Corrosion rate determination method and device and computer program product

Abstract

The invention discloses a corrosion rate determination method and device and a computer program product, and relates to the technical field of offshore engineering.The corrosion rate determination method is applied to an offshore metal structure and comprises the steps that the first corrosion rate and the second corrosion rate are obtained, the first corrosion rate is obtained through calculation according to an electrochemical corrosion formula, and the second corrosion rate is obtained through calculation according to an electrochemical corrosion formula; the second corrosion rate is obtained by a machine learning model, the electrochemical corrosion formula comprises corrosion parameters of the metal structure, and the machine learning model is used for receiving the input corrosion parameters and outputting the corrosion rate of the metal structure; and determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure. By adopting the technical scheme, the problem of how to improve the prediction accuracy of the corrosion rate of the offshore metal structure is solved.

Description

Technical Field

[0001] The present application relates to the field of offshore engineering technology, and in particular to a method, device and computer program product for determining a corrosion rate. Background Art

[0002] In the field of offshore engineering technology, the metal structures of offshore wind power equipment are prone to severe chemical corrosion when exposed to marine environments containing corrosive gases such as hydrogen, hydrogen sulfide and carbon monoxide for a long time. Traditional corrosion monitoring methods usually rely on a single sensor for corrosion environment monitoring, which cannot fully capture the combined impact of multiple environmental factors on the corrosion rate. Traditional methods such as linear polarization resistance method and electrochemical impedance spectroscopy can only monitor local corrosion rates. The simple correlation model between gas concentration and corrosion rate cannot accurately describe the complex relationship between gas concentration and corrosion rate, which in turn leads to the inability to accurately predict the corrosion rate of offshore metal structures.

[0003] Therefore, in the related art, there is a problem of how to improve the prediction accuracy of the corrosion rate of offshore metal structures.

[0004] Regarding the problem of how to improve the prediction accuracy of the corrosion rate of offshore metal structures in related technologies, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method, device and computer program product for determining a corrosion rate, so as to at least solve the problem in the related art of how to improve the prediction accuracy of the corrosion rate of offshore metal structures.

[0007] According to one aspect of an embodiment of the present application, a method for determining a corrosion rate is provided and applied to an offshore metal structure, comprising: obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure; determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents the predicted corrosion rate of the metal structure.

[0008] In an exemplary embodiment, before obtaining the first corrosion rate and the second corrosion rate, the method includes: obtaining the corrosion parameters through sensors disposed on the metal structure, wherein the corrosion parameters include the corrosion current of the offshore metal structure and gas concentrations of multiple gases.

[0009] In an exemplary embodiment, obtaining the first corrosion rate and the second corrosion rate includes: calculating the first corrosion rate using the following electrochemical corrosion formula:

[0010]

[0011] Among them, CR phys represents the first corrosion rate, I corr represents corrosion current, K represents the unit conversion constant, n represents the number of electron transfers in the corrosion reaction, F represents the Faraday constant, and ρ represents the density of the offshore metal structure.

[0012] In an exemplary embodiment, the corrosion current is determined by the following formula:

[0013]

[0014] Among them, I corr represents corrosion current, I0 represents reference current without gas influence, m represents the number of gas species, C i represents the concentration of the i-th gas, α i Represents the linear influence factor of gas concentration on corrosion rate, β ij Represents the influence factor of the coupling effect between the i-th gas and the j-th gas on the corrosion rate.

[0015] In an exemplary embodiment, obtaining a first corrosion rate and a second corrosion rate includes: simulating the corrosion process by computational fluid dynamics and finite element method to obtain a target gas concentration; inputting the target gas concentration and preset corrosion parameters into the machine learning model to obtain a second corrosion rate output by the machine learning model, wherein the preset corrosion parameters do not include gas concentration, and the machine learning model is trained using the historical corrosion parameters of the offshore metal structure as a training data set and the historical corrosion rate of the offshore metal structure as a verification data set.

[0016] In an exemplary embodiment, determining a target corrosion rate according to the first corrosion rate and the second corrosion rate includes: calculating a first accuracy of the first corrosion rate and a second accuracy of the second corrosion rate through historical corrosion data of the offshore metal structure; determining a first weighting factor of the first corrosion rate and a second weighting factor of the second corrosion rate according to the first accuracy and the second accuracy; and determining the target corrosion rate by the following formula:

[0017] CR final =ω1·CR phys +ω2·CR pred ;

[0018] Among them, CR final represents the target corrosion rate, CR phys represents the first corrosion rate, CR pred The second corrosion rate is identified, ω1 represents the first weight factor, and ω2 represents the second weight factor.

[0019] According to another aspect of an embodiment of the present application, a device for determining a corrosion rate is also provided, including: an acquisition module for acquiring a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure; a determination module is used to determine a target corrosion rate based on the first corrosion rate and the second corrosion rate, and the target corrosion rate represents the predicted corrosion rate of the metal structure.

[0020] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the corrosion rate when running.

[0021] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the corrosion rate determination method through the computer program.

[0022] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0023] Through this application, the first corrosion rate of the offshore metal structure can be calculated by the electrochemical corrosion formula, the second corrosion rate of the offshore metal structure can be calculated based on the machine learning algorithm, and the first corrosion rate and the second corrosion rate can be combined to determine the final predicted target corrosion rate of the offshore metal structure. This solves the problem of how to improve the prediction accuracy of the corrosion rate of the offshore metal structure in the related art, and achieves the effect of improving the prediction accuracy of the corrosion rate of the offshore metal structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining a corrosion rate according to an embodiment of the present application;

[0027] Figure 2 is a flow chart of a method for determining a corrosion rate according to an embodiment of the present application;

[0028] Figure 3 It is a structural block diagram of a device for determining a corrosion rate according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a method for determining a corrosion rate according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0032] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the corrosion rate in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal 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.

[0033] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0034] In this embodiment, a method for determining the corrosion rate is provided. Figure 2 is a flow chart of a method for determining a corrosion rate according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0035] Step S202, obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure;

[0036] Optionally, in the above step S202, the corrosion parameters include gas concentration, temperature, humidity, pH value, corrosion current, open circuit potential, etc.

[0037] Step S204: determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

[0038] Through the above steps, the first corrosion rate of the offshore metal structure can be calculated by the electrochemical corrosion formula, the second corrosion rate of the offshore metal structure can be calculated based on the machine learning algorithm, and the first corrosion rate and the second corrosion rate can be combined to determine the final predicted target corrosion rate of the offshore metal structure. This solves the problem of how to improve the prediction accuracy of the corrosion rate of the offshore metal structure in the related art, and achieves the effect of improving the prediction accuracy of the corrosion rate of the offshore metal structure.

[0039] In an exemplary embodiment, before obtaining the first corrosion rate and the second corrosion rate, the method includes: obtaining the corrosion parameters through sensors disposed on the metal structure, wherein the corrosion parameters include the corrosion current of the offshore metal structure and gas concentrations of multiple gases.

[0040] Optionally, in the above embodiment, the sensors on the metal structure include electrochemical sensors, gas sensors, optical sensors, ultrasonic sensors, etc. By constructing a multi-type sensor network, real-time monitoring of corrosion parameters can be achieved.

[0041] Optionally, in the above embodiment, the gas concentrations of the plurality of gases include the gas concentrations of corrosive gases such as hydrogen, hydrogen sulfide, and carbon monoxide.

[0042] In an exemplary embodiment, obtaining the first corrosion rate and the second corrosion rate includes: calculating the first corrosion rate using the following electrochemical corrosion formula:

[0043]

[0044] Among them, CR phys represents the first corrosion rate, I corrrepresents corrosion current, K represents the unit conversion constant, n represents the number of electron transfers in the corrosion reaction, F represents the Faraday constant, and ρ represents the density of the offshore metal structure.

[0045] Optionally, in the above embodiment, the unit conversion constant is used to convert the electrochemical parameters in the corrosion process, such as corrosion current, into physical measurements of material corrosion (such as thickness loss of metal materials) so as to more intuitively quantify the impact of corrosion on the material. Specifically, K can be taken as 0.129, and F is the Faraday constant of 96485 C / mol.

[0046] In an exemplary embodiment, the corrosion current is determined by the following formula:

[0047]

[0048] Among them, I corr represents corrosion current, I0 represents reference current without gas influence, m represents the number of gas species, C i represents the concentration of the i-th gas, α i Represents the linear influence factor of gas concentration on corrosion rate, β ij Represents the influence factor of the coupling effect between the i-th gas and the j-th gas on the corrosion rate.

[0049] Optionally, in the above embodiment, the influence of factors such as temperature and pH on the corrosion current may be further considered.

[0050] Through the above embodiments, the synergistic effects of various gases on the corrosion rate of offshore metals are taken into account when calculating the corrosion current, thereby improving the prediction accuracy of the corrosion rate.

[0051] In an exemplary embodiment, obtaining a first corrosion rate and a second corrosion rate includes: simulating the corrosion process by computational fluid dynamics and finite element method to obtain a target gas concentration; inputting the target gas concentration and preset corrosion parameters into the machine learning model to obtain a second corrosion rate output by the machine learning model, wherein the preset corrosion parameters do not include gas concentration, and the machine learning model is trained using the historical corrosion parameters of the offshore metal structure as a training data set and the historical corrosion rate of the offshore metal structure as a verification data set.

[0052] Optionally, in the above embodiment, CFD (Computational Fluid Dynamics) can be used to simulate the diffusion and flow characteristics of the corrosive gas around the metal structure to obtain the gas concentration distribution. FEM (Finite Element Method) is used to simulate the corrosion process of the metal structure. Considering the influence of factors such as gas concentration, flow rate and pressure on the corrosion rate, the gas concentration can be calculated by the following formula:

[0053]

[0054] Among them, C is the concentration of corrosive gas, D is the diffusion coefficient, and v is the flow rate.

[0055] Optionally, in the above embodiment, a machine learning algorithm (such as a random forest algorithm, etc.) may be used to model the data collected by the sensor to predict the corrosion rate under specific conditions:

[0056] CR pred =ML(X1,X2,...,X n )

[0057] Among them, X i For different input variables, such as gas concentration, temperature, humidity, etc., the above gas concentration is obtained by simulating the corrosion process through the above-mentioned computational fluid dynamics and finite element method. Parameters such as temperature and humidity can be preset parameters. Multiple groups of preset parameters can be set to improve the comprehensiveness and accuracy of corrosion rate prediction. ML represents a machine learning model.

[0058] In an exemplary embodiment, determining a target corrosion rate according to the first corrosion rate and the second corrosion rate includes: calculating a first accuracy of the first corrosion rate and a second accuracy of the second corrosion rate through historical corrosion data of the offshore metal structure; determining a first weighting factor of the first corrosion rate and a second weighting factor of the second corrosion rate according to the first accuracy and the second accuracy; and determining the target corrosion rate by the following formula:

[0059] CR final =ω1·CR phys +ω2·CR pred ;

[0060] Among them, CR final represents the target corrosion rate, CR phys represents the first corrosion rate, CR pred The second corrosion rate is identified, ω1 represents the first weight factor, and ω2 represents the second weight factor.

[0061] Optionally, in the above embodiment, ω1 and ω2 are weight factors reflecting the credibility of the two data, which can be corrected by the Bayesian updating method.

[0062] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0063] In this embodiment, a device for determining a corrosion rate is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0064] Figure 3 : is a structural block diagram of a device for determining a corrosion rate according to an embodiment of the present application, the device comprising:

[0065] an acquisition module 32, configured to acquire a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is configured to receive the input corrosion parameters and output the corrosion rate of the metal structure;

[0066] The determination module 34 is configured to determine a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

[0067] Through the above device, the first corrosion rate of the offshore metal structure can be calculated by the electrochemical corrosion formula, the second corrosion rate of the offshore metal structure can be calculated based on the machine learning algorithm, and the first corrosion rate and the second corrosion rate can be combined to determine the final predicted target corrosion rate of the offshore metal structure. This solves the problem of how to improve the prediction accuracy of the corrosion rate of the offshore metal structure in the related art, and achieves the effect of improving the prediction accuracy of the corrosion rate of the offshore metal structure.

[0068] In an exemplary embodiment, the acquisition module 32 is further used to acquire the corrosion parameters through sensors disposed on the metal structure, wherein the corrosion parameters include the corrosion current of the offshore metal structure and the gas concentrations of multiple gases.

[0069] In an exemplary embodiment, the acquisition module 32 is further configured to calculate the first corrosion rate using the following electrochemical corrosion formula:

[0070]

[0071] Among them, CR phys represents the first corrosion rate, I corr represents corrosion current, K represents the unit conversion constant, n represents the number of electron transfers in the corrosion reaction, F represents the Faraday constant, and ρ represents the density of the offshore metal structure.

[0072] In an exemplary embodiment, the acquisition module 32 is further configured to determine the corrosion current by the following formula:

[0073]

[0074] Among them, I corr represents corrosion current, I0 represents reference current without gas influence, m represents the number of gas species, C i represents the concentration of the i-th gas, α i Represents the linear influence factor of gas concentration on corrosion rate, β ij Represents the influence factor of the coupling effect between the i-th gas and the j-th gas on the corrosion rate.

[0075] In an exemplary embodiment, the acquisition module 32 is also used to simulate the corrosion process through computational fluid dynamics and finite element method to obtain a target gas concentration; the target gas concentration and preset corrosion parameters are input into the machine learning model to obtain a second corrosion rate output by the machine learning model, wherein the preset corrosion parameters do not include gas concentration, and the machine learning model is trained using the historical corrosion parameters of the offshore metal structure as a training data set and the historical corrosion rate of the offshore metal structure as a verification data set.

[0076] In an exemplary embodiment, the determination module 34 is further used to calculate a first accuracy of the first corrosion rate and a second accuracy of the second corrosion rate through the historical corrosion data of the offshore metal structure; determine a first weighting factor of the first corrosion rate and a second weighting factor of the second corrosion rate according to the first accuracy and the second accuracy; and determine the target corrosion rate through the following formula:

[0077] CR final =ω1·CR phys +ω2·CR pred ;

[0078] Among them, CR final represents the target corrosion rate, CR phys represents the first corrosion rate, CR pred The second corrosion rate is identified, ω1 represents the first weight factor, and ω2 represents the second weight factor.

[0079] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0080] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0081] S1, obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure;

[0082] S2, determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

[0083] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0084] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0085] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0086] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0087] S1, obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure;

[0088] S2, determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

[0089] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0090] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0091] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0092] S1, obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure;

[0093] S2, determining a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

[0094] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0095] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0096] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a corrosion rate, characterized in that: Metal structures used in offshore applications include: Obtaining a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure; A target corrosion rate is determined based on the first corrosion rate and the second corrosion rate, the target corrosion rate representing a predicted corrosion rate of the metal structure.

2. The method according to claim 1, characterized in that Before obtaining the first corrosion rate and the second corrosion rate, the method includes: The corrosion parameters are obtained by sensors arranged on the metal structure, wherein the corrosion parameters include the corrosion current of the offshore metal structure and the gas concentrations of multiple gases.

3. The method according to claim 1, characterized in that Obtaining a first corrosion rate and a second corrosion rate, including: The first corrosion rate is calculated by the following electrochemical corrosion formula: Among them, CR phys represents the first corrosion rate, I corr represents corrosion current, K represents the unit conversion constant, n represents the number of electron transfers in the corrosion reaction, F represents the Faraday constant, and ρ represents the density of the offshore metal structure.

4. The method according to claim 3, characterized in that The corrosion current is determined by the following formula: Among them, I corr represents corrosion current, I0 represents reference current without gas influence, m represents the number of gas species, C i represents the concentration of the i-th gas, α i Represents the linear influence factor of gas concentration on corrosion rate, β ij Represents the influence factor of the coupling effect between the i-th gas and the j-th gas on the corrosion rate.

5. The method according to claim 2, characterized in that: Obtaining a first corrosion rate and a second corrosion rate, including: The corrosion process is simulated by computational fluid dynamics and finite element method to obtain the target gas concentration; The target gas concentration and preset corrosion parameters are input into the machine learning model to obtain a second corrosion rate output by the machine learning model, wherein the preset corrosion parameters do not include gas concentration, and the machine learning model is trained using the historical corrosion parameters of the offshore metal structure as a training data set and the historical corrosion rate of the offshore metal structure as a verification data set.

6. The method according to claim 1, characterized in that Determining a target corrosion rate according to the first corrosion rate and the second corrosion rate includes: calculating a first accuracy of the first corrosion rate and a second accuracy of the second corrosion rate using historical corrosion data of the offshore metal structure; determining a first weighting factor for the first corrosion rate and a second weighting factor for the second corrosion rate according to the first accuracy and the second accuracy; The target corrosion rate is determined by the following formula: CR final =ω1·CR phys +ω2·CR pred ; Among them, CR final represents the target corrosion rate, CR phys represents the first corrosion rate, CR pred The second corrosion rate is identified, ω1 represents the first weight factor, and ω2 represents the second weight factor.

7. A device for determining a corrosion rate, characterized in that: include: An acquisition module, used to acquire a first corrosion rate and a second corrosion rate, wherein the first corrosion rate is calculated according to an electrochemical corrosion formula, and the second corrosion rate is obtained by a machine learning model, wherein the electrochemical corrosion formula includes corrosion parameters of the metal structure, and the machine learning model is used to receive the input corrosion parameters and output the corrosion rate of the metal structure; A determination module is used to determine a target corrosion rate according to the first corrosion rate and the second corrosion rate, wherein the target corrosion rate represents a predicted corrosion rate of the metal structure.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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