Temperature control method and system based on snail stock solution separation process

Abstract

The invention relates to the technical field of temperature control based on a specific process, and discloses a temperature control method and system based on a snail stock solution separation process, and the method comprises the steps: monitoring the temperature data of each key device in the snail stock solution separation process in real time, and carrying out the preprocessing and feature extraction; a temperature control instruction is generated through a PID control algorithm and is adjusted by the PLC, the operation state of the system is monitored in real time, fault alarm is carried out, the operation process of the system is recorded, and a report record is generated and visually displayed. Through multi-dimensional temperature monitoring, a complete system architecture, a customized solution and visual display, the precision and efficiency of temperature control are remarkably improved, the stability of a snail stock solution separation process and the product quality are ensured, and an efficient and reliable temperature control technical scheme is provided for related industries.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature control based on specific processes, and in particular to a temperature control method and system based on snail original liquid separation process. BACKGROUND

[0002] In the traditional snail original liquid separation process, temperature control is usually focused on a single device or a few key points. This local temperature control method cannot effectively respond to temperature fluctuations that may occur during the process. Traditional temperature monitoring methods also lack deep analysis and processing of temperature data. Therefore, there is a need for a method to comprehensively monitor and control the temperature of key devices in the snail original liquid separation process to ensure the stability of the entire process and product quality.

[0003] The snail original liquid separation process has a complex process flow involving multiple key devices. Existing temperature control systems often lack a complete system architecture to achieve comprehensive monitoring, control, monitoring, alarm, and reporting of temperature, which not only increases the complexity of data management and system maintenance, but also reduces the reliability and efficiency of the system.

[0004] The snail original liquid separation process has its unique temperature requirements and process characteristics. Traditional temperature control methods often lack customized solutions for this process, resulting in poor temperature control during the process, affecting the separation efficiency of the snail original liquid and product quality. Therefore, a customized temperature control method and system specifically for the snail original liquid separation process is needed to improve process stability and product quality.

[0005] In existing temperature control systems, data reporting and display are usually simple, and operators and managers have difficulty quickly understanding the system's running status and historical data through existing data display methods, which not only reduces work efficiency, but also may miss some important abnormal situations. SUMMARY

[0006] The present application is a temperature control method and system based on snail original liquid separation process to solve the problems in the prior art.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a temperature control method based on snail original liquid separation process, comprising:

[0008] Step S1, real-time monitoring of temperature data of key devices involved in each process stage of snail original liquid separation process;

[0009] Step S2, pre-processing and feature extraction of the temperature data to obtain a temperature feature vector;

[0010] Step S3, generating temperature control instructions through a PID control algorithm according to the temperature feature vector;

[0011] Step S4, the PLC receives and executes the temperature control instructions to adjust the temperature of the key devices involved in each process stage of the snail raw liquid separation process;

[0012] Step S5, real-time monitoring of the system running state during the execution of the temperature adjustment process;

[0013] Step S6, issuing an alarm signal and recording an alarm log when the temperature in any required device in any process stage exceeds the preset range or the system running state fails;

[0014] Step S7, recording the running process of the temperature control system and generating a report record;

[0015] Step S8, displaying the report record through a visual method.

[0016] Further, the key devices involved in each process stage of the snail raw liquid separation process include constant temperature warehouses, standing containers, centrifuges, sterilization equipment, enzymatic reaction kettles, ultrafiltration equipment, and freeze-drying equipment;

[0017] In step S1, the following sub-steps are further included:

[0018] S1-1, deploying temperature sensors on the inner walls and bottoms of the constant temperature warehouses, the inner walls and bottoms of the standing containers, the rotors and housings of the centrifuges, the inner cavities of the sterilization equipment, different height positions in the enzymatic reaction kettles, the liquid inlets, liquid outlets, and filter membrane surfaces of the ultrafiltration equipment, and the cold traps, drying chambers, and material trays of the freeze-drying equipment;

[0019] S1-2, real-time temperature monitoring of each process stage in the snail raw liquid separation process through the temperature sensors to obtain temperature data, including temperature values, collection time stamps, and device IDs.

[0020] Further, in step S2, the following sub-steps are further included:

[0021] S2-1, verifying the time continuity and integrity of the temperature data through a time series analysis method, and requiring retransmission of the temperature data that fails the verification;

[0022] S2-2, identifying and removing outliers in the temperature data, and further performing noise reduction and smoothing processing on the temperature data through a moving average method to obtain preprocessed temperature data;

[0023] S2-3, obtaining distribution characteristics of the temperature data by a clustering analysis method according to the pre-processed temperature data, and further obtaining correlation characteristics and correlation strength information between the pre-processed temperature data by a correlation analysis method, the correlation characteristics including a correlation coefficient, a correlation direction, and a correlation variation trend;

[0024] S2-4, integrating the distribution characteristics, the correlation characteristics, and the correlation strength information into a temperature feature vector by a principal component analysis method.

[0025] Further, in step S3, the following sub-steps are further included:

[0026] S3-1, presetting process temperatures of each process stage in the snail stock solution separation process;

[0027] S3-2, generating a temperature control instruction by a PID control algorithm according to the temperature feature vector and the preset process temperatures, the temperature control instruction including device information to be adjusted and temperature adjustment parameters of the corresponding device, and further performing validity verification on the temperature control instruction by a SPIN model checking tool, if the verification fails, re-generating the temperature control instruction and performing the validity verification again until the verification passes;

[0028] S3-3, issuing the temperature control instruction to PLC controllers of key devices involved in each process stage in the snail stock solution separation process by a Modbus protocol.

[0029] Further, in step S4, the following sub-steps are further included:

[0030] S4-1, the PLC controllers of the key devices involved in each process stage in the snail stock solution separation process receive and execute the temperature control instruction;

[0031] S4-2, monitoring running states of each device in the temperature adjustment operation process in real time by a feedback monitoring method, obtaining real-time feedback data of the temperature adjustment of each device, and further performing feedback adjustment on each device by a PLC closed-loop control method.

[0032] Further, in step S5, the following sub-steps are further included:

[0033] S5-1, obtaining running states of a data acquisition stage by monitoring signal strength of a temperature sensor and a CRC checking method;

[0034] S5-2, obtaining running states of an instruction generation stage by tracking a generation path of the temperature control instruction and comparing output results, the generation path including generation time, generation conditions, verification results, packaging formats, and transmission paths;

[0035] S5-3, obtaining the running state of the instruction execution stage by monitoring the communication link connection state and data response time of the PLC controller, the communication link connection state including a physical connection state, signal strength, and bit error rate.

[0036] Further, in step S6, the following sub-steps are further included:

[0037] S6-1, presetting the temperature variation range required by each stage of the snail stock solution separation process, comparing the temperature data of the required device of the process stage, and determining whether it exceeds the preset variation range, and generating a temperature alarm information when it is determined that the preset variation range is exceeded, the temperature alarm information including device ID and device temperature;

[0038] S6-2, according to the running state of the temperature control system itself, including communication state, data transmission accuracy and running parameter index, through fault diagnosis method, judge whether the system running state appears fault, when it is judged that the fault appears, generate running alarm information, the running alarm information includes fault module and fault reason;

[0039] S6-3, displaying the temperature alarm information and running alarm information on the operator's monitoring interface through the industrial automation monitoring system, and at the same time pushing the alarm information to the relevant management personnel through the SMS and mobile terminal APP pushing mode, recording the time when the alarm signal is generated and generating the alarm log.

[0040] Further, in step S7, the following sub-steps are further included:

[0041] S7-1, formatting the temperature data, temperature feature vector, temperature control instruction, system running state and alarm log according to the JSON format, and generating the formatted process information;

[0042] S7-2, data arrangement of the formatted process information is carried out through the pre-defined aggregation rule, and the report record is generated;

[0043] S7-3, checking the integrity and accuracy of the report record, and storing the report record passed by the checking in the local database of the system.

[0044] Further, in step S8, the following sub-steps are further included:

[0045] S8-1, converting the report record into a data structure that can be used for visualization through a data analysis method, the data structure including JSON structure, XML structure and CSV structure;

[0046] S8-2, according to the structured report record, a visualization chart of the process flow of the snail stock solution separation process is obtained by a chart generation method, and the visualization chart includes a time series chart, a column chart, a line chart, a scatter chart and a heat map;

[0047] S8-3, a user-friendly visualization interface is designed on a Tableau platform, and the visualization chart is displayed through the visualization interface.

[0048] A temperature control system based on the snail stock solution separation process for implementing the above method comprises:

[0049] A temperature sensor module is configured to monitor the temperature of key devices involved in each process stage of the snail stock solution separation process in real time, and obtain temperature data.

[0050] A data processing module is configured to pre-process and extract features of the temperature data, and obtain a temperature feature vector.

[0051] An instruction generation module is configured to generate a temperature control instruction by a PID control algorithm according to the temperature feature vector.

[0052] A temperature control module is configured to receive and execute the temperature control instruction, and adjust the temperature of the key devices involved in each process stage of the snail stock solution separation process.

[0053] A system monitoring module is configured to monitor the system running state in the process of executing the temperature adjustment in real time.

[0054] A fault alarm module is configured to send an alarm signal when the temperature of the device required in any process stage exceeds a preset range or the system running state fails, and record an alarm log.

[0055] A report generation module is configured to record the running process of the temperature control system, and generate a report record.

[0056] A report display module is configured to display the report record by a visualization method.

[0057] The technical solution provided by the present application has at least the following beneficial effects:

[0058] The application proposes a complete temperature control system architecture, including a temperature sensor module, a data processing module, an instruction generation module, a temperature control module, a system monitoring module, a fault alarm module, a report generation module and a report display module, so as to realize comprehensive monitoring, control, alarm and report of the temperature of the snail raw liquid separation process, improve the automation and intelligent level of the system, effectively reduce the complexity and maintenance cost of the system through the integrated system architecture, and also improve the reliability and operation efficiency of the system, and ensure the stability of the whole process and the product quality.

[0059] The application proposes a special temperature control method and system for the characteristics of the snail raw liquid separation process, pre-sets the temperature threshold and change range of each process stage, combines the PID control algorithm and the model verification tool, meets the special requirements of the snail raw liquid separation process on temperature control, and the customized solution can provide strong technical support for the development of the snail raw liquid related industry, and has remarkable economic and social benefits.

[0060] The application converts the running process of the temperature control system into a visual data structure, designs a user-friendly visual interface on the Tableau platform, realizes intuitive display of the running state of the system, and the visual display method can enable the operating personnel and the management personnel to quickly and accurately understand the running state and historical data of the system, discover and handle abnormal conditions in time, thereby significantly improving the work efficiency and reducing the operation risk, and providing strong data support for the management and decision of the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0062] Figure 1 The method flowchart provided for the embodiments of the application is provided.

[0063] Figure 2 The system structure diagram provided for the embodiments of the application is provided. DETAILED DESCRIPTION

[0064] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the following describes in detail the specific implementation, structure, features and effects of a temperature control method and system based on snail stock solution separation process according to the present application, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0066] The following examples are for illustrative purposes only and are not intended to limit the scope of the present application.

[0067] The following describes in detail the specific scheme of a temperature control method and system based on snail stock solution separation process provided by the present application, in conjunction with the accompanying drawings.

[0068] Please refer to Figure 1 which shows a method flowchart of a temperature control method based on snail stock solution separation process provided by one embodiment of the present application, which includes the following steps:

[0069] Step S1, real-time monitoring of temperature data of key devices involved in each process stage of snail stock solution separation process;

[0070] The key devices involved in each process stage of snail stock solution separation process include constant temperature bin, standing container, centrifuge, sterilization equipment, enzymatic reaction kettle, ultrafiltration equipment and freeze-drying equipment;

[0071] In step S1, the following sub-steps are further included:

[0072] S1-1, deploying temperature sensors on the inner wall and bottom of the constant temperature bin, the inner wall and bottom of the standing container, the rotor and outer shell of the centrifuge, the inner cavity of the sterilization equipment, different height positions in the enzymatic reaction kettle, the liquid inlet, liquid outlet and filter membrane surface of the ultrafiltration equipment, and the cold trap, drying chamber and material tray of the freeze-drying equipment;

[0073] S1-2, real-time temperature monitoring of each process stage of snail stock solution separation process by temperature sensors to obtain temperature data, including temperature value, collection time stamp and device ID.

[0074] It should be noted that the standing container is used for standing treatment of the stock solution in the early stage of snail stock solution separation process, so that the impurities and precipitates in the stock solution naturally settle, thereby preliminarily separating out the relatively pure liquid part.

[0075] The centrifuge is used to separate different components in the snail stock solution according to the density difference under the action of centrifugal force, which can quickly and effectively separate the solid particles and cell fragments in the stock solution, and improve the purity of the stock solution.

[0076] The sterilization equipment is used to sterilize the preliminarily separated snail stock solution, which can kill bacteria, viruses, fungi and other microorganisms in the stock solution to ensure the health and safety of the stock solution. The sterilization equipment mentioned in the patent adopts high-temperature sterilization method, which inactivates microorganisms by heating the stock solution to a certain temperature and keeping it for a period of time, thereby achieving the purpose of sterilization.

[0077] The enzymatic reaction kettle is used to add specific enzymes to decompose proteins, polysaccharides and other macromolecules in the stock solution under suitable temperature and conditions, thereby improving the biological activity and solubility of the stock solution.

[0078] The ultrafiltration equipment is used to further separate and purify the snail stock solution after enzymatic treatment through the pore size screening effect of the ultrafiltration membrane, which can effectively remove macromolecular impurities, suspended solids and part of microorganisms in the stock solution while retaining the effective components in the stock solution, thereby improving the purity and quality of the stock solution.

[0079] The freeze-drying equipment is used to freeze-dry the snail stock solution purified by ultrafiltration to remove water in the stock solution and produce dry snail stock solution powder, which maximizes the retention of effective components and biological activity in the stock solution and prolongs the shelf life of the product, while also facilitating subsequent transportation and storage.

[0080] In step S2, the temperature data is preprocessed and feature extracted to obtain a temperature feature vector;

[0081] In step S2, the following sub-steps are further included:

[0082] S2-1, by time series analysis method, the time continuity and integrity of the temperature data are verified, and the temperature data that fails to pass the verification is required to be retransmitted;

[0083] S2-2, by Z-score method, the outliers in the temperature data are identified and removed, and further by moving average method, the noise in the temperature data is denoised and smoothed to obtain preprocessed temperature data;

[0084] S2-3, according to the preprocessed temperature data, the distribution characteristics of the temperature data are obtained by clustering analysis method, and further by correlation analysis method, the correlation characteristics and correlation strength information between the preprocessed temperature data are obtained, the correlation characteristics include correlation coefficient, correlation direction and correlation trend;

[0085] S2-4, by principal component analysis method, the distribution characteristics, correlation characteristics and correlation strength information are integrated into a temperature feature vector.

[0086] It is necessary to point out that time series analysis is a statistical technique for verifying the time continuity and integrity of temperature data, checking whether the temperature data is continuously collected within the preset time interval, and whether there is data loss or time interval anomaly. If it is found that the data is not continuous or complete, the system will require re-collection of data to ensure the accuracy of subsequent analysis.

[0087] Z-score method is a statistical method for evaluating the deviation of data points from the average value of the data set. It identifies outliers in temperature data by calculating the Z-score of each data point, which is calculated as follows:

[0088]

[0089] where X is the temperature data point, μ is the average value of the temperature data set, and σ is the standard deviation of the temperature data set.

[0090] If the Z-score of a certain temperature data point exceeds the preset threshold, for example , it is considered to be an outlier and needs to be removed.

[0091] Moving average method is a method for smoothing time series data by calculating the average value of a series of data points to reduce random fluctuations in the data. Common moving average methods include simple moving average and exponential moving average. Specifically, by calculating the average value of temperature data within a certain time window, the average value is used to replace each data point within the window, thereby reducing the noise in the data.

[0092] Clustering analysis is an unsupervised learning method for dividing data points in a data set into several clusters, so that the similarity of data points in the same cluster is high, while the similarity of data points in different clusters is low, thereby obtaining the distribution characteristics of the data.

[0093] Correlation analysis is a statistical method for evaluating the linear relationship between two or more variables, where the correlation coefficient ranges from -1 to 1, where 1 indicates complete positive correlation, -1 indicates complete negative correlation, and 0 indicates no correlation. This method reveals the intrinsic relationship between temperature data, providing more abundant information for subsequent feature vector generation.

[0094] Principal component analysis is a dimensionality reduction technique that converts high-dimensional data into low-dimensional data while preserving as much variance information as possible from the original data. This method can extract the main features of the data and integrate complex temperature data features into a simple feature vector, facilitating subsequent temperature control instruction generation and system monitoring.

[0095] Step S3, generate temperature control instructions according to the temperature feature vector through the PID control algorithm;

[0096] In step S3, the following sub-steps are also included:

[0097] S3-1, preset the process temperature of each process stage in the snail stock solution separation process;

[0098] S3-2, generate temperature control instructions according to the temperature feature vector and the preset process temperature through the PID control algorithm, the temperature control instructions include device information to be adjusted and temperature adjustment parameters of the corresponding device, further verify the effectiveness of the temperature control instructions through the SPIN model checking tool, if the verification fails, regenerate the temperature control instructions and verify again until the verification passes;

[0099] S3-3, issue the temperature control instructions to the PLC controllers of the key devices involved in each process stage of the snail stock solution separation process through the Modbus protocol.

[0100] It should be noted that the PID control algorithm: PID control algorithm is a common feedback control algorithm, which adjusts the control quantity through three parameters of proportion, integral and differential, the formula is as follows:

[0101]

[0102] In the formula, is the control quantity, is the deviation between the set value and the actual value, is the proportional coefficient, is the integral coefficient, is the differential coefficient.

[0103] An application example in the snail stock solution separation process is given: assuming that the set temperature of a process stage is , the actual temperature is , the deviation is

[0104] , and then the control quantity u(t) is calculated through the PID controller, which is used to adjust the power of the heating or cooling device to make the actual temperature close to the set temperature.

[0105] SPIN model checking tool is a tool for verifying the correctness of concurrent systems and protocols, the verification content includes whether the temperature control instructions meet the preset temperature threshold and variation range, whether it will cause system instability or running failure, to ensure that the generated temperature control instructions meet the system design requirements, avoid system running failure or product quality problems caused by incorrect temperature control instructions.

[0106] Modbus protocol is a serial communication protocol that supports RS-232, RS-485 and other physical media, widely used in industrial automation field, to realize reliable communication between control system and field devices, and ensure accurate transmission of temperature control instructions.

[0107] PLC, programmable logic controller, is a computer device widely used in industrial automation control, which connects with field devices through input-output interface, responds quickly to temperature control instructions, accurately controls temperature of each process stage, and ensures stability of process and product quality.

[0108] Step S4, PLC receives and executes temperature control instructions to adjust the temperature of key devices involved in each process stage of snail raw liquid separation process;

[0109] In step S4, the following sub-steps are included:

[0110] S4-1, the PLC controller of the key devices involved in each process stage of snail raw liquid separation process receives and executes temperature control instructions;

[0111] S4-2, real-time monitoring of the running state of each device during temperature adjustment operation is realized by feedback monitoring method, real-time feedback data of temperature adjustment of each device is obtained, and further feedback adjustment of each device is realized by PLC closed-loop control method.

[0112] It should be noted that feedback monitoring method is a method of collecting system running state data in real time and comparing it with preset target value to evaluate whether system running meets expectations, which can timely find deviation and abnormal situation in temperature adjustment process and provide accurate data support for subsequent feedback adjustment, ensuring accuracy and reliability of temperature adjustment.

[0113] PLC closed-loop control method is a control method based on feedback information for automatic adjustment, in closed-loop control system, controller automatically adjusts control quantity according to real-time data obtained by feedback monitoring, to make system output as close as possible to preset target value, ensuring temperature of each process stage stable within preset range, improving accuracy and reliability of temperature control, thus ensuring stability of snail raw liquid separation process and product quality.

[0114] Step S5, real-time monitoring of system running state during temperature adjustment process;

[0115] In step S5, the following sub-steps are included:

[0116] S5-1, running state of data acquisition stage is obtained by monitoring signal strength of temperature sensor and CRC check method;

[0117] S5-2, obtain the running state of the instruction generation stage by tracking the generation path of the temperature control instruction and comparing the output results, the generation path including the generation time, generation condition, verification result, packaging format and transmission path;

[0118] S5-3, obtain the running state of the instruction execution stage by monitoring the communication link connection state and data response time of the PLC controller, the communication link connection state including the physical connection state, signal strength and error code rate.

[0119] It should be noted that the signal strength of the temperature sensor is usually represented in the form of voltage, current or digital signal, which reflects the size and reliability of the temperature change detected by the sensor. If the signal strength is too low, it may indicate that the sensor is faulty or poorly connected, and if the signal strength is too high, it may indicate that the sensor is disturbed or damaged.

[0120] The CRC check method is a technique for verifying data integrity and accuracy, which is used to check whether the data has been tampered with, lost or damaged during transmission or storage, to avoid control errors caused by data loss or damage during data collection, transmission and storage.

[0121] Tracking the generation path of the temperature control instruction means recording and analyzing the entire process of the temperature control instruction from generation to issuance, including the generation, verification, packaging and transmission of the instruction, to ensure that the generation and transmission of the temperature control instruction meet the preset specifications and logic, to timely discover and correct errors or abnormalities in the instruction generation process through subsequent steps, and to improve the reliability and stability of the system.

[0122] Comparing the output results means comparing the actual output results with the preset target value or expected results to evaluate whether the temperature adjustment achieves the expected effect. If there is a deviation between the actual results and the target value, the system will generate corresponding alarm information or adjust the control strategy to ensure the effectiveness and accuracy of the temperature adjustment operation and ensure the stability of the system operation and product quality.

[0123] Step S6, when the temperature of the device required in any process stage exceeds the preset range or the system running state fails, an alarm signal is sent and an alarm log is recorded;

[0124] In step S6, the following sub-steps are further included:

[0125] S6-1, preset the temperature variation range required in each stage of the snail stock solution separation process by comparing the temperature data of the device required in the process stage, and determining whether it exceeds the preset variation range, and generating a temperature alarm information when it is determined that it exceeds the preset variation range, the temperature alarm information including the device ID and device temperature;

[0126] S6-2, according to the temperature control system itself running state, including communication state, data transmission accuracy and running parameter index, through fault diagnosis method, judge whether the system running state appears fault, when judging as appearing fault, generate running alarm information, running alarm information includes fault module and fault reason;

[0127] S6-3, through industrial automation monitoring system, temperature alarm information and running alarm information are displayed on the operator's monitoring interface, and at the same time, alarm information is pushed to relevant management personnel through short message and mobile terminal APP push mode, the time of alarm signal generation is recorded and alarm log is generated.

[0128] It should be noted that an example of the temperature threshold value and temperature change range required in each stage of the snail stock solution separation process is given:

[0129]

[0130] The fault diagnosis method refers to identifying and locating system faults by analyzing system running state data, including checking whether the communication link is normal, monitoring the running state of PLC controller, such as CPU load, memory usage and I / O interface state, whether there is data loss or transmission delay, this method is usually based on the preset fault diagnosis model or algorithm, the various running parameters of the system are monitored and evaluated to determine whether there is a fault and the specific location and reason of the fault.

[0131] Industrial automation monitoring system is a system for real-time monitoring and management of industrial production process, which realizes centralized monitoring, fault alarm, data recording and remote control of production process by collecting and analyzing running data of field devices.

[0132] Step S7, record the running process of the temperature control system, and generate report record;

[0133] In step S7, the following sub-steps are further included:

[0134] S7-1, the temperature data, temperature feature vector, temperature control instruction, system running state and alarm log are formatted according to JSON format, and formatted flow information is generated;

[0135] S7-2, the formatted flow information is data arranged through pre-defined aggregation rule, and report record is generated;

[0136] S7-3, check the integrity and accuracy of the report record, and store the checked report record in the local database of the system.

[0137] It should be noted that predefined aggregation rules refer to a set of pre-defined logic and algorithms used to organize and summarize formatted process information, thereby generating meaningful reporting records. Specifically, these include:

[0138] Classification: Data is classified according to different dimensions such as time, device, and operation type;

[0139] Statistics: Perform statistical analysis on the categorized data to calculate the average temperature, temperature variation range, and alarm frequency for each device;

[0140] Summary: Summarize the statistical results into a complete report record for easy review and analysis later.

[0141] Through predefined aggregation rules, this patented method can effectively record and organize various data during operation, generate detailed reporting records, and provide strong support for system optimization and management.

[0142] Step S8: Display the report records using visualization methods;

[0143] Step S8 further includes the following sub-steps:

[0144] S8-1 uses data parsing methods to transform reporting records into data structures that can be used for visualization. These data structures include JSON, XML, and CSV structures.

[0145] S8-2, Based on the structured reporting records, a visual chart of the snail stock solution separation process is generated using chart generation methods. The visual chart includes time series charts, bar charts, line charts, scatter plots, and heat maps.

[0146] S8-3 is used to design a user-friendly visualization interface on the Tableau platform, which displays visual charts.

[0147] It should be noted that JSON is a lightweight data interchange format that is easy for humans to read and write, as well as easy for machines to parse and generate. It is text-based, uses key-value pairs to represent data, and supports multiple data types such as strings, numbers, booleans, and arrays.

[0148] XML is a markup language used for storing and transmitting data. It uses custom tags to describe the structure and content of data. It is flexible, easy to extend, and suitable for storing and transmitting complex data. It is especially suitable for expressing hierarchical data. Converting report records into XML format can facilitate subsequent processing and data visualization.

[0149] CSV is a simple text file format commonly used to store tabular data, with each column of data separated by commas and each row representing a record. It is simple, easy to understand and process, making it ideal for structured data representation.

[0150] Tableau is a powerful data analysis and visualization tool widely used in business intelligence and data analysis. It supports multiple data sources such as relational databases, Excel files, and CSV files, and can quickly generate various visualization charts. Applications include:

[0151] Importing JSON, XML, or CSV format report records into the Tableau platform, based on structured report records, generating various visualization charts, including:

[0152] Time series chart: shows the trend of device temperature over time;

[0153] Bar chart: shows the average temperature and alarm frequency of different devices;

[0154] Line chart: shows the trend of device temperature;

[0155] Scatter plot: shows the relationship between device temperature and alarm frequency;

[0156] Heat map: shows the distribution of device temperature;

[0157] User-friendly interface: design a user-friendly visualization interface to enable relevant management personnel to intuitively view and analyze the system's running status.

[0158] Please refer to Figure 2 , which shows a system structure diagram of a temperature control system based on a snail raw liquid separation process, the system includes:

[0159] Temperature sensor module, for real-time monitoring of the temperature of key devices involved in each process stage of the snail raw liquid separation process, obtaining temperature data;

[0160] Data processing module, for pre-processing and feature extraction of temperature data, obtaining temperature feature vector;

[0161] Instruction generation module, for generating temperature control instructions based on temperature feature vector through PID control algorithm;

[0162] Temperature control module, for receiving and executing temperature control instructions, adjusting the temperature of key devices involved in each process stage of the snail raw liquid separation process;

[0163] System monitoring module, for real-time monitoring of system running status during temperature adjustment process;

[0164] a fault alarm module, configured to send an alarm signal and record an alarm log when the temperature of any device required in any process stage exceeds a preset range or a system operating state fails;

[0165] a report generation module, configured to record an operation process of the temperature control system and generate a report record;

[0166] a report display module, configured to display the report record by a visual method.

[0167] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A temperature control method based on a snail stock solution separation process, characterized by, The method comprises the following steps: Step S1, real-time monitoring of the temperature data of the key devices involved in each process stage of the snail raw liquid separation process; Step S2, pre-processing and feature extraction of the temperature data to obtain a temperature feature vector; Step S3, generating a temperature control instruction through a PID control algorithm based on the temperature feature vector; Step S4, PLC receiving and executing the temperature control instruction to adjust the temperature of the key devices involved in each process stage of the snail raw liquid separation process; Step S5, real-time monitoring of the system running state during the execution of the temperature adjustment process; Step S6, issuing an alarm signal and recording an alarm log when the temperature of the required device in any process stage exceeds the preset range or the system running state fails; Step S7, recording the running process of the temperature control system and generating a report record; Step S8, displaying the report record through a visualization method.

2. The temperature control method based on a snail broth separation process according to claim 1, characterized in that: The key devices involved in each process stage of the snail raw liquid separation process include constant temperature warehouses, standing containers, centrifuges, sterilization equipment, enzymatic reaction kettles, ultrafiltration equipment, and freeze-drying equipment; In step S1, the following sub-steps are further included: S1-1, deploying temperature sensors on the inner walls and bottoms of the constant temperature warehouses, the inner walls and bottoms of the standing containers, the rotors and housings of the centrifuges, the inner cavities of the sterilization equipment, different height positions in the enzymatic reaction kettles, the liquid inlets, outlets, and filter membrane surfaces of the ultrafiltration equipment, and the cold traps, drying chambers, and material trays of the freeze-drying equipment; S1-2, real-time temperature monitoring of each process stage of the snail raw liquid separation process through the temperature sensors to obtain temperature data, which includes temperature values, collection time stamps, and device IDs.

3. The temperature control method based on the snail raw liquid separation process according to claim 1, wherein: In step S2, the following sub-steps are further included: S2-1, verifying the time continuity and integrity of the temperature data through a time series analysis method, and requiring retransmission of the temperature data that fails the verification; S2-2, identifying and removing outliers in the temperature data through a Z-score method, and further performing noise reduction and smoothing processing on the temperature data through a moving average method to obtain pre-processed temperature data; S2-3, obtaining the distribution characteristics of the temperature data through a clustering analysis method based on the pre-processed temperature data, and further obtaining the correlation characteristics and correlation strength information between the pre-processed temperature data through a correlation analysis method, the correlation characteristics including correlation coefficients, correlation directions, and correlation trends; S2-4, integrating the distribution characteristics, correlation characteristics, and correlation strength information into a temperature feature vector through a principal component analysis method.

4. The temperature control method based on the snail raw liquid separation process according to claim 1, wherein: In step S3, the following sub-steps are further included: S3-1, presetting the process temperature of each process stage of the snail raw liquid separation process; S3-2, generating temperature control instructions by a PID control algorithm according to the temperature feature vector and a preset process temperature, the temperature control instructions including to-be-adjusted device information and temperature adjustment parameters of corresponding devices, further verifying effectiveness of the temperature control instructions by a SPIN model checking tool, if the verification fails, re-generating the temperature control instructions and again verifying effectiveness, until the verification passes; S3-3, issuing the temperature control instructions to PLC controllers of key devices involved in each process stage of the snail stock solution separation process through a Modbus protocol.

5. The temperature control method based on the snail stock solution separation process according to claim 1, wherein in step S4, further comprising the following sub-steps: S4-2, monitoring running states of each device in a temperature adjustment operation process in real time by a feedback monitoring method, obtaining real-time feedback data of temperature adjustment of each device, and further adjusting each device by a PLC closed-loop control method. S4-1, the PLC controllers of the key devices involved in each process stage in the snail stock solution separation process receive and execute the temperature control instructions ； 6. The temperature control method based on the snail stock solution separation process according to claim 1, wherein in step S5, further comprising the following sub-steps: S5-1, obtaining a running state of a data acquisition stage by a signal strength monitoring method of a temperature sensor and a CRC checking method; S5-2, obtaining a running state of an instruction generation stage by a generation path of the temperature control instructions and a comparison result of output results, the generation path including generation time, generation conditions, verification results, packaging formats and transmission paths; S5-3, obtaining a running state of an instruction execution stage by monitoring a communication link connection state of the PLC controller and a data response time, the communication link connection state including a physical connection state, a signal strength and a bit error rate.

7. The temperature control method based on the snail stock solution separation process according to claim 1, wherein in step S6, further comprising the following sub-steps: S6-1, presetting a change range of a required temperature of each stage of the snail stock solution separation process according to a process flow, comparing temperature data of devices required by the process stage, judging whether the temperature data exceeds the preset change range, generating a temperature alarm information when it is judged that the preset change range is exceeded, the temperature alarm information including a device ID and a device temperature; S6-2, judging whether a system running state appears a fault according to the system running state, including a communication state, data transmission accuracy and running parameter indexes, generating a running alarm information when it is judged that the fault appears, the running alarm information including a fault module and a fault reason; S6-3, displaying the temperature alarm information and the running alarm information on a monitoring interface of an operator by an industrial automation monitoring system, and simultaneously pushing the alarm information to relevant management personnel by a short message and a mobile terminal APP pushing method, recording a time when the alarm signal is generated and generating an alarm log.

8. The temperature control method based on the snail stock solution separation process according to claim 1, wherein ​ ​ ​ In step S7, the following sub-steps are further included: S7-1, format the temperature data, temperature feature vector, temperature control instruction, system running state and alarm log in JSON format to generate formatted process information; S7-2, data arrangement of the formatted process information through pre-defined aggregation rules to generate a report record; S7-3, check the integrity and accuracy of the report record, and store the checked report record in the local database of the system.

9. The temperature control method based on the snail stock solution separation process according to claim 1, characterized in that: In step S8, the following sub-steps are further included: S8-1, convert the report record into a data structure that can be used for visualization through a data analysis method, including JSON structure, XML structure and CSV structure; S8-2, according to the structured report record, obtain the visualization chart of the process flow of the snail stock solution separation process through a chart generation method, including time series chart, column chart, line chart, scatter chart and heat map; S8-3, design a user-friendly visualization interface on the Tableau platform to display the visualization chart through the visualization interface.

10. A temperature control system for implementing the method of claim 1 based on a snail stock solution separation process, characterized by, Comprising: A temperature sensor module for real-time monitoring of the temperature in the key devices involved in each process stage of the snail stock solution separation process to obtain temperature data; A data processing module for pre-processing and feature extraction of the temperature data to obtain a temperature feature vector; An instruction generation module for generating temperature control instructions based on the temperature feature vector through a PID control algorithm; A temperature control module for receiving and executing temperature control instructions to adjust the temperature of the key devices involved in each process stage of the snail stock solution separation process; A system monitoring module for real-time monitoring of the system running state during the execution of temperature adjustment process; A fault alarm module for issuing an alarm signal when the temperature in any device required in the process stage exceeds the preset range or the system running state fails, and recording the alarm log; A report generation module for recording the running process of the temperature control system and generating a report record; A report display module for displaying the report record through a visualization method.

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