Intelligent regulation and control system and method for precise preparation of solid sample

Abstract

The invention discloses an intelligent regulation and control system and method for precise preparation of solid samples, and relates to the field of system automatic control, the intelligent regulation and control system comprises a precise feeding system, a precise press-fit forming system, a graded sorting and discharging system and an intelligent regulation and control core module, all subsystems are sequentially arranged in the flowing direction of the samples, and a dual-data interaction link is established with the intelligent regulation and control core module; a closed-loop regulation and control system is formed, and the accurate feeding system can realize sample temperature and humidity pretreatment; the precise press-fit forming system has precise press-fit and full-parameter monitoring functions; the grading sorting discharging system integrates multi-dimensional nondestructive testing and intelligent sorting functions, an intelligent regulation and control core module adopts an MCU main control and deep learning edge processing unit framework, a multi-dimensional preparation parameter library is pre-stored, all preparation parameters are dynamically optimized by analyzing multi-source feedback data and applying an improved closed-loop control algorithm, full-process self-adaptive precise control is achieved, and the intelligent sorting discharging system is high in reliability and high in reliability. And the preparation quality and efficiency are ensured.

Description

Technical Field

[0001] This invention relates to the field of automatic system control, and in particular to an intelligent control system and method for the precise preparation of solid samples. Background Technology

[0002] In the sample pretreatment stage of heavy metal analysis and geological testing, solid samples such as soil and mineral powders need to be prepared into fixed-size molded parts (such as discs) before subsequent testing and analysis. The semi-manual or simple automated preparation methods commonly used in existing technologies have many drawbacks: 1. Lack of integrated collaborative control mechanism: The feeding, pressing, and discharging processes are independent of each other, and parameter adjustments rely on manual experience. It is impossible to achieve adaptive optimization based on sample status and environmental changes, resulting in poor stability of the preparation process; 2. Insufficient temperature and humidity control precision: Only humidity or temperature is controlled individually, without coordinated temperature and humidity regulation. In addition, the low sensing accuracy and single adjustment method can easily lead to condensation, cracking or sticking of samples, affecting the molding quality. 3. Low matching degree between feeding and pressing parameters: The feeding amount adopts a single weighing and repeating core, the deviation compensation method is simple, the pressing process only monitors pressure and displacement, and does not combine temperature parameters for collaborative optimization, resulting in poor sample size consistency and internal density. 4. Lack of fault tolerance and self-learning ability: When the sensor fails, the system needs to be shut down for repair. It cannot achieve fault self-healing, and the system performance cannot be continuously optimized through operating data. The accuracy will decrease after long-term use. 5. Incomplete traceability system: Only some preparation parameters are recorded, lacking full-process data traceability capabilities, which cannot meet the quality control requirements of third-party testing institutions.

[0003] To address the aforementioned issues, this invention proposes an intelligent control system and method for precise preparation of solid samples. By constructing a closed-loop control system that integrates temperature and humidity coordination, multi-parameter coupling optimization, fault self-healing, and full-chain traceability, the system achieves automated, high-precision, and adaptive control of the entire solid sample preparation process, overcoming the shortcomings of existing technologies. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides an intelligent control system and method for the precise preparation of solid samples.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a precise preparation and intelligent control system for solid samples, comprising: a precise feeding system, a precision compression molding system, a grading and sorting discharge system, and an intelligent control core module. The precise feeding system, the precision compression molding system, and the grading and sorting discharge system are arranged sequentially along the sample flow direction, and each establishes a wired and wireless dual data interaction link with the intelligent control core module to form a closed-loop control system. The precision feeding system includes a storage bin, a variable frequency screw feeding device, a conveying channel, an integrated temperature and humidity sensing component, and an ultrasonic heat pump dual-mode humidity control device. The integrated temperature and humidity sensing component is used to simultaneously collect the temperature and humidity feedback signals of the solid sample and transmit them to the intelligent control core module. The ultrasonic heat pump dual-mode humidity control device receives the control command from the intelligent control core module and performs ultrasonic humidification, heat pump dehumidification, or constant temperature humidification to pre-process the sample temperature and humidity to the target range. The precision pressing molding system includes a molding cavity, a high-precision servo press, a ceramic liquid-cooled composite temperature control system, and a full-parameter pressing monitoring component. The ceramic liquid-cooled composite temperature control system and the intelligent control core module form a temperature control closed loop. The full-parameter pressing monitoring component collects pressure, displacement, and molding cavity temperature signals in real time during the pressing process. The high-precision servo press outputs precise pressing force and stroke according to control commands. The graded sorting and unloading system includes an intelligent sorting mechanism, a traceable conveyor line, and a multi-dimensional non-destructive testing module. The multi-dimensional non-destructive testing module integrates dimensional visual inspection, density detection, and surface defect identification functions, and outputs quality inspection feedback signals to the intelligent control core module. The intelligent control core module adopts a hybrid control architecture of MCU main control + deep learning edge processing unit. It has a pre-stored multi-dimensional preparation parameter library based on different sample types. By analyzing the multi-source feedback data collected by each system in real time, it uses an improved closed-loop control algorithm to dynamically optimize and coordinate the adjustment of feeding amount, pressing pressure curve, molding cavity temperature and humidity control parameters to achieve adaptive and precise control of the entire preparation process.

[0006] In a preferred embodiment of the present invention, the integrated temperature and humidity sensing component adopts a high-precision capacitive composite sensor with a temperature detection range of -10℃ to 60℃, a humidity detection range of 0-95%RH, a humidity accuracy of ±1.8%RH, a temperature accuracy of ±0.3℃, and outputs a continuous temperature and humidity feedback signal. The ultrasonic heat pump dual-mode humidity control device integrates an ultrasonic atomizing humidifier and a heat pump dehumidifier, and forms a linkage control link with the frequency conversion control module of the frequency conversion screw feeding device. The intelligent control core module intelligently switches between the following control modes based on the difference and rate of change between the temperature and humidity feedback signal and the preset adaptive temperature and humidity, using a multi-dimensional logical judgment algorithm: Mode 1, High Temperature and High Humidity Control: When the ambient temperature is >32℃ and the sample humidity is >suitable humidity +6%RH, the intelligent control core module outputs intermittent heat pump dehumidification command and pulse ultrasonic humidification fine-tuning command, and at the same time outputs a speed reduction command to the variable frequency screw feeder to extend the sample residence time and achieve uniform temperature and humidity regulation. Mode 2, High Temperature and Low Humidity Control: When the ambient temperature is >32℃ and the sample humidity is <adaptive humidity -6%RH, the intelligent control core module outputs a low-power continuous ultrasonic humidification command and simultaneously outputs a speed fine-tuning command to the variable frequency screw feeding device, which, together with the preheating of the molding cavity, achieves moldability optimization. Mode 3, Low Temperature and High Humidity Control: When the ambient temperature is <12℃ and the sample humidity is > the appropriate humidity +6%RH, the intelligent control core module first outputs a preheating command to the ceramic liquid cooling composite temperature control system. After the forming cavity temperature reaches the preset threshold, it outputs a feeding control command to the precision feeding system. At the same time, the heat pump dehumidifier is started to operate at low power to avoid sample condensation and adhesion.

[0007] In a preferred embodiment of the present invention, a dual-dimensional verification module for feeding is provided at the connection between the conveying channel and the molding cavity. The dual-dimensional verification module for feeding includes a high-precision weighing sensor and a volume detection sensor, which are used to detect the weight and volume parameters of the sample before it enters the mold and output feedback signals. After receiving the feedback signal, the intelligent control core module uses a two-parameter error compensation algorithm to determine the feed rate deviation. If a deviation exists, it outputs a parameter adjustment command. In the next feeding cycle, the feeding time / frequency conversion speed is compensated, or the pressing pressure and holding time parameters are dynamically fine-tuned during the current pressing process to achieve real-time closed-loop compensation for feeding deviation.

[0008] In a preferred embodiment of the present invention, a full-parameter monitoring component for pressing is provided in the molding cavity. The full-parameter monitoring component for pressing integrates a high-response-speed pressure sensor, a high-precision displacement sensor and a multi-point temperature sensor, which is used to collect the three-dimensional curves of pressure, displacement and temperature in real time throughout the pressing process. The intelligent control core module compares and analyzes the collected three-dimensional curves with the standard curves of the corresponding sample types in the parameter library; If an abnormal curve shape is detected, the system can determine that it is due to uneven material distribution, changes in the state of the molding cavity, or fluctuations in temperature and humidity, and take corrective measures immediately.

[0009] In a preferred embodiment of the present invention, the ceramic liquid-cooled composite temperature control system includes ceramic heating elements uniformly arranged around the molding cavity, spiral liquid-cooled pipelines, and multi-point fiber optic temperature sensors embedded in the molding cavity wall. The multi-point fiber optic temperature sensors are used to monitor the temperature of different areas within the molding cavity in real time, thereby achieving differentiated and precise temperature control. For highly adsorbent soil samples, the temperature control range of the molding chamber is 38-48℃. If the actual humidity of the sample fed by the precision feeding system is 4%RH or higher than the appropriate humidity, the intelligent control core module will automatically raise the temperature by 3-4℃ and use the residual heat of the molding chamber to assist in the evaporation of moisture and shorten the sample stabilization time. For mineral powder samples, the temperature control range of the molding chamber is 20-27℃. This range can be dynamically adjusted according to the working conditions of the ultrasonic heat pump dual-mode humidity control device. Under low temperature and high humidity conditions, the sample can be preheated appropriately according to the instructions of the intelligent control core module to avoid condensation. Under high temperature conditions, a cooling medium is introduced through the liquid cooling pipeline to maintain the reference temperature range and prevent high temperature from causing particle oxidation or lattice changes.

[0010] In a preferred embodiment of the present invention, the deep learning edge processing unit of the intelligent control core module is based on the MindSporeLite framework and has a built-in deep learning-based preparation process prediction and optimization model. This model can analyze the most recent 150-250 sets of complete preparation data in real time and also has the following functions: Predicting trends: When the pass rate of a certain type of sample is detected to decrease for three consecutive groups or the pass rate is lower than 92% in a single instance, the deep learning edge processing unit can predict potential process drift in advance and actively trigger multi-parameter coupling adjustment. Multi-parameter coupled optimization: The adjustment process is based on the model to perform coordinated optimization of multiple parameters such as feed rate, temperature and humidity, molding cavity temperature, pressing pressure and holding time, in order to find the global optimal solution; Self-learning update: After adjustment, if the overall pass rate of 12 consecutive groups of samples is ≥96%, then this set of new optimized parameters will be used as the dynamic benchmark parameters for this type of sample and updated to the sample preparation parameter library to achieve continuous self-learning and performance improvement of the system.

[0011] In a preferred embodiment of the present invention, the intelligent control core module further includes a redundancy backup and intelligent fault self-healing module, which achieves comprehensive fault identification and intelligent handling through sensor redundancy configuration, real-time equipment status monitoring, and AI fault mode recognition. Sensor anomaly detection: Real-time monitoring of data from each sensor. If data changes suddenly, exceeds the reasonable range, there is no feedback signal, or the signal drift exceeds the threshold, it is immediately determined to be a sensor failure. Fault classification and intelligent handling: Minor fault: The system automatically switches to the backup sensor, and at the same time calls the average value of the sensor’s most recent 15 sets of historical data. It combines the data with data from other relevant sensors to perform data fusion and cross-validation, generates reliable data, continues to run, and records fault logs in the background. It prompts the user to perform calibration through the human-machine interface. Critical Fault: Immediately initiate the safety shutdown procedure. The touchscreen displays detailed fault type, location, and possible causes, and alarms are triggered by a buzzer and three-color indicator lights. At the same time, the fault information is encrypted and uploaded to the cloud database, accompanied by remote diagnostic guidance and a visual file of repair steps. This supports remote diagnostics and rapid troubleshooting, minimizing downtime.

[0012] In a preferred embodiment of the present invention, the sample lifecycle dual traceability unit is further included, which includes a micro RFID chip implantation device, an ultraviolet inkjet printer, a local encrypted storage module, and a cloud data interaction interface. The micro RFID chip implantation device is used to implant a unique micro RFID chip inside each qualified sample, and the ultraviolet inkjet printer is used to generate a unique QR code on the sample surface. The chip and the QR code are associated with the same identification ID, which includes batch, preparation time, equipment number and operator information. The local encrypted storage module is synchronized with the intelligent control core module in real time, recording the entire preparation process parameter chain corresponding to the unique ID. The parameter chain includes feeding parameters, temperature and humidity adjustment data, pressing curve, and detection results. The sample lifecycle dual traceability unit supports seamless integration with the laboratory information management system, enabling end-to-end data traceability and reverse querying from sample preparation and analysis to report generation.

[0013] Another technical solution adopted in this invention is a method for precise preparation and intelligent control of solid samples, used in the above-mentioned system, comprising the following steps: S1. Intelligent parameter initialization: The system automatically identifies the sample type through the raw material batch identification module, or the user selects the sample type through the human-computer interaction interface of the intelligent control core module. The system automatically calls the basic parameters in the pre-stored multi-dimensional preparation parameter library. At the same time, based on the preparation data and optimization results of the previous batch, the initial parameters of the current batch are intelligently fine-tuned, and users can manually modify key parameters based on experience. S2. Precision quantitative feeding: The intelligent control core module drives the operation of the frequency conversion screw feeder of the precision feeding system to transport the solid sample in the storage bin to the conveying channel. The feeding amount is calculated by the frequency conversion speed and the preset flow coefficient. When the sample is transported to the inlet of the forming cavity, the weight-volume dual parameter detection is completed by the feeding dual-dimensional verification module. When the target feeding amount is reached, the feeder stops. If a feeding amount deviation is detected, the system drives the feeder to perform micro-feeding or parameter compensation in the next cycle. S3. Dynamic temperature and humidity adjustment and feedback: The integrated temperature and humidity sensing component detects the sample temperature and humidity in real time. The intelligent control core module dynamically selects and executes the best temperature and humidity adjustment mode based on the difference between the detection result and the preset suitable temperature and humidity. During the adjustment process, the temperature and humidity change rate is continuously monitored and fed back to the ceramic liquid cooling composite temperature control system in real time, providing it with the basis for preheating / cooling decisions. S4. Pre-pressure detection and intelligent temperature control coordination: Samples that meet the temperature and humidity standards are sent into the molding cavity. The intelligent control core module starts the ceramic liquid cooling composite temperature control system based on the final temperature and humidity values ​​and sample type fed back from step S3, and accurately raises the temperature of the molding cavity to the dynamically optimized target temperature and stabilizes it. Then, the high-precision servo press is driven to move the upper press head downwards at the pre-press pressure. The full-parameter monitoring component of the press detects the pre-press process. If it is determined that the sample distribution is uneven, the lower press head is controlled to vibrate slightly at a frequency of 8-12Hz to adjust until the sample distribution is uniform. S5. Closed-loop pressing molding: The intelligent control core module drives the high-precision servo press machine to descend according to the optimized target pressing pressure curve, compacting the sample in the molding cavity. After maintaining the preset holding time, the pressing full parameter monitoring component detects the final molding parameters. The system compares the actual pressure-displacement-temperature three-dimensional curve with the standard curve. If a slight deviation is found, the system adaptively adjusts the pressure, stroke, or holding time parameters of the next pressing, forming a closed-loop optimization. S6. Multidimensional Non-destructive Testing and Sorting: The dimensional accuracy, density, and surface defects of the molded sample are tested. If the test is qualified, it proceeds to the next step; if it is unqualified, the intelligent sorting mechanism sends it to the waste channel and records the reason for the unqualification. S7. Sample Dual Traceability Coding: The micro RFID chip implantation device and ultraviolet inkjet printer of the sample full life cycle dual traceability unit work synchronously. The RFID chip is implanted inside the qualified sample and the QR code is printed on the surface. The local encrypted storage module associates the code with all process parameters from step S2 to step S5 and stores it in encrypted form, while uploading it to the cloud. S8. Intelligent discharge and continuous circulation: The intelligent control core module drives the intelligent sorting mechanism to eject the coded qualified samples, which are then sent to the designated collection device via a traceable conveyor line with coded identification. The system then automatically triggers the next round of preparation process, repeating steps S2-S8 to achieve continuous, unattended automated production.

[0014] In a preferred embodiment of the present invention, in step S3, if the absolute difference between the sample humidity and the suitable humidity is >6%RH, the intelligent control core module simultaneously starts the corresponding adjustment device and the variable frequency screw feeding device to operate at low speed, so as to uniformly adjust the temperature and humidity of the sample. If 3%RH≤absolute difference≤6%RH, start the regulator to operate at low power. If the absolute difference is less than 3%RH, only the integrated temperature and humidity sensor will be used for monitoring, and the adjustment device will not be activated.

[0015] This invention addresses the shortcomings of the prior art and has the following beneficial effects: (1) This invention features a fully automated design that automatically identifies raw material batches, precisely feeds quantitatively, dynamically adjusts temperature and humidity, performs pre-pressing detection, close-loop pressing, multi-dimensional non-destructive testing, dual traceability coding, and continuous cyclic discharge. It eliminates the need for manual intervention in weighing, feeding, sorting, coding, and other operations, effectively reducing human error and labor intensity. The system can automatically trigger the next round of preparation process, achieving continuous unattended operation, adapting to the needs of batch sample preparation in the laboratory, and significantly improving preparation efficiency. Compared with semi-manual preparation methods, the efficiency improvement is substantial.

[0016] (2) This invention employs a linkage design between an integrated temperature and humidity sensing component and an ultrasonic heat pump dual-mode humidity control device to achieve synchronous acquisition and coordinated control of temperature and humidity, overcoming the shortcomings of separate temperature and humidity control and lagging adjustment in existing technologies. Through a multi-dimensional logical judgment algorithm, it intelligently switches between three operating modes: high temperature and high humidity, high temperature and low humidity, and low temperature and high humidity. It also employs differentiated strategies such as intermittent dehumidification, pulsed humidification, and preheating linkage of the forming chamber, enabling precise control of sample humidity within the suitable range of ±3%RH and temperature control accuracy of ±0.3℃. This design effectively avoids forming defects such as adhesion of highly adsorbent soil samples, oxidation and cracking of mineral powder samples, and condensation of fine-particle clay samples, significantly improving the adaptability of samples with different characteristics under complex environmental conditions.

[0017] (3) This invention integrates weight and volume dual parameter detection through the dual-dimensional verification module of feeding, and combined with the dual-parameter error compensation algorithm, it can correct feeding deviation in real time. Compared with the traditional single weighing verification, the feeding accuracy is improved to ±0.02g, which effectively avoids the problem of uneven sample size and density caused by feeding deviation. At the same time, the pressing full parameter monitoring component collects the three-dimensional curves of pressure, displacement and temperature in real time. By comparing with the standard curve, the abnormality prediction can be realized. The material distribution can be adjusted by the 8-12Hz high frequency micro-vibration of the pressing head, and the pressing pressure and holding time can be dynamically adjusted to ensure that the density uniformity deviation of the sample is ≤0.03g / cm³, and the size accuracy is controlled within ±0.05mm. The overall pass rate of batch preparation is greatly improved.

[0018] (4) This invention achieves real-time detection and graded processing of sensor anomalies through the redundancy backup and intelligent fault self-healing design of the intelligent control core module. For minor faults of sensors such as temperature, humidity and pressure, it can automatically switch to backup sensors and generate reliable data by combining historical data fusion verification to ensure continuous operation of the system. For serious faults, it can quickly trigger safety shutdown and audible and visual alarms, and upload fault information and maintenance instructions to the cloud to support remote diagnosis and greatly shorten the fault troubleshooting time. At the same time, the wired and wireless dual data interaction link design avoids system paralysis caused by single link interruption, further improving the operational stability in batch preparation scenarios, and the continuous operation rate of the equipment can reach 100%.

[0019] (5) The intelligent control core module of this invention incorporates a deep learning prediction and optimization model based on the MindSporeLite framework, which can analyze 150-250 sets of historical preparation data in real time. When the sample pass rate is detected to be continuously decreasing or below the threshold, it can predict process drift in advance and trigger multi-parameter coupling optimization. By coordinating the adjustment of key parameters such as feed rate, temperature and humidity, pressing pressure, and molding cavity temperature, the global optimal solution is found, and the parameter library is automatically updated when the pass rate is ≥96% for 12 consecutive sets, so as to achieve continuous improvement in system performance. This design allows the system to adapt to various types of samples such as highly adsorbent soil, mineral powder, and fine-grained clay without manual re-adjustment of parameters, greatly reducing the dependence on manual experience.

[0020] (6) This invention adds a dual traceability unit for the entire life cycle of the sample. Through the dual encoding of micro RFID chip implantation and ultraviolet inkjet coding, the unique identification association between the sample interior and surface is realized. Compared with the traditional single surface coding, the traceability information is less likely to be lost. The entire preparation process parameter chain, including feeding, temperature and humidity adjustment, pressing, and detection data, is stored locally and synchronized in the cloud. It supports seamless connection with the laboratory information management system, realizing full-link traceability and reverse query from sample preparation, analysis and testing to report generation. It fully meets the quality control requirements of third-party testing institutions and improves the credibility and traceability of experimental results. Attached Figure Description

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is a logical architecture diagram of the intelligent control system for precise preparation of solid samples according to a preferred embodiment of the present invention; Figure 2 This is a flowchart illustrating the temperature and humidity control process of the precision feeding system according to a preferred embodiment of the present invention. Figure 3 This is a flowchart of the temperature control module of the compression molding system according to a preferred embodiment of the present invention; Figure 4 This is a flowchart of the graded sorting and discharge system and dual traceability unit of a preferred embodiment of the present invention; Figure 5 This is a flowchart of a preferred embodiment of the intelligent control method for precise preparation of solid samples according to the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or it can be fixed through another intermediate component. When a component is said to be "connected to" another component, it can be directly connected to the other component or it may be fixed through another intermediate component. When a component is said to be "set on" another component, it can be set directly on the other component or it may be set through another intermediate component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] like Figures 1 to 4 As shown, a precise solid sample preparation intelligent control system includes a precise feeding system, a precision compression molding system, a grading and sorting discharge system, and an intelligent control core module. The precise feeding system, precision compression molding system, and grading and sorting discharge system are arranged sequentially along the sample flow direction, and each establishes a wired and wireless dual data interaction link with the intelligent control core module to form a closed-loop control system. Through the full-link data interaction and sequential arrangement of the precise feeding system, precision compression molding system, grading and sorting discharge system, and intelligent control core module, the intelligent control core module uniformly receives feedback signals from each system and outputs collaborative control commands. The entire process is automated, completing core steps such as sample feeding, temperature and humidity pretreatment, compression molding, quality inspection, traceability coding, and discharge. No manual intervention is required for weighing, feeding, sorting, coding, etc., eliminating the dependence on manual labor in traditional preparation methods.

[0026] The precision feeding system includes a storage bin, a variable frequency screw feeder, a conveying channel, an integrated temperature and humidity sensor, and an ultrasonic heat pump dual-mode humidity control device. It is used to achieve precise quantitative delivery and temperature and humidity pretreatment of samples. The integrated temperature and humidity sensor synchronously collects temperature and humidity feedback signals from solid samples and transmits them to the intelligent control core module, providing basic data for control decisions. The ultrasonic heat pump dual-mode humidity control device receives control commands from the intelligent control core module and executes ultrasonic humidification, heat pump dehumidification, or constant temperature humidification actions to pretreat the sample temperature and humidity to the target range, providing a stable material basis for subsequent pressing and molding.

[0027] Specifically, the integrated temperature and humidity sensing component employs a high-precision capacitive composite sensor with a temperature detection range of -10℃ to 60℃ and a humidity detection range of 0-95%RH. The humidity accuracy is ±1.8%RH, and the temperature accuracy is ±0.3℃, enabling it to output continuous and stable temperature and humidity feedback signals. Compared to traditional single-dimensional, low-precision sensing methods, this design can capture the temperature and humidity changes of the sample in real time and accurately during transport, avoiding adjustment misalignment caused by data lag or errors. This ensures that the intelligent control core module can obtain the true state of the sample, providing reliable data support for the formulation of subsequent adjustment strategies.

[0028] The ultrasonic heat pump dual-mode humidity control device integrates an ultrasonic atomizing humidifier and a heat pump dehumidifier, forming a linked control link with the variable frequency control module of the variable frequency screw feeder. This device overcomes the limitations of traditional single dehumidification or humidification methods. By linking control with the variable frequency screw feeder speed (e.g., reducing the speed to extend sample residence time when temperature and humidity deviations are large), it ensures that the sample fully contacts the regulating medium (atomized water vapor or dry airflow) within the conveying channel, avoiding localized temperature and humidity unevenness. Compared to traditional static temperature and humidity control methods, this design allows for highly consistent temperature and humidity pretreatment of batch samples, providing a stable material basis for subsequent pressing and molding processes, thereby improving the dimensional accuracy and performance consistency of the final molded samples.

[0029] The intelligent control core module, based on the difference and rate of change between the temperature and humidity feedback signal and the preset adaptive temperature and humidity, intelligently switches between the following control modes through a multi-dimensional logical judgment algorithm to achieve precise adaptation to different environmental conditions and sample characteristics: Mode 1, High Temperature and High Humidity Control: When the ambient temperature is >32℃ and the sample humidity is >suitable humidity +6%RH, the system determines that it is a high humidity and easy adhesion condition. At this time, the intelligent control core module outputs intermittent heat pump dehumidification command and pulse ultrasonic humidification fine adjustment command, and at the same time outputs a speed reduction command to the variable frequency screw feeder to extend the sample residence time and achieve uniform temperature and humidity regulation, avoiding local overheating or incomplete dehumidification. Mode 2, High Temperature and Low Humidity Control: When the ambient temperature is >32℃ and the sample humidity is <adaptive humidity -6%RH, the system determines that it is a low humidity and brittle chemical condition. At this time, the intelligent control core module outputs a low-power continuous ultrasonic humidification command and outputs a speed fine-tuning command to the variable frequency screw feeding device. Combined with the preheating of the molding cavity, the molding performance is optimized to prevent cracking after pressing due to excessive dryness of the material. Mode 3, Low Temperature and High Humidity Control: When the ambient temperature is <12℃ and the sample humidity is > suitable humidity +6%RH, the system determines that it is a condensation-prone condition. At this time, the intelligent control core module first outputs a preheating command to the ceramic liquid cooling composite temperature control system. After the forming cavity temperature reaches the preset threshold, it outputs a feeding control command to the precision feeding system. At the same time, the heat pump dehumidifier is started to operate at low power, which fundamentally avoids condensation of the sample in the cold forming cavity due to a sudden drop in temperature, thus ensuring the forming quality.

[0030] It is conceivable that this could also include common ambient temperature operating conditions, Mode 4, Ambient Temperature Operating Condition Control: When 12℃≤ambient temperature≤32℃ and sample humidity>adaptive humidity+6%RH, the intelligent control core module outputs a continuous medium-power dehumidification command to the heat pump dehumidifier, maintaining the reference speed of the variable frequency screw feeder, and achieving efficient dehumidification through precise humidity control. When 12℃≤ambient temperature≤32℃ and sample humidity<compatible humidity-6%RH, a low-power intermittent humidification command is output to the ultrasonic atomizing humidifier, while the feeding speed is finely adjusted to extend the residence time and ensure uniform humidity increase. When the ambient temperature is 12℃≤Ambient temperature≤32℃ and the sample humidity is within the suitable range of ±3%RH, only the integrated temperature and humidity sensing component is used for monitoring, and the adjustment device is not activated to reduce energy consumption.

[0031] The intelligent control core module automatically switches adjustment modes based on the difference and rate of change between the real-time temperature and humidity of the sample and the preset suitable temperature and humidity, eliminating the need for manual parameter adjustments according to environmental changes. This intelligent adaptation capability not only reduces the skill requirements and labor intensity of operators but also avoids improper adjustments caused by human judgment errors, ensuring the stability and reliability of the temperature and humidity pretreatment process, making it particularly suitable for multi-batch, continuous sample preparation scenarios.

[0032] Furthermore, a dual-dimensional feed verification module is installed at the connection between the conveying channel and the molding cavity. This module includes a high-precision weighing sensor and a volume detection sensor, used to perform weight-volume dual-parameter detection on the sample before it enters the mold and output feedback signals. Compared to the traditional single-weighing verification method, weight-volume dual-parameter detection can more accurately determine the feed deviation and avoid the problem of misjudgment caused by single-weighing due to changes in sample bulk density.

[0033] After receiving feedback signals, the intelligent control core module uses a dual-parameter error compensation algorithm to determine the feed rate deviation. If a deviation exists, it outputs parameter adjustment instructions: first, it compensates for the feeding time / variable speed in the next feeding cycle to correct the deviation at its source; second, it dynamically fine-tunes the pressing pressure and holding time parameters during the current pressing process to offset the impact of feed rate fluctuations on the molding effect. This design solves the problem of inconsistent sample preparation conditions caused by traditional manual weighing errors and insufficient feeding accuracy, ensuring a high degree of uniformity in the preparation basis of each sample.

[0034] The precision compression molding system includes a molding cavity, a high-precision servo press, a ceramic liquid-cooled composite temperature control system, and a full-parameter monitoring component for compression molding. It is used to achieve precise temperature-controlled compression molding of samples. The ceramic liquid-cooled composite temperature control system, together with the intelligent control core module, forms a closed-loop temperature control system, dynamically adjusting the molding cavity temperature based on sample type and temperature and humidity pretreatment results. The full-parameter monitoring component collects pressure, displacement, and molding cavity temperature signals in real time during the compression process, providing data support for optimizing the compression process. The high-precision servo press outputs precise compression force and stroke according to control commands, ensuring optimal molding results.

[0035] Specifically, a full-parameter monitoring component for pressing is installed inside the molding cavity. This component integrates a high-response-speed pressure sensor, a high-precision displacement sensor, and multi-point temperature sensors to collect three-dimensional curves of pressure, displacement, and temperature in real time throughout the pressing process. The intelligent control core module compares and analyzes the collected three-dimensional curves with standard curves of the corresponding sample type in the parameter library. If abnormal curve morphology is detected (such as pressure peak shift, uneven displacement changes, or excessive temperature fluctuations), the system can quickly determine that it is due to uneven material distribution, changes in the molding cavity state, or temperature and humidity fluctuations, and immediately take corrective measures (such as slightly vibrating the press head at a frequency of 8-12Hz to adjust material distribution and fine-tuning the pressing pressure). Compared to the traditional pressing process that "only controls the result and does not monitor the process," this design achieves visualization and controllability of the pressing process, can provide early warning of abnormal conditions, and significantly reduces the molding failure rate.

[0036] The ceramic liquid-cooled composite temperature control system includes ceramic heating elements uniformly arranged around the molding cavity, a spiral liquid-cooled pipeline, and multi-point fiber optic temperature sensors embedded in the molding cavity wall. The multi-point fiber optic temperature sensors are used to monitor the temperature of different areas within the molding cavity in real time, achieving differentiated and precise temperature control. The ceramic heating elements offer advantages such as rapid heating and high temperature control accuracy, while the spiral liquid-cooled pipeline quickly removes excess heat. Together, they achieve rapid adjustment and stable maintenance of the molding cavity temperature. The multi-point fiber optic temperature sensors avoid the limitations of single-point temperature measurement, ensuring uniform temperature distribution within the molding cavity and preventing sample molding defects caused by localized temperature differences.

[0037] To address the characteristics of different sample types, the ceramic liquid-cooled composite temperature control system employs a differentiated temperature control strategy: For highly adsorbent soil samples, the temperature control range of the molding chamber is 38-48℃. If the actual humidity of the sample fed by the precision feeding system is 4%RH or higher than the appropriate humidity, the intelligent control core module will automatically raise the temperature by 3-4℃ and use the residual heat of the molding chamber to assist in the evaporation of moisture and shorten the sample stabilization time. For mineral powder samples, the temperature control range of the molding chamber is 20-27℃. This range can be dynamically adjusted according to the working conditions of the ultrasonic heat pump dual-mode humidity control device. Under low temperature and high humidity conditions, the sample can be preheated appropriately according to the instructions of the intelligent control core module to avoid condensation. Under high temperature conditions, a cooling medium (such as ethylene glycol aqueous solution) is introduced through the liquid cooling pipeline to maintain the reference temperature range and prevent high temperature from causing particle oxidation or lattice changes.

[0038] This differentiated temperature control design based on sample type and actual humidity adapts to the physicochemical properties of different samples and can cope with changes in complex environmental conditions. It fundamentally avoids problems such as condensation, oxidation, and cracking caused by improper temperature, thus improving the stability of molding quality. The precision compression molding system's feed verification, process monitoring, and temperature control functions are deeply integrated with the intelligent control core module, forming a collaborative optimization system of "feed parameters - temperature and humidity status - compression curve - molding temperature." This multi-dimensional collaborative control mode ensures precise matching of key parameters during the pressing process, significantly improving the uniformity of the molded sample's dimensions (diameter, thickness) and ensuring the consistent density of the sample's internal structure, thus guaranteeing the accuracy of subsequent testing and analysis.

[0039] The graded sorting and unloading system includes an intelligent sorting mechanism, a traceable conveyor line, and a multi-dimensional non-destructive testing module, used to achieve quality inspection and qualified sorting of formed samples. Among them, the multi-dimensional non-destructive testing module integrates dimensional visual inspection, density detection, and surface defect recognition functions, and outputs quality inspection feedback signals to the intelligent control core module. The intelligent sorting mechanism automatically sorts qualified and unqualified samples based on the inspection results. The traceable conveyor line has a coding recognition function, which can accurately transport qualified coded samples to designated collection devices.

[0040] Specifically, the multidimensional non-destructive testing module integrates dimensional visual inspection (accuracy ±0.01mm), density inspection (accuracy ±0.01g / cm³), and surface defect recognition (identifying scratches and dents ≥0.1mm). Compared to traditional manual visual inspection or single-dimensional inspection, it can quickly and accurately confirm sample quality, avoiding the outflow of non-conforming products due to human fatigue and subjective judgment errors. Combined with an intelligent sorting mechanism, it can automatically separate qualified and non-conforming samples into different channels (qualified samples enter the dual traceability process, while non-conforming samples are sent to the waste channel), and record the reasons for non-conformity (such as dimensional deviations, uneven density, surface defects, etc.). No manual intervention is required, which improves inspection efficiency and ensures the consistency of the quality of outgoing samples, solving the problems of low efficiency and large errors in traditional inspection methods.

[0041] The intelligent control core module adopts a hybrid control architecture of MCU main control + deep learning edge processing unit. It pre-stores a multi-dimensional preparation parameter library based on different sample types (such as highly absorbent soil, mineral powder, and fine-grained clay). Through real-time analysis of multi-source feedback data (temperature, humidity, feed rate, pressing curve, test results, etc.) collected from various systems, it dynamically optimizes and coordinates the adjustment of feed rate, pressing pressure curve, and temperature and humidity control parameters in the molding cavity using an improved closed-loop control algorithm, achieving adaptive and precise control throughout the entire preparation process. Compared to traditional single MCU control, the hybrid architecture of MCU + deep learning edge processing unit ensures real-time response to control commands while possessing data mining and intelligent optimization capabilities, meeting the requirements for high-precision and intelligent preparation.

[0042] Specifically, the deep learning edge processing unit of the intelligent control core module is based on the MindSporeLite framework and has a built-in deep learning-based preparation process prediction and optimization model. This model can analyze the most recent 150-250 sets of complete preparation data in real time and also has the following functions: Predicting trends: When the pass rate of a certain type of sample is detected to decrease for three consecutive groups or the single pass rate is lower than 92%, the deep learning edge processing unit can predict potential process drift in advance (such as pressure curve shift caused by mold cavity wear, and adjustment deviation caused by sensor aging), and actively trigger multi-parameter coupling adjustment without manual intervention. Multi-parameter coupling optimization: The adjustment process is no longer a fine-tuning of a single parameter, but a collaborative optimization of multiple parameters such as feed rate, temperature and humidity, molding cavity temperature, pressing pressure and holding time based on the model to find the global optimal solution; for example, when the sample humidity is too high, the system not only adjusts the dehumidification power, but also optimizes the feed speed and molding cavity temperature in conjunction to ensure that the parameters of each link are accurately matched. Self-learning update: After adjustment, if the overall pass rate of 12 consecutive groups of samples is ≥96%, then this set of new optimized parameters will be used as the dynamic benchmark parameters for this type of sample and updated to the sample preparation parameter library to achieve continuous self-learning and performance improvement of the system.

[0043] This design differs from traditional control systems that only fine-tune a single parameter. Through multi-parameter coupling optimization and self-learning updates, it significantly improves the adaptability of preparation parameters, enabling the system to adapt to multiple types of samples without manual parameter readjustment. This significantly reduces reliance on human experience and allows for continuous optimization of preparation performance, ensuring long-term stability and accuracy.

[0044] Furthermore, the intelligent control core module also features redundant backup and intelligent fault self-healing modules. Through redundant sensor configuration, real-time equipment status monitoring, and AI fault mode recognition, it achieves comprehensive fault identification and intelligent handling, solving the problem of shutdown due to minor faults in traditional systems and improving equipment uptime. Sensor anomaly detection: Real-time monitoring of data from various sensors (temperature, humidity, pressure, displacement, weighing, etc.). If data changes suddenly, exceeds the reasonable range, there is no feedback signal, or the signal drift exceeds the threshold, it is immediately determined to be a sensor failure. Fault classification and intelligent handling: Minor faults: The system does not immediately shut down, but automatically switches to the backup sensor. At the same time, it calls the average of the last 15 sets of historical data from that sensor, combines it with data from other relevant sensors to perform data fusion and cross-validation, generates reliable data, continues to run, and records fault logs in the background. The system prompts the user to perform calibration through the human-machine interface. For example, when the temperature and humidity sensor experiences slight drift, the system can combine the molding cavity temperature sensor data with historical temperature and humidity data to generate reliable temperature and humidity values, ensuring the continuous operation of the control logic. Serious Fault: Immediately initiate the safety shutdown procedure. The touchscreen displays detailed fault type, location, and possible causes (e.g., "No feedback from the pressure sensor, please check the wiring") and triggers an alarm via buzzer and three-color indicator light. Simultaneously, the fault information is encrypted and uploaded to the cloud database, accompanied by remote diagnostic guidance and a visual file of repair steps. This supports remote diagnostics and rapid troubleshooting, minimizing downtime.

[0045] In addition, the intelligent control core module also supports human-computer interaction. Users can select sample types, manually modify key parameters, and view preparation process data and fault logs via the touch screen, making the operation convenient and intuitive.

[0046] The present invention also includes a dual traceability unit for the entire life cycle of samples. The dual traceability unit for the entire life cycle of samples includes a micro RFID chip implantation device, an ultraviolet inkjet printer, a local encrypted storage module and a cloud data interaction interface, which is used to realize full-chain traceability management of samples and meet the quality control requirements of third-party testing institutions.

[0047] Specifically, the micro RFID chip implantation device is used to implant a unique micro RFID chip inside each qualified sample, and the ultraviolet inkjet printer is used to generate a unique QR code on the sample surface. The chip and the QR code are associated with the same identification ID, which includes batch number, preparation time, equipment number, and operator information. Compared with the traditional single surface coding method, the dual coding design of internal RFID chip + surface QR code can effectively avoid the traceability failure problem caused by surface code wear and detachment, ensuring the durability and reliability of traceability information.

[0048] The local encrypted storage module synchronizes with the intelligent control core module in real time, recording the entire preparation process parameter chain corresponding to the unique ID. The parameter chain includes feeding parameters (feeding speed, feed rate, double verification data), temperature and humidity control data (temperature and humidity before and after control, control mode, control time), pressing curve (pressure-displacement-temperature three-dimensional data, holding time), and detection results (size, density, surface defect detection data). At the same time, the parameter chain is uploaded to the cloud database through the cloud data interaction interface to achieve dual backup of local and cloud, avoiding data loss.

[0049] The dual-traceability unit for the entire sample lifecycle supports seamless integration with the Laboratory Information Management System (LIMS), enabling end-to-end data traceability and reverse lookup from sample preparation and analysis to report generation. When subsequent analysis and testing detect data anomalies, the corresponding preparation process parameters can be quickly located via the sample's RFID chip or QR code to pinpoint the root cause of the problem. Simultaneously, batch information allows for rapid lookup of preparation data for all samples within the same batch, facilitating quality audits and batch problem investigation, significantly improving laboratory management efficiency and data reliability.

[0050] like Figure 5 As shown, a method for precise preparation and intelligent control of solid samples, used in the aforementioned intelligent control system for precise preparation of solid samples, includes the following steps: S1. Intelligent Parameter Initialization: The system automatically identifies the sample type through the raw material batch identification module, or the user selects the sample type through the human-computer interaction interface of the intelligent control core module. The system automatically calls the basic parameters from the pre-stored multi-dimensional preparation parameter library. Simultaneously, based on the preparation data and optimization results of the previous batch, it intelligently fine-tunes the initial parameters of the current batch. Users can manually modify key parameters based on experience, such as pressing pressure, holding time, and suitable temperature and humidity. This step achieves intelligent initialization of preparation parameters, ensuring parameter adaptability and improving operational flexibility to meet the needs of different users.

[0051] S2. Precision Quantitative Feeding: The intelligent control core module drives the variable frequency screw feeder of the precision feeding system to transport the solid sample in the storage bin to the conveying channel. The feeding amount is calculated by the variable frequency speed and the preset flow coefficient. When the sample is transported to the molding cavity inlet, the weight-volume dual parameter detection is completed by the feeding dual-dimensional verification module. The feeder stops when the target feeding amount is reached. If a feeding amount deviation is detected, the system drives the feeder to perform micro-feeding or parameter compensation in the next cycle. This step ensures the accuracy and consistency of the sample quantity entering the mold through dual parameter verification and real-time compensation.

[0052] S3. Dynamic Temperature and Humidity Adjustment and Feedback: The integrated temperature and humidity sensing component detects the sample temperature and humidity in real time. The intelligent control core module dynamically selects and executes the optimal temperature and humidity adjustment mode based on the difference between the detection result and the preset suitable temperature and humidity, such as high temperature and high humidity, high temperature and low humidity, low temperature and high humidity, or normal temperature mode. During the adjustment process, the temperature and humidity change rate is continuously monitored and fed back to the ceramic liquid-cooled composite temperature control system in real time, providing it with a basis for preheating / cooling decisions.

[0053] In a preferred embodiment of the present invention, in step S3, if the absolute difference between the sample humidity and the suitable humidity is >6%RH, the intelligent control core module simultaneously starts the corresponding adjustment device and the variable frequency screw feeder to operate at low speed, extending the sample residence time and making the sample uniformly regulated in temperature and humidity; if 3%RH≤absolute difference≤6%RH, the adjustment device is started to operate at low power to precisely fine-tune the temperature and humidity; if the absolute difference is <3%RH, only the integrated temperature and humidity sensing component is kept monitoring, and the adjustment device is not started to reduce energy consumption.

[0054] S4. Pre-compression detection and intelligent temperature control coordination: Samples meeting temperature and humidity standards are sent into the molding cavity. The intelligent control core module, based on the final temperature and humidity values ​​and sample type feedback from step S3, activates the ceramic liquid-cooled composite temperature control system to precisely raise the molding cavity temperature to the dynamically optimized target temperature and stabilize it. Subsequently, a high-precision servo press drives the upper pressure head downwards at pre-compression pressure. The full-parameter monitoring component detects the pre-compression process. If uneven sample distribution is detected, the lower pressure head is controlled to vibrate slightly at a frequency of 8-12Hz to adjust until the sample distribution is uniform. This step, through pre-compression detection and vibration adjustment, ensures uniform sample distribution within the molding cavity, avoiding molding defects caused by localized accumulation.

[0055] S5. Closed-Loop Compression Molding: The intelligent control core module drives the high-precision servo press to descend according to the optimized target compression pressure curve, compacting the sample in the molding cavity. After maintaining the preset holding time, the compression full-parameter monitoring component detects the final molding parameters. The system compares the actual pressure-displacement-temperature three-dimensional curve with the standard curve. If a slight deviation is found, the system adaptively adjusts the pressure, stroke, or holding time parameters for the next compression, forming a closed-loop optimization. This step, through process monitoring and closed-loop optimization, continuously improves the stability and accuracy of compression molding.

[0056] S6. Multidimensional Non-destructive Testing and Sorting: The molded samples are tested for dimensional accuracy, density, and surface defects. If the tests are satisfactory, the sample proceeds to the next step; if not, the intelligent sorting mechanism sends it to the waste channel and records the reason for non-compliance (such as dimensional deviation, uneven density, surface scratches, etc.). This step achieves precise screening of sample quality, ensuring that unqualified products do not flow into subsequent stages.

[0057] S7. Sample Dual Traceability Coding: The micro RFID chip implantation device and ultraviolet inkjet printer of the sample full life cycle dual traceability unit work synchronously to implant RFID chips inside qualified samples and print QR codes on the surface; the local encrypted storage module associates the code with all process parameters from steps S2 to S5 and stores it in encrypted form, while uploading it to the cloud to complete the traceability binding of the entire sample preparation process.

[0058] S8. Intelligent Discharge and Continuous Circulation: The intelligent control core module drives the intelligent sorting mechanism to eject coded and qualified samples, which are then transported to the designated collection device via a traceable conveyor line with coded identification. The system then automatically triggers the next round of the preparation process, repeating steps S2-S8 to achieve continuous, unattended automated production. This step enables continuous circulation of the preparation process, significantly improving the efficiency of batch sample preparation and reducing the cost of manual intervention.

[0059] Example 1: Highly Adsorbent Soil Sample (High Temperature and High Humidity Conditions) 1. Sample and operating conditions Sample parameters: Soil sample for heavy metal testing in farmland, particle size ≤2mm, initial temperature 30℃, initial humidity 32% (>suitable humidity +6%RH, i.e. 26%), high adsorption water content, easy to stick together and clump; Environmental conditions: The laboratory environment in summer has a temperature of 34℃ (>32℃) and an ambient humidity of 70%, which is considered a high temperature and high humidity condition. Preparation requirements: Batch preparation of 120 sets of samples for atomic absorption spectroscopy detection, requiring no clumping or cracking, support for full-chain traceability, and continuous equipment operation without downtime.

[0060] 2. System Configuration and Parameter Initialization Core modules enabled: Precision feeding system (including integrated temperature and humidity sensing components and ultrasonic-heat pump dual-mode humidity control device), precision compression molding system (ceramic-liquid cooling composite temperature control system), graded sorting and discharging system, intelligent control core module (MCU+MindSporeLite edge processing unit), and sample full life cycle dual traceability unit. Initialization parameters (the system automatically calls the parameter library of highly adsorbent soil samples + the optimization results of the previous batch): The reference speed of the variable frequency screw feeder is 14 r / min, and the target feed rate is 5.2 g. The molding cavity reference temperature is 42℃, and the pressing pressure curve is: pre-pressure 5MPa → main pressure 18MPa → holding pressure 12MPa, holding pressure time 12s; Temperature and humidity regulation thresholds: When the absolute difference is greater than 6%RH, the heat pump dehumidification and ultrasonic fine-tuning will be activated; when the absolute difference is between 3% and 6%RH, the heat pump will operate at low power.

[0061] 3. Preparation process control (real-time data of key parameters) S1-S2: Parameter Initialization and Precision Feeding The system automatically identifies "highly adsorbent soil samples" through the raw material batch identification module. After calling up the basic parameters and combining the data from the previous batch (pass rate 95.8%), it fine-tunes the forming chamber temperature to 43℃ and the feeding speed to 13.8r / min. The variable frequency screw feeder operates at 13.8 r / min. The sample is conveyed to the inlet of the forming cavity through the conveying channel. The feeding dual-dimensional verification module detects: weight 5.21g (deviation +0.01g), volume 1.86cm³ (theoretical volume 1.85cm³). The deviation is within the allowable range, and the feeder stops.

[0062] S3: Dynamic temperature and humidity control Real-time detection of integrated temperature and humidity sensing components: when the sample temperature is 30℃ and the humidity is 32%, and the difference between the sample temperature and the adaptive humidity is 10%RH (>6%RH), the intelligent control core module triggers the high temperature and high humidity working mode. Output commands: Heat pump dehumidifier operates intermittently (runs for 3 seconds and stops for 1 second, power 1200W), ultrasonic atomizing humidifier is pulsed and fine-tuned (pulse frequency 2Hz, power 300W), variable frequency screw feeder is reduced to 8r / min (residual time extended to 15s). Adjustment results: After 15 seconds, the sample temperature was 31℃ and the humidity was 25% (entering the adaptation range of 22±4%RH). The adjustment data was fed back to the ceramic-liquid cooling composite temperature control system in real time, triggering the molding cavity temperature to be maintained at 43℃.

[0063] S4: Pre-pressure detection and temperature control in tandem Samples meeting temperature and humidity standards are sent into the molding cavity, where the ceramic-liquid cooling composite temperature control system has stabilized the cavity temperature at 43℃. The high-precision servo press press descends with a pre-pressure of 5MPa above the press head. The data collected by the full-parameter monitoring component of the press press shows that the pressure curve is stable, the displacement deviation is ≤0.01mm, and the sample distribution is uniform, requiring no vibration adjustment.

[0064] S5: Closed-loop compression molding The servo press operates according to the optimized pressure curve: 5MPa (pre-pressure 2s) → 18MPa (main pressure 5s) → 12MPa (holding pressure 12s). The compression full-parameter monitoring component collects three-dimensional curves: peak pressure 18.02MPa, final displacement 9.98mm, cavity temperature 42.8℃. The deviation from the standard curve is ≤1%. The system records the parameters and keeps them consistent for the next set.

[0065] S6: Multidimensional Nondestructive Testing The multidimensional non-destructive testing module test results are as follows: diameter 50.02mm (deviation +0.02mm), thickness 9.97mm (deviation -0.03mm), density 1.82g / cm³ (uniformity deviation 0.02g / cm³), no surface defects, and the product is deemed qualified.

[0066] S7: Dual Traceability Coding The miniature RFID chip implantation device implants a chip (ID: ST-20250810-001) inside the sample, and a UV inkjet printer prints the corresponding QR code, which includes the batch number (TS-20250810), preparation time (2025-08-10 14:32:15), and device number (SC-007). The local encrypted storage module synchronously records the parameter chain: feed amount 5.21g, temperature and humidity before adjustment (30℃ / 32%RH), temperature and humidity after adjustment (31℃ / 25%RH), pressing curve data, and test results, and synchronously uploads them to the cloud.

[0067] S8: Intelligent Discharge and Circulation The intelligent sorting mechanism ejects qualified samples, which are then sent to the collection box via a traceability conveyor line. The system automatically triggers the next round of preparation, operating continuously without human intervention.

[0068] 4. Implementation Results and Core Advantages Batch preparation results: The overall pass rate of 120 samples was 97.5%, with 5 samples failing (3 samples with a dimensional deviation of 0.06 mm and 2 samples with minor surface defects). There were no clumping or cracking caused by improper temperature and humidity. Core Advantage Verification: Temperature and humidity coordinated control: Under high temperature and high humidity environment, the ultrasonic-heat pump dual-mode regulation can accurately reduce the humidity from 32% to 25%, with a deviation of ≤1%RH, and the molding cavity temperature is stabilized at 43±0.3℃, which meets the dehumidification requirements of highly adsorbent samples. Dual traceability reliability: Ten groups of samples were randomly selected, and the complete parameter chain could be read by RFID reader and QR code scanning. The data was consistent with the cloud data, and the traceability success rate was 100%. Continuous operation capability: 120 sets of samples were prepared without downtime, and the equipment was 100% uptime.

[0069] Example 2: Mineral powder sample (high temperature and low humidity conditions) 1. Sample and operating conditions Sample parameters: Quartz mineral powder sample, particle size ≤1mm, initial temperature 35℃, initial humidity 14% (< suitable humidity -6%RH, i.e. 16%), easily oxidized at high temperature, easily cracked at low humidity; Environmental conditions: Dry autumn environment, temperature 33℃ (>32℃), ambient humidity 15%, judged as high temperature and low humidity conditions; Preparation requirements: Prepare 80 sets of samples in batches for X-ray fluorescence spectroscopy detection. The samples must be free from oxidation and cracking, and the density uniformity deviation must be ≤0.02g / cm³.

[0070] 2. System Configuration and Parameter Initialization Core modules enabled: precision feeding system, precision compression molding system (liquid cooling circuit enhanced configuration), graded sorting and discharging system, and intelligent control core module (deep learning edge processing unit). Initialization parameters (mineral powder sample parameter library + previous batch optimization results): The reference speed of the variable frequency screw feeder is 12 r / min, and the target feed rate is 4.8 g. The molding cavity reference temperature is 24℃, and the pressing pressure curve is: pre-pressure 6MPa → main pressure 20MPa → holding pressure 14MPa, holding pressure time 15s; Temperature and humidity adjustment thresholds: Ultrasonic humidification is activated when the absolute difference is greater than 6%RH, and ultrasonic low-power humidification is activated when the absolute difference is between 3% and 6%RH.

[0071] 3. Preparation process control (real-time data of key parameters) S1-S2: Parameter Initialization and Precision Feeding The system automatically identifies the sample type, calls up the basic parameters, and combines the data from the previous batch (pass rate 94.5%) to fine-tune the feeding speed to 11.5 r / min and the main pressing pressure to 20.5 MPa. The variable frequency screw feeder is running, and the dual-dimensional verification module detects the following: weight 4.79g (deviation -0.01g), volume 1.71cm³ (theoretical volume 1.72cm³). The deviation is acceptable, and the feeder stops.

[0072] S3: Dynamic temperature and humidity control Temperature and humidity integrated sensor component detection: Sample temperature 35℃, humidity 14%, the difference between the sample temperature and the adapted humidity is 8%RH (>6%RH), triggering high temperature and low humidity working condition mode; Output command: The ultrasonic atomizing humidifier operates continuously at low power (power 400W), and the speed of the variable frequency screw feeder is finely adjusted to 10r / min (residence time 12s). Adjustment results: After 12 seconds, the sample temperature reached 34℃ and the humidity reached 20% (entering the adaptation range), which was fed back to the temperature control system to maintain the molding cavity at a reference temperature of 24℃.

[0073] S4: Pre-pressure detection and temperature control in tandem The sample is fed into the molding cavity, and the ceramic-liquid cooling composite temperature control system is circulated with an ethylene glycol aqueous solution (temperature 18℃) through the liquid cooling circuit to stabilize the cavity temperature at 24.1℃; Pre-compression process detection: The pressure curve is stable, the sample is evenly distributed, and no vibration adjustment is required.

[0074] S5: Closed-loop compression molding The servo press operates according to the optimized curve: 6MPa (pre-pressure 3s) → 20.5MPa (main pressure 6s) → 14MPa (holding pressure 15s). The pressure monitoring component collected three-dimensional curves: pressure peak 20.48MPa, displacement 9.99mm, cavity temperature 24.0℃. Compared with the standard curve, it was found that the pressure curve of the 30th group of samples was slightly deviated (deviation 2.5%). The system adaptively adjusted the main pressure of the next group to 20.8MPa.

[0075] S6: Multidimensional Nondestructive Testing Test results: The size deviation of qualified samples is ≤ ±0.04 mm, the density uniformity deviation is ≤ 0.02 g / cm³, and there are no oxidation or cracking defects; after the adjustment of the 30th group, the pressure curve deviation of subsequent samples is ≤ 1%.

[0076] S7-S8: Dual Traceability and Recycled Discharge Qualified samples are embedded with RFID chips and printed with QR codes, and the parameter chain is stored and uploaded; the system prepares continuously in cycles without human intervention.

[0077] 4. Implementation Results and Core Advantages Batch preparation results: The overall pass rate of 80 samples was 98.75%, with only 1 sample failing due to a density deviation of 0.04 g / cm³. There were no oxidized or cracked samples. Core Advantage Verification: Multi-parameter coupling optimization: After analyzing 30 sets of data, the AI ​​module adaptively adjusts the pressing pressure, thereby increasing the subsequent sample qualification rate to 100%, demonstrating its self-learning ability. High-temperature oxidation prevention: The liquid cooling circuit stably maintains the molding cavity at 24°C, avoiding high-temperature oxidation of mineral powder; X-ray fluorescence spectroscopy shows no deviation in elemental content. Low humidity cracking prevention: Ultrasonic humidification increases the sample humidity from 14% to 20%, resulting in no cracking after molding, thus solving the molding problem under traditional low humidity conditions.

[0078] Example 3: Fine-grained clay sample (low temperature and high humidity conditions + fault self-healing verification) 1. Sample and operating conditions Sample parameters: Fine-grained clay sample, particle size ≤0.5mm, initial temperature 10℃, initial humidity 30% (>suitable humidity +6%RH, i.e. 26%), prone to condensation at low temperatures and prone to sticking together at high humidity; Environmental conditions: The laboratory environment in winter has a temperature of 11℃ (<12℃) and an ambient humidity of 75%, which is judged as a low temperature and high humidity condition. Preparation requirements: Prepare 100 sets of samples in batches, requiring no condensation and no adhesion, to verify the system's self-healing ability.

[0079] 2. System Configuration and Parameter Initialization Enabling core modules: Full system + redundant backup and intelligent fault self-healing module (backup temperature and humidity sensor); Initialization parameters (fine-grained clay sample parameter library): The reference speed of the variable frequency screw feeder is 13 r / min, and the target feed rate is 5.0 g. The target temperature for preheating the molding cavity is 30℃. The pressing pressure curve is as follows: pre-pressure 4MPa → main pressure 17MPa → holding pressure 13MPa, holding time 14s.

[0080] 3. Preparation process control (real-time data of key parameters + fault simulation) S1-S2: Parameter Initialization and Precision Feeding The user manually selects the sample type, the system calls up the basic parameters, and the molding cavity starts preheating. The variable frequency screw feeder is running, and the dual-dimensional verification module detects the following: weight 5.02g (deviation +0.02g), volume 1.79cm³, deviation is acceptable, and the feeder stops.

[0081] S3: Dynamic temperature and humidity control Temperature and humidity integrated sensor component detection: Sample temperature 10℃, humidity 30%, triggering low temperature and high humidity working condition mode; Output command: Heat pump dehumidifier operates at low power (800W), molding cavity continues to preheat to 30℃ (time 20s); After preheating, the sample is sent into the conveying channel. After 18 seconds of adjustment, the humidity drops to 24% and the temperature rises to 15℃, entering the adaptation range.

[0082] S4: Pre-pressure detection and temperature control in tandem The sample was fed into the molding cavity (temperature 30.2℃). During the pre-pressing process, the full-parameter monitoring component of the pressing detected uneven sample distribution (displacement deviation 0.03mm). The pressing head was controlled to vibrate slightly at a frequency of 10Hz for 2s. After adjustment, the distribution was uniform.

[0083] S5: Closed-loop compression molding The servo press operates according to the curve, and after the pressure holding is completed, the three-dimensional curve is found to be without deviation from the standard curve.

[0084] Fault self-healing verification (during sample preparation of group 50) Fault Trigger: Sudden signal drift in the integrated temperature and humidity sensor (humidity detection value of 35%, exceeding the reasonable range, judged as a minor fault); System processing: Automatically switch to the backup temperature and humidity sensor, retrieve the average value of the most recent 15 sets of historical data from the original sensor (24.2%RH), and cross-validate it with the data from the molding cavity temperature sensor (30.1℃) to generate reliable data of 24.3%RH, and continue running; The background system logs the following fault information: "Temperature and humidity sensor 1 signal drift (deviation 3.2%RH), backup sensor has been switched, calibration is recommended." A pop-up message appears on the human-machine interface.

[0085] S6-S8: Multidimensional Nondestructive Testing and Cyclic Testing The 20 sets of samples prepared during the fault period had a 95% pass rate and no defects such as condensation or adhesion. Once qualified samples are coded and discharged, the system continues to cycle until 100 sets are completed.

[0086] 4. Implementation Results and Core Advantages Batch preparation results: The overall pass rate of 100 samples was 96%, with 4 samples failing (2 samples had dimensional deviations and 2 samples had surface adhesion). There was no downtime during the malfunction, and the operating rate was 100%. Core Advantage Verification: Low-temperature anti-condensation: The molding cavity is preheated to 30℃, and with the temperature and humidity control, the sample does not condense, solving the problem of low-temperature preparation of fine-particle clay. Fault self-healing capability: Automatically switches to backup sensor in case of minor faults without downtime, ensuring continuous batch production, with fault handling time <0.5s; Adhesion control: The heat pump dehumidification and molding cavity preheating work together to reduce the sample humidity from 30% to 24%, eliminating adhesion defects and ensuring stable molding results.

[0087] Summary table of core data for the implementation example: Table 1 The above three sets of embodiments cover solid samples with different characteristics (high adsorption, easy oxidation, easy condensation) and typical environmental conditions (high temperature and high humidity, high temperature and low humidity, low temperature and high humidity), and verify the system's fault self-healing capability. Implementation data shows that this invention, through coordinated temperature and humidity control, multi-parameter coupling optimization, redundancy backup, and dual traceability design, can achieve high precision (size deviation ≤ ±0.05 mm), high stability (overall pass rate ≥ 96%), and full automation in solid sample preparation. It fully meets the batch sample preparation needs in fields such as heavy metal analysis and geological testing, and solves core problems in existing technologies such as improper temperature and humidity control, single parameter optimization, system downtime due to malfunctions, and incomplete traceability.

[0088] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These are all equivalent modifications and improvements made to the above embodiments based on the essential technology of the present invention, and all of these fall within the protection scope of the present invention.

Claims

1. A smart control system for precise preparation of solid samples, characterized in that, include: The system includes a precision feeding system, a precision compression molding system, a grading and sorting discharge system, and an intelligent control core module. The precision feeding system, precision compression molding system, and grading and sorting discharge system are arranged sequentially along the sample flow direction, and each of them establishes a wired and wireless dual data interaction link with the intelligent control core module to form a closed-loop control system. The precision feeding system includes a storage bin, a variable frequency screw feeding device, a conveying channel, an integrated temperature and humidity sensing component, and an ultrasonic heat pump dual-mode humidity control device. The integrated temperature and humidity sensing component is used to simultaneously collect the temperature and humidity feedback signals of the solid sample and transmit them to the intelligent control core module. The ultrasonic heat pump dual-mode humidity control device receives the control command from the intelligent control core module and performs ultrasonic humidification, heat pump dehumidification, or constant temperature humidification to pre-process the sample temperature and humidity to the target range. The precision pressing molding system includes a molding cavity, a high-precision servo press, a ceramic liquid-cooled composite temperature control system, and a full-parameter pressing monitoring component. The ceramic liquid-cooled composite temperature control system and the intelligent control core module form a temperature control closed loop. The full-parameter pressing monitoring component collects pressure, displacement, and molding cavity temperature signals in real time during the pressing process. The high-precision servo press outputs precise pressing force and stroke according to control commands. The graded sorting and unloading system includes an intelligent sorting mechanism, a traceable conveyor line, and a multi-dimensional non-destructive testing module. The multi-dimensional non-destructive testing module integrates dimensional visual inspection, density detection, and surface defect identification functions, and outputs quality inspection feedback signals to the intelligent control core module. The intelligent control core module adopts a hybrid control architecture of MCU main control + deep learning edge processing unit. It has a pre-stored multi-dimensional preparation parameter library based on different sample types. By analyzing the multi-source feedback data collected by each system in real time, it uses an improved closed-loop control algorithm to dynamically optimize and coordinate the adjustment of feeding amount, pressing pressure curve, molding cavity temperature and humidity control parameters to achieve adaptive and precise control of the entire preparation process.

2. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The integrated temperature and humidity sensing component adopts a high-precision capacitive composite sensor with a temperature detection range of -10℃ to 60℃, a humidity detection range of 0-95%RH, a humidity accuracy of ±1.8%RH, a temperature accuracy of ±0.3℃, and outputs continuous temperature and humidity feedback signals. The ultrasonic heat pump dual-mode humidity control device integrates an ultrasonic atomizing humidifier and a heat pump dehumidifier, and forms a linkage control link with the frequency conversion control module of the frequency conversion screw feeding device. The intelligent control core module intelligently switches between the following control modes based on the difference and rate of change between the temperature and humidity feedback signal and the preset adaptive temperature and humidity, using a multi-dimensional logical judgment algorithm: Mode 1, High Temperature and High Humidity Control: When the ambient temperature is >32℃ and the sample humidity is >suitable humidity +6%RH, the intelligent control core module outputs intermittent heat pump dehumidification command and pulse ultrasonic humidification fine-tuning command, and at the same time outputs a speed reduction command to the variable frequency screw feeder to extend the sample residence time and achieve uniform temperature and humidity regulation. Mode 2, High Temperature and Low Humidity Control: When the ambient temperature is >32℃ and the sample humidity is <adaptive humidity -6%RH, the intelligent control core module outputs a low-power continuous ultrasonic humidification command and simultaneously outputs a speed fine-tuning command to the variable frequency screw feeding device, which, together with the preheating of the molding cavity, achieves moldability optimization. Mode 3, Low Temperature and High Humidity Control: When the ambient temperature is <12℃ and the sample humidity is > the appropriate humidity +6%RH, the intelligent control core module first outputs a preheating command to the ceramic liquid cooling composite temperature control system. After the forming cavity temperature reaches the preset threshold, it outputs a feeding control command to the precision feeding system. At the same time, the heat pump dehumidifier is started to operate at low power to avoid sample condensation and adhesion.

3. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The connection between the conveying channel and the molding cavity is provided with a dual-dimensional verification module for feeding. The dual-dimensional verification module for feeding includes a high-precision weighing sensor and a volume detection sensor, which are used to detect the weight and volume dual parameters of the sample before it enters the mold and output feedback signals. After receiving the feedback signal, the intelligent control core module uses a two-parameter error compensation algorithm to determine the feed rate deviation. If a deviation exists, it outputs a parameter adjustment command. In the next feeding cycle, the feeding time / frequency conversion speed is compensated, or the pressing pressure and holding time parameters are dynamically fine-tuned during the current pressing process to achieve real-time closed-loop compensation for feeding deviation.

4. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The molding cavity is equipped with a full-parameter monitoring component for pressing. The full-parameter monitoring component for pressing integrates a high-response-speed pressure sensor, a high-precision displacement sensor and a multi-point temperature sensor, which is used to collect the three-dimensional curves of pressure, displacement and temperature in real time throughout the pressing process. The intelligent control core module compares and analyzes the collected three-dimensional curves with the standard curves of the corresponding sample types in the parameter library; If an abnormal curve shape is detected, the system can determine that it is due to uneven material distribution, changes in the state of the molding cavity, or fluctuations in temperature and humidity, and take corrective measures immediately.

5. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The ceramic liquid-cooled composite temperature control system includes ceramic heating elements uniformly arranged around the molding cavity, spiral liquid-cooled pipelines, and multi-point fiber optic temperature sensors embedded in the molding cavity wall. The multi-point fiber optic temperature sensors are used to monitor the temperature of different areas within the molding cavity in real time, achieving differentiated and precise temperature control. For highly adsorbent soil samples, the temperature control range of the molding chamber is 38-48℃. If the actual humidity of the sample fed by the precision feeding system is 4%RH or higher than the appropriate humidity, the intelligent control core module will automatically raise the temperature by 3-4℃ and use the residual heat of the molding chamber to assist in the evaporation of moisture and shorten the sample stabilization time. For mineral powder samples, the temperature control range of the molding chamber is 20-27℃. This range can be dynamically adjusted according to the working conditions of the ultrasonic heat pump dual-mode humidity control device. Under low temperature and high humidity conditions, the sample can be preheated appropriately according to the instructions of the intelligent control core module to avoid condensation. Under high temperature conditions, a cooling medium is introduced through the liquid cooling pipeline to maintain the reference temperature range and prevent high temperature from causing particle oxidation or lattice changes.

6. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The deep learning edge processing unit of the intelligent control core module is based on the MindSporeLite framework and has a built-in deep learning-based preparation process prediction and optimization model. This model can analyze the most recent 150-250 sets of complete preparation data in real time and also has the following functions: Predicting trends: When the pass rate of a certain type of sample is detected to decrease for three consecutive groups or the pass rate is lower than 92% in a single instance, the deep learning edge processing unit can predict potential process drift in advance and actively trigger multi-parameter coupling adjustment. Multi-parameter coupled optimization: The adjustment process is based on the model to perform coordinated optimization of multiple parameters such as feed rate, temperature and humidity, molding cavity temperature, pressing pressure and holding time, in order to find the global optimal solution; Self-learning update: After adjustment, if the overall pass rate of 12 consecutive groups of samples is ≥96%, then this set of new optimized parameters will be used as the dynamic benchmark parameters for this type of sample and updated to the sample preparation parameter library to achieve continuous self-learning and performance improvement of the system.

7. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: The intelligent control core module also features redundancy backup and intelligent fault self-healing modules, achieving comprehensive fault identification and intelligent handling through sensor redundancy configuration, real-time equipment status monitoring, and AI fault mode recognition. Sensor anomaly detection: Real-time monitoring of data from each sensor. If data changes suddenly, exceeds the reasonable range, there is no feedback signal, or the signal drift exceeds the threshold, it is immediately determined to be a sensor failure. Fault classification and intelligent handling: Minor fault: The system automatically switches to the backup sensor, and at the same time calls the average value of the sensor’s most recent 15 sets of historical data. It combines the data with data from other relevant sensors to perform data fusion and cross-validation, generates reliable data, continues to run, and records fault logs in the background. It prompts the user to perform calibration through the human-machine interface. Critical Fault: Immediately initiate the safety shutdown procedure. The touchscreen displays detailed fault type, location, and possible causes, and alarms are triggered by a buzzer and three-color indicator lights. At the same time, the fault information is encrypted and uploaded to the cloud database, accompanied by remote diagnostic guidance and a visual file of repair steps. This supports remote diagnostics and rapid troubleshooting, minimizing downtime.

8. The intelligent control system for precise preparation of solid samples according to claim 1, characterized in that: It also includes a dual traceability unit for the entire life cycle of a sample, which includes a micro RFID chip implantation device, an ultraviolet inkjet printer, a local encrypted storage module, and a cloud data interaction interface; The micro RFID chip implantation device is used to implant a unique micro RFID chip inside each qualified sample, and the ultraviolet inkjet printer is used to generate a unique QR code on the sample surface. The chip and the QR code are associated with the same identification ID, which includes batch, preparation time, equipment number and operator information. The local encrypted storage module is synchronized with the intelligent control core module in real time, recording the entire preparation process parameter chain corresponding to the unique ID. The parameter chain includes feeding parameters, temperature and humidity adjustment data, pressing curve, and detection results. The sample lifecycle dual traceability unit supports seamless integration with the laboratory information management system, enabling end-to-end data traceability and reverse querying from sample preparation and analysis to report generation.

9. A method for precise preparation and intelligent control of solid samples, characterized in that: For use in the system according to any one of claims 1-8, the method comprises the following steps: S1. Intelligent parameter initialization: The system automatically identifies the sample type through the raw material batch identification module, or the user selects the sample type through the human-computer interaction interface of the intelligent control core module. The system automatically calls the basic parameters in the pre-stored multi-dimensional preparation parameter library. At the same time, based on the preparation data and optimization results of the previous batch, the initial parameters of the current batch are intelligently fine-tuned, and users can manually modify key parameters based on experience. S2. Precision quantitative feeding: The intelligent control core module drives the operation of the frequency conversion screw feeder of the precision feeding system to transport the solid sample in the storage bin to the conveying channel. The feeding amount is calculated by the frequency conversion speed and the preset flow coefficient. When the sample is transported to the inlet of the forming cavity, the weight-volume dual parameter detection is completed by the feeding dual-dimensional verification module. When the target feeding amount is reached, the feeder stops. If a feeding amount deviation is detected, the system drives the feeder to perform micro-feeding or parameter compensation in the next cycle. S3. Dynamic temperature and humidity adjustment and feedback: The integrated temperature and humidity sensing component detects the sample temperature and humidity in real time. The intelligent control core module dynamically selects and executes the best temperature and humidity adjustment mode based on the difference between the detection result and the preset suitable temperature and humidity. During the adjustment process, the temperature and humidity change rate is continuously monitored and fed back to the ceramic liquid cooling composite temperature control system in real time, providing it with the basis for preheating / cooling decisions. S4. Pre-pressure detection and intelligent temperature control coordination: Samples that meet the temperature and humidity standards are sent into the molding cavity. The intelligent control core module starts the ceramic liquid cooling composite temperature control system based on the final temperature and humidity values ​​and sample type fed back from step S3, and accurately raises the temperature of the molding cavity to the dynamically optimized target temperature and stabilizes it. Then, the high-precision servo press is driven to move the upper press head downwards at the pre-press pressure. The full-parameter monitoring component of the press detects the pre-press process. If it is determined that the sample distribution is uneven, the lower press head is controlled to vibrate slightly at a frequency of 8-12Hz to adjust until the sample distribution is uniform. S5. Closed-loop pressing molding: The intelligent control core module drives the high-precision servo press machine to descend according to the optimized target pressing pressure curve, compacting the sample in the molding cavity. After maintaining the preset holding time, the pressing full parameter monitoring component detects the final molding parameters. The system compares the actual pressure-displacement-temperature three-dimensional curve with the standard curve. If a slight deviation is found, the system adaptively adjusts the pressure, stroke, or holding time parameters of the next pressing, forming a closed-loop optimization. S6. Multidimensional Non-destructive Testing and Sorting: The dimensional accuracy, density, and surface defects of the molded sample are tested. If the test is qualified, it proceeds to the next step; if it is unqualified, the intelligent sorting mechanism sends it to the waste channel and records the reason for the unqualification. S7. Sample Dual Traceability Coding: The micro RFID chip implantation device and ultraviolet inkjet printer of the sample full life cycle dual traceability unit work synchronously. The RFID chip is implanted inside the qualified sample and the QR code is printed on the surface. The local encrypted storage module associates the code with all process parameters from step S2 to step S5 and stores it in encrypted form, while uploading it to the cloud. S8. Intelligent discharge and continuous circulation: The intelligent control core module drives the intelligent sorting mechanism to eject the coded qualified samples, which are then sent to the designated collection device via a traceable conveyor line with coded identification. The system then automatically triggers the next round of preparation process, repeating steps S2-S8 to achieve continuous, unattended automated production.

10. The intelligent control method for precise preparation of solid samples according to claim 9, characterized in that: In step S3, if the absolute difference between the sample humidity and the suitable humidity is >6%RH, the intelligent control core module simultaneously starts the corresponding adjustment device and the frequency conversion screw feeding device to operate at low speed, so that the sample can be uniformly regulated in temperature and humidity. If 3%RH≤absolute difference≤6%RH, start the regulator to operate at low power. If the absolute difference is less than 3%RH, only the integrated temperature and humidity sensor will be used for monitoring, and the adjustment device will not be activated.

Images