judgment conditions. The specific adjustment relationship is as follows: in, The adjusted decision threshold, As the baseline decision threshold, The pulse's time width characteristics are measured in real time. The base time width, and The constant obtained through calibration has the dimension of the reciprocal of time to ensure that the dimensions on both sides of the equation are consistent. This mechanism enables the system to dynamically compensate for signal changes caused by environmental factors, thereby effectively distinguishing them from the actual thermodynamic imbalance trend.

[0031] To prevent the output of obviously unreasonable instructions due to potential anomalies in the main control logic, the system also establishes a decision safety boundary based on different physical principles. This boundary is achieved by adding an independent temperature sensor to measure the absolute temperature of the controlled temperature zone. The core logic processing unit is further configured to perform a safety arbitration verification between the control instruction and the absolute temperature value measured by the independent temperature sensor before issuing the control instruction to the temperature controller actuator, and execute the instruction only if the verification passes. The specific procedures for the safety arbitration verification include: when the control instruction is a heating instruction, verifying whether the absolute temperature value is lower than the upper limit of the safe temperature stored in a memory; and when the control instruction is a cooling instruction, verifying whether the absolute temperature value is higher than the lower limit of the safe temperature stored in memory. In addition to pre-arbitration, the system also has the ability of post-verification and self-diagnosis, which is achieved by multiplexing a single control output into a single... This is achieved by detecting the health of the system itself. Specifically, after the core logic processing unit issues a control command to the temperature control actuator, it is configured to continuously monitor the actual characteristics of the phase drift and amplitude changes of the subsequent response signal within a second set time window. At the same time, the core logic processing unit compares the actual characteristics with the expected response characteristics associated with the control command stored in the memory. Based on the comparison results, the system can determine whether there is a fault in the working link composed of the disturbance probe generator, dynamic response receiver, and temperature control actuator. If the actual response does not match the expected response, it can determine that there is a fault in the core link of the system itself and trigger the corresponding alarm or safety mode. This mechanism of pre-arbitration and post-verification works together to enable the system to distinguish between external process deviations and internal component failures, thereby improving the reliability and maintainability of the entire control system in actual production applications.

[0032] Example 1: In a continuously operating capsule drying production line environment, the target process temperature of the controlled temperature zone is set at 50°C. This environment faces two intertwined operational challenges: firstly, weak high-frequency temperature disturbances caused by the periodic sweeping airflow from the central air conditioning system; secondly, due to production procedures, a brief full-process humidification and disinfection operation is performed every hour by an automated spray system, which instantaneously introduces low-temperature water mist into the environment, causing simultaneous changes in both temperature and humidity in the controlled temperature zone. When the intelligent temperature control system for the capsule production process is activated, its core logic processing unit first executes... The adaptive resonant frequency calibration procedure determines the frequency that maximizes the amplitude of the response signal as the reference frequency, and then the system enters a continuous online monitoring state. When faced with the weak temperature disturbances caused by the periodic air sweeping of the central air conditioning, although there are brief phase drifts and amplitude fluctuations in the response signal collected by the dynamic response receiver, the core logic processing unit does not trigger the temperature control actuator because its duration and change pattern do not meet the preset judgment conditions. The temperature control actuator remains silent. This operating state avoids the large number of small-amplitude power adjustment actions that must be performed in the traditional control mode to offset such fluctuations.

[0033] When an automated sprinkler system performs humidification and disinfection operations, it faces a complex challenge: the evaporation of low-temperature water mist causes a rapid drop in temperature within the controlled zone, creating a genuine thermodynamic imbalance; simultaneously, the sharp increase in air humidity alters the acoustic impedance of the heat transfer medium. At this point, two technical features within the system work synergistically: firstly, the core logic processing unit measures the time width of each pulse in the response signal. It can detect the increase in air damping in real time, and based on The relationship dynamically increases the decision threshold used for trend judgment. On the other hand, the strong thermodynamic imbalance caused by the sudden drop in temperature leads to phase drift and amplitude variation in the response signal, exceeding the dynamically raised decision threshold while still meeting the requirements for duration and monotonicity. This collaborative mechanism allows the core logic processing unit to effectively distinguish a signal caused by a real temperature change from a background signal caused by humidity changes, based on a higher decision threshold, and thus issue a discontinuous pulse-like heating command. Before issuing this heating command, it must undergo a safety arbitration verification process using independent temperature sensors. The core logic processing unit compares the heating command with the absolute temperature values ​​measured by the independent temperature sensors to confirm the current... Once the temperature is below the upper limit of the safe temperature set in the memory and the verification is passed, the temperature control actuator will execute the heating pulse. After the command is issued, the core logic processing unit compares the actual characteristics of the subsequent response signal with the expected response characteristics stored in the memory within the second set time window. This confirms that the heating behavior has caused the expected phase and amplitude changes, thus completing a closed-loop verification of the health of the entire working link. Finally, the temperature of the controlled temperature zone is quickly guided back to a stable state by a one-time energy injection. The entire process does not rely on continuous compensation of the static scalar of temperature, but rather transforms a multivariable coupled control problem into an intermittent intervention process based on trend prediction through the analysis of the dynamic response characteristics of the system and the coordination of multiple mechanisms.

[0034] Example 2: To objectively verify the effectiveness of the technical solution of this invention in terms of energy efficiency and control stability, an experimental platform was built comprising two thermodynamically isolated controlled temperature zones, A and B, of the same specifications. Controlled temperature zone A was equipped with an industrial standard proportional-integral-derivative PID control system as a control group, while controlled temperature zone B was equipped with the intelligent temperature control system of this invention as the experimental group. Both controlled temperature zones were equipped with the same model of temperature control actuator and independent temperature sensors for data recording. A central disturbance generator synchronously applied a consistent thermodynamic disturbance sequence to both controlled temperature zones. The disturbance sequence of this experiment was designed to simulate common complex disturbance scenarios in industrial production, and its total duration was set to 360 seconds. The duration of 0 seconds was set to balance the representativeness of the test and the analyzability of the data. The sequence consisted of two phases. The first phase was from 0 to 2400 seconds. During this period, the central disturbance generator applied a continuous weak sinusoidal thermal disturbance with a period of 60 seconds and an amplitude equivalent to a temperature change of ±0.1°C to the two controlled temperature zones to simulate continuous background noise in the environment. The second phase was from 2400 to 3600 seconds. At the 2400-second mark, the central disturbance generator performed a momentary step cooling operation equivalent to a temperature change of -1.5°C to simulate sudden and large-scale heat loss events such as opening and closing equipment doors. Throughout the entire test, the target process temperature in both controlled temperature zones was set to 50.0°C.

[0035] After the experiment started, under the continuous weak sinusoidal thermal disturbance in the first stage, the PID control system deployed in the controlled temperature zone A was observed to be in a state of frequent small-amplitude switching action to continuously compensate for the temperature deviation caused by the disturbance. In the controlled temperature zone B, the core logic processing unit of the intelligent temperature control system of the present invention, because it determined that the phase drift and amplitude change characteristics of the disturbance did not meet its preset trend imbalance condition, its temperature control actuator remained silent for most of the time. Entering the second stage, after being subjected to a step cooling operation at 2400 seconds, the PID control system in the controlled temperature zone A immediately responded to the rapid drop in temperature, drove the temperature control actuator to compensate at maximum power, and experienced an overshoot of 0.4°C during the temperature recovery process. In contrast, the system in the controlled temperature zone B, after determining that an irreversible imbalance trend had formed, only issued a discontinuous pulse control command, and its temperature smoothly recovered to near the set value without significant overshoot. The specific quantitative data are shown in Table 1.

[0036] Table 1: A comparison of performance data of controlled temperature zone A and controlled temperature zone B at key time points during the test.

[0037]

[0038] The data presented in Table 1 shows that the high number of actions and high energy consumption of the PID control system are a direct result of its reliance on temperature error scalar compensation. The silent state of the system in the face of weak disturbances and its single, low overshoot intervention when dealing with severe disturbances demonstrate the effectiveness of the core logic processing unit's inherent mechanism of distinguishing the nature of disturbances by analyzing phase drift and amplitude changes, and proactively guiding only the true imbalance trend. Based on the data from this comparative experiment, an objective conclusion can be drawn: under the same disturbance conditions, compared to the traditional PID control system, the intelligent temperature control system of this invention significantly reduces the operating frequency of the temperature controller actuator and the total energy consumption of the system while maintaining the temperature within the process requirements.

[0039] Example 3: This example combines Figures 1 to 3 A description of a temperature intelligent control system for a capsule manufacturing process, such as... Figure 1As shown in the figure, this diagram illustrates the interaction between energy flow and information flow. A disturbance probe generator applies periodic, weak energy disturbances to the controlled temperature zone, i.e., the capsule manufacturing process environment. A dynamic response receiver is responsible for acquiring the response signal carrying the thermodynamic state. To ensure signal quality, this receiver integrates components for signal acquisition and noise reduction, such as an acoustic damping material microcavity, a back-to-back microphone unit, and a differential amplifier circuit. The processed response signal is transmitted to the core logic processing unit. This unit analyzes the phase drift and amplitude changes of the signal to proactively determine the system imbalance trend and generates control commands to be verified. These commands are then sent to… Before the temperature control actuator can be executed, it must be verified by a safety arbitration verification module. This module performs an independent safety boundary verification based on the absolute temperature data of the controlled temperature zone measured by an independent temperature sensor. Only commands that pass the verification will be executed by the temperature control actuator, thereby realizing discontinuous pulse control of the controlled temperature zone. In addition, the core logic processing unit will compare the actual response characteristics after the control execution with the pre-stored expected response for self-diagnosis of the working link health, and can output a warning when a link failure is detected, thus forming a closed-loop control system with trend prediction, safety arbitration and self-diagnosis capabilities.

[0040] like Figure 2 As shown, the horizontal axis of the graph represents time in seconds, the left vertical axis represents ambient humidity in percentage relative humidity (%RH), and the right vertical axis represents pulse width in milliseconds (ms). The three curves in the graph represent ambient humidity, pulse width, and pulse width, respectively. And the decision threshold, which characterizes the pulse width of the acoustic damping effect of the heat transfer medium after a step increase in ambient humidity from 40%RH to approximately 80%RH at about 153 seconds. This also increases in size, and the core logic processing unit is based on this. The changes in the pulse width dynamically and non-linearly increased the decision threshold. This process clearly demonstrates the system's intrinsic mechanism of indirectly sensing changes in ambient humidity by measuring pulse width characteristics and adaptively adjusting the sensitivity of its internal decision logic.

[0041] like Figure 3As shown, the raw response signal generated by the dynamic response receiver first enters the 1.0 signal dynamic characteristic analysis module. This module separates the phase drift and amplitude change characteristics, as well as the pulse time width characteristics. The former is sent to the 2.0 system thermodynamic trend determination module. This module judges the data in the D1 decision threshold library and generates a pending control command. This command then enters the 3.0 execution command safety arbitration module. This module simultaneously retrieves the absolute temperature value from the independent temperature sensor and the data from the D2 safety limit library to arbitrate the command. The final pulse control command that passes the arbitration is sent to the temperature control actuator; otherwise, it is sent to the other actuator. The system triggers a security arbitration alarm and notifies system maintenance. At the same time, the pulse time width feature extracted by module 1.0 is sent to the link health verification module 4.0. This module generates dynamic calibration coefficients based on this feature to update the D1 decision threshold library. On the other hand, it also compares the data in the D3 expected feature library with the subsequent original response signal based on the instruction execution record generated by module 3.0 to verify the system link health. When an anomaly is detected, a link fault alarm is triggered. This fully demonstrates the internal connection and data interaction path of the four core logics: signal analysis, trend judgment, security arbitration, and link self-test.

[0042] Example 4: In a specific deployment scenario, before the intelligent temperature control system of the present invention is put into online operation, it needs to perform an offline parameter calibration and model generation procedure for the specific controlled temperature zone it serves, namely a newly built capsule drying chamber. This procedure aims to transform multiple key benchmark parameters and models related to decision logic within the system from a general algorithm framework into deterministic values ​​that match the thermodynamic and acoustic characteristics of the specific chamber. When the calibration procedure is started, the intelligent temperature control system to be calibrated is installed in the drying chamber, and the chamber is operated under controlled and stable benchmark environmental conditions, that is, the internal temperature is stably maintained at the target process temperature of 50.0°C, and the relative humidity is stably maintained at a known low level, 40%RH. Under these benchmark conditions, the core logic processing unit drives the disturbance probe generator to work continuously at its benchmark frequency for ten minutes, and simultaneously collects the response signals fed back by the dynamic response receiver. The core logic processing unit then analyzes the time width characteristics of all response pulses collected within the ten minutes. Perform statistical analysis, calculate the arithmetic mean, and use this mean as the baseline time width. Stored in the system's memory.

[0043] While maintaining the aforementioned baseline environmental conditions, the procedure proceeds to the decision threshold calibration stage. Through an external controller, a series of short heating pulses with minimal energy, sufficient to elicit a measurable response, are applied to the temperature control actuator within the cavity. After each pulse, the core logic processing unit records the peak value and duration of the phase drift and amplitude change of the induced response signal. After repeating this process one hundred times, the core logic processing unit obtains a statistical distribution database of the response characteristics of this specific cavity to weak thermal disturbances. Based on this database, the statistical average of the phase drift plus three standard deviations is set as the phase threshold, and a corresponding first preset time length is set to distinguish between real thermal disturbances and random signal noise at a high confidence level. Subsequently, the procedure proceeds to... During the generation phase of the expected response characteristic model for system self-testing, the core logic processing unit actively sends a discontinuous pulse control command—a heating command lasting 0.5 seconds—to the temperature control actuator inside the cavity through its own control output port. Within a subsequently set second time window, it high-frequency samples and records the complete time-series curve of the phase drift and amplitude changes of the response signal. This operation is repeated ten times to eliminate random errors from single measurements. The core logic processing unit then averages these ten recorded time-series curves to generate a dynamic response time-series curve representing the expected dynamic response of a standard heating operation within the cavity. This curve is then digitized and stored in memory as an expected response characteristic. Finally, to determine the dynamic calibration coefficient used for humidity compensation... The system adjusts the environmental conditions within the cavity by using a humidifier to precisely control the relative humidity at a known high level while maintaining a constant temperature of 50.0°C. Under these new stable conditions, the system measures and calculates a new average value for the pulse time width characteristic. At this point, the core logic processing unit possesses... and Two data points, and based on the preset calibration target, which requires a 50% reduction in the sensitivity of the decision threshold at 80% RH humidity, that is... Therefore, from the relational formula Solving for the constant The value is then fixed in the system, thus completing the entire offline calibration procedure. The intelligent temperature control system has been transformed from a general-purpose unit into a dedicated control system whose internal key parameters and models are adapted to a specific physical environment.

[0044] Example 5: In a low-temperature drying scenario with high requirements for process control reliability and continuous operation for several days, the system has a built-in risk control procedure to address potential failures of its internal components or potential anomalies in control logic. This ensures the functional integrity and decision-making effectiveness of the entire control system during long-term unattended operation. To address potential performance drift in the sensor link during long-term operation, the core logic processing unit is configured to execute an online end-to-end system health self-check at the beginning of each preset one-hour operating cycle. During this self-check, the system temporarily suspends its conventional thermodynamic trend-based control decision logic. The system processes and outputs a standardized excitation pulse with deterministic amplitude and duration to the disturbance probe generator via the core logic processing unit. Simultaneously, the dynamic response receiver synchronously acquires the response signal generated by the excitation pulse and measures its amplitude. The core logic processing unit then compares the amplitude of the response signal measured online with a reference response amplitude that was measured and stored in the memory during the offline calibration phase of the system. If the deviation between the two exceeds a preset tolerance threshold, the system determines that the health of the core sensing link composed of the disturbance probe generator and the dynamic response receiver has deviated, and actively issues a system alarm that requires maintenance.

[0045] To address the possibility that the core logic processing unit might output control commands that contradict physical reality due to unforeseen software defects or special harmonic interference, the system's built-in security arbitration verification mechanism is further configured as a trend consistency verification procedure. Before the core logic processing unit makes a control decision based on the analysis of phase drift and amplitude changes and issues a corresponding command to the temperature controller actuator, the command must pass this verification. Specifically, if the decision is a heating command, its basis must be the determination that there is a current cooling trend. In this case, the arbitration mechanism will verify whether the first derivative of the temperature measured by independent temperature sensors within the past time window is also negative. The heating command is only allowed to pass and be sent to the temperature controller actuator if and only if both sensors, based on different physical principles, indicate the same temperature change trend. If the two trends contradict each other, the command is rejected, and an alarm for control logic anomaly is triggered. This mechanism of trend cross-verification based on independent channels provides a layer of protection based on physical facts for the final output of the system.

[0046] Example 6: In a specific systems engineering practice, to ensure the portability and performance consistency of the intelligent temperature control system of the present invention across different physical units, a standardized hardware module optimization and safety parameter determination procedure needs to be executed in advance. This procedure aims to determine the key physical parameters of the micro-cavity constituting the dynamic response receiver and provide an objective basis for setting the decision threshold for the safety arbitration verification mechanism applied to different product processes. This procedure first enters the physical parameter optimization stage around the micro-cavity, and its core objective is to maximize the signal acquisition of the back-to-back microphone units and differential amplifier circuits. Common-mode rejection ratio (CMRR) of the link; to this end, in a controlled acoustic testing environment, a signal generator was used to generate near-field signals simulating disturbance probes and far-field signals simulating ambient noise. An experimental design method was used to systematically change two key physical parameters of the micro-cavity: the distance between the two microphone units and the density of the acoustic damping material used for encapsulation. Under each set of parameters, the output signal after processing by the differential amplifier circuit was measured and recorded, and its CMRR was calculated. Finally, the set of physical parameters that yielded the maximum CMRR was determined as the standardized design specification for the hardware module.

[0047] When determining the upper and lower limits of safe temperature in the safety arbitration verification mechanism, it is necessary to conduct independent material thermal property analysis for specific processed products, i.e., specific batches of capsules. Specifically, differential scanning calorimetry is used to perform temperature scanning on capsule samples to determine the critical temperature at which irreversible physical or chemical changes occur, such as the thermal decomposition initiation temperature of the material. Then, a safety margin of 5°C is subtracted from the measured critical temperature, and the resulting value is set as the upper limit of safe temperature stored in the system's memory. The determination of the lower limit of safe temperature follows a similar procedure, which is set by testing the temperature point at which the product may deteriorate in quality at low temperatures. This procedure directly links the absolute safety boundary of the system with the objective physical properties of the processed material, providing a traceable physical property basis for the safe operation of the system.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A temperature intelligent control system for a capsule manufacturing process, characterized in that, include: The disturbance probe generator is configured to apply a periodic, weak energy disturbance with a reference frequency to the heat transfer medium within a controlled temperature zone. A dynamic response receiver is configured to acquire the response signal of the heat transfer medium to energy disturbances. The core logic processing unit, connected to the disturbance probe generator and the dynamic response receiver, is configured to: continuously measure the phase drift and amplitude change of the response signal relative to the energy disturbance; and when the phase drift and amplitude change together satisfy a judgment condition, issue a discontinuous pulse-type control command to a temperature control actuator in the process. Furthermore, after the core logic processing unit issues a control command to the temperature control actuator, it is further configured to: continuously monitor the actual characteristics of the phase drift and amplitude change of the subsequent response signal within a second set time window; compare the actual characteristics with an expected response characteristic associated with the control command stored in a memory; and, based on the comparison result, determine whether there is a fault in the working link composed of the disturbance probe generator, the dynamic response receiver, and the temperature control actuator.

2. The intelligent temperature control system for a capsule manufacturing process according to claim 1, characterized in that, The dynamic response receiver includes at least two microphone units arranged back-to-back; and the core logic processing unit is configured to receive the response signal after processing by a differential amplifier circuit.

3. The intelligent temperature control system for a capsule manufacturing process according to claim 2, characterized in that, The disturbance probe generator and at least two microphone units are encapsulated together in a miniature cavity made of acoustic damping material.

4. The intelligent temperature control system for a capsule manufacturing process according to claim 1, characterized in that, The core logic processing unit is further configured to: when the system starts up, determine the resonant frequency with the largest amplitude by scanning a frequency range and comparing the amplitude of the response signal at each frequency, and set the resonant frequency as the reference frequency.

5. The intelligent temperature control system for a capsule manufacturing process according to claim 1, characterized in that, The determination criteria are: the absolute value of the phase drift continuously exceeds a phase threshold within a first set time length, and the amplitude value of the acquired response signal shows a monotonically changing trend within this time length.

6. The intelligent temperature control system for a capsule manufacturing process according to claim 1, characterized in that, The core logic processing unit is further configured to: measure the time width feature of each pulse in the response signal; generate a dynamic calibration coefficient based on the time width feature; and use the dynamic calibration coefficient to adjust a decision threshold applied to the judgment condition.

7. The intelligent temperature control system for a capsule manufacturing process according to claim 6, characterized in that, The core logic processing unit adjusts the decision threshold using the following relationship: in, The adjusted decision threshold; This serves as a baseline decision threshold; The measured pulse's time width characteristic; As a reference time width; and It is a constant obtained through calibration.

8. The intelligent temperature control system for a capsule manufacturing process according to claim 1, characterized in that, The system also includes an independent temperature sensor for measuring the absolute temperature of the controlled temperature zone; and the core logic processing unit is further configured to: before issuing a control command to the temperature control actuator, perform a safety arbitration verification between the control command and the absolute temperature value measured by the independent temperature sensor, and execute the control command only if the verification passes.

9. The intelligent temperature control system for a capsule manufacturing process according to claim 8, characterized in that, The safety arbitration verification includes: when the control command is a heating command, verifying whether the absolute temperature value is lower than a safe upper temperature limit stored in a memory; and when the control command is a cooling command, verifying whether the absolute temperature value is higher than a safe lower temperature limit stored in a memory.

Images