A method and system for developing data management of a nuclear power simulation model

Abstract

The present application relates to the technical field of data processing, and discloses a nuclear power simulation model development data management method and system, which comprises the following steps: extracting the development data of the nuclear power simulation model, dividing the simulation grid into different subdomains, and allocating the I / O bandwidth of the subdomains; determining the effective access frequency and the weighted effective access frequency of the subdomains at the current time step, calculating the hotspot center, determining the predicted access probability of the future time step, and determining the data of the time step preloaded to the first storage layer according to the predicted access probability; determining the dynamic compression ratio of the subdomains at the current time step and the compression mode of the data of the subdomains at the adjacent time step, writing the compression result into the second storage layer, selecting part of the data of the second storage layer for compression according to the value of the effective access frequency, and writing the compressed data into the third storage layer, so as to realize the management of the development data of the nuclear power simulation model. The present application can improve the management accuracy and efficiency of the nuclear power simulation model data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a nuclear power simulation model development data management method and system. BACKGROUND

[0002] Nuclear power simulation model data management refers to a series of operations such as effective organization, storage, access, update and maintenance of various data related to simulation models in the process of nuclear power plant simulation, so as to ensure the accuracy, integrity and availability of data, thereby supporting the development, operation, maintenance and optimization of simulation models. Generally, a simulation calculation needs to iterate hundreds of thousands of time steps on a grid of hundreds of millions, generating super large data. When managing and storing such super large data, all original data sets of subdomains are usually written into one or a few super large files, and are compressed and stored in a unified fixed format for long-term preservation.

[0003] However, there are spatial and temporal differences between the local data of nuclear power simulation model data. Specifically, the cell size, physical field gradient and access frequency of different subdomains often cause spatial differences between local data, and the data change amplitude of each subdomain between adjacent time steps often causes temporal differences between local data. However, the nuclear power simulation model data management directly manages the data of different subdomains according to the same format, which may cause I / O load imbalance during storage, and also reduce cache hit rate during retrieval. SUMMARY

[0004] The present application provides a nuclear power simulation model development data management method and system to solve the problem that the nuclear power simulation model data is managed according to a unified format, resulting in I / O load imbalance during data storage and reducing cache hit rate during data retrieval. The technical solution adopted is as follows:

[0005] In a first aspect, an embodiment of the present application provides a nuclear power simulation model development data management method, which includes the following steps:

[0006] Extracting the development data of the nuclear power simulation model, dividing the simulation grid into different subdomains, and allocating the I / O bandwidth of the subdomain according to the number of cells of the subdomain;

[0007] Based on the number of times the subdomain is accessed in the current time step and all time steps within the preset time window before the current time step, the effective access frequency of the subdomain in the current time step is determined. Combining the number of times the subdomain is accessed in all time steps within the time window, the weighted effective access frequency of the time window is determined. Based on the effective access frequency, the hot spot center is calculated. Based on the distance between the geometric center coordinates of the subdomain and the hot spot center, and the interval between the current time step and future time steps, the predicted access probability of future time steps is determined. Based on the predicted access probability, the data of the time steps to be preloaded to the first storage layer is determined.

[0008] Based on the differences between data in adjacent time steps and the volume differences between different subdomains, the dynamic compression ratio of the subdomain in the current time step is determined. Based on the dynamic compression ratio of the subdomain in the time step, the compression method of the data in the subdomain in adjacent time steps is determined, and the compression result is written to the second storage layer. Combining the effective access frequency of the subdomain in the current time step, a portion of the data in the second storage layer is selected for compression and written to the third storage layer, thereby realizing the management of nuclear power simulation model development data.

[0009] Furthermore, the formula for calculating the effective access frequency of the subdomain at the current time step is:

[0010]

[0011] in, Indicates the first Each subdomain at the current time step Valid access frequency; Indicates the length of the preset time window; Indicates the current time step; Indicates the first The subdomain in the first The number of times a step is accessed; Represents the natural constant; This indicates the preset attenuation coefficient.

[0012] Furthermore, the specific method for obtaining the weighted effective access frequency of the time window is as follows:

[0013] The effective access frequency of a time step is used as the weight of the number of times a subdomain is accessed in a time step. The number of times all time steps are accessed within a preset time window is weighted and summed to obtain the weighted effective access frequency of the time window.

[0014] Furthermore, the specific method for calculating hotspot centers based on effective access frequency includes:

[0015] Calculate the geometric center coordinates of the subdomains, use the effective access frequency of the time step as the weight of the geometric center coordinates of the subdomains, and sum the weighted geometric center coordinates of all subdomains to obtain the hot spot center.

[0016] Furthermore, the formula for calculating the predicted access probability at the future time step is:

[0017]

[0018] in, Indicates the first Each subdomain at the current time step The next future Predicted access probability at each time step; Indicates the current time step The next future One time step; Indicates the first Each subdomain at the current time step The Euclidean distance between the geometric center coordinates and the hotspot center at time; This represents the normalization function.

[0019] Furthermore, the specific method for determining the data to be preloaded to the first storage layer at the time step based on the predicted access probability includes:

[0020] The third quartile of the predicted access probability of all future time steps after the current time step is denoted as the second partitioning threshold. Data of future time steps after the current time step whose predicted access probability is greater than the second partitioning threshold are preloaded into the first storage layer.

[0021] Furthermore, the specific method for obtaining the dynamic compression ratio of the subdomain at the current time step is as follows:

[0022] The sum of squares of the differences between the corresponding field variables of the corresponding units in the same subdomain at the current time step and the previous adjacent time step is denoted as the first sum of squares of the corresponding units in the same subdomain at the current time step; the volume of the unit is used as the weight of the first sum of squares of the unit, and the first sum of squares of all units in the same subdomain at the current time step is weighted and summed, and the normalized value of the weighted sum is denoted as the difference between adjacent time steps of the same subdomain at the current time step;

[0023] The median of the differences between all subdomains at the current time step and their adjacent time steps is denoted as the sensitivity parameter at the current time step; the sum of the differences between the subdomains at the current time step and their adjacent time step differences and the sensitivity parameter at the current time step is denoted as the relative differences between the subdomains at the current time step and their adjacent time steps.

[0024] The maximum value of the relative adjacent time step difference of the sub-domain at the current time step and the preset third division threshold is recorded as the dynamic compression ratio of the sub-domain at the current time step.

[0025] Further, the method for determining the compression mode of the data of the sub-domain at the adjacent time step according to the dynamic compression ratio of the sub-domain at the time step comprises the following specific method:

[0026] The difference between the corresponding field variables of the same sub-domain at the current time step and the previous adjacent time step of the current time step is recorded as the difference data block of the same sub-domain at the current time step.

[0027] When the dynamic compression ratio of the sub-domain at the current time step is greater than or equal to the fourth division threshold, the difference data block is losslessly compressed.

[0028] When the dynamic compression ratio of the sub-domain at the current time step is greater than or equal to the third division threshold and less than the fourth division threshold, the dynamic compression ratio of the sub-domain at the current time step is taken as the value of the compression ratio, the difference data block is lossy quantized, and the lossy quantized difference data block is losslessly compressed.

[0029] Further, the specific method for selecting part of the data of the second storage layer for compression comprises the following specific method:

[0030] The first six quantiles of the effective access frequencies of all the sub-domains at the current time step are recorded as the fifth division threshold, and the data of the sub-domains at the corresponding time step and all the time steps before the corresponding time step are selected for compression.

[0031] In a second aspect, the embodiments of the present application further provide a nuclear power simulation model development data management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method in any one of the above aspects when executing the computer program.

[0032] The present application has the following beneficial effects:

[0033] The application first allocates I / O bandwidth of the sub-domain according to the number of units of the sub-domain, realizes dynamic adjustment of I / O load, eliminates file lock competition and realizes load balancing, because of the time locality of the access hotness of the nuclear power simulation model, the read-write performance closer to the current step can represent the access tendency of the next step, the weighted effective access frequency of the time window is determined by weighting the access frequency according to the interval between different time steps in the time window, to avoid misjudging the cooled area as a hotspot, then, in order to avoid blind prefetching and wasting cache, the predicted access probability of the future time step is determined according to the distance between the geometric center coordinate of the sub-domain and the hotspot center and the interval between the current time step and the future time step, so that the predicted access probability meets that the units closer to the current hotspot center in space have a higher probability of being accessed next step, and the units closer to the current time in time have a higher probability of being accessed, and the data of the time step preloaded to the first storage layer is determined according to the predicted access probability; further, the compression ratio of each time step is adaptively determined according to the difference of the development data of adjacent time steps, that is, the dynamic compression ratio of the sub-domain in the current time step is determined, and compression is performed according to the adaptively determined compression ratio, and the compression result is written into the second storage layer; finally, when the data of the sub-domain is not frequently accessed for a long time, the necessity of real-time calculation and high-frequency analysis of the data of the time step corresponding to the sub-domain is relatively low, in order to speed up the reading and writing of other sub-domain data with relatively strong necessity, at the same time, in order to save storage space, the historical differential data of these sub-domains is more strongly compressed, and the compression result is stored in the third storage layer, realizing the management of the nuclear power simulation model development data, solving the problem that the data of the nuclear power simulation model is managed according to a unified format, resulting in I / O load imbalance during data storage and reducing cache hit rate during data retrieval, and improving the management accuracy and management efficiency of the nuclear power simulation model data. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0035] Figure 1 The flowchart of the nuclear power simulation model development data management method provided by an embodiment of the present application;

[0036] Figure 2 The flowchart of the effective access frequency acquisition provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0038] Please refer to Figure 1 which shows a flow chart of a nuclear power simulation model development data management method provided by an embodiment of the present application, and the method comprises the following steps:

[0039] In step S001, the development data of the nuclear power simulation model is extracted, the simulation grid is divided into different sub-domains, and the I / O bandwidth of the sub-domains is allocated according to the number of cells of the sub-domains.

[0040] The development data of the nuclear power simulation model is extracted. If the development data of the nuclear power simulation model is read and written according to a centralized single large file, all processes will compete for the same POSIX file lock, resulting in queuing and waiting. In order to shorten the I / O delay and eliminate the competition for the file lock, the development data of each sub-domain is read and written in a parallel manner. Specifically, the simulation grid is divided into different sub-domains according to spatial connectivity, and the development data of different sub-domains is written into different files.

[0041] Among them, the I / O load imbalance refers to the uneven distribution of data input and output operations among storage devices or hardware components in a computer system, resulting in some devices becoming bottlenecks due to excessive workload, while other devices are not fully utilized.

[0042] In the finite element solver, the number of field variables and the storage bytes of each cell are basically fixed, so the number of cells and the final I / O byte number are in a linear relationship, and the number of cells can be used as a weight to allocate the I / O task amount. Moreover, the number of cells is an existing integer field in the grid file, and no sampling is required. In order to ensure that the reading and writing among the sub-domains are conflict-free, the simulation grid is divided according to the spatial connectivity of the grid. In the pre-processing stage, the grid is regarded as an undirected graph, and a graph partitioning algorithm is used to obtain a cell-level partition mapping table. Then, in the solver, the cells are sent to each sub-domain according to the partition mapping table.

[0043] Specifically, preferably, as an embodiment of the present application, the unit file is exported in the pre-processing tool, the adjacent surface of each unit is parsed using PyMesh, the undirected graph of the unit and the adjacent unit is established, the partition is performed using ParMETIS in spatial connectivity, all sub-domains are obtained, a separate binary file is created for each sub-domain, and a mapping file corresponding to the unit and the sub-domain is generated. In the embodiment, ANSYS Meshing is selected as the pre-processing tool; the number of sub-domains is set to 128, the number of sub-domains is set according to the number of units in the file, and it is ensured that there are units in each sub-domain; 128 MPI processes are started by the solver, and each process reads and writes files for different sub-domains according to the mapping file; the adjacent surface of each unit is parsed using PyMesh, and the partition is performed using ParMETIS in spatial connectivity, which are both known technologies and will not be described in detail.

[0044] The I / O bandwidth is allocated according to the number of units of each sub-domain, and the I / O bandwidth of the sub-domain is proportional to the number of units of the sub-domain in the cumulative sum of the number of units of all sub-domains. Therefore, the I / O bandwidth of the sub-domain with more units is larger.

[0045] After each time step or working condition is completed, the actual time consumption of each sub-domain is extracted, and if the actual time consumption of the sub-domain is greater than the second quartile of the time consumption average, the sub-domain is re-partitioned and the mapping file is rewritten before the next iteration using ParMETIS, so as to continuously maintain the I / O load balance.

[0046] It can be understood that the re-partitioning of the sub-domain and the rewriting of the mapping file can realize dynamic adjustment of the I / O load, eliminate file lock competition and realize load balancing.

[0047] At this point, the development data of the nuclear power simulation model, the sub-domains and the I / O bandwidth of the sub-domains are obtained.

[0048] In step S002, the effective access frequency of the sub-domain in the current time step is determined according to the number of times the sub-domain is accessed in the current time step and all time steps within a preset time window before the current time step, the weighted effective access frequency of the time window is determined in combination with the number of times all time steps within the time window are accessed, the hot spot center is calculated according to the effective access frequency, the prediction access probability of the future time step is determined according to the distance between the geometric center coordinates of the sub-domain and the hot spot center and the interval between the current time step and the future time step, and the data of the time step preloaded to the first storage layer is determined according to the prediction access probability.

[0049] There is strong spatiotemporal continuity between the various development data. When the solver calculates the current cell, it must access the values ​​of all adjacent cells of the current cell. If a cell is accessed, its adjacent cells will also be accessed in the same time step or the next time step. Therefore, the current access hotspot area may still be a hotspot in the next time step.

[0050] Nuclear power simulation models typically employ small-step explicit or semi-implicit recommendations for time progression. Solutions for the same physical field are almost continuous within adjacent time steps. Therefore, the solver needs to repeatedly read the field variables of the same batch of cells and their adjacent cells, resulting in some subdomains being accessed much more frequently than others. If traditional on-demand disk reading is used, adjacent development data may be written to different subdomain files, leading to high latency due to cross-node data retrieval for each read. Therefore, it is necessary to calculate the probability of the next access by statistically analyzing the access patterns and spatiotemporal characteristics of each subdomain. When the probability exceeds a threshold, the subdomain's data is prematurely pulled into the cache, with LRU (Least Recently Used) managing the cache capacity. LRU cache management is a well-known technique and will not be elaborated further.

[0051] Because the access frequency in nuclear power simulation models exhibits significant temporal locality, the read / write performance closer to the current step is more representative of the access tendency for the next step. Therefore, it is necessary to weight the access frequency according to the interval between different time steps within the time window to avoid misclassifying cooled-down areas as hotspots. Specifically, this application uses a time-decreasing weight, causing the weight of historical access counts to decay rapidly, thereby retaining the weight of recent access counts and making the calculation of effective access frequency more consistent with reality.

[0052] The effective access frequency of the subdomain at the current time step is determined based on the number of times the subdomain is accessed at the current time step and at all time steps within the preset time window before the current time step.

[0053]

[0054] in, Indicates the first Each subdomain at the current time step Valid access frequency; This indicates the length of the preset time window. In this embodiment, the length of the time window is set to 100. Indicates the current time step; Indicates the first The subdomain in the th The number of times a step is accessed; Represents the natural constant; represents a preset decay coefficient, the decay coefficient is used for controlling a forgetting speed of the historical access times, the decay coefficient should be greater than or equal to 0.05 and less than or equal to 0.1, and the value of the decay coefficient in the embodiment is 0.05.

[0055] It can be understood that, when the current time step is greater than the length of the time window, the effective access frequency of the current time step is started to be calculated; the greater the decay coefficient is, the faster the forgetting speed of the historical access times is, and the more the calculation of the effective access frequency depends on the recent data.

[0056] An effective access frequency acquisition flowchart is shown in FIG. 4. Figure 2

[0057] In the running process of the solver, a counter is allocated to each subdomain, when each subdomain is read and written once, the count of the counter is increased by one and a time stamp is recorded, when the count of the counter reaches the value of the length of the time window, the effective access frequency of the time step is taken as the weight of the number of times that the subdomain is accessed in the time step, the number of times that all time steps in the preset time window are accessed is weighted and summed to obtain the weighted effective access frequency of the time window.

[0058] The geometric center coordinates of the subdomain are calculated according to the vertex coordinates of the subdomain, the effective access frequency of the time step is taken as the weight of the geometric center coordinates of the subdomain, the geometric center coordinates of all subdomains are weighted and summed to obtain the hotspot center. The third quartile of all time steps in the preset time window is recorded as the first division threshold, and the subdomain whose cumulative sum of the number of times that is accessed is greater than the first division threshold in all time steps in the preset time window is recorded as the hotspot subdomain.

[0059] The vertex coordinates of the subdomain can be directly extracted from the nuclear power simulation model, and the acquisition of the vertex coordinates of the subdomain is a known technology and will not be described in detail.

[0060] In the nuclear power simulation model, physical quantities exhibit the characteristics of continuous diffusion in space and time, that is, the closer the unit is to the current hotspot center in space, the higher the probability of being accessed in the next step is, and the closer the unit is to the current time in time, the higher the probability of being accessed is. Therefore, in order to avoid blind prefetching waste of cache, the prediction access probability of the future time step is determined according to the distance between the geometric center coordinates of the subdomain and the hotspot center and the interval between the current time step and the future time step.

[0061] The calculation formula of the prediction access probability of the future time step is:

[0062]

[0063] wherein, represents the number of times that the i-th subdomain is accessed in the current time step t, and represents the number of times that the i-th subdomain is accessed in the future time step t + k. ​the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step; the prediction access probability of the future time step after the current time step;

[0064] the third quartile of the prediction access probability of all future time steps after the current time step is recorded as the second division threshold, and the data of the future time steps after the current time step with the prediction access probability greater than the second division threshold is preloaded to the first storage layer, and the cache is managed using the LRU strategy.

[0065] the first storage layer carrier is an NVMe SSD or NVRAM local to the server node, used to store the data of the hotspot subdomain with high probability access in the next step; the cache data is managed using the LRU strategy, which is a well-known technology and will not be described again. In addition, in addition to the first storage layer, there are also a second storage layer and a third storage layer; the second storage layer carrier is a parallel system file or a distributed SSD pool, and the second storage layer is used to store the compressed differential data of the subdomain and the complete data of the nearest time step; the third storage layer carrier is an object storage or a tape library, used to store the compressed package after difference merging. The parallel system file is Lustre, GPFS, BeeGFS, and the object storage is S3, Ceph.

[0066] At this point, the data of the preloaded time step is determined.

[0067] In step S003, the dynamic compression ratio of the subdomain in the current time step is determined according to the difference between the data of the adjacent time steps of the subdomain and the volume difference of different subdomains, the compression mode of the data of the adjacent time steps of the subdomain is determined according to the dynamic compression ratio of the subdomain in the time step, and the compression result is written to the second storage layer. In combination with the value of the effective access frequency of the subdomain in the current time step, part of the data of the second storage layer is selected for compression and written to the third storage layer, realizing the management of the nuclear power simulation model development data.

[0068] ​A simulation calculation needs to iterate hundreds of millions of grids for tens of thousands to hundreds of thousands of time steps, generating massive data, and most of the field variables in the region change very little in adjacent time steps. If direct lossless saving is performed, a large amount of storage space will be consumed, and I / O delay will also be caused. However, if all data are uniformly compressed in a format, the accuracy of the hot area cannot be guaranteed. Therefore, the compression ratio of each time step needs to be adaptively determined according to the difference between the development data of adjacent time steps, and compression needs to be performed according to the adaptively determined compression ratio.

[0069] The dynamic compression ratio of the subdomain in the current time step is determined according to the difference between the data of the adjacent time steps of the subdomain and the volume difference of different subdomains.

[0070] The sum of squares of the corresponding field variables of the corresponding cells of the same subdomain in the current time step and the previous adjacent time step of the current time step is denoted as the first sum of squares of the corresponding cells of the same subdomain in the current time step. The volume of the cell is used as the weight of the first sum of squares of the cell, and the first sum of squares of all cells of the same subdomain in the current time step is weighted and summed. The normalized value of the result of the weighted sum is denoted as the adjacent time step difference of the same subdomain in the current time step.

[0071] In this embodiment, the normalized value is calculated by using a sigmoid function, wherein the sigmoid function is a known technology and will not be described here. As other implementation manners, the implementer can use other methods of the prior art, such as a tanh function, etc.; the field variable of the time step is the development data of the corresponding cell of the time step; and the volume of the cell is obtained by using a known technology, which will not be described here.

[0072] The median of the adjacent time step differences of all subdomains in the current time step is denoted as the sensitivity parameter of the current time step, and the sum of the adjacent time step difference of the subdomain in the current time step and the sensitivity parameter of the current time step is denoted as the relative adjacent time step difference of the subdomain in the current time step. The maximum value of the relative adjacent time step difference of the subdomain in the current time step and the third division threshold value is denoted as the dynamic compression ratio of the subdomain in the current time step.

[0073] The sensitivity parameter of the current time step can make the value of the dynamic compression ratio of nearly half of the subdomains close to 0.5, so as to ensure that these subdomains are neither excessively compressed nor excessively retained.

[0074] The third division threshold value is a preset parameter, and the value of the third division threshold value should be greater than or equal to 0.2 and less than or equal to 0.5. In this embodiment, the value of the third division threshold value is 0.2.

[0075] The greater the difference between the adjacent time steps of the current time step of the sub-domain, the greater the change in the physical field generated in the current time step and the previous adjacent time step of the current time step of the sub-domain, the more important the data of the current time step, the more necessary it is to retain high-precision data in the compression process, and the greater the compression ratio should be selected for compression. At this time, the dynamic compression ratio of the sub-domain in the current time step is greater.

[0076] According to the dynamic compression ratio of the sub-domain at the time step, the compression mode of the data of the adjacent time step of the sub-domain is determined, and the compression result is written into the second storage layer.

[0077] The difference between the corresponding field variables of the same sub-domain at the current time step and the previous adjacent time step of the current time step is denoted as the difference data block of the same sub-domain at the current time step. When the dynamic compression ratio of the sub-domain in the current time step is greater than or equal to the fourth division threshold, the difference data block is losslessly compressed by using the LZ4-HC level 9 algorithm; when the dynamic compression ratio of the sub-domain in the current time step is greater than or equal to the third division threshold and less than the fourth division threshold, the dynamic compression ratio of the sub-domain in the current time step is taken as the value of the compression ratio, the difference data block is lossy quantized, and the lossy quantized difference data block is losslessly compressed by using the LZ4 level 6 algorithm. The compression result of the difference data block is written into the second storage layer.

[0078] The fourth division threshold is a preset parameter, the value of the fourth division threshold should be greater than 0.2 and less than or equal to 1, and the value of the fourth division threshold in the embodiment is 0.9. The lossless compression by using the LZ4-HC level 9 algorithm and the LZ4 level 6 algorithm are all known technologies, and will not be described in detail.

[0079] When the data of the sub-domain is not frequently accessed for a long time, it indicates that the necessity of real-time calculation and high-frequency analysis on the data of the time step corresponding to the sub-domain is relatively low. In order to speed up the reading and writing of other sub-domain data with relatively strong necessity, and at the same time, in order to save storage space, the historical difference data of these sub-domains can be more strongly compressed, and the compressed result is stored in the third storage layer.

[0080] The first sixth quantile of the effective access frequency of all sub-domains at the current time step is denoted as the fifth division threshold, the difference data blocks of the sub-domains with the effective access frequency less than the fifth division threshold at the current time step and all time steps before the current time step are selected, the selected difference data blocks are arranged in chronological order, the complete data of the sub-domain is obtained, the complete data of the sub-domain is compressed by using a compression method based on wavelet change, and the compressed data is written into the third storage layer, and the storage space of these data in the second storage layer is released.

[0081] Thus, the management of the nuclear power simulation model development data is realized.

[0082] Based on the same inventive concept as the above method, the embodiments of the present application also provide a nuclear power simulation model development data management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above nuclear power simulation model development data management methods when executing the computer program.

[0083] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data management method for developing nuclear power simulation models, characterized in that, The method includes the following steps: Extract the development data of the nuclear power simulation model, divide the simulation mesh into different subdomains, and allocate the I / O bandwidth of the subdomains according to the number of cells in each subdomain. Based on the number of times the subdomain is accessed in the current time step and all time steps within the preset time window before the current time step, the effective access frequency of the subdomain in the current time step is determined. Combining the number of times the subdomain is accessed in all time steps within the time window, the weighted effective access frequency of the time window is determined. Based on the effective access frequency, the hot spot center is calculated. Based on the distance between the geometric center coordinates of the subdomain and the hot spot center, and the interval between the current time step and future time steps, the predicted access probability of future time steps is determined. Based on the predicted access probability, the data of the time steps to be preloaded to the first storage layer is determined. Based on the differences between data in adjacent time steps and the volume differences between different subdomains, the dynamic compression ratio of the subdomain in the current time step is determined. Based on the dynamic compression ratio of the subdomain in the time step, the compression method of the data in the subdomain in adjacent time steps is determined, and the compression result is written to the second storage layer. Combining the effective access frequency of the subdomain in the current time step, a portion of the data in the second storage layer is selected for compression and written to the third storage layer, thereby realizing the management of nuclear power simulation model development data. The formula for calculating the effective access frequency of the subdomain at the current time step is: in, Indicates the first Each subdomain at the current time step Valid access frequency; Indicates the length of the preset time window; Indicates the current time step; Indicates the first The subdomain in the first The number of times a step is accessed; Represents the natural constant; This indicates the preset attenuation coefficient; The specific method for obtaining the dynamic compression ratio of the subdomain at the current time step is as follows: The sum of squares of the differences between the corresponding field variables of the corresponding units in the same subdomain at the current time step and the previous adjacent time step is denoted as the first sum of squares of the corresponding units in the same subdomain at the current time step; the volume of the unit is used as the weight of the first sum of squares of the unit, and the first sum of squares of all units in the same subdomain at the current time step is weighted and summed, and the normalized value of the weighted sum is denoted as the difference between adjacent time steps of the same subdomain at the current time step; The median of the differences between all subdomains at the current time step and their adjacent time steps is denoted as the sensitivity parameter at the current time step; the sum of the differences between the subdomains at the current time step and their adjacent time step differences and the sensitivity parameter at the current time step is denoted as the relative differences between the subdomains at the current time step and their adjacent time steps. The maximum value between the difference between the subdomain and its relative adjacent time steps at the current time step and the preset third partitioning threshold is denoted as the dynamic compression ratio of the subdomain at the current time step.

2. The nuclear power simulation model development data management method according to claim 1, characterized in that, The specific method for obtaining the weighted effective access frequency of the time window is as follows: The effective access frequency of a time step is used as the weight of the number of times a subdomain is accessed in a time step. The number of times all time steps are accessed within a preset time window is weighted and summed to obtain the weighted effective access frequency of the time window.

3. The nuclear power simulation model development data management method according to claim 1, characterized in that, The specific method for calculating hotspot centers based on effective access frequency is as follows: Calculate the geometric center coordinates of the subdomains, use the effective access frequency of the time step as the weight of the geometric center coordinates of the subdomains, and sum the weighted geometric center coordinates of all subdomains to obtain the hot spot center.

4. The nuclear power simulation model development data management method according to claim 1, characterized in that, The formula for calculating the predicted access probability at the future time step is: in, Indicates the first Each subdomain at the current time step The next future Predicted access probability at each time step; Indicates the current time step The next future One time step; Indicates the first Each subdomain at the current time step The Euclidean distance between the geometric center coordinates and the hotspot center at time; This represents the normalization function.

5. The nuclear power simulation model development data management method according to claim 1, characterized in that, The specific method for determining the data to be preloaded to the first storage layer at the time step based on the predicted access probability includes: The third quartile of the predicted access probability of all future time steps after the current time step is denoted as the second partitioning threshold. Data of future time steps after the current time step whose predicted access probability is greater than the second partitioning threshold are preloaded into the first storage layer.

6. The nuclear power simulation model development data management method according to claim 1, characterized in that, The method for determining the compression method of data in adjacent time steps based on the dynamic compression ratio of the subdomain at each time step includes the following specific methods: The difference between the corresponding field variables of the same subdomain at the current time step and the previous adjacent time step is denoted as the difference data block of the same subdomain at the current time step. When the dynamic compression ratio of the subdomain at the current time step is greater than or equal to the fourth partitioning threshold, the differential data block is subjected to lossless compression. When the dynamic compression ratio of the subdomain at the current time step is greater than or equal to the third partition threshold and less than the fourth partition threshold, the dynamic compression ratio of the subdomain at the current time step is used as the value of the compression ratio. Lossy quantization is performed on the differential data block, and lossless compression is performed on the lossily quantized differential data block.

7. The nuclear power simulation model development data management method according to claim 1, characterized in that, The specific method for selecting and compressing a portion of the data in the second storage layer is as follows: The first quartile of the effective access frequency of all subdomains at the current time step is recorded as the fifth partitioning threshold. Data of subdomains whose effective access frequency is less than the fifth partitioning threshold at the corresponding time step and all previous time steps are compressed.

8. A nuclear power plant simulation model development data management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

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