Intelligent Detection Method and System for Storage Tanks Based on Internet of Things Technology

Abstract

This invention belongs to the field of storage tank inspection technology, and particularly relates to an intelligent inspection method and system for storage tanks based on Internet of Things (IoT) technology. First, it constructs a high-fidelity three-dimensional digital twin model and integrates real-time sensor data, static attribute data, and historical archive data, breaking down information silos. Second, it evaluates and calculates the dynamic risk index and benchmark risk index of the storage tank based on multi-source fusion data. Then, it calculates the comprehensive risk value of the storage tank based on the evaluation results of the dynamic and benchmark risk indices. Next, it determines the risk level of the storage tank based on the comprehensive risk value calculation results. Finally, it issues an early warning based on the risk level determination result and simultaneously outputs maintenance strategies. This enables a precise shift from planned maintenance to predictive maintenance, allowing for early identification of slowly accumulating potential risks in storage tanks and providing sufficient early warning for high-risk, aging storage tanks.

Description

Technical Field

[0001] This invention belongs to the field of storage tank inspection technology, specifically relating to a smart inspection method and system for storage tanks based on Internet of Things (IoT) technology. Background Technology

[0002] As a critical infrastructure in the fields of petrochemicals and energy reserves, the safe and stable operation of storage tanks is of paramount importance. Traditional methods of storage tank inspection and risk management mainly rely on manual periodic inspections, offline inspections, and management systems based on static information. These methods have many limitations, including low inspection efficiency and difficulty in detecting sudden defects or risks in real time. With the development of IoT, digital twins, and artificial intelligence technologies, storage tank inspection is gradually evolving towards intelligence, real-time monitoring, and visualization.

[0003] However, traditional intelligent detection methods for storage tanks fail to dynamically and quantitatively integrate real-time operating status, long-term service performance, and environmental factors, resulting in delayed or inaccurate risk warnings. This makes it difficult to achieve a precise transition from planned maintenance to predictive maintenance, which in turn prevents the system from identifying slowly accumulating potential risks in the early stages and from providing sufficient early warning for high-risk, aging storage tanks. Consequently, it is difficult to support true predictive maintenance and asset integrity management decisions.

[0004] To address the aforementioned issues, this application presents a method and system for intelligent detection of storage tanks based on Internet of Things (IoT) technology. Summary of the Invention

[0005] To address the shortcomings of the prior art mentioned in the background section, this application proposes an intelligent detection method and system for storage tanks based on Internet of Things (IoT) technology. First, a high-fidelity three-dimensional digital twin model is constructed to achieve multi-source data fusion. Second, the dynamic risk index and benchmark risk index of the storage tank are evaluated and calculated based on the multi-source fused data. Next, the comprehensive risk value of the storage tank is calculated based on the evaluation results of the dynamic risk index and the benchmark risk index. Then, the risk level of the storage tank is determined based on the calculation result of the comprehensive risk value. Finally, an early warning is issued based on the risk level determination result, and maintenance strategies are simultaneously output to solve the problems in the background section.

[0006] Firstly, to achieve the above objectives, this application provides a smart detection method for storage tanks based on Internet of Things (IoT) technology, which includes the following specific steps:

[0007] S1. Construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data;

[0008] S2. Dynamic risk index of storage tanks based on real-time sensor data Conduct an assessment;

[0009] S3. Benchmark risk index for storage tanks based on static attribute data and historical archive data. Conduct an assessment;

[0010] S4, Based on dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations;

[0011] The dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. The calculation includes the following specific steps:

[0012] S41, Reliability Factor for Real-Time Sensor Data Based on Data Quality Assessment Credibility factor The calculation formula is:

[0013]

[0014] in, The number of effective detection points, This represents the total number of testing sites. This is the actual delay time. Maximum allowable delay time;

[0015] S42. Obtain Dynamic Risk Index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Calculate the overall risk value. The calculation formula is:

[0016]

[0017] in, As a reliability factor for real-time sensing data, when the reliability of real-time sensing data is high, the overall risk value depends more on dynamic risk; when the reliability of real-time sensing data is low, the overall risk value depends more on baseline risk.

[0018] S5. Based on the comprehensive risk value The calculation results are used to determine the risk level of the storage tank;

[0019] S6. Issue early warnings based on the risk level assessment results and simultaneously output maintenance strategies.

[0020] Based on the above-mentioned preferred approach, the construction of a three-dimensional digital twin model of the storage tank, and the integration of real-time sensor data, static attribute data, and historical archive data, includes the following steps:

[0021] S11. Based on the geometric dimensions, structural parameters and material information of the storage tank, a high-fidelity three-dimensional digital twin model of the storage tank is constructed using three-dimensional modeling tools;

[0022] S12. Collect real-time sensing data of the storage tank through the Internet of Things gateway. The real-time sensing data includes temperature, pressure, liquid level, corrosion rate, and vibration data, and synchronize it to the three-dimensional digital twin model. The corrosion rate of the storage tank wall is monitored in real time using a corrosion sensor. The corrosion sensor can be a resistance probe, an inductance probe, or an electrochemical noise sensor. The real-time corrosion rate is calculated by measuring the changes in resistance, inductance, or electrochemical noise caused by corrosion of the probe.

[0023] S13. Extract the static attribute data of the storage tank from the enterprise asset management system. The static attribute data includes design life, construction year, material specifications, and design pressure.

[0024] S14. Extract historical archive data of the storage tank from the historical database. The historical archive data includes inspection reports, maintenance records, accident records, and corrosion detection data.

[0025] S15. Establish a unified data identifier and timestamp to associate and map real-time sensor data, static attribute data, historical archive data, and corresponding components of the 3D digital twin model.

[0026] Based on the above scheme, the preferred method is to assess the dynamic risk index of the storage tank based on real-time sensor data, including the following steps:

[0027] S21. Extract corrosion monitoring data and deformation monitoring data from real-time sensor data;

[0028] S22. Calculate the overall corrosion risk index of the storage tank based on corrosion monitoring data. Overall corrosion risk index The calculation formula is:

[0029]

[0030] in, Let be the remaining wall thickness at the i-th detection point at time t. Design the wall thickness for the storage tank. , These are the weighting coefficients, and ;

[0031] S23. Calculate the overall deformation defect risk index of the storage tank based on deformation monitoring data. Overall Deformation Defect Risk Index The calculation formula is:

[0032]

[0033] in, This refers to the maximum allowable settlement limit for the storage tank. This represents the maximum settlement at time t. This represents the minimum settlement at time t. , These are the weighting coefficients, and , Let be the settlement at the j-th defect location at time t. The number of defect locations involved in the calculation;

[0034] S24, Based on the overall corrosion risk index The calculation results and the overall deformation defect risk index The calculation results are used to calculate the dynamic risk index of the storage tank. Dynamic risk index The calculation formula is:

[0035]

[0036] in, , These are the weighting coefficients. For storage tanks storing corrosive media Larger values ​​are not suitable for tanks storing heavy media or located in areas with unstable foundations. The value is relatively large;

[0037] S25, Regarding the dynamic risk index Normalization is performed, mapping the result to the interval [0,1]. The normalization formula is as follows:

[0038]

[0039] in, It serves as a dynamic risk reference value, set based on design specifications, historical data, or engineering experience.

[0040] Based on the above scheme, the preferred embodiment is the benchmark risk index for storage tanks based on static attribute data and historical archive data. The assessment includes the following specific steps:

[0041] S31. Extract baseline risk assessment parameters for storage tanks from static attribute data and historical archive data. These baseline risk assessment parameters include the service life. Design life Maximum historical corrosion rate Maximum permissible corrosion rate Environmental corrosion level ;

[0042] S32. Calculate the benchmark risk index of the storage tank based on the obtained benchmark risk assessment parameters. Benchmark risk index The calculation formula is:

[0043]

[0044] in, The number of years is the factor. As a historical corrosive factor, Environmental factors are determined according to the environmental corrosion level. Values ​​range from [0.1, 1.0], with larger values ​​for stronger corrosiveness.

[0045] The preferred option based on the above scheme is the one based on the comprehensive risk value. The calculation results are used to determine the risk level of the storage tank, including the following steps:

[0046] S51. Classify risk levels and set a low-risk threshold based on the risk level. Medium risk threshold Severe risk threshold ;

[0047] S52, Based on comprehensive risk value The size determines the risk level, when It is judged as green level when; It is judged as yellow level when; It is judged as orange level when; It is classified as red level at that time.

[0048] In a preferred embodiment of the above scheme, the step of issuing early warnings based on risk level assessment results and simultaneously outputting maintenance strategies includes the following steps:

[0049] S61. Generate early warning signals based on the determined risk level: Green level: No early warning triggered; Yellow level: Attention warning triggered, inspection suggestions generated; Orange level: Early warning triggered, special inspection suggestions generated; Red level: Emergency warning triggered, immediate action suggestions generated.

[0050] S62. Visualize the risk status on a three-dimensional digital twin model and mark the overall risk level of the storage tank with different colors;

[0051] S63. Generate a maintenance strategy report based on the risk level. The maintenance strategy report includes the current risk level, main risk sources, recommended measures, and recommended processing time.

[0052] S64. Send early warning information and maintenance strategy reports through preset channels.

[0053] Secondly, this application provides an intelligent detection system for storage tanks based on Internet of Things (IoT) technology, which specifically includes: a multi-source data integration and twin modeling module: used to construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data;

[0054] Dynamic risk calculation module: used to calculate the dynamic risk index of storage tanks based on real-time sensor data. Conduct an assessment;

[0055] Benchmark Risk Assessment Module: Used to determine the benchmark risk index of storage tanks based on static attribute data and historical archive data. Conduct an assessment;

[0056] Comprehensive Risk Value Calculation Module: Used for calculating risk values ​​based on dynamic risk indices. The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations;

[0057] Risk level determination module: used to determine the risk level based on the comprehensive risk value. The calculation results are used to determine the risk level of the storage tank;

[0058] Visualized early warning and decision output module: used to issue early warnings based on risk level assessment results and simultaneously output maintenance strategies.

[0059] Thirdly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0060] The processor executes the aforementioned intelligent tank detection method based on Internet of Things (IoT) technology by calling the computer program stored in the memory.

[0061] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned intelligent tank detection method based on Internet of Things technology.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] This invention first constructs a high-fidelity 3D digital twin model and unifies and integrates real-time sensor data, static attribute data, and historical archive data, breaking down information silos and forming a complete and traceable dynamic mapping of the physical entity of the storage tank in virtual space. This allows managers to intuitively and comprehensively grasp the health status of the storage tank throughout its entire lifecycle, from design and construction to service and maintenance, providing a unified data foundation for accurate decision-making. Secondly, based on multi-source fusion data, the dynamic risk index and benchmark risk index of the storage tank are assessed and calculated separately. Then, based on the assessment results of the dynamic risk index and the benchmark risk index, the comprehensive risk value of the storage tank is calculated, innovatively decomposing the risk. The system consists of a dynamic risk index and a benchmark risk index. The dynamic risk index is calculated based on real-time monitoring data such as corrosion and deformation defects, and can sensitively reflect the immediate changes in the condition of the storage tank. The benchmark risk index is calculated based on static attribute data such as design age, historical corrosion, and environmental level, as well as historical archive data. The combination of the two overcomes the one-sidedness of a single assessment. Then, the risk level of the storage tank is determined based on the calculation result of the comprehensive risk value. Finally, an early warning is issued based on the risk level determination result, and maintenance strategies are output simultaneously. This can achieve a precise shift from planned maintenance to predictive maintenance, identify potential risks that slowly accumulate in the storage tank at an early stage, and provide sufficient early warning for high-risk old storage tanks. Attached Figure Description

[0064] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0065] Figure 1 This is a schematic diagram of the overall process of the intelligent detection method for storage tanks based on Internet of Things technology according to the present invention;

[0066] Figure 2 This is a flowchart of the intelligent detection method for storage tanks based on Internet of Things technology according to the present invention.

[0067] Figure 3 This is a schematic diagram of the framework of the intelligent tank detection system based on Internet of Things technology of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0069] Example 1

[0070] To address the technical problems raised in the background art, this application provides a preferred embodiment: such as Figures 1-3 As shown, the intelligent detection method for storage tanks based on Internet of Things (IoT) technology includes the following specific steps:

[0071] S1. Construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data;

[0072] S2. Dynamic risk index of storage tanks based on real-time sensor data Conduct an assessment;

[0073] S3. Benchmark risk index for storage tanks based on static attribute data and historical archive data. Conduct an assessment;

[0074] S4, Based on dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations;

[0075] The dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. The calculation includes the following specific steps:

[0076] S41, Reliability Factor for Real-Time Sensor Data Based on Data Quality Assessment Credibility factor The calculation formula is:

[0077]

[0078] in, The number of effective detection points, This represents the total number of testing sites. This is the actual delay time. Maximum allowable delay time;

[0079] S42. Obtain Dynamic Risk Index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Calculate the overall risk value. The calculation formula is:

[0080]

[0081] in, As a reliability factor for real-time sensing data, when the reliability of real-time sensing data is high, the overall risk value depends more on dynamic risk; when the reliability of real-time sensing data is low, the overall risk value depends more on baseline risk.

[0082] S5. Based on the comprehensive risk value The calculation results are used to determine the risk level of the storage tank;

[0083] S6. Issue early warnings based on the risk level assessment results and simultaneously output maintenance strategies.

[0084] The advantages of this embodiment compared to the prior art are as follows: First, by constructing a high-fidelity three-dimensional digital twin model and unifying and integrating real-time sensor data, static attribute data, and historical archive data, information silos are broken down, forming a complete and traceable dynamic mapping of the physical entity of the storage tank in virtual space. Second, based on multi-source fusion data, the dynamic risk index and the benchmark risk index of the storage tank are evaluated and calculated separately. Then, based on the evaluation results of the dynamic risk index and the benchmark risk index, the comprehensive risk value of the storage tank is calculated. Then, based on the calculation result of the comprehensive risk value, the risk level of the storage tank is determined. Finally, based on the risk level determination result, an early warning is issued, and maintenance strategies are output simultaneously. This can achieve a precise shift from planned maintenance to predictive maintenance, identify potential risks that slowly accumulate in the storage tank at an early stage, and provide sufficient early warning for high-risk old storage tanks.

[0085] It should be noted that: by introducing a credibility factor According to the credibility factor The value can dynamically adjust the weight of the dynamic risk index and the benchmark risk index. When the collected real-time sensor data is reliable, the risk judgment relies more on the current dynamic risk. When the real-time sensor data is delayed or lost, the system can automatically switch to relying on the more stable benchmark risk, which can significantly improve the robustness and reliability of the risk assessment system and avoid misjudgments caused by data quality problems.

[0086] Furthermore:

[0087] In an optional embodiment, a three-dimensional digital twin model of the storage tank is constructed, integrating real-time sensor data, static attribute data, and historical archive data, including the following steps:

[0088] S11. Based on the geometric dimensions, structural parameters and material information of the storage tank, a high-fidelity three-dimensional digital twin model of the storage tank is constructed using three-dimensional modeling tools;

[0089] S12. Collect real-time sensing data of the storage tank through the Internet of Things gateway. The real-time sensing data includes temperature, pressure, liquid level, corrosion rate, and vibration data, and synchronize it to the three-dimensional digital twin model. The corrosion rate of the storage tank wall is monitored in real time using a corrosion sensor. The corrosion sensor can be a resistance probe, an inductance probe, or an electrochemical noise sensor. The real-time corrosion rate is calculated by measuring the changes in resistance, inductance, or electrochemical noise caused by corrosion of the probe.

[0090] S13. Extract the static attribute data of the storage tank from the enterprise asset management system. The static attribute data includes design life, construction year, material specifications, and design pressure.

[0091] S14. Extract historical archive data of the storage tank from the historical database. The historical archive data includes inspection reports, maintenance records, accident records, and corrosion detection data.

[0092] S15. Establish a unified data identifier and timestamp to associate and map real-time sensor data, static attribute data, historical archive data, and corresponding components of the 3D digital twin model.

[0093] In an optional embodiment, the dynamic risk index of the storage tank is assessed based on real-time sensor data, including the following steps:

[0094] S21. Extract corrosion monitoring data and deformation monitoring data from real-time sensor data;

[0095] S22. Calculate the overall corrosion risk index of the storage tank based on corrosion monitoring data. Overall corrosion risk index The calculation formula is:

[0096]

[0097] in, Let be the remaining wall thickness at the i-th detection point at time t. Design the wall thickness for the storage tank. , These are the weighting coefficients, and ;

[0098] S23. Calculate the overall deformation defect risk index of the storage tank based on deformation monitoring data. Overall Deformation Defect Risk Index The calculation formula is:

[0099]

[0100] in, This refers to the maximum allowable settlement limit for the storage tank. This represents the maximum settlement at time t. This represents the minimum settlement at time t. , These are the weighting coefficients, and , Let be the settlement at the j-th defect location at time t. The number of defect locations involved in the calculation;

[0101] S24, Based on the overall corrosion risk index The calculation results and the overall deformation defect risk index The calculation results are used to calculate the dynamic risk index of the storage tank. Dynamic risk index The calculation formula is:

[0102]

[0103] in, , These are the weighting coefficients. For storage tanks storing corrosive media Larger values ​​are not suitable for tanks storing heavy media or located in areas with unstable foundations. The value is relatively large;

[0104] S25, Regarding the dynamic risk index Normalization is performed, mapping the result to the interval [0,1]. The normalization formula is as follows:

[0105]

[0106] in, It serves as a dynamic risk reference value, set based on design specifications, historical data, or engineering experience.

[0107] It should be noted that: when hour When the value is in the interval [0,1], It was believed at the time that the extreme risk had been reached. This standardizes all risk values ​​to the same scale, making comparisons and threshold settings easier. It also limits the upper limit of risk values ​​to a maximum of 1, thus avoiding the impact of extreme values.

[0108] Overall corrosion risk index The first term in the calculation formula It can assess the overall corrosion status of the storage tank, reflecting its general health condition. It can assess the most severe localized corrosion, highlighting the weakest link in the system, and avoids the masking effect of averages, ensuring that serious localized problems are not overlooked due to a generally good overall condition. It can directly represent the wall thickness reduction rate and is a core indicator for corrosion assessment;

[0109] Overall Deformation Defect Risk Index The first term in the calculation formula Assessing the overall settlement level reflects the overall stability of the foundation; the latter item Assessing uneven settlement is a key factor leading to stress concentration and structural damage in storage tanks.

[0110] Furthermore:

[0111] In an optional embodiment, a benchmark risk index for the storage tank is based on static attribute data and historical archive data. The assessment includes the following specific steps:

[0112] S31. Extract baseline risk assessment parameters for storage tanks from static attribute data and historical archive data. These baseline risk assessment parameters include the service life. Design life Maximum historical corrosion rate Maximum permissible corrosion rate Environmental corrosion level ;

[0113] S32. Calculate the benchmark risk index of the storage tank based on the obtained benchmark risk assessment parameters. Benchmark risk index The calculation formula is:

[0114]

[0115] in, The number of years is the factor. As a historical corrosive factor, Environmental factors are determined according to the environmental corrosion level. Values ​​range from [0.1, 1.0], with larger values ​​for stronger corrosiveness.

[0116] It should be noted that: the years factor With historical corrosion factors All passed The function is restricted to the range [0, 1.0], while ensuring environmental factors are considered. Within the range of [0.1, 1.0], the impact of extreme values ​​can be avoided.

[0117] In an optional embodiment, based on the comprehensive risk value The calculation results are used to determine the risk level of the storage tank, including the following steps:

[0118] S51. Classify risk levels and set a low-risk threshold based on the risk level. Medium risk threshold Severe risk threshold ;

[0119] S52, Based on comprehensive risk value The size determines the risk level, when It is judged as green level when; It is judged as yellow level when; It is judged as orange level when; It is classified as red level at that time.

[0120] In an optional embodiment, an early warning is issued based on the risk level determination result, and a maintenance strategy is output simultaneously, including the following steps:

[0121] S61. Generate early warning signals based on the determined risk level: Green level: No early warning triggered; Yellow level: Attention warning triggered, inspection suggestions generated; Orange level: Early warning triggered, special inspection suggestions generated; Red level: Emergency warning triggered, immediate action suggestions generated.

[0122] S62. Visualize the risk status on a three-dimensional digital twin model and mark the overall risk level of the storage tank with different colors;

[0123] S63. Generate a maintenance strategy report based on the risk level. The maintenance strategy report includes the current risk level, main risk sources, recommended measures, and recommended processing time.

[0124] S64. Send early warning information and maintenance strategy reports through preset channels.

[0125] Example 2

[0126] Based on the same inventive concept as in Embodiment 1, such as Figure 1 As shown, this embodiment provides an intelligent detection system for storage tanks based on Internet of Things (IoT) technology, which specifically includes: a multi-source data integration and twin modeling module: used to construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data;

[0127] Dynamic risk calculation module: used to calculate the dynamic risk index of storage tanks based on real-time sensor data. Conduct an assessment;

[0128] Benchmark Risk Assessment Module: Used to determine the benchmark risk index of storage tanks based on static attribute data and historical archive data. Conduct an assessment;

[0129] Comprehensive Risk Value Calculation Module: Used for calculating risk values ​​based on dynamic risk indices. The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations;

[0130] Risk level determination module: used to determine the risk level based on the comprehensive risk value. The calculation results are used to determine the risk level of the storage tank;

[0131] Visualized early warning and decision output module: used to issue early warnings based on risk level assessment results and simultaneously output maintenance strategies.

[0132] The parameters and steps for implementing the corresponding functions of each unit module in the intelligent tank detection system based on Internet of Things technology of the present invention described above can be referred to the parameters and steps in the embodiments of the intelligent tank detection method based on Internet of Things technology mentioned above, and will not be repeated here.

[0133] Example 3

[0134] Based on the same inventive concept as Embodiment 1, this embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0135] The processor executes the aforementioned intelligent tank detection method based on Internet of Things technology by calling the computer program stored in the memory.

[0136] It should be noted that all computer programs for the intelligent tank detection method based on Internet of Things (IoT) technology are implemented using the C programming language.

[0137] Example 4

[0138] Based on the same inventive concept as in Embodiment 1, this embodiment proposes a computer-readable storage medium having an erasable and rewritable computer program stored thereon.

[0139] When the computer program runs on the computer device, it enables the computer device to perform the aforementioned intelligent tank detection method based on Internet of Things (IoT) technology.

[0140] For example, computer-readable storage media can be read-only memory, random access memory, read-only optical disc, magnetic tape, floppy disk, and optical data storage devices.

[0141] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for IoT devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0142] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0143] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0148] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0149] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0150] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A smart detection method for storage tanks based on Internet of Things (IoT) technology, characterized in that, Includes the following steps: S1. Construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data; S2. Dynamic risk index of storage tanks based on real-time sensor data Conduct an assessment; S3. Benchmark risk index for storage tanks based on static attribute data and historical archive data. Conduct an assessment; S4, Based on dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations; The dynamic risk index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. The calculation includes the following specific steps: S41, Reliability Factor for Real-Time Sensor Data Based on Data Quality Assessment Credibility factor The calculation formula is: ； in, The number of effective detection points, This represents the total number of testing sites. This is the actual delay time. Maximum allowable delay time; S42. Obtain Dynamic Risk Index The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Calculate the overall risk value. The calculation formula is: ； in, As a reliability factor for real-time sensing data, when the reliability of real-time sensing data is high, the overall risk value depends more on dynamic risk; when the reliability of real-time sensing data is low, the overall risk value depends more on baseline risk. S5. Based on the comprehensive risk value The calculation results are used to determine the risk level of the storage tank; S6. Issue early warnings based on the risk level assessment results and simultaneously output maintenance strategies.

2. The intelligent detection method for storage tanks based on Internet of Things technology according to claim 1, characterized in that: The construction of a three-dimensional digital twin model of the storage tank, integrating real-time sensor data, static attribute data, and historical archive data, includes the following steps: S11. Based on the geometric dimensions, structural parameters and material information of the storage tank, a high-fidelity three-dimensional digital twin model of the storage tank is constructed using three-dimensional modeling tools; S12. Collect real-time sensor data of the storage tank through the Internet of Things gateway. The real-time sensor data includes temperature, pressure, liquid level, corrosion rate, and vibration data, and synchronize it to the three-dimensional digital twin model. S13. Extract the static attribute data of the storage tank from the enterprise asset management system. The static attribute data includes design life, construction year, material specifications, and design pressure. S14. Extract historical archive data of the storage tank from the historical database. The historical archive data includes inspection reports, maintenance records, accident records, and corrosion detection data. S15. Establish a unified data identifier and timestamp to associate and map real-time sensor data, static attribute data, historical archive data, and corresponding components of the 3D digital twin model.

3. The intelligent detection method for storage tanks based on Internet of Things technology according to claim 2, characterized in that: The dynamic risk index of storage tanks is assessed based on real-time sensor data, including the following steps: S21. Extract corrosion monitoring data and deformation monitoring data from real-time sensor data; S22. Calculate the overall corrosion risk index of the storage tank based on corrosion monitoring data. Overall corrosion risk index The calculation formula is: ； in, Let be the remaining wall thickness at the i-th detection point at time t. Design the wall thickness for the storage tank. , These are the weighting coefficients, and ; S23. Calculate the overall deformation defect risk index of the storage tank based on deformation monitoring data. Overall Deformation Defect Risk Index The calculation formula is: ； in, This refers to the maximum allowable settlement limit for the storage tank. This represents the maximum settlement at time t. This represents the minimum settlement at time t. , These are the weighting coefficients, and , Let be the settlement at the j-th defect location at time t. The number of defect locations involved in the calculation; S24, Based on the overall corrosion risk index The calculation results and the overall deformation defect risk index The calculation results are used to calculate the dynamic risk index of the storage tank. Dynamic risk index The calculation formula is: ； in, , These are the weighting coefficients. For storage tanks storing corrosive media Larger values ​​are not suitable for tanks storing heavy media or located in areas with unstable foundations. The value is relatively large; S25, Regarding the dynamic risk index Normalization is performed, mapping the result to the interval [0,1]. The normalization formula is as follows: ； in, It serves as a dynamic risk reference value, set based on design specifications, historical data, or engineering experience.

4. The intelligent detection method for storage tanks based on Internet of Things technology according to claim 3, characterized in that: The benchmark risk index for storage tanks based on static attribute data and historical archive data. The assessment includes the following specific steps: S31. Extract baseline risk assessment parameters for storage tanks from static attribute data and historical archive data. These baseline risk assessment parameters include the service life. Design life Maximum historical corrosion rate Maximum permissible corrosion rate Environmental corrosion level ; S32. Calculate the benchmark risk index of the storage tank based on the obtained benchmark risk assessment parameters. Benchmark risk index The calculation formula is: ； in, The number of years is the factor. As a historical corrosive factor, Environmental factors are determined according to the environmental corrosion level. Values ​​range from [0.1, 1.0], with larger values ​​for stronger corrosiveness.

5. The intelligent detection method for storage tanks based on Internet of Things technology according to claim 4, characterized in that: Based on comprehensive risk value The calculation results are used to determine the risk level of the storage tank, including the following steps: S51. Classify risk levels and set a low-risk threshold based on the risk level. Medium risk threshold Severe risk threshold ; S52, Based on comprehensive risk value The size determines the risk level, when It is judged as green level when; It is judged as yellow level when; It is judged as orange level when; It is classified as red level at that time.

6. The intelligent detection method for storage tanks based on Internet of Things technology according to claim 5, characterized in that: The process of issuing early warnings based on risk level assessment results and simultaneously outputting maintenance strategies includes the following steps: S61. Generate early warning signals based on the determined risk level: Green level: No early warning triggered; Yellow level: Attention warning triggered, inspection suggestions generated; Orange level: Early warning triggered, special inspection suggestions generated; Red level: Emergency warning triggered, immediate action suggestions generated. S62. Visualize the risk status on a three-dimensional digital twin model and mark the overall risk level of the storage tank with different colors; S63. Generate a maintenance strategy report based on the risk level. The maintenance strategy report includes the current risk level, main risk sources, recommended measures, and recommended processing time. S64. Send early warning information and maintenance strategy reports through preset channels.

7. A smart tank detection system based on Internet of Things (IoT) technology, implemented based on the smart tank detection method based on IoT technology as described in any one of claims 1-5, characterized in that, Specifically, it includes: a multi-source data integration and twin modeling module: used to construct a three-dimensional digital twin model of the storage tank and integrate real-time sensor data, static attribute data and historical archive data; Dynamic risk calculation module: used to calculate the dynamic risk index of storage tanks based on real-time sensor data. Conduct an assessment; Benchmark Risk Assessment Module: Used to determine the benchmark risk index of storage tanks based on static attribute data and historical archive data. Conduct an assessment; Comprehensive Risk Value Calculation Module: Used for calculating risk values ​​based on dynamic risk indices. The assessment results and the benchmark risk index The assessment results affect the overall risk value of the storage tank. Perform calculations; Risk level determination module: used to determine the risk level based on the comprehensive risk value. The calculation results are used to determine the risk level of the storage tank; Visualized early warning and decision output module: used to issue early warnings based on risk level assessment results and simultaneously output maintenance strategies.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor, characterized in that: the processor executes the intelligent detection method for storage tanks based on Internet of Things technology as described in any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform the intelligent tank detection method based on Internet of Things technology as described in any one of claims 1-6.

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