High-precision titration intelligent control method and system

Abstract

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

Description

Technical Field

[0001] The present invention relates to the field of intelligent titration control, and in particular to a high-precision intelligent titration 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 automation control technology, sensor technology, data communication technology, computer technology, chemometrics, machine learning, Internet of Things and other technologies, thus giving birth to titration intelligent control technology.

[0003] However, the existing intelligent titration control technology often has 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 technology for 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 the existing high-precision titration technology, such as the lack of 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 the existing high-precision titration technology, such as the sample information processing is not intelligent and efficient enough, the equipment integration and automatic preprocessing are insufficient, the sensor calibration and monitoring are imperfect, the titration parameter setting and scheme optimization lack adaptability, the data acquisition fusion and endpoint judgment are not accurate enough, the result calculation verification and storage lack systematicity, and the system self-cleaning maintenance and status monitoring are not intelligent.

[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 prompt function, connect to the cloud database and regularly update the information database, use rule reasoning and combine machine learning to generate pre-processing solutions, and evaluate the feasibility and efficiency of the solutions;

[0008] With the help of standardized industrial communication protocols, the intelligent titration equipment and automated sample pretreatment equipment can be seamlessly integrated, and an equipment status monitoring network can be established to perform automated sample pretreatment.

[0009] Automatically select calibration solutions and standard substances according to preset plans and sensor types, and carry out 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 titration, and use fault diagnosis methods to promptly detect and diagnose faults.

[0010] Based on the detailed information of the sample and the pretreatment results, the chemometric algorithm is used to calculate the initial parameters of the titration. In the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters according to the data collected by multiple sensors. A titration model library for various chemical systems is constructed and the model is adaptively updated during titration using an online learning algorithm. Before the formal titration, a comprehensive simulation experiment is performed using computer simulation technology and different schemes are compared and analyzed.

[0011] Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-mark and align data from different sensors, use 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 framework to build neural network models such as multi-layer perceptron, recurrent neural network and its variants, and train and optimize the model by adjusting various factors of the model;

[0013] The content and concentration of the substance to be measured are accurately calculated based on the titration reaction equation and the actual amount of titrant used, and the calibration error of the titrant concentration, the sample measurement error and the sensor measurement error are fully considered. The error propagation theory is used to perform error analysis, the uncertainty range is given, and a multi-level result verification system is established;

[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 they do not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on 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 sample information entry interface with intelligent prompt function is constructed, connected to the cloud database and the information database is updated regularly, rule reasoning is used in combination with machine learning to generate pre-processing schemes, and the feasibility and efficiency of the schemes are evaluated, including:

[0016] The information entry interface is optimized, with an intuitive, concise human-computer interaction interface with intelligent prompts. When the operator enters the sample type, a drop-down menu or auto-fill option will automatically pop up for the common sources, estimated concentration range, and main components of the sample type.

[0017] Database update and management: establish connection with cloud database, regularly update sample information base from authoritative chemical analysis database, scientific research institutions’ shared data platform 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 of pretreatment scheme, set database administrator permissions, and allow professionals to review, amend and supplement the database;

[0018] Intelligent generation and evaluation of pretreatment plans, using 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 seamless integration of intelligent titration equipment and automated sample pretreatment equipment is achieved by using standardized industrial communication protocols, and an equipment status monitoring network is established to perform automated sample pretreatment, including:

[0020] Equipment integration and communication optimization, using standardized industrial communication protocols, seamlessly integrating intelligent titration equipment with automated sample pretreatment equipment, establishing an equipment status monitoring network, and real-time monitoring of the operating parameters of the pretreatment equipment. When equipment fails or is abnormal, the equipment automatically switches to backup equipment or suspends the titration process, and issues an alarm to notify the operator;

[0021] Precise metering and mixing control, using high-precision electronic balances and precision pipettes equipped with automatic calibration functions to weigh and measure samples, regularly calibrating metering equipment, and introducing intelligent control algorithms in stirring and ultrasonic mixing operations to automatically adjust stirring speed, ultrasonic power and time according to the nature of the sample;

[0022] The removal of interfering substances and quality inspection 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 inspection link, a combination of online spectral analysis technology and chemical sensor arrays is used to conduct real-time and comprehensive inspections of the clarity, pH, and ion concentration of the solution, establish a multi-parameter quality inspection model, and use machine learning algorithms to analyze the inspection data.

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

[0024] Automatic calibration program optimization, design of fully automatic calibration process, the equipment automatically selects appropriate calibration solution and standard substance according to the preset calibration plan and sensor type, and controls the calibration equipment to perform calibration operation. During the calibration process, high-precision measuring instruments are used as reference standards to compare and correct the sensor's measurement values ​​in real time;

[0025] 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 discover the sensor's performance degradation and faults. Use a model-based fault diagnosis method to accurately diagnose sensor faults.

[0026] Sensor layout and protection optimization: According to the shape and size of the titration container, 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.

[0027] Furthermore, the chemometric algorithm is used to calculate the initial parameters of the titration based on the sample detailed information and the pretreatment results, and a real-time feedback control algorithm is used in the titration process. According to the data collected by multiple sensors, the parameters are adjusted dynamically and flexibly, a titration model library of 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 by computer simulation technology and different schemes are compared and analyzed, including:

[0028] Intelligent calculation and dynamic adjustment of parameters: Based on the detailed information of the sample and the preprocessing 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.

[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 of a specific chemical system, and use online learning algorithms to adaptively update the model during the titration process;

[0030] Titration scheme simulation and optimization verification: Before the formal titration, the titration scheme is simulated using computer simulation technology. 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 high-speed data acquisition card is used to collect multi-sensor data in real time, a data synchronization mechanism is established to accurately time-mark and align different sensor data, a deep learning data fusion algorithm is used, a visual interface is developed to display data and an analysis tool is embedded, including:

[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 data fusion algorithm based on deep learning to judge the titration process, introducing an attention mechanism, so that the algorithm automatically focuses on the most important feature information in the data for judging 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 build neural network models such as multi-layer perceptrons, recurrent neural networks and their variants, and trains and optimizes the model by adjusting various factors of the model, 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: In the process of model prediction, 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 to continuously improve 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 content and concentration of the substance to be measured are accurately calculated based on the titration reaction equation and the actual amount of titrant, the calibration error of the titrant concentration, the sample measurement error and the sensor measurement error are fully considered, the error propagation theory is used to perform error analysis, the uncertainty range is given, and a multi-level result verification system is established, including:

[0040] Result calculation and error analysis: According to the titration reaction equation and the actual amount of titrant, the content or concentration of the substance to be measured is accurately calculated. In the calculation process, the calibration error of the titrant concentration, the sample measurement 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 for 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 this 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: store all relevant data of the titration experiment in a relational or non-relational database, establish data index and query units, and facilitate operators to quickly retrieve and query historical data according to sample number, experiment date and test substance, back up and archive data regularly, and use data encryption technology to encrypt sensitive data.

[0043] Furthermore, the intelligent self-cleaning program described in the development automatically selects the appropriate cleaning solution and method according to the titration test type, titrant and sample properties, monitors the cleaning effect with the help of sensors during cleaning, and automatically adjusts the parameters if it does not meet the standards, establishes an equipment maintenance management unit, formulates maintenance plans and gives advance reminders based on the equipment usage time and operation times, uses the Internet of Things and big data to monitor the status of key components, and warns of failures, and adjusts 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 according to the type of titration test, the titrant used and the nature of the sample. During the cleaning process, sensors are used to monitor the cleaning effect. If the cleaning does not meet the standards, the equipment automatically adjusts the cleaning parameters or repeats the cleaning steps.

[0045] Equipment maintenance plan and reminder: Establish equipment maintenance management unit, formulate maintenance plan according to equipment usage time, operation times and life cycle of key components. Before the maintenance plan expires, the equipment will automatically send out maintenance reminder notifications. At the same time, establish maintenance record files to record the time, content, maintenance personnel and operation status of each maintenance.

[0046] Intelligent condition monitoring and fault warning use 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 according to the fault warning information and postpone or transfer the affected experimental tasks to other normal equipment.

[0047] Compared with the prior art, the beneficial effects of the present invention are: sample information management is assisted by intelligent prompts and cloud databases, equipment integration and automated preprocessing are guaranteed by standardized protocols, sensor application is optimized by precise calibration and performance monitoring, intelligent setting and updating of titration parameters and schemes are achieved through chemometrics and online learning, efficient processing of multi-sensor data is achieved through high-speed acquisition and deep learning algorithms, accurate prediction and judgment of endpoints are ensured through multi-level verification and error analysis, accuracy and reliability of results are improved through intelligent self-cleaning and Internet of Things technology support system maintenance and guarantee, which 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 higher efficiency, higher precision and higher intelligence.

[0048] On the other hand, the present application also provides a high-precision titration intelligent control system, comprising:

[0049] Sample information management module: 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 pre-processing solutions, and evaluate the feasibility and effectiveness of the solutions;

[0050] Equipment integration and monitoring module: With the help of standardized industrial communication protocols, the intelligent titration equipment and the automated sample pretreatment equipment are seamlessly integrated, and an equipment status monitoring network is established to perform automated sample pretreatment.

[0051] Sensor calibration and diagnosis module: automatically selects calibration solutions and standard substances according to preset plans and sensor types, 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, continuously monitors key indicators during titration, and uses fault diagnosis methods to promptly detect and diagnose faults.

[0052] Titration parameters and scheme module: Based on the detailed information of the sample and the pretreatment results, the chemometric algorithm is used to calculate the initial parameters of the titration. In the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters according to the data collected by multiple sensors. A titration model library for various chemical systems is constructed and the model is adaptively updated during titration using an online learning algorithm. Before the formal titration, a comprehensive simulation experiment is performed using computer simulation technology and different schemes are compared and analyzed.

[0053] Data acquisition and fusion module: Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-mark and align data from different sensors, use deep learning data fusion algorithms, develop a visual interface to display data and embed analysis tools;

[0054] Endpoint prediction and judgment module: collects titration experimental data covering different chemical systems and concentration ranges, uses deep learning framework to build neural network models such as multi-layer perceptron, recurrent neural network and its variants, and optimizes the model by adjusting various factors of the model;

[0055] 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 measurement error and sensor measurement error, use the error propagation theory to perform error analysis, give the uncertainty range, and establish a multi-level result verification system;

[0056] System maintenance and guarantee module: 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 they do not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on 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.

[0057] 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, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A flowchart of a high-precision titration intelligent control method provided by an embodiment of the present invention;

[0060] Figure 2 A functional block diagram of a high-precision titration intelligent control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] 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 described herein. On the contrary, these embodiments are provided in order 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, in the absence of conflict, the embodiments of the present invention and the features described 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 combination with the embodiments.

[0062] Reference Figure 1 In some embodiments of the present application, a high-precision titration intelligent control method comprises:

[0063] 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.

[0064] Step S200: Achieve seamless integration of intelligent titration equipment and automated sample pretreatment equipment by means of standardized industrial communication protocols, establish an equipment status monitoring network, and perform automated sample pretreatment.

[0065] Step S300: Automatically select calibration solutions and standard substances according to the preset plan and sensor type, and carry out calibration operations. Use high-precision measuring instruments as reference standards to compare and correct the sensor's measured values ​​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 discover and diagnose faults.

[0066] Step S400: Calculate the initial parameters of the titration using a chemometric algorithm based on the sample detailed information and pretreatment results. Use a real-time feedback control algorithm during the titration process to dynamically and flexibly adjust the parameters based on data collected by multiple sensors. Build a titration model library for various chemical systems and use an online learning algorithm to adaptively update the model during titration. Use computer simulation technology to fully simulate the experiment and compare and analyze different schemes before the formal titration.

[0067] 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-mark and align different sensor data, use a deep learning data fusion algorithm, develop a visual interface to display data and embed analysis tools.

[0068] 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.

[0069] 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 consider the titrant concentration calibration error, sample measurement error and sensor measurement error, use the error propagation theory to perform error analysis, give the uncertainty range, and establish a multi-level result verification system.

[0070] Step S800: Develop an intelligent self-cleaning program to automatically select suitable cleaning solutions and methods based on the titration test type, titrant and sample properties. During cleaning, use sensors to monitor the cleaning effect and automatically adjust parameters if it does not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on 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.

[0071] Specifically, an intuitive, concise human-computer interaction interface with intelligent prompt function is designed. When the operator enters the sample type, the system automatically pops up a drop-down menu or auto-fill options for the common sources, estimated concentration ranges and main components of the sample type, so that the operator can quickly and accurately enter information. For example, when "food samples" are selected, common food types such as fruits, vegetables, meat, etc. will be prompted, and different estimated ingredient ranges will be corresponding, such as vitamin C, sugars and other ingredients that may be contained in fruits and their approximate concentration ranges. A cloud database connection is established, and the sample information library is regularly updated from authoritative chemical analysis databases, scientific research institutions' shared data platforms, and a large amount of sample data accumulated by internal laboratories. Data mining technology is used to analyze and organize new data, and useful information is extracted to optimize the pretreatment scheme recommendation algorithm. At the same time, database administrator permissions are set to allow professionals to review, revise and supplement the database to ensure the accuracy and completeness of the data, and the pretreatment scheme is generated by combining a rule-based reasoning system with a machine learning algorithm. The rule reasoning system determines the neutralization pretreatment method for certain acidic samples based on chemical knowledge and empirical rules, such as the principle of acid-base neutralization reaction. The machine learning algorithm learns from a large amount of historical sample preprocessing data and recommends more accurate preprocessing steps based on the similarity of sample information. After generating a plan, the system automatically evaluates the feasibility and efficiency of the plan, such as estimating the preprocessing time, cost, and possible errors, to provide a reference for operators.

[0072] It is understandable that operations such as building a sample information entry interface with intelligent prompt function and connecting it to a 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.

[0073] Specifically, standardized industrial communication protocols (such as Profibus, ModbusTCP, etc.) are used to seamlessly integrate the intelligent titration system with the automated sample pretreatment equipment to achieve high-speed and stable data transmission and command control. An equipment status monitoring network is established to monitor the operating parameters of the pretreatment equipment (such as motor speed, temperature, pressure, etc.) in real time to ensure the normal operation of the equipment. When the equipment fails or is abnormal, the system automatically switches to the backup equipment or suspends the titration process, and issues an alarm to notify the operator. For the weighing and measurement of samples, 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, and they are equipped with automatic calibration functions. The metering equipment is calibrated regularly to ensure accuracy. In the stirring and ultrasonic mixing operations, intelligent control algorithms are introduced to automatically adjust the stirring speed, ultrasonic power and time according to the properties of the sample (such as viscosity and density) to achieve the best mixing effect. For example, for samples with high viscosity, the stirring speed and ultrasonic power are appropriately increased, and the processing time is extended. An intelligent chemical reagent addition system is developed to accurately calculate and automatically add the appropriate amount of removal reagent according to the type and content of interfering substances that may exist in the sample. For example, when determining the content of a specific protein in a biological sample, the enzyme-linked immunosorbent assay (ELISA) principle is used to automatically add specific antibodies to remove interference from other irrelevant proteins. In the quality inspection process, a combination of online spectral analysis technology and chemical sensor arrays is used to conduct real-time and comprehensive inspections of multiple parameters such as solution clarity, pH, and ion concentration. By establishing a multi-parameter quality inspection model and using machine learning algorithms to analyze the inspection data, it is accurately determined whether the pre-treated sample meets the titration requirements. If not, the system automatically adjusts the pre-treatment parameters or starts the secondary processing process.

[0074] It is understandable that the use of standardized industrial communication protocols to achieve seamless equipment integration, establish a monitoring network, and carry out automated preprocessing ensures high-speed and stable data transmission and precise command control between intelligent titration equipment and automated sample preprocessing equipment, and can monitor equipment operating parameters in real time, detect and handle faults in a timely manner, and ensure the normal operation of equipment. Automated preprocessing not only improves the accuracy and consistency of operations, but also greatly improves work efficiency, reduces human errors and operation time, makes the titration process smoother and more reliable, and creates good equipment operation and sample processing conditions for obtaining high-precision titration results.

[0075] Specifically, a fully automatic calibration process is designed. The system automatically selects appropriate calibration solutions and standard substances 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 (such as high-precision potentiometers, spectrophotometers, etc.) are used as reference standards to compare and correct the sensor's measured values ​​in real time. For example, for pH sensor calibration, a standard buffer solution with an accurate pH value (accurate to 0.001) is used to perform multi-point calibration at different temperatures, and the temperature compensation algorithm is used to correct the measurement error. A sensor performance monitoring model is established to monitor the sensor's response time, stability, sensitivity and other key performance indicators in real time during the titration process. By comparing and analyzing the performance data under normal working conditions, sensor performance degradation or failure can be discovered in a timely manner. A model-based fault diagnosis method, such as a combination of Kalman filter and neural network, is used to accurately diagnose sensor faults. For example, when an abnormal potential drift occurs in a potential sensor, the system can determine whether it is due to sensor electrode contamination, electrolyte leakage or circuit failure, and give corresponding maintenance suggestions. According to the shape and size of the titration vessel, the layout position of the sensor probe is optimized using fluid mechanics simulation software 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 a position 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 a detachable anti-corrosion and anti-pollution sheath, and the sheath surface is automatically cleaned regularly to extend the service life of the sensor.

[0076] It is understandable that the calibration solution and material are first automatically selected and calibrated, combined with high-precision instruments as reference, to ensure the accuracy of the initial sensor measurement. Real-time comparison and correction of measurement values ​​can dynamically maintain measurement accuracy. Establishing a performance monitoring model and continuously monitoring key indicators during titration can accurately grasp the sensor status. The use of fault diagnosis methods can timely detect and diagnose faults, avoid data deviations caused by sensor failures, ensure the reliability of sensor data throughout the titration process, and thus improve the accuracy and credibility of the titration results.

[0077] Specifically, based on the detailed information of the sample and the pretreatment results, the chemometric algorithm is used to accurately calculate the initial parameters of the titration. For example, according to the chemical reaction equation and the estimated content of the substance to be measured in the sample, the theoretical titrant dosage range is calculated, and the initial titration volume and concentration are determined on this basis. 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. For example, when it is found that the potential change rate of the solution is too fast, the titration speed is appropriately reduced to avoid excessive titration. A titration model library for various chemical systems is established, and each model contains the reaction kinetic 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. For example, when encountering a new chemical substance or a complex mixed system, the system automatically records the data during the titration process, and by comparing and analyzing with similar systems in the model library, the model parameters are adjusted or a new sub-model is created to improve the accuracy and efficiency of the titration of the chemical system. Before the formal titration, the titration scheme is simulated using computer simulation technology. 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. The optimal titration scheme is selected by comparing and analyzing the simulation results of different titration schemes. At the same time, the endpoint judgment conditions are optimized and verified during the simulation process to ensure that the titration endpoint can be accurately judged under different experimental conditions.

[0078] It is understandable that the use of chemometric algorithms to calculate initial parameters combined with real-time feedback control algorithms can adjust parameters in real time based on multi-sensor data, effectively respond to changes in the titration process, and reduce error accumulation. Building 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. The computer simulation experiments and comparative analysis of solutions before the formal titration predict possible problems in advance and select the optimal solution, which greatly improves the accuracy, scientificity and efficiency of the titration operation, ensuring that high-precision titration results can be obtained in various complex chemical analysis scenarios.

[0079] Specifically, a high-speed data acquisition card (with a sampling frequency of more than 1kHz) is used to collect multi-sensor data in real time to ensure that the rapidly changing signals in the solution can be captured. A data synchronization mechanism is established to time-mark and align the data collected by different sensors to ensure the synchronization and accuracy of the data. For example, when collecting potential and pH data, the acquisition time error of the two is ensured to be within the microsecond level in order to perform accurate data analysis and fusion. A data fusion algorithm based on deep learning is used, such as a combination of convolutional neural network (CNN) and long short-term memory network (LSTM). CNN is used to extract the spatial features of sensor data, such as the color distribution features in the color sensor image; LSTM is used to learn the time series features of the data, such as the trend of potential and pH data changing with the titration time. By fusing the two, the titration process can be judged more comprehensively and accurately. At the same time, the attention mechanism is introduced to enable the algorithm to automatically focus on the most important feature information in the data for judging the titration endpoint, improve the fusion efficiency and accuracy, and develop a real-time data visualization interface to display the data collected by multiple sensors in intuitive forms such as curves and charts, so that operators can observe the titration process in real time. At the same time, data analysis tools such as data trend analysis and correlation analysis are embedded in the visual interface. Operators can analyze the data at any time to determine whether the titration process is normal, whether there are abnormal fluctuations or interference factors. If an abnormality is found, the system provides corresponding countermeasures, such as pausing the titration, checking the sensor or recalibrating.

[0080] It is understandable that the high-speed acquisition card can capture the rapidly changing signals in the solution to ensure the timeliness and integrity of the data; the data synchronization mechanism accurately processes the data of different sensors to ensure the accuracy and consistency of the data, providing a reliable basis for subsequent analysis. The deep learning data fusion algorithm can deeply explore the value of multi-sensor data and more accurately judge the titration process. The visual interface and analysis tools make it easier for operators to intuitively understand the titration status, discover abnormalities in time, and use analysis tools to conduct in-depth research, effectively improving the operability, controllability and intelligence level of the titration process, and further ensuring the high accuracy and reliability of the titration results.

[0081] Specifically, a large amount of titration experimental data of different chemical systems and different concentration ranges is collected, including the data sequence after multi-sensor fusion and the corresponding titration endpoint information. A deep learning framework (such as TensorFlow, PyTorch, etc.) is used to build a neural network model, such as a multi-layer perceptron (MLP), a recurrent neural network (RNN) and its variants (such as a gated recurrent unit GRU, a long short-term memory network LSTM), etc. The model is trained using these data, and the model performance is optimized by adjusting the model structure, number of layers, number of neurons, and optimization algorithms (such as stochastic gradient descent SGD, Adam optimizer, etc.) to improve the accuracy of titration endpoint prediction. In the model prediction process, an uncertainty assessment mechanism is introduced, such as a Bayesian neural network or a Monte Carlo dropout method, to evaluate the uncertainty of the model prediction results. When the uncertainty is high, the system automatically takes corresponding measures, such as increasing the frequency of data collection, extending the fine titration stage time, or adjusting the endpoint judgment threshold. At the same time, online learning technology is used to adaptively adjust the model according to new titration experimental data, continuously improve the adaptability of the model to different chemical systems and experimental conditions, and adopt a multi-model fusion strategy to fuse the prediction results of different types of machine learning models (such as neural network models, support vector machine SVM models, decision tree models, etc.). For example, through weighted averaging, voting methods or stacked ensemble learning, the advantages of multiple models are combined to improve the reliability of titration endpoint judgment. In the fusion process, the weights are dynamically adjusted according to the performance of different models in different chemical systems to ensure that accurate endpoint judgment results can be obtained in various situations.

[0082] It is understandable that rich data can enable 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 prediction accuracy of the titration endpoint 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.

[0083] Specifically, the content or concentration of the substance to be measured is accurately calculated based on the titration reaction equation and the actual amount of titrant. In the calculation process, the calibration error of the titrant concentration, the sample measurement error, the sensor measurement error and other factors are taken into account, and the error propagation theory is used to analyze the results and give the uncertainty range of the measurement results. For example, the Gaussian error propagation law is used to calculate the concentration error of the substance to be measured caused by the titrant concentration error and the volume measurement error, and it is compared with the preset error allowable range, and a multi-level result verification system is established. In addition to cross-validation and repeated titration experiments using different titration endpoint judgment methods, standard substance verification and inter-laboratory comparison verification are also introduced. Titration experiments are performed regularly using standard substances of known concentration, and the measurement results are compared with the standard values ​​to evaluate the accuracy and reliability of the system. At the same time, participate in inter-laboratory comparison projects, compare and analyze the titration results of this laboratory with those of other laboratories, find potential problems and make timely improvements. During the verification process, if the result exceeds the error tolerance range, the system automatically starts the traceability analysis program to find possible sources of error from sample collection, pretreatment, titration process to result calculation, and generates a detailed error report. All relevant data of the titration experiment (including sample information, titration parameters, multi-sensor data curves, calculation results, verification information, error analysis reports, etc.) are stored in relational databases (such as MySQL, Oracle, etc.) or non-relational databases (such as MongoDB, etc.). A data index and query system is established to facilitate operators to quickly retrieve and query historical data based on sample number, experimental date, test substance and other information. At the same time, data is backed up and archived regularly to ensure data security and integrity. Data encryption technology is used to encrypt sensitive data (such as sample source, operator information, etc.) to prevent data leakage.

[0084] It is understandable that accurate calculations can produce more accurate result values, and comprehensive consideration and analysis of various errors can clearly define the reliability range of the results, so that operators have a clear understanding of the data quality. The multi-level result verification system further verifies the accuracy of the results from multiple dimensions. Through cross-validation of different methods, comparison of standard substances, and horizontal comparison between laboratories, it effectively eliminates the interference of accidental errors and systematic errors, greatly improving the accuracy, credibility and authority of the titration results, and providing solid data support for scientific research, quality control, etc.

[0085] Specifically, develop an intelligent self-cleaning program to automatically select the appropriate cleaning solution and cleaning method according to the type of titration test, the titrant used, and the nature of the sample. For example, after an acid titration test, first clean the burette and container with an alkaline solution and then rinse with clean water; for samples containing organic matter, use an organic solvent for cleaning. During the cleaning process, use sensors to monitor the cleaning effect, such as by detecting the conductivity, pH, or residual concentration of a specific substance in the solution after cleaning to determine whether it is clean. If the cleaning does not meet the standards, the system automatically adjusts the cleaning parameters or repeats the cleaning steps, establishes an equipment maintenance management system, and formulates a maintenance plan based on factors such as the equipment's usage time, number of operations, and life cycle of key components. For example, it is stipulated that the burette should be replaced every 100 uses, and the sensor should be fully calibrated and maintained every 3 months. Before the maintenance plan expires, the system automatically issues a maintenance reminder notification, including the content of the maintenance, the tools and spare parts required, and the maintenance step instructions. At the same time, a maintenance record file is established to record the time, content, maintenance personnel and operating status of each maintenance, so as to provide data support for the long-term stable operation of the equipment. The key components of the system (such as motors, pumps, sensors, circuits, etc.) are monitored in real time using Internet of Things technology and big data analysis. Sensors are installed on the components to collect operating parameter data such as temperature, vibration, current, and voltage, and the data is transmitted to the system control center via wireless transmission. Machine learning algorithms are used to analyze these data and establish equipment failure prediction models. For example, support vector machines (SVM) or neural network models are used to predict whether the motor is about to fail based on parameters such as the vibration frequency and current change of the motor. When the model predicts a failure risk, the system sends out early warning information to notify the operator to inspect and repair, so as to avoid sudden equipment failure leading to experimental interruption or data loss. At the same time, the system automatically adjusts the titration plan according to the failure warning information, and postpones or transfers the affected experimental tasks to other normal equipment.

[0086] It is understandable that the intelligent self-cleaning program can automatically match the cleaning plan according to the experimental characteristics and monitor the effect, ensure the cleanliness of the instrument, reduce sample residue and cross contamination, and ensure the accuracy of subsequent titration. The equipment maintenance management unit makes plans and gives advance reminders based on the use of the equipment. With the help of the Internet of Things and big data, it monitors the status of key components and warns of failures, which can effectively prevent experimental interruptions and data losses caused by sudden equipment failures, ensure stable operation of the equipment, extend its service life, reasonably arrange maintenance resources, and flexibly adjust the titration plan, making the entire titration workflow more scientific, orderly, efficient and reliable.

[0087] A high-precision titration intelligent control method in the above embodiment assists sample information management through intelligent prompts and cloud databases, standardizes equipment integration and automated preprocessing through standardized protocols, optimizes sensor applications through precise calibration and performance monitoring, realizes intelligent setting and updating of titration parameters and schemes through chemometrics and online learning, achieves efficient processing of multi-sensor data through high-speed acquisition and deep learning algorithms, ensures accurate prediction and judgment of endpoints through multi-model strategies, improves result accuracy and reliability through multi-level verification and error analysis, and supports system maintenance and assurance through intelligent self-cleaning and Internet of Things technologies. 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 higher efficiency, higher precision and higher intelligence.

[0088] 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:

[0089] Sample information management module: 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 pre-processing solutions, and evaluate the feasibility and efficiency of the solutions;

[0090] Equipment integration and monitoring module: With the help of standardized industrial communication protocols, the intelligent titration equipment and the automated sample pretreatment equipment are seamlessly integrated, and an equipment status monitoring network is established to perform automated sample pretreatment.

[0091] Sensor calibration and diagnosis module: automatically selects calibration solutions and standard substances according to preset plans and sensor types, 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, continuously monitors key indicators during titration, and uses fault diagnosis methods to promptly detect and diagnose faults.

[0092] Titration parameters and scheme module: Based on the detailed information of the sample and the pretreatment results, the chemometric algorithm is used to calculate the initial parameters of the titration. In the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters according to the data collected by multiple sensors. A titration model library for various chemical systems is constructed and the model is adaptively updated during titration using an online learning algorithm. Before the formal titration, a comprehensive simulation experiment is performed using computer simulation technology and different schemes are compared and analyzed.

[0093] Data acquisition and fusion module: Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-mark and align data from different sensors, use deep learning data fusion algorithms, develop a visual interface to display data and embed analysis tools;

[0094] Endpoint prediction and judgment module: collects titration experimental data covering different chemical systems and concentration ranges, uses deep learning framework to build neural network models such as multi-layer perceptron, recurrent neural network and its variants, and optimizes the model by adjusting various factors of the model;

[0095] 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 measurement error and sensor measurement error, use the error propagation theory to perform error analysis, give the uncertainty range, and establish a multi-level result verification system;

[0096] System maintenance and guarantee module: 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 they do not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on 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.

[0097] 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, which will not be described in detail here.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant 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 within 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 prompt function, connect to the cloud database and regularly update the information database, use rule reasoning and combine machine learning to generate pre-processing solutions, and evaluate the feasibility and efficiency of the solutions; With the help of standardized industrial communication protocols, the intelligent titration equipment and automated sample pretreatment equipment can be seamlessly integrated, and an equipment status monitoring network can be established to perform automated sample pretreatment. Automatically select calibration solutions and standard substances according to preset plans and sensor types, and carry out 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 titration, and use fault diagnosis methods to promptly detect and diagnose faults. Based on the detailed information of the sample and the pretreatment results, the chemometric algorithm is used to calculate the initial parameters of the titration. In the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters according to the data collected by multiple sensors. A titration model library for various chemical systems is constructed and the model is adaptively updated during titration using an online learning algorithm. Before the formal titration, a comprehensive simulation experiment is performed using computer simulation technology and different schemes are compared and analyzed. Use high-speed data acquisition cards to collect multi-sensor data in real time, establish a data synchronization mechanism to accurately time-mark and align data from different sensors, use 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 framework to build neural network models such as multi-layer perceptron, recurrent neural network and its variants, and train and optimize the model by adjusting various factors of the model; The content and concentration of the substance to be measured are accurately calculated based on the titration reaction equation and the actual amount of titrant used, and the calibration error of the titrant concentration, the sample measurement error and the sensor measurement error are fully considered. The error propagation theory is used to perform error analysis, the uncertainty range is given, and a multi-level result verification system is established; 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 they do not meet the standards. Establish an equipment maintenance management unit, formulate maintenance plans based on 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 sample information entry interface with intelligent prompt function is constructed, connected to the cloud database and regularly updates the information database, uses rule reasoning and combines machine learning to generate pre-processing solutions, and evaluates the feasibility and efficiency of the solutions, including: The information entry interface is optimized, with an intuitive, concise human-computer interaction interface with intelligent prompts. When the operator enters the sample type, a drop-down menu or auto-fill option will automatically pop up for the common sources, estimated concentration range, and main components of the sample type. Database update and management: establish connection with cloud database, regularly update sample information base from authoritative chemical analysis database, scientific research institutions’ shared data platform 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 of pretreatment scheme, set database administrator permissions, and allow professionals to review, amend and supplement the database; Intelligent generation and evaluation of pretreatment plans, using 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, seamlessly integrating intelligent titration equipment with automated sample pretreatment equipment, establishing an equipment status monitoring network, and real-time monitoring of the operating parameters of the pretreatment equipment. When equipment fails or is abnormal, the equipment automatically switches to backup equipment or suspends the titration process, and issues an alarm to notify the operator; Precise metering and mixing control, using high-precision electronic balances and precision pipettes equipped with automatic calibration functions to weigh and measure samples, regularly calibrating metering equipment, and introducing intelligent control algorithms in stirring and ultrasonic mixing operations to automatically adjust stirring speed, ultrasonic power and time according to the nature of the sample; The removal of interfering substances and quality inspection 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 inspection link, a combination of online spectral analysis technology and chemical sensor arrays is used to conduct real-time and comprehensive inspections of the clarity, pH, and ion concentration of the solution, establish a multi-parameter quality inspection model, and use machine learning algorithms to analyze the inspection data.

4. A high-precision titration intelligent control method according to claim 1, characterized in that: The calibration solution and standard substance are automatically selected according to the preset plan and sensor type, and the calibration operation is carried out. The high-precision measuring instrument is used as a reference standard to compare and correct the measured value of the sensor in real time. At the same time, a performance monitoring model is established to continuously monitor key indicators during the titration process, and the fault diagnosis method is used to timely discover and diagnose the fault, including: Automatic calibration program optimization, design of fully automatic calibration process, the equipment automatically selects appropriate calibration solution and standard substance according to the preset calibration plan and sensor type, and controls the calibration equipment to perform calibration operation. During the calibration process, high-precision measuring instruments are used as reference standards to compare and correct the sensor's measurement values ​​in real time; 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 discover the sensor's 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 container, 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 chemometric algorithm is used to calculate the initial parameters of the titration based on the sample detailed information and pretreatment results, and a real-time feedback control algorithm is used in the titration process. According to the data collected by multiple sensors, the parameters are adjusted dynamically and flexibly. A titration model library of various chemical systems is constructed and the online learning algorithm is used to adaptively update the model during titration. Before the formal titration, a comprehensive simulation experiment is performed by computer simulation technology and different schemes are compared and analyzed, including: Intelligent calculation and dynamic adjustment of parameters: Based on the detailed information of the sample and the preprocessing 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, establish a titration model library for various chemical systems, each model contains the reaction kinetics equation, endpoint judgment criteria and parameter optimization strategy of a specific chemical system, and use online learning algorithms to adaptively update the model during the titration process; Titration scheme simulation and optimization verification: Before the formal titration, the titration scheme is simulated using computer simulation technology. 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 method uses a high-speed data acquisition card to collect multi-sensor data in real time, establishes a data synchronization mechanism to accurately time-mark and align different sensor data, uses a deep learning data fusion algorithm, develops a visual interface to display data, and embeds analysis tools, including: 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 data fusion algorithm based on deep learning to judge the titration process, introducing an attention mechanism, so that the algorithm automatically focuses on the most important feature information in the data for judging 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, and uses a deep learning framework to build neural network models such as multi-layer perceptrons, recurrent neural networks and their variants. The model is trained and optimized by adjusting various factors of the model, 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: In the process of model prediction, 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 to continuously improve 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, fully considers the calibration error of the titrant concentration, the sample measurement error and the sensor measurement error, uses the error propagation theory to perform error analysis, gives the uncertainty range, and establishes a multi-level result verification system, including: Result calculation and error analysis: According to the titration reaction equation and the actual amount of titrant, the content or concentration of the substance to be measured is accurately calculated. In the calculation process, the calibration error of the titrant concentration, the sample measurement 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 for 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 this 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: store all relevant data of the titration experiment in a relational or non-relational database, establish data index and query units, and facilitate operators to quickly retrieve and query historical data according to sample number, experiment date and test substance, back up and archive data regularly, and use data encryption technology to encrypt sensitive data.

9. A high-precision titration intelligent control method according to claim 1, characterized in that: The developed intelligent self-cleaning program automatically selects the appropriate cleaning solution and method according to the titration test type, titrant and sample properties, monitors the cleaning effect with the help of sensors during cleaning, and automatically adjusts the parameters if it does not meet the standards, establishes an equipment maintenance management unit, formulates maintenance plans and gives advance reminders based on the equipment usage time and operation times, uses the Internet of Things and big data to monitor the status of key components, and warns of failures, and adjusts the titration plan, including: Intelligent self-cleaning program: Develop an intelligent self-cleaning program that automatically selects the appropriate cleaning solution and cleaning method according to the type of titration test, the titrant used and the nature of the sample. During the cleaning process, sensors are used to monitor the cleaning effect. If the cleaning does not meet the standards, the equipment automatically adjusts the cleaning parameters or repeats the cleaning steps. Equipment maintenance plan and reminder: Establish equipment maintenance management unit, formulate maintenance plan according to equipment usage time, operation times and life cycle of key components. Before the maintenance plan expires, the equipment will automatically send out maintenance reminder notifications. At the same time, establish maintenance record files to record the time, content, maintenance personnel and operation status of each maintenance. Intelligent condition monitoring and fault warning use 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 according to 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 prompt function, connects to the cloud database and regularly updates the information database, uses rule reasoning and combines machine learning to generate pretreatment plans, and evaluates the feasibility and efficiency of the 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 diagnosis module automatically selects calibration solutions and standard substances according to the preset 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. At the same time, it establishes a performance monitoring model, continuously monitors key indicators during the titration process, and uses fault diagnosis methods to promptly discover and diagnose faults. The titration parameter and scheme module uses the chemometric algorithm to calculate the initial parameters of the titration based on the sample detailed information and pretreatment results. In the titration process, a real-time feedback control algorithm is used to dynamically and flexibly adjust the parameters according to the data collected by multiple sensors. A titration model library for various chemical systems is constructed and the online learning algorithm is used to adaptively update the model during titration. Before the formal titration, the computer simulation technology is used to fully simulate the experiment and compare and analyze different schemes. Data acquisition and fusion module, using high-speed data acquisition cards to collect multi-sensor data in real time, establishing a data synchronization mechanism to accurately time-mark and align data from different sensors, using deep learning data fusion algorithms, developing a visual interface to display data and embed analysis tools; The endpoint prediction and judgment module collects titration experimental data covering different chemical systems and concentration ranges, uses the deep learning framework to build neural network models such as multi-layer perceptron, recurrent neural network and its variants, and optimizes the model by adjusting various factors of the model; 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 considers the calibration error of the titrant concentration, the sample measurement error and the sensor measurement error, uses the error propagation theory to perform error analysis, gives the uncertainty range, and establishes a multi-level result verification system; The system maintenance and guarantee module develops an intelligent self-cleaning program, which automatically selects the appropriate cleaning solution and method according to the titration test type, titrant and sample properties. During cleaning, sensors are used to monitor the cleaning effect. If the parameters are not up to standard, the parameters are automatically adjusted. An equipment maintenance management unit is established, and maintenance plans are formulated and reminders are given in advance 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.

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