Oil storage tank leakage detection and response system and method based on AI

Abstract

The invention relates to the field of oil storage leakage detection, in particular to an AI-based oil storage tank leakage detection and response system and method. The method comprises the following steps: acquiring oil tank projection information and oil tank state data of an oil tank to be tested, constructing a corresponding three-dimensional model, building a simulation test environment, and performing simulation test on the three-dimensional model by means of the simulation test environment to obtain test data; analyzing the test data, constructing a test time axis, and further analyzing the test data by means of the test time axis to obtain a response time line, a leakage risk area and a risk response signal; analyzing all areas of the oil storage tank to be detected by means of the leakage risk area to obtain a risk spreading area chain; analyzing the risk spreading area chain by combining the oil storage tank to be detected, and establishing an oil tank leakage risk map; and generating a risk prevention scheme according to the risk response signal and the oil tank leakage risk map. The leakage detection precision and the oil storage safety can be improved.

Description

Technical Field

[0001] The present invention relates to the field of oil storage leakage detection, and in particular to an AI-based oil storage tank leakage detection and response system and method. Background Art

[0002] With the widespread use of oil, natural gas, and their derivatives in the energy system, oil storage tanks, as key storage and transportation facilities, are widely deployed in refineries, petrochemicals, ports, and oil depots. Oil storage tanks are typically used to store flammable and explosive liquids such as crude oil, gasoline, and diesel. Their operational safety is directly related to the safety of corporate assets, personnel, and the environment.

[0003] Chinese patent publication number CN113326610B discloses a gas station oil tank leak detection and early warning method based on oil height soft measurement prediction, including: based on oil quantity conservation analysis, taking into account the characteristics of oil height and temperature in gas station storage tanks that meet the requirements of multiple regression analysis, constructing a data sample that predicts the current oil height by using the current temperature, oil quantity and the oil height, temperature and oil quantity at past moments; combining machine learning and other technologies to utilize the operating data of the liquid level meter system to establish a unified oil height soft measurement model that can be used under different oil quantities; performing an error comparison analysis between the real-time measurement results of the oil height and the prediction results of the model, thereby effectively detecting possible leakage faults in the oil tank when the oil tank leakage detection instrument fails.

[0004] In the existing technology, leak detection of oil storage tank entities is costly and labor-intensive. It is difficult to accurately detect micro-leakage and chronic leakage in oil storage tanks, and there are detection errors, which increases the possibility of safety hazards in oil storage tanks, becoming a problem that needs to be solved urgently. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the background technology and propose an AI-based oil tank leakage detection and response method.

[0006] The technical solution of the present invention is an AI-based oil tank leak detection and response method, comprising the following steps:

[0007] S1. Obtaining the oil tank projection information and oil tank status data of the oil storage tank to be tested, constructing a corresponding three-dimensional model, setting up a simulation test environment, and performing a simulation test on the three-dimensional model using the simulation test environment to obtain test data;

[0008] S2. Analyze the test data and construct a test timeline. Using the test timeline, further analyze the test data to obtain a response timeline, leakage risk areas, and risk response signals.

[0009] S3. Analyze all areas of the oil storage tank to be tested with the help of leakage risk areas to obtain a risk spread area chain; analyze the risk spread area chain in combination with the oil storage tank to be tested and establish an oil tank leakage risk map; generate a risk prevention plan based on the risk response signal and the oil tank leakage risk map.

[0010] Preferably, the process of obtaining the oil tank projection information and oil tank status data of the oil storage tank to be tested, constructing a corresponding three-dimensional model, setting up a simulation test environment, and performing a simulation test on the three-dimensional model with the help of the simulation test environment to obtain the test data includes:

[0011] The FARO Focus device is used to perform three-dimensional laser scanning on the oil storage tank to be tested, and obtain oil tank point cloud data; the oil tank projection information includes the overall size of the oil tank, the overall structure of the oil tank, and the surface information of the oil tank; the oil tank status data includes oil status data and internal and external environmental data; the oil status includes oil type and oil volume; the internal and external environmental data includes internal and external temperature values ​​and internal and external pressure values;

[0012] Use CAD software to construct a three-dimensional model of the oil tank to be tested based on the acquired point cloud data of each oil tank;

[0013] Build a simulation test environment, use AI algorithms and simulation test environment to simulate the 3D model and obtain test data;

[0014] The simulation test includes a pressure simulation test and a temperature simulation test; the test data includes pressure test results and temperature test results; the pressure test results include the pressure monitoring value of each area and the pressure test time; the pressure monitoring values ​​include the monitoring temperature values ​​and the odor concentration values; the temperature test results include the temperature monitoring values ​​of each area and the temperature test time; the temperature monitoring values ​​include the monitoring pressure values ​​and the odor concentration values.

[0015] Preferably, the process of building a simulation test environment includes:

[0016] The simulation test environment includes a target unit, a monitoring unit, an environmental test unit and a state simulation unit; the target unit is used to input the three-dimensional model of the oil storage tank to be tested and the oil state data; the state simulation unit includes a state input terminal and a simulation control terminal, and the state input terminal is used to input internal and external environmental data.

[0017] Preferably, the test data is analyzed and a test timeline is constructed. With the help of the test timeline, the process of further analyzing the test data is as follows:

[0018] Mark the start time of the pressure test time and the temperature test time in the test data of each area onto several coordinate axes respectively, and mark the pressure test time and the temperature test time in sequence along the start time to construct a test time axis;

[0019] According to the pressure monitoring values ​​and temperature monitoring values ​​corresponding to the pressure test time and the temperature test time, a timeline is constructed at the time point of the corresponding test time axis. Monitoring nodes and odor concentration nodes are set on the timeline, and the pressure monitoring values ​​and temperature monitoring values ​​are respectively marked in the nodes of the timeline of the corresponding time until all the test data are completely marked on the test time axis. The pressure test records and temperature test records of each area are obtained; the pressure test records and temperature test records are analyzed.

[0020] Preferably, the process of analyzing the pressure test records and the temperature test records to obtain the response timeline, the leakage risk area, and the risk response signal includes:

[0021] Set a trend change threshold and a concentration change threshold. If the difference between the change trend of the monitoring nodes and odor concentration nodes recorded by the pressure test and the change trend of the monitoring nodes and odor concentration nodes recorded by the temperature test in the same area and adjacent timelines is less than or equal to the trend change threshold, and the difference in odor concentration between the pressure test records and the temperature test records of the starting timeline and the ending timeline is less than or equal to the concentration change threshold, then mark the timeline as a silent timeline.

[0022] If the difference between the change trend of the monitoring nodes in the pressure test record and the change trend of the monitoring nodes in the temperature test record in the same area and the adjacent timeline is greater than the trend change threshold, or the difference in odor concentration between the pressure test record and the temperature test record of the starting timeline and the ending timeline is greater than the concentration change threshold, then the timeline is marked as a response timeline;

[0023] The test data of the area corresponding to the test time axis where all timelines are silent timelines are deleted on the test time axis; the area corresponding to the test time axis where there are response timelines is recorded as a leakage risk area, and a risk response signal is generated.

[0024] Preferably, all areas of the oil storage tank to be tested are analyzed using the leakage risk area to obtain a risk spread area chain, which includes:

[0025] Taking each leakage risk area as the center, the adjacent areas of the leakage risk area are traversed in turn, and the adjacent risk relationship between the adjacent leakage risk areas is constructed;

[0026] Calculate the ratio of the total number of response timelines to the total time in the leakage risk area, and record it as the risk factor of the leakage risk area; record the area corresponding to the maximum risk factor of the leakage risk area with an adjacent risk relationship as the influence area; construct the influence direction according to the order of risk factors from large to small, and assign adjacent risk relationships to obtain adjacent risk vectors; mark the risk factors into the leakage risk area, and the adjacent risk vectors and leakage risk areas constitute a risk spread area chain.

[0027] Preferably, the risk spreading area chain is analyzed in combination with the oil storage tank to be tested, and an oil tank leakage risk map is established; and the process of generating a risk prevention plan based on the risk response signal and the oil tank leakage risk map includes:

[0028] Construct an outer wall area map of the oil storage tank to be tested and mark each risk spread area chain on the outer wall area map. If there are multiple unconnected risk spread area chains within the outer wall area map, construct the potential risk relationship between the influence areas of each risk spread area chain to obtain the oil tank leakage risk map;

[0029] When a risk response signal is received, the oil tank leakage risk map is sent to the maintenance personnel. The maintenance personnel inspect the areas marked on the oil tank leakage risk map, carry out targeted prevention for different risk situations in each area, and generate a risk prevention plan.

[0030] The present invention also discloses an AI-based oil tank leakage detection and response system, including a management center, which is communicatively connected to an oil tank simulation test module, a leakage risk module, and a leakage prevention module:

[0031] The oil tank simulation test module is used to obtain the oil tank projection information and oil tank status data of the oil storage tank to be tested, and to construct a corresponding three-dimensional model and set up a simulation test environment. With the help of the simulation test environment, the three-dimensional model is simulated and tested to obtain test data.

[0032] The leakage risk module is used to analyze test data and construct a test timeline. With the help of the test timeline, the test data can be further analyzed to obtain the response timeline, leakage risk area and risk response signal.

[0033] The leakage prevention module is used to analyze all areas of the oil storage tank to be tested with the help of leakage risk areas to obtain a risk spread area chain; in combination with the oil storage tank to be tested, the risk spread area chain is analyzed to establish an oil tank leakage risk map; based on the risk response signal and the oil tank leakage risk map, a risk prevention plan is generated.

[0034] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: obtaining the oil tank projection information and oil tank status data of the oil storage tank to be tested, and constructing a corresponding three-dimensional model, setting up a simulation test environment, and using the simulation test environment to simulate the three-dimensional model to achieve leakage enhancement testing of the oil storage tank, thereby reducing the cost of oil storage tank leakage detection; analyzing the test data, constructing a test timeline, and using the test timeline to further analyze the test data to obtain a response timeline, leakage risk area and risk response signal, thereby improving the accuracy of oil storage tank leakage detection; using the leakage risk area to analyze all areas of the oil storage tank to be tested, and obtain a risk spread area chain; combining the oil storage tank to be tested, analyzing the risk spread area chain, and establishing an oil tank leakage risk map; generating a risk prevention plan based on the risk response signal and the oil tank leakage risk map; increasing the risk management and control capabilities of the oil storage tank, and improving oil storage safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0036] Example 1, as Figure 1 As shown, the present invention proposes an AI-based oil tank leakage detection and response method, which includes the following steps:

[0037] S1. Obtaining the oil tank projection information and oil tank status data of the oil storage tank to be tested, constructing a corresponding three-dimensional model, setting up a simulation test environment, and performing a simulation test on the three-dimensional model using the simulation test environment to obtain test data;

[0038] S2. Analyze the test data and construct a test timeline. Using the test timeline, further analyze the test data to obtain a response timeline, leakage risk areas, and risk response signals.

[0039] S3. Analyze all areas of the oil storage tank to be tested with the help of leakage risk areas to obtain a risk spread area chain; analyze the risk spread area chain in combination with the oil storage tank to be tested and establish an oil tank leakage risk map; generate a risk prevention plan based on the risk response signal and the oil tank leakage risk map.

[0040] It should be further explained that, in the specific implementation process, the tank projection information and tank status data of the oil storage tank to be tested are obtained, and the corresponding three-dimensional model is constructed. A simulation test environment is set up, and the three-dimensional model is simulated and tested with the help of the simulation test environment. The process of obtaining test data is as follows:

[0041] The oil storage tank to be tested is an oil storage tank that needs to be leak tested;

[0042] The FARO Focus device is used to perform three-dimensional laser scanning on the oil storage tank to be tested, and obtain oil tank point cloud data; the oil tank projection information includes the overall size of the oil tank, the overall structure of the oil tank, and the surface information of the oil tank; the oil tank status data includes oil status data and internal and external environmental data; the oil status includes oil type and oil volume; the internal and external environmental data includes internal and external temperature values ​​and internal and external pressure values;

[0043] Specifically, the oil storage tank to be tested is equipped with a temperature sensor, a density sensor, and a pressure sensor, etc., for obtaining the oil tank status data of the oil storage tank to be tested;

[0044] Use CAD software to construct a three-dimensional model of the oil tank to be tested based on the acquired point cloud data of each oil tank;

[0045] The simulation test environment includes a target unit, a monitoring unit, an environmental test unit and a state simulation unit;

[0046] The target unit is used to input the three-dimensional model of the oil storage tank to be tested and the oil status data; the monitoring unit includes a temperature sensor, a pressure sensor, and an odor sensor, etc., which are used to monitor various data of the simulated test of the oil storage tank to be tested and cooperate with the environmental testing unit to adjust various parameters; the monitoring unit is installed on the outer wall of the oil tank of the three-dimensional model of the oil storage tank to be tested, and the monitoring unit device is numbered X;

[0047] Specifically, the spatial distances between the monitoring units on the outer wall of the oil storage tank to be tested are equal, and the edge positions of the data acquisition ranges of the sensors in the monitoring units at spatial locations on the outer walls of adjacent oil storage tanks partially overlap. That is, the data acquisition ranges of all sensors in all monitoring units cover the entire outer wall range of the three-dimensional model of the oil storage tank to be tested.

[0048] The environmental testing unit includes a temperature control terminal and a pressure control terminal, etc. The state simulation unit includes a state input terminal and a simulation control terminal. The state input terminal is used to input internal and external environmental data; the simulation control terminal is used to input fluid mechanics control equations, thermodynamic equations, and stress intensity factors to simulate the changes of oil in the oil storage tank to be tested and the changes of the outer wall of the oil storage tank to be tested.

[0049] Through AI algorithms and simulated test environments, simulate the 3D model to obtain test data;

[0050] Specifically, AI algorithms include but are not limited to neural network algorithms, finite element algorithms, environmental simulation training algorithms, fluid mechanics simulation, thermal conduction coupling simulation, and fracture mechanics simulation algorithms;

[0051] The simulation test includes a pressure simulation test and a temperature simulation test; the test data includes pressure test results and temperature test results; the pressure test results include pressure monitoring values ​​of each area and pressure test time; the pressure monitoring values ​​include monitoring temperature values ​​and odor concentration values; the temperature test results include temperature monitoring values ​​of each area and temperature test time; the temperature monitoring values ​​include monitoring pressure values ​​and odor concentration values;

[0052] Specifically, each area refers to the area monitored by the monitoring unit; the pressure simulation test refers to taking the internal and external pressure values ​​as the internal and external pressure starting values, and continuously increasing (or decreasing) the internal (or external) pressure values ​​until the outer wall of the oil storage tank to be tested changes, and stopping increasing (or decreasing) the internal (or external) pressure value; the temperature simulation test refers to taking the internal and external temperature values ​​as the internal and external temperature starting values, and continuously increasing (or decreasing) the internal (or external) temperature values, and stopping increasing (or decreasing) the internal (or external) temperature value until the outer wall of the oil storage tank to be tested changes, and stopping increasing (or decreasing) the internal (or external) temperature value, and the pressure simulation test time is the same as the temperature simulation test time.

[0053] It should be further explained that, during the specific implementation process, the test data is analyzed, a test timeline is constructed, and the test data is further analyzed using the test timeline to obtain the response timeline, leakage risk area, and risk response signal. The process is as follows:

[0054] Mark the start time of the pressure test time and the temperature test time in the test data of each area onto several coordinate axes respectively, and mark the pressure test time and the temperature test time in sequence along the start time to construct a test time axis;

[0055] According to the pressure monitoring values ​​and temperature monitoring values ​​corresponding to the pressure test time and the temperature test time, a timeline is constructed at the time point of the corresponding test time axis. The timeline is provided with monitoring nodes and odor concentration nodes, and the pressure monitoring values ​​and temperature monitoring values ​​are respectively marked to the nodes of the timeline of the corresponding time until all the test data are marked on the test time axis. Then, the pressure test record and temperature test record of each area are obtained;

[0056] Set a trend change threshold and a concentration change threshold. If the difference between the change trend of the monitoring nodes and odor concentration nodes recorded by the pressure test and the change trend of the monitoring nodes and odor concentration nodes recorded by the temperature test in the same area and adjacent timelines is less than or equal to the trend change threshold, and the difference in odor concentration between the pressure test records and the temperature test records of the starting timeline and the ending timeline is less than or equal to the concentration change threshold, then mark the timeline as a silent timeline.

[0057] If the difference between the change trend of the monitoring nodes in the pressure test record and the change trend of the monitoring nodes in the temperature test record in the same area and the adjacent timeline is greater than the trend change threshold, or the difference in odor concentration between the pressure test record and the temperature test record of the starting timeline and the ending timeline is greater than the concentration change threshold, then the timeline is marked as a response timeline;

[0058] The test data of the area corresponding to the test time axis where all timelines are silent timelines are deleted on the test time axis; the area corresponding to the test time axis where there are response timelines is recorded as a leakage risk area, and a risk response signal is generated.

[0059] It should be further explained that, during the specific implementation process, all areas of the oil storage tank to be tested are analyzed with the help of leakage risk areas to obtain a risk spread area chain. The risk spread area chain is analyzed in conjunction with the oil storage tank to be tested to establish an oil tank leakage risk map. Based on the risk response signal and the oil tank leakage risk map, the process of generating a risk prevention plan is as follows:

[0060] Taking each leakage risk area as the center, the adjacent areas of the leakage risk area are traversed in turn, and the adjacent risk relationship between the adjacent leakage risk areas is constructed;

[0061] Calculate the ratio of the total number of response timelines to the total time in the leakage risk area and record it as the risk factor of the leakage risk area. Record the area corresponding to the maximum risk factor of the leakage risk area with adjacent risk relationships as the influence area. Construct the influence direction according to the order of risk factors from large to small, and assign adjacent risk relationships to obtain adjacent risk vectors. Mark the risk factors into the leakage risk area, and the adjacent risk vectors and leakage risk areas form a risk spread area chain.

[0062] It should be noted that the direction of influence only exists between adjacent regions, and cross-regional influence can be reflected by several consecutive adjacent regions;

[0063] Construct the outer wall area map of the oil storage tank to be tested, mark each risk spread area chain on the outer wall area map, if there are multiple unconnected risk spread area chains in the outer wall area map, then construct the potential risk relationship between the influence areas of each risk spread area chain to obtain the oil tank leakage risk map; if

[0064] When a risk response signal is received, the oil tank leakage risk map is sent to the maintenance personnel. The maintenance personnel inspect the areas marked on the oil tank leakage risk map, carry out targeted prevention for different risk situations in each area, and generate a risk prevention plan.

[0065] In a second embodiment, an AI-based oil tank leakage detection and response system proposed in the present invention is applied to the AI-based oil tank leakage detection and response method described in the first embodiment, and specifically includes a management center, which is communicatively connected to an oil tank simulation test module, a leakage risk module, and a leakage prevention module:

[0066] The oil tank simulation test module is used to obtain the oil tank projection information and oil tank status data of the oil storage tank to be tested, and to construct a corresponding three-dimensional model and set up a simulation test environment. With the help of the simulation test environment, the three-dimensional model is simulated and tested to obtain test data.

[0067] The leakage risk module is used to analyze test data and construct a test timeline. With the help of the test timeline, the test data can be further analyzed to obtain the response timeline, leakage risk area and risk response signal.

[0068] The leakage prevention module is used to analyze all areas of the oil storage tank to be tested with the help of leakage risk areas to obtain a risk spread area chain; in combination with the oil storage tank to be tested, the risk spread area chain is analyzed to establish an oil tank leakage risk map; based on the risk response signal and the oil tank leakage risk map, a risk prevention plan is generated.

[0069] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. An AI-based oil tank leak detection and response method, characterized in that: The following steps are involved: S1. Obtaining the oil tank projection information and oil tank status data of the oil storage tank to be tested, constructing a corresponding three-dimensional model, setting up a simulation test environment, and performing a simulation test on the three-dimensional model using the simulation test environment to obtain test data; S2. Analyze the test data and construct a test timeline. Using the test timeline, further analyze the test data to obtain a response timeline, leakage risk areas, and risk response signals. S3. Analyze all areas of the oil storage tank to be tested using the leakage risk area to obtain a risk spread area chain; Combined with the oil storage tanks to be tested, the risk spreading area chain is analyzed and an oil tank leakage risk map is established; Generate risk prevention plans based on risk response signals and oil tank leakage risk maps.

2. The AI-based oil tank leak detection and response method according to claim 1 is characterized in that: Obtain the tank projection information and tank status data of the oil storage tank to be tested, build a corresponding 3D model, set up a simulation test environment, and use the simulation test environment to perform simulation testing on the 3D model. The process of obtaining test data includes: The FARO Focus device is used to perform three-dimensional laser scanning on the oil storage tank to be tested, and obtain oil tank point cloud data; the oil tank projection information includes the overall size of the oil tank, the overall structure of the oil tank, and the surface information of the oil tank; the oil tank status data includes oil status data and internal and external environmental data; the oil status includes oil type and oil volume; the internal and external environmental data includes internal and external temperature values ​​and internal and external pressure values; Use CAD software to construct a three-dimensional model of the oil tank to be tested based on the acquired point cloud data of each oil tank; Build a simulation test environment, use AI algorithms and simulation test environment to simulate the 3D model and obtain test data; The simulation test includes a pressure simulation test and a temperature simulation test; the test data includes pressure test results and temperature test results; the pressure test results include the pressure monitoring value of each area and the pressure test time; the pressure monitoring values ​​include the monitoring temperature values ​​and the odor concentration values; the temperature test results include the temperature monitoring values ​​of each area and the temperature test time; the temperature monitoring values ​​include the monitoring pressure values ​​and the odor concentration values.

3. The AI-based oil tank leak detection and response method according to claim 2 is characterized in that: The process of setting up a simulation test environment includes: The simulation test environment includes a target unit, a monitoring unit, an environmental test unit and a state simulation unit; the target unit is used to input the three-dimensional model of the oil storage tank to be tested and the oil state data; the state simulation unit includes a state input terminal and a simulation control terminal, and the state input terminal is used to input internal and external environmental data.

4. The AI-based oil tank leak detection and response method according to claim 3 is characterized in that: Analyze the test data and build a test timeline. With the help of the test timeline, the process of further analyzing the test data is as follows: Mark the start time of the pressure test time and the temperature test time in the test data of each area onto several coordinate axes respectively, and mark the pressure test time and the temperature test time in sequence along the start time to construct a test time axis; According to the pressure monitoring values ​​and temperature monitoring values ​​corresponding to the pressure test time and the temperature test time, a timeline is constructed at the time point of the corresponding test time axis. Monitoring nodes and odor concentration nodes are set on the timeline, and the pressure monitoring values ​​and temperature monitoring values ​​are respectively marked in the nodes of the timeline of the corresponding time until all the test data are completely marked on the test time axis. The pressure test records and temperature test records of each area are obtained; the pressure test records and temperature test records are analyzed.

5. The AI-based oil tank leak detection and response method according to claim 4 is characterized in that: The process of analyzing pressure test records and temperature test records to obtain response timelines, leakage risk areas, and risk response signals includes: Set a trend change threshold and a concentration change threshold. If the difference between the change trend of the monitoring nodes and odor concentration nodes recorded by the pressure test and the change trend of the monitoring nodes and odor concentration nodes recorded by the temperature test in the same area and adjacent timelines is less than or equal to the trend change threshold, and the difference in odor concentration between the pressure test records and the temperature test records of the starting timeline and the ending timeline is less than or equal to the concentration change threshold, then mark the timeline as a silent timeline. If the difference between the change trend of the monitoring nodes in the pressure test record and the change trend of the monitoring nodes in the temperature test record in the same area and the adjacent timeline is greater than the trend change threshold, or the difference in odor concentration between the pressure test record and the temperature test record of the starting timeline and the ending timeline is greater than the concentration change threshold, then the timeline is marked as a response timeline; The test data of the area corresponding to the test time axis where all timelines are silent timelines are deleted on the test time axis; the area corresponding to the test time axis where there are response timelines is recorded as a leakage risk area, and a risk response signal is generated.

6. The AI-based oil tank leakage detection and response method according to claim 1 or 5, characterized in that: The process of analyzing all areas of the oil storage tank to be tested using the leakage risk area and obtaining the risk spread area chain includes: Taking each leakage risk area as the center, the adjacent areas of the leakage risk area are traversed in turn, and the adjacent risk relationship between the adjacent leakage risk areas is constructed; Calculate the ratio of the total number of response timelines to the total time in the leakage risk area, and record it as the risk factor of the leakage risk area; record the area corresponding to the maximum risk factor of the leakage risk area with an adjacent risk relationship as the influence area; construct the influence direction according to the order of risk factors from large to small, and assign adjacent risk relationships to obtain adjacent risk vectors; mark the risk factors into the leakage risk area, and the adjacent risk vectors and leakage risk areas constitute a risk spread area chain.

7. The AI-based oil tank leak detection and response method according to claim 6 is characterized in that: Combined with the oil storage tanks to be tested, the risk spreading area chain is analyzed and an oil tank leakage risk map is established; Based on the risk response signals and the oil tank leakage risk map, the process of generating a risk prevention plan includes: Construct an outer wall area map of the oil storage tank to be tested and mark each risk spread area chain on the outer wall area map. If there are multiple unconnected risk spread area chains within the outer wall area map, construct the potential risk relationship between the influence areas of each risk spread area chain to obtain the oil tank leakage risk map; When a risk response signal is received, the oil tank leakage risk map is sent to the maintenance personnel. The maintenance personnel inspect the areas marked on the oil tank leakage risk map, carry out targeted prevention for different risk situations in each area, and generate a risk prevention plan.

8. An AI-based oil tank leak detection and response system, specifically applied to the AI-based oil tank leak detection and response method according to any one of claims 1 to 7, comprising a management center, characterized in that: The management center is connected to the oil tank simulation test module, leakage risk module and leakage prevention module: The oil tank simulation test module is used to obtain the oil tank projection information and oil tank status data of the oil storage tank to be tested, and to construct a corresponding three-dimensional model and set up a simulation test environment. With the help of the simulation test environment, the three-dimensional model is simulated and tested to obtain test data. The leakage risk module is used to analyze test data and construct a test timeline. With the help of the test timeline, the test data can be further analyzed to obtain the response timeline, leakage risk area and risk response signal. The leakage prevention module is used to analyze all areas of the oil tank to be tested with the help of leakage risk areas to obtain the risk spread area chain; combined with the oil tank to be tested, the risk spread area chain is analyzed to establish the oil tank leakage risk map; Generate risk prevention plans based on risk response signals and oil tank leakage risk maps.

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