A high-precision titration intelligent control method and system

Abstract

The application relates to the field of titration intelligent control, and discloses a high-precision titration intelligent control method and system, which comprises the following steps: sample information input and preprocessing planning, sample automatic preprocessing, multi-sensor system calibration and preparation, intelligent titration parameter setting and scheme optimization, titration process multi-sensor data real-time acquisition and fusion, titration end point prediction and judgment based on machine learning, result calculation, verification and storage, system self-cleaning, maintenance and state monitoring. Through the collaborative operation of multiple modules and a series of intelligent technical means, the accuracy, reliability, automation degree and adaptability of high-precision titration in each link are comprehensively improved, and the high requirements for titration precision and efficiency in complex and various chemical analysis scenes are met.

Description

Technical Field

[0001] The present invention relates to the field of titration intelligent control, and in particular to a high-precision titration intelligent control method and system. Background Art

[0002] With the advancement of modern science and technology, in order to meet the higher accuracy, efficiency and flexibility requirements of titration in chemical analysis, as well as to cope with the increasingly complex sample analysis and large-scale data processing needs, on the basis of traditional titration technology, it has integrated multiple fields such as automation control technology, sensor technology, data communication technology, computer technology, chemometrics, machine learning, and the Internet of Things, thus giving rise to titration intelligent control technology.

[0003] However, existing intelligent titration control technologies often have problems such as lack of intelligent prompts and integration with cloud databases in sample information management, insufficient equipment integration and automated preprocessing, inaccurate and imperfect sensor calibration and performance monitoring, lack of chemometrics and online learning support for titration parameter and scheme setting and updating, insufficient utilization of high-speed acquisition and deep learning algorithms in multi-sensor data processing, lack of multi-model fusion strategy guarantee for endpoint prediction and judgment, lack of multi-level considerations for result verification and error analysis, and lack of intelligent self-cleaning and Internet of Things technologies in system maintenance and guarantee. As a result, it performs poorly in terms of intelligence, precision, automation, adaptability, and stability, making it difficult to efficiently and accurately meet the high-quality requirements of titration work in complex chemical analysis.

[0004] Therefore, it is necessary to design a high-precision titration intelligent control method and system to solve the problems in existing high-precision titration technology, such as insufficient intelligent and efficient sample information processing, insufficient equipment integration and automated preprocessing, imperfect sensor calibration and monitoring, lack of adaptability in titration parameter setting and scheme optimization, inaccurate data acquisition fusion and endpoint judgment, lack of systematicity in result calculation, verification and storage, and unintelligent system self-cleaning, maintenance and status monitoring. Summary of the Invention

[0005] In view of this, the present invention proposes a high-precision titration intelligent control method and system, aiming to solve the problems in existing high-precision titration technology, such as insufficient intelligent and efficient sample information processing, insufficient equipment integration and automated preprocessing, imperfect sensor calibration and monitoring, lack of adaptability in titration parameter setting and scheme optimization, insufficient accuracy in data acquisition fusion and endpoint judgment, lack of systematicity in result calculation, verification and storage, and unintelligent system self-cleaning, maintenance and status monitoring.

[0006] In one aspect, the present invention provides a high-precision titration intelligent control method, comprising:

[0007] Build a sample information entry interface with intelligent prompts, connect to the cloud database and regularly update the information database, use rule reasoning and combine machine learning to generate specific implementation methods for pretreatment plans, and evaluate the feasibility and efficiency of pretreatment plans;

[0008] Leveraging standardized industrial communication protocols, seamless integration of intelligent titration equipment and automated sample pretreatment equipment is achieved, enabling the establishment of an equipment status monitoring network for automated sample pretreatment.

[0009] Automatically select calibration solutions and standard substances based on the preset calibration plan and sensor type, and perform calibration operations. High-precision measuring instruments are used as reference standards to compare and correct sensor measurements in real time. A performance monitoring model is established to continuously monitor key indicators during titration, and fault diagnosis methods are used to promptly detect and diagnose faults.

[0010] Based on detailed sample information and pretreatment results, chemometric algorithms are used to calculate initial titration parameters. A real-time feedback control algorithm is employed during the titration process to dynamically and flexibly adjust parameters based on data collected by multiple sensors. A titration model library for various chemical systems is constructed, and online learning algorithms are used to adaptively update the models during titration. Computer simulation technology is used to comprehensively simulate experiments and compare and analyze different schemes before the actual titration.

[0011] Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-stamp and align data from different sensors, apply deep learning data fusion algorithms, develop a visual interface to display data, and embed analysis tools;

[0012] Collect titration experimental data covering different chemical systems and concentration ranges, use deep learning frameworks to build multi-layer perceptron, recurrent neural network and its variant neural network models, and optimize the model by adjusting various factors to train and predict the titration endpoint under different chemical systems and concentration ranges;

[0013] Accurately calculate the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully consider the titrant concentration calibration error, sample measurement error and sensor measurement error, use error propagation theory to perform error analysis, provide uncertainty ranges, and establish a multi-level result verification system;

[0014] Develop an intelligent self-cleaning program to automatically select suitable cleaning solutions and methods based on the titration test type, titrant, and sample properties. Use sensors to monitor the cleaning effect during cleaning, and automatically adjust parameters if it does not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on the equipment usage time and number of operations, and provide advance reminders. Use the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust titration plans.

[0015] Furthermore, the aforementioned construction of a sample information entry interface with intelligent prompting functions, connected to a cloud database and regularly updating the information database, utilizing rule reasoning combined with machine learning to generate implementation methods for pretreatment plans, and evaluating the feasibility and efficiency of pretreatment plans, includes:

[0016] The information entry interface has been optimized to be intuitive, concise, and have intelligent prompts. When the operator enters the sample type, a drop-down menu or auto-fill options will automatically pop up showing the common sources, estimated concentration ranges, and main components of that sample type.

[0017] Database update and management: establish connections with cloud databases, regularly update the sample information database from authoritative chemical analysis databases, scientific research institutions' shared data platforms, and sample data accumulated by internal laboratories; use data mining technology to analyze and organize new data, extract useful information, and optimize the recommended algorithm for pretreatment solutions; set database administrator permissions to allow professionals to review, modify, and supplement the database;

[0018] Intelligent generation and evaluation of pretreatment plans uses a combination of rule-based reasoning units and machine learning algorithms to generate pretreatment plans. The rule-based reasoning unit determines the neutralization pretreatment method of the sample based on chemical knowledge and empirical rules. The machine learning algorithm learns from a large amount of historical sample pretreatment data and recommends more accurate pretreatment steps based on the similarity of sample information. After the plan is generated, it automatically evaluates the feasibility and efficiency of the plan.

[0019] Furthermore, the aforementioned seamless integration of intelligent titration equipment and automated sample pre-processing equipment by means of standardized industrial communication protocols, establishment of an equipment status monitoring network, and automated sample pre-processing include:

[0020] Equipment integration and communication optimization: Using standardized industrial communication protocols, intelligent titration equipment is seamlessly integrated with automated sample pretreatment equipment. An equipment status monitoring network is established to monitor the operating parameters of the pretreatment equipment in real time. When equipment fails or is abnormal, the equipment automatically switches to a backup device or suspends the titration process, and issues an alarm to notify the operator.

[0021] Precise metering and mixing control: high-precision electronic balances and precision pipettes equipped with automatic calibration functions are used to weigh and measure samples. Measuring equipment is calibrated regularly. Intelligent control algorithms are introduced in stirring and ultrasonic mixing operations to automatically adjust stirring speed, ultrasonic power, and time according to the properties of the sample.

[0022] Intelligent removal of interfering substances and quality detection, construction of an intelligent chemical reagent adding unit, accurate calculation and automatic addition of appropriate removal reagents according to the types and contents of interfering substances that may exist in the sample, real-time comprehensive detection of the clarity, acidity and alkalinity, and ion concentration of the solution in the quality detection link by using an online spectral analysis technology and a chemical sensor array combination, establishment of a multi-parameter quality detection model, and analysis of the detection data by using a machine learning algorithm.

[0023] Further, the calibration solution and standard substance are automatically selected according to a preset calibration plan and a sensor type, and calibration operation is carried out, a high-precision measuring instrument is used as a reference standard to real-time compare and correct the measured value of the sensor, and a performance monitoring model is established to continuously monitor key indicators during titration, and a fault diagnosis method is used to timely discover and diagnose faults, including:

[0024] Automatic calibration program optimization, design of a fully automatic calibration process, automatic selection of appropriate calibration solutions and standard substances by the equipment according to a preset calibration plan and a sensor type, and control of the calibration equipment to carry out calibration operation, real-time comparison and correction of the measured value of the sensor by using a high-precision measuring instrument as a reference standard during calibration;

[0025] Sensor performance monitoring and fault diagnosis, establishment of a sensor performance monitoring model to real-time monitor the response time, stability and sensitivity of the sensor during titration, comparison and analysis with the performance data under the normal working state to timely discover the performance decline and faults of the sensor, and accurate diagnosis of the sensor faults by using a model-based fault diagnosis method;

[0026] Sensor layout and protection optimization, optimization of the layout position of the sensor probe by using a fluid mechanics simulation software according to the shape and size of the titration container, and design of special protection devices for the sensor probe.

[0027] Further, the titration initial parameters are calculated by using a chemometrics algorithm based on sample detailed information and pretreatment results, a real-time feedback control algorithm is used during titration, parameters are dynamically and flexibly adjusted according to the data collected by multiple sensors, a titration model library of multiple chemical systems is constructed, and an online learning algorithm is used to update the model adaptively during titration, and different schemes are comprehensively simulated by computer simulation technology before formal titration and compared and analyzed, including:

[0028] Parameter intelligent calculation and dynamic adjustment, accurate calculation of the initial parameters of titration by using a chemometrics algorithm based on sample detailed information and pretreatment results, and dynamic adjustment of the titration parameters according to the data collected by multiple sensors during titration by using a real-time feedback control algorithm;

[0029] Adaptive update of chemical system models. Establish a titration model library for various chemical systems. Each model contains the reaction kinetics equation, endpoint judgment criteria, and parameter optimization strategy for a specific chemical system. During the titration process, the model is adaptively updated using an online learning algorithm.

[0030] Titration scheme simulation and optimization verification: Before the formal titration, computer simulation technology is used to simulate the titration scheme. According to the sample information, titration parameters and chemical system model, various changes in the solution during the titration process are simulated to predict possible problems and errors. By comparing and analyzing the simulation results of different titration schemes, the optimal titration scheme is selected, and the endpoint judgment conditions are optimized and verified during the simulation process.

[0031] Furthermore, the aforementioned use of a high-speed data acquisition card to collect multi-sensor data in real time, establishing a data synchronization mechanism to accurately time-stamp and align data from different sensors, applying a deep learning data fusion algorithm, developing a visual interface to display data and embedding analysis tools, includes:

[0032] High-speed data acquisition and synchronization: Use high-speed data acquisition cards to collect multi-sensor data in real time, capture rapidly changing signals in the solution, establish a data synchronization mechanism, and time-stamp and align data collected by different sensors;

[0033] Improved multi-sensor data fusion algorithm, using a deep learning-based data fusion algorithm to determine the titration process, and introducing an attention mechanism, so that the algorithm automatically focuses on the most important feature information in the data for determining the titration endpoint;

[0034] Data visualization and real-time analysis: develop a real-time data visualization interface to display the data collected by multiple sensors in intuitive forms such as curves and charts. Embed data analysis tools in the visualization interface to analyze the data at any time and judge the titration process. If any abnormality is found, provide corresponding response measures.

[0035] Furthermore, the collection covers titration experimental data of different chemical systems and concentration ranges, and uses a deep learning framework to construct a multi-layer perceptron, a recurrent neural network, and its variant neural network models. By adjusting various factors of the model, the training and optimization model is optimized to predict the titration endpoint under different chemical systems and concentration ranges, including:

[0036] Model training and optimization: collect titration experimental data of different chemical systems and different concentration ranges, use deep learning framework to build neural network model, use experimental data to train the model, and optimize model performance by adjusting the model structure, number of layers, number of neurons and optimization algorithm;

[0037] Model uncertainty assessment and adaptive adjustment: During the model prediction process, an uncertainty assessment mechanism is introduced to evaluate the uncertainty of the model prediction results. When the uncertainty is high, corresponding measures are automatically taken. Using online learning technology, the model is adaptively adjusted according to the new titration experimental data, continuously improving the model's adaptability to different chemical systems and experimental conditions;

[0038] Multi-model fusion and decision-making adopts a multi-model fusion strategy to fuse the prediction results of different types of machine learning models. During the fusion process, the weights are dynamically adjusted according to the performance of different models in different chemical systems.

[0039] Furthermore, the titration reaction equation and the actual amount of titrant used are used to accurately calculate the content and concentration of the substance to be measured, fully considering the titrant concentration calibration error, sample amount error and sensor measurement error, using error propagation theory to perform error analysis, giving the uncertainty range, and establishing a multi-level result verification system, including:

[0040] Result calculation and error analysis: Based on the titration reaction equation and the actual amount of titrant used, the content or concentration of the substance to be measured is accurately calculated. During the calculation process, the calibration error of the titrant concentration, the sample amount error and the sensor measurement error are fully considered. The error propagation theory is used to analyze the results and the uncertainty range of the measurement results is given.

[0041] Result verification and quality control: establish a multi-level result verification system, adopt different titration endpoint judgment methods, conduct cross-validation and repeated titration experiments, introduce standard substance verification and inter-laboratory comparison verification, regularly use standard substances of known concentration to conduct titration experiments, compare the measurement results with the standard values, evaluate the accuracy and reliability of the measurement, participate in inter-laboratory comparison projects, compare and analyze the titration results of the laboratory with those of other laboratories, identify potential problems and make timely improvements. During the verification process, if the result exceeds the allowable error range, the equipment automatically starts the traceability analysis program to find the source of the error from sample collection, pretreatment, titration process to result calculation, and generate a detailed error report;

[0042] Data storage and management: All relevant data of the titration experiment are stored in a relational database or a non-relational database, and data index and query units are established to facilitate operators to quickly retrieve and query historical data based on sample number, experiment date and test substance. Data is backed up and archived regularly, and sensitive data is encrypted using data encryption technology.

[0043] Furthermore, the intelligent self-cleaning program described above automatically selects the appropriate cleaning solution and method based on the titration test type, titrant, and sample properties. During cleaning, sensors are used to monitor the cleaning effect, and parameters are automatically adjusted if the cleaning effect does not meet the standards. An equipment maintenance management unit is established to formulate maintenance plans and provide advance reminders based on the equipment usage time and number of operations. The Internet of Things and big data are used to monitor the status of key components, warn of faults, and adjust the titration plan, including:

[0044] Intelligent self-cleaning program: Develop an intelligent self-cleaning program that automatically selects the appropriate cleaning solution and cleaning method based on the type of titration test, the titrant used, and the nature of the sample. During the cleaning process, sensors monitor the cleaning effect. If the cleaning effect is not up to standard, the equipment automatically adjusts the cleaning parameters or repeats the cleaning steps.

[0045] Equipment maintenance planning and reminders: Establish an equipment maintenance management unit to formulate maintenance plans based on the equipment's usage time, number of operations, and the life cycle of key components. Before the maintenance plan expires, the equipment will automatically issue maintenance reminders. At the same time, a maintenance record file will be established to record the time, content, maintenance personnel, and the equipment's operating status after each maintenance.

[0046] Intelligent condition monitoring and fault warning utilize IoT technology and big data analysis to conduct real-time condition monitoring of key equipment components. Sensors are installed on the components to collect operating parameter data such as temperature, vibration, current, and voltage. The data is transmitted wirelessly to the equipment control center, and machine learning algorithms are used to analyze this data to establish an equipment failure prediction model. When the model predicts a failure risk, the equipment will issue a warning message in advance to notify the operator to conduct inspection and repairs. At the same time, the equipment will automatically adjust the titration plan based on the fault warning information, and the affected experimental tasks will be postponed or transferred to other normal equipment.

[0047] On the other hand, the present application also provides a high-precision titration intelligent control system, comprising:

[0048] The sample information management module builds a sample information entry interface with intelligent prompts, connects to the cloud database and regularly updates the information database. It uses rule reasoning and combines machine learning to generate specific implementation methods for pretreatment plans and evaluates the feasibility and efficiency of pretreatment plans.

[0049] The equipment integration and monitoring module uses standardized industrial communication protocols to achieve seamless integration of intelligent titration equipment and automated sample pretreatment equipment, establish an equipment status monitoring network, and perform automated sample pretreatment;

[0050] The sensor calibration and diagnostics module automatically selects calibration solutions and standard substances based on the preset calibration plan and sensor type, and performs calibration operations. It uses high-precision measuring instruments as reference standards to compare and correct sensor measurements in real time. It also establishes a performance monitoring model to continuously monitor key indicators during titration and uses fault diagnosis methods to promptly detect and diagnose faults.

[0051] The titration parameter and scheme module uses chemometric algorithms to calculate initial titration parameters based on sample details and pretreatment results. A real-time feedback control algorithm is used during the titration process to dynamically and flexibly adjust parameters based on data collected by multiple sensors. A titration model library for various chemical systems is constructed, and online learning algorithms are used to adaptively update the models during titration. Computer simulation technology is used to comprehensively simulate experiments and compare and analyze different schemes before the actual titration.

[0052] The data acquisition and fusion module uses high-speed data acquisition cards to collect multi-sensor data in real time, establishes a data synchronization mechanism to accurately time-stamp and align data from different sensors, applies deep learning data fusion algorithms, develops a visual interface to display data, and embeds analytical tools;

[0053] The endpoint prediction and judgment module collects titration experimental data covering different chemical systems and concentration ranges, uses a deep learning framework to build multi-layer perceptron, recurrent neural network and its variant neural network models, and optimizes the training model by adjusting various factors of the model to predict the titration endpoint under different chemical systems and concentration ranges;

[0054] The result processing and storage module accurately calculates the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully considering the titrant concentration calibration error, sample measurement error and sensor measurement error, and uses error propagation theory to perform error analysis, provide uncertainty ranges, and establish a multi-level result verification system;

[0055] The system maintenance and assurance module develops an intelligent self-cleaning program, automatically selects appropriate cleaning solutions and methods based on the titration test type, titrant, and sample properties, uses sensors to monitor the cleaning effect during cleaning, and automatically adjusts parameters if it does not meet the standards. It also establishes an equipment maintenance management unit, formulates maintenance plans based on the equipment usage time and number of operations, and provides advance reminders. It uses the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust titration plans.

[0056] It is understandable that the above-mentioned high-precision titration intelligent control system and the high-precision titration intelligent control method have the same beneficial effects, and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0058] Figure 1 A flow chart of a high-precision titration intelligent control method provided by an embodiment of the present invention;

[0059] Figure 2 This is a functional block diagram of a high-precision titration intelligent control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0061] Reference Figure 1 In some embodiments of the present application, a high-precision titration intelligent control method includes:

[0062] Step S100: Build a sample information entry interface with intelligent prompt function, connect to the cloud database and regularly update the information database, use rule reasoning and combine machine learning to generate a preprocessing plan, and evaluate the feasibility and efficiency of the plan.

[0063] Step S200: Achieve seamless integration of intelligent titration equipment and automated sample pre-processing equipment using standardized industrial communication protocols, establish an equipment status monitoring network, and perform automated sample pre-processing.

[0064] Step S300: Automatically select calibration solutions and standard substances according to the preset plan and sensor type, and perform calibration operations. Use high-precision measuring instruments as reference standards to compare and correct sensor measurements in real time. At the same time, establish a performance monitoring model, continuously monitor key indicators during the titration process, and use fault diagnosis methods to promptly detect and diagnose faults.

[0065] Step S400: Based on the sample detailed information and pretreatment results, the initial titration parameters are calculated using a chemometric algorithm. During the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters based on the data collected by multiple sensors. A titration model library for various chemical systems is constructed, and an online learning algorithm is used to adaptively update the model during titration. Before the formal titration, a comprehensive simulation experiment is performed using computer simulation technology to compare and analyze different schemes.

[0066] Step S500: Use a high-speed data acquisition card to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-tag and align different sensor data, apply deep learning data fusion algorithms, develop a visual interface to display data and embed analysis tools.

[0067] Step S600: Collect titration experimental data covering different chemical systems and concentration ranges, use a deep learning framework to build neural network models such as multi-layer perceptrons, recurrent neural networks and their variants, and train and optimize the model by adjusting various factors of the model.

[0068] Step S700: Accurately calculate the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully considering the titrant concentration calibration error, sample measurement error and sensor measurement error, use error propagation theory to perform error analysis, provide uncertainty ranges, and establish a multi-level result verification system.

[0069] Step S800: Develop an intelligent self-cleaning program to automatically select the appropriate cleaning solution and method based on the titration test type, titrant, and sample properties. During cleaning, use sensors to monitor the cleaning effect and automatically adjust the parameters if it does not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on the equipment usage time and number of operations, and provide advance reminders. Use the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust the titration plan.

[0070] Specifically, the system has designed an intuitive, concise human-computer interaction interface with intelligent prompts. When the operator enters a sample type, a drop-down menu or auto-populates options automatically displaying common sources, estimated concentration ranges, and main ingredients for that sample type, facilitating quick and accurate information entry. For example, when selecting "Food Samples," common food categories such as fruits, vegetables, and meat are displayed, along with corresponding estimated ingredient ranges, such as the potential content of vitamin C and sugars in fruit, along with their approximate concentration ranges. A cloud database connection is established, and the sample information database is regularly updated from authoritative chemical analysis databases, shared data platforms of scientific research institutions, and a large amount of sample data accumulated by internal laboratories. Data mining techniques are used to analyze and organize new data, extracting useful information to optimize the pretreatment solution recommendation algorithm. Database administrator permissions are also set to allow professionals to review, modify, and supplement the database to ensure data accuracy and completeness. Pretreatment solutions are generated using a combination of a rule-based reasoning system and machine learning algorithms. The rule-based reasoning system uses chemical knowledge and empirical rules, such as the principles of acid-base neutralization reactions, to determine the neutralization pretreatment method for certain acidic samples. Machine learning algorithms, by learning from a large amount of historical sample preprocessing data, recommend more precise preprocessing steps based on sample similarities. After generating a plan, the system automatically evaluates its feasibility and efficiency, estimating preprocessing time, cost, and potential errors, providing a reference for operators.

[0071] It is understandable that operations such as building a sample information entry interface with intelligent prompt function and connecting it to the cloud database can make the high-precision titration intelligent control method more intelligent and efficient in the sample information acquisition link. Intelligent prompts can reduce human input errors and speed up information entry. The update of the cloud database and the application of rule reasoning and machine learning can accurately generate pretreatment plans based on massive data and scientific rules. The evaluation of the feasibility and efficiency of the plan can avoid problems in advance and optimize resource allocation, laying a solid foundation for the accuracy and efficiency of subsequent titration operations, and improving the intelligence level and reliability of the overall titration process.

[0072] Specifically, standardized industrial communication protocols (such as Profibus and ModbusTCP) are used to seamlessly integrate the intelligent titration system with automated sample pretreatment equipment, enabling high-speed, stable data transmission and command control. An equipment status monitoring network is established to monitor pretreatment equipment operating parameters (such as motor speed, temperature, and pressure) in real time to ensure proper operation. In the event of a device failure or anomaly, the system automatically switches to a backup device or pauses the titration process, issuing an alarm to notify the operator. For sample weighing and measurement, high-precision electronic balances (with an accuracy of up to 0.0001g) and precision pipettes (with an accuracy of up to 0.1μL) are used, equipped with automatic calibration functions, and the measuring equipment is regularly calibrated to ensure accuracy. During stirring and ultrasonic mixing operations, intelligent control algorithms are introduced to automatically adjust stirring speed, ultrasonic power, and time based on sample properties (such as viscosity and density) to achieve optimal mixing. For example, for highly viscous samples, stirring speed and ultrasonic power are appropriately increased, and processing time is extended. An intelligent chemical reagent addition system has been developed to accurately calculate and automatically add the appropriate amount of removal reagent based on the type and content of interfering substances in the sample. For example, when measuring the content of specific proteins in biological samples, the enzyme-linked immunosorbent assay (ELISA) principle is used to automatically add specific antibodies to remove interference from other unrelated proteins. During quality testing, a combination of online spectral analysis technology and chemical sensor arrays is used to conduct real-time, comprehensive testing of multiple parameters such as solution clarity, pH, and ion concentration. By establishing a multi-parameter quality testing model and using machine learning algorithms to analyze test data, the system accurately determines whether the pretreated sample meets titration requirements. If not, the system automatically adjusts pretreatment parameters or initiates a secondary processing step.

[0073] Understandably, the seamless integration of equipment, the establishment of a monitoring network, and automated preprocessing using standardized industrial communication protocols ensure high-speed, stable data transmission and precise command control between intelligent titration equipment and automated sample preprocessing equipment. This enables real-time monitoring of equipment operating parameters, prompt detection and resolution of faults, and guaranteed normal operation. Automated preprocessing not only improves operational accuracy and consistency, but also significantly enhances work efficiency, reduces human error and operation time, and makes the titration process smoother and more reliable, creating optimal equipment operation and sample processing conditions for obtaining high-precision titration results.

[0074] Specifically, a fully automated calibration process was designed. Based on the preset calibration plan and sensor type, the system automatically selects appropriate calibration solutions and standard materials and controls the calibration equipment to perform calibration operations. During the calibration process, high-precision measuring instruments (such as high-precision potentiometers and spectrophotometers) are used as reference standards to compare and correct sensor measurements in real time. For example, for pH sensor calibration, standard buffer solutions with precise pH values ​​(accurate to 0.001) are used. Multi-point calibration is performed at different temperatures, and a temperature compensation algorithm is used to correct measurement errors. A sensor performance monitoring model is established to monitor key performance indicators (KPIs) such as sensor response time, stability, and sensitivity in real time during the titration process. By comparing and analyzing performance data with normal operating conditions, sensor performance degradation or failure can be detected promptly. Model-based fault diagnosis methods, such as a combination of a Kalman filter and a neural network, are employed to accurately diagnose sensor faults. For example, when an abnormal potential drift occurs in a potentiometric sensor, the system can determine whether it is due to sensor electrode contamination, electrolyte leakage, or circuit failure, and provide corresponding repair recommendations. Based on the shape and size of the titration vessel, the system uses fluid dynamics simulation software to optimize the layout of the sensor probe to ensure that the sensor can quickly and accurately sense changes in the solution. For example, in a stirred titration vessel, the sensor probe is placed away from the stirring blade but in an area where the solution is fully mixed to avoid interference with the sensor measurement caused by stirring. At the same time, special protective devices are designed for the sensor probe, such as removable anti-corrosion and anti-pollution sheaths, and the sheath surface is regularly and automatically cleaned to extend the service life of the sensor.

[0075] As you can understand, automatically selecting the calibration solution and substance and performing the calibration, combined with high-precision instruments as a reference, ensures the accuracy of the sensor's initial measurements. Real-time comparison and correction of measured values ​​dynamically maintains measurement accuracy. Establishing a performance monitoring model and continuously monitoring key indicators during titration provides a precise understanding of sensor status. Utilizing fault diagnosis methods, faults can be promptly detected and diagnosed, avoiding data deviations caused by sensor failures. This ensures the reliability of sensor data throughout the titration process, thereby improving the accuracy and credibility of titration results.

[0076] Specifically, based on detailed sample information and preprocessing results, a chemometric algorithm is used to accurately calculate the initial titration parameters. For example, based on the chemical reaction equation and the estimated content of the analyte in the sample, the theoretical titrant dosage range is calculated, and the initial titration volume and concentration are determined based on this range. During the titration process, a real-time feedback control algorithm is used to dynamically adjust the titration parameters based on data collected by multiple sensors. For example, if the rate of change of the solution's potential is detected to be too rapid, the titration speed is appropriately reduced to avoid overtitration. A library of titration models for various chemical systems has been established, each containing the reaction kinetics equations, endpoint judgment criteria, and parameter optimization strategies for a specific chemical system. During the titration process, an online learning algorithm is used to adaptively update the model. For example, when encountering a new chemical substance or a complex mixture, the system automatically records the titration data during the titration process. By comparing and analyzing the data with similar systems in the model library, the model parameters are adjusted or new sub-models are created to improve the accuracy and efficiency of the titration of the chemical system. Before the actual titration, computer simulation technology is used to simulate the titration scheme. Based on sample information, titration parameters, and chemical system models, various changes in the solution during the titration process are simulated to predict potential problems and errors. The optimal titration scheme is selected by comparing and analyzing the simulation results of different titration schemes. Furthermore, endpoint determination criteria are optimized and verified during the simulation to ensure accurate determination of the titration endpoint under different experimental conditions.

[0077] It is understandable that the use of chemometric algorithms to calculate initial parameters and combined with real-time feedback control algorithms can adjust parameters in real time based on multi-sensor data, effectively responding to changes in the titration process and reducing error accumulation. Constructing a titration model library and using online learning algorithms for adaptive updates can cope with different chemical systems and continuously optimize the model to fit the actual situation. Computer simulation experiments and comparative analysis of solutions before formal titration can predict possible problems in advance and screen out the optimal solution, greatly improving the accuracy, scientificity and efficiency of titration operations, ensuring high-precision titration results in various complex chemical analysis scenarios.

[0078] Specifically, a high-speed data acquisition card (with a sampling frequency exceeding 1kHz) is used to collect multi-sensor data in real time, ensuring the capture of rapidly changing signals in the solution. A data synchronization mechanism is established to time-stamp and align data collected by different sensors to ensure data synchronization and accuracy. For example, when collecting potential and pH data, the acquisition time error is maintained within microseconds for accurate data analysis and fusion. Deep learning-based data fusion algorithms, such as a combination of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are employed. CNNs extract spatial features from sensor data, such as the color distribution in color sensor images, while LSTMs learn temporal features, such as the trends in potential and pH data over titration time. By fusing these two approaches, a more comprehensive and accurate assessment of titration progress can be achieved. Furthermore, an attention mechanism is introduced, enabling the algorithm to automatically focus on the most important features in the data for determining the titration endpoint, improving fusion efficiency and accuracy. A real-time data visualization interface is developed to display data collected by multiple sensors in intuitive forms such as curves and charts, allowing operators to observe the titration process in real time. Data analysis tools, such as trend analysis and correlation analysis, are embedded in the visualization interface. Operators can analyze data at any time to determine whether the titration process is normal and whether there are any abnormal fluctuations or interference factors. If anomalies are detected, the system provides appropriate countermeasures, such as pausing the titration, checking the sensor, or recalibrating.

[0079] As you can see, the high-speed acquisition card captures rapidly changing signals in the solution, ensuring data timeliness and integrity. The data synchronization mechanism precisely processes data from different sensors, ensuring accuracy and consistency, and providing a reliable foundation for subsequent analysis. Deep learning data fusion algorithms can deeply tap into the value of multi-sensor data, allowing for more accurate judgment of titration progress. The visual interface and analysis tools allow operators to intuitively understand the titration status, promptly identify anomalies, and conduct in-depth research with the help of analytical tools. This effectively improves the operability, controllability, and intelligence of the titration process, further ensuring the high accuracy and reliability of titration results.

[0080] Specifically, a large amount of titration experimental data from diverse chemical systems and concentration ranges is collected, including data sequences fused from multiple sensors and corresponding titration endpoint information. Deep learning frameworks (such as TensorFlow and PyTorch) are used to construct neural network models, such as multi-layer perceptrons (MLPs), recurrent neural networks (RNNs), and their variants (such as gated recurrent units (GRUs) and long short-term memory (LSTMs). This data is used to train the models. Model performance is optimized by adjusting the model structure, number of layers, number of neurons, and optimization algorithms (such as stochastic gradient descent (SGD) and the Adam optimizer) to improve the accuracy of titration endpoint prediction. During the model prediction process, uncertainty assessment mechanisms, such as Bayesian neural networks or Monte Carlo dropout methods, are introduced to assess the uncertainty of the model's predictions. When uncertainty is high, the system automatically takes appropriate measures, such as increasing data collection frequency, extending the fine titration phase, or adjusting the endpoint threshold. At the same time, online learning techniques are used to adaptively adjust the model based on new titration experimental data, continuously improving the model's adaptability to different chemical systems and experimental conditions. A multi-model fusion strategy is employed to combine the prediction results of different machine learning models (such as neural network models, support vector machines (SVM) models, and decision tree models). For example, through weighted averaging, voting, or stacked ensemble learning, the advantages of multiple models are combined to improve the reliability of titration endpoint determination. During the fusion process, the weights of different models are dynamically adjusted based on their performance in different chemical systems to ensure accurate endpoint determination results in all situations.

[0081] It is understandable that rich data allows the model to fully learn the titration characteristics under different chemical systems and concentration ranges. The various neural network models constructed can be trained and optimized by adjusting factors, which can more accurately fit the complex titration process, thereby effectively improving the accuracy of the prediction of the titration end point and enhancing the generalization ability of the model, so that it can better cope with various titration scenarios, reduce errors caused by differences in chemical systems and concentration changes, and improve the accuracy and reliability of the entire titration process.

[0082] Specifically, according to the titration reaction equation and the actual amount of titrant, the content or concentration of the measured substance is accurately calculated. In the calculation process, considering the factors such as the calibration error of the titrant concentration, the sample volume error, the sensor measurement error, etc., the error propagation theory is used to analyze the error of the result, and the uncertainty range of the measurement result is given. For example, using the Gaussian error propagation law, the error of the concentration of the measured substance caused by the error of the titrant concentration and the volume measurement error is calculated, and compared with the preset error allowed range, a multi-level result verification system is established. In addition to cross-validation and repeated titration experiments using different titration end point judgment methods, standard substance verification and inter-laboratory comparison verification are also introduced. Periodically use standard substances with known concentrations for titration experiments, compare the measurement results with the standard values, and evaluate the accuracy and reliability of the system. At the same time, participate in inter-laboratory comparison projects, compare the titration results of this laboratory with those of other laboratories, find potential problems and improve them in time. In the verification process, if the result is outside the error allowed range, the system automatically starts the traceability analysis program, finds the possible error sources from sample collection, pretreatment, titration process to result calculation, and generates a detailed error report. All related data of the titration experiment (including sample information, titration parameters, multi-sensor data curves, calculation results, verification information, error analysis report, etc.) are stored in a relational database (such as MySQL, Oracle, etc.) or a non-relational database (such as MongoDB, etc.). Establish a data index and query system to facilitate operators to quickly retrieve and query historical data according to sample number, experiment date, measured substance, etc. At the same time, regularly backup and archive the data to ensure the safety and integrity of the data. Use data encryption technology to encrypt sensitive data (such as sample source, operator information, etc.) to prevent data leakage.

[0083] It can be understood that accurate calculation can obtain more accurate result values, and comprehensive consideration and analysis of various errors can clearly define the reliability interval of the result, so that the operator has a clear idea of the data quality. The multi-level result verification system further verifies the accuracy of the result from multiple dimensions, cross- verifies by different methods, compares with standard substances, and compares horizontally between laboratories, effectively eliminates accidental errors and systematic errors, greatly improves the accuracy, reliability and authority of the titration result, and provides solid data support for scientific research, quality control, etc.

[0084] Specifically, an intelligent self-cleaning program has been developed to automatically select the appropriate cleaning solution and method based on the type of titration test, the titrant used, and the sample properties. For example, after an acidic titration test, the burette and container are first cleaned with an alkaline solution and then rinsed with water. Samples containing organic matter are cleaned with an organic solvent. During the cleaning process, sensors monitor the cleaning effect, such as by measuring the conductivity, pH, or residual concentration of specific substances in the post-cleaning solution to determine whether it is completely clean. If the cleaning is not up to standard, the system automatically adjusts the cleaning parameters or repeats the cleaning steps. An equipment maintenance management system has been established to develop maintenance plans based on factors such as equipment usage time, number of runs, and the life cycle of key components. For example, burettes are required to be replaced every 100 uses, and sensors are required to undergo full calibration and maintenance every three months. Before the maintenance plan expires, the system automatically issues a maintenance reminder, including the maintenance details, required tools and spare parts, and instructions for the maintenance steps. A maintenance record archive is also established, documenting the time, content, maintenance personnel, and post-maintenance operational status of each maintenance session. This provides data support for the long-term stable operation of the equipment. IoT technology and big data analytics are used to monitor the real-time status of key system components (such as motors, pumps, sensors, and circuits). Sensors are installed on these components to collect operating parameter data such as temperature, vibration, current, and voltage, which are then wirelessly transmitted to the system control center. Machine learning algorithms are used to analyze this data and develop equipment failure prediction models. For example, a support vector machine (SVM) or neural network model can be used to predict impending motor failure based on parameters such as motor vibration frequency and current fluctuations. When the model predicts a failure risk, the system issues an early warning, notifying operators to conduct inspections and repairs, thus preventing unexpected equipment failures from causing experimental interruptions or data loss. Furthermore, the system automatically adjusts titration plans based on these warnings, deferring or relocating affected experiments to functioning equipment.

[0085] Understandably, the intelligent self-cleaning program automatically matches cleaning solutions based on experimental characteristics and monitors their effectiveness, ensuring instrument cleanliness, reducing sample carryover and cross-contamination, and guaranteeing the accuracy of subsequent titrations. The equipment maintenance management unit develops plans and provides advance reminders based on equipment usage. Leveraging the Internet of Things and big data to monitor the status of key components and provide early warnings of failures, it effectively prevents experimental interruptions and data loss caused by sudden equipment failures, ensures stable equipment operation, extends its service life, rationally allocates maintenance resources, and flexibly adjusts titration plans, making the entire titration workflow more scientific, orderly, efficient, and reliable.

[0086] The above embodiment provides a high-precision intelligent titration control method, which uses intelligent prompts and cloud databases to assist in sample information management, standardized protocols to ensure equipment integration and automated preprocessing, precise calibration and performance monitoring to optimize sensor applications, chemometrics and online learning to achieve intelligent setting and updating of titration parameters and schemes, high-speed acquisition and deep learning algorithms to achieve efficient processing of multi-sensor data, multi-model strategies to ensure accurate prediction and judgment of endpoints, multi-level verification and error analysis to improve result accuracy and reliability, and intelligent self-cleaning and Internet of Things technology to support system maintenance and assurance. It comprehensively improves the intelligence, precision, and automation level of titration, significantly enhances the adaptability and stability of the system, greatly improves the quality and efficiency of chemical analysis titration work, and leads titration technology to a new stage of greater efficiency, higher precision, and higher intelligence.

[0087] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a high-precision titration intelligent control system, including:

[0088] Sample Information Management Module: Builds a sample information entry interface with intelligent prompts, connects to the cloud database and regularly updates the information base, uses rule-based reasoning combined with machine learning to generate pre-processing plans, and evaluates the feasibility and efficiency of the plans;

[0089] Equipment integration and monitoring module: Leveraging standardized industrial communication protocols, seamlessly integrate intelligent titration equipment with automated sample pretreatment equipment, establish an equipment status monitoring network, and perform automated sample pretreatment.

[0090] Sensor Calibration and Diagnosis Module: Automatically selects calibration solutions and standard substances based on preset plans and sensor types, performs calibration operations, uses high-precision measuring instruments as reference standards, compares and corrects sensor measurements in real time, establishes a performance monitoring model, continuously monitors key indicators during titration, and uses fault diagnosis methods to promptly detect and diagnose faults.

[0091] Titration Parameters and Scheme Module: This module uses chemometric algorithms to calculate initial titration parameters based on sample details and pretreatment results. A real-time feedback control algorithm is employed during the titration process to dynamically and flexibly adjust parameters based on data collected by multiple sensors. A library of titration models for various chemical systems is constructed, and online learning algorithms are used to adaptively update the models during titration. Computer simulation technology is used to comprehensively simulate experiments and compare and analyze different schemes before the actual titration.

[0092] Data acquisition and fusion module: Utilizes high-speed data acquisition cards to collect multi-sensor data in real time, establishes a data synchronization mechanism to accurately time-stamp and align data from different sensors, applies deep learning data fusion algorithms, develops a visual interface to display data, and embeds analytical tools;

[0093] Endpoint prediction and judgment module: This module collects titration experimental data covering different chemical systems and concentration ranges, uses a deep learning framework to build neural network models such as multi-layer perceptrons, recurrent neural networks, and their variants, and optimizes the model by adjusting various model elements.

[0094] Result processing and storage module: Accurately calculate the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully consider the titrant concentration calibration error, sample amount error and sensor measurement error, use error propagation theory to perform error analysis, provide uncertainty range, and establish a multi-level result verification system;

[0095] System maintenance and assurance module: Develop an intelligent self-cleaning program to automatically select the appropriate cleaning solution and method based on the titration test type, titrant, and sample properties. During cleaning, use sensors to monitor the cleaning effect and automatically adjust the parameters if it does not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on the equipment usage time and number of operations, and provide advance reminders. Use the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust the titration plan.

[0096] It is understandable that the above-mentioned high-precision titration intelligent control system and the high-precision titration intelligent control method have the same beneficial effects, and will not be described in detail here.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A high-precision titration intelligent control method, characterized in that: include: Build a sample information entry interface with intelligent prompts, connect to the cloud database and regularly update the information database, use rule reasoning and combine machine learning to generate specific implementation methods for pretreatment plans, and evaluate the feasibility and efficiency of pretreatment plans; Leveraging standardized industrial communication protocols, seamless integration of intelligent titration equipment and automated sample pretreatment equipment is achieved, enabling the establishment of an equipment status monitoring network for automated sample pretreatment. Automatically select calibration solutions and standard substances based on the preset calibration plan and sensor type, and perform calibration operations. High-precision measuring instruments are used as reference standards to compare and correct sensor measurements in real time. A performance monitoring model is established to continuously monitor key indicators during titration, and fault diagnosis methods are used to promptly detect and diagnose faults. Based on detailed sample information and pretreatment results, chemometric algorithms are used to calculate initial titration parameters. A real-time feedback control algorithm is employed during the titration process to dynamically and flexibly adjust parameters based on data collected by multiple sensors. A titration model library for various chemical systems is constructed, and online learning algorithms are used to adaptively update the models during titration. Computer simulation technology is used to comprehensively simulate experiments and compare and analyze different schemes before the actual titration. Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-stamp and align data from different sensors, apply deep learning data fusion algorithms, develop a visual interface to display data, and embed analysis tools; Collect titration experimental data covering different chemical systems and concentration ranges, use deep learning frameworks to build multi-layer perceptron, recurrent neural network and its variant neural network models, and optimize the model by adjusting various factors to train and predict the titration endpoint under different chemical systems and concentration ranges; Accurately calculate the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully consider the titrant concentration calibration error, sample measurement error and sensor measurement error, use error propagation theory to perform error analysis, provide uncertainty ranges, and establish a multi-level result verification system; Develop an intelligent self-cleaning program to automatically select suitable cleaning solutions and methods based on the titration test type, titrant, and sample properties. Use sensors to monitor the cleaning effect during cleaning, and automatically adjust parameters if it does not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on the equipment usage time and number of operations, and provide advance reminders. Use the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust titration plans.

2. A high-precision titration intelligent control method according to claim 1, characterized in that: The aforementioned construction of a sample information entry interface with intelligent prompting functions, connecting to a cloud database and regularly updating the information database, utilizing rule reasoning combined with machine learning to generate implementation methods for pretreatment plans, and evaluating the feasibility and efficiency of pretreatment plans, includes: The information entry interface has been optimized to be intuitive, concise, and have intelligent prompts. When the operator enters the sample type, a drop-down menu or auto-fill options will automatically pop up showing the common sources, estimated concentration ranges, and main components of that sample type. Database update and management: establish connections with cloud databases, regularly update the sample information database from authoritative chemical analysis databases, scientific research institutions' shared data platforms, and sample data accumulated by internal laboratories; use data mining technology to analyze and organize new data, extract useful information, and optimize the recommended algorithm for pretreatment solutions; set database administrator permissions to allow professionals to review, modify, and supplement the database; Intelligent generation and evaluation of pretreatment plans uses a combination of rule-based reasoning units and machine learning algorithms to generate pretreatment plans. The rule-based reasoning unit determines the neutralization pretreatment method of the sample based on chemical knowledge and empirical rules. The machine learning algorithm learns from a large amount of historical sample pretreatment data and recommends more accurate pretreatment steps based on the similarity of sample information. After the plan is generated, it automatically evaluates the feasibility and efficiency of the plan.

3. A high-precision titration intelligent control method according to claim 1, characterized in that: The aforementioned method of achieving seamless integration of intelligent titration equipment and automated sample pretreatment equipment with the help of standardized industrial communication protocols, establishing an equipment status monitoring network, and performing automated sample pretreatment includes: Equipment integration and communication optimization: Using standardized industrial communication protocols, intelligent titration equipment is seamlessly integrated with automated sample pretreatment equipment. An equipment status monitoring network is established to monitor the operating parameters of the pretreatment equipment in real time. When equipment fails or is abnormal, the equipment automatically switches to a backup device or suspends the titration process, and issues an alarm to notify the operator. Precise metering and mixing control: high-precision electronic balances and precision pipettes equipped with automatic calibration functions are used to weigh and measure samples. Measuring equipment is calibrated regularly. Intelligent control algorithms are introduced in stirring and ultrasonic mixing operations to automatically adjust stirring speed, ultrasonic power, and time according to the properties of the sample. Interference substance removal and quality detection are intelligent, and an intelligent chemical reagent addition unit is constructed. According to the type and content of interfering substances that may exist in the sample, the appropriate amount of removal reagent is accurately calculated and automatically added. In the quality detection process, a combination of online spectral analysis technology and chemical sensor arrays is used to conduct real-time and comprehensive detection of the clarity, pH, and ion concentration of the solution. A multi-parameter quality detection model is established, and the detection data is analyzed using machine learning algorithms.

4. A high-precision titration intelligent control method according to claim 1, characterized in that: The aforementioned automatic selection of calibration solutions and standard substances based on the preset calibration plan and sensor type, and the implementation of calibration operations, using high-precision measuring instruments as reference standards, real-time comparison and correction of sensor measurements, and the establishment of a performance monitoring model to continuously monitor key indicators during the titration process, using fault diagnosis methods to promptly discover and diagnose faults, including: The automatic calibration procedure is optimized and a fully automatic calibration process is designed. The equipment automatically selects the appropriate calibration solution and standard substance according to the preset calibration plan and sensor type, and controls the calibration equipment to perform calibration operations. During the calibration process, high-precision measuring instruments are used as reference standards to perform real-time comparison and correction of sensor measurements. Sensor performance monitoring and fault diagnosis: Establish a sensor performance monitoring model to monitor the sensor's response time, stability, and sensitivity in real time during the titration process. By comparing and analyzing the performance data with that under normal working conditions, timely detect sensor performance degradation and faults. Use a model-based fault diagnosis method to accurately diagnose sensor faults. Sensor layout and protection optimization: According to the shape and size of the titration vessel, the layout position of the sensor probe is optimized using fluid mechanics simulation software, and a special protection device is designed for the sensor probe.

5. A high-precision titration intelligent control method according to claim 1, characterized in that: The aforementioned method uses a chemometric algorithm to calculate the initial titration parameters based on detailed sample information and pretreatment results. During the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters based on data collected by multiple sensors. A titration model library for various chemical systems is constructed, and an online learning algorithm is used to adaptively update the model during titration. Before the formal titration, computer simulation technology is used to fully simulate the experiment and compare and analyze different schemes, including: Intelligent parameter calculation and dynamic adjustment: Based on the detailed information of the sample and the pre-processing results, the chemometric algorithm is used to accurately calculate the initial parameters of the titration. During the titration process, a real-time feedback control algorithm is used to dynamically adjust the titration parameters according to the data collected by multiple sensors. Adaptive update of chemical system models. A titration model library for various chemical systems is established. Each model contains the reaction kinetics equation, endpoint judgment criteria, and parameter optimization strategy for a specific chemical system. During the titration process, the model is adaptively updated using an online learning algorithm. Titration scheme simulation and optimization verification: Before the formal titration, computer simulation technology is used to simulate the titration scheme. According to the sample information, titration parameters and chemical system model, various changes in the solution during the titration process are simulated to predict possible problems and errors. By comparing and analyzing the simulation results of different titration schemes, the optimal titration scheme is selected, and the endpoint judgment conditions are optimized and verified during the simulation process.

6. A high-precision titration intelligent control method according to claim 1, characterized in that: The aforementioned use of high-speed data acquisition cards to collect multi-sensor data in real time, establishing a data synchronization mechanism to accurately time-stamp and align data from different sensors, applying deep learning data fusion algorithms, developing a visual interface to display data, and embedding analysis tools, includes: High-speed data acquisition and synchronization: Use high-speed data acquisition cards to collect multi-sensor data in real time, capture rapidly changing signals in the solution, establish a data synchronization mechanism, and time-stamp and align data collected by different sensors; Improved multi-sensor data fusion algorithm, using a deep learning-based data fusion algorithm to determine the titration process, and introducing an attention mechanism, so that the algorithm automatically focuses on the most important feature information in the data for determining the titration endpoint; Data visualization and real-time analysis: develop a real-time data visualization interface to display the data collected by multiple sensors in intuitive forms such as curves and charts. Embed data analysis tools in the visualization interface to analyze the data at any time and judge the titration process. If any abnormality is found, provide corresponding response measures.

7. A high-precision titration intelligent control method according to claim 1, characterized in that: The collection covers titration experimental data of different chemical systems and concentration ranges. A multi-layer perceptron, recurrent neural network, and its variant neural network models are constructed using a deep learning framework. The model is trained and optimized by adjusting various factors of the model to predict the titration endpoint under different chemical systems and concentration ranges, including: Model training and optimization: collect titration experimental data of different chemical systems and different concentration ranges, use deep learning framework to build neural network model, use experimental data to train the model, and optimize model performance by adjusting the model structure, number of layers, number of neurons and optimization algorithm; Model uncertainty assessment and adaptive adjustment: During the model prediction process, an uncertainty assessment mechanism is introduced to evaluate the uncertainty of the model prediction results. When the uncertainty is high, corresponding measures are automatically taken. Using online learning technology, the model is adaptively adjusted according to the new titration experimental data, continuously improving the model's adaptability to different chemical systems and experimental conditions; Multi-model fusion and decision-making adopts a multi-model fusion strategy to fuse the prediction results of different types of machine learning models. During the fusion process, the weights are dynamically adjusted according to the performance of different models in different chemical systems.

8. A high-precision titration intelligent control method according to claim 1, characterized in that: The method accurately calculates the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully considers the titrant concentration calibration error, sample measurement error and sensor measurement error, uses error propagation theory to perform error analysis, provides uncertainty ranges, and establishes a multi-level result verification system, including: Result calculation and error analysis: Based on the titration reaction equation and the actual amount of titrant used, the content or concentration of the substance to be measured is accurately calculated. During the calculation process, the calibration error of the titrant concentration, the sample amount error and the sensor measurement error are fully considered. The error propagation theory is used to analyze the results and the uncertainty range of the measurement results is given. Result verification and quality control: establish a multi-level result verification system, adopt different titration endpoint judgment methods, conduct cross-validation and repeated titration experiments, introduce standard substance verification and inter-laboratory comparison verification, regularly use standard substances of known concentration to conduct titration experiments, compare the measurement results with the standard values, evaluate the accuracy and reliability of the measurement, participate in inter-laboratory comparison projects, compare and analyze the titration results of the laboratory with those of other laboratories, identify potential problems and make timely improvements. During the verification process, if the result exceeds the allowable error range, the equipment automatically starts the traceability analysis program to find the source of the error from sample collection, pretreatment, titration process to result calculation, and generate a detailed error report; Data storage and management: All relevant data of the titration experiment are stored in a relational database or a non-relational database, and data index and query units are established to facilitate operators to quickly retrieve and query historical data based on sample number, experiment date and test substance. Data is backed up and archived regularly, and sensitive data is encrypted using data encryption technology.

9. A high-precision titration intelligent control method according to claim 1, characterized in that: The intelligent self-cleaning program described above automatically selects the appropriate cleaning solution and method based on the titration test type, titrant, and sample properties. During cleaning, sensors monitor the cleaning effect and automatically adjust parameters if it does not meet the standards. An equipment maintenance management unit is established to formulate maintenance plans and provide advance reminders based on equipment usage time and number of operations. The Internet of Things and big data are used to monitor the status of key components, issue early warnings for faults, and adjust the titration plan, including: Intelligent self-cleaning program: Develop an intelligent self-cleaning program that automatically selects the appropriate cleaning solution and cleaning method based on the type of titration test, the titrant used, and the nature of the sample. During the cleaning process, sensors monitor the cleaning effect. If the cleaning effect is not up to standard, the equipment automatically adjusts the cleaning parameters or repeats the cleaning steps. Equipment maintenance planning and reminders: Establish an equipment maintenance management unit to formulate maintenance plans based on the equipment's usage time, number of operations, and the life cycle of key components. Before the maintenance plan expires, the equipment will automatically issue maintenance reminders. At the same time, a maintenance record file will be established to record the time, content, maintenance personnel, and the equipment's operating status after each maintenance. Intelligent condition monitoring and fault warning utilize Internet of Things technology and big data analysis to conduct real-time condition monitoring of key components of the equipment. Sensors are installed on the components to collect operating parameter data such as temperature, vibration, current, and voltage. The data is transmitted to the equipment control center via wireless transmission. Machine learning algorithms are used to analyze these data and establish an equipment failure prediction model. When the model predicts a failure risk, the equipment will issue a warning message in advance to notify the operator to conduct inspection and maintenance. At the same time, the equipment will automatically adjust the titration plan based on the fault warning information and postpone or transfer the affected experimental tasks to other normal equipment.

10. A high-precision titration intelligent control system, characterized in that: include: The sample information management module builds a sample information entry interface with intelligent prompts, connects to the cloud database and regularly updates the information database. It uses rule reasoning and combines machine learning to generate specific implementation methods for pretreatment plans and evaluates the feasibility and efficiency of pretreatment plans. The equipment integration and monitoring module uses standardized industrial communication protocols to achieve seamless integration of intelligent titration equipment and automated sample pretreatment equipment, establish an equipment status monitoring network, and perform automated sample pretreatment; The sensor calibration and diagnostics module automatically selects calibration solutions and standard substances based on the preset calibration plan and sensor type, and performs calibration operations. It uses high-precision measuring instruments as reference standards to compare and correct sensor measurements in real time. It also establishes a performance monitoring model to continuously monitor key indicators during titration and uses fault diagnosis methods to promptly detect and diagnose faults. The titration parameter and scheme module uses chemometric algorithms to calculate initial titration parameters based on sample details and pretreatment results. A real-time feedback control algorithm is used during the titration process to dynamically and flexibly adjust parameters based on data collected by multiple sensors. A titration model library for various chemical systems is constructed, and online learning algorithms are used to adaptively update the models during titration. Computer simulation technology is used to comprehensively simulate experiments and compare and analyze different schemes before the actual titration. The data acquisition and fusion module uses high-speed data acquisition cards to collect multi-sensor data in real time, establishes a data synchronization mechanism to accurately time-stamp and align data from different sensors, applies deep learning data fusion algorithms, develops a visual interface to display data, and embeds analytical tools; The endpoint prediction and judgment module collects titration experimental data covering different chemical systems and concentration ranges, uses a deep learning framework to build multi-layer perceptron, recurrent neural network and its variant neural network models, and optimizes the training model by adjusting various factors of the model to predict the titration endpoint under different chemical systems and concentration ranges; The result processing and storage module accurately calculates the content and concentration of the substance to be measured based on the titration reaction equation and the actual amount of titrant used, fully considering the titrant concentration calibration error, sample measurement error and sensor measurement error, and uses error propagation theory to perform error analysis, provide uncertainty ranges, and establish a multi-level result verification system; The system maintenance and assurance module develops an intelligent self-cleaning program, automatically selects appropriate cleaning solutions and methods based on the titration test type, titrant, and sample properties, uses sensors to monitor the cleaning effect during cleaning, and automatically adjusts parameters if it does not meet the standards. It also establishes an equipment maintenance management unit, formulates maintenance plans based on the equipment usage time and number of operations, and provides advance reminders. It uses the Internet of Things and big data to monitor the status of key components, warn of faults, and adjust titration plans.

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