Temperature correction method, device and equipment for non-uniform temperature field of containment and medium

Abstract

The application provides a containment non-uniform temperature field temperature correction method, device, equipment and medium, and the optimization method comprises the following steps: collecting the data of the monitoring points in each region to obtain a training data set; dividing each region along a thickness direction to generate a plurality of sub-regions with equal thickness, and obtaining to-be-inverted parameters of each sub-region; inputting the to-be-inverted parameters of each sub-region and a plurality of temperature deviation data into a finite element model to generate strain calculation data of each sub-region under the temperature deviation data; calculating the inversion parameters of all sub-regions; and optimizing the finite element model according to the inversion parameters. Through the containment non-uniform temperature field temperature correction method, device, equipment and medium provided by the application, the analysis accuracy of the containment structure under complex temperature conditions can be improved.

Description

Technical Field

[0001] The present invention relates to the field of nuclear power, and in particular to a method, device, equipment and medium for correcting a non-uniform temperature field of a containment vessel. Background Art

[0002] In nuclear power plants, the containment serves as the third safety barrier, and the safety and reliability of its structure are of paramount importance. The containment typically utilizes a biaxially prestressed concrete cylinder structure. As the nuclear power plant ages, the containment will shrink and creep under the action of biaxial prestressing. This phenomenon occurs rapidly in the early stages, but becomes more gradual over time. However, the temperature strain caused by temperature changes may exceed the effects of shrinkage and creep at this point. If the influence of temperature strain cannot be effectively eliminated, it will be difficult to accurately extract shrinkage and creep data from actual monitoring data to reliably evaluate and predict the structural safety and reliability of the containment, which may ultimately affect the safe operation of the nuclear power plant.

[0003] Current technology typically assumes that uniform temperature within concrete produces stress-free, uniformly distributed strain, while temperature gradients produce strain-free stress. Strain-temperature corrections are based on this assumption. Furthermore, the thermal expansion coefficient of the containment concrete is typically set to a fixed value. However, the containment is a statically indeterminate structure, and its temperature field is non-uniform and unstable due to the influence of sunlight and seasonal variations. Furthermore, concrete itself is a heterogeneous material, and due to construction factors, the properties of each part may vary significantly. Using uniform material parameters may result in deviations between the calculated results and the actual situation.

[0004] The existing finite element model of the containment has shortcomings in dealing with the strain correction caused by non-uniform temperature fields. Therefore, it is necessary to further study and improve the model to improve the analysis accuracy of the containment structure under complex temperature conditions, so as to better ensure the safety of nuclear power plants. Summary of the Invention

[0005] The object of the present invention is to provide a method, device, equipment and medium for correcting the non-uniform temperature field of a containment vessel, which can improve the analysis accuracy of the containment vessel structure under complex temperature conditions.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention provides a method for correcting a non-uniform temperature field of a containment vessel, comprising:

[0008] Dividing the cross section of the containment into a plurality of equally divided regions, collecting data from monitoring points in each region to obtain a training data set, wherein the training data set includes: temperature deviation data of all regions, strain monitoring data of all monitoring points, and relative strain data of all monitoring points;

[0009] Dividing each of the regions along the thickness direction to generate a plurality of sub-regions of equal thickness, obtaining the concrete expansion coefficient of each sub-region and expressing it as a corresponding parameter to be inverted, wherein each of the sub-regions includes at least one monitoring point, and the parameter to be inverted is within a value range;

[0010] Inputting the parameters to be inverted and a plurality of temperature deviation data of each sub-region into a finite element model, and generating strain calculation data of the monitoring points of each sub-region under different temperature deviation data;

[0011] Calculating the mean square error of the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, obtaining the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and expressing it as the inversion parameter of the sub-region;

[0012] The value range of the parameters to be inverted in the finite element model is optimized according to the inversion parameters of each sub-region to generate an optimized finite element model, and the temperature of the containment under the non-uniform temperature field is corrected according to the optimized finite element model.

[0013] In one embodiment of the present invention, the step of collecting data from monitoring points in each of the areas to obtain a training data set includes:

[0014] A plurality of monitoring points are set at equal intervals within each of the regions along the thickness direction, and an initial data set is collected from all monitoring points; wherein the monitoring points close to the outside of the containment shell are denoted as outside monitoring points, and the initial data set includes: strain monitoring data and temperature monitoring data from all monitoring points at each time of each day within a preset time period;

[0015] Fitting and sorting the temperature monitoring data of the outer monitoring points of each day in the initial data set according to each time to obtain the optimal time in each area of ​​each day, filtering the data in the initial data set according to the optimal time to generate an intermediate data set;

[0016] According to the comparison result of the goodness of fit of each area in the intermediate data set and the preset goodness threshold, the strain monitoring data of all monitoring points in each area at the optimal time under the compared days and the temperature monitoring data of each area are retained to generate a training data set.

[0017] In one embodiment of the present invention, the temperature monitoring data of the external monitoring points of each day in the initial data set are fitted and sorted according to each time to obtain the optimal time in each area of ​​each day, and the data in the initial data set are filtered according to the optimal time to generate the intermediate data set, including:

[0018] Calculate the average of the temperature monitoring data of other monitoring points at all times in each area of ​​each day;

[0019] Performing linear fitting on a plurality of temperature monitoring data of the outer monitoring points of each of the areas on each day with the corresponding mean value to calculate the goodness of fit of each of the areas at each moment on each day;

[0020] Sort the goodness of fit corresponding to each area at all times of each day, and obtain the time corresponding to the best goodness of fit for each area of ​​each day, and use the time as the best time for the corresponding area of ​​that day;

[0021] An intermediate data set is generated based on the strain monitoring data of all monitoring points corresponding to each area at the optimal time every day, the temperature monitoring data of all monitoring points and the goodness of fit of the area.

[0022] In one embodiment of the present invention, the step of retaining the strain monitoring data of all monitoring points in each region at the optimal time and the temperature monitoring data of each region on the days that pass the comparison based on the goodness of fit of each region in the intermediate data set and a preset goodness threshold, and generating a training data set includes:

[0023] The goodness of fit of each of the regions on each day is judged against a preset goodness threshold, and data whose goodness of fit of each of the regions on the same day is greater than the preset goodness threshold is screened out to generate a target data set; wherein the target data set includes: the temperature deviation value of each of the regions at the optimal time and the strain monitoring data of all monitoring points on the days that pass the screening; the temperature deviation value represents the difference between the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell for each of the regions; the temperature fitting value is calculated by fitting the depth of the monitoring point inside the shell and the corresponding temperature monitoring data to obtain the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell;

[0024] The earliest day among the screened days is obtained as the initial day, the strain monitoring data of all monitoring points on the initial day is used as the initial value, the strain monitoring data of all monitoring points on other days are subtracted from the initial value to calculate the relative value, and a training data set is obtained; wherein the training data set includes: temperature deviation data of all areas, strain monitoring data of all monitoring points, and relative strain data of all monitoring points on other days.

[0025] In one embodiment of the present invention, the step of inputting the parameters to be inverted and the plurality of temperature deviation data of each sub-region into the finite element model and generating the strain calculation data of the monitoring points of each sub-region under different temperature deviation data includes:

[0026] Taking a value for the parameter to be inverted for each sub-region within a value range;

[0027] Inputting the parameters to be inverted and a certain temperature deviation data of each sub-region into the finite element model, and calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data;

[0028] Inputting the parameters to be inverted and another temperature deviation data of each sub-region into a finite element model, and calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data;

[0029] A linear fitting function is generated using the temperature deviation data as an independent variable and the strain calculation data as a dependent variable, and a strain calculation data set under different temperature deviation data is generated according to the linear fitting function, wherein the strain calculation data set includes the strain calculation data of the monitoring points of all sub-areas.

[0030] In one embodiment of the present invention, after the step of inputting the parameters to be inverted and the plurality of temperature deviation data of each sub-region into the finite element model to generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data, the method further includes:

[0031] Taking multiple values ​​of the parameter to be inverted for each sub-region within a value range until the number of values ​​taken reaches a preset number;

[0032] After each value is taken, the strain calculation data of the monitoring point of each sub-region under different temperature deviation data is repeatedly calculated, and after obtaining different linear fitting functions, a corresponding strain calculation data set is generated.

[0033] In one embodiment of the present invention, the step of calculating the mean square error between the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, obtaining the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and expressing the parameter as the inversion parameter of the sub-region includes:

[0034] After each value is taken, the mean square error between the strain calculation data set and the corresponding relative strain data of the monitoring point in each sub-region is calculated;

[0035] The mean square error is screened to obtain the to-be-inverted parameter corresponding to the minimum mean square error, which is expressed as the inversion parameter of the monitoring points in all sub-areas.

[0036] The present invention also provides a temperature correction device for a non-uniform temperature field of a containment vessel, comprising:

[0037] A first partitioning module is configured to partition the cross section of the containment vessel into a plurality of equally divided regions, collect data from monitoring points in each region, and obtain a training data set, wherein the training data set includes temperature deviation data of all regions, strain monitoring data of all monitoring points, and relative strain data of all monitoring points;

[0038] A second division module is configured to divide each of the regions along a thickness direction to generate a plurality of sub-regions of equal thickness, obtain a concrete expansion coefficient of each sub-region, and express the coefficient as a corresponding parameter to be inverted, wherein each sub-region includes at least one monitoring point, and the parameter to be inverted is within a value range;

[0039] a parameter optimization module, configured to input the parameters to be inverted and a plurality of temperature deviation data of each sub-region into a finite element model, and generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data;

[0040] a parameter calculation module, configured to calculate the mean square error between the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, and obtain the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and express it as the inversion parameter of the sub-region; and

[0041] The model optimization module is used to optimize the value range of the parameters to be inverted in the finite element model according to the inversion parameters of each sub-region to generate an optimized finite element model, and to correct the temperature of the containment under the non-uniform temperature field according to the optimized finite element model.

[0042] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the temperature correction method for the non-uniform temperature field of the containment are implemented.

[0043] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a temperature correction method for a non-uniform temperature field of a containment vessel.

[0044] As described above, the present invention provides a method, device, equipment, and medium for correcting the non-uniform temperature field of a containment vessel. By more precisely accounting for the containment vessel's hyperstatic state, material heterogeneity, and the non-uniform distribution of the temperature field, this method can more accurately predict the strain of the containment vessel under different temperature conditions. Compared to traditional simplified correction methods, this method significantly improves the accuracy of strain and temperature corrections by utilizing actual monitoring data and finite element models. This precise temperature correction method allows for a better understanding of the containment vessel's actual state, avoiding unnecessary excessive maintenance or repairs, and thus optimizing maintenance costs and resource allocation.

[0045] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 Flowchart of a method for correcting a non-uniform temperature field in a containment vessel according to an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of a containment vessel in one embodiment of the present invention;

[0049] Figure 3 Schematic diagram of a temperature correction device for a non-uniform temperature field of a containment vessel according to an embodiment of the present invention;

[0050] Figure 4 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0051] In the figure: 10, first area; 20, second area; 30, third area; 40, fourth area; 100, first division module; 200, second division module; 300, parameter optimization module; 400, parameter calculation module; 500, model optimization module; 1, electronic device; 12, memory; 13, processor. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] See also Figure 1 The present invention provides a method for correcting the temperature of a containment vessel's non-uniform temperature field. This method can optimize the containment vessel's finite element model so that the optimized finite element model can improve the accuracy of analyzing the containment vessel structure under complex temperature conditions, thereby better ensuring the safety of the nuclear power plant. The optimization method may include the following steps:

[0054] Step S10: Divide the cross section of the containment into multiple equally divided regions, collect data from monitoring points in each region, and obtain a training data set. The training data set includes: temperature deviation data of all regions, strain monitoring data of all monitoring points, and relative strain data of all monitoring points.

[0055] Step S20: Divide each region along the thickness direction to generate multiple sub-regions of equal thickness, obtain the concrete expansion coefficient of each sub-region, and express it as a corresponding parameter to be inverted, wherein each sub-region includes at least one monitoring point, and the parameter to be inverted is within a value range;

[0056] Step S30: input the parameters to be inverted and multiple temperature deviation data of each sub-region into the finite element model to generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data;

[0057] Step S40: Calculate the mean square error between the strain calculation data and the corresponding relative strain data of the monitoring points in all sub-regions, and obtain the parameter to be inverted corresponding to the minimum mean square error of the monitoring points in each sub-region, which is expressed as the inversion parameter of the sub-region;

[0058] Step S50: Optimize the value range of the parameters to be inverted in the finite element model according to the inversion parameters of each sub-region to generate an optimized finite element model, and correct the temperature of the containment under the non-uniform temperature field according to the optimized finite element model.

[0059] In one embodiment, when step S10 is executed, specifically, step S10 may include the following steps:

[0060] Step S11: multiple monitoring points are set at equal intervals in each region along the thickness direction, and an initial data set of all monitoring points is collected; wherein the monitoring points close to the outside of the containment shell are denoted as outside monitoring points, and the initial data set includes: strain monitoring data and temperature monitoring data of all monitoring points at each time of each day within a preset time period;

[0061] Step S12: fitting and sorting the temperature monitoring data of the outer monitoring points of each day in the initial data set according to each time to obtain the optimal time in each area of ​​each day, filtering the data in the initial data set according to the optimal time to generate an intermediate data set;

[0062] Step S13: Based on the comparison result of the goodness of fit of each region in the intermediate data set with the preset goodness threshold, the strain monitoring data of all monitoring points in each region at the optimal time under the compared days and the temperature monitoring data of each region are retained to generate a training data set.

[0063] In one embodiment, when executing step S11, specifically, a nuclear power plant containment vessel that has been tensioned for more than three years can be selected as the research object. At this time, the shrinkage and creep of the containment vessel have entered a stable stage, and it can be assumed that within a shorter sampling period, the strain is mainly caused by temperature changes. The internal temperature of the containment vessel is basically constant under normal operating conditions, while the external temperature changes are more significant due to the influence of environmental factors such as sunlight and seasonal changes. The temperature distribution of the outer cylinder wall refers to the temperature distribution law of the sunlight cylinder wall of the cooling tower. It is assumed to be uniformly distributed in the cylinder height direction, and unevenly distributed in the cylinder circumference direction according to different sunlight angles.

[0064] In one embodiment, temperature and strain sensors are embedded in the containment vessel at various locations and depths to monitor temperature and strain changes. An online data acquisition system is installed, and sampling intervals are set to continuously collect strain and temperature data. The sampling interval can be 1 or 2 hours, with no specific limit.

[0065] In one embodiment, the containment vessel's cross section can be vertically divided into multiple equally spaced zones. The temperature within each zone is assumed to be stable, i.e., the temperature distribution is linear across the cross section at any height, and the temperature distribution is uniform across the entire zone. Within each zone, multiple monitoring points are set at equal intervals along the thickness, each of which can be equipped with a temperature sensor and a strain sensor. For example, four monitoring points can be set within each zone.

[0066] In one embodiment, an initial dataset is collected from all monitoring points, including strain and temperature monitoring data from all monitoring points at every time of day (every two hours, such as 0:00, 2:00, 4:00, etc., through 22:00) over a preset time period. This method allows for the acquisition of strain and temperature variation data for the containment under different conditions, including at different locations, depths, and time. The preset time period can be one year, two years, or other specific time period, but there is no specific limit.

[0067] In one embodiment, during data collection, for example, at a particular monitoring point, the temperature at 0:00 on a particular day is 25°C and the strain is 120 με; at 2:00, the temperature is 24°C and the strain is 119 με, and so on, forming a complete time dataset for that monitoring point on that particular day. Subsequently, an initial dataset can be generated based on the time datasets of all monitoring points each day. This initial dataset can include strain and temperature monitoring data for all monitoring points at every moment of each day within a preset time period.

[0068] In one embodiment, when step S12 is executed, specifically, step S12 may include the following steps:

[0069] Step S121: Calculate the average of the temperature monitoring data of other monitoring points in each area at all times every day;

[0070] Step S122: linearly fit the temperature monitoring data of the outer monitoring points of each area every day to the corresponding mean value to calculate the goodness of fit of each area at each time every day;

[0071] Step S123: sort the goodness of fit corresponding to each area at all times of each day, obtain the time corresponding to the best goodness of fit for each area of ​​each day, and use this time as the best time for the corresponding area of ​​that day;

[0072] Step S124 : Generate an intermediate data set based on the strain monitoring data of all monitoring points corresponding to each area at the optimal time of each day, the temperature monitoring data of all monitoring points, and the goodness of fit of the area.

[0073] In one embodiment, when executing step S121, specifically, since the surface temperature of the containment changes dramatically due to environmental factors such as sunlight and wind speed on the same day, resulting in unstable cross-sectional temperature field, it is necessary to find the moment when the temperature field is most stable, that is, the optimal moment.

[0074] In one embodiment, the temperature monitoring data of all monitoring points in each area can be calculated every day. At each moment, the mean of the temperature monitoring data of all monitoring points except the outer monitoring points is calculated. In this way, the mean temperature of each area at each moment on the same day can be obtained.

[0075] In one embodiment, when executing step S122, specifically, for each area, multiple temperature monitoring data of monitoring points outside the area are selected and linearly fitted with the temperature mean at the corresponding time. Through linear fitting, the goodness of fit R of each area at each time of each day can be calculated. 2 . Goodness of fit R 2It can be used to measure the strength of the linear relationship between temperature monitoring data and the mean, R 2 The closer the value is to 1, the more stable the temperature field is at that moment. For example, the temperature monitoring data of the external monitoring point at 12:00 (for example, 24°C) is linearly fitted with the mean value of 25°C, and the goodness of fit R at that moment is calculated. 2 Repeat the above steps to calculate the R of the area at 0 o'clock, 2 o'clock, 4 o'clock...22 o'clock, etc. 2 value.

[0076] In one embodiment, when executing step S123, specifically, the goodness of fit R of each region at all times can be calculated every day. 2 Sort by. Goodness of fit R 2 It reflects the strength of the linear relationship between the temperature field and the mean at that moment. For example, the monitoring time of a certain area on the same day is 0 o'clock, 2 o'clock, 4 o'clock...22 o'clock, a total of 12 moments, and each moment has a corresponding goodness of fit R 2 These R 2 Sort the values ​​and find R 2 The moment with the highest value.

[0077] In one embodiment, R 2 The time with the highest value is regarded as the optimal time of the day for the area. This time represents the time when the temperature field of the area is most stable on the same day. For example, if the R 2 The value is 0.98, which is the highest among all times of the day, so 16:00 is determined to be the optimal time for this area.

[0078] In one embodiment, when executing step S124, specifically, at the determined optimal time, the strain monitoring data and temperature monitoring data of all monitoring points in the area can be collected, and the goodness of fit R at that time can be recorded. 2 Then, the above data can be integrated to generate an intermediate data set. The intermediate data set can include: strain monitoring data, temperature monitoring data and corresponding goodness of fit R of all monitoring points in each region at the optimal time. 2 value.

[0079] In one embodiment, when step S13 is executed, specifically, step S13 may include the following steps:

[0080] Step S131: The goodness of fit of each region on each day is judged against a preset goodness threshold, and data whose goodness of fit of each region on the same day is greater than the preset goodness threshold is screened out to generate a target data set; wherein the target data set includes: the temperature deviation value of each region at the optimal time on the days that pass the screening, and the strain monitoring data of all monitoring points, the temperature deviation value represents the difference between the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell for each region, and the temperature fitting value is calculated by fitting the depth of the monitoring point inside the shell and the corresponding temperature monitoring data to calculate the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell;

[0081] Step S132: Take the earliest day among the days that pass the screening as the initial day, use the strain monitoring data of all monitoring points on the initial day as the initial value, subtract the strain monitoring data of all monitoring points on other days from the initial value to calculate the relative value, and obtain a training data set; wherein the training data set includes: temperature deviation data of all areas, strain monitoring data of all monitoring points, and relative strain data of all monitoring points on other days.

[0082] In one embodiment, when executing step S131, specifically, the goodness of fit R of each region can be calculated every day. 2 Make a judgment and compare it with the preset goodness threshold [R 2 ] (e.g. 0.95) for comparison. By screening out the goodness of fit R of each region on the same day 2 If the values ​​of the temperature field on that day are all greater than the preset threshold value, it can be considered that the temperature field data on that day has high stability and reliability.

[0083] In one embodiment, for the days that pass the screening, the temperature fitting value of each area at the optimal time can be calculated. The temperature fitting value is calculated by fitting the depth of the monitoring point inside the shell and the corresponding temperature monitoring data. First, the depth value and corresponding temperature monitoring data of each monitoring point can be obtained. Then, a linear fit is performed using the depth and temperature data of all monitoring points to calculate the temperature fitting value of the outside and inside of the shell. Finally, the temperature deviation value is calculated. The temperature deviation value represents the difference between the temperature fitting value of the outside and inside of the shell for each area.

[0084] See also Figure 2As shown in Table 1, in one embodiment, for example, when the containment vessel is divided into four regions, the four regions may include a first region 10, a second region 20, a third region 30, and a fourth region 40. The temperature monitoring data of the four monitoring points in the first region 10 may be K1, K2, K3, and K4, respectively. The temperature monitoring data of the four monitoring points in the second region 20 may be K5, K6, K7, and K8, respectively. The temperature monitoring data of the four monitoring points in the third region 30 may be K9, K10, K11, and K12, respectively. The temperature monitoring data of the four monitoring points in the fourth region 40 may be K13, K14, K15, and K16, respectively.

[0085] Table 1: Temperature deviation data (°C) for four regions.

[0086]

[0087]

[0088] In one embodiment, strain monitoring data for all monitoring points in each region at the optimal time on the days that pass the screening process is collected. The above calculation results are integrated to generate a target dataset. The target dataset may include the temperature deviation value for each region at the optimal time and the strain monitoring data for all monitoring points on the days that pass the screening process.

[0089] In one embodiment, when executing step S132, specifically, the earliest day among the days that pass the screening can be selected as the initial day. For example, if the screened days are Day 1, Day 2, and Day 3, and Day 1 is the earliest day, then Day 1 is selected as the initial day. Subsequently, strain monitoring data for all monitoring points on the initial day can be obtained as the initial value.

[0090] In one embodiment, the strain monitoring data of all monitoring points on other days (not the initial day) can be subtracted from the initial value to calculate the relative strain data. Finally, the temperature deviation data of all regions and the strain monitoring data of all monitoring points can be integrated to generate a training data set.

[0091] See also Figure 2As shown in Table 2, in one embodiment, for example, when the containment vessel is divided into four regions, the four regions may include a first region 10, a second region 20, a third region 30, and a fourth region 40. The relative strain data of the four monitoring points in the first region 10 may be K1T, K1V, K2T, and K2V, respectively. The relative strain data of the four monitoring points in the second region 20 may be K5T, K5V, K6T, and K6V, respectively. The relative strain data of the four monitoring points in the third region 30 may be K9T, K9V, K10T, and K10V, respectively. The relative strain data of the four monitoring points in the fourth region 40 may be K13T, K13V, K14T, and K14V, respectively.

[0092] Table 2: Relative strain data of four regions (μmm / mm)

[0093] K1T K1V K2T K2V K5T K5V K6T K6V K9T K9V K10T K10V K13T K13V K14T K14V 9.70 11.87 10.16 10.86 43.07 4.53 45.43 45.02 -15.96 -26.34 43.17 34.07 11.09 -15.81 42.89 30.09 9.64 11.87 10.04 10.86 -42.73 4.83 44.24 44.53 -15.91 -26.35 42.29 33.25 9.69 -17.23 43.25 29.46 9.58 11.87 9.94 10.86 -.42.49 5.01 42.72 43.83 -15.69 -26.32 40.98 32.18 860 -18.10 43.36 28.42 9.54 11.87 9.85 10.85 -41.72 5.72 41.53 43.15 -15.35 -25.95 39.85 31.01 7.99 -18.64 43.28 27.63 9.52 11.86 9.78 10.84 -39.33 9.51 42.66 43.27 -14.58 -24.93 39.42 30.43 9.01 -18.44 42.89 27.05 9.51 11.85 9.75 10.82 -35.91 14.87 47.47 45.01 -13.51 -23.87 39.41 30.65 11,48 -1731 42.35 27.11 9.51 11.84 9.74 10.81 -37.15 14.05 50.39 46.48 -12.99 -23.48 39.23 31.56 13.49 -16.34 41.95 27.70 9.51 11.84 9.73 10.79 -40.61 10.40 50.80 47.35 -13.56 -22.81 41.76 33.90 15.21 -15.50 41.91 28.37 9.51 11.82 9.71 10.77 43.00 7.46 50.10 47.66 -10.21 -19.81 45.11 37.55 18.84 -14.48 41,80 30.11 9.49 11.81 9.70 10.75 44.30 5.64 49.17 47.44 -13.20 -22.11 48.71 38.70 21.85 -13.52 42.52 31.71 … … … … … … … … … … … … … … … …

[0094] In one embodiment, when executing step S20, each region may be further divided along the thickness direction to generate multiple sub-regions of equal thickness. Each sub-region should include at least one monitoring point to facilitate data collection and analysis. For example, each region may be divided into two sub-regions of equal thickness, in which case each sub-region may include two monitoring points.

[0095] In one embodiment, the concrete expansion coefficient of each sub-region can be obtained, and these concrete expansion coefficients can be used as parameters to be inverted for subsequent numerical simulation and inversion analysis. The concrete expansion coefficient can be obtained through experimental measurement.

[0096] In one embodiment, when step S30 is executed, specifically, step S30 may include the following steps:

[0097] Step S31, taking values ​​for the parameters to be inverted in each sub-region within a value range;

[0098] Step S32: input the parameters to be inverted and a certain temperature deviation data of each sub-region into the finite element model, and calculate the strain calculation data of the monitoring point of each sub-region under the temperature deviation data;

[0099] Step S33: input the parameters to be inverted and another temperature deviation data of each sub-region into the finite element model, and calculate the strain calculation data of the monitoring point of each sub-region under the temperature deviation data;

[0100] Step S34: Generate a linear fitting function using the temperature deviation data as an independent variable and the strain calculation data as a dependent variable, and generate a strain calculation data set under different temperature deviation data according to the linear fitting function, wherein the strain calculation data set includes the strain calculation data of the monitoring points of all sub-areas;

[0101] Step S35, taking values ​​for the parameter to be inverted in each sub-region multiple times within the value range until the number of times reaches a preset number;

[0102] Step S36: After each value is taken, the strain calculation data of the monitoring point in each sub-region under different temperature deviation data is repeatedly calculated, and after obtaining different linear fitting functions, a corresponding strain calculation data set is generated.

[0103] In one embodiment, when executing step S31 and step S32, specifically, the value range of the parameter to be inverted may be 0.6×10 -5 ℃ -1 ≤x d ≤1.4×10 -5 ℃ -1 First, each parameter to be inverted can be set to a value within the range of values. For example, all eight parameters to be inverted can be set to 1.0×10 -5 ℃ -1 Subsequently, the parameters to be inverted and a certain temperature deviation data (eg, 40°C) of each sub-region can be input into the finite element model to calculate the strain calculation data of the monitoring point of each sub-region under the 40°C temperature deviation data.

[0104] In one embodiment, when step S33 is executed, specifically, after calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data of 40°C, the temperature deviation data can be revalued, for example, the temperature deviation data can be revalued to 0°C. Subsequently, the parameters to be inverted for each sub-region and another temperature deviation data (e.g., 0°C) can be input into the finite element model to calculate the strain calculation data of the monitoring point of each sub-region under the temperature deviation data of 0°C.

[0105] In one embodiment, when executing step S34, specifically, since the temperature deviation data and the strain calculation data are in a linear relationship, after calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data of 40°C and the strain calculation data of the monitoring point of each sub-region under the temperature deviation data of 0°C, the temperature deviation data can be used as the independent variable and the strain calculation data can be used as the dependent variable for fitting to generate a linear fitting function. Subsequently, the parameters to be inverted in each sub-region can be taken as values ​​(1.0×10 -5 ℃ -1 ), according to the linear fitting function, the strain calculation data set under different temperature deviation data is calculated, wherein the strain calculation data set includes the strain calculation data of the monitoring points of all sub-areas.

[0106] In one embodiment, when executing step S35, specifically, in the above-mentioned calculation process, the parameter to be inverted in each sub-region is only valued once, and the corresponding linear fitting function is generated. At this time, the value of the parameter to be inverted in each sub-region may not be accurate enough. Therefore, it is necessary to perform multiple value acquisitions on the parameter to be inverted in each sub-region within the value range until the number of value acquisitions reaches a preset number. For example, the Monte Carlo random value method can be used for value acquisition. The size of the preset number of times can be unlimited, for example, it can be 500, 1000, etc., and the specific numerical value can be set according to actual needs.

[0107] In one embodiment, when executing step S36, specifically, each time a value is taken for the parameter to be inverted for each subregion, the value of the parameter to be inverted for each subregion is input into the finite element model under temperature deviation data of 40°C and 0°C, and the strain calculation data of the monitoring point in each subregion under the temperature deviation data of 40°C and 0°C are calculated. In this case, each time a value is taken for the parameter to be inverted for each subregion, a different linear fitting function can be obtained, and a different strain calculation data set can be generated.

[0108] In one embodiment, when step S40 is executed, specifically, step S40 may include the following steps:

[0109] Step S41: after each value is taken, calculating the mean square error between the strain calculation data set and the corresponding relative strain data of the monitoring point in each sub-region;

[0110] Step S42: screening the mean square error to obtain the to-be-inverted parameter corresponding to the minimum mean square error, which is expressed as the inversion parameter of the monitoring points of all sub-areas.

[0111] In one embodiment, when executing step S41, specifically, after taking values ​​for the parameters to be inverted for each sub-region each time, the mean square error F(X) between the calculated data set and the corresponding relative strain data of the monitoring points of each sub-region may be calculated.

[0112] In one embodiment, when executing step S42, specifically, because the parameter to be inverted in each sub-region needs to be evaluated multiple times within a range of values ​​until the number of evaluations reaches a preset number, a mean square error (MSE) can be generated after each evaluation. In this case, the number of MSEs can be the number of preset times. Since the magnitude of each MSE can vary, a multi-population genetic algorithm can be used to transform the inversion problem of the thermal expansion coefficient of concrete into an optimal solution that minimizes the difference between a set of strain calculation data and the relative strain data results for solving the parameter to be inverted. That is, all MSEs can be compared to obtain the MSE with the smallest value, expressed as: Where F(X) is the objective function, i.e., mean square error. X={x1,x2,...,x D} is the parameter to be inverted, and D is the number of parameters to be inverted. i (X, K) is the strain calculation value of the i-th monitoring point when the inversion parameter X is used and the temperature deviation data is K. ε i (K) is the relative strain data of the i-th monitoring point when the temperature deviation data is K. n is the number of monitoring points. T is the temperature monitoring data corresponding to the temperature deviation data K.

[0113] In one embodiment, after the minimum mean square error is obtained, the parameters to be inverted of the corresponding monitoring points in each sub-region can be obtained under the mean square error, and expressed as the inversion parameters of all sub-regions.

[0114] In one embodiment, when executing step S50, specifically, after obtaining the inversion parameters of all sub-areas, the corresponding parameters in the finite element model can be updated according to the inversion parameters to ensure that the finite element model more accurately reflects the actual physical phenomena, thereby improving the accuracy and reliability of the simulation, and correcting the temperature of the containment under the non-uniform temperature field according to the optimized finite element model.

[0115] As can be seen, the above scheme, by more precisely accounting for the containment's hyperstatic state, material heterogeneity, and non-uniform temperature distribution, can more accurately predict the containment's strain under different temperature conditions. Compared to traditional simplified correction methods, the accuracy of strain and temperature corrections is significantly improved by utilizing actual monitoring data and finite element models. This precise temperature correction method allows for a better understanding of the containment's actual condition, avoiding unnecessary over-maintenance or repairs, thereby optimizing maintenance costs and resource allocation.

[0116] See also Figure 3 The present invention also provides a temperature correction device for a non-uniform temperature field in a containment vessel. This correction device can apply the aforementioned optimization method to optimize the finite element model of the containment vessel. This optimized finite element model improves the analysis accuracy of the containment vessel structure under complex temperature conditions, thereby better ensuring the safety of the nuclear power plant. The correction device can include a first partitioning module 100, a second partitioning module 200, a parameter optimization module 300, a parameter calculation module 400, and a model optimization module 500.

[0117] In one embodiment, the first partitioning module 100 can be used to divide the cross-section of the containment shell, generate multiple equally divided areas, collect data from monitoring points in each area, and obtain a training data set. The training data set includes: temperature deviation data of all areas, strain monitoring data of all monitoring points, and relative strain data of all monitoring points.

[0118] In one embodiment, the first partitioning module 100 can be specifically used to set multiple monitoring points at equal intervals in each area along the thickness direction, and collect an initial data set of all monitoring points; wherein, the monitoring points close to the outside of the containment shell are represented as outside monitoring points, and the initial data set includes: strain monitoring data and temperature monitoring data of all monitoring points at each moment of each day within a preset time period; the temperature monitoring data of the outside monitoring points of each day in the initial data set are fitted and sorted according to each moment to obtain the optimal moment of each area in each day, and the data in the initial data set are filtered according to the optimal moment to generate an intermediate data set; based on the comparison result of the goodness of fit of each area in the intermediate data set with the preset goodness threshold, the strain monitoring data of all monitoring points in each area at the optimal moment under the compared days and the temperature monitoring data of each area are retained, and a training data set is generated.

[0119] In one embodiment, the first partitioning module 100 can also be specifically used to calculate the mean of the temperature monitoring data of other monitoring points in each area of ​​each day at all times; linearly fit the multiple temperature monitoring data of the outer monitoring points of each area of ​​each day with the corresponding mean to calculate the goodness of fit of each area of ​​each day at each time; sort the goodness of fit corresponding to each area of ​​each day at all times, and obtain the time corresponding to the optimal goodness of fit for each area of ​​each day, which is used as the optimal time of the corresponding area of ​​the day; generate an intermediate data set based on the strain monitoring data of all monitoring points corresponding to each area of ​​each day at the optimal time, the temperature monitoring data of all monitoring points and the goodness of fit of the area.

[0120] In one embodiment, the first partitioning module 100 can also be specifically used to judge the goodness of fit of each area of ​​each day with a preset goodness threshold, filter out data whose goodness of fit of each area on the same day is greater than the preset goodness threshold, and generate a target data set; wherein the target data set includes: the temperature deviation value of each area at the optimal time and the strain monitoring data of all monitoring points on the days that pass the screening, the temperature deviation value represents the difference between the temperature fitting value of the outside of the shell and the temperature fitting value of the inside of the shell for each area, and the temperature fitting value is calculated by fitting the depth of the monitoring point inside the shell and the corresponding temperature monitoring data to calculate the temperature fitting value of the outside of the shell and the temperature fitting value of the inside of the shell; on the days that pass the screening, the earliest day is obtained as the initial day, the strain monitoring data of all monitoring points on the initial day is used as the initial value, the strain monitoring data of all monitoring points on other days are subtracted from the initial value to calculate the relative value, and a training data set is obtained; wherein the training data set includes: the temperature deviation data of all areas, the strain monitoring data of all monitoring points, and the relative strain data of all monitoring points on other days.

[0121] In one embodiment, the second division module 200 can be used to divide each area along the thickness direction to generate multiple sub-areas of equal thickness, obtain the concrete expansion coefficient of each sub-area, and express it as the corresponding parameter to be inverted, wherein each sub-area includes at least one monitoring point, and the parameter to be inverted is within the value range.

[0122] In one embodiment, the parameter optimization module 300 may be used to input the parameters to be inverted and multiple temperature deviation data of each sub-region into the finite element model to generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data.

[0123] In one embodiment, the parameter optimization module 300 can be specifically used to take values ​​for the parameters to be inverted for each sub-region within a value range; input the parameters to be inverted for each sub-region and a certain temperature deviation data into a finite element model, and calculate the strain calculation data of the monitoring points of each sub-region under the temperature deviation data; input the parameters to be inverted for each sub-region and another temperature deviation data into the finite element model, and calculate the strain calculation data of the monitoring points of each sub-region under the temperature deviation data; generate a linear fitting function using the temperature deviation data as an independent variable and the strain calculation data as a dependent variable, and generate a strain calculation data set under different temperature deviation data according to the linear fitting function, wherein the strain calculation data set includes the strain calculation data of the monitoring points of all sub-regions; take values ​​for the parameters to be inverted for each sub-region multiple times within the value range until the number of value taking reaches a preset number; after each value taking, repeatedly calculate the strain calculation data of the monitoring points of each sub-region under different temperature deviation data, and after obtaining different linear fitting functions, generate the corresponding strain calculation data set.

[0124] In one embodiment, the parameter calculation module 400 can be used to calculate the mean square error of the strain calculation data and the corresponding relative strain data of the monitoring points of all sub-areas, and obtain the parameters to be inverted corresponding to the minimum mean square error of the monitoring points in each sub-area, which are expressed as the inversion parameters of the sub-area.

[0125] In one embodiment, the parameter calculation module 400 can be specifically used to calculate the mean square error between the strain calculation data set and the corresponding relative strain data of the monitoring points in each sub-area after each value is taken; the mean square error is screened to obtain the parameter to be inverted corresponding to the minimum mean square error, which is expressed as the inversion parameter of the monitoring points in all sub-areas.

[0126] In one embodiment, the model optimization module 500 can be used to optimize the value range of the parameters to be inverted in the finite element model according to the inversion parameters of each sub-region to generate an optimized finite element model, and correct the temperature of the containment under the non-uniform temperature field according to the optimized finite element model.

[0127] The specific definition of the correction device can be found in the definition of the optimization method above and will not be repeated here. Each module in the correction device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules can be embedded in or independent of the memory of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the memory can call and execute the corresponding operations of each module.

[0128] See also Figure 4 In one embodiment, the electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a temperature correction program for the non-uniform temperature field of the containment.

[0129] In one embodiment, the memory 12 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 12 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1. Furthermore, the memory 12 can also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 12 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code for temperature correction of the non-uniform temperature field of the containment, but can also be used to temporarily store data that has been output or is about to be output.

[0130] In one embodiment, the processor 13 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the electronic device 1, connecting the various components of the entire electronic device 1 using various interfaces and circuits. It executes programs or modules stored in the memory 12 (e.g., a temperature correction program for the non-uniform temperature field of the containment vessel) and accesses data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0131] In one embodiment, the processor 13 executes the operating system and various installed application programs of the electronic device 1. The processor 13 executes the application programs to implement the steps in the above-mentioned method for correcting the non-uniform temperature field of the containment.

[0132] In one embodiment, the computer program may be divided into one or more modules, one or more of which are stored in the memory 12 and executed by the processor 13 to complete the present application. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a first division module 100, a second division module 200, a parameter optimization module 300, a parameter calculation module 400, and a model optimization module 500.

[0133] The embodiments of the present invention disclosed above are intended only to illustrate the present invention. They do not describe all details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for correcting the non-uniform temperature field of a containment vessel, characterized in that: include: Dividing the cross section of the containment into a plurality of equally divided regions, collecting data from monitoring points in each region to obtain a training data set, the training data set including: temperature deviation data of all regions, strain monitoring data of all monitoring points, and relative strain data of all monitoring points; Dividing each of the regions along the thickness direction to generate a plurality of sub-regions of equal thickness, obtaining the concrete expansion coefficient of each sub-region and expressing it as a corresponding parameter to be inverted, wherein each of the sub-regions includes at least one monitoring point, and the parameter to be inverted is within a value range; Inputting the parameters to be inverted and a plurality of temperature deviation data of each sub-region into a finite element model, and generating strain calculation data of the monitoring points of each sub-region under different temperature deviation data; Calculating the mean square error of the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, obtaining the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and expressing it as the inversion parameter of the sub-region; The value range of the parameters to be inverted in the finite element model is optimized according to the inversion parameters of each sub-region to generate an optimized finite element model, and the temperature of the containment under the non-uniform temperature field is corrected according to the optimized finite element model.

2. The temperature correction method for the non-uniform temperature field of a containment vessel according to claim 1, characterized in that: The step of collecting data from monitoring points in each area to obtain a training data set includes: A plurality of monitoring points are set at equal intervals within each of the regions along the thickness direction, and an initial data set is collected from all monitoring points; wherein the monitoring points close to the outside of the containment shell are denoted as outside monitoring points, and the initial data set includes: strain monitoring data and temperature monitoring data from all monitoring points at each time of each day within a preset time period; Fitting and sorting the temperature monitoring data of the outer monitoring points of each day in the initial data set according to each time to obtain the optimal time in each area of ​​each day, filtering the data in the initial data set according to the optimal time to generate an intermediate data set; According to the comparison result of the goodness of fit of each area in the intermediate data set and the preset goodness threshold, the strain monitoring data of all monitoring points in each area at the optimal time under the compared days and the temperature monitoring data of each area are retained to generate a training data set.

3. The temperature correction method for the non-uniform temperature field of a containment vessel according to claim 2, characterized in that: The steps of fitting and sorting the temperature monitoring data of the outer monitoring points of each day in the initial data set according to each time to obtain the optimal time in each area of ​​each day, and filtering the data in the initial data set according to the optimal time to generate the intermediate data set include: Calculate the average of the temperature monitoring data of other monitoring points at all times in each area of ​​each day; Performing linear fitting on a plurality of temperature monitoring data of the outer monitoring points of each of the areas on each day with the corresponding mean value to calculate the goodness of fit of each of the areas at each moment on each day; Sort the goodness of fit corresponding to each area at all times of each day, and obtain the time corresponding to the best goodness of fit for each area of ​​each day, and use the time as the best time for the corresponding area of ​​that day; An intermediate data set is generated based on the strain monitoring data of all monitoring points corresponding to each area at the optimal time every day, the temperature monitoring data of all monitoring points and the goodness of fit of the area.

4. The temperature correction method for the non-uniform temperature field of a containment vessel according to claim 2, characterized in that: The step of retaining the strain monitoring data of all monitoring points in each region at the optimal time and the temperature monitoring data of each region on the days that pass the comparison and generating a training data set based on the comparison result of the goodness of fit of each region in the intermediate data set with a preset goodness threshold comprises: The goodness of fit of each of the regions on each day is judged against a preset goodness threshold, and data whose goodness of fit of each of the regions on the same day is greater than the preset goodness threshold is screened out to generate a target data set; wherein the target data set includes: the temperature deviation value of each of the regions at the optimal time and the strain monitoring data of all monitoring points on the days that pass the screening; the temperature deviation value represents the difference between the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell for each of the regions; the temperature fitting value is calculated by fitting the depth of the monitoring point inside the shell and the corresponding temperature monitoring data to obtain the temperature fitting value of the outer side of the shell and the temperature fitting value of the inner side of the shell; The earliest day among the screened days is obtained as the initial day, the strain monitoring data of all monitoring points on the initial day is used as the initial value, the strain monitoring data of all monitoring points on other days are subtracted from the initial value to calculate the relative value, and a training data set is obtained; wherein the training data set includes: temperature deviation data of all areas, strain monitoring data of all monitoring points, and relative strain data of all monitoring points on other days.

5. The temperature correction method for the non-uniform temperature field of a containment vessel according to claim 1, characterized in that: The step of inputting the parameters to be inverted and the plurality of temperature deviation data of each sub-region into the finite element model to generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data comprises: Taking a value for the parameter to be inverted for each sub-region within a value range; Inputting the parameters to be inverted and a certain temperature deviation data of each sub-region into the finite element model, and calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data; Inputting the parameters to be inverted and another temperature deviation data of each sub-region into a finite element model, and calculating the strain calculation data of the monitoring point of each sub-region under the temperature deviation data; A linear fitting function is generated using the temperature deviation data as an independent variable and the strain calculation data as a dependent variable, and a strain calculation data set under different temperature deviation data is generated according to the linear fitting function, wherein the strain calculation data set includes the strain calculation data of the monitoring points of all sub-areas.

6. The method for correcting the non-uniform temperature field of a containment vessel according to claim 5, characterized in that: After the step of inputting the parameters to be inverted and the plurality of temperature deviation data of each sub-region into the finite element model to generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data, the method further includes: Taking multiple values ​​of the parameter to be inverted for each sub-region within a value range until the number of values ​​taken reaches a preset number; After each value is taken, the strain calculation data of the monitoring point of each sub-region under different temperature deviation data is repeatedly calculated, and after obtaining different linear fitting functions, a corresponding strain calculation data set is generated.

7. The method for correcting the non-uniform temperature field of a containment vessel according to claim 6, characterized in that: The step of calculating the mean square error between the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, obtaining the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and expressing the parameter as the inversion parameter of the sub-region comprises: After each value is taken, the mean square error between the strain calculation data set and the corresponding relative strain data of the monitoring point in each sub-region is calculated; The mean square error is screened to obtain the to-be-inverted parameter corresponding to the minimum mean square error, which is expressed as the inversion parameter of the monitoring points in all sub-areas.

8. A temperature correction device for a non-uniform temperature field of a containment vessel, characterized in that: include: A first partitioning module is configured to partition the cross section of the containment vessel into a plurality of equally divided regions, collect data from monitoring points in each region, and obtain a training data set, wherein the training data set includes temperature deviation data of all regions, strain monitoring data of all monitoring points, and relative strain data of all monitoring points; A second division module is configured to divide each of the regions along a thickness direction to generate a plurality of sub-regions of equal thickness, obtain a concrete expansion coefficient of each sub-region, and express the coefficient as a corresponding parameter to be inverted, wherein each sub-region includes at least one monitoring point, and the parameter to be inverted is within a value range; a parameter optimization module, configured to input the parameters to be inverted and a plurality of temperature deviation data of each sub-region into a finite element model, and generate strain calculation data of the monitoring points of each sub-region under different temperature deviation data; a parameter calculation module, configured to calculate the mean square error between the strain calculation data and the corresponding relative strain data of the monitoring points of all the sub-regions, and obtain the parameter to be inverted corresponding to the minimum mean square error of the monitoring points of each sub-region, and express it as the inversion parameter of the sub-region; and The model optimization module is used to optimize the value range of the parameters to be inverted in the finite element model according to the inversion parameters of each sub-region to generate an optimized finite element model, and to correct the temperature of the containment under the non-uniform temperature field according to the optimized finite element model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for correcting the temperature of the non-uniform temperature field of the containment vessel according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for correcting the temperature of the non-uniform temperature field of the containment vessel according to any one of claims 1 to 7 are implemented.

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