Joint prediction method, device and equipment for noise of offshore platform and medium

Abstract

The invention relates to the technical field of noise prediction, and discloses an offshore platform noise joint prediction method, device and equipment, and a medium, which can analyze a drawing file of an offshore platform to extract structural feature data of the offshore platform, predict structural noise distribution of the offshore platform based on the structural feature data of the offshore platform and a statistical energy method, and predict noise distribution of the offshore platform based on the statistical energy method. And predicting air noise distribution of the offshore platform based on the structural feature data of the offshore platform and a sound ray tracking method, and superposing the structural noise distribution and the air noise distribution of the offshore platform to obtain overall noise distribution of the offshore platform. According to the method, the statistical energy method and the sound ray tracking method are combined for noise joint prediction to obtain the overall noise distribution of the offshore platform, and the accuracy of noise prediction of the offshore platform can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of noise prediction, and in particular to a method, device, equipment and medium for joint prediction of offshore platform noise. Background Art

[0002] With the development of science and technology, offshore oil and natural gas extraction technology continues to improve.

[0003] Offshore platforms are crucial facilities for offshore oil and gas extraction. They contain numerous noisy equipment. Even under normal conditions, these devices, including main engines, compressors, pumps, motors, and valves, generate high-intensity noise and vibration, which can propagate into living quarters, severely impacting the health and safety of offshore workers.

[0004] Related technologies can monitor the generation and distribution of offshore platform noise and implement corresponding measures to prevent or reduce noise, thereby minimizing the impact of noise on offshore workers. However, the accuracy of offshore platform noise determined by these technologies is relatively low. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for joint prediction of offshore platform noise, which are used to solve the defect of low accuracy of offshore platform noise determined by related technologies and improve the accuracy of offshore platform noise determination.

[0006] In a first aspect, the present invention provides a method for joint prediction of offshore platform noise, comprising:

[0007] Parsing the drawing files of the offshore platform to extract structural characteristic data of the offshore platform;

[0008] Based on the structural characteristic data of the offshore platform and the statistical energy method, predicting the structure-borne noise distribution of the offshore platform; and based on the structural characteristic data of the offshore platform and the ray tracing method, predicting the airborne noise distribution of the offshore platform;

[0009] The structural noise distribution and the air noise distribution of the offshore platform are superimposed to obtain the overall noise distribution of the offshore platform.

[0010] Optionally, the predicting the structure-borne noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a statistical energy method includes:

[0011] Based on the structural characteristic data of the offshore platform, a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform are established in vibration and noise simulation software, and the modal density and loss factor of the beam-column structure model, each of the plate models, and each of the acoustic cavity models are determined;

[0012] Determining the coupling relationship between the beam-column structure model, each of the plate models, and each of the acoustic cavity models according to the modal density and the loss factor, and setting the model excitation and frequency range;

[0013] Creating a corresponding energy balance equation based on the beam-column structure model, each of the plate models, each of the acoustic cavity models, the property parameters, the coupling relationship, the model excitation, and the frequency range;

[0014] The energy balance equation is solved to obtain the structural noise distribution of the offshore platform.

[0015] Optionally, the structural characteristic data of the offshore platform includes beam and column structural characteristic data, plate structural characteristic data, and acoustic cavity structural characteristic data;

[0016] The method includes establishing a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform in vibration and noise simulation software based on the structural characteristic data of the offshore platform, including:

[0017] Based on the beam-column structural characteristic data of the offshore platform, a beam-column structural model of the offshore platform is established in the vibration and noise simulation software, and on the basis of the beam-column structural model, each plate model and each acoustic cavity model is established according to the plate structure characteristic data and the acoustic cavity structure characteristic data.

[0018] Optionally, the predicting of the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the ray tracing method includes:

[0019] Based on the structural characteristic data of the offshore platform, creating a platform structure model in air noise simulation software;

[0020] Under conditions of normal temperature and pressure air, setting sound wave propagation medium parameters of the platform structure model, and assigning source strength parameters to the noise sources of the platform structure model based on sound power test data of high-noise equipment on each layer of the offshore platform;

[0021] An acoustic ray tracing calculation is performed based on the platform structure model, the sound wave propagation medium parameters and the source intensity parameters to obtain the air noise distribution of the offshore platform.

[0022] Optionally, creating a platform structure model in air noise simulation software based on the structural characteristic data of the offshore platform includes:

[0023] Based on the structural characteristic data of the offshore platform, identifying noisy equipment, large-volume equipment, rooms, large-volume systemic equipment, thick and thin pipes, and non-equipment items;

[0024] Creating models corresponding to the noise device, the large-volume device, and the room in the air noise simulation software, creating a corresponding simplified model based on the large-volume systematic device, and creating a corresponding obstacle model based on the non-equipment item if the volume and noise propagation shielding effect of the non-equipment item exceed a set threshold; determining that the noise propagation shielding effect of the thick and thin pipes is less than the set threshold, ignoring the modeling of the thick and thin pipes;

[0025] The platform structure model is created based on the models corresponding to the noise device, the large-volume device and the room, the simplified model and the obstacle model; wherein the platform structure model includes the models corresponding to the noise device, the large-volume device and the room, the simplified model and the obstacle model.

[0026] Optionally, the simplified model is a cube or a cylinder.

[0027] Optionally, superimposing the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform includes:

[0028] performing spatial position alignment on the structure-borne noise distribution and the airborne noise distribution of the offshore platform to obtain the structure-borne noise and the airborne noise at multiple spatial positions of the offshore platform;

[0029] For any of the spatial locations, performing sound energy conversion on the structure-borne noise and air noise at the spatial location to obtain structure-borne noise energy and air noise energy at the spatial location, adding the structure-borne noise energy and air noise energy at the spatial location to obtain total sound energy at the spatial location, and converting the total sound energy at the spatial location into a corresponding sound pressure level to obtain the sound pressure level at the spatial location;

[0030] Each of the spatial positions in the offshore platform and the corresponding sound pressure levels are taken as the overall noise distribution.

[0031] In a second aspect, the present invention provides a joint prediction device for offshore platform noise, comprising:

[0032] A parsing unit, configured to parse a drawing file of an offshore platform to extract structural characteristic data of the offshore platform;

[0033] A first prediction unit is configured to predict the structure-borne noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a statistical energy method;

[0034] a second prediction unit, configured to predict the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a ray tracing method;

[0035] The superposition unit is used to superimpose the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform.

[0036] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the joint prediction method for offshore platform noise according to the first aspect or any corresponding embodiment thereof.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for joint prediction of offshore platform noise according to the first aspect or any corresponding embodiment thereof.

[0038] The method, device, equipment, and medium for joint prediction of offshore platform noise provided by the present invention can analyze offshore platform drawings and documents to extract structural characteristic data of the offshore platform. Based on the structural characteristic data and the statistical energy method, the offshore platform's structure-borne noise distribution is predicted. Furthermore, based on the structural characteristic data and the ray tracing method, the offshore platform's airborne noise distribution is predicted. The structural noise distribution and the airborne noise distribution of the offshore platform are superimposed to obtain the overall noise distribution of the offshore platform. The present invention combines the statistical energy method and the ray tracing method to perform joint noise prediction to obtain the overall noise distribution of the offshore platform, effectively improving the accuracy of offshore platform noise prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A flowchart of a method for joint prediction of offshore platform noise provided by an embodiment of the present invention;

[0041] Figure 2 A flowchart of another method for joint prediction of offshore platform noise provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the structure of a joint prediction device for offshore platform noise provided by an embodiment of the present invention;

[0043] Figure 4 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0045] It's important to note that the occupational noise exposure limit for offshore platform workers must not exceed 85dB. Some research indicates that process operators in offshore platform production areas are exposed to noise levels exceeding the relevant standards, with average levels reaching 94.6dB(A). This suggests that offshore platform workers face a high risk of hearing loss.

[0046] When noise levels on offshore platforms exceed standards, the first step is to understand their distribution characteristics and propagation patterns to accurately predict and develop appropriate prevention and control measures. Noise simulation for offshore platforms is an effective approach. Noise simulation technology generally involves building a three-dimensional model based on actual conditions, assigning noise sources to the model through a value assignment method, and programming the model based on sound propagation theory. Leveraging the vast computing power of computers, the propagation of sound waves within the three-dimensional model is calculated, enabling acoustic calculations for large areas and complex sound sources and receiving points.

[0047] Acoustic professional software in related technologies has been initially applied in the noise optimization design of offshore platforms. Related technologies can predict noise based on the statistical energy method. However, due to the limitations of the statistical energy method itself in dealing with noise problems and its relatively complicated use, it has not played its due role in optimization design.

[0048] Due to the limitations of the statistical energy method's own theory, when using the statistical energy method model to predict spatial noise, only the average noise response of the acoustic cavity subsystem can be obtained, and the noise response result of a certain position in the acoustic cavity cannot be accurately obtained. Therefore, the internal acoustic cavity must be reasonably divided. However, in cases where the internal space is large, or there are problems with outdoor sound propagation, the number of acoustic cavities will increase, resulting in cumulative errors and serious deviations in the analysis. Moreover, this artificial division of the acoustic cavity does not conform to the actual physical scenario. In addition, since the statistical energy method requires re-modeling and meshing when the model changes, the optimization design of noise control measures will cause a large workload, which is not convenient for repeatedly adjusting and measuring different settings for noise reduction measures.

[0049] Taking the above problems into consideration, the inventors of the present invention decided to develop a hybrid noise calculation method based on the statistical energy method, absorbing the sound propagation algorithm widely used in the fields of environmental noise and occupational noise, to improve the overall efficiency of the prediction technology and support the low-noise design of offshore platforms.

[0050] The ray method, finite element method, statistical energy method and other related technologies can all predict noise propagation, but each method has its advantages and disadvantages. The ray method is mainly used for noise prediction in large-scale and large-scale situations. It is suitable for predictions at higher frequencies and can reflect various physical characteristics in the sound propagation process, but it cannot calculate vibration propagation and structural sound radiation. The statistical energy method is suitable for the calculation of complex systems and can realize the calculation of vibration propagation and structural sound. However, for large-scale outdoor sound propagation, it is difficult to reflect the influence of obstacles in the propagation path, and the calculation error is difficult to control. The ray method and the statistical energy analysis method are both suitable for the medium and high frequency ranges with sufficiently high modal density. The error of the simulation results in the low-frequency range will gradually increase. Therefore, the finite element method is generally used to solve low-frequency problems. However, due to the unit size limitations of the finite element model, the application of the finite element method to offshore platforms with larger sizes can only be limited to an extremely low frequency range.

[0051] Offshore platforms are exceptional, ultra-large structures towering above the sea. Their primary structure is steel, where vibrations from equipment are minimally dissipated and propagate far. The vibrations generated by operating equipment (especially high-vibration, heavy equipment) propagate through the foundation to the platform structure, rapidly spreading outward throughout the deck and accommodation building, radiating structure-borne sound into the spaces above the deck and into the rooms within the accommodation building. Structural sound prediction relies on calculating the vibration propagation of steel structures. For large and complex platform structures, the statistical energy method is a more appropriate approach.

[0052] In addition to the platform's structural noise, air noise propagation is equally important to noise prediction and is an important component of it. Therefore, the noise prediction technology of offshore operating platforms requires the establishment of a joint calculation method to accurately predict the noise distribution in key places such as offshore platform living areas and production areas.

[0053] The following combination Figure 1-Figure 2 The present invention describes the joint prediction method of offshore platform noise.

[0054] like Figure 1 As shown, this embodiment proposes a first joint prediction method for offshore platform noise, which may include the following steps:

[0055] S101. Parse the drawing file of the offshore platform to extract structural feature data of the offshore platform.

[0056] The offshore platform may be a fixed offshore platform or a non-fixed offshore platform, such as a floating platform or a mobile platform.

[0057] Among them, the drawing files of the offshore platform include the three-dimensional structure of the offshore platform drawn using drawing software.

[0058] Among them, the structural characteristic data of the offshore platform may include equipment type, equipment quantity, equipment location, equipment connection relationship, hierarchical structure, material data, load data and ancillary facilities, etc.

[0059] Specifically, this embodiment can parse the drawing file of the offshore platform to extract the structural feature data of the offshore platform.

[0060] Specifically, this embodiment can parse the 3D structure of an offshore platform in its drawing files, extract the assembly hierarchy (such as the deck, support columns, and pile foundations) within the 3D structure through feature tree traversal, extract the geometric parameters of the faces and edges within the 3D structure through boundary representation analysis, calculate the volume and center of gravity of related equipment and structures, and identify structural feature data such as bolt hole arrays and rib layouts through parametric feature recognition. This embodiment can also obtain equipment material specifications from the title bar and material table in the drawing files.

[0061] Optionally, this embodiment may first align the coordinate systems of the drawing files to ensure uniformity of multiple drawing data (such as general arrangement drawings and local details), and then extract the structural feature data of the offshore platform's three-dimensional structure from the drawing files.

[0062] S102. Predict the structural noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the statistical energy method.

[0063] Specifically, this embodiment can predict the structural noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the statistical energy method.

[0064] Optionally, step S102 may include:

[0065] Based on the structural characteristic data of the offshore platform, a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform were established in the vibration and noise simulation software. The modal density and loss factor of the beam-column structure model, each plate model, and each acoustic cavity model were determined.

[0066] Based on the modal density and loss factor, determine the coupling relationship between the beam-column structure model, each plate model, and each acoustic cavity model, and set the model excitation and frequency range;

[0067] Create the corresponding energy balance equation based on the beam-column structural model, each plate model, each acoustic cavity model, attribute parameters, coupling relationships, model excitation, and frequency range;

[0068] The energy balance equation is solved to obtain the structure-borne noise distribution of the offshore platform.

[0069] Specifically, this embodiment can predict offshore platform structure-borne noise based on the statistical energy method. This embodiment combines extracted structural feature data, drawings, and actual conditions to establish the overall platform beam-column structure. Based on this beam-column structure, a plate subsystem and acoustic cavity subsystem are created. The model subsystem parameters are set based on the actual structure, outfitting materials, and parameters. The subsystem modal density is calculated, the loss factor is determined, the subsystems are connected, and the excitation and frequency ranges are set. Finally, the corresponding solution calculations are performed to obtain the offshore platform's structure-borne noise distribution.

[0070] It should be noted that this embodiment allows for boundary conditions, which act as constraints on certain subsystems of the model. System excitations and boundary constraints are often assigned energy values. System excitations can be achieved by adding vibration, energy, and other data at specific locations.

[0071] Optionally, the structural characteristic data of the offshore platform includes beam-column structural characteristic data, plate structural characteristic data, and acoustic cavity structural characteristic data. In this case, based on the structural characteristic data of the offshore platform, a beam-column structural model, multiple plate models, and multiple acoustic cavity models of the offshore platform are established in the vibration and noise simulation software, including:

[0072] Based on the structural characteristic data of the beams and columns of the offshore platform, a beam-column structural model of the offshore platform is established in the vibration and noise simulation software. On the basis of the beam-column structural model, each plate model and each acoustic cavity model are established according to the plate structure characteristic data and the acoustic cavity structure characteristic data.

[0073] S103. Predict the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the sound ray tracing method.

[0074] Specifically, this embodiment can predict the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the sound ray tracing method.

[0075] Optionally, step S103 may include:

[0076] Based on the structural characteristic data of the offshore platform, a platform structure model is created in the air noise simulation software;

[0077] Under the conditions of normal temperature and pressure air, the sound wave propagation medium parameters of the platform structure model are set, and based on the sound power test data of high-noise equipment on each layer of the offshore platform, the source intensity parameters of the noise source of the platform structure model are assigned;

[0078] According to the platform structure model, sound wave propagation medium parameters and source intensity parameters, sound ray tracing calculation is performed to obtain the air noise distribution of the offshore platform.

[0079] Optionally, the above-mentioned creation of a platform structure model in air noise simulation software based on the structural characteristic data of the offshore platform includes:

[0080] Based on the structural characteristic data of the offshore platform, noisy equipment, large-volume equipment, rooms, large-volume system equipment, thick and thin pipes, and non-equipment items are identified;

[0081] In the air noise simulation software, models corresponding to noisy equipment, large-volume equipment, and rooms are created. A corresponding simplified model is created based on the large-volume systematic equipment. If the volume and noise propagation blocking effect of non-equipment items exceed the set threshold, a corresponding obstacle model is created based on the non-equipment items. If the noise propagation blocking effect of thick and thin pipes is less than the set threshold, the modeling of thick and thin pipes is ignored.

[0082] A platform structure model is created based on the models, simplified models, and obstacle models corresponding to the noise equipment, large-volume equipment, and rooms; wherein the platform structure model includes the models, simplified models, and obstacle models corresponding to the noise equipment, large-volume equipment, and rooms.

[0083] Optionally, simplify the model to a cube or cylinder.

[0084] It should be noted that, considering that the actual structure of the platform equipment is relatively complex, this embodiment can combine the extracted structural feature data, drawings and actual conditions to establish a structural model of the offshore platform in the air noise simulation software, and perform simplified processing in the noise simulation prediction model based on relevant processing principles, mainly including the creation of models for noise equipment and large-volume equipment and rooms that affect noise propagation. Large-volume systematic equipment is simplified into cubes or cylinders in the model. Among them, various thick and thin pipes on the offshore platform have little effect on noise propagation and are ignored during modeling. Other non-equipment items piled on the offshore platform, if they are large in volume or have a significant shielding effect on noise propagation, are created as obstacles in the model. The medium for sound wave propagation is set according to the parameters of air at normal temperature and pressure. The source strength parameters of the model are assigned according to the sound power test results of the main high-noise equipment on each layer of the platform.

[0085] S104: Superimpose the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform.

[0086] Specifically, in this embodiment, after determining the resultant noise distribution of the offshore platform and the air noise distribution, the resultant noise distribution of the offshore platform and the air noise distribution are superimposed to obtain the overall noise distribution of the offshore platform.

[0087] Optionally, step S104 may include:

[0088] The structure-borne noise distribution and airborne noise distribution of the offshore platform are spatially aligned to obtain the structure-borne noise and airborne noise at multiple spatial locations on the offshore platform.

[0089] For any spatial position, the structure-borne noise and air noise at the spatial position are converted into sound energy to obtain the structure-borne noise energy and air noise energy at the spatial position, the structure-borne noise energy and air noise energy at the spatial position are added together to obtain the total sound energy at the spatial position, and the total sound energy at the spatial position is converted into the corresponding sound pressure level to obtain the sound pressure level at the spatial position;

[0090] Each spatial position in the offshore platform and the corresponding sound pressure level are taken as the overall noise distribution.

[0091] Specifically, this embodiment can output the structural noise and airborne noise calculation data of the platform model deck space respectively, and superimpose the two types of data to obtain the overall noise result of the platform deck space.

[0092] The joint offshore platform noise prediction method proposed in this embodiment can analyze offshore platform drawings and documents to extract the offshore platform's structural characteristic data. Based on this structural characteristic data and the statistical energy method, the offshore platform's structure-borne noise distribution is predicted. Furthermore, based on this structural characteristic data and the ray tracing method, the offshore platform's airborne noise distribution is predicted. The structural and airborne noise distributions of the offshore platform are superimposed to obtain the overall noise distribution of the offshore platform. This embodiment combines the statistical energy method and the ray tracing method to perform joint noise prediction to obtain the overall noise distribution of the offshore platform, effectively improving the accuracy of offshore platform noise prediction.

[0093] like Figure 2 As shown, this embodiment proposes a second joint prediction method for offshore platform noise. This method analyzes offshore platform noise, obtains offshore platform structural design and layout data, extracts platform structural information from the structural design and layout data, and extracts platform design and analog data, namely, offshore platform structural characteristic data. Subsequently, this embodiment sets platform acoustic parameters, noise source intensity, vibration excitation, and personnel arrangements based on the platform design and analog data.

[0094] Specifically, this embodiment can perform 3D modeling of the platform structure in vibration noise simulation software based on the platform structural data and platform acoustic parameters, establish subsystems, define properties and divide grids to obtain a platform simulation model, namely a statistical energy analysis acoustic simulation model, apply loads and vibration excitations in the statistical energy analysis acoustic simulation model, and then perform platform cabin structural sound prediction to obtain the structural noise distribution of the offshore platform.

[0095] Specifically, this embodiment can create an open / semi-open space 3D acoustic model in air noise simulation software based on the platform structure data, then define properties in the model, apply loads according to the noise source intensity, and perform platform air noise prediction to obtain the air noise distribution of the offshore platform.

[0096] Specifically, this embodiment combines platform cabin structure-borne noise predictions with platform airborne noise predictions to predict platform noise. This superposition of the platform cabin structure-borne noise predictions and platform airborne noise predictions yields the overall noise distribution of the offshore platform. Finally, this embodiment analyzes the overall noise distribution of the offshore platform and provides recommendations to address the noise, thereby proposing appropriate noise mitigation measures to reduce the impact of noise on platform operators.

[0097] like Figure 3 As shown, this embodiment proposes a joint prediction device for offshore platform noise, which may include:

[0098] The parsing unit 301 is used to parse the drawing file of the offshore platform to extract the structural characteristic data of the offshore platform;

[0099] The first prediction unit 302 is configured to predict the structure-borne noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a statistical energy method;

[0100] The second prediction unit 303 is used to predict the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the sound ray tracing method;

[0101] The superposition unit 304 is configured to superimpose the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform.

[0102] It should be noted that the processing of the parsing unit 301, the first prediction unit 302, the second prediction unit 303 and the superposition unit 304 and the beneficial effects thereof can be referred to in the respective Figure 1 Steps S101 to S104 in the above are not described in detail.

[0103] Optionally, the first prediction unit 302 is further configured to:

[0104] Based on the structural characteristic data of the offshore platform, a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform were established in the vibration and noise simulation software. The modal density and loss factor of the beam-column structure model, each plate model, and each acoustic cavity model were determined.

[0105] Based on the modal density and loss factor, determine the coupling relationship between the beam-column structure model, each plate model, and each acoustic cavity model, and set the model excitation and frequency range;

[0106] Create the corresponding energy balance equation based on the beam-column structural model, each plate model, each acoustic cavity model, attribute parameters, coupling relationships, model excitation, and frequency range;

[0107] The energy balance equation is solved to obtain the structure-borne noise distribution of the offshore platform.

[0108] Optionally, the structural characteristic data of the offshore platform includes beam and column structural characteristic data, plate structural characteristic data, and acoustic cavity structural characteristic data;

[0109] The first prediction unit 302 is further configured to:

[0110] Based on the structural characteristic data of the beams and columns of the offshore platform, a beam-column structural model of the offshore platform is established in the vibration and noise simulation software. On the basis of the beam-column structural model, each plate model and each acoustic cavity model are established according to the plate structure characteristic data and the acoustic cavity structure characteristic data.

[0111] Optionally, the second prediction unit 303 is further configured to:

[0112] Based on the structural characteristic data of the offshore platform, a platform structure model is created in the air noise simulation software;

[0113] Under the conditions of normal temperature and pressure air, the sound wave propagation medium parameters of the platform structure model are set, and based on the sound power test data of high-noise equipment on each layer of the offshore platform, the source intensity parameters of the noise source of the platform structure model are assigned;

[0114] According to the platform structure model, sound wave propagation medium parameters and source intensity parameters, sound ray tracing calculation is performed to obtain the air noise distribution of the offshore platform.

[0115] Optionally, the second prediction unit 303 is further configured to:

[0116] Based on the structural characteristic data of the offshore platform, noisy equipment, large-volume equipment, rooms, large-volume system equipment, thick and thin pipes, and non-equipment items are identified;

[0117] In the air noise simulation software, models corresponding to noisy equipment, large-volume equipment, and rooms are created. A corresponding simplified model is created based on the large-volume systematic equipment. If the volume and noise propagation blocking effect of non-equipment items exceed the set threshold, a corresponding obstacle model is created based on the non-equipment items. If the noise propagation blocking effect of thick and thin pipes is less than the set threshold, the modeling of thick and thin pipes is ignored.

[0118] A platform structure model is created based on the models, simplified models, and obstacle models corresponding to the noise equipment, large-volume equipment, and rooms; wherein the platform structure model includes the models, simplified models, and obstacle models corresponding to the noise equipment, large-volume equipment, and rooms.

[0119] Optionally, simplify the model to a cube or cylinder.

[0120] Optionally, the superimposing unit 304 is further configured to:

[0121] The structure-borne noise distribution and airborne noise distribution of the offshore platform are spatially aligned to obtain the structure-borne noise and airborne noise at multiple spatial locations on the offshore platform.

[0122] For any spatial position, the structure-borne noise and air noise at the spatial position are converted into sound energy to obtain the structure-borne noise energy and air noise energy at the spatial position, the structure-borne noise energy and air noise energy at the spatial position are added together to obtain the total sound energy at the spatial position, and the total sound energy at the spatial position is converted into the corresponding sound pressure level to obtain the sound pressure level at the spatial position;

[0123] Each spatial position in the offshore platform and the corresponding sound pressure level are taken as the overall noise distribution.

[0124] The joint offshore platform noise prediction device proposed in this embodiment can analyze offshore platform drawings and files to extract structural characteristic data of the offshore platform. Based on this structural characteristic data and the statistical energy method, it predicts the offshore platform's structure-borne noise distribution. Furthermore, based on this structural characteristic data and the ray tracing method, it predicts the offshore platform's airborne noise distribution. The structural noise distribution and airborne noise distribution of the offshore platform are superimposed to obtain the offshore platform's overall noise distribution. This invention combines the statistical energy method and the ray tracing method to perform joint noise prediction to obtain the offshore platform's overall noise distribution, effectively improving the accuracy of offshore platform noise prediction.

[0125] The offshore platform noise joint prediction device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0126] The embodiment of the present invention also provides a computer device having the above Figure 3 The joint prediction device for offshore platform noise is shown.

[0127] See also Figure 4 , a structural diagram of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0128] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0129] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0130] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0132] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0133] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 various embodiments of the present invention.

Claims

1. A joint prediction method for offshore platform noise, characterized in that: include: Parsing the drawing files of the offshore platform to extract structural characteristic data of the offshore platform; Based on the structural characteristic data of the offshore platform and the statistical energy method, predicting the structure-borne noise distribution of the offshore platform; and based on the structural characteristic data of the offshore platform and the ray tracing method, predicting the airborne noise distribution of the offshore platform; The structural noise distribution and air noise distribution of the offshore platform are superimposed to obtain the overall noise distribution of the offshore platform.

2. The method according to claim 1, characterized in that The method of predicting the structure-borne noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the statistical energy method includes: Based on the structural characteristic data of the offshore platform, a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform are established in vibration and noise simulation software, and the modal density and loss factor of the beam-column structure model, each of the plate models, and each of the acoustic cavity models are determined; Determining the coupling relationship between the beam-column structure model, each of the plate models, and each of the acoustic cavity models according to the modal density and the loss factor, and setting the model excitation and frequency range; Creating a corresponding energy balance equation based on the beam-column structure model, each of the plate models, each of the acoustic cavity models, the property parameters, the coupling relationship, the model excitation, and the frequency range; The energy balance equation is solved to obtain the structural noise distribution of the offshore platform.

3. The method according to claim 2, characterized in that The structural characteristic data of the offshore platform includes beam and column structural characteristic data, plate structural characteristic data and acoustic cavity structural characteristic data; The method includes establishing a beam-column structure model, multiple plate models, and multiple acoustic cavity models of the offshore platform in vibration and noise simulation software based on the structural characteristic data of the offshore platform, including: Based on the beam-column structural characteristic data of the offshore platform, a beam-column structural model of the offshore platform is established in the vibration and noise simulation software, and on the basis of the beam-column structural model, each plate model and each acoustic cavity model is established according to the plate structure characteristic data and the acoustic cavity structure characteristic data.

4. The method according to claim 1, wherein The method of predicting the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and the ray tracing method includes: Based on the structural characteristic data of the offshore platform, creating a platform structure model in air noise simulation software; Under conditions of normal temperature and pressure air, setting sound wave propagation medium parameters of the platform structure model, and assigning source strength parameters to the noise sources of the platform structure model based on sound power test data of high-noise equipment on each layer of the offshore platform; An acoustic ray tracing calculation is performed based on the platform structure model, the sound wave propagation medium parameters and the source intensity parameters to obtain the air noise distribution of the offshore platform.

5. The method according to claim 4, characterized in that The step of creating a platform structure model in air noise simulation software based on the structural characteristic data of the offshore platform includes: Based on the structural characteristic data of the offshore platform, identifying noisy equipment, large-volume equipment, rooms, large-volume systemic equipment, thick and thin pipes, and non-equipment items; Creating models corresponding to the noise device, the large-volume device, and the room in the air noise simulation software, creating a corresponding simplified model based on the large-volume systematic device, and creating a corresponding obstacle model based on the non-equipment item if the volume and noise propagation shielding effect of the non-equipment item exceed a set threshold; determining that the noise propagation shielding effect of the thick and thin pipes is less than the set threshold, ignoring the modeling of the thick and thin pipes; The platform structure model is created based on the models corresponding to the noise device, the large-volume device and the room, the simplified model and the obstacle model; wherein the platform structure model includes the models corresponding to the noise device, the large-volume device and the room, the simplified model and the obstacle model.

6. The method according to claim 5, characterized in that The simplified model is a cube or a cylinder.

7. The method according to claim 1, characterized in that The superposition of the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform includes: performing spatial position alignment on the structure-borne noise distribution and the airborne noise distribution of the offshore platform to obtain the structure-borne noise and the airborne noise at multiple spatial positions of the offshore platform; For any of the spatial locations, performing sound energy conversion on the structure-borne noise and air noise at the spatial location to obtain structure-borne noise energy and air noise energy at the spatial location, adding the structure-borne noise energy and air noise energy at the spatial location to obtain total sound energy at the spatial location, and converting the total sound energy at the spatial location into a corresponding sound pressure level to obtain the sound pressure level at the spatial location; Each of the spatial positions in the offshore platform and the corresponding sound pressure levels are taken as the overall noise distribution.

8. A joint prediction device for offshore platform noise, characterized in that: include: A parsing unit, configured to parse a drawing file of an offshore platform to extract structural characteristic data of the offshore platform; A first prediction unit is configured to predict the structure-borne noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a statistical energy method; a second prediction unit, configured to predict the air noise distribution of the offshore platform based on the structural characteristic data of the offshore platform and a sound ray tracing method; The superposition unit is used to superimpose the structural noise distribution and the air noise distribution of the offshore platform to obtain the overall noise distribution of the offshore platform.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the offshore platform noise joint prediction method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the offshore platform noise joint prediction method according to any one of claims 1 to 7.

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