Method and device for determining posterior distribution of corrosion rate, storage medium and electronic device

Abstract

The invention discloses a method and device for determining posterior distribution of a corrosion rate, a storage medium and an electronic device, and relates to the field of computers.The method for determining posterior distribution of the corrosion rate comprises the steps that prior distribution P (theta) of the corrosion rate of a target area is established according to historical data of the target area, the prior distribution is used for indicating the predicted distribution of the corrosion rate; determining a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and the electrochemical impedance spectroscopy data of the target area; according to the prior distribution and the likelihood function, posterior distribution of the corrosion rate is determined, and the posterior distribution is used for indicating actual distribution of the corrosion rate; by the adoption of the scheme, the problem that in the related technology, a multi-pile steel structure in an offshore wind power infrastructure is located in a complex corrosion environment, and a common corrosion monitoring method is difficult to comprehensively monitor various corrosion forms is solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular, to a method and device for determining a posterior distribution of a corrosion rate, a storage medium, and an electronic device. Background Art

[0002] In offshore wind power infrastructure, multi-pile underwater steel structures are exposed to extreme marine environments for a long time, and are subject to multiple corrosion threats such as salt water, waves, marine biological erosion, and ocean acidification. These factors make the corrosion rate of steel structures difficult to predict and changeable, and traditional corrosion monitoring technology is unable to cope with this challenge. For example, although single-mode corrosion monitoring such as linear polarization resistance (LPR) or electrochemical impedance spectroscopy (EIS) can provide certain corrosion rate information, they are each limited to monitoring specific types of corrosion behavior or performing best under specific environmental conditions, and it is difficult to fully and accurately reflect the complex and changing corrosion environment.

[0003] Regarding the related technologies, the multi-pile steel structures in offshore wind power infrastructure are in a complex corrosion environment, and the commonly used corrosion monitoring methods are difficult to comprehensively monitor various corrosion forms. No effective solution has been proposed yet.

[0004] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining the posterior distribution of corrosion rate, a storage medium and an electronic device, so as to at least solve the problem in the related art that the multi-pile steel structures in the offshore wind power infrastructure are in a complex corrosion environment, and the commonly used corrosion monitoring methods are difficult to comprehensively monitor various corrosion forms.

[0006] According to one aspect of an embodiment of the present application, a method for determining a posterior distribution of a corrosion rate is provided, comprising: establishing a prior distribution P(θ) of the corrosion rate of a target area based on historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate; determining a likelihood function corresponding to the corrosion rate based on linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; determining a posterior distribution of the corrosion rate based on the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

[0007] In an exemplary embodiment, determining the likelihood function corresponding to the corrosion rate based on the linear polarization resistance data and electrochemical impedance spectrum data of the target area includes: collecting linear polarization resistance data of the target area through a linear polarization resistance sensor to obtain the linear polarization resistance data, and establishing a first likelihood function corresponding to the corrosion rate based on the linear polarization resistance data; collecting electrochemical impedance spectrum data of the target area through an electrochemical impedance spectrum sensor to obtain the electrochemical impedance spectrum data, and establishing a second likelihood function corresponding to the corrosion rate based on the electrochemical impedance spectrum data, wherein the likelihood function includes the first likelihood function and the second likelihood function.

[0008] In an exemplary embodiment, establishing a first likelihood function corresponding to the corrosion rate according to the linear polarization resistance data includes: calculating a preliminary estimate of the corrosion rate according to the linear polarization resistance data: Among them, v corr is the preliminary estimate, B is the material property constant, R p is the linear polarization resistance data; establishing the first likelihood function according to the preliminary estimate in, is the noise corresponding to the linear polarization resistance sensor.

[0009] In an exemplary embodiment, establishing a second likelihood function corresponding to the corrosion rate according to the electrochemical impedance spectroscopy data includes: performing an equivalent circuit fitting on the electrochemical impedance spectroscopy data to obtain the linear polarization resistance data R p and the capacitance data C of the target area d ,in, Z(ω) is the electrochemical impedance spectroscopy data, Z(ω) is a complex number, R s is the solution resistance of the target area, ω is the signal frequency; the second likelihood function is established according to the linear polarization resistance data and the capacitance data Wherein, f(θ, ω) is used to indicate the correlation between the corrosion rate and the signal frequency, is the noise corresponding to the electrochemical impedance spectroscopy sensor.

[0010] In an exemplary embodiment, determining the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function includes: determining a first posterior distribution of the corrosion rate according to a first likelihood function corresponding to the prior distribution and the linear polarization resistance data, and determining a second posterior distribution of the corrosion rate according to a second likelihood function corresponding to the prior distribution and the electrochemical impedance spectroscopy data, wherein the likelihood function includes the first likelihood function and the second likelihood function; and performing weighted summation of the first posterior distribution and the second posterior distribution to obtain the posterior distribution.

[0011] In an exemplary embodiment, the method further includes: determining the corrosion rate variability of the corrosion rate according to the posterior distribution; and when it is determined that the corrosion rate variability increases, using the formula R(t)=-(E(t)+λ·Δv corr (t)) calculates the instantaneous reward of the sensor, and adjusts the sampling frequency of the sensor according to the instantaneous reward, where R(t) is the instantaneous reward, E(t) is the sensor power consumption of the sensor, Δv corr (t) is the corrosion rate variability, λ is a trade-off parameter, and the sensor is used to collect the linear polarization resistance data and / or the electrochemical impedance spectroscopy data.

[0012] According to another aspect of an embodiment of the present application, a device for determining a posterior distribution of a corrosion rate is also provided, including: an establishment module for establishing a prior distribution P(θ) of the corrosion rate of a target area based on historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate; a first determination module for determining a likelihood function corresponding to the corrosion rate based on linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; and a second determination module for determining the posterior distribution of the corrosion rate based on the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

[0013] According to another aspect of an embodiment 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 method for determining the posterior distribution of the above-mentioned corrosion rate when running.

[0014] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the posterior distribution of the corrosion rate through the computer program.

[0015] 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.

[0016] Through the present application, a prior distribution P(θ) of the corrosion rate of the target area is first established based on the historical data of the target area, wherein the prior distribution is used to indicate the predicted distribution of the corrosion rate; the likelihood function corresponding to the corrosion rate is determined based on the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; the posterior distribution of the corrosion rate is determined based on the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate; the above scheme is adopted to solve the problem in the related art that the multi-pile steel structure in the offshore wind power infrastructure is in a complex corrosion environment, and the commonly used corrosion monitoring method is difficult to comprehensively monitor various corrosion forms. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] 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.

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining a posterior distribution of a corrosion rate according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining a posterior distribution of a corrosion rate according to an embodiment of the present application;

[0021] Figure 3 It is a structural block diagram of a device for determining a posterior distribution of a corrosion rate according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] 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.

[0023] 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.

[0024] 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 for determining a posterior distribution of a corrosion rate according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only 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.

[0025] 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 posterior distribution of 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 may 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.

[0026] 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.

[0027] The following is an introduction to the professional terms that appear in this application:

[0028] OWF: Offshore Wind Farm, offshore wind farm;

[0029] CP: Cathodic Protection, cathodic protection;

[0030] EIS: Electrochemical Impedance Spectroscopy, electrochemical impedance spectroscopy;

[0031] LPR: Linear Polarization Resistance, linear polarization resistance;

[0032] SHM: Structural Health Monitoring, structural health monitoring;

[0033] RUL: Remaining Useful Life, remaining life prediction;

[0034] Bayesian Inference: Bayesian inference;

[0035] RL: Reinforcement Learning, reinforcement learning, used for dynamic scheduling and system optimization.

[0036] In this embodiment, a method for determining the posterior distribution of the corrosion rate is provided. Figure 2 is a flow chart of a method for determining a posterior distribution of a corrosion rate according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0037] Step S202: establishing a priori distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the priori distribution is used to indicate a predicted distribution of the corrosion rate;

[0038] Step S204: determining a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area;

[0039] Step S206: determining the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate.

[0040] Through the above steps, a prior distribution P(θ) of the corrosion rate of the target area is first established based on the historical data of the target area, wherein the prior distribution is used to indicate the predicted distribution of the corrosion rate; a likelihood function corresponding to the corrosion rate is determined based on the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; a posterior distribution of the corrosion rate is determined based on the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate; the above scheme is adopted to solve the problem in the related technology that the multi-pile steel structure in the offshore wind power infrastructure is in a complex corrosion environment, and the commonly used corrosion monitoring method is difficult to comprehensively monitor various corrosion forms.

[0041] In an exemplary embodiment, determining the likelihood function corresponding to the corrosion rate based on the linear polarization resistance data and electrochemical impedance spectrum data of the target area includes: collecting linear polarization resistance data of the target area through a linear polarization resistance sensor to obtain the linear polarization resistance data, and establishing a first likelihood function corresponding to the corrosion rate based on the linear polarization resistance data; collecting electrochemical impedance spectrum data of the target area through an electrochemical impedance spectrum sensor to obtain the electrochemical impedance spectrum data, and establishing a second likelihood function corresponding to the corrosion rate based on the electrochemical impedance spectrum data, wherein the likelihood function includes the first likelihood function and the second likelihood function.

[0042] LPR sensor: Linear polarization resistance sensor. It is based on the linear polarization resistance method and calculates the corrosion rate of the metal by measuring the current change on the metal surface under a small polarization voltage. This method is suitable for monitoring uniform corrosion.

[0043] EIS sensor: Electrochemical impedance spectroscopy sensor. It is based on electrochemical impedance spectroscopy. By applying AC voltage and measuring the current response, it analyzes the impedance changes at different frequencies to obtain detailed information on the corrosion state of the metal surface, including local corrosion phenomena such as pitting corrosion and crevice corrosion.

[0044] Therefore, in the embodiment of the present application, the linear polarization resistance data of the target area is first collected by the LRP sensor to obtain the linear polarization resistance data, and a first likelihood function corresponding to the corrosion rate is established based on the linear polarization resistance data; on the other hand, the electrochemical impedance spectrum data of the target area is collected by the EIS sensor to obtain the electrochemical impedance spectrum data, and a second likelihood function corresponding to the corrosion rate is established based on the electrochemical impedance spectrum data, wherein the likelihood function includes a first likelihood function and a second likelihood function.

[0045] Optionally, establishing a first likelihood function corresponding to the corrosion rate according to the linear polarization resistance data includes: calculating a preliminary estimate of the corrosion rate according to the linear polarization resistance data: Among them, v corr is the preliminary estimate, B is the material property constant, R p is the linear polarization resistance data; establishing the first likelihood function according to the preliminary estimate in, is the noise corresponding to the linear polarization resistance sensor.

[0046] After measuring the real-time polarization resistance (i.e. the linear polarization resistance data mentioned above) through LPR, a preliminary estimate of the corrosion rate is calculated This estimate is used as the likelihood function in Bayesian inference (i.e., the first likelihood function) Among them, the measured polarization resistance R p Related to the corrosion rate θ, is the noise or uncertainty in the LPR measurement.

[0047] Optionally, establishing a second likelihood function corresponding to the corrosion rate according to the electrochemical impedance spectroscopy data includes: performing equivalent circuit fitting on the electrochemical impedance spectroscopy data to obtain the linear polarization resistance data R p and the capacitance data C of the target area d ,in, Z(ω) is the electrochemical impedance spectroscopy data, Z(ω) is a complex number, R sis the solution resistance of the target area, ω is the signal frequency; the second likelihood function is established according to the linear polarization resistance data and the capacitance data Wherein, f(θ, ω) is used to indicate the correlation between the corrosion rate and the signal frequency, is the noise corresponding to the electrochemical impedance spectroscopy sensor.

[0048] EIS measures the impedance spectrum at multiple frequencies to obtain the frequency response Z(ω) of the polarization resistance (i.e., the electrochemical impedance spectrum data mentioned above), and uses equivalent circuit fitting to obtain R p and C d This data is used for the EIS likelihood function of Bayesian inference (i.e., the second likelihood function), where f(θ, ω) is the relationship between the corrosion rate and the EIS frequency response, is the uncertainty of the EIS measurement.

[0049] Optionally, determining the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function includes: determining a first posterior distribution of the corrosion rate according to a first likelihood function corresponding to the prior distribution and the linear polarization resistance data, and determining a second posterior distribution of the corrosion rate according to a second likelihood function corresponding to the prior distribution and the electrochemical impedance spectroscopy data, wherein the likelihood function includes the first likelihood function and the second likelihood function; and performing weighted summation of the first posterior distribution and the second posterior distribution to obtain the posterior distribution.

[0050] Since the data obtained from multi-mode monitoring may be noisy or inconsistent, they need to be fused through Bayesian inference to optimize the assessment of corrosion status.

[0051] Bayesian formula:

[0052]

[0053] Where, P(θ|D): the posterior distribution of the corrosion rate θ given the real-time data D;

[0054] P(θ): prior distribution, based on historical data or model prediction;

[0055] P(D|θ): likelihood function of observed data D under parameter θ;

[0056] P(D): Normalization constant to ensure that the probability distribution sums to 1.

[0057] According to the Bayesian formula and the prior distribution, the first posterior distribution and the second posterior distribution corresponding to the first likelihood function and the second likelihood function are calculated respectively; then the first posterior distribution and the second posterior distribution are weightedly summed to obtain the final posterior distribution, thereby completing data fusion.

[0058] This completes the Bayesian fusion of LPR and EIS data. When the impedance Z(ω) measured by EIS and the R measured by LPR are p When there are certain differences, Bayesian inference can adjust the estimation of corrosion rate through prior knowledge P(θ) and real-time data P(D|θ) to reduce the error of single data source.

[0059] Optionally, the method further comprises: determining the corrosion rate variability of the corrosion rate according to the posterior distribution; and when it is determined that the corrosion rate variability increases, using the formula R(t)=-(E(t)+λ·Δv corr (t)) calculates the instantaneous reward of the sensor, and adjusts the sampling frequency of the sensor according to the instantaneous reward, where R(t) is the instantaneous reward, E(t) is the sensor power consumption of the sensor, Δv corr (t) is the corrosion rate variability, λ is a trade-off parameter, and the sensor is used to collect the linear polarization resistance data and / or the electrochemical impedance spectroscopy data.

[0060] Optionally, the system can adjust the monitoring frequency through a reinforcement learning algorithm based on the form of the posterior distribution when the uncertainty in determining the corrosion rate increases, making the monitoring frequency more flexible. In order to maximize monitoring efficiency, the system dynamically schedules the monitoring frequency of the sensor through a reinforcement learning algorithm, with the goal of optimizing the sensor sampling frequency based on environmental conditions, corrosion rate changes, and sensor health status.

[0061] State space: includes current sensor data (such as EIS and LPR measurements) and sensor power consumption.

[0062] Action Space: Adjust the sampling frequency of each sensor or activate alternate sensors.

[0063] Reward function: R(t) = -(E(t) + λ · Δv corr (t)), where R(t) is the instantaneous reward of the system, which is used to adjust the sensor sampling frequency, and E(t) is the sensor power consumption. The higher the sampling frequency, the greater the power consumption. corr (t) is the uncertainty of corrosion rate estimation. The lower the frequency, the larger the estimation error. λ is a trade-off parameter that controls the balance between power consumption and monitoring accuracy.

[0064] When the corrosion rate v corrWhen the change in is large (calculated by Bayesian inference from LPR and EIS data), the system will increase the sampling frequency to more accurately capture the corrosion progress; when the environment is relatively stable, the sampling frequency will be reduced to save energy.

[0065] Specifically, reinforcement learning continuously optimizes the monitoring frequency scheduling through the state-action-reward mechanism. The system maximizes the reward function R(t) through training to achieve the best balance between accuracy and energy efficiency. Among them, π * (s) is the optimal policy, which determines the action to be taken in state s. γ is the discount factor, which weighs the long-term reward against the short-term reward.

[0066] 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.

[0067] In this embodiment, a device for determining the posterior distribution of the corrosion rate is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, which have been described and will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for 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.

[0068] Figure 3 : is a structural block diagram of a device for determining a posterior distribution of a corrosion rate according to an embodiment of the present application, the device comprising:

[0069] An establishing module 32, configured to establish a prior distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate;

[0070] A first determination module 34, configured to determine a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and the electrochemical impedance spectroscopy data of the target area;

[0071] The second determination module 36 is used to determine the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate.

[0072] Through the above-mentioned device, a prior distribution P(θ) of the corrosion rate of the target area is first established based on the historical data of the target area, wherein the prior distribution is used to indicate the predicted distribution of the corrosion rate; the likelihood function corresponding to the corrosion rate is determined based on the linear polarization resistance data and electrochemical impedance spectrum data of the target area; the posterior distribution of the corrosion rate is determined based on the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate; the above-mentioned scheme is adopted to solve the problem in the related technology that the multi-pile steel structure in the offshore wind power infrastructure is in a complex corrosion environment, and the commonly used corrosion monitoring method is difficult to comprehensively monitor various corrosion forms.

[0073] Optionally, the above-mentioned first determination module 34 is also used to collect linear polarization resistance data of the target area through a linear polarization resistance sensor to obtain the linear polarization resistance data, and establish a first likelihood function corresponding to the corrosion rate based on the linear polarization resistance data; collect electrochemical impedance spectrum data of the target area through an electrochemical impedance spectrum sensor to obtain the electrochemical impedance spectrum data, and establish a second likelihood function corresponding to the corrosion rate based on the electrochemical impedance spectrum data, wherein the likelihood function includes the first likelihood function and the second likelihood function.

[0074] Optionally, the first determination module 34 is further configured to calculate a preliminary estimated value of the corrosion rate according to the linear polarization resistance data: Among them, v corr is the preliminary estimate, B is the material property constant, R p is the linear polarization resistance data; establishing the first likelihood function according to the preliminary estimate in, is the noise corresponding to the linear polarization resistance sensor.

[0075] Optionally, the first determination module 34 is further configured to perform equivalent circuit fitting on the electrochemical impedance spectroscopy data to obtain the linear polarization resistance data R p and the capacitance data C of the target area d ,in, Z(ω) is the electrochemical impedance spectroscopy data, Z(ω) is a complex number, R s is the solution resistance of the target area, ω is the signal frequency; the second likelihood function is established according to the linear polarization resistance data and the capacitance data Wherein, f(θ, ω) is used to indicate the correlation between the corrosion rate and the signal frequency, is the noise corresponding to the electrochemical impedance spectroscopy sensor.

[0076] Optionally, the second determination module 36 is further used to determine a first posterior distribution of the corrosion rate based on a first likelihood function corresponding to the prior distribution and the linear polarization resistance data, and to determine a second posterior distribution of the corrosion rate based on a second likelihood function corresponding to the prior distribution and the electrochemical impedance spectrum data, wherein the likelihood function includes the first likelihood function and the second likelihood function; and weighted summation of the first posterior distribution and the second posterior distribution is performed to obtain the posterior distribution.

[0077] Optionally, the second determination module 36 is further configured to determine, when it is determined that the corrosion rate variability increases, the following formula R(t)=-(E(t)+λ·Δv corr (t)) calculates the instantaneous reward of the sensor, and adjusts the sampling frequency of the sensor according to the instantaneous reward, where R(t) is the instantaneous reward, E(t) is the sensor power consumption of the sensor, Δv corr (t) is the corrosion rate variability, λ is a trade-off parameter, and the sensor is used to collect the linear polarization resistance data and / or the electrochemical impedance spectroscopy data.

[0078] 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 running.

[0079] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0080] S1, establishing a prior distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate;

[0081] S2, determining a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area;

[0082] S3, determining a posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

[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, establishing a prior distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate;

[0088] S2, determining a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area;

[0089] S3, determining a posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

[0090] 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.

[0091] 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.

[0092] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0093] S1, establishing a prior distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate;

[0094] S2, determining a likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and electrochemical impedance spectroscopy data of the target area;

[0095] S3, determining a posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

[0096] 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.

[0097] 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.

[0098] 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 the posterior distribution of corrosion rate, characterized in that: include: Establishing a prior distribution P(θ) of the corrosion rate of the target area according to historical data of the target area, wherein the prior distribution is used to indicate a predicted distribution of the corrosion rate; Determining a likelihood function corresponding to the corrosion rate according to linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; A posterior distribution of the corrosion rate is determined according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate an actual distribution of the corrosion rate.

2. The method according to claim 1, characterized in that Determining the likelihood function corresponding to the corrosion rate according to the linear polarization resistance data and the electrochemical impedance spectrum data of the target area includes: Collecting linear polarization resistance data of the target area through a linear polarization resistance sensor to obtain the linear polarization resistance data, and establishing a first likelihood function corresponding to the corrosion rate according to the linear polarization resistance data; The electrochemical impedance spectroscopy data of the target area is collected by an electrochemical impedance spectroscopy sensor to obtain the electrochemical impedance spectroscopy data, and a second likelihood function corresponding to the corrosion rate is established according to the electrochemical impedance spectroscopy data, wherein the likelihood function includes the first likelihood function and the second likelihood function.

3. The method according to claim 2, characterized in that Establishing a first likelihood function corresponding to the corrosion rate according to the linear polarization resistance data includes: A preliminary estimate of the corrosion rate was calculated from the linear polarization resistance data: Among them, v corr is the preliminary estimate, B is the material property constant, R p is the linear polarization resistance data; The first likelihood function is established based on the preliminary estimate in, is the noise corresponding to the linear polarization resistance sensor.

4. The method according to claim 2, characterized in that: Establishing a second likelihood function corresponding to the corrosion rate according to the electrochemical impedance spectroscopy data includes: The electrochemical impedance spectroscopy data is fitted with an equivalent circuit to obtain the linear polarization resistance data R p and the capacitance data C of the target area d ,in, Z(ω) is the electrochemical impedance spectroscopy data, Z(ω) is a complex number, R s is the solution resistance of the target area, ω is the signal frequency; The second likelihood function is established based on the linear polarization resistance data and the capacitance data. Wherein, f(θ, ω) is used to indicate the correlation between the corrosion rate and the signal frequency, is the noise corresponding to the electrochemical impedance spectroscopy sensor.

5. The method according to claim 1, characterized in that Determining the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function includes: Determine a first posterior distribution of the corrosion rate according to a first likelihood function corresponding to the prior distribution and the linear polarization resistance data, and determine a second posterior distribution of the corrosion rate according to a second likelihood function corresponding to the prior distribution and the electrochemical impedance spectroscopy data, wherein the likelihood function includes the first likelihood function and the second likelihood function; A weighted sum is performed on the first posterior distribution and the second posterior distribution to obtain the posterior distribution.

6. The method according to claim 1, characterized in that The method further comprises: determining a corrosion rate variability of the corrosion rate based on the posterior distribution; In the case where the corrosion rate variability is determined to increase, the formula R(t) = -(E(t) + λ·Δv corr (t)) calculates the instantaneous reward of the sensor, and adjusts the sampling frequency of the sensor according to the instantaneous reward, where R(t) is the instantaneous reward, E(t) is the sensor power consumption of the sensor, Δv corr (t) is the corrosion rate variability, λ is a trade-off parameter, and the sensor is used to collect the linear polarization resistance data and / or the electrochemical impedance spectroscopy data.

7. A device for determining a posterior distribution of a corrosion rate, characterized in that: include: An establishing module, used to establish a prior distribution P(θ) of the corrosion rate of the target area according to the historical data of the target area, wherein the prior distribution is used to indicate the predicted distribution of the corrosion rate; A first determination module, configured to determine a likelihood function corresponding to the corrosion rate according to linear polarization resistance data and electrochemical impedance spectroscopy data of the target area; The second determination module is used to determine the posterior distribution of the corrosion rate according to the prior distribution and the likelihood function, wherein the posterior distribution is used to indicate the actual distribution of the corrosion rate.

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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