Corrosion early warning management method and device for storage environment

Abstract

The invention discloses a corrosion early warning management method and device for a storage environment. The method comprises the following steps: obtaining a storage environment monitoring data set and a monitoring sample measurement data set; the monitoring sample measurement data set comprises a set of sample measurement sequences obtained by measuring a monitoring sample at a plurality of historical moments; the sample measurement sequence comprises a resistance value sequence, a mass value sequence, a corrosion amount sequence and a thickness value sequence; performing clustering analysis processing on the storage environment monitoring data set to obtain a data classification model and a measurement data set of each type; constructing a corrosion early warning model of the type by utilizing the measurement data set of each type; and processing the acquired environment monitoring data and sample measurement data of the to-be-predicted storage environment by using the data classification model and the corrosion early warning model to obtain a corrosion early warning value of the to-be-predicted storage environment.

Description

Technical Field

[0001] The invention relates to the field of corrosion early warning, and in particular to a corrosion early warning management method and device for a storage environment. Background Art

[0002] Material reserves are an important part of security work and play an irreplaceable role in material security in various operations. Whether it is equipment sealing or use, anti-corrosion management is particularly important. For some high-value equipment and products, once rust occurs, the economic losses and safety hazards caused are irreparable, and its severity is difficult to measure in numbers. Therefore, monitoring the rust state of equipment and warning of the occurrence of rust in advance have extremely important economic and safety value. At present, how to achieve rapid and accurate warning of equipment corrosion based on storage environment monitoring data has become a key issue that needs to be solved urgently. Summary of the invention

[0003] The present invention mainly solves the problem of how to realize rapid and accurate early warning of equipment corrosion based on a storage environment. The present invention discloses a corrosion early warning management method and device for a storage environment.

[0004] In a first aspect of the embodiment of the present application, a corrosion early warning management method for a storage environment is disclosed, comprising:

[0005] S1, obtain a storage environment monitoring data set and a monitoring sample measurement data set; the monitoring sample measurement data set includes a set of sample measurement sequences obtained by measuring the monitoring samples at several historical moments; the sample measurement sequence includes a resistance value sequence, a mass value sequence, a corrosion amount sequence and a thickness value sequence; the storage environment monitoring data set includes a set of environmental measurement sequences obtained by measuring the storage environment at several historical moments; the environmental measurement sequence includes a temperature sequence, a humidity sequence, an oxygen content sequence and a harmful gas content sequence; the environmental measurement sequence and the sample measurement sequence establish a one-to-one correspondence according to the acquisition time;

[0006] S2, performing cluster analysis on the storage environment monitoring data set to obtain a data classification model and a measurement data set of each type;

[0007] S3, using each type of measurement data set to construct a corrosion early warning model of the type;

[0008] S4, using the data classification model and the corrosion warning model, the collected environmental monitoring data and sample measurement data of the storage environment to be predicted are processed to obtain the corrosion warning value of the storage environment to be predicted.

[0009] The data classification model and the corrosion early warning model are used to process the collected environmental monitoring data and sample measurement data of the storage environment to be predicted to obtain the corrosion early warning value of the storage environment to be predicted, including:

[0010] S41, using a data classification model, processing the collected environmental monitoring data of the storage environment to be predicted to obtain the type of the environmental monitoring data;

[0011] S42, using the corrosion warning model corresponding to the type, processing the environmental monitoring data and sample measurement data of the storage environment to be predicted, to obtain a corrosion warning value of the storage environment to be predicted.

[0012] The cluster analysis process is performed on the storage environment monitoring data set to obtain a data classification model and each type of measurement data set, including:

[0013] Each environmental measurement sequence in the storage environment monitoring data set is represented as an environmental matrix; the row vector of the environmental matrix includes a temperature sequence, a humidity sequence, an oxygen content sequence, and a harmful gas content sequence;

[0014] Perform cluster analysis on all environmental matrices to obtain the type information of environmental matrices and the environmental matrices contained in each type;

[0015] Using the environmental measurement sequences and sample measurement sequences corresponding to all environmental matrices of the same type, a measurement data set of the corresponding type is constructed;

[0016] For all environment matrices of the same type, kernel matrix calculation and boundary value calculation are performed respectively to obtain kernel matrix values ​​and boundary values ​​corresponding to the type;

[0017] Using all types of kernel matrix values ​​and boundary values, a data classification model is constructed.

[0018] The expression for calculating the kernel matrix is:

[0019]

[0020] Among them, a i,kj is the element of the kth row and jth column of the ith environment matrix of the same type, P is the number of environment matrices contained in the type, ω1 and ω2 are the preset first weighting factor and the second weighting factor respectively, α kj is the element of the k-th row and j-th column of the kernel matrix corresponding to the type, is the mean of the j-th column elements of the i-th environment matrix of the same type, is the mean of the elements in the kth row of the i-th environment matrix of the same type, and K is the column dimension of the environment matrix.

[0021] The boundary value calculation expression is:

[0022]

[0023] Among them, b kj is the range value of the elements in the kth row and jth column of all environment matrices of the same type, μ kj ,δ kj and η kj are the mean, variance and median values ​​of the elements in the kth row and jth column of all environment matrices of the same type, β kj is the boundary value of the element in the kth row and jth column of the environment matrix of the type.

[0024] The discriminant expression of the data classification model is:

[0025] φ kj -α kj |≤β kj , 1≤k≤J,1≤j≤K,

[0026] Among them, φ kj is the element of the kth row and jth column of the environment matrix to be classified, J is the row dimension of the environment matrix, φ kj -α kj |≤β kj is the discriminant expression corresponding to the element in the kth row and jth column of the environment matrix, and J is the row dimension of the environment matrix;

[0027] When each element of the environment matrix to be classified satisfies the corresponding discriminant expression, it is determined that the environment matrix to be classified belongs to the type corresponding to the kernel matrix value and the boundary value.

[0028] The method of using each type of measurement data set to construct a corrosion early warning model of the type includes:

[0029] S31, taking each type of environmental measurement sequence as a system measurement vector, taking the sample measurement sequence of the type as a target state vector, and constructing a system state transfer equation of the type using a linear system state equation modeling method;

[0030] The expression of the system state transfer equation is:

[0031] x(k+1)=Φ(k+1,k)x(k)+G(k)w(k),

[0032] z(k)=H(k)x(k)+v(k),,

[0033] Wherein, Φ(k+1,k) represents the state transfer matrix at time k, H(k) represents the measurement matrix at time k, G(k) represents the input matrix at time k, w(k) and v(k) represent the state noise and measurement noise at time k, respectively, x(k) represents the target state vector at time k, z(k) represents the system measurement vector at time k, and x(k) represents the predicted target state vector at time k+1;

[0034] S32, solving each type of system state transfer equation to obtain a corrosion measurement value prediction model of the type;

[0035] S33, constructing a corrosion early warning evaluation model; the expression of the corrosion early warning evaluation model is:

[0036]

[0037]

[0038] Among them, yf is the corrosion warning value, H2() is the second-order Hermitian function, and p i is the i-th corrosion warning component, A ij is the element of the i-th row and j-th column of the environmental measurement matrix constructed by using the environmental measurement sequence. The 1st to 4th rows of the environmental measurement matrix are respectively the temperature sequence, the humidity sequence, the oxygen content sequence, and the harmful gas content sequence. i is the i-th standard value, z1 to z4 are the temperature standard value, humidity standard value, oxygen content standard value, and harmful gas content standard value, respectively. is the mean of the i-th row vector of the environmental measurement matrix, M and N are the row dimension and column dimension of the environmental measurement matrix respectively, and exp represents the power operation of the constant e;

[0039] S34, fuse the corrosion warning assessment model and each type of corrosion measurement value prediction model to obtain the corrosion warning model of the type; in the corrosion warning model, use the output of the corrosion measurement value prediction model as the input of the corrosion warning assessment model; the output of the corrosion warning assessment model is the corrosion warning value of the storage environment; the input of the corrosion measurement value prediction model is the input of the corrosion warning model; the output of the corrosion warning model is the output of the corrosion warning assessment model; the input of the corrosion measurement value prediction model is the environmental measurement sequence and sample measurement sequence of the storage environment.

[0040] In a second aspect of the embodiment of the present application, a corrosion early warning management device for a storage environment is disclosed, the device comprising:

[0041] A memory storing executable program code;

[0042] a processor coupled to the memory;

[0043] The processor calls the executable program code stored in the memory to execute the corrosion early warning management method for the storage environment.

[0044] In a third aspect of an embodiment of the present application, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the corrosion early warning management method for a storage environment.

[0045] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the corrosion early warning management method of the storage environment.

[0046] The beneficial effects of the present invention are:

[0047] The present invention is based on the storage environment, obtains a storage environment monitoring data set and a monitoring sample measurement data set to achieve rapid and accurate early warning of equipment corrosion.

[0048] The present invention obtains a storage environment monitoring data set and a monitoring sample measurement data set, performs cluster analysis on the storage environment monitoring data set, obtains a data classification model and a measurement data set of each type, and uses the data classification model and the corrosion warning model to process the collected environmental monitoring data and sample measurement data of the storage environment to be predicted, and obtains the corrosion warning value of the storage environment to be predicted. Before the corrosion warning is performed, the present invention first classifies various types of storage environments according to the collected storage environment monitoring data set, and establishes a special corrosion warning model according to the classification results, thereby improving the accuracy and applicable scenarios of the corrosion warning.

[0049] In the process of constructing a data classification model, the present invention establishes a calculation model of a kernel matrix and a boundary value, ensuring that the calculated kernel matrix and boundary value can reflect the characteristics of each type of data, thereby ensuring the accuracy of data classification.

[0050] In the process of constructing the equipment corrosion early warning model, the present invention first establishes a spatial description equation for the sample corrosion process, and then constructs a corrosion assessment model based on the equation. Through this two-step approach, the complexity of the corrosion early warning is reduced and the real-time nature of the corrosion early warning is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the content of the present invention, an embodiment is given here.

[0053] Figure 1 It is a flow chart for implementing the method of the present invention.

[0054] In a first aspect of the embodiment of the present application, a corrosion early warning management method for a storage environment is disclosed, comprising:

[0055] S1, obtain a storage environment monitoring data set and a monitoring sample measurement data set; the monitoring sample measurement data set includes a set of sample measurement sequences obtained by measuring the monitoring samples at several historical moments; the sample measurement sequence includes a resistance value sequence, a mass value sequence, a corrosion amount sequence and a thickness value sequence, specifically, the measurement results of the resistance value, corrosion amount, mass value and thickness value of the flat resistance probe of the corrosion sensor; the storage environment monitoring data set includes a set of environmental measurement sequences obtained by measuring the storage environment at several historical moments; the environmental measurement sequence includes a temperature sequence, a humidity sequence, an oxygen content sequence, and a harmful gas content sequence; a one-to-one correspondence is established between the environmental measurement sequence and the sample measurement sequence according to the acquisition time;

[0056] S2, performing cluster analysis on the storage environment monitoring data set to obtain a data classification model and a measurement data set of each type;

[0057] S3, using each type of measurement data set to construct a corrosion early warning model of the type;

[0058] S4, using the data classification model and the corrosion early warning model, processing the collected environmental monitoring data and sample measurement data of the storage environment to be predicted, and obtaining the corrosion early warning value of the storage environment to be predicted;

[0059] The storage environment monitoring data set and the monitoring sample measurement data set obtained can be collected by sensors.

[0060] The data classification model and the corrosion early warning model are used to process the collected environmental monitoring data and sample measurement data of the storage environment to be predicted to obtain the corrosion early warning value of the storage environment to be predicted, including:

[0061] S41, using a data classification model, processing the collected environmental monitoring data of the storage environment to be predicted to obtain the type of the environmental monitoring data;

[0062] S42, using the corrosion warning model corresponding to the type, processing the environmental monitoring data and sample measurement data of the storage environment to be predicted, to obtain a corrosion warning value of the storage environment to be predicted.

[0063] The cluster analysis process is performed on the storage environment monitoring data set to obtain a data classification model and each type of measurement data set, including:

[0064] Each environmental measurement sequence in the storage environment monitoring data set is represented as an environmental matrix; the row vector of the environmental matrix includes a temperature sequence, a humidity sequence, an oxygen content sequence, and a harmful gas content sequence;

[0065] Perform cluster analysis on all environmental matrices to obtain the type information of environmental matrices and the environmental matrices contained in each type;

[0066] Using the environmental measurement sequences and sample measurement sequences corresponding to all environmental matrices of the same type, a measurement data set of the corresponding type is constructed;

[0067] For all environment matrices of the same type, kernel matrix calculation and boundary value calculation are performed respectively to obtain kernel matrix values ​​and boundary values ​​corresponding to the type;

[0068] Using all types of kernel matrix values ​​and boundary values, a data classification model is constructed.

[0069] The expression for calculating the kernel matrix is:

[0070]

[0071] Among them, a i,kj is the element of the kth row and jth column of the ith environment matrix of the same type, P is the number of environment matrices contained in the type, ω1 and ω2 are the preset first weighting factor and the second weighting factor respectively, α kj is the k-th row and j-th column element of the kernel matrix corresponding to the type, is the mean of the j-th column elements of the i-th environment matrix of the same type, is the mean of the elements in the kth row of the i-th environment matrix of the same type, and K is the column dimension of the environment matrix.

[0072] The boundary value calculation expression is:

[0073]

[0074] Among them, b kj is the range value of the elements in the kth row and jth column of all environment matrices of the same type, μ kj ,δ kj and η kj are the mean, variance and median values ​​of the elements in the kth row and jth column of all environment matrices of the same type, β kj is the boundary value of the element in the kth row and jth column of the environment matrix of the type.

[0075] The discriminant expression of the data classification model is:

[0076] φkj -α kj |≤β kj , 1≤k≤J,1≤j≤K,

[0077] Among them, φ kj is the element of the kth row and jth column of the environment matrix to be classified, J is the row dimension of the environment matrix, φ kj -α kj |≤β kj is the discriminant expression corresponding to the element in the kth row and jth column of the environment matrix, and J is the row dimension of the environment matrix;

[0078] When each element of the environment matrix to be classified satisfies the corresponding discriminant expression, it is determined that the environment matrix to be classified belongs to the type corresponding to the kernel matrix value and the boundary value.

[0079] The method of using each type of measurement data set to construct a corrosion early warning model of the type includes:

[0080] S31, taking each type of environmental measurement sequence as a system measurement vector, taking the sample measurement sequence of the type as a target state vector, and constructing a system state transfer equation of the type using a linear system state equation modeling method;

[0081] The expression of the system state transfer equation is:

[0082]

[0083] Wherein, Φ(k+1,k) represents the state transfer matrix at time k, H(k) represents the measurement matrix at time k, G(k) represents the input matrix at time k, w(k) and v(k) represent the state noise and measurement noise at time k, respectively, x(k) represents the target state vector at time k, z(k) represents the system measurement vector at time k, and x(k) represents the predicted target state vector at time k+1;

[0084] S32, solving each type of system state transfer equation to obtain a corrosion measurement value prediction model of the type;

[0085] S33, constructing a corrosion early warning evaluation model; the expression of the corrosion early warning evaluation model is:

[0086]

[0087]

[0088] Among them, yf is the corrosion warning value, H2() is the second-order Hermitian function, and p i is the i-th corrosion warning component, A ijis the element of the i-th row and j-th column of the environmental measurement matrix constructed by using the environmental measurement sequence. The 1st to 4th rows of the environmental measurement matrix are respectively the temperature sequence, the humidity sequence, the oxygen content sequence, and the harmful gas content sequence. i is the i-th standard value, z1 to z4 are the temperature standard value, humidity standard value, oxygen content standard value, and harmful gas content standard value, respectively. is the mean of the i-th row vector of the environmental measurement matrix, M and N are the row dimension and column dimension of the environmental measurement matrix respectively, and exp represents the power operation of the constant e;

[0089] S34, fusing the corrosion early warning evaluation model and each type of corrosion measurement value prediction model to obtain the type of corrosion early warning model; in the corrosion early warning model, using the output of the corrosion measurement value prediction model as the input of the corrosion early warning evaluation model; the output of the corrosion early warning evaluation model is the corrosion early warning value of the storage environment; the input of the corrosion measurement value prediction model is the input of the corrosion early warning model; the input of the corrosion measurement value prediction model is the environmental measurement sequence and the sample measurement sequence of the storage environment;

[0090] The data classification model is used to process the collected environmental monitoring data of the storage environment to be predicted to obtain the type of environmental monitoring data, which is to represent the environmental monitoring data as an environmental matrix and solve the environmental matrix. The environmental monitoring data of the storage environment to be predicted is an environmental measurement sequence; the sample measurement data of the storage environment to be predicted is a sample measurement sequence;

[0091] The state transfer equation of each type of system is solved by using the Kalman filter method to solve Φ(k+1,k), H(k) and G(k).

[0092] Before performing S2, it also includes data preprocessing of the storage environment monitoring data set and the monitoring sample measurement data set;

[0093] The data preprocessing includes: data cleaning processing, time registration processing and mode checking processing;

[0094] The row mode inspection process comprises:

[0095] S231, for each type of data attribute of the data set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, filter modeling is performed to obtain regression models of the data attributes of each type;

[0096] S232, using the regression model, calculating and processing the independent variable to obtain a regression data value; determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the second data set; if it is less than or equal to the first regression discrimination threshold, not processing the data;

[0097] S233, performing fusion processing on all data of the data set after executing S231 to S232 to obtain a preprocessed data set;

[0098] The filtering modeling may adopt linear autoregressive filtering or α-β filtering;

[0099] The data collection information is a data sequence number value or a data collection time value or a frequency value corresponding to the data;

[0100] The time registration process is to unify different types of data to the same time reference; the time registration process can adopt the internal push / extrapolation method, the Lagrange three-point interpolation method, etc.;

[0101] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0102] The corrosion warning value is used to characterize the possibility of corrosion in the storage environment to be predicted. The larger the value, the more likely the storage environment to be predicted is to be corroded.

[0103] The harmful gases include sulfur dioxide, hydrogen sulfide and chlorides;

[0104] The environmental measurement sequences and sample measurement sequences corresponding to all environmental matrices are the environmental measurement sequences corresponding to the environmental matrices and the sample measurement sequences corresponding to the environmental measurement sequences;

[0105] ω1 and ω2 are respectively the preset first weighting factor and the second weighting factor, and the values ​​are 0.4 and 0.3 respectively.

[0106] The cluster analysis may adopt density-based clustering, graph clustering, spectral clustering, etc.

[0107] The Hermitian function is a Hermite function.

[0108] In a second aspect of an embodiment of the present invention, a corrosion early warning management device for a storage environment is disclosed, the device comprising:

[0109] A memory storing executable program code;

[0110] a processor coupled to the memory;

[0111] The processor calls the executable program code stored in the memory to execute the corrosion early warning management method for the storage environment.

[0112] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the corrosion early warning management method for a storage environment.

[0113] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the corrosion early warning management method for the storage environment.

[0114] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A corrosion early warning management method for a storage environment, characterized in that: include: S1, obtain a storage environment monitoring data set and a monitoring sample measurement data set; the monitoring sample measurement data set includes a set of sample measurement sequences obtained by measuring the monitoring samples at several historical moments; the sample measurement sequence includes a resistance value sequence, a mass value sequence, a corrosion amount sequence and a thickness value sequence; the storage environment monitoring data set includes a set of environmental measurement sequences obtained by measuring the storage environment at several historical moments; the environmental measurement sequence includes a temperature sequence, a humidity sequence, an oxygen content sequence and a harmful gas content sequence; the environmental measurement sequence and the sample measurement sequence establish a one-to-one correspondence according to the acquisition time; S2, performing cluster analysis on the storage environment monitoring data set to obtain a data classification model and a measurement data set of each type; S3, using each type of measurement data set to construct a corrosion early warning model of the type; S4, using the data classification model and the corrosion warning model, the collected environmental monitoring data and sample measurement data of the storage environment to be predicted are processed to obtain the corrosion warning value of the storage environment to be predicted.

2. The corrosion early warning management method for storage environment according to claim 1, characterized in that: The data classification model and the corrosion early warning model are used to process the collected environmental monitoring data and sample measurement data of the storage environment to be predicted to obtain the corrosion early warning value of the storage environment to be predicted, including: S41, using a data classification model, processing the collected environmental monitoring data of the storage environment to be predicted to obtain the type of the environmental monitoring data; S42, using the corrosion warning model corresponding to the type, processing the environmental monitoring data and sample measurement data of the storage environment to be predicted, to obtain a corrosion warning value of the storage environment to be predicted.

3. The corrosion early warning management method for storage environment according to claim 2, characterized in that: The cluster analysis process is performed on the storage environment monitoring data set to obtain a data classification model and each type of measurement data set, including: Each environmental measurement sequence in the storage environment monitoring data set is represented as an environmental matrix; the row vector of the environmental matrix includes a temperature sequence, a humidity sequence, an oxygen content sequence, and a harmful gas content sequence; Perform cluster analysis on all environmental matrices to obtain the type information of environmental matrices and the environmental matrices contained in each type; Using the environmental measurement sequences and sample measurement sequences corresponding to all environmental matrices of the same type, a measurement data set of the corresponding type is constructed; For all environment matrices of the same type, kernel matrix calculation and boundary value calculation are performed respectively to obtain kernel matrix values ​​and boundary values ​​corresponding to the type; Using all types of kernel matrix values ​​and boundary values, a data classification model is constructed.

4. The corrosion early warning management method for storage environment according to claim 3, characterized in that: The expression for calculating the kernel matrix is: Among them, a i,kj is the element of the kth row and jth column of the ith environment matrix of the same type, P is the number of environment matrices contained in the type, ω1 and ω2 are the preset first weighting factor and the second weighting factor respectively, α kj is the element of the k-th row and j-th column of the kernel matrix corresponding to the type, is the mean of the j-th column elements of the i-th environment matrix of the same type, is the mean of the k-th row elements of the i-th environment matrix of the same type, and K is the column dimension of the environment matrix.

5. The corrosion early warning management method for storage environment according to claim 4, characterized in that: The boundary value calculation expression is: Among them, b kj is the range value of the elements in the kth row and jth column of all environment matrices of the same type, μ kj , δ kj and η kj are the mean, variance and median values ​​of the elements in the kth row and jth column of all environment matrices of the same type, β kj is the boundary value of the element in the kth row and jth column of the environment matrix of the type.

6. The corrosion early warning management method for storage environment according to claim 5, characterized in that: The discriminant expression of the data classification model is: f kj -a kj |≤β kj ,1≤k≤J,1≤j≤K, Among them, φ kj is the element of the kth row and jth column of the environment matrix to be classified, J is the row dimension of the environment matrix, φ kj -α kj |≤β kj is the discriminant expression corresponding to the element in the kth row and jth column of the environment matrix, and J is the row dimension of the environment matrix; When each element of the environment matrix to be classified satisfies the corresponding discriminant expression, it is determined that the environment matrix to be classified belongs to the type corresponding to the kernel matrix value and the boundary value.

7. The corrosion early warning management method for storage environment according to claim 1, characterized in that: The method of using each type of measurement data set to construct a corrosion early warning model of the type includes: S31, taking each type of environmental measurement sequence as a system measurement vector, taking the sample measurement sequence of the type as a target state vector, and constructing a system state transfer equation of the type using a linear system state equation modeling method; The expression of the system state transfer equation is: Wherein, Φ(k+1,k) represents the state transfer matrix at time k, H(k) represents the measurement matrix at time k, G(k) represents the input matrix at time k, w(k) and v(k) represent the state noise and measurement noise at time k, respectively, x(k) represents the target state vector at time k, z(k) represents the system measurement vector at time k, and x(k) represents the predicted target state vector at time k+1; S32, solving each type of system state transfer equation to obtain a corrosion measurement value prediction model of the type; S33, constructing a corrosion early warning evaluation model; the expression of the corrosion early warning evaluation model is: Among them, yf is the corrosion warning value, H2() is the second-order Hermitian function, and p i is the i-th corrosion warning component, A ij is the element of the i-th row and j-th column of the environmental measurement matrix constructed by using the environmental measurement sequence. The 1st to 4th rows of the environmental measurement matrix are respectively the temperature sequence, the humidity sequence, the oxygen content sequence, and the harmful gas content sequence. i is the i-th standard value, z1 to z4 are the temperature standard value, humidity standard value, oxygen content standard value, and harmful gas content standard value, respectively. is the mean of the i-th row vector of the environmental measurement matrix, M and N are the row dimension and column dimension of the environmental measurement matrix respectively, and exp represents the power operation of the constant e; S34, fuse the corrosion warning assessment model and each type of corrosion measurement value prediction model to obtain the corrosion warning model of the type; in the corrosion warning model, use the output of the corrosion measurement value prediction model as the input of the corrosion warning assessment model; the output of the corrosion warning assessment model is the corrosion warning value of the storage environment; the input of the corrosion measurement value prediction model is the input of the corrosion warning model; the output of the corrosion warning model is the output of the corrosion warning assessment model; the input of the corrosion measurement value prediction model is the environmental measurement sequence and sample measurement sequence of the storage environment.

8. A corrosion early warning management device for a storage environment, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the corrosion early warning management method for a storage environment according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the corrosion early warning management method for a storage environment as described in any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the corrosion early warning management method for a storage environment as described in any one of claims 1 to 7.

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