Digital sulfuric acid real-time monitoring management system and method

Abstract

The invention relates to the technical field of intelligent production data monitoring, in particular to a digital sulfuric acid real-time monitoring management system and method. The system comprises a data acquisition module used for acquiring parameters in a sulfuric acid production process; the data transmission module is used for transmitting the acquired data to the system and encrypting the transmitted data; the data analysis module is used for processing original data, mining a potential association relationship among production parameters, and analyzing and predicting a historical trend of the production data; the real-time monitoring module is used for displaying key parameters and an equipment operation state, and the fault diagnosis module is used for constructing a fault diagnosis model, performing feature extraction and classification processing on production data, outputting a fault diagnosis result, and identifying potential fault points by analyzing a parameter change trend and a mutual relation; and the early warning module is used for setting an early warning rule and sending an early warning notification by adopting a plurality of early warning modes. According to the technical scheme, the reliability, real-time performance, intelligence and greenization of sulfuric acid production can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production data monitoring, and in particular to a digital sulfuric acid real-time monitoring and management system and method. Background Art

[0002] As an important basic chemical raw material, sulfuric acid is widely used in fertilizers, pesticides, metallurgy, petroleum, chemicals, textiles, printing and dyeing, and other fields. The stability and safety of its production process are crucial to ensuring the normal operation of the sulfuric acid industry. With the rapid development of industrial automation and information technology, higher requirements are being placed on the real-time monitoring and management of the sulfuric acid production process.

[0003] Traditional sulfuric acid production management relies primarily on manual inspections and regular data logging, which presents numerous limitations. First, manual inspections make it difficult to achieve continuous, real-time monitoring of the production process, making it easy to miss unusual changes in key parameters. This leads to delayed fault detection, impacting production efficiency and product quality. Second, data records are often stored in paper or simple spreadsheets, resulting in fragmented data storage and the need for manual review to locate specific records, making rapid retrieval impossible. Furthermore, paper records are susceptible to chemical contamination in the sulfuric acid industry environment and are prone to yellowing and brittleness due to contact with acidic substances, making long-term preservation difficult. This also hinders efficient data analysis and mining, hindering the ability to provide robust data support for production decision-making. Furthermore, traditional management models also have shortcomings in data transmission. The single data transmission method lacks flexibility and security, making it susceptible to interference from external environmental factors, leading to the risk of data loss, tampering, and falsification, posing a serious threat to production safety. Furthermore, traditional management models lack the ability to predict equipment failures and process anomalies, resulting in a high risk of unplanned downtime, which in turn impacts production continuity.

[0004] In recent years, although some sulfuric acid production companies have begun to introduce automated monitoring systems, these systems often have single functions and lack the comprehensive ability to conduct real-time analysis of the causes of equipment failures and corrosion conditions, as well as the comprehensiveness and reliability of system judgments. Summary of the Invention

[0005] The purpose of the present invention is to provide a digital sulfuric acid real-time monitoring and management system and method to improve the reliability and real-time performance, intelligence and greenness of sulfuric acid production.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a digital sulfuric acid real-time monitoring and management system, comprising:

[0007] The data acquisition module is used to collect various parameters in the sulfuric acid production process through sensors. The data acquisition module sets different data acquisition frequencies according to the changing characteristics of each parameter;

[0008] The data transmission module is used to transmit the collected data to the system, using a combination of wired and wireless transmission methods, and encrypting the transmitted data;

[0009] The data analysis module is used to clean, standardize and normalize the collected raw data, explore the potential correlation between different production parameters, and analyze and predict the historical trends of production data through time series analysis methods;

[0010] The real-time monitoring module is used to display the key parameters and real-time operating status of the equipment during the sulfuric acid production process.

[0011] The fault diagnosis module is used to build a fault diagnosis model, extract features and classify the collected production data, output fault diagnosis results, and identify potential fault points by analyzing the changing trends and mutual relationships of multiple parameters;

[0012] The early warning module sets detailed early warning rules according to the safety standards and process requirements of the sulfuric acid production process, and uses multiple early warning methods to send early warning notifications.

[0013] Beneficial effects of the basic solution: The data acquisition module sets different data acquisition frequencies based on the changing characteristics of each parameter. It can collect fast-changing parameters at high frequencies and slow-changing parameters at low frequencies. This ensures the integrity and timeliness of key data while avoiding unnecessary data redundancy, improving the accuracy and efficiency of data acquisition and enabling the collected data to more truly reflect the actual situation of the sulfuric acid production process.

[0014] The combination of wired and wireless transmission methods allows for flexible adaptation to diverse production environments, ensuring stable data transmission. Furthermore, encryption of transmitted data effectively prevents data theft or tampering during transmission, guaranteeing data security and accuracy.

[0015] The data analysis module explores the potential correlations between different production parameters, analyzes and predicts the historical trends of production data through time series analysis methods, and can predict the changing trends in the production process in advance.

[0016] The real-time monitoring module displays key parameters and equipment operating status in the sulfuric acid production process, enabling operators to understand the situation at the production site in real time and intuitively, detect abnormal situations in a timely manner and take corresponding measures.

[0017] The fault diagnosis module builds a fault diagnosis model, extracts features, and classifies collected production data to accurately identify equipment fault types and causes. By analyzing the changing trends and interrelationships of multiple parameters to identify potential fault points, it enables early warning and prevention of faults, reduces the impact of equipment failures on production, reduces equipment maintenance costs and downtime, and improves the practicality and intelligence of equipment.

[0018] The early warning module sets detailed early warning rules according to the safety standards and process requirements of the sulfuric acid production process, and can issue early warning signals in a timely and accurate manner.

[0019] This technical solution achieves comprehensive, real-time monitoring and management of the sulfuric acid production process through the collaborative operation of multiple functional modules, ensuring system accuracy, intelligence, and efficiency. Through practical application, this system can effectively improve sulfuric acid production efficiency, ensure production safety, and reduce production costs, providing strong support for the sustainable development of enterprises. It has broad application prospects and promotional value.

[0020] As an implementable preferred solution, it supports multi-terminal access and distinguishes the normal, warning and alarm states of parameters through different colors.

[0021] As an implementable preferred solution, in the data acquisition module, a thermocouple sensor is used for temperature monitoring, a piezoresistive pressure sensor is used for pressure monitoring, an electromagnetic flowmeter is used for flow monitoring, and an electromagnetic sensor is used for concentration monitoring.

[0022] As an implementable preferred solution, when the data transmission module encrypts the transmitted data, it generates the key and initialization vector IV required for AES encryption at the sending end, encrypts the data using the AES algorithm, and performs CRC check on the encrypted data.

[0023] As an implementable preferred solution, the data analysis module removes noise data, outliers and duplicate data when cleaning the collected raw data; uses the Z-score normalization method to standardize the cleaned data, and maps the concentration data to the [0,1] interval through normalization.

[0024] As an implementable preferred solution, the fault diagnosis module extracts features and classifies the collected production data through convolutional layers, pooling layers, and fully connected layers; during the fault diagnosis process, multiple production parameters are reduced in dimension through principal component analysis to extract the most representative principal components for fault diagnosis; production data are clustered according to similarity using a cluster analysis algorithm, and the specific fault point is determined in combination with the equipment's working principle and historical data.

[0025] As an implementable preferred solution, the early warning module sets detailed early warning rules according to the safety standards and process requirements of the sulfuric acid production process, sends early warning notifications via text messages and / or emails, and displays early warning information on a real-time monitoring interface.

[0026] As an implementable preferred solution, it also includes a system management module for establishing a user authority management mechanism, recording all operations and events during system operation, and discovering system anomalies and security risks through analysis of system logs.

[0027] As an implementable preferred solution, the data acquisition module also establishes a parameter prediction model through an LSTM neural network to predict the changing trend of each parameter in the future and dynamically adjusts the sensor acquisition frequency according to the trend.

[0028] In a second aspect, the embodiments of the present disclosure provide a digital sulfuric acid real-time monitoring and management method, which utilizes the above-mentioned digital sulfuric acid real-time monitoring and management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a structural diagram of a digital sulfuric acid real-time monitoring and management system.

[0030] Figure 2 Schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It will be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain the present application, rather than to limit the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered to be isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0032] In addition, unless otherwise defined, technical or scientific terms used in the description of the present invention should have the common meanings understood by those skilled in the art in the art to which the present invention belongs.

[0033] The present invention will be further described in detail below with reference to the accompanying drawings:

[0034] Reference numerals: electronic device 500 , processor 501 , communication interface 502 , memory 503 , bus 504 .

[0035] Example 1

[0036] Reference Figure 1The embodiment of the present disclosure provides a digital sulfuric acid real-time monitoring and management system, including a data acquisition module, a data transmission module, a data analysis module, a real-time monitoring module, a fault diagnosis module, an early warning module and a system management module.

[0037] The data acquisition module is used to collect and calculate various parameters in the sulfuric acid production process through sensors. Various types of high-precision sensors are deployed in key links in sulfuric acid production, such as converters, pipelines, and storage tanks. Thermocouple sensors are used for temperature monitoring, which have the characteristics of fast response speed and high measurement accuracy. They can accurately capture temperature changes in the converter and meet the strict temperature monitoring requirements of the sulfuric acid production process. Pressure monitoring uses a piezoresistive pressure sensor, which has high sensitivity and good stability and can accurately measure the pressure conditions in the pipeline in real time. Flow monitoring uses an electromagnetic flowmeter, whose measurement accuracy is not affected by factors such as fluid density and viscosity, and can reliably obtain sulfuric acid flow data. Concentration monitoring uses an electromagnetic sensor, which can quickly and accurately detect the concentration of sulfuric acid.

[0038] In this embodiment, the collected parameters include the physical properties of common gases in sulfuric acid production (oxygen, nitrogen, hydrogen, air, water, water vapor, sulfur dioxide, sulfur trioxide, carbon monoxide, carbon dioxide, nitrogen oxides, ammonia, fluorine, and hydrogen fluoride); the physical properties of dry air (P = 101.3 KPa); the density of dry air at different temperatures (kg / m 3 ), specific heat capacity of dry air (kJ / (kg·℃)), heat transfer coefficient of dry air (10 2 ×λ(×4.19kJ / (m·h·℃)), thermal diffusivity of dry air (10 2 ×a(m 2 / h), viscosity of dry air (μPa·s), kinematic viscosity of dry air (μm 2 / s), Prandtl number of dry air (Pr); water content of saturated vapor pressure; SO2 parameter query involves the density of SO2 aqueous solution at 15℃ (g / cm 3 ), SO2 concentration in solution (g / L), liquid SO2 density (g / cm 3 ), the solubility of SO2 in acid and fuming acid (g / 100g fuming sulfuric acid); the parameter query of sulfur involves the saturated vapor density of sulfur (kg / m 3), heat of vaporization of sulfur at 120-646°C (kJ / kg), heat of dissociation of sulfur (cal / mol) / (cal / g) / (kJ / kg), heat of combustion of sulfur (cal / mol) / (cal / g) / (kJ / kg), heat of fusion of sulfur at 101.3 kPa (cal / g) / (kJ / kg), transition heat of sulfur (cal / g) / (KJ / Kg); physical properties of liquid sulfur include specific heat capacity of liquid sulfur at 30°C (kJ / (kg·K), density of liquid sulfur (kg / m 3 ), viscosity of liquid sulfur (Pa·s); fuming sulfuric acid parameter query includes density of fuming acid at 15-45℃ (kg / m 3 ), concentration of fuming sulfuric acid at 20℃ (g / L), viscosity of fuming acid at 15-50℃ and 25-80℃ (mPa·s); Sulfuric acid parameter query includes density of sulfuric acid at 0-100℃ (kg / m 3 ), heat capacity of sulfuric acid at -20-150℃ (kJ / (kg·K)), heat enthalpy of sulfuric acid at 20-250℃ (kJ / kg), thermal conductivity of sulfuric acid (λ(W / (m·K))), electrical conductivity of sulfuric acid (S / cm); water parameter query includes viscosity of water at different temperatures (mPa·s); physical properties of saturated water include pressure of saturated water at different temperatures (under 101kPa conditions), density of saturated water (kg / m 3 ), enthalpy of saturated water (kJ / kg), specific heat capacity of saturated water (Cp), thermal conductivity of saturated water (kJ / (m·h·℃)), thermal diffusivity of saturated water (m 2 / h), viscosity of saturated water (μPa·s), kinematic viscosity of saturated water (μm 2 / s), volume expansion coefficient of saturated water β×10 4 (℃) Surface tension of saturated water σ×10 4 (kg / m3), Prandtl number of saturated water (Pr); the physical properties of saturated water vapor include the pressure of saturated water vapor at different temperatures ((×98kPa)(absolute)), the density of saturated water vapor (kg / m3) 3 ), enthalpy of saturated water vapor (kJ / kg), heat of vaporization of saturated water vapor (kJ / kg), specific heat capacity of saturated water vapor (kJ / (kg·℃)), thermal diffusivity of saturated water vapor (kJ / (m·h·℃)), thermal conductivity of saturated water vapor (a×10 3 (m 2 / h)), viscosity of saturated water vapor (μPa·s), kinematic viscosity of saturated water vapor (μm 2 / s), Prandtl number of saturated water vapor (Pγ). Common gas properties include the density of oxygen (kg / m 3), gas constant (k(kg·m / (kg·℃))), melting point of gas (℃), heat of fusion of gas (J / g), boiling point of gas at 101.3kPa (℃), latent heat of vaporization of gas at 101.3kPa (kJ / kg), specific heat capacity per kilogram of gas at 20℃, 101.3kPa (kJ / (kg·℃)), thermal conductivity of gas under standard conditions (W / (m·K)), viscosity of gas at different temperatures (μPa·s).

[0039] The data acquisition module sets different data collection frequencies based on the changing characteristics of various parameters during the sulfuric acid production process. For parameters such as temperature and pressure that change rapidly and have a significant impact on production safety, a high-frequency data collection strategy of once per second is adopted to ensure that instantaneous parameter changes are captured in a timely manner. For relatively stable parameters such as flow rate and concentration, data is collected every few seconds, ensuring data accuracy while avoiding data redundancy caused by excessive data collection. In response to abnormal situations in the production process, such as sudden parameter changes or equipment failures, the system automatically triggers an emergency data collection mechanism, increasing the collection frequency to multiple times per second, providing richer data support for subsequent fault diagnosis and resolution.

[0040] The data transmission module is used to transmit collected data to the system, using a combination of wired and wireless transmission methods. In areas with short distances and low interference, such as between sensors and data collectors inside a workshop, wired transmission, such as industrial Ethernet, is preferred. Industrial Ethernet has the advantages of fast transmission speeds, high stability, and strong anti-interference capabilities, ensuring reliable data transmission. For scenarios with long distances, difficult wiring, or requiring mobile device access, such as sensors on outdoor storage tanks, wireless transmission technologies such as 4G / 5G networks or LoRa wireless communication technology are used. 4G / 5G networks have fast transmission speeds and can achieve real-time remote data transmission; LoRa technology has the advantages of low power consumption and long-distance transmission, making it suitable for scenarios with high requirements on power consumption and transmission distance.

[0041] The transmitted data is encrypted. On the sending end, the key and initialization vector (IV) required for AES encryption are generated to add randomness to the encryption process, preventing identical plaintext blocks from being encrypted into identical ciphertext blocks. The data to be transmitted is encrypted using the AES algorithm, and a CRC check is performed on the encrypted data. The check result is sent along with the encrypted data. On the receiving end, the CRC check is performed on the received data. If the check passes, the data is decrypted using the key. If the receiving end detects an error in the data, it requests the sending end to resend the data to ensure data accuracy.

[0042] The data analysis module cleans the collected raw data, removing noise, outliers, and duplicates. Reasonable data thresholds are set, and data points that exceed these thresholds are marked and processed. For example, for temperature data, during normal production, the temperature inside the converter should fluctuate within a certain range. If a temperature value far exceeds the normal range and does not align with the changing trends of other relevant parameters, it is identified as an outlier and removed. Duplicate data is identified and deleted using a data comparison algorithm to ensure data uniqueness and accuracy.

[0043] The cleaned data were standardized and normalized. Using the Z-score method, the data were converted to a standard normal distribution with a mean of 0 and a standard deviation of 1. Concentration data were normalized to the interval [0, 1], eliminating dimensional differences between data, making them comparable and improving the accuracy and reliability of data analysis.

[0044] The data analysis module also explores potential correlations between different production parameters. Analysis revealed a strong positive correlation between temperature and pressure within the converter; as temperature increases, pressure also rises. Time series analysis methods are used to analyze and forecast historical trends in production data. By establishing an autoregressive integrated moving average (ARIMA) model, sulfuric acid flow rate data is modeled and forecasted, enabling early detection of potential trends in the production process and providing a basis for production decision-making.

[0045] The real-time monitoring module displays key parameters and equipment operating status during the sulfuric acid production process, preferably using graphical displays such as dashboards, line charts, and bar charts. It displays the current values and trends of parameters such as converter temperature, pressure, flow rate, and concentration in real time. Different colors are used to distinguish between normal, warning, and alarm states of parameters (green for normal, yellow for warning, and red for alarm), allowing users to quickly identify abnormalities in the production process.

[0046] The real-time monitoring module supports multi-terminal access, allowing users to view production data and equipment operating status anytime and anywhere through personal computers, mobile phones or tablets.

[0047] The fault diagnosis module is used to build a fault diagnosis model, collecting a large amount of normal and fault data from the sulfuric acid production process to train and optimize the model. The collected production data serves as the model input. After feature extraction and classification processing through convolutional layers, pooling layers, and fully connected layers, the model outputs the fault diagnosis results. For example, for converter fault diagnosis, multiple parameter data, such as converter temperature, pressure, and conversion rate, are preprocessed and input into a trained CNN model. The model can accurately determine whether a converter fault exists and what type of fault it is.

[0048] During the fault diagnosis process, production data is deeply mined and analyzed. By using the principal component analysis (PCA) method, multiple production parameters are subjected to dimensionality reduction processing to extract the most representative principal components for fault diagnosis. In the present embodiment, when analyzing the fault of sulfuric acid production equipment, multiple parameters such as temperature, pressure, flow, vibration, etc. are subjected to PCA processing to obtain the main components, which reflect the key information of the equipment operation status. Using the cluster analysis algorithm, the production data are clustered according to similarity. If a certain type of data is significantly different from the normal data clustering, it is judged that the production status corresponding to this type of data may have a fault. The clustering results are further analyzed, and the specific fault point is determined in combination with the working principle and historical data of the equipment.

[0049] The fault diagnosis module identifies potential fault points by analyzing the changing trends and interrelationships of multiple parameters. For example, in the sulfuric acid production process, suppose the converter's temperature and conversion rate exhibit abnormal changes simultaneously. Normally, temperature and conversion rate maintain a certain proportional relationship. As temperature rises, so does conversion rate, but the increase is within a reasonable range. If the temperature rises but the conversion rate does not, analyzing the changing trends and interrelationships of these two parameters, combined with historical data and equipment operating experience, suggests a catalyst failure within the converter, leading to an uneven reaction and, in turn, abnormal temperature and pressure. The system issues a potential fault warning, prompting personnel to promptly inspect and optimize the process equipment to prevent further escalation of the fault.

[0050] The early warning module sets detailed warning rules based on the safety standards and process requirements of the sulfuric acid production process. For example, for temperature parameters, when the temperature inside the converter exceeds 80% of the normal operating upper limit, the system issues a yellow warning; when the temperature exceeds the normal operating upper limit, a red alarm is issued. For pressure parameters, different warning thresholds are also set to ensure timely warning information when pressure abnormalities occur.

[0051] The early warning module uses multiple alert methods to ensure that relevant personnel receive timely warning information. When the system triggers an alert, a notification is sent via SMS, WeChat, email, and other means. The SMS notification includes specific information about the alert, such as the fault type, location, and time of the alert. Simultaneously, the alert information is displayed as a pop-up window on the real-time monitoring interface, accompanied by an audio prompt to draw attention.

[0052] The system management module establishes a strict user rights management mechanism to ensure system data security and operational compliance. Users are divided into different roles, such as administrators, production personnel, and technicians, based on their responsibilities and work requirements. Administrators have the highest authority and can fully manage and configure the system, including user information management, rights allocation, and system parameter settings. Production personnel can only view production data and perform basic operations, such as starting and stopping equipment. Technicians can perform data analysis, fault diagnosis, and system maintenance.

[0053] The system management module also records all operations and events during system operation, including user logins, data modifications, device operations, and warning messages. System logs are stored using a combination of timestamps and operation records for easy traceability and query. Analysis of system logs enables timely identification of system anomalies and security risks, providing a basis for system optimization and troubleshooting.

[0054] Example 2

[0055] The technical features that distinguish this embodiment from the above embodiments are that the data acquisition module also establishes a parameter prediction model through an LSTM neural network, dynamically adjusts the sensor acquisition frequency, improves the system's monitoring accuracy and response speed to changes in sulfuric acid production process parameters, and effectively ensures production safety and stability. Specifically, it includes the following:

[0056] Based on the normal variation characteristics of various parameters during sulfuric acid production, reasonable initial collection frequencies are set for different parameters. For example, for parameters such as temperature and pressure, which change rapidly and have a significant impact on production safety, the initial collection frequency is set to once per second; for relatively stable parameters such as flow rate and concentration, the initial collection frequency is set to once every 5 seconds.

[0057] Collect historical data on various parameters of the sulfuric acid production process for at least the past week, covering both normal production conditions and those under varying degrees of abnormal operating conditions. Clean the collected historical data to remove noise, outliers, and duplicates, and perform standardization and normalization.

[0058] Build an LSTM neural network model using Python's TensorFlow or PyTorch framework. The model consists of three to five layers of LSTM units. The number of hidden layer neurons is adjusted based on data characteristics and model training results, typically between 64 and 256. The input layer receives processed historical data, and the output layer outputs predicted values for each parameter for the next 30 seconds.

[0059] Use the optimized historical data to train the LSTM model. During training, use the mean squared error (MSE) loss function to measure the difference between the predicted value and the true value. Use the Adam optimizer to iteratively update the model parameters using gradient descent. Set an appropriate number of training rounds (e.g., 500-1000 rounds) and learning rate (e.g., 0.001-0.0001) to ensure model convergence and good generalization.

[0060] During system operation, the LSTM neural network parameter prediction model uses real-time predictions to predict the changing trends of various parameters over a period of time (e.g., 30 seconds). The system sets warning thresholds for each parameter, determined based on sulfuric acid production process standards and safety regulations. For example, if the normal operating range of the converter temperature is 410-420°C, the upper limit of the temperature warning threshold can be set at 425°C and the lower limit at 400°C.

[0061] When the predicted value approaches the warning threshold (e.g., reaching 90% of the threshold), the dynamic frequency adjustment unit automatically increases the acquisition frequency of the corresponding parameter to 200Hz, ensuring that even subtle changes in the parameter can be accurately captured. Simultaneously, the high-speed cache area is activated to store transient data at a frequency of 200Hz. The cache area is constructed using high-speed flash memory chips with fast read and write capabilities, ensuring that data is not lost even with high-frequency acquisition.

[0062] When the parameters return to the normal range and stabilize for a period of time (such as 5 minutes), the dynamic frequency adjustment unit will restore the acquisition frequency to the initial setting value, stop the high-frequency storage in the cache area, and transfer the cached data to the data transmission module according to the normal process to ensure the rational use of system resources.

[0063] The data analysis module processes high-frequency transient data as it receives it. It employs a more efficient data cleaning algorithm to quickly identify and remove outliers and noise from high-frequency data. It also uses a sliding window algorithm to perform real-time analysis on transient data stored in the cache, promptly identifying parameter mutations and short-term trend changes, providing more accurate data support for fault diagnosis and early warning.

[0064] By adding a dynamic frequency adjustment unit to the data acquisition module and integrating an LSTM neural network to establish a parameter prediction model, this embodiment can predict abnormal changes in parameters during the sulfuric acid production process in advance, promptly increase the acquisition frequency when the parameters approach the danger threshold, and obtain more accurate transient data. This data is processed by subsequent modules, making fault diagnosis more timely and accurate, and early warning more forward-looking, greatly improving the performance of the digital sulfuric acid real-time monitoring and management system, ensuring the safe and stable operation of the sulfuric acid production process, reducing production risks, and improving production efficiency.

[0065] The disclosed embodiment also provides a digital sulfuric acid real-time monitoring and management method, which utilizes the above-mentioned digital sulfuric acid real-time monitoring and management system.

[0066] Those skilled in the art will appreciate that all or part of the process of realizing a kind of digital sulfuric acid real-time monitoring and management system can be accomplished by instructing the relevant hardware through a computer program, and described program can be stored in a non-volatile computer-readable storage medium, and this program, when executed, can include the process of each embodiment of a digital sulfuric acid real-time monitoring and management system. Wherein, any quoting to memory, storage, database or other media used in each embodiment provided by the application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] The present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the aforementioned method for predicting the prognosis of craniocerebral injury based on big data and a knowledge graph are implemented. In the present application, the processor serves as the control center of the computer method and can be either a physical processor or a virtual processor.

[0068] Reference Figure 2 The electronic device 500 includes: at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. The bus 504 is used to implement connection and communication between these components, the communication interface 502 is used to communicate signaling or data with other node devices, and the memory 503 stores machine-readable instructions executable by the processor 501. When the electronic device 500 is running, the processor 501 communicates with the memory 503 via the bus 504. When the machine-readable instructions are called by the processor 501, the steps of the above-mentioned method for predicting the prognosis of craniocerebral injury based on big data and knowledge graphs are executed.

[0069] The above contents are merely embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. A person of ordinary skill in the art is aware of all common technical knowledge in the technical field to which the invention belongs before the filing date or priority date, is able to obtain all existing technologies in the field, and has the ability to apply conventional experimental means before that date. A person of ordinary skill in the art can, under the guidance of this application, improve and implement this scheme in combination with his or her own abilities. Some typical known structures or known methods should not become an obstacle for a person of ordinary skill in the art to implement this application. It should be pointed out that for a person of ordinary skill in the art, several variations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection claimed in this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A digital sulfuric acid real-time monitoring and management system, characterized in that: include: The data acquisition module is used to collect various parameters in the sulfuric acid production process through sensors. The data acquisition module sets different data acquisition frequencies according to the changing characteristics of each parameter; The data transmission module is used to transmit the collected data to the system, using a combination of wired and wireless transmission methods, and encrypting the transmitted data; The data analysis module is used to clean, standardize and normalize the collected raw data, explore the potential correlation between different production parameters, and analyze and predict the historical trends of production data through time series analysis methods; The real-time monitoring module is used to display the key parameters and real-time operating status of the equipment during the sulfuric acid production process. The fault diagnosis module is used to build a fault diagnosis model, extract features and classify the collected production data, output fault diagnosis results, and identify potential fault points by analyzing the changing trends and mutual relationships of multiple parameters; The early warning module sets early warning rules according to the safety standards and process requirements of the sulfuric acid production process, and uses multiple early warning methods to send early warning notifications.

2. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that, It supports multi-terminal access and uses different colors to distinguish the normal, warning and alarm status of parameters.

3. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: In the data acquisition module, a thermocouple sensor is used for temperature monitoring, a piezoresistive pressure sensor is used for pressure monitoring, an electromagnetic flowmeter is used for flow monitoring, and an electromagnetic sensor is used for concentration monitoring.

4. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: When the data transmission module encrypts the transmitted data, it generates the key and initialization vector IV required for AES encryption at the sending end, encrypts the data using the AES algorithm, and performs CRC check on the encrypted data.

5. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: The data analysis module cleans the collected raw data by removing noise data, outliers and duplicate data; standardizes the cleaned data using the Z-score standardization method, and maps the concentration data to the [0, 1] interval through normalization.

6. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: The fault diagnosis module extracts features and classifies the collected production data through convolutional layers, pooling layers, and fully connected layers. During the fault diagnosis process, multiple production parameters are reduced in dimension through principal component analysis to extract the most representative principal components for fault diagnosis. The production data is clustered according to similarity using a cluster analysis algorithm, and the specific fault point is determined based on the equipment's working principle and historical data.

7. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: The early warning module sets detailed early warning rules according to the safety standards and process requirements of the sulfuric acid production process, sends early warning notifications via text messages and / or emails, and displays early warning information on a real-time monitoring interface.

8. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: It also includes a system management module, which is used to establish a user authority management mechanism, record all operations and events during system operation, and discover system anomalies and security risks through analysis of system logs.

9. A digital sulfuric acid real-time monitoring and management system according to claim 1, characterized in that: The data acquisition module also establishes a parameter prediction model through the LSTM neural network to predict the changing trend of each parameter in the future and dynamically adjusts the sensor acquisition frequency according to the trend.

10. A digital sulfuric acid real-time monitoring and management method, characterized in that: A digital sulfuric acid real-time monitoring and management system as described in any one of items 1-9 above is used.

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