Intelligent monitoring and emergency response method and system for fuel gas storage tank transportation

Abstract

The invention relates to an intelligent monitoring and emergency response method for fuel gas storage tank transportation. According to the method, through matching transportation demand data and a fuel gas storage tank database, fuel gas tank transportation data including transportation quantity, model and basic parameters are generated, through comparing actual factory data and safety threshold data of the fuel gas tank, a safety fluctuation threshold value of the fuel gas tank is determined, and in the transportation process, state data of the fuel gas tank are monitored in real time. Combining real-time transportation data to construct a transportation state data set; analyzing the data set by using a machine self-learning algorithm, generating a state change trend of the gas tank, and predicting future changes; and when a prediction result shows a potential risk, the system gives out an early warning and automatically generates an emergency response strategy so as to ensure that the state of the gas tank is kept within a safety threshold. According to the method, through an intelligent monitoring and response mechanism, the safety and efficiency of gas tank transportation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas storage tank transportation, and in particular to an intelligent monitoring and emergency response method and system for gas storage tank transportation. Background Art

[0002] At present, gas tank transportation mainly relies on two forms: compressed natural gas (CNG) and liquefied natural gas (LNG). CNG compresses natural gas to 200kgf / cm² under high pressure, reducing the volume of natural gas by 200 times and storing it in gas storage tanks, which is convenient for automobile transportation and suitable for short-distance transportation.

[0003] Existing transportation monitoring technologies often rely on regular inspections and manual monitoring during the transportation process. It is difficult to respond to abnormal situations that may occur during the transportation process in a timely manner and lacks real-time performance. Manual inspections not only affect transportation efficiency, but also pose safety risks due to untimely inspections, reducing the safety of gas storage tank transportation. Therefore, improvements are needed. Summary of the invention

[0004] In order to improve the efficiency and safety of gas storage tank transportation, the present application provides an intelligent monitoring and emergency response method and system for gas storage tank transportation.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions: An intelligent monitoring and emergency response method for gas storage tank transportation, the method comprising the steps of: Acquire transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; Acquire the actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas tanks, wherein the safety fluctuation threshold is used to indicate the fluctuation range of the gas tanks in a safe state during transportation; Acquire the status data of the gas tank in transportation, and record the status data of the gas tank based on a preset public time axis to construct a gas tank status data set; Acquire real-time transportation data, the real-time transportation data including real-time environmental data and real-time road information, input the real-time transportation data into the gas tank status data set, and associate the real-time transportation data with the gas tank status data based on the common time axis to construct a transportation status data set; The preset transportation status analysis model analyzes the transportation status data set based on a machine self-learning algorithm to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transportation data change; Acquire transportation route data and environmental prediction data, and make predictions based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; Extracting the gas tank state data feature for predicting the change trend of the gas tank state, and comparing the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation where the threshold is exceeded; If there is a situation where the threshold is exceeded, an early warning signal is issued and an emergency response strategy is generated based on the transportation route data and the environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within the safety threshold.

[0006] By adopting the above technical solution, the key parameters of the gas tanks in the transportation process are monitored and analyzed in real time and compared with the preset safety thresholds to ensure the safety of transportation. This method reduces human errors, improves response speed, optimizes transportation efficiency and reduces costs through an automated monitoring system. At the same time, it can dynamically adjust transportation strategies according to real-time environmental and road information, and enhances adaptability to different environmental conditions. In addition, this solution improves the decision-making quality of transportation managers through data-driven decision support, improves customer satisfaction, and helps reduce the impact on the environment, thereby achieving the goals of efficient, safe and environmentally friendly gas tank transportation.

[0007] In a preferred example, the present application can be further configured as follows: the transport status data set is analyzed based on a machine self-learning algorithm in a preset transport status analysis model to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transport data change, including the following steps: Extracting state features related to the gas tank state data from the transport state data set, the state features including the pressure valve data features and the internal pressure data features of the gas tank; Extracting transportation data features from the transportation status data set, wherein the transportation data features include vehicle speed, vehicle turning angle amplitude, and transportation environment data features; The transport status analysis model analyzes the extracted status features and transport data features, generates a real-time gas tank status change trend through the change rules of the status features, and the real-time gas tank status change trend is associated with the transport data features, thereby analyzing the changes in the real-time gas tank status change trend according to the changes in the transport data features.

[0008] By adopting the above technical solutions, the transportation status analysis model comprehensively considers the status characteristics and transportation data characteristics of the gas tank, and analyzes the correlation and change rules between these characteristics through machine learning algorithms. For example, the model may find that at a specific speed, the fluctuation of the internal pressure of the tank is significantly correlated with the vehicle turning angle. Based on these analyses, the model can predict the state change trend of the gas tank under different transportation conditions, and associate the real-time gas tank state change trend with the transportation data characteristics. The transportation strategy can be adjusted in real time, such as recommending slowing down or avoiding sharp turns, to reduce the adverse effects on the gas tank and ensure that the gas tank is always in a safe state during transportation.

[0009] In a preferred example, the present application can be further configured as follows: in the step of acquiring the transportation route data and the environmental prediction data, and predicting based on the real-time gas tank state change trend to generate a predicted gas tank state change trend, the steps are as follows: Acquire transportation route data, and extract road condition feature data from the transportation route data, The road condition characteristic data is classified into traveled road condition data and to-be-traveled road condition data according to the actual transportation situation, the traveled road condition data is used to construct the transportation status data set, and the to-be-traveled road condition data is used to analyze and predict the predicted gas tank status change trend of subsequent transportation; Comparing the to-be-traveled road condition data with the traveled road condition data to generate road condition difference data; Based on the correlation between the real-time gas tank status change trend and the transportation data feature, the real-time gas tank status change trend is analyzed through road condition difference data to generate a road condition predicted status change trend, and the road condition predicted status change trend is used to generate a predicted gas tank status change trend.

[0010] By adopting the above technical solution, combined with the real-time gas tank status change trend and road condition difference data, the possible status changes of the gas tank under different road conditions are analyzed and predicted, and the road condition prediction status change trend is generated. The road condition prediction status change trend provides support for decision-making during the transportation process and helps avoid possible risks.

[0011] In a preferred example, the present application can be further configured as follows: in the step of acquiring the transportation route data and the environmental prediction data, and predicting based on the real-time gas tank state change trend to generate a predicted gas tank state change trend, the following steps are also included: Acquiring environmental prediction data, and comparing the environmental prediction data with the real-time environmental data in the real-time transportation data to generate environmental difference data; Based on the correlation between the real-time gas tank state change trend and the transportation data feature, the real-time gas tank state change trend is analyzed through the environmental difference data to generate an environmental prediction state change trend, and the environmental prediction state change trend is used to generate a predicted gas tank state change trend; Specifically, the preset predicted state change model analyzes the road condition predicted state change trend and the environment predicted state change trend based on a machine self-learning algorithm to generate a predicted gas tank state change trend.

[0012] By adopting the above technical solution, using environmental difference data and real-time gas tank status change trends, the system analyzes and predicts the impact of environmental changes on the gas tank status, and generates environmental prediction status change trends. The environmental prediction status change trends enable transportation managers to prepare in advance for environmental factors such as extreme weather to ensure transportation safety. The predicted gas tank status change trends take into account the potential impact of road conditions and environmental factors. This prediction result can provide decision support for transportation managers and help take measures in advance to ensure the safety of gas tanks during transportation.

[0013] In a preferred example, the present application can be further configured as follows: after the step of analyzing the road condition prediction state change trend and the environment prediction state change trend based on the machine self-learning algorithm by the preset prediction state change model to generate the prediction gas tank state change trend, the following steps are included: Acquire actual gas tank status data, and generate actual gas tank status change trend according to the actual gas tank status data; Comparing the actual gas tank state change trend with the predicted gas tank state change trend to generate trend deviation data, and constructing a trend deviation data set; The preset prediction deviation analysis model analyzes the trend deviation data set based on a machine self-learning algorithm to generate a prediction compensation factor, and the prediction compensation factor is used to correct the predicted gas tank state change trend, thereby improving the accuracy of the prediction.

[0014] By adopting the above technical solutions, the prediction model can continuously learn from the actual transportation process and optimize itself, thereby improving the accuracy of the prediction and the safety of transportation. Through continuous feedback and adjustment, the prediction model can better adapt to various complex situations in the actual transportation process and reduce the risks caused by inaccurate predictions.

[0015] In a preferred example, the present application can be further configured as follows: the emergency response strategy includes adjusting the transportation route, checking for abnormal gas tanks, issuing alarm signals, considering the potential impact of the accident on the environment and formulating corresponding environmental protection and pollution control measures.

[0016] In a preferred example, the present application can be further configured as follows: the machine self-learning algorithm includes a deep learning algorithm, a support vector machine algorithm and a random forest algorithm.

[0017] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions: An intelligent monitoring and emergency response device for gas storage tank transportation, the device comprising: A gas tank transportation data generating unit, used for acquiring transportation demand data, and matching the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; A safety fluctuation threshold generating unit, used for obtaining actual factory data of all gas cylinders to be transported, and comparing the actual factory data of the gas cylinders to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas cylinders, wherein the safety fluctuation threshold is used to indicate a fluctuation range of the gas cylinders in a safe state during transportation; A gas tank status data set construction unit, used for acquiring the status data of the gas tank in transportation, and recording the status data of the gas tank based on a preset common time axis to construct a gas tank status data set; A transport status data set construction unit, used for acquiring real-time transport data, the real-time transport data including real-time environmental data and real-time road surface information, inputting the real-time transport data into the gas tank status data set, and associating the real-time transport data with the gas tank status data based on the common time axis to construct the transport status data set; A real-time gas tank state change trend generating unit, which is used to pre-set a transportation state analysis model to analyze the transportation state data set based on a machine self-learning algorithm to generate a real-time gas tank state change trend, wherein the real-time gas tank state change trend is used to represent the gas tank state change trend corresponding to the transportation data change; A predicted gas tank state change trend generating unit, used for acquiring transportation route data and environmental prediction data, and making predictions based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; A threshold comparison and judgment unit, used for extracting the gas tank state data feature for predicting the change trend of the gas tank state, and comparing the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation of exceeding the threshold; The emergency response strategy generation unit is used to send out a warning signal when a situation exceeds a threshold value and generate an emergency response strategy based on the transportation route data and the environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within a safe threshold value.

[0018] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent monitoring and emergency response method for gas storage tank transportation when executing the computer program.

[0019] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent monitoring and emergency response method for gas storage tank transportation.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. Real-time monitoring and analysis of key parameters of gas tanks during transportation, and comparison with preset safety thresholds to ensure transportation safety. This method reduces human errors, improves response speed, optimizes transportation efficiency, and reduces costs through an automated monitoring system. At the same time, it can also dynamically adjust transportation strategies based on real-time environmental and road information, and enhances adaptability to different environmental conditions. In addition, this solution improves the decision-making quality of transportation managers through data-driven decision support, improves customer satisfaction, and helps reduce the impact on the environment, achieving the goals of efficient, safe, and environmentally friendly gas tank transportation; 2. The transportation status analysis model comprehensively considers the status characteristics of the gas tank and the transportation data characteristics, and analyzes the correlation and change rules between these characteristics through machine learning algorithms. For example, the model may find that at a specific speed, the fluctuation of the internal pressure of the tank is significantly correlated with the vehicle turning angle. Based on these analyses, the model can predict the state change trend of the gas tank under different transportation conditions, and associate the real-time gas tank state change trend with the transportation data characteristics. The transportation strategy can be adjusted in real time, such as recommending slowing down or avoiding sharp turns, to reduce the adverse effects on the gas tank and ensure that the gas tank is always in a safe state during transportation; 3. Combine the real-time gas tank status change trend and road condition difference data to analyze and predict the possible status changes of the gas tank under different road conditions, generate the road condition prediction status change trend, use the environmental difference data and the real-time gas tank status change trend to systematically analyze and predict the impact of environmental changes on the gas tank status, generate the environmental prediction status change trend, and the environmental prediction status change trend enables transportation managers to prepare in advance to deal with environmental factors such as extreme weather to ensure transportation safety. The prediction of the gas tank status change trend takes into account the potential impact of road conditions and environmental factors. This prediction result can provide decision support for transportation managers and help take measures in advance to ensure the safety of gas tanks during transportation; 4. Enable the prediction model to continuously learn and self-optimize from the actual transportation process, thereby improving the accuracy of the prediction and the safety of transportation. Through continuous feedback and adjustment, the prediction model can better adapt to various complex situations in the actual transportation process and reduce the risks caused by inaccurate predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the steps of an intelligent monitoring and emergency response method for gas storage tank transportation in one embodiment of the present application; Figure 2 It is a principle block diagram of an intelligent monitoring and emergency response device for gas storage tank transportation in one embodiment of the present application; Figure 3 It is a schematic diagram of an electronic device in an embodiment of the present application.

[0022] Figure Number: 1. Gas tank transportation data generation unit; 2. Safety fluctuation threshold generation unit; 3. Gas tank status data set construction unit; 4. Transportation status data set construction unit; 5. Real-time gas tank status change trend generation unit; 6. Prediction of gas tank status change trend generation unit; 7. Threshold comparison and judgment unit; 8. Emergency response strategy generation unit. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent monitoring and emergency response method for gas storage tank transportation, which specifically includes the following steps: S10: Acquire transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data; The gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the pre-set gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; Specifically, assuming that a customer needs to transport 100 gas cylinders of model A, the basic parameters of each gas cylinder (such as maximum pressure and temperature range) and safety thresholds (such as maximum transportation pressure of 1.5MPa and maximum transportation temperature of 60°C) have been preset in the gas storage tank database. According to customer needs and database information, transportation data is generated, including the transportation quantity 100, model A, and the corresponding basic parameters and safety thresholds, to ensure accurate data support before transportation, providing a basis for subsequent safety monitoring and emergency response.

[0025] S20: Acquire actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas tanks; Among them, the safety fluctuation threshold is used to indicate the fluctuation range of the gas tank in a safe state during transportation; Specifically, before the gas tank leaves the factory, the actual factory pressure and temperature of each gas tank are recorded and compared with the safety threshold to determine the allowable pressure and temperature fluctuation range under normal transportation conditions. For example, if the factory pressure is 1.4MPa, the safety fluctuation threshold may be set to 1.4MPa to 1.5MPa, providing a specific safety range for real-time monitoring and facilitating timely detection of abnormal situations.

[0026] S30: Acquire the status data of the gas tank in transportation, and record the status data of the gas tank based on a preset public time axis to construct a gas tank status data set; Specifically, during the transportation process, the pressure and temperature of the gas tank are recorded every 10 minutes, and these data are recorded in the database in chronological order to build a gas tank status data set. Through continuous monitoring, any changes in the gas tank status can be discovered in time, providing complete data for subsequent analysis.

[0027] S40: acquiring real-time transportation data, and inputting the real-time transportation data into the gas tank status data set, and associating the real-time transportation data with the gas tank status data based on the common time axis to construct a transportation status data set; The real-time transportation data includes real-time environmental data and real-time road surface information.

[0028] S50: The preset transportation status analysis model analyzes the transportation status data set based on a machine self-learning algorithm to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to indicate the gas tank status change trend corresponding to the transportation data change; Among them, the machine self-learning algorithm includes deep learning algorithm, support vector machine algorithm and random forest algorithm.

[0029] S60: Acquire transportation route data and environmental prediction data, and make a prediction based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; Among them, based on real-time environmental and road information, the pressure and temperature change trends of gas tanks in the next few hours are predicted to prepare for possible extreme situations.

[0030] S70: extracting the gas tank state data feature for predicting the gas tank state change trend, and comparing the gas tank state data feature for predicting the gas tank state change trend with the safety threshold data to determine whether there is a situation where the threshold is exceeded; Specifically, the gas tank status data features for predicting the changing trend of the gas tank status are extracted, such as the pressure and temperature of the gas tank, and the predicted pressure and temperature are compared with the safety threshold. For example, it is predicted that the pressure in the gas tank may reach 1.6MPa in a certain period of time in the future, which exceeds the set safety threshold, so as to timely discover potential safety risks and buy time for taking preventive measures.

[0031] S80: If there is a situation where the threshold is exceeded, a warning signal is issued and an emergency response strategy is generated according to the transportation route data and the environmental prediction data to adjust the state change trend of the gas tank to control the gas tank state data characteristics to be within the safety threshold; Specifically, emergency response strategies include adjusting transportation routes, checking for abnormal gas tanks, issuing alarm signals, considering the potential impact of accidents on the environment and formulating corresponding environmental protection and pollution control measures; among them, taking the adjustment of transportation routes as an example: once it is predicted that the pressure exceeds the safety threshold, the system automatically issues an early warning and, based on the current location and route data, recommends that the driver slow down or choose a flatter route to reduce the pressure in the gas tank. Through timely emergency response, risks can be effectively controlled to ensure transportation safety.

[0032] In summary, by real-time monitoring and analysis of key parameters of gas tanks during transportation and comparing them with preset safety thresholds, the safety of transportation is ensured. This method reduces human errors, improves response speed, optimizes transportation efficiency, and reduces costs through an automated monitoring system. At the same time, it can dynamically adjust transportation strategies based on real-time environmental and road information, and enhances adaptability to different environmental conditions. In addition, this solution improves the decision-making quality of transportation managers through data-driven decision support, improves customer satisfaction, and helps reduce the impact on the environment, achieving the goals of efficient, safe, and environmentally friendly gas tank transportation.

[0033] In S50: the preset transportation status analysis model analyzes the transportation status data set based on the machine self-learning algorithm to generate a real-time gas tank status change trend, and the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transportation data change. This step includes the following steps: S51: extracting state features related to the gas tank state data from the transport state data set, the state features including the pressure valve data features of the gas tank and the internal pressure data features of the tank body; Specifically, during the transportation process, the system collects pressure valve data of the gas tank through sensors, such as real-time data of the pressure valve and the real-time pressure inside the tank. These data are input into the transportation status analysis model as state features. By accurately capturing the key operating parameters of the gas tank, the physical state of the gas tank during transportation can be more accurately predicted, providing key information for safety monitoring.

[0034] S52: extracting transportation data features from the transportation status data set, wherein the transportation data features include vehicle speed, vehicle turning angle amplitude, and transportation environment data features; Specifically, the speed and turning angle of the transport vehicle, as well as external environmental data such as temperature, humidity and road conditions are collected. These transport data characteristics are input into the analysis model together with the gas tank status data. Considering the impact of the transport environment and vehicle operating status on the gas tank, the external factors of the gas tank status change can be more fully understood, and the accuracy and reliability of the prediction can be improved.

[0035] S53: the transport state analysis model analyzes the extracted state features and transport data features, generates a real-time gas tank state change trend according to the change rule of the state features, and the real-time gas tank state change trend is associated with the transport data features, so as to analyze the change of the real-time gas tank state change trend according to the change of the transport data features; Specifically, the transportation status analysis model comprehensively considers the status characteristics of the gas tank and the transportation data characteristics, and analyzes the correlation and change patterns between these characteristics through machine learning algorithms. For example, the model may find that at a specific speed, the fluctuation of the internal pressure of the tank is significantly correlated with the vehicle's turning angle. Based on these analyses, the model can predict the state change trend of the gas tank under different transportation conditions, and associate the real-time gas tank state change trend with the transportation data characteristics. It can adjust the transportation strategy in real time, such as recommending slowing down or avoiding sharp turns, to reduce the adverse effects on the gas tank and ensure that the gas tank is always in a safe state during transportation.

[0036] In summary, this solution achieves accurate extraction and in-depth analysis of the state characteristics of gas tanks during transportation, greatly improving the accuracy of state change trend prediction. This intelligent monitoring method based on machine self-learning algorithm can evaluate transportation risks in real time and optimize transportation operations, thereby significantly improving transportation safety and efficiency. In addition, by reducing human intervention, it reduces the possibility of errors and delays, enhances adaptability to different transportation environments, and improves the data-driven ability of operational decisions.

[0037] In the step of S60: acquiring the transportation route data and the environmental prediction data, and predicting based on the real-time gas tank state change trend to generate a predicted gas tank state change trend, the following steps are included: S61: Acquire transportation route data, and extract road condition feature data from the transportation route data. Wherein, the road condition characteristic data is classified into traveled road condition data and to-be-traveled road condition data according to the actual transportation situation, the traveled road condition data is used to construct the transportation status data set, and the to-be-traveled road condition data is used to analyze and predict the predicted gas tank status change trend of subsequent transportation; It is further explained that detailed data of the transportation route are collected, including road surface type, traffic flow, slope and other road condition characteristics of the sections that have been traveled and those to be traveled. These data are divided into the road condition data that have been traveled and the road condition data that are to be traveled. The former is used to construct the current transportation status data set, and the latter is used to predict the future status. Through the classification and analysis of the road condition data, the impact of different road conditions on the status of gas tanks can be more accurately predicted, providing a basis for safe transportation.

[0038] S62: Compare the road condition data to be driven with the road condition data to generate road condition difference data; It can compare the data of road conditions that have been driven and those to be driven, and identify differences. For example, if the road to be driven has a large slope change or traffic congestion, the road condition difference data can help predict the potential impact of future sections on the status of the gas tank and take countermeasures in advance.

[0039] S63: Based on the correlation between the real-time gas tank state change trend and the transportation data feature, the real-time gas tank state change trend is analyzed through the road condition difference data to generate a road condition predicted state change trend, and the road condition predicted state change trend is used to generate a predicted gas tank state change trend; Specifically, by combining the real-time gas tank status change trend and road condition difference data, the system analyzes and predicts the possible status changes of the gas tank under different road conditions, and generates the road condition prediction status change trend. The road condition prediction status change trend provides support for decision-making during the transportation process and helps avoid possible risks.

[0040] S64: Obtaining environmental prediction data, and comparing the environmental prediction data with the real-time environmental data in the real-time transportation data to generate environmental difference data; Specifically, weather forecast data for a period of time in the future, including temperature, humidity, wind speed, etc., is obtained and compared with the current real-time environmental data to generate environmental difference data. The environmental difference data helps to predict the potential impact of environmental changes on the status of gas tanks and improve transportation adaptability.

[0041] S65: Based on the correlation between the real-time gas tank state change trend and the transportation data feature, the real-time gas tank state change trend is analyzed through the environmental difference data to generate an environmental predicted state change trend, and the environmental predicted state change trend is used to generate a predicted gas tank state change trend; Specifically, by using environmental difference data and real-time gas tank status change trends, the system analyzes and predicts the impact of environmental changes on the gas tank status and generates environmental prediction status change trends. The environmental prediction status change trends enable transportation managers to prepare in advance to deal with environmental factors such as extreme weather and ensure transportation safety. By generating the environmental prediction state change trend, the prediction accuracy of the state change during the transportation of gas tanks is improved. Moreover, by analyzing the environmental prediction state change trend, real-time adjustment and optimization of the transportation strategy are carried out, thereby enhancing the safety and efficiency of the transportation process.

[0042] S66: The preset predicted state change model analyzes the predicted road state change trend and the predicted environment state change trend based on a machine self-learning algorithm to generate a predicted gas tank state change trend; Regarding how to generate the predicted gas tank state change trend in step S66, in the embodiment of the present application, the steps include: Data integration and feature extraction: First, the system collects and integrates information from different sensors and data sources, including the pressure, temperature, location and other status data of the gas tank, as well as the speed, turning angle and external environment data of the transport vehicle (such as temperature, humidity, road conditions, etc.). These data are used to extract key features related to the status of the gas tank and the transportation environment.

[0043] Road condition prediction and state change trend analysis: Using machine self-learning algorithms, the system analyzes the differences between the road condition data that has been driven and the road condition data to be driven, and identifies key road condition features that may affect the state of the gas tank, such as road bumps, slope changes, etc. These features are used to predict the possible state changes of the gas tank under similar road conditions.

[0044] Analysis of environmental prediction status change trends: Similarly, the system analyzes the differences between real-time environmental data and environmental prediction data to identify environmental factors that may affect the status of the gas tank, such as temperature changes, humidity changes, etc. These factors are crucial to predicting the status changes of the gas tank under different environmental conditions.

[0045] Prediction model training and application: Through machine self-learning algorithms, such as neural networks in deep learning, the system will train a prediction model that can output the state change trend of gas tanks in the future based on the input state characteristics and transportation data characteristics. This model will continuously optimize and adjust itself through newly collected data.

[0046] Generation of prediction results: Ultimately, the system will generate a predicted gas tank state change trend based on the predicted road condition state change trend and the predicted environment state change trend. This predicted gas tank state change trend takes into account the potential impact of road conditions and environmental factors. This prediction result can provide decision support for transportation managers and help take measures in advance to ensure the safety of gas tanks during transportation.

[0047] After the step S66: the preset predicted state change model analyzes the road condition predicted state change trend and the environment predicted state change trend based on the machine self-learning algorithm to generate a predicted gas tank state change trend, the following steps are included: S661: Acquire actual gas tank status data, and generate an actual gas tank status change trend according to the actual gas tank status data; S662: Compare the actual gas tank state change trend with the predicted gas tank state change trend to generate trend deviation data, and construct a trend deviation data set; Specifically, the real-time collected gas tank status data is compared with the status change trend predicted by the machine self-learning algorithm to analyze the deviation between the two. These deviation data are used to construct a trend deviation data set, which provides a feedback mechanism for the prediction model, allowing the model to continuously learn and adjust to reduce prediction errors.

[0048] S663: The preset prediction deviation analysis model analyzes the trend deviation data set based on a machine self-learning algorithm to generate a prediction compensation factor, wherein the prediction compensation factor is used to correct the predicted gas tank state change trend, thereby improving the prediction accuracy; Specifically, using the trend deviation data set, the forecast deviation analysis model analyzes the causes and patterns of deviations through a machine self-learning algorithm and generates forecast compensation factors. These factors are used to adjust and optimize the forecast model to more accurately reflect actual state changes. The application of forecast compensation factors enables the forecast model to dynamically adapt to changes in the actual transportation process, improving the accuracy and reliability of the forecast.

[0049] It should be noted that this solution enables the prediction model to continuously learn and self-optimize from the actual transportation process, thereby improving the accuracy of the prediction and the safety of transportation. Through continuous feedback and adjustment, the prediction model can better adapt to various complex situations in the actual transportation process and reduce the risks caused by inaccurate predictions.

[0050] In the embodiment of the present application, for example, suppose we have a fleet of gas tank transport vehicles, each of which is equipped with sensors to monitor its pressure and temperature in real time. During the transportation process, we collect a large amount of data about road conditions (such as bumps and slopes), environmental conditions (such as temperature and humidity), and gas tank status.

[0051] S661: Get actual gas tank status data: For example, during transportation, we found that the actual pressure reading of a gas tank suddenly rose, exceeding the change trend of the normal transportation status. This actual status data was recorded and used for subsequent analysis.

[0052] S662: Generate trend deviation data set: For example, we compared the actual pressure reading of this gas tank with the pressure change trend predicted by our prediction model and found a significant deviation. This deviation is recorded in the trend deviation dataset for further analysis.

[0053] S663: Generate prediction compensation factor: For example, using the trend deviation data set, our prediction deviation analysis model uses a machine learning algorithm to analyze that the increase in pressure readings may be caused by an unusually bumpy road surface. The model learns that under similar road conditions, the pressure reading of the gas tank may be higher than the predicted value by a certain value, which is our prediction compensation factor.

[0054] In practice, this prediction compensation factor can be used to adjust our prediction model in real time. When our system predicts that we are about to enter a bumpy road, it will automatically add the compensation factor to the predicted pressure value to provide a more accurate prediction, so that transportation managers can take measures in advance, such as adjusting the transportation speed or route, to ensure that the status of the gas tank remains within a safe range.

[0055] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0056] In one embodiment, an intelligent monitoring and emergency response device for gas storage tank transportation is provided, and the intelligent monitoring and emergency response device for gas storage tank transportation corresponds to the intelligent monitoring and emergency response method for gas storage tank transportation in the above embodiment. Figure 2 As shown, the intelligent monitoring and emergency response device for gas storage tank transportation includes a gas tank transportation data generating unit 1, which is used to obtain transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; A safety fluctuation threshold value generating unit 2 is used to obtain the actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold value of the gas tanks, wherein the safety fluctuation threshold value is used to indicate the fluctuation range of the gas tanks in a safe state during transportation; A gas tank status data set construction unit 3, used for acquiring the status data of the gas tank in transportation, and recording the status data of the gas tank based on a preset common time axis to construct a gas tank status data set; A transport status data set construction unit 4 is used to obtain real-time transport data, the real-time transport data including real-time environmental data and real-time road information, and input the real-time transport data into the gas tank status data set, and associate the real-time transport data with the gas tank status data based on the common time axis to construct the transport status data set; A real-time gas tank state change trend generating unit 5, which is pre-set with a transport state analysis model to analyze the transport state data set based on a machine self-learning algorithm to generate a real-time gas tank state change trend, wherein the real-time gas tank state change trend is used to represent the gas tank state change trend corresponding to the transport data change; A predicted gas tank state change trend generating unit 6 is used to obtain transportation route data and environmental prediction data, and to make a prediction based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; A threshold comparison and judgment unit 7 is used to extract the gas tank state data feature for predicting the change trend of the gas tank state, and compare the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation where the threshold is exceeded; The emergency response strategy generation unit 8 is used to issue a warning signal when a situation exceeds a threshold and generate an emergency response strategy based on the transportation route data and environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within a safe threshold.

[0057] For the specific definition of the intelligent monitoring and emergency response device for gas storage tank transportation, please refer to the definition of the intelligent monitoring and emergency response method for gas storage tank transportation mentioned above, which will not be repeated here. Each module in the above-mentioned intelligent monitoring and emergency response device for gas storage tank transportation can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0058] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent monitoring and emergency response method for gas storage tank transportation is implemented.

[0059] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; Acquire the actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas tanks, wherein the safety fluctuation threshold is used to indicate the fluctuation range of the gas tanks in a safe state during transportation; Acquire the status data of the gas tank in transportation, and record the status data of the gas tank based on a preset public time axis to construct a gas tank status data set; Acquire real-time transportation data, the real-time transportation data including real-time environmental data and real-time road information, input the real-time transportation data into the gas tank status data set, and associate the real-time transportation data with the gas tank status data based on the common time axis to construct a transportation status data set; The preset transportation status analysis model analyzes the transportation status data set based on a machine self-learning algorithm to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transportation data change; Acquire transportation route data and environmental prediction data, and make predictions based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; Extracting the gas tank state data feature for predicting the change trend of the gas tank state, and comparing the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation where the threshold is exceeded; If there is a situation where the threshold is exceeded, an early warning signal is issued and an emergency response strategy is generated based on the transportation route data and the environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within the safety threshold.

[0060] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters and safety threshold data corresponding to each model of gas tank; Acquire the actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas tanks, wherein the safety fluctuation threshold is used to indicate the fluctuation range of the gas tanks in a safe state during transportation; Acquire the status data of the gas tank in transportation, and record the status data of the gas tank based on a preset public time axis to construct a gas tank status data set; Acquire real-time transportation data, the real-time transportation data including real-time environmental data and real-time road information, input the real-time transportation data into the gas tank status data set, and associate the real-time transportation data with the gas tank status data based on the common time axis to construct a transportation status data set; The preset transportation status analysis model analyzes the transportation status data set based on a machine self-learning algorithm to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transportation data change; Acquire transportation route data and environmental prediction data, and make predictions based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; Extracting the gas tank state data feature for predicting the change trend of the gas tank state, and comparing the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation where the threshold is exceeded; If there is a situation where the threshold is exceeded, an early warning signal is issued and an emergency response strategy is generated based on the transportation route data and the environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within the safety threshold.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0062] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0063] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent monitoring and emergency response method for gas storage tank transportation, characterized in that: The method comprises the steps of: acquiring transportation demand data, and matching the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data comprises the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database comprises the basic parameters and safety threshold data corresponding to each model of the gas tank; Acquire the actual factory data of all gas tanks to be transported, and compare the actual factory data of the gas tanks to be transported with the safety threshold data to generate a safety fluctuation threshold of the gas tanks, wherein the safety fluctuation threshold is used to indicate the fluctuation range of the gas tanks in a safe state during transportation; Acquire the status data of the gas tank in transportation, and record the status data of the gas tank based on a preset public time axis to construct a gas tank status data set; Acquire real-time transportation data, the real-time transportation data including real-time environmental data and real-time road information, input the real-time transportation data into the gas tank status data set, and associate the real-time transportation data with the gas tank status data based on the common time axis to construct a transportation status data set; The preset transportation status analysis model analyzes the transportation status data set based on a machine self-learning algorithm to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transportation data change; Acquire transportation route data and environmental prediction data, and make predictions based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; Extracting the gas tank state data feature for predicting the change trend of the gas tank state, and comparing the gas tank state data feature for predicting the change trend of the gas tank state with the safety threshold data to determine whether there is a situation where the threshold is exceeded; If there is a situation where the threshold is exceeded, an early warning signal is issued and an emergency response strategy is generated based on the transportation route data and the environmental prediction data to adjust the gas tank state change trend to control the gas tank state data characteristics within the safety threshold.

2. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 1 is characterized in that: The step of analyzing the transport status data set based on the machine self-learning algorithm in the preset transport status analysis model to generate a real-time gas tank status change trend, wherein the real-time gas tank status change trend is used to represent the gas tank status change trend corresponding to the transport data change includes the following steps: Extracting state features related to the gas tank state data from the transport state data set, the state features including the pressure valve data features and the internal pressure data features of the gas tank; Extracting transportation data features from the transportation status data set, wherein the transportation data features include vehicle speed, vehicle turning angle amplitude, and transportation environment data features; The transport status analysis model analyzes the extracted status features and transport data features, generates a real-time gas tank status change trend through the change rules of the status features, and the real-time gas tank status change trend is associated with the transport data features, thereby analyzing the changes in the real-time gas tank status change trend according to the changes in the transport data features.

3. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 2 is characterized in that: The step of acquiring the transportation route data and the environmental prediction data, and predicting based on the real-time gas tank state change trend to generate a predicted gas tank state change trend includes the following steps: Acquire transportation route data, and extract road condition feature data from the transportation route data, The road condition characteristic data is classified into traveled road condition data and to-be-traveled road condition data according to the actual transportation situation, the traveled road condition data is used to construct the transportation status data set, and the to-be-traveled road condition data is used to analyze and predict the predicted gas tank status change trend of subsequent transportation; Comparing the to-be-traveled road condition data with the traveled road condition data to generate road condition difference data; Based on the correlation between the real-time gas tank status change trend and the transportation data feature, the real-time gas tank status change trend is analyzed through road condition difference data to generate a road condition predicted status change trend, and the road condition predicted status change trend is used to generate a predicted gas tank status change trend.

4. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 3 is characterized in that: In the step of acquiring the transportation route data and the environmental prediction data, and predicting based on the real-time gas tank state change trend to generate a predicted gas tank state change trend, the following steps are also included: Acquiring environmental prediction data, and comparing the environmental prediction data with the real-time environmental data in the real-time transportation data to generate environmental difference data; Based on the correlation between the real-time gas tank state change trend and the transportation data feature, the real-time gas tank state change trend is analyzed through the environmental difference data to generate an environmental prediction state change trend, and the environmental prediction state change trend is used to generate a predicted gas tank state change trend; Specifically, the preset predicted state change model analyzes the road condition predicted state change trend and the environment predicted state change trend based on a machine self-learning algorithm to generate a predicted gas tank state change trend.

5. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 4 is characterized in that: After the step of analyzing the road condition prediction state change trend and the environment prediction state change trend based on the machine self-learning algorithm by the preset prediction state change model to generate the prediction state change trend of the gas tank, the following steps are included: Acquire actual gas tank status data, and generate actual gas tank status change trend according to the actual gas tank status data; Comparing the actual gas tank state change trend with the predicted gas tank state change trend to generate trend deviation data, and constructing a trend deviation data set; The preset prediction deviation analysis model analyzes the trend deviation data set based on a machine self-learning algorithm to generate a prediction compensation factor, and the prediction compensation factor is used to correct the predicted gas tank state change trend, thereby improving the accuracy of the prediction.

6. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 1 is characterized in that: The emergency response strategy includes adjusting the transportation route, checking the gas tanks with abnormalities, issuing alarm signals, considering the potential impact of the accident on the environment and formulating corresponding environmental protection and pollution control measures.

7. The intelligent monitoring and emergency response method for gas storage tank transportation according to claim 1 is characterized in that: The machine self-learning algorithm includes a deep learning algorithm, a support vector machine algorithm and a random forest algorithm.

8. An intelligent monitoring and emergency response device for gas storage tank transportation, applied to an intelligent monitoring and emergency response method for gas storage tank transportation as claimed in any one of claims 1 to 7, characterized in that: The device comprises: A gas tank transportation data generating unit (1) is used to obtain transportation demand data, and match the transportation demand data with a preset gas storage tank database to generate corresponding gas tank transportation data, wherein the gas tank transportation data includes the transportation quantity, the model of the transported gas tank and the basic parameters corresponding to the gas tank, wherein the preset gas storage tank database includes the basic parameters corresponding to each model of gas tank and safety threshold data; A safety fluctuation threshold value generating unit (2) is used to obtain actual factory data of all gas cylinders to be transported, and compare the actual factory data of the gas cylinders to be transported with the safety threshold value data to generate a safety fluctuation threshold value of the gas cylinders, wherein the safety fluctuation threshold value is used to indicate a fluctuation range within which the gas cylinders are in a safe state during transportation; A gas tank status data set construction unit (3), used for acquiring the status data of the gas tank in transportation, and recording the status data of the gas tank based on a preset common time axis to construct a gas tank status data set; a transport status data set construction unit (4), used for acquiring real-time transport data, the real-time transport data including real-time environmental data and real-time road surface information, inputting the real-time transport data into the gas tank status data set, and associating the real-time transport data with the gas tank status data based on the common time axis to construct the transport status data set; A real-time gas tank state change trend generating unit (5), which is used to pre-set a transportation state analysis model to analyze the transportation state data set based on a machine self-learning algorithm to generate a real-time gas tank state change trend, wherein the real-time gas tank state change trend is used to represent the gas tank state change trend corresponding to the transportation data change; A predicted gas tank state change trend generating unit (6), used for acquiring transportation route data and environmental prediction data, and making a prediction based on the real-time gas tank state change trend to generate a predicted gas tank state change trend; A threshold comparison and judgment unit (7) is used to extract the gas tank state data feature for predicting the gas tank state change trend, and compare the gas tank state data feature for predicting the gas tank state change trend with the safety threshold data to determine whether there is a situation where the threshold is exceeded; The emergency response strategy generating unit (8) is used to send out a warning signal when a situation exceeds a threshold value and generate an emergency response strategy according to the transportation route data and the environmental prediction data, so as to adjust the state change trend of the gas tank to control the state data characteristics of the gas tank to be within a safe threshold value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the intelligent monitoring and emergency response method for gas storage tank transportation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of an intelligent monitoring and emergency response method for gas storage tank transportation as described in any one of claims 1 to 7 are implemented.

Images