Safe transportation method and system for oil tank truck

Abstract

The invention discloses a safe transportation method and system for an oil tank truck, and relates to the field of dangerous goods transportation, and the method comprises the steps that the first on-off state of a sea valve and the second on-off state of an oil pump power takeoff are obtained; acquiring position data of a target oil tank truck; and generating warning prompt information according to the position data, the first switch state and the second switch state. According to the safe transportation method for the oil tank truck, the state sensors are installed on the sea valve and the oil pump power takeoff, the on-off state of equipment is monitored in real time, and it is ensured that state information of the equipment can be obtained in real time. The problem that equipment is not closed due to human negligence is avoided, and the transportation safety of the oil tank truck is improved.

Description

Technical Field

[0001] The present specification relates to the field of dangerous goods transportation, and more specifically, the present application relates to a method and system for safe transportation of oil tankers. Background Art

[0002] During transportation, tank trucks usually need to load, unload or transport flammable and explosive liquid substances (such as gasoline, diesel, etc.). In order to ensure transportation safety, key safety control equipment such as seabed valves and oil pump power take-offs are installed on tank trucks. The seabed valve is used to control the outflow of oil in the tank, and the oil pump power take-off is used to drive the oil pump for liquid loading and unloading.

[0003] In the related technologies, traditional tank trucks mainly rely on manual operation. The driver may forget to close the seabed valve or the oil pump power take-off due to negligence, resulting in oil leakage or equipment idling during transportation, posing the risk of environmental pollution, equipment damage or safety accidents. The related technologies lack real-time monitoring means for the status of seabed valves and oil pump power take-offs, and cannot promptly know the open or closed status of the equipment. There is a lack of linkage between vehicle driving and equipment status, and it is impossible to determine whether the equipment status matches the driving scenario. Traditional solutions usually rely on manual inspections or discover problems afterwards, lack an active alarm mechanism, and are prone to miss safety interventions at critical moments.

[0004] Therefore, it is necessary to propose a safe transportation method and system for oil tank trucks to at least solve some of the above problems. Summary of the invention

[0005] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0006] In the first aspect, the present application proposes a method for safe transportation of oil tankers, comprising:

[0007] Acquire a first switch state of a subsea valve and a second switch state of an oil pump power take-off;

[0008] Obtain the location data of the target tanker;

[0009] Generate warning information according to the above-mentioned position data, the above-mentioned first switch state and the above-mentioned second switch state.

[0010] In a feasible implementation manner, the generating of warning prompt information according to the position data, the first switch state and the second switch state includes:

[0011] When the first switch state indicates that the target tanker is turned on and the position data indicates that the target tanker is traveling at a speed greater than a preset speed, a first warning prompt message is generated, wherein the first warning prompt message includes a parking warning message and a seabed valve closing prompt message; and / or,

[0012] When the second switch state indicates that the target tanker truck is turned on and the position data indicates that the target tanker truck is traveling at a speed greater than a preset speed, a second warning prompt message is generated, wherein the second warning prompt message includes a prompt message for turning off the oil pump power take-off; and / or,

[0013] When the first switch state and the second switch state are both turned on and the position data indicates that the driving speed of the target tanker is greater than a preset speed, a third warning prompt information is generated, wherein the third warning prompt information includes a parking warning information, a seabed valve closing prompt information and an oil pump power take-off closing prompt information.

[0014] In a feasible implementation, it also includes:

[0015] When the first switch state and / or the second switch state indicates on, the target personnel completes the operation corresponding to the warning prompt information, and the first switch state and / or the second switch state still indicates on, obtaining the seabed valve pressure information, the seabed valve external shooting information and the oil pump power take-off operation parameter information;

[0016] Upload the above-mentioned seabed valve pressure information, the above-mentioned seabed valve external shooting information and the above-mentioned oil pump power take-off operation parameter information to the scheduling processing model of the cloud server to obtain the fault identification result and processing method;

[0017] The above-mentioned fault identification result and the above-mentioned processing method are sent to the mobile terminal corresponding to the above-mentioned target person, so that the above-mentioned target person can perform corresponding operations on the above-mentioned target tanker based on the above-mentioned fault identification result and the above-mentioned processing method.

[0018] In a feasible implementation manner, the specific steps of the scheduling processing model of the cloud server obtaining the fault identification result and processing method include:

[0019] Determine the abnormal pressure information of the subsea valve according to the above-mentioned subsea valve pressure information and the coincidence information of the historical pressure curve of the subsea valve;

[0020] Determine the abnormal leakage information of the subsea valve according to the dripping speed and dripping density information of the oil droplets obtained from the external photographing information of the subsea valve;

[0021] Determine the abnormal information of the oil pump power take-off according to the above-mentioned oil pump power take-off operation parameter information and theoretical operation parameter information;

[0022] Obtaining the oil type information of the target oil tanker;

[0023] Determine a fault identification result according to the oil type information, the abnormal pressure information of the seabed valve, the abnormal leakage information of the seabed valve and the abnormal information of the oil pump power take-off;

[0024] The processing method corresponding to the fault identification result is generated according to the fault identification result and the correspondence between the historical fault identification results and the historical processing methods.

[0025] In a feasible implementation manner, when a target camera unit is provided in the area corresponding to the subsea valve of the target oil tanker, the external photographing information of the subsea valve is obtained by the target camera unit and uploaded to the cloud server; when a target camera unit is not provided in the area corresponding to the subsea valve of the target oil tanker, the external photographing information of the subsea valve is obtained by the target personnel using the target terminal and uploaded to the cloud server through the target APP.

[0026] The specific steps of determining the dripping speed of the oil droplets and the dripping density information of the oil droplets include:

[0027] Extracting the first frame image, the second frame image and the time interval information between the first frame image and the second frame image according to the external shooting information of the seabed valve;

[0028] Performing image segmentation based on the first frame image based on a preset threshold value to separate the background image and the oil drop image;

[0029] Selecting image feature points based on the oil drop image;

[0030] Determine the dripping speed based on the position tracking information of the image feature points and the time interval information;

[0031] The drip density information is determined based on the pixel ratios of the oil drop image and the background image.

[0032] In a feasible implementation manner, the above fault identification result includes a target fault type and a fault severity;

[0033] The fault identification result is determined based on the oil type information, the abnormal pressure information of the seabed valve, the abnormal leakage information of the seabed valve and the abnormal information of the oil pump power take-off, including:

[0034] Using expert knowledge to construct a Bayesian initial network, wherein the Bayesian initial network includes a first node, a first edge, and a first conditional probability table, the first node includes a first input node and a first output node, the first input node includes an oil type node, a subsea valve pressure abnormality node, and a subsea valve leakage abnormality node, the first output node includes a fault type node and a fault severity node, the first edge includes an input node and a fault type node dependency, an input node and a fault severity node dependency, and a fault type node and a fault severity node dependency, and the first conditional probability table is used to characterize the probability influence of the first parent node on the first child node;

[0035] Training the first conditional probability table through the first historical data to generate a target Bayesian network;

[0036] Inputting the above oil product type information, the above seabed valve pressure abnormality information, and the above seabed valve leakage abnormality information into the above target Bayesian network to obtain the posterior probability of each fault type;

[0037] The target fault type and the fault severity are determined according to the posterior probability of each fault type.

[0038] In a feasible implementation manner, the above-mentioned processing method includes the type of processing measures and the urgency of the operation;

[0039] The above-mentioned processing method corresponding to the above-mentioned fault identification result is generated according to the above-mentioned fault identification result and the correspondence between the historical fault identification results and the historical processing methods, including:

[0040] Constructing a Bayesian initial extended network, wherein the Bayesian initial extended network includes a second node, a second edge, and a second conditional probability table, the second node includes a second input node and a second output node, the second input node includes a fault type node and a fault severity node, the second output node includes a treatment measure type node and an operation urgency node, the second edge includes a fault severity node and a treatment measure type node dependency, a fault type node and a treatment measure type node dependency, a historical processing method correspondence and a treatment measure type node dependency, and a treatment measure type node and an operation urgency node dependency, and the second conditional probability table is used to characterize the probability influence of the second parent node on the second child node;

[0041] Training the second conditional probability table through the second historical data to generate a target Bayesian extended network;

[0042] Inputting the above fault identification results into the above target Bayesian extended network to obtain the posterior probabilities of different treatment measures;

[0043] The above-mentioned treatment measure type and the above-mentioned operation urgency are determined according to the posterior probability and the posterior probability of each treatment measure.

[0044] In a feasible implementation, it also includes:

[0045] In the case where the above-mentioned types of treatment measures include at least two and the urgency deviation of each of the above-mentioned operation urgency is less than a preset deviation, obtaining the target person's task completion preference information;

[0046] The target processing action type is determined based on the above-mentioned completion task preference information.

[0047] In a second aspect, the present application proposes a tanker truck safety transportation system, the tanker truck safety transportation system comprises a local end, the local end comprises a first acquisition unit, a second acquisition unit and a generation unit,

[0048] The first acquisition unit is configured to acquire a first switch state of the seabed valve and a second switch state of the oil pump power take-off;

[0049] The second acquisition unit is configured to acquire the position data of the target tanker;

[0050] The generating unit is configured to generate warning information according to the position data, the first switch state and the second switch state.

[0051] In a feasible implementation manner, the above-mentioned oil tanker safe transportation system further includes a cloud server, and the above-mentioned cloud server includes a third acquisition unit and a sending unit.

[0052] The third acquisition unit is configured to acquire a fault identification result and a processing method according to the subsea valve pressure information, the external photographic information of the subsea valve and the operating parameter information of the oil pump power take-off according to the scheduling processing model;

[0053] The sending unit is configured to send the fault identification result and the processing method to the mobile terminal corresponding to the target person, so that the target person can perform corresponding operations on the target tanker based on the fault identification result and the processing method.

[0054] The safe transportation method for oil tankers proposed in this application monitors the switch status of the equipment in real time by installing status sensors on the seabed valve and the oil pump power take-off to ensure that its status information can be obtained in real time. Avoid the problem of equipment not being closed due to human negligence and improve the accuracy of operation. Real-time perception of equipment status to form a safe closed loop. Through the GPS module or Beidou positioning system, the vehicle's geographical location, driving status and other information are collected in real time, and scenario analysis is performed in combination with the equipment status data. The linkage analysis of the equipment status and the vehicle status is realized to determine whether the equipment is turned on or off in accordance with the current transportation scenario requirements. According to the logical matching of the equipment status and the vehicle location status, the system can automatically generate warning prompt information, automatically judge the normal and abnormal status through intelligent algorithms, and avoid the misjudgment or omission of manual identification in traditional methods. A multi-level alarm mechanism of local prompts and remote notifications is proposed, and the abnormal status can be mastered by users at multiple levels at the same time, improving the problem response speed. The system automatically obtains the equipment status and vehicle status data through sensors and GPS modules, and performs real-time analysis and judgment without the need for additional operation by the driver. Reduce the impact of human factors on system safety and avoid accidents caused by negligent or misoperation. Improve the intelligence and automation level of the operation of the tanker during transportation. During transportation, if the seabed valve is not closed, it may cause oil leakage, pollute the environment or cause a fire; if the oil pump power take-off is not closed, it may cause the equipment to idle or be damaged. Through real-time monitoring and warning, the system can remind relevant personnel to take measures before the problem occurs. Effectively reduce environmental pollution and property losses caused by abnormal equipment status during transportation. This method relies on sensors, GPS modules, communication modules and data processing logic, has a simple structure, can be easily integrated with existing tanker equipment, can be quickly deployed, and is suitable for the transformation of existing tankers. The safe transportation method for tankers proposed in this application, in view of the insufficient monitoring and alarm lag problems in the prior art, significantly improves the safety and intelligence level of tanker transportation through real-time monitoring, logical judgment, multi-level alarm and other means. It can not only detect abnormal equipment status in the first time, but also quickly notify relevant personnel through local and remote prompts to avoid accidents. Compared with traditional technologies, this method has higher real-time, reliability and convenience, and provides a comprehensive safety guarantee solution for the tanker transportation industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0056] Figure 1 A schematic diagram of a process of a safe transportation method for a tanker truck provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of a vehicle-side display interface of a tanker truck safety transportation system provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of a vehicle-side display interface of another oil tanker safe transportation system provided in an embodiment of the present application;

[0059] Figure 4 A structural schematic diagram of a tanker truck safe transportation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0061] Figure 1 A schematic diagram of a process of a safe transportation method of a tanker truck provided in an embodiment of the present application, the method may specifically include:

[0062] In the first aspect, the present application proposes a method for safe transportation of oil tankers, comprising:

[0063] S110, obtaining a first switch state of the seabed valve and a second switch state of the oil pump power take-off;

[0064] S120, obtaining the location data of the target tanker;

[0065] S130, generating warning prompt information according to the above-mentioned position data, the above-mentioned first switch state and the above-mentioned second switch state.

[0066] For example, the present application method proposes a monitoring method based on the status of the seabed valve, the status of the oil pump power take-off, and the location data of the target oil tanker for the safety of the oil tanker during transportation. By acquiring and analyzing these key data in real time, warning information is generated to prevent safety hazards caused by equipment not being shut down or improper operation.

[0067] The subsea valve is responsible for controlling the outflow of oil in the tank and is an important device to prevent leakage during transportation. A status sensor can be installed on the subsea valve, or a subsea valve with data feedback function can be directly installed to monitor in real time whether the valve is in the "open" or "closed" state. The status sensor returns the "first switch state" (such as OPEN or CLOSED). The first switch state can also be the opening information, for example: open 20%, open 40%, etc.

[0068] The oil pump power take-off is a key component that drives the oil pump for loading and unloading operations. The start and stop status of the power take-off is monitored in real time through the status sensor. Return to the "second switch state" (such as ON or OFF). Similar to the first switch state, the second switch state can also be load information, such as: 20% load, 40% load. The oil pump power take-off should be kept closed when not loading and unloading to prevent the equipment from idling, increasing energy consumption or causing equipment damage and danger.

[0069] Through the GPS module or Beidou positioning system, the geographical location (longitude, latitude) and status information (such as speed, driving direction) of the tanker are collected in real time. The geographical location is the specific location of the tanker at present. The driving status includes whether it is stationary (such as parking for loading and unloading) or whether it is moving (such as in transportation).

[0070] Determine the scene the vehicle is in based on its location and status. For example, in the loading and unloading area, the device is allowed to be in the "on" state. During driving, the device must be in the "off" state. Location information can help identify whether there is a mismatch between the device status and the vehicle driving status.

[0071] The warning information is generated based on the following: whether the seabed valve is closed, whether the oil pump power take-off is closed, and whether the vehicle position and driving status are in motion. If the vehicle is stationary (loading and unloading area), the device is turned on without warning. If the vehicle is in driving state, the device is turned off without warning.

[0072] If the vehicle is in motion but the device is not turned off, it is considered abnormal and a warning message is generated. If the vehicle is stationary but the device is turned off, it indicates that there may be an operation omission.

[0073] Generate warning information based on abnormal situations, such as:

[0074] A: “Warning: The vehicle is moving and the seabed valve is not closed!”

[0075] B: "Warning: The vehicle is moving and the fuel pump power take-off is on!"

[0076] C: "Prompt: The vehicle is stationary, but the device is not turned on. Please check the operating procedures."

[0077] Warning information can include local prompts and remote notifications. Local prompts send out sound and light alarms through the vehicle-mounted equipment to remind the driver. Remote notifications can send warning information to the cloud server monitoring platform or the manager's terminal through the 4G / 5G module.

[0078] The safe transportation method of oil tanker proposed in this application monitors the switch status of the equipment in real time by installing status sensors on the submarine valve and the oil pump power take-off to ensure that its status information (such as "open / closed" or "opening", "load") can be obtained in real time. Avoid the problem of equipment not being closed due to human negligence and improve the accuracy of operation. Real-time perception of equipment status to form a safe closed loop. Through the GPS module or Beidou positioning system, the vehicle's geographical location (latitude and longitude), driving status (such as stationary or moving) and other information are collected in real time, and scenario analysis is performed in combination with the equipment status data. The linkage analysis of the equipment status and the vehicle status is realized to determine whether the equipment is open or closed in accordance with the requirements of the current transportation scenario. According to the logical matching of the equipment status and the vehicle position status, the system can automatically generate warning prompt information, automatically judge the normal and abnormal status through intelligent algorithms, and avoid the misjudgment or omission of manual identification in traditional methods. A multi-level alarm mechanism of local prompts and remote notifications is proposed, and the abnormal status can be mastered by users at multiple levels at the same time, which improves the problem response speed. The system automatically obtains the equipment status and vehicle status data through sensors and GPS modules, and performs real-time analysis and judgment without the need for additional operation by the driver. Reduce the impact of human factors on system safety and avoid accidents caused by negligent or misoperation. Improve the intelligence and automation level of operation during tanker transportation. During transportation, if the seabed valve is not closed, it may cause oil leakage, pollute the environment or cause a fire; if the oil pump power take-off is not closed, it may cause the equipment to idle or be damaged. Through real-time monitoring and warning, the system can remind relevant personnel to take measures before the problem occurs. Effectively reduce environmental pollution and property losses caused by abnormal equipment status during transportation. This method relies on sensors, GPS modules, communication modules and data processing logic, has a simple structure, can be easily integrated with existing tanker equipment, can be quickly deployed, and is suitable for the transformation of existing tankers. The safe transportation method for tankers proposed in this application, aimed at the problems of insufficient monitoring and alarm lag in the prior art, significantly improves the safety and intelligence level of tanker transportation through real-time monitoring, logical judgment, multi-level alarm and other means. It can not only detect abnormal equipment status in the first time, but also quickly notify relevant personnel through local and remote prompts to avoid accidents. Compared with traditional technologies, this method has higher real-time, reliability and convenience, and provides a comprehensive safety guarantee solution for the tanker transportation industry.

[0079] In a feasible implementation manner, step S130 generating warning prompt information according to the position data, the first switch state and the second switch state specifically further includes S130-A, S130-B and S130-C:

[0080] S130-A, when the first switch state indicates that the target tanker is turned on and the position data indicates that the target tanker is traveling at a speed greater than a preset speed, generating a first warning prompt message, wherein the first warning prompt message includes a parking warning message and a seabed valve closing prompt message; and / or,

[0081] S130-B, when the second switch state indicates that it is turned on and the position data indicates that the target tanker truck has a travel speed greater than a preset speed, generating a second warning prompt message, wherein the second warning prompt message includes a prompt message for turning off the oil pump power take-off; and / or,

[0082] S130-C. When both the first switch state and the second switch state are turned on and the position data indicates that the travel speed of the target tanker is greater than a preset speed, a third warning prompt information is generated, wherein the third warning prompt information includes a parking warning information, a seabed valve closing prompt information, and an oil pump power take-off closing prompt information.

[0083] Exemplarily, step S130-A: if the vehicle is running and the speed is greater than a preset value, and the seabed valve is not closed, it is determined to be an abnormal state. At this time, there is a risk of oil leakage, and the driver needs to stop the vehicle immediately and close the seabed valve.

[0084] The generated warning information may include:

[0085] 1. Local prompt: Sound and light alarms are issued, and the following information is displayed: "Warning: The vehicle is moving, please stop!" "Close the seabed valve to ensure safety!"

[0086] 2. Remote notification: Upload the above warning information to the cloud server monitoring platform or the administrator terminal through the 4G / 5G module.

[0087] Step S130-B: If the vehicle is running and the speed is greater than the preset value, and the oil pump power take-off is in the on state, it is determined to be an abnormal state. At this time, there is a risk of equipment idling, increased energy consumption or equipment damage, and the driver needs to immediately turn off the oil pump power take-off.

[0088] The generated warning information may include:

[0089] 1. Local prompt: Sound and light alarms are issued, and the following message is displayed: "Warning: The oil pump power take-off is running, please turn it off!"

[0090] 2. Remote notification: Upload the above warning information to the cloud server monitoring platform or the administrator terminal through the 4G / 5G module.

[0091] Step S130-C: If the vehicle is running and the speed is greater than the preset value, and the seabed valve and the oil pump power take-off are not closed, it is determined to be a serious abnormal state. At this time, there is a high risk of oil leakage, equipment damage and safety accidents, and the driver needs to take immediate measures.

[0092] The generated warning information may include:

[0093] 1. Local prompt: A high-priority sound and light alarm is issued, and the following information is displayed: "Warning: The vehicle is moving, please stop immediately!" "Close the seabed valve and oil pump power take-off to ensure safety!"

[0094] 2. Remote notification: Upload the above warning information to the cloud server monitoring platform or the management personnel terminal through the 4G / 5G module and mark it as a high-priority alarm for quick response.

[0095] like Figure 2 As shown, it is a schematic diagram of the vehicle-side display interface of a tank truck safety transportation system provided by an embodiment of the present application. The warning prompt information may include a text prompt area and an image prompt area. The text prompt area displays text prompt information for the target personnel to understand the abnormal situation by viewing the text. The image prompt area includes a simple picture of the tank truck, as well as regional icons of the inspection area of ​​the seabed valve and the oil pump power take-off. The icons of the abnormal area are reminded in "red" or other warning ways, so that the target personnel can quickly understand the abnormal area and conduct investigation.

[0096] The method proposed in this application can transmit warning information through various means such as sound and light alarms, local displays, and remote notifications, ensuring that drivers and managers can be informed of abnormal conditions in a timely manner. This embodiment generates hierarchical warning information for different abnormal conditions of tank trucks through the step-by-step logic of S130-A, S130-B, and S130-C, which not only improves the system's intelligent judgment ability, but also provides accurate guidance based on actual scenarios. This design can significantly reduce safety hazards during transportation and provide an efficient and intelligent safety management solution for the tank truck transportation industry.

[0097] In a feasible implementation manner, after completing step S130, the implementation further includes S140-S160:

[0098] S140, when the first switch state and / or the second switch state indicates on, the target person completes the operation corresponding to the warning prompt information, and the first switch state and / or the second switch state still indicates on, obtaining the seabed valve pressure information, the seabed valve external shooting information, and the oil pump power take-off operation parameter information;

[0099] S150 Upload the subsea valve pressure information, the external photographic information of the subsea valve, and the operating parameter information of the oil pump power take-off to the scheduling processing model of the cloud server to obtain a fault identification result and a processing method;

[0100] S160 sends the above-mentioned fault identification result and the above-mentioned processing method to the mobile terminal corresponding to the above-mentioned target person, so that the above-mentioned target person can perform corresponding operations on the above-mentioned target tanker based on the above-mentioned fault identification result and the above-mentioned processing method.

[0101] For example, when the first switch state (subsea valve state) and / or the second switch state (oil pump power take-off state) is displayed as "on", it is detected that the target personnel have completed the operation corresponding to the early warning prompt, but the device state still displays "on". The real-time pressure value of the subsea valve is obtained through the pressure sensor to determine whether it is in an abnormal state.

[0102] The camera captures real-time images around the subsea valve to check for external physical obstacles (such as debris blockage) or abnormal operation (such as mechanical structure that is not completely closed) or oil drop leakage.

[0103] The collected operating parameter information of the oil pump power take-off includes the speed, torque, operating status and other parameters of the power take-off. The power take-off should not be operated when the vehicle is driving. If abnormal operating parameters are detected, it may be that the system has not cut off the power take-off or the related sensor is faulty.

[0104] The three types of data collected (subsea valve pressure information, subsea valve external shooting information, and oil pump power take-off operating parameter information) are uploaded to the cloud server. The cloud server contains a preset scheduling processing model that can analyze the equipment status and fault causes. The scheduling processing model integrates multi-source data (pressure, image, parameter) to generate a complete equipment operation status diagram. Machine learning or rule matching methods can be used to combine historical data and expert experience bases to analyze whether the current equipment has a fault and the specific type of fault. According to the identified fault type, the corresponding solution is matched from the processing knowledge base.

[0105] The fault identification results and processing methods generated in the cloud are sent to the target personnel's mobile devices (such as mobile phones and tablets) through wireless networks. Fault identification results: "The subsea valve is not closed, and abnormal pressure may be caused by blockage." Processing method: "Please clear obstacles around the subsea valve and close the valve again; check the status of the pressure sensor." The target personnel inspect and operate the equipment according to the results and methods provided by the mobile terminal.

[0106] Through steps S140-S160, this embodiment realizes intelligent monitoring and efficient processing of equipment abnormalities, which not only improves the safety of the tanker transportation process, but also provides convenient operation guidance for the target personnel. This solution is particularly suitable for high-risk liquid transportation scenarios, such as petroleum, chemicals, etc.

[0107] In a feasible implementation manner, the specific steps of the scheduling processing model of the cloud server obtaining the fault identification result and processing method include:

[0108] S210, determining the abnormal pressure information of the subsea valve according to the above-mentioned subsea valve pressure information and the coincidence information of the historical pressure curve of the subsea valve;

[0109] S220, determining the abnormal leakage information of the subsea valve according to the dripping speed and the dripping density information of the oil droplets obtained from the external photographing information of the subsea valve;

[0110] S230, determining abnormal information of the oil pump power take-off according to the above-mentioned oil pump power take-off operating parameter information and theoretical operating parameter information;

[0111] S240, obtaining the oil type information of the target oil tanker;

[0112] S250, determining a fault identification result according to the oil type information, the abnormal pressure information of the seabed valve, the abnormal leakage information of the seabed valve, and the abnormal information of the oil pump power take-off;

[0113] S260: Generate the processing method corresponding to the fault identification result according to the fault identification result and the correspondence between historical fault identification results and historical processing methods.

[0114] Exemplarily, the abnormal pressure information of the subsea valve is determined according to the subsea valve pressure information and the coincidence information of the historical pressure curve, and the real-time pressure information of the subsea valve is obtained using a pressure sensor, and the normal operating pressure curve of the subsea valve is extracted from the historical database.

[0115] The overlap between the real-time pressure data and the historical pressure curve is calculated through a signal comparison algorithm (such as the dynamic time warping algorithm, DTW). A high overlap indicates that the subsea valve is working normally, while a low overlap indicates that the current pressure state deviates from the normal state and there may be an abnormality.

[0116] Use an external camera to obtain real-time images or videos of the subsea valve area. Use visual technology to analyze the behavior of oil droplets in the image, and calculate the movement speed of oil droplets per unit time based on the displacement trajectory of the oil droplets. Count the number of dripping oil droplets within a set time, and determine the degree of leakage based on the dripping speed and density threshold.

[0117] Collect the operating parameters of the oil pump power take-off in real time, including but not limited to: speed, output torque, current or energy consumption. Obtain the theoretical operating parameters of the oil pump power take-off from the equipment database. Compare the real-time parameters with the theoretical parameters and analyze the deviation.

[0118] Determine possible abnormalities based on the deviation type. If the speed is too high, there may be misoperation or mechanical failure. If the torque is abnormal, the equipment may be overloaded or the internal machinery may be stuck. If the running time is too long, it means that the equipment was not shut down according to the specifications.

[0119] Obtain information about the type of oil currently being transported from the tanker's database. Possible oil types include: petroleum, diesel, liquefied gas, chemicals, etc. The oil type affects the risk level and handling priority of the fault. Highly volatile oils (such as gasoline) have a higher risk of leakage and require a quick response. Non-volatile oils (such as lubricants) have a lower risk and can be handled later.

[0120] The above-mentioned oil type information, the above-mentioned abnormal seabed valve pressure information, the above-mentioned abnormal seabed valve leakage information and the above-mentioned oil pump power take-off abnormal information are comprehensively considered to determine the fault identification result. For example: If the pressure is abnormal + the leakage is serious, it is judged as "the seabed valve is not completely closed, resulting in serious leakage." If only the power take-off operating parameters are abnormal, it is judged as "the power take-off is overloaded." Generate specific fault identification results based on the fault identification results, such as: "The seabed valve is leaking seriously, and transportation must be stopped immediately and checked." "The power take-off is operating abnormally, which may be due to equipment overload."

[0121] Based on the historical fault identification results and the corresponding processing method relationship library, similar historical cases and processing solutions are found. The generated processing methods are sent to the target personnel's mobile terminal to provide operation guidance.

[0122] The method provided in this embodiment improves the accuracy of fault identification by jointly analyzing multi-source data such as pressure, images, and operating parameters. The best treatment plan is generated in combination with historical cases to reduce the decision-making pressure of operators. The method provided in this embodiment allows for rapid fault identification and treatment plan generation, facilitating timely response. This embodiment achieves intelligent identification of key equipment failures in tank trucks through multi-dimensional analysis of equipment operation data, and generates targeted treatment plans, providing strong guarantees for safety and operational efficiency during tank truck transportation.

[0123] In a feasible implementation manner, when a target camera unit is provided in the area corresponding to the subsea valve of the target oil tanker, the external photographing information of the subsea valve is obtained by the target camera unit and uploaded to the cloud server; when a target camera unit is not provided in the area corresponding to the subsea valve of the target oil tanker, the external photographing information of the subsea valve is obtained by the target personnel using the target terminal and uploaded to the cloud server through the target APP.

[0124] The specific steps of determining the dripping speed of the oil droplets and the dripping density information of the oil droplets include:

[0125] S310, extracting the first frame image, the second frame image, and the time interval information between the first frame image and the second frame image according to the external shooting information of the seabed valve;

[0126] S320, performing image segmentation based on the first frame image based on a preset threshold value to separate the background image and the oil drop image;

[0127] S330, selecting image feature points based on the oil drop image;

[0128] S340, determining the dripping speed based on the position tracking information of the image feature points and the time interval information;

[0129] S350, determining the drip density information based on the pixel ratios of the oil drop image and the background image.

[0130] For example, when the target tanker is equipped with a fixed camera unit, the target camera unit is fixedly installed in the seabed valve area to collect external image data of oil droplets in real time. The camera unit directly uploads the collected images to the cloud server via a wireless network.

[0131] There is no fixed camera unit on the target tanker, and the target personnel can use the terminal to shoot. During the inspection, the target personnel use a mobile terminal (such as a mobile phone or tablet) to shoot the external image of the subsea valve through the target APP, and the image data is uploaded to the cloud server through the APP.

[0132] Extract two frames of images from the uploaded image sequence: the first frame of image and the second frame of image. Determine the time interval between the two frames of image by using the timestamp collected by the camera unit or the terminal.

[0133] The first frame image is segmented to separate the background area from the oil drop area. The background image is a part with a larger area and smooth grayscale value. The oil drop image is a part with a small area, bright spots or darker spots, and the grayscale value or color characteristics are significantly different from the background. The background image part without oil droplets and the oil drop image part containing oil droplets can be generated according to the preset grayscale or brightness threshold.

[0134] Select a feature point in the oil droplet area, which can usually be the center of the oil droplet. Use a feature point detection algorithm such as the Shi-Tomasi algorithm to extract stable and easy-to-track feature points.

[0135] Mark the position (x1, y1) of the oil drop in the first frame image.

[0136] In the second frame image, the motion trajectory of the feature point in the first frame is tracked by the optical flow method to obtain the new position (x2, y2) of the feature point.

[0137] Calculate the pixel displacement of the oil droplet according to the position of the feature point:

[0138]

[0139] Convert pixel displacement to actual physical displacement based on the camera calibration parameters :

[0140]

[0141] is the ratio of pixels to physical distance.

[0142] The dripping speed is:

[0143]

[0144] Count the number of pixels in the oil drop image and the background image:

[0145] Calculate the pixel ratio of the oil droplet area:

[0146]

[0147] in, is the number of oil droplet pixels, 背景 is the number of background pixels.

[0148] The method provided in this embodiment is compatible with both fixed camera units and manual shooting scenarios, and uses image processing technology to achieve automatic leakage analysis, reduce human intervention, and accurately judge the degree of leakage through dual analysis of dripping speed and density.

[0149] In a feasible implementation manner, the above fault identification result includes a target fault type and a fault severity;

[0150] The above S250 specifically includes S2501-S2505:

[0151] S2501, constructing a Bayesian initial network using expert knowledge, wherein the Bayesian initial network includes a first node, a first edge, and a first conditional probability table, the first node includes a first input node and a first output node, the first input node includes an oil type node, a subsea valve pressure abnormality node, and a subsea valve leakage abnormality node, the first output node includes a fault type node and a fault severity node, the first edge includes an input node and a fault type node dependency, an input node and a fault severity node dependency, and a fault type node and a fault severity node dependency, and the first conditional probability table is used to characterize the probability influence of the first parent node on the first child node;

[0152] S2502, training the first conditional probability table through the first historical data to generate a target Bayesian network;

[0153] S2503, inputting the above oil product type information, the above seabed valve pressure abnormality information, and the above seabed valve leakage abnormality information into the above target Bayesian network to obtain the posterior probability of each fault type;

[0154] S2504: Determine the target fault type and the fault severity according to the posterior probability of each fault type.

[0155] For example, a Bayesian network is a model based on probabilistic reasoning, which is constructed through nodes and dependencies (edges), can represent the causal relationship between variables, and quantify this relationship through a conditional probability table (CPT).

[0156] The Bayesian initial network is constructed, and the input variables of the first input node include the oil type node, the oil type node, the subsea valve pressure abnormality node, and the subsea valve leakage abnormality node. The oil type node is used to indicate the type of oil transported by the tanker (such as gasoline, diesel, etc.). The subsea valve pressure abnormality node is used to indicate whether the pressure state of the subsea valve is abnormal. The subsea valve leakage abnormality node is used to indicate the leakage degree outside the subsea valve.

[0157] The target variables output by the first output node include a fault type node and a fault severity node. The fault type node indicates the current specific fault type (such as leakage, blockage, etc.). The fault severity node indicates the severity of the fault (such as minor, medium, severe).

[0158] The edge is the dependency between the input node and the fault type node (e.g., the oil type affects the probability of the fault type). The first edge includes the dependency between the input node and the fault severity node and the dependency between the fault type node and the fault severity node.

[0159] The first conditional probability table is used to characterize the probability influence of the first parent node on the first child node. For example, if the oil type is a highly volatile oil (such as gasoline), the probability of leakage may be high. If the pressure anomaly of the subsea valve is large, the probability of blockage may be high. The initial conditional probability table is manually set through expert knowledge and constructed based on empirical knowledge of equipment operation and failure.

[0160] The conditional probability table is trained using the first historical data (historical equipment operation records, fault data). The value of the conditional probability table is updated using the maximum likelihood estimation (MLE) to make it more consistent with the actual data distribution.

[0161] The real-time collected data (oil type information, abnormal seabed valve pressure information, abnormal seabed valve leakage information) is input into the Bayesian network. The posterior probability of each fault type is calculated using the Bayesian inference formula:

[0162]

[0163] According to the posterior probability value, the fault type with the highest posterior probability is selected as the target fault type. According to the probability relationship between the fault type node and the severity node, the severity of the fault is further determined.

[0164] In a feasible implementation manner, the above-mentioned processing method includes the type of processing measures and the urgency of the operation;

[0165] The above S260 specifically includes S2601-S2604:

[0166] S2601, constructing a Bayesian initial extended network, wherein the Bayesian initial extended network includes a second node, a second edge, and a second conditional probability table, the second node includes a second input node and a second output node, the second input node includes a fault type node and a fault severity node, the second output node includes a treatment measure type node and an operation urgency node, the second edge includes a fault severity node and a treatment measure type node dependency, a fault type node and a treatment measure type node dependency, a historical processing method correspondence relationship and a treatment measure type node dependency, and a treatment measure type node and an operation urgency node dependency, and the second conditional probability table is used to characterize the probability influence of the second parent node on the second child node;

[0167] S2602, training the second conditional probability table through the second historical data to generate a target Bayesian extended network;

[0168] S2603, inputting the above fault identification result into the above target Bayesian extended network to obtain the posterior probability of different treatment measures;

[0169] S2604: Determine the type of processing measure and the urgency of the operation according to the posterior probability of each processing measure.

[0170] Exemplarily, when constructing a Bayesian initial expansion network, the second input node includes a fault type node and a fault severity node. The fault type node comes from the target fault type obtained in S250, and the fault severity node comes from the target fault severity obtained in S250.

[0171] The second output node includes a treatment measure type node and an operation urgency node. The treatment measure type node indicates the specific treatment measure for the fault (such as replacing a valve, remotely shutting down the device, etc.). The operation urgency node indicates the urgency of the treatment measure (such as immediate treatment, regular inspection, etc.).

[0172] The second edge includes: the dependency between the fault type node and the treatment measure type node (different fault types correspond to different treatment measures). The dependency between the fault severity node and the treatment measure type node (severe faults require a higher level of treatment). The dependency between the treatment measure type node and the operation urgency node (for example, complex treatment measures usually require a higher level of urgency).

[0173] Use the second historical data (historical fault handling records and effect evaluation) to train the conditional probability table. Optimize the probability relationship between the handling measures and the urgency of the operation corresponding to each fault type and severity.

[0174] Input the fault identification results (fault type and severity) into the Bayesian expansion network. Calculate the posterior probability of each treatment type:

[0175]

[0176]

[0177] Based on the posterior probability of each treatment type, the treatment with the highest probability was selected as the final treatment.

[0178] The treatment measures include but are not limited to transferring oil products, waiting for rescue on the spot, continuing transportation to the destination and emergency unloading. For example:

[0179] 1. If the leakage of the subsea valve is slight and of moderate severity, the tanker truck should be driven to the nearest gas station or oil receiving point to unload the oil, provided that the vehicle can be driven safely. A spare vehicle should be dispatched to transfer the oil to a safe transport vehicle.

[0180] 2. In case of severe leakage from the seabed valve and the severity is high, the vehicle cannot be driven safely and must wait for rescue on the spot. The vehicle is prohibited from running and the valve is closed, waiting for the dispatch of an emergency rescue vehicle to transfer the oil.

[0181] 3. In the case of a minor fault in the oil pump power take-off and low severity, the fault will not pose a major risk to the transportation process. It is recommended to complete the current transportation task before repairing it.

[0182] 4. In the event of a complete failure of the subsea valve or an uncontrollable and severe leakage, the dispatcher will immediately arrange for the oil to be unloaded at the nearest receiving point to reduce the risk of dangerous goods during transportation.

[0183] In a feasible implementation, it also includes:

[0184] In the case where the above-mentioned types of treatment measures include at least two and the urgency deviation of each of the above-mentioned operation urgency is less than a preset deviation, obtaining the target person's task completion preference information;

[0185] The target processing action type is determined based on the above-mentioned completion task preference information.

[0186] Exemplarily, when the generated treatment measures types include at least two or more, the difference in the operation urgency of each treatment measure is small (that is, the deviation is less than the preset urgency deviation threshold). There are multiple reasonable options for treatment measures, but the specific operation method needs to be finally determined based on the preferences of the target personnel.

[0187] Urgency deviation indicates the difference in urgency between different types of treatment measures. For example: Treatment measure A (Urgency: High). Treatment measure B (Urgency: Medium-High). If the difference in urgency between the two is less than the preset deviation threshold, the two are considered to be close in urgency. The setting of the preset deviation can prevent the system from directly selecting a treatment measure when the difference in urgency is small, while ignoring the preferences of the target personnel.

[0188] The target person's task completion preference information is the target person's individual or team's preference when performing tasks. Preferences may be related to the following factors:

[0189] Efficiency first: prefer to solve problems quickly.

[0190] Safety first: prefer to ensure maximum safety.

[0191] Economic priority: preferring the lowest-cost solution.

[0192] Preference information can be preset or collected dynamically by the system. Based on the target person's previous operation records, their preferences can be inferred. For example, if the target person chooses to unload oil quickly many times, their preference may be "efficiency first". The system can directly ask the target person for his current preference through his terminal device. In the scenario of team operation, preferences can also be determined by team preset rules. For example, a fleet can set the same preference information for completing tasks.

[0193] When the difference in urgency is small, the generated multiple treatment measures are sorted according to the target personnel's task completion preferences, and the type of measure that best meets the preferences is selected as the final treatment plan. Treatment measure A: Go to the nearest receiving point to unload the oil (efficiency first). Treatment measure B: Wait for the rescue vehicle on the spot (safety first). If the target personnel's preference is "efficiency first", treatment measure A will be selected. Generate a navigation route to reach the task completion preference information, such as Figure 3 As shown, the navigation route is displayed in the display interface of the display terminal on the vehicle side.

[0194] In summary, this application designs an intelligent and dynamic safety management method for equipment status monitoring, abnormal handling, and multi-scenario fault response in the safe transportation of tank trucks. By monitoring the real-time status of the subsea valve and the oil pump power take-off, combined with the location information and driving status of the target vehicle, it is possible to quickly identify abnormal situations where the equipment and vehicle status do not match. A multi-level alarm mechanism is set up to ensure that drivers and managers can know the potential safety risks at the first time, respond quickly, and avoid safety accidents caused by abnormal status. By analyzing the fault type and severity of multidimensional data such as pressure, images, and operating parameters combined with the Bayesian network model, potential equipment failures can be quickly identified and accurate fault identification results can be generated. When the urgency of multiple treatment measures is close, the system can introduce the preference information of the target personnel and adjust the priority of the treatment measures according to actual needs. By analyzing the historical operation records or real-time preference information of the target personnel, a more suitable treatment plan is provided to improve the scientificity and satisfaction of task execution. Combined with the equipment status and vehicle location information, the linkage analysis of the equipment status and the vehicle driving scene is realized, and the equipment opening or closing state is automatically judged to meet the requirements of the transportation scene. This method can be easily integrated with existing tanker equipment, suitable for rapid transformation and deployment of existing tankers, and reduces implementation costs. This method is particularly suitable for high-risk liquid transportation scenarios. Through intelligent monitoring and fault handling of equipment and transportation status, it can significantly reduce the risk of accidents. Generate hierarchical treatment measures according to the type of fault to avoid resource waste or safety hazards caused by over- or under-processing. The system uses historical data and real-time data to continuously train the Bayesian network model to optimize the accuracy of fault identification and the priority ranking of treatment methods. Dynamically adjust the fault handling strategy to improve the system's adaptability to new scenarios and new fault modes. This method significantly improves the safety and management efficiency of tanker transportation by introducing multi-level technologies such as real-time monitoring, intelligent analysis, dynamic processing, and personalized preference support. This method has high real-time performance, reliability, and scalability, and provides an intelligent and scientific safety assurance solution for the tanker transportation industry.

[0195] Second, Figure 4 As shown, it is a structural schematic diagram of a tank truck safety transportation system 40 proposed in this application. The tank truck safety transportation system includes a local end 401, and the local end includes a first acquisition unit 4011, a second acquisition unit 4012 and a generation unit 4013.

[0196] The first acquisition unit 4011 is configured to acquire a first switch state of the seabed valve and a second switch state of the oil pump power take-off;

[0197] The second acquisition unit 4012 is configured to acquire the location data of the target tanker;

[0198] The generating unit 4013 is configured to generate warning information according to the position data, the first switch state and the second switch state.

[0199] In a feasible implementation manner, the oil tanker safe transportation system 40 further includes a cloud server 402, and the cloud server 402 includes a third acquisition unit 4021 and a sending unit 4022.

[0200] The third acquisition unit 4021 is configured to acquire a fault identification result and a processing method according to the subsea valve pressure information, the external photographic information of the subsea valve and the operating parameter information of the oil pump power take-off according to the scheduling processing model;

[0201] The sending unit 4022 is configured to send the fault identification result and the processing method to the mobile terminal corresponding to the target person, so that the target person can perform corresponding operations on the target tanker based on the fault identification result and the processing method.

[0202] The above embodiments 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, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A method for safe transportation of oil tankers, characterized in that: include: Acquire a first switch state of a subsea valve and a second switch state of an oil pump power take-off; Obtain the location data of the target tanker; Generate warning information according to the position data, the first switch state and the second switch state.

2. The method for safe transportation of oil tanker trucks according to claim 1 is characterized in that: The generating of warning prompt information according to the position data, the first switch state and the second switch state includes: When the first switch state indicates that it is turned on and the position data indicates that the speed of the target tanker truck is greater than a preset speed, a first warning prompt information is generated, wherein the first warning prompt information includes a parking warning information and a seabed valve closing prompt information; and / or, When the second switch state indicates that it is turned on and the position data indicates that the speed of the target tanker truck is greater than a preset speed, a second warning prompt information is generated, wherein the second warning prompt information includes a prompt information for turning off the oil pump power take-off; and / or, When the first switch state and the second switch state are both turned on and the position data indicates that the driving speed of the target tanker is greater than a preset speed, a third warning prompt information is generated, wherein the third warning prompt information includes a parking warning information, a seabed valve closing prompt information, and an oil pump power take-off closing prompt information.

3. The method for safe transportation of oil tanker trucks according to claim 1 is characterized in that: Also includes: When the first switch state and / or the second switch state indicates on, the target person completes the operation corresponding to the warning prompt information, and the first switch state and / or the second switch state still indicates on, obtaining the seabed valve pressure information, the seabed valve external shooting information and the oil pump power take-off operation parameter information; Uploading the subsea valve pressure information, the subsea valve external shooting information and the oil pump power take-off operation parameter information to the scheduling processing model of the cloud server to obtain the fault identification result and processing method; The fault identification result and the processing method are sent to a mobile terminal corresponding to the target person, so that the target person performs corresponding operations on the target tanker truck based on the fault identification result and the processing method.

4. The method for safe transportation of oil tanker trucks according to claim 3 is characterized in that: The specific steps of the scheduling processing model of the cloud server obtaining the fault identification result and processing method include: Determining abnormal pressure information of the subsea valve according to the subsea valve pressure information and the coincidence information of the historical pressure curve of the subsea valve; Determine the abnormal leakage information of the subsea valve according to the dripping speed and dripping density information of the oil droplets obtained from the external photographing information of the subsea valve; Determine the abnormal information of the oil pump power take-off according to the operating parameter information of the oil pump power take-off and the theoretical operating parameter information; Obtaining oil type information of the target oil tanker; Determine a fault identification result according to the oil type information, the abnormal pressure information of the seabed valve, the abnormal leakage information of the seabed valve, and the abnormal information of the oil pump power take-off; The processing method corresponding to the fault identification result is generated according to the fault identification result and the correspondence between historical fault identification results and historical processing methods.

5. The method for safe transportation of oil tanker trucks according to claim 4 is characterized in that: In the case where a target camera unit is provided in the area corresponding to the subsea valve of the target tanker, the external photographing information of the subsea valve is obtained by the target camera unit and uploaded to the cloud server; in the case where a target camera unit is not provided in the area corresponding to the subsea valve of the target tanker, the external photographing information of the subsea valve is obtained by the target personnel using the target terminal and uploaded to the cloud server through the target APP; The specific steps of determining the dripping speed of the oil droplets and the dripping density information of the oil droplets include: Extracting the first frame image, the second frame image and the time interval information between the first frame image and the second frame image according to the external shooting information of the submarine valve; Performing image segmentation based on a preset threshold according to the first frame image to separate the background image and the oil drop image; Selecting image feature points based on the oil drop image; Determine the dripping speed based on the position tracking information of the image feature points and the time interval information; The drip density information is determined based on pixel ratios of the oil drop image and the background image.

6. The method for safe transportation of oil tanker trucks according to claim 4 is characterized in that: The fault identification result includes the target fault type and fault severity; The determining of the fault identification result according to the oil type information, the abnormal pressure information of the seabed valve, the abnormal leakage information of the seabed valve and the abnormal information of the oil pump power take-off includes: Using expert knowledge to construct a Bayesian initial network, wherein the Bayesian initial network includes a first node, a first edge, and a first conditional probability table, the first node includes a first input node and a first output node, the first input node includes an oil type node, a subsea valve pressure abnormality node, and a subsea valve leakage abnormality node, the first output node includes a fault type node and a fault severity node, the first edge includes an input node and a fault type node dependency, an input node and a fault severity node dependency, and a fault type node and a fault severity node dependency, and the first conditional probability table is used to characterize the probability influence of the first parent node on the first child node; Training the first conditional probability table through first historical data to generate a target Bayesian network; Inputting the oil type information, the abnormal pressure information of the seabed valve, and the abnormal leakage information of the seabed valve into the target Bayesian network to obtain the posterior probability of each fault type; The target fault type and the fault severity are determined according to the posterior probability of each fault type.

7. The method for safe transportation of oil tanker trucks according to claim 6 is characterized in that: The treatment method includes the type of treatment measures and the urgency of the operation; The generating the processing method corresponding to the fault identification result according to the corresponding relationship between the fault identification result and the historical fault identification results and the historical processing methods comprises: Constructing a Bayesian initial extended network, wherein the Bayesian initial extended network includes a second node, a second edge, and a second conditional probability table, the second node includes a second input node and a second output node, the second input node includes a fault type node and a fault severity node, the second output node includes a treatment measure type node and an operation urgency node, the second edge includes a fault severity node and a treatment measure type node dependency, a fault type node and a treatment measure type node dependency, a historical processing method correspondence relationship and a treatment measure type node dependency, and a treatment measure type node and an operation urgency node dependency, and the second conditional probability table is used to characterize the probability influence of the second parent node on the second child node; Training the second conditional probability table by second historical data to generate a target Bayesian extended network; Inputting the fault identification result into the target Bayesian extended network to obtain the posterior probabilities of different treatment measures; The type of treatment measure and the operation urgency are determined according to the posterior probability of each treatment measure.

8. The method for safe transportation of oil tanker trucks according to claim 7 is characterized in that: Also includes: In the case where the types of treatment measures include at least two and the urgency deviation of each of the urgency levels of the operation is less than a preset deviation, obtaining the task completion preference information of the target person; A target processing action type is determined based on the task completion preference information.

9. A safe transportation system for oil tank trucks, characterized in that: The oil tanker safe transportation system comprises a local end, wherein the local end comprises a first acquisition unit, a second acquisition unit and a generation unit. The first acquisition unit is configured to acquire a first switch state of the seabed valve and a second switch state of the oil pump power take-off; The second acquisition unit is configured to acquire the position data of the target tanker; The generating unit is configured to generate warning prompt information according to the position data, the first switch state and the second switch state.

10. The oil tanker safe transportation system according to claim 9, characterized in that: The oil tanker safe transportation system further includes a cloud server, which includes a third acquisition unit and a sending unit. The third acquisition unit is configured to acquire a fault identification result and a processing method according to the subsea valve pressure information, the external photographing information of the subsea valve and the operating parameter information of the oil pump power take-off according to the scheduling processing model; The sending unit is configured to send the fault identification result and the processing method to a mobile terminal corresponding to the target person, so that the target person performs corresponding operations on the target tanker truck based on the fault identification result and the processing method.

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