Reactor core model correction and quadrant power tilt calculation method based on data assimilation

Abstract

The invention discloses a data assimilation-based reactor core model correction method, device and equipment, and the method can change the characteristics of the power level of each assembly in a reactor core through the adjustment of characteristic parameters, and carries out the parameter assimilation of the parameter values of the characteristic parameters in a reactor core theoretical model. The accuracy of simulating the theoretical power level of the component by the corrected reactor core theoretical model can be improved; and if at least one time of correction is the parameter value of the first characteristic parameter which does not change along with the operation of the nuclear reaction unit in the previous (i-1) times of correction process of the reactor core theoretical model, it is proved that the first characteristic parameter at each component in the reactor core theoretical model after the (i-1)-th time of correction is balanced and solidified, the interference of the parameter in the ith correction process can be avoided, and the accuracy of simulating the theoretical power level of the component by the reactor core theoretical model after the ith correction can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of nuclear reactors, and in particular to a core model correction and quadrant power tilt calculation method based on data assimilation. Background Art

[0002] In the field of nuclear reactor physics, the current power levels of various core components are mainly determined based on the regular measurement activities of detectors. However, in the period between two measurements, it is impossible to determine the changes in the power levels of various core components in this way. The power levels of various core components can only be simulated through the core theoretical model. However, since the core is always in a dynamic development process, the local and global state parameters of the core are constantly changing, and the power levels of various core components are also constantly changing. The core theoretical model has a low accuracy in predicting the changing trend of the power levels of various core components. Summary of the invention

[0003] The present application aims to at least solve the technical problems existing in the prior art. To this end, the present application proposes a core model correction and quadrant power tilt calculation method based on data assimilation, which can improve the power level of each core component simulated by the core theoretical model and the accuracy of quadrant power tilt calculation.

[0004] In a first aspect, an embodiment of the present application provides a core model correction method based on data assimilation, the method comprising:

[0005] Obtaining the theoretical power level of each component in the core obtained in the i-th simulation based on the core theoretical model after the i-1th correction, wherein the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core; i is a positive integer greater than 1;

[0006] Acquiring a measured power level corresponding to a theoretical power level of each of the components, wherein the measured power level is obtained by measuring the components in the core;

[0007] If there is at least one operation of parameter assimilation on the parameter value of the first characteristic parameter in the previous i-1 times of correction of the core theoretical model, then based on the theoretical power level of each of the components obtained by the i-th simulation and the corresponding measured power level, parameter assimilation is performed on the parameter value of the second characteristic parameter in the core theoretical model, so that the deviation between the theoretical power level of each of the components obtained by the simulation of the corrected core theoretical model and the measured power level of each of the components is less than a target threshold;

[0008] The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each of the components, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

[0009] The embodiment of the present application adopts the above technical solution, which has at least the following beneficial effects:

[0010] The method utilizes the adjustment of characteristic parameters to change the characteristics of the power level of each component in the core. By correcting the parameter values ​​of the characteristic parameters in the core theoretical model, the accuracy of the corrected core theoretical model in simulating the theoretical power level of the component can be improved. Furthermore, if during the first i-1 corrections to the core theoretical model, at least one correction is made to the parameter value of the first characteristic parameter that does not change with the operation of the nuclear reactor unit, it is proved that the first characteristic parameter at each component in the core theoretical model after the i-1 correction has reached equilibrium and solidified, and it is possible to avoid interference from the parameter during the i-th correction process. Therefore, when the core theoretical model is corrected for the i-th time, the characteristic parameters in the core theoretical model that are different from the first characteristic parameters can be corrected, and the accuracy of the corrected core theoretical model in simulating the theoretical power level of the component can be improved.

[0011] In some embodiments of the present application, after obtaining the measured power level corresponding to the theoretical power level of each of the components, the method further includes:

[0012] If there is no parameter assimilation operation on the parameter value of the first characteristic parameter in the process of correcting the characteristic parameters in the core theoretical model for the first i-1 times, then based on the theoretical power levels of each of the components obtained by the i-th simulation and the corresponding measured power levels, the parameter value of the first characteristic parameter in the core theoretical model is corrected so that the deviation between the theoretical power level of each of the components obtained by the corrected core theoretical model simulation and the measured power level of each of the components is less than the target threshold.

[0013] In some embodiments of the present application, the parameter assimilation of the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the i-th simulation and the corresponding measured power level includes:

[0014] Determine the positive or negative correlation between the parameter value of the second characteristic parameter at the location of each of the components and the theoretical power level of each of the components;

[0015] Determining the magnitude relationship between the theoretical power level of each of the components obtained by simulating the core theoretical model and the measured power level of each of the components;

[0016] According to the positive and negative correlation and the size relationship, the parameter value of the second characteristic parameter in the core theoretical model is corrected.

[0017] In some embodiments of the present application, the parameter assimilation of the parameter value of the second characteristic parameter in the core theoretical model according to the positive and negative correlation and the size relationship includes:

[0018] If the theoretical power level of the target component is less than the measured power level of the target component, and there is a positive correlation between the parameter value of the second characteristic parameter and the theoretical power level, then increasing the parameter value of the second characteristic parameter in the core theoretical model; the target component is any one of the components;

[0019] If the theoretical power level of the target component is less than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, reducing the parameter value of the second characteristic parameter in the core theoretical model;

[0020] If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, increasing the parameter value of the second characteristic parameter in the core theoretical model;

[0021] If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a positive correlation between the parameter value of the second characteristic parameter and the theoretical power level, then the parameter value of the second characteristic parameter in the core theoretical model is reduced.

[0022] In some embodiments of the present application, before obtaining the theoretical power level of each component in the core obtained in the i-th simulation based on the core theoretical model corrected for the i-1th time, the method further includes:

[0023] Acquire the initial theoretical power level of each component in the core obtained based on the initial core theoretical model simulation;

[0024] Acquiring an initial measured power level corresponding to an initial theoretical power level of each of the components;

[0025] Based on the initial theoretical power levels of each of the components and the corresponding initial measured power levels, the parameter value of the first characteristic parameter in the initial core theoretical model is corrected to obtain the core theoretical model after the first correction, and the deviation between the theoretical power level of each of the components simulated by the core theoretical model after the first correction and the initial measured power level of each of the components is less than the target threshold.

[0026] In some embodiments of the present application, the first characteristic parameter includes the flow rate of the coolant / moderator at the core inlet; and the second characteristic parameter includes the water gap.

[0027] In a second aspect, an embodiment of the present application provides a method for calculating core quadrant power tilt, the method comprising:

[0028] Simulating and obtaining theoretical power levels of various components in the core based on a target model, wherein the target model is the core theoretical model corrected in the core model correction method based on data assimilation described in the first aspect;

[0029] The core quadrant power tilt state is calculated according to the theoretical power level of each of the components obtained by simulating the target model.

[0030] The embodiment of the present application adopts the above technical solution, which has at least the following beneficial effects:

[0031] This method utilizes the modified core theoretical model of the first aspect to simulate the theoretical power level of each component in the core, and then utilizes the simulated theoretical power level of each component in the core to calculate the quadrant power tilt state of the core. Since the simulated theoretical power level of each component in the core is accurate, the core quadrant power tilt state is calculated with higher accuracy.

[0032] In a third aspect, an embodiment of the present application provides a core model correction device based on data assimilation, the device comprising:

[0033] A theoretical power level acquisition unit, used to acquire the theoretical power level of each component in the core obtained by the i-th simulation based on the core theoretical model after the i-1th correction, wherein the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core; i is a positive integer greater than 1;

[0034] a measured power level acquisition unit, used to acquire a measured power level corresponding to a theoretical power level of each of the components, wherein the measured power level is obtained by measuring the components in the core;

[0035] a core theoretical model correction unit, configured to, if there is at least one operation of parameter assimilation on the parameter value of the first characteristic parameter in the previous (i-1) correction processes of the core theoretical model, perform parameter assimilation on the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the ith simulation and the corresponding measured power level, so that the deviation between the theoretical power level of each component obtained by the simulation of the corrected core theoretical model and the measured power level of each component is less than a target threshold;

[0036] The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each of the components, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

[0037] In a fourth aspect, an embodiment of the present application provides an electronic device comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the core model correction method based on data assimilation of the first aspect and the core quadrant power tilt calculation method of the second aspect.

[0038] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned first aspect of the core model correction method based on data assimilation and the above-mentioned second aspect of the core quadrant power tilt calculation method.

[0039] It can be understood that the beneficial effects of the third to fifth aspects compared with the related art are the same as the beneficial effects of the first aspect compared with the related art. Please refer to the relevant description in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0041] Figure 1 It is a flow chart of an embodiment of a core model correction method based on data assimilation provided by the present application;

[0042] Figure 2 It is a structural schematic diagram of an embodiment of a core model correction device based on data assimilation provided by the present application;

[0043] Figure 3 It is a schematic diagram of the structure of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0044] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0045] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0046] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0047] Before introducing the specific embodiments, the technical concepts of this application are first introduced:

[0048] The theoretical core model is a model constructed based on the existing three-dimensional core nuclear design program, according to the core design parameters and core loading plan of the nuclear power plant unit. The model can simulate the theoretical power level of each component in the core of the nuclear reactor unit after presetting various parameters.

[0049] Core power tilt (TILT) refers to the asymmetry of power in each quadrant during core operation. This is a relatively common phenomenon in pressurized water reactor cores and a long-standing problem in core operation and fuel management. The factors that cause quadrant power tilt are numerous and complex and involve the mutual coupling of core neutron-thermal-fuel multi-physics fields. It is difficult to analyze the root cause in actual engineering. It mainly relies on regular detector measurement activities to determine the current core power tilt state.

[0050] Reference Figure 1 In one embodiment of the present application, a core model correction method based on data assimilation is provided, the method comprising the following steps S110 to S130:

[0051] Step S110, obtaining the theoretical power level of each component in the core obtained in the i-th simulation based on the core theoretical model after the i-1th correction, wherein the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core.

[0052] Step S120: obtaining a measured power level corresponding to a theoretical power level of each component, where the measured power level is obtained by measuring the components in the core.

[0053] Step S130: If there is at least one operation of assimilation of the parameter value of the first characteristic parameter in the previous i-1 correction processes of the core theoretical model, then based on the theoretical power levels of each component obtained by the i-th simulation and the corresponding measured power levels, the parameter value of the second characteristic parameter in the core theoretical model is assimilated, so that the deviation between the theoretical power level of each component obtained by the simulation of the corrected core theoretical model and the measured power level of each component is less than the target threshold.

[0054] The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each component, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

[0055] In step S110, the core theoretical model here can be designed by the core segment program SMART in the SCIENCE nuclear design package, or other core programs with equivalent functions. It should be noted that different types of nuclear power plant units require conventional core information, such as the number, type, size, and arrangement of fuel assemblies, as well as the power, temperature, and flow rate of the core design; the relevant design information of each unit may be slightly different.

[0056] Normally, the power level of each component in the core of a nuclear reactor is measured regularly to perform evaluation based on the measurement (such as evaluation of quadrant tilt). However, between two measurements, the power level of each component needs to be simulated and estimated through a model. In order to improve the accuracy of the simulated theoretical power level of the core theoretical model, the characteristic parameters in the model are adjusted in this embodiment to achieve correction of the model, so that the deviation between the theoretical power level of each component simulated by the corrected core theoretical model and the corresponding measured power level is less than a preset target threshold (the threshold can be set based on experience).

[0057] It should be noted that the modification process of the characteristic parameters in the core theoretical model is the assimilation process of the characteristic parameters in the core theoretical model. In the mainstream data assimilation algorithm, the measured value is fully trusted, that is, the true value is assumed to be equal to the measured value. In this embodiment, the measured power level is fully trusted, that is, the measured power level is considered as the target value of the theoretical correction, and the influence of the measurement error is not considered.

[0058] Since there are many factors that affect the power distribution level of each component in the core, measurement errors, reconstruction theory methods, cross-section databases, reactivity cross-sections, core macroscopic operation status, etc. all have a certain influence. However, in the process of model correction, only a single characteristic parameter is considered to be adjusted in each correction, so that the theoretical results of the core theoretical model simulation are close to the measured results. And use this single characteristic parameter to cover the comprehensive results of all other factors, and use this unique characteristic parameter to characterize the deviation between the measured results and the theoretical calculations.

[0059] Therefore, each time the power level of each component is measured, due to the time interval from the previous measurement, the deviation of the core theoretical model will increase within the time interval. At this time, the core theoretical model needs to be corrected to reduce the deviation between the theoretical power level output by the core theoretical model and the measured power level. Step S110 assumes that the current is the i-th correction process, where i is a positive integer greater than 1; before the i-th time, there are i-1 correction processes, and each correction process is implemented based on the model corrected last time. For example, for the i-th time, the core theoretical model corrected for the i-1th time is used. By cumulatively correcting the model one by one, the accuracy of the model simulating the theoretical power level can be continuously improved.

[0060] In step S120, the measured power level corresponding to the theoretical power level of each component is obtained. For example, a core has 157 components. The theoretical power level of the 157 components can be obtained through the model, and the measured power level of the 157 components can be measured through the flux diagram test and other means. For example, the flux diagram measurement test is usually once a month. Here, in order to make the measured power level more realistic, the measured power level of each component after the flux diagram measurement test of the core measurement system is post-processed. The post-processing here can be filtering and other operations to filter out interference factors, so that the measured power level of the component obtained by processing is more realistic, thereby improving the accuracy of subsequent corrections.

[0061] In step S130, the first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each component, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit. In this way, the first characteristic parameter can be adjusted first to achieve the assimilation of the first characteristic parameter in the model, so that the first characteristic parameter in the revised core theoretical model can achieve the optimal distribution, that is, in the subsequent correction process, the characteristic parameter can also maintain a balanced state and solidify, and will not interfere with subsequent operations.

[0062] Therefore, in step S130, when the i-th correction is made, it is first determined whether the parameter value of the first characteristic parameter in the model has been adjusted at least once in the previous i-1 times. If it has been adjusted, it proves that the distribution level of the characteristic parameter in the model corrected for the i-th time has reached the theoretically optimal state (because the deviation between the theoretical power level of the output caused by the first characteristic parameter and the true value is small). At this time, during the i-th correction, because the characteristic parameter has been solidified in the model, no interference will occur, and only the characteristic parameters other than the first characteristic parameter need to be considered. In this embodiment, the second characteristic parameter is adjusted so that the theoretical power level simulated by the model after the i-th correction is close to the measured power level.

[0063] In some embodiments of the present application, the first characteristic parameter refers to a characteristic parameter that does not change with the operation of the nuclear reactor unit. For example, the first characteristic parameter is the flow rate of the coolant / moderator at the core inlet. In a pressurized water reactor, the coolant / moderator is light water containing boric acid. The flow distribution in the core has different values ​​or relative values ​​(i.e., flow distribution levels) at the locations of various components. By adaptively adjusting the flow distribution level at the core inlet in the core theoretical model, the power distribution level of each component in the core theoretical model can be changed.

[0064] Because the difference between the theoretical power level and the measured power level generated by a nuclear reactor in the early stage of its life mainly depends on the flow distribution level, the flow distribution level at the core inlet in the model can be adjusted first, so that the theoretically optimal flow distribution level at the core inlet exists in the revised model, and the flow distribution of each component is balanced and solidified. For example, when making the first correction, the flow distribution level at the core inlet in the core theoretical model is adjusted. It should be noted that the flow distribution level at the core inlet does not change with the operation of the reactor. The flow distribution level at the core inlet is caused by the flow distributor at the reactor coolant inlet. Due to the uneven manufacturing and assembly, this deviation will no longer change after correction. However, the flow at each component inside the reactor will change. This is calculated based on various operating conditions of the reactor and is not considered here.

[0065] The second characteristic parameter may be a characteristic parameter that does not change with the operation of the nuclear reactor unit, or may be a characteristic parameter that changes with the operation of the nuclear reactor unit, for example, the second characteristic parameter is the water gap. Because the deformation of the fuel assembly is an important factor affecting TILT, the fuel assembly may deform or displace in the reactor, thereby causing a change in the assembly gap (water gap), so as the core continues to operate, the deformation of the fuel assembly will gradually accumulate, and the distribution of the water gap will gradually change.

[0066] This embodiment has at least the following beneficial effects:

[0067] This method can change the characteristics of the power level of each component in the core by adjusting the characteristic parameters. By correcting the parameter values ​​of the characteristic parameters in the core theoretical model, the accuracy of the corrected core theoretical model in simulating the component theoretical power level can be improved.

[0068] Moreover, if, during the first i-1 core theoretical model corrections, at least one correction is made to the parameter value of the first characteristic parameter that does not change with the operation of the nuclear reactor unit, it proves that the first characteristic parameter of each component in the core theoretical model after the i-1th correction has reached equilibrium and solidified, which can avoid interference from the parameter during the i-th correction process, so that when the core theoretical model is corrected for the i-th time, the characteristic parameters in the core theoretical model that are different from the first characteristic parameters can be corrected, thereby improving the accuracy of the theoretical power level of the component simulated by the core theoretical model after the i-th correction.

[0069] In some embodiments of the present application, after step S120, the following steps are further included:

[0070] Step S120: if there is no parameter assimilation operation on the parameter value of the first characteristic parameter in the previous i-1 times of modifying the characteristic parameters in the core theoretical model, then based on the theoretical power level of each component obtained by the i-th simulation and the corresponding measured power level, the parameter value of the first characteristic parameter in the core theoretical model is modified so that the deviation between the theoretical power level of each component obtained by the modified core theoretical model simulation and the measured power level of each component is less than the target threshold value;

[0071] Since the first characteristic parameter refers to a characteristic parameter that does not change with the operation of the nuclear reactor unit, if the parameter value of the first characteristic parameter is not corrected during the first i-1 corrections to the characteristic parameters in the core theoretical model, for example, the second characteristic parameter is corrected, then when the model is corrected for the i-th time, the parameter value of the first characteristic parameter in the model can be corrected so that the parameter value of the first characteristic parameter at each component in the corrected model reaches a balance and solidifies, thereby improving the accuracy of the subsequent corrected model simulation theoretical power level.

[0072] In some embodiments of the present application, the step S130 of correcting the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the i-th simulation and the corresponding measured power level includes:

[0073] Step S1310: determine the positive or negative correlation between the parameter value of the second characteristic parameter at the location of each component and the theoretical power level of each component.

[0074] Step S1320: Determine the magnitude relationship between the theoretical power level of each component obtained by simulating the core theoretical model and the measured power level of each component.

[0075] Step S1330: According to the positive and negative correlation and the size relationship, the parameter value of the second characteristic parameter in the core theoretical model is corrected.

[0076] In step S1310, by adjusting the parameter value of the second characteristic parameter in the model, the model can simulate and obtain the theoretical power level of each component, and by comparison, the positive and negative correlation between the parameter value of the second characteristic parameter of each component and the theoretical power level of each component can be determined, for example, the greater the flow distribution level, the better the moderation condition, and the higher the power level of the fuel assembly. Alternatively, a table preset in advance is used to record the positive and negative correlation between the flow distribution level at the location of each component and the theoretical power level of each component (which can be obtained through experiments), and then the result is obtained by looking up the table.

[0077] In step S1320, the magnitude relationship can be clearly determined by comparing the values ​​of the two. For example, the theoretical power level of component 1 is greater than the measured power level, and the theoretical power level of component 2 is greater than the measured power level.

[0078] In step S1330, the second characteristic parameter value in the core theoretical model is corrected according to the positive and negative correlation and the magnitude relationship. If the theoretical power level of each component simulated by the core theoretical model is higher than the measured power level of the component, and there is a positive correlation between the second characteristic parameter (such as water gap) and the theoretical power level, then the second characteristic parameter in the core theoretical model should be reduced, so that the theoretical power level of each component simulated by the modified core theoretical model is close to the measured power level of the component.

[0079] It should be noted that it is also possible to preset a correspondence between a theoretical power level and a corresponding measured power level and the parameter value of the second characteristic parameter. For example, based on the deviation between the theoretical power level and the measured power level, a corresponding adjustment value is set for the deviation. This relationship is stored in the form of a key value. When in use, one only needs to look up the table to obtain the adjustment plan.

[0080] It should be noted that if the first characteristic parameter is to be corrected, the correction process of the first characteristic parameter is similar, for example,

[0081] (1) Determine the positive or negative correlation between the parameter value of the first characteristic parameter at the location of each component and the theoretical power level of each component.

[0082] (2) Determine the relationship between the theoretical power level of each component simulated by the core theoretical model and the measured power level of each component.

[0083] (3) Based on the positive and negative correlation and the size relationship, the parameter value of the first characteristic parameter in the core theoretical model is corrected.

[0084] I will not go into details here.

[0085] This method can adjust the parameter values ​​of characteristic parameters through data comparison and positive and negative correlation. Compared with the table lookup solution, the adjustment solution is more accurate.

[0086] In some embodiments of the present application, the parameter value of the second characteristic parameter in the core theoretical model is corrected according to the positive and negative correlation and the size relationship, including the following:

[0087] (1) if the theoretical power level of the target component is less than the measured power level of the target component, and the parameter value of the second characteristic parameter is positively correlated with the theoretical power level, increasing the parameter value of the second characteristic parameter in the core theoretical model; the target component is any one of the components;

[0088] (2) If the theoretical power level of the target component is less than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, reduce the parameter value of the second characteristic parameter in the core theoretical model.

[0089] (3) If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, increase the parameter value of the second characteristic parameter in the core theoretical model.

[0090] (4) If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a positive correlation between the parameter value of the second characteristic parameter and the theoretical power level, reduce the parameter value of the second characteristic parameter in the core theoretical model.

[0091] This embodiment lists four situations for correcting the parameter value of the characteristic parameter in the core theoretical model according to the positive and negative correlation and the size relationship. According to the above four situations, the parameter value of the second characteristic parameter in the core theoretical model can be corrected, so that the theoretical power level simulated by the corrected model can be close to the measured power level, thereby improving the accuracy of the theoretical power level simulated by the model.

[0092] It should be noted that the correction process for the first characteristic parameter is similar, for example,

[0093] (1) If the theoretical power level of the target component is less than the measured power level of the target component, and there is a positive correlation between the parameter value of the first characteristic parameter and the theoretical power level, the parameter value of the first characteristic parameter in the core theoretical model is increased; the target component is any one of the components.

[0094] (2) If the theoretical power level of the target component is less than the measured power level of the target component, and there is a negative correlation between the parameter value of the first characteristic parameter and the theoretical power level, reduce the parameter value of the first characteristic parameter in the core theoretical model.

[0095] (3) If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a negative correlation between the parameter value of the first characteristic parameter and the theoretical power level, increase the parameter value of the first characteristic parameter in the core theoretical model.

[0096] (4) If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a positive correlation between the parameter value of the first characteristic parameter and the theoretical power level, reduce the parameter value of the first characteristic parameter in the core theoretical model.

[0097] In some embodiments of the present application, the correction of the parameter value of the second characteristic parameter in the core theoretical model according to the positive and negative correlation and the magnitude relationship includes the following process:

[0098] Step S1331: Preset a proportionality coefficient.

[0099] Step S1332: Multiply the parameter value of the second characteristic parameter in the core theoretical model by the proportionality coefficient, where the proportionality coefficient is associated with the positive and negative correlation and the magnitude relationship.

[0100] In this embodiment, each time the parameter value of the target characteristic parameter in the core theoretical model is corrected, it is to multiply the parameter value of the characteristic parameter in the core theoretical model by a proportionality coefficient, which can control the consistency of the adjustment. Moreover, since the correction process of the model often requires multiple adjustments, when multiplying by a proportionality coefficient, the adjustment process can be refined and the efficiency of model correction can be improved. For example: if it is necessary to improve the flow distribution level, multiply by the coefficient c (c > 1), and the coefficient c can be 1.1, 1.2, 1.3. If it is necessary to reduce, multiply by the coefficient c (0 < c < 1), and the coefficient c can be 0.9, 0.8, 0.7. The value of the coefficient c is associated with the positive and negative correlation and the magnitude relationship.

[0101] It should be noted that it is also possible not to use the proportionality coefficient, but to make fine-tuning according to experience.

[0102] In some embodiments of the present application, before step S110 obtains the theoretical power levels of the various components in the core obtained by the i-th simulation based on the core theoretical model corrected in the (i - 1)-th time, the method further includes:

[0103] Obtain the initial theoretical power levels of the various components in the core obtained by simulating based on the initial core theoretical model;

[0104] Acquiring an initial measured power level corresponding to an initial theoretical power level of each component;

[0105] Based on the initial theoretical power level of each component and the corresponding initial measured power level, the parameter value of the first characteristic parameter in the initial core theoretical model is corrected to obtain the core theoretical model after the first correction, and the deviation between the theoretical power level of each component simulated by the core theoretical model after the first correction and the initial measured power level of each component is less than the target threshold.

[0106] The method provided in this embodiment adjusts the first characteristic parameter when the core theoretical model is corrected for the first time, and then in subsequent corrections, there is no need to consider the interference caused by the first characteristic parameter, but the second characteristic parameter that changes with the operation of the core can be considered and corrected. Moreover, because the correction is made for the first time, the interference caused by the first characteristic parameter in the subsequent correction of the core theoretical model can be avoided to the greatest extent, thereby improving the accuracy of subsequent core theoretical model simulations.

[0107] The following are two applicable scenarios for this application:

[0108] Scenario 1: Safety monitoring of each core component.

[0109] Normally, the actual measurement of the power level of each component in the core requires an interval of one month. Therefore, between two regular actual measurements, it is impossible to truly measure and detect the power level of each component. At this time, it is necessary to simulate the theoretical power level through the core theoretical model, and simulate the theoretical power levels of each component in the core through the simulated theoretical power level, and then use the simulated data to monitor the safety of each component.

[0110] Through the processing of the above steps S110 to S130, the theoretical power level simulated by the modified core theoretical model can be as close to the actual level of the component as possible, thereby simulating the theoretical power level of each component between two periodic actual measurements, thereby achieving safety monitoring.

[0111] Scenario 2: Tilt monitoring of each quadrant of the core.

[0112] The calculation formula of core quadrant power tilt value is:

[0113]

[0114] Among them, P i (i=1,2,3,4) represents the average power level in each quadrant; P (avg)Represents the average power level of the entire stack; that is, the ratio of the average power in the quadrant where the core TILT is the largest to the average power level of the entire stack.

[0115] Therefore, after obtaining the accurate simulated theoretical power level, the tilt state of the quadrant can be calculated using the above formula.

[0116] Therefore, an embodiment of the present application provides a method for calculating core quadrant power tilt, the method comprising:

[0117] Step S210: obtaining theoretical power levels of various components in the core based on the target model simulation, wherein the target model is a corrected core theoretical model in the core model correction method based on data assimilation.

[0118] Step S220: Calculate the core quadrant power tilt state according to the theoretical power level of each component obtained by the target model simulation.

[0119] It should be noted that the quadrant power tilt state calculation is realized by the above core quadrant power tilt value calculation formula, and a quadrant power tilt state with high accuracy can be calculated by this method. It will not be described in detail here, and please refer to the description of the subsequent embodiments for details.

[0120] In order to facilitate understanding by those skilled in the art, taking the above scenario 2 as an example, a method for calculating the core quadrant power tilt is provided, and the method flow is as follows:

[0121] Step S910: Based on the existing three-dimensional core design program, a core theoretical model is constructed according to the core design parameters and core loading plan of the nuclear power plant unit.

[0122] For example, taking the CPR1000 unit as an example, the core has 157 components, and 157 theoretical power levels can be obtained through theoretical calculation of the core theoretical model. Note: Pa (i) , where i=1,2,......,157.

[0123] At the same time, the core quadrant power tilt value is calculated: TILT(k), where k is one of four quadrants: 1, 2, 3, and 4. It should be noted that in the general initial state and the reference symmetric design, TILT = 1.00, that is, the power of each quadrant of the whole reactor is uniform. It should be noted that there are many ways to divide the quadrants, but this does not affect this embodiment.

[0124] Step S920: Regularly obtain measured data.

[0125] Based on the RIC (core measurement system) flux diagram measurement test work carried out regularly (about once a month) in nuclear power plants, regular measured power levels and core TILT values ​​are obtained.

[0126] For example, taking the CPR1000 unit as an example, the core has 157 components, that is, 157 measured power levels are obtained through test measurement and post-processing. (i) , where i=1,2,......,157.

[0127] Step S930: Fusing the measured data and the theoretical model.

[0128] (1) The first revision process;

[0129] In this embodiment, the measured value is fully trusted. The measured power level is considered as the target value of the theoretical correction. The corresponding technical means will be taken later to correct the theoretical model so that the theoretical power level Pa (i) Close to the measured power level Pt (i) .

[0130] Obtaining the measured power level of each of the above components;

[0131] The flow distribution level at the core inlet in the core theoretical model is adjusted to correct the theoretical power level of each component in the core theoretical model. Then, by repeatedly adjusting the inlet flow distribution parameters in the core theoretical model, an optimal inlet flow distribution level is finally obtained so that the core theoretical power level Pa (i) Approaching the first measurement power level Pt (i) .

[0132] The specific revision process includes:

[0133] Analyze the effect of the selected flow distribution level increase / decrease on the power level of the assembly and determine its positive / negative correlation. Generally speaking, the larger the flow level, the better the moderation conditions and the higher the power level of the fuel assembly.

[0134] First, an initial level of traffic distribution is given.

[0135] Then, the theoretical power level Pa of the core theoretical model under the condition of the initial level of flow distribution is solved (i) .

[0136] Then, in numerical order (from component 1 to component 157), the theoretical power level Pa is compared in turn. i and the measured power level Pt (i) The size of , i = 1, 2, ..., 157.

[0137] If Pt (i) >Pa (i) ; then improve the flow distribution level at the component, so that Pa (i) Increase; if Pt (i) <Pa (i); then reduce the flow distribution level at the component, so that Pa (i) Reduce.

[0138] When adjusting (increasing or decreasing), it can be adjusted proportionally; it is assumed that the correction weight coefficients of the initial flow level are consistent and are all 1.0.

[0139] If the flow distribution level is increased, the coefficient c (c>1) is multiplied, such as 1.1, 1.2, 1.3. If the flow distribution level is reduced, the coefficient c (c<1) is multiplied, such as 0.9, 0.8, 0.7.

[0140] For the flow distribution level, only a small adjustment is made each time. The inlet flow levels of each component in the core theoretical model need to be adjusted repeatedly for many times. Finally, a reasonable flow distribution level of the core theoretical model will be obtained, so that the core theoretical power distribution is consistent with the measured power level distribution, that is,

[0141] Pt (i) -Pa (i) <0.001, i=1,2,...,157

[0142] Finally, the burnup calculation and analysis were carried out using the revised core theoretical model to simulate the actual operation of the core until the periodic flux diagram test time, so as to predict the change trend of TILT in this stage. It should be noted that the core quadrant power tilt is calculated based on the power distribution. When the core power distribution is determined, the core quadrant power tilt will also be determined.

[0143] The first correction assimilates the core inlet flow parameters, ignores other uncertain factors that cause TILT, assumes that in the early life of a nuclear reactor unit, the gap between theory and practice depends on the flow difference, corrects the core theoretical model by correcting the flow distribution, and keeps this flow distribution unchanged during the subsequent operation of the unit. This can effectively describe the uneven flow distribution problem that may exist in the actual core.

[0144] (2) The second revision process;

[0145] Collect measured power levels of individual components;

[0146] Based on the first revised model, the water gap distribution in the core theoretical model is adjusted (covering the comprehensive results of all other factors that may affect TILT) to correct the theoretical power levels of each component in the core theoretical model. Then, by repeatedly adjusting the water gap distribution in the core theoretical model, the core theoretical power level Pa (i) Approximate measurement power level Pt (i) .

[0147] The specific correction process is similar to the first correction process mentioned above and will not be repeated here.

[0148] Based on the core theoretical model after assimilating the water gap distribution parameters, the burnup calculation analysis is carried out to simulate the core operation status and quadrant power tilt state (TILT value) of the actual operation of the core to the next stage (nearly one month), so as to achieve short-term prediction of TILT change trend.

[0149] Since fuel assembly deformation is an important influencing factor of TILT, and the fuel assembly may be deformed or displaced in the stack, resulting in changes in the assembly gap (water gap), the flow distribution has been assimilated, corrected and solidified during the first correction process. Therefore, this application uses the water gap distribution as the characteristic parameter for the second correction of the model. Taking the water gap as the main factor can not only approximately replace other comprehensive influences, but also effectively solve the fuel assembly deformation problem that may exist in the actual core.

[0150] (3) The third to Nth correction processes, like the previous correction process type, are to adjust the water gap distribution in the core theoretical model to achieve core model correction based on data assimilation. This is because as the reactor unit operates, the deformation of the fuel assembly will gradually accumulate, so each subsequent correction process can only adjust the water gap distribution. Through regular correction, long-term prediction of TILT change trends can be achieved.

[0151] like Figure 2 Some embodiments of the present application provide a core model correction device based on data assimilation, the device comprising:

[0152] The theoretical power level acquisition unit 1100 is used to acquire the theoretical power level of each component in the core obtained by the i-th simulation based on the core theoretical model after the i-1th correction, and the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core;

[0153] The measured power level acquisition unit 1200 is used to acquire the measured power level corresponding to the theoretical power level of each component, and the measured power level is obtained by measuring the components in the core;

[0154] The core theoretical model correction unit 1300 is used to perform parameter assimilation on the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the ith simulation and the corresponding measured power level if there is at least one operation of parameter assimilation on the parameter value of the first characteristic parameter in the previous i-1 times of correction of the core theoretical model, so that the deviation between the theoretical power level of each component obtained by the simulation of the corrected core theoretical model and the measured power level of each component is less than a target threshold value;

[0155] The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each component, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

[0156] It should be noted that the core model correction device based on data assimilation provided in this embodiment and the above-mentioned core model correction method based on data assimilation are based on the same inventive concept, so the relevant contents of the above-mentioned core model correction method based on data assimilation are also applicable to the contents of the core model correction method device based on data assimilation, and therefore, they will not be repeated here.

[0157] like Figure 3 The embodiment of the present application further provides an electronic device, the electronic device comprising:

[0158] At least one hydrogen fuel cell;

[0159] at least one memory;

[0160] at least one processor;

[0161] at least one program;

[0162] The program is stored in the memory, and the processor executes at least one program to implement the core model correction method and core quadrant power tilt calculation method based on data assimilation in the present disclosure.

[0163] The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer, etc.

[0164] The electronic device according to the embodiment of the present application is described in detail below.

[0165] The processor 1600 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present disclosure;

[0166] The memory 1700 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1700 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1700, and the processor 1600 calls and executes the core model correction method based on data assimilation and the core quadrant power tilt calculation method of the embodiment of the present disclosure.

[0167] Input / output interface 1800, used to implement information input and output;

[0168] The communication interface 1900 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0169] Bus 2000 , which transmits information between various components of the device (e.g., processor 1600 , memory 1700 , input / output interface 1800 , and communication interface 1900 );

[0170] The processor 1600 , the memory 1700 , the input / output interface 1800 , and the communication interface 1900 are connected to each other in communication within the device via the bus 2000 .

[0171] An embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned core model correction method based on data assimilation and core quadrant power tilt calculation method.

[0172] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0173] The embodiments described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0174] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present disclosure and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0175] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0177] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0178] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0179] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0182] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store programs.

[0183] The above is a specific description of the preferred implementation of the embodiments of the present application, but the embodiments of the present application are not limited to the above-mentioned implementation methods. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the embodiments of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the embodiments of the present application.

[0184] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A core model correction method based on data assimilation, characterized in that: The method comprises: Obtaining the theoretical power level of each component in the core obtained in the i-th simulation based on the core theoretical model after the i-1th correction, wherein the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core; i is a positive integer greater than 1; Acquiring a measured power level corresponding to a theoretical power level of each of the components, wherein the measured power level is obtained by measuring the components in the core; If there is at least one operation of parameter assimilation on the parameter value of the first characteristic parameter in the previous i-1 times of correction of the core theoretical model, then based on the theoretical power level of each of the components obtained by the i-th simulation and the corresponding measured power level, parameter assimilation is performed on the parameter value of the second characteristic parameter in the core theoretical model, so that the deviation between the theoretical power level of each of the components obtained by the simulation of the corrected core theoretical model and the measured power level of each of the components is less than a target threshold; The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each of the components, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

2. The core model correction method based on data assimilation according to claim 1 is characterized in that: After obtaining the measured power levels corresponding to the theoretical power levels of the components, the method further includes: If there is no parameter assimilation operation on the parameter value of the first characteristic parameter in the process of correcting the characteristic parameters in the core theoretical model for the first i-1 times, then based on the theoretical power levels of each of the components obtained by the i-th simulation and the corresponding measured power levels, the parameter value of the first characteristic parameter in the core theoretical model is corrected so that the deviation between the theoretical power level of each of the components obtained by the corrected core theoretical model simulation and the measured power level of each of the components is less than the target threshold.

3. The core model correction method based on data assimilation according to claim 1 is characterized in that: The parameter assimilation of the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the i-th simulation and the corresponding measured power level includes: Determine the positive or negative correlation between the parameter value of the second characteristic parameter at the location of each of the components and the theoretical power level of each of the components; Determining the magnitude relationship between the theoretical power level of each of the components obtained by simulating the core theoretical model and the measured power level of each of the components; According to the positive and negative correlation and the size relationship, the parameter value of the second characteristic parameter in the core theoretical model is corrected.

4. The core model correction method based on data assimilation according to claim 3 is characterized in that: The step of performing parameter assimilation on the parameter value of the second characteristic parameter in the core theoretical model according to the positive and negative correlation and the size relationship includes: If the theoretical power level of the target component is less than the measured power level of the target component, and there is a positive correlation between the parameter value of the second characteristic parameter and the theoretical power level, then increasing the parameter value of the second characteristic parameter in the core theoretical model; the target component is any one of the components; If the theoretical power level of the target component is less than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, reducing the parameter value of the second characteristic parameter in the core theoretical model; If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a negative correlation between the parameter value of the second characteristic parameter and the theoretical power level, increasing the parameter value of the second characteristic parameter in the core theoretical model; If the theoretical power level of the target component is greater than the measured power level of the target component, and there is a positive correlation between the parameter value of the second characteristic parameter and the theoretical power level, then the parameter value of the second characteristic parameter in the core theoretical model is reduced.

5. The core model correction method based on data assimilation according to claim 1 is characterized in that: Before obtaining the theoretical power level of each component in the core obtained in the i-th simulation based on the core theoretical model corrected for the i-1th time, the method further includes: Acquire the initial theoretical power level of each component in the core obtained based on the initial core theoretical model simulation; Acquiring an initial measured power level corresponding to an initial theoretical power level of each of the components; Based on the initial theoretical power levels of each of the components and the corresponding initial measured power levels, the parameter value of the first characteristic parameter in the initial core theoretical model is corrected to obtain the core theoretical model after the first correction, and the deviation between the theoretical power level of each of the components simulated by the core theoretical model after the first correction and the initial measured power level of each of the components is less than the target threshold.

6. The core model correction method based on data assimilation according to claim 1 is characterized in that: The first characteristic parameter includes the flow rate of the coolant / moderator at the core inlet; the second characteristic parameter includes the water gap.

7. A method for calculating core quadrant power tilt, characterized in that: The method comprises: The theoretical power level of each component in the core is obtained based on the target model simulation, wherein the target model is the core theoretical model corrected in the core model correction method based on data assimilation according to any one of claims 1 to 6; The core quadrant power tilt state is calculated according to the theoretical power level of each of the components obtained by simulating the target model.

8. A core model correction device based on data assimilation, characterized in that: The device comprises: A theoretical power level acquisition unit, used to acquire the theoretical power level of each component in the core obtained by the i-th simulation based on the core theoretical model after the i-1th correction, wherein the core theoretical model is constructed according to the nuclear reactor unit corresponding to the core; i is a positive integer greater than 1; a measured power level acquisition unit, used to acquire a measured power level corresponding to a theoretical power level of each of the components, wherein the measured power level is obtained by measuring the components in the core; a core theoretical model correction unit, configured to, if there is at least one operation of parameter assimilation on the parameter value of the first characteristic parameter in the previous (i-1) correction processes of the core theoretical model, perform parameter assimilation on the parameter value of the second characteristic parameter in the core theoretical model based on the theoretical power level of each component obtained by the ith simulation and the corresponding measured power level, so that the deviation between the theoretical power level of each component obtained by the simulation of the corrected core theoretical model and the measured power level of each component is less than a target threshold; The first characteristic parameter and the second characteristic parameter are both characteristic parameters that affect the power level of each of the components, and the first characteristic parameter is a characteristic parameter that does not change with the operation of the nuclear reactor unit, and the second characteristic parameter is different from the first characteristic parameter.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the core model correction method based on data assimilation as described in any one of claims 1 to 6 and the core quadrant power tilt calculation method as described in claim 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a core model correction method based on data assimilation as described in any one of claims 1 to 6 and a core quadrant power tilt calculation method as described in claim 7.

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