Chemical production workshop protection method based on data analysis

Abstract

The invention relates to a chemical production workshop protection method based on data analysis, and aims to monitor environmental parameters such as gas concentration, temperature, pressure and humidity in a workshop in real time through a multi-sensor network. The system comprises a gas sensor, a temperature sensor, a pressure sensor and a humidity sensor, and sensor data are transmitted to a central control system through the Internet of Things technology. The system performs intelligent analysis on data based on a Bayesian network, a support vector machine and a time sequence analysis model, dynamically identifies potential safety hazards, and automatically adjusts protection strategies according to analysis results, including emergency measures such as ventilation triggering, cooling, equipment closing and the like. The system also has a self-learning function, and improves the intelligence and adaptivity of protection through reinforcement learning optimization protection measures. The method can effectively improve the safety management level of the chemical workshop, reduces the accident risk, and is suitable for intelligent safety protection of various chemical production environments.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical workshop protection, and in particular to a chemical production workshop protection method based on data analysis. Background Art

[0002] Chemical production workshops involve a large number of dangerous chemicals, flammable and explosive substances, and toxic and harmful gases. During the production process, a slight mistake may cause serious safety accidents. These accidents not only threaten the health of workers, but may also cause significant property losses and environmental pollution. Therefore, safety protection in chemical production workshops has always been the focus of the industry.

[0003] Traditional chemical workshop protection methods mainly rely on fixed safety devices and regular manual inspections, such as installing fire protection systems, ventilation equipment, gas leak alarms, etc. Although these measures can reduce safety hazards to a certain extent, they have the following shortcomings:

[0004] Delayed response: Traditional protection measures usually rely on passive response after the incident. For example, the alarm device can only take effect after a gas leak or equipment failure occurs, but the danger already exists and may have had an adverse effect on personnel or equipment.

[0005] Lack of real-time monitoring and early warning capabilities: Traditional systems have limited monitoring methods and are unable to conduct real-time, comprehensive monitoring of environmental parameters in the workshop (such as gas concentration, temperature, pressure, humidity, etc.), resulting in the inability to discover safety hazards in a timely manner.

[0006] Lack of intelligent analysis: Traditional safety devices are unable to conduct complex safety analysis and decision-making adjustments. They can usually only be triggered based on simple conditions, and lack comprehensive analysis based on multiple parameters and dimensions. Due to the complex chemical production environment, the abnormality of a single parameter is often not enough to determine the specific situation of safety hazards.

[0007] With the rapid development of the Internet of Things, sensor technology and data analysis technology, introducing advanced sensor networks and big data analysis technology into the safety protection of chemical workshops can greatly improve the automation and intelligence level of workshop safety management.

[0008] At present, some modern chemical companies have begun to deploy basic sensor networks to monitor gas leaks, temperature changes and pressure fluctuations. However, these systems still have the problem of insufficient intelligence, which is mainly manifested in the following points:

[0009] Sensor data is highly isolated: Data from different types of sensors are often managed in a decentralized manner, making it impossible to achieve data fusion and comprehensive analysis, resulting in the system being unable to make accurate judgments on complex safety hazards.

[0010] Lack of dynamic adjustment capabilities: Existing protection systems usually respond according to fixed preset conditions and are difficult to dynamically adjust and optimize according to environmental changes. The risks in modern chemical production processes are variable and dynamic, and a single response strategy often cannot effectively deal with complex dangerous situations.

[0011] Lack of self-learning function: At present, the protection systems of most chemical workshops do not have self-learning functions, and cannot perform pattern recognition and risk prediction based on historical data and real-time data, nor can they automatically optimize the decision-making strategies of protective measures.

[0012] In order to overcome the above problems, the present invention proposes a chemical production workshop protection method based on data analysis. By deploying a variety of sensors, such as gas sensors, temperature sensors, pressure sensors and humidity sensors, the environmental parameters in the workshop are collected in real time, and the data are intelligently analyzed through big data analysis, machine learning and other technologies. The system can not only monitor the safety status in the workshop in real time, but also dynamically adjust the protection strategy according to the changes in the environment, and even continuously optimize the safety protection measures through the self-learning mechanism. Summary of the invention

[0013] The purpose of the present invention is to provide a chemical production workshop protection method based on data analysis to address the deficiencies of the prior art and to solve the problems raised in the background technology.

[0014] To achieve the above object, the present invention provides the following technical solution: a chemical production workshop protection method based on data analysis, comprising the following steps:

[0015] Deploy sensors at key locations in chemical production workshops, including MQ-7 electrochemical gas sensors, MQ-135 semiconductor gas sensors, temperature sensors, pressure sensors, and SHT10 humidity sensors;

[0016] The sensor collects environmental parameters such as gas concentration, temperature, pressure and humidity in the workshop in real time;

[0017] The parameter data collected by the sensor is transmitted to the central control system through the Internet of Things technology;

[0018] The central control system conducts big data analysis based on preset thresholds, historical data, and real-time monitoring data to identify potential safety hazards;

[0019] Dynamically adjust protection strategies based on analysis results, including triggering protection equipment activation and alarm systems.

[0020] As a preferred technical solution of the present invention, the central control system uses a Bayesian network model to analyze sensor data and calculate the probability of potential safety hazards, as follows: The concentration of harmful gases detected by the gas sensor Gi With its safety threshold T G The ratio r i =G i / T G The formula for calculating the risk of hidden dangers is:

[0021]

[0022] Among them, P(H|D) is the conditional probability of hidden danger occurrence, P(D|H) is the probability of data monitored when the hidden danger occurs, P(H) is the prior probability of the hidden danger, and P(D) is the marginal probability of the observed data.

[0023] As a preferred technical solution of the present invention, the gas sensor includes but is not limited to:

[0024] MQ-7 electrochemical gas sensor: used to detect the concentration of carbon monoxide gas in the workshop;

[0025] MQ-135 semiconductor gas sensor: used to monitor the concentration of harmful gases such as ammonia, benzene and formaldehyde in the workshop;

[0026] SHT10 humidity sensor: used to measure the air humidity in the workshop in real time.

[0027] As a preferred technical solution of the present invention, the central control system calculates the potential impact of various environmental parameters on equipment and workers in real time based on big data analysis and machine learning algorithms, and takes the following dynamic protection measures:

[0028] When the MQ-7 gas sensor detects that the carbon monoxide concentration exceeds 50ppm, the system automatically triggers the ventilation equipment;

[0029] When the temperature sensor detects that the temperature exceeds 100°C, the spray system is automatically activated;

[0030] When the pressure sensor detects that the pressure exceeds the safety threshold, it automatically shuts off the power to the dangerous equipment.

[0031] As a preferred technical solution of the present invention, the temperature sensor is a K-type thermocouple sensor with a measurement range of -120°C-1200°C. When it is detected that the temperature in the workshop exceeds the set threshold, the system predicts potential danger according to the following algorithm:

[0032]

[0033] Among them, T avg is the average temperature, if T avg If the safety threshold is continuously exceeded, the system will immediately trigger an alarm.

[0034] As a preferred technical solution of the present invention, the pressure sensor is a BMP280 piezoelectric sensor, which is used to monitor the pressure changes in the workshop. When the pressure changes exceed the set safety range, the system triggers the corresponding protection mechanism, including automatically closing the pipeline valve and notifying the staff.

[0035] As a preferred technical solution of the present invention, the central control system uses a time series analysis model to analyze historical data and predict potential dangerous trends. The formula is:

[0036]

[0037] Among them, y t is the monitoring value at time t. The system predicts equipment failure or environmental changes by analyzing the fluctuation trend in the data sequence.

[0038] As a preferred technical solution of the present invention, the system includes a risk classification model based on a support vector machine (SVM) for classifying and evaluating multiple types of risks of real-time monitoring data. The specific optimization objective function is as follows:

[0039]

[0040] Among them, w is the weight vector, b is the bias term, C is the regularization parameter, and y i is the label, x i is a feature vector, and the classification result is used to adjust the protection strategy of the workshop.

[0041] As a preferred technical solution of the present invention, the system has a reinforcement learning function, which optimizes the protection measures based on the Q-learning algorithm. The system dynamically adjusts the protection strategy through the following reward function:

[0042]

[0043] Among them, Q(s,a) is the value of taking action a in the current state s, α is the learning rate, γ is the discount factor, and r is the immediate reward. The system optimizes the protection strategy through continuous learning.

[0044] As a preferred technical solution of the present invention, the system displays the data collected by various sensors in the workshop and the analysis results through a data visualization module, and generates a safety report for workshop managers to make safety decisions and risk assessments.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can monitor the environmental changes in the chemical workshop in real time by deploying various types of sensors, including gas sensors, temperature sensors, pressure sensors and humidity sensors. Compared with the traditional periodic inspection or passive response system, the present invention can timely discover potential dangerous factors through data analysis at the early stage of hidden dangers, such as excessive concentration of harmful gases, abnormal equipment temperature or pressure, etc. Once an abnormal situation is detected, the system will immediately trigger the corresponding emergency equipment, such as starting ventilation equipment, cooling equipment or shutting down dangerous equipment, which significantly improves the speed and efficiency of protective response and reduces the probability of accidents.

[0046] The present invention uses the fusion analysis of multi-dimensional sensor data, combined with advanced algorithms such as Bayesian networks, support vector machines (SVM) and time series analysis, to intelligently evaluate multiple environmental parameters of the workshop. Unlike traditional systems that can only monitor a single parameter, the present invention can comprehensively consider multiple factors such as gas, temperature, pressure, etc., conduct a comprehensive risk assessment, and dynamically adjust the protection strategy. In this way, the system can more accurately identify complex dangerous situations, not only relying on the anomaly of a single parameter, but also making intelligent decisions based on the correlation between multiple factors, thereby improving the accuracy and effectiveness of protective measures.

[0047] The present invention adopts machine learning methods such as reinforcement learning to enable the system to have self-learning capabilities. The system can gradually optimize the protection strategy according to the change patterns of historical data and real-time data. For example, the system can learn and automatically select the most effective protection plan by constantly trying different emergency measures. In addition, the system can also predict future risks through time series and take protective measures in advance, realizing the transition from passive response to active prevention. With the accumulation of data, the intelligence level of system protection will continue to improve, making workshop safety protection more scientific, accurate and dynamic. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flowchart of the chemical production workshop protection method based on data analysis proposed by the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The following is combined with Figure 1 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0051] Example 1: Protection system based on gas concentration monitoring

[0052] In the chemical workshop, the MQ-7 electrochemical gas sensor is deployed to detect the concentration of carbon monoxide. Assume that the CO safety concentration threshold set by the system is 50ppm.

[0053] Data example: At a certain moment, the CO concentration read by the MQ-7 sensor is 70ppm, which exceeds the set threshold. Calculation process:

[0054] First, calculate the ratio of the current gas concentration to the safety threshold:

[0055]

[0056] Due to r i >1, that is, the concentration exceeds the safe value, the system immediately triggers the workshop's ventilation equipment and sounds an alarm to notify workers to evacuate.

[0057] Bayesian network risk calculation: Assume that historical data shows that when the CO concentration reaches 70ppm, there is a 30% chance of an accident. The Bayesian network formula is as follows:

[0058]

[0059] Where: P(D|H)=0.7 indicates the probability that the gas concentration is 70 ppm when the accident occurs.

[0060] P(H)=0.3 is the prior probability of an accident occurring, and P(D)=0.5 is the probability of observing a gas concentration of 70 ppm.

[0061] The conditional probability of an accident occurring is calculated as:

[0062]

[0063] Therefore, under this condition, the probability of an accident is 42%, the system activates a high-risk alarm and takes further measures.

[0064] Example 2: Safety protection system based on temperature monitoring

[0065] Assume that the temperature threshold in the chemical workshop is 100°C, and the current temperature detected by the K-type thermocouple temperature sensor deployed in the workshop is

[0066] Data example: The data read are: temperature T1 = 95°C (1 minute ago), temperature T2 = 100°C (30 seconds ago), temperature T3 = 110°C (current).

[0067] Calculation process: The system uses the ARIMA model to predict future temperature trends, assuming that the model parameters are:

[0068] φ1=0.6(autoregressive coefficient),

[0069] θ1=0.2 (moving average coefficient).

[0070] Apply the time series model formula:

[0071] y t =α+φ1y t-1 +∈ t +θ1∈ t-1

[0072] Among them, the temperature prediction at the current time t is:

[0073] y t =110+0.6·(110-100)+0.2·(100-95)=110+6+1=117℃

[0074] The forecast results show that the temperature will rise to 117°C in the future, exceeding the safe value. The system immediately triggers cooling equipment (such as the sprinkler system) and issues a high temperature alarm to the staff.

[0075] Example 3: Protective measures based on pressure change monitoring

[0076] The gas tank in the chemical workshop is equipped with a BMP280 pressure sensor to detect pressure changes. Assume that the pressure safety threshold is 150kPa.

[0077] Data example: The pressure data at the current time point t0 is: P0 = 130 kPa (starting pressure), P1 = 140 kPa (30 seconds ago), P2 = 155 kPa (current).

[0078] Calculation process: Calculate the pressure change rate ΔP:

[0079] ΔP=P2-P1=155-140=15kPa

[0080] Since the pressure change exceeds the set safety range (assuming the change cannot exceed 10kPa), the system immediately triggers the automatic closure of the pipeline valve and notifies the staff to check the equipment.

[0081] Risk analysis: The system uses Bayesian networks to analyze the probability of equipment failure. Assume that when the equipment fails, the probability of the pressure change exceeding 10kPa is 80%, and the prior probability of equipment failure is 15%.

[0082] Use the Bayesian formula to calculate the probability of equipment failure:

[0083]

[0084] The probability of failure is 48%, and the system reminds the operator to check the equipment and take preventive measures.

[0085] Example 4: Intelligent protection system based on multi-sensor data fusion

[0086] Assume that MQ-7 electrochemical gas sensor (to detect CO concentration), K-type thermocouple temperature sensor (to detect temperature) and BMP280 pressure sensor (to detect pressure) are deployed in the workshop at the same time.

[0087] Data example: CO concentration: 60ppm (read by MQ-7); temperature: 105°C (read by K-type thermocouple); pressure: 160kPa (read by BMP280).

[0088] Calculation process: The SVM classification function is:

[0089] The system uses the SVM algorithm to classify multidimensional data to determine whether the current state is dangerous. Assume that the weight vector of the SVM model is w = [0.5, 0.3, 0.2],

[0090] The input feature vector is x=[60,105,160], and the bias term is b=10.

[0091] f(x)=w T x+b=0.5·60+0.3·105+0.2·160+10=30+31.5+32+10

[0092] =103.5

[0093] If the system judgment threshold is 100, f(x)=103.5>100, it means that the current state is dangerous, and the system automatically initiates emergency measures.

[0094] Example 5: Dynamic protection strategy adjustment based on reinforcement learning

[0095] Assume that the gas concentration and temperature control system in a workshop uses a reinforcement learning algorithm to optimize the ventilation strategy.

[0096] Data example: Current gas concentration: 55ppm; Current temperature: 102℃.

[0097] Calculation process: The system selects the optimal protective measures through the Q-learning algorithm. Assuming the current state is s1 (gas concentration exceeds the standard), the action taken by the system is to increase ventilation, and the reward is to reduce the concentration. The Q value update formula is:

[0098]

[0099] Among them: Q(s1,a1) = 50 is the Q value of the current strategy, r = 10 is the immediate reward for reducing the concentration, γ = 0.9 is the discount factor, α = 0.5 is the learning rate, max a Q(s2,a)=60 is the optimal Q value of the future state.

[0100] The Q value is updated to: Q(s1,a1)=50+0.5[10+0.9·60-50]=50+0.5

[0101] [10+54-50]=50+0.5·14=57 The system selects the updated strategy and continues to optimize the ventilation plan.

[0102] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A chemical production workshop protection method based on data analysis, characterized in that: The following steps are involved: Deploy sensors at key locations in chemical production workshops, including MQ-7 electrochemical gas sensors, MQ-135 semiconductor gas sensors, temperature sensors, pressure sensors, and SHT10 humidity sensors; The sensor collects environmental parameters such as gas concentration, temperature, pressure and humidity in the workshop in real time; The parameter data collected by the sensor is transmitted to the central control system through the Internet of Things technology; The central control system conducts big data analysis based on preset thresholds, historical data, and real-time monitoring data to identify potential safety hazards; Dynamically adjust protection strategies based on analysis results, including triggering protection equipment activation and alarm systems.

2. The protection method according to claim 1, characterized in that: The central control system uses the Bayesian network model to analyze the sensor data and calculate the probability of potential safety hazards. Specifically, the concentration of harmful gases detected by the gas sensor G i With its safety threshold T G The ratio r i =G i / T G The formula for calculating the risk of hidden dangers is: Among them, P(H|D) is the conditional probability of hidden danger occurrence, P(D|H) is the probability of data monitored when the hidden danger occurs, P(H) is the prior probability of the hidden danger, and P(D) is the marginal probability of the observed data.

3. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The gas sensor includes but is not limited to: MQ-7 electrochemical gas sensor: used to detect the concentration of carbon monoxide gas in the workshop; MQ-135 semiconductor gas sensor: used to monitor the concentration of harmful gases such as ammonia, benzene and formaldehyde in the workshop; SHT10 humidity sensor: used to measure the air humidity in the workshop in real time.

4. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The central control system calculates the potential impact of various environmental parameters on equipment and workers in real time based on big data analysis and machine learning algorithms, and takes the following dynamic protective measures: When the MQ-7 gas sensor detects that the carbon monoxide concentration exceeds 50ppm, the system automatically triggers the ventilation equipment; When the temperature sensor detects that the temperature exceeds 100°C, the spray system is automatically activated; When the pressure sensor detects that the pressure exceeds the safety threshold, it automatically shuts off the power to the dangerous equipment.

5. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The temperature sensor is a K-type thermocouple sensor with a measurement range of -120°C to 1200°C. When the temperature in the workshop is detected to exceed the set threshold, the system predicts potential danger according to the following algorithm: Among them, T avg is the average temperature, if T avg If the safety threshold is continuously exceeded, the system will immediately trigger an alarm.

6. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The pressure sensor is a BMP280 piezoelectric sensor, which is used to monitor pressure changes in the workshop. When the pressure changes exceed the set safety range, the system triggers the corresponding protection mechanism, including automatically closing the pipeline valve and notifying the staff.

7. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The central control system uses a time series analysis model to analyze historical data and predict potential dangerous trends. The formula is: Among them, y t is the monitoring value at time t. The system predicts equipment failure or environmental changes by analyzing the fluctuation trend in the data sequence.

8. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The system includes a risk classification model based on support vector machine (SVM) for classifying and evaluating multiple types of risks in real-time monitoring data. The specific optimization objective function is as follows: Among them, w is the weight vector, b is the bias term, C is the regularization parameter, and y i is the label, x i is a feature vector, and the classification result is used to adjust the protection strategy of the workshop.

9. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The system has reinforcement learning capabilities and optimizes protection measures based on the Q-learning algorithm. The system dynamically adjusts protection strategies through the following reward functions: Among them, Q(s,a) is the value of taking action a in the current state s, α is the learning rate, γ is the discount factor, and r is the immediate reward. The system optimizes the protection strategy through continuous learning.

10. The chemical production workshop protection method based on data analysis according to claim 1 is characterized in that: The system uses the data visualization module to display the data collected by various sensors in the workshop and the analysis results, and generates safety reports for workshop managers to make safety decisions and risk assessments.

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