Non-uniform grid data calculation method based on hierarchical calling rule and related device

Abstract

The invention discloses a non-uniform grid data calculation method based on a hierarchy calling rule and a related device, and the method comprises the steps: firstly analyzing an input parameter of a calculation task, and selecting corresponding calculation models according to a parameter type, the calculation models comprise a C-type model, a B-type model and an A-type model, and the models are divided according to a dependency relationship; thirdly, constructing a directed acyclic graph according to a hierarchical calling rule, representing a dependency relationship between tasks and excluding cyclic calling; then, a task execution sequence is generated based on the graph, and is ranked according to a hierarchical call rule. And performing parallel optimization on the task execution sequence according to the computing resource state, and allocating the task execution sequence to a distributed computing engine for execution. And finally, storing the result in a database, and supporting a subsequent task to directly call a historical result. The method aims at solving the problems of disordered dependency, high redundancy, low efficiency and the like of calculation tasks, and the organization efficiency and performance of building energy-saving calculation are improved.

Description

Technical Field

[0001] The present invention relates to the field of building energy-saving calculation, and in particular to a non-uniform grid data calculation method based on hierarchical calling rules and a related device. Background Art

[0002] In the field of building energy efficiency calculations, data types mainly include traditional raster data, satellite remote sensing data, meteorological station data, etc. These data forms have their own characteristics: (1) Raster data is usually used for large-scale spatial analysis, and data points are evenly distributed on a grid with a fixed resolution. Its advantage is that the calculation rules are simple and suitable for standardized modeling. However, in order to improve accuracy, the size of the grid unit must be reduced, which will lead to a sharp increase in data volume, increase storage requirements, and increase the computational burden (for example, when the resolution is increased from 10 km to 1 km, the data volume will increase 100 times); (2) Satellite remote sensing data has high spatial and temporal resolution, but the processing cost is high; (3) Meteorological station data is mainly based on field measurements, with relatively limited data points, making it difficult to fully cover the target area.

[0003] In recent years, with the advancement of building energy efficiency research, datasets based on non-uniform grids and their computational models have gradually gained application. These gridded datasets are divided according to the characteristics of the study area and influencing factors. This allows for a more rational spatial distribution of data, significantly reducing data storage volume, eliminating redundant information, and improving data management and computational efficiency while maintaining data accuracy. However, gridded datasets rely on their computational models (calculation methods). Only by inputting the appropriate input parameters and performing model calculations can the required data be obtained.

[0004] However, the current calculation of gridded data still mainly relies on local software such as ArcGIS, MATLAB and Python. Although these tools can process gridded data, they have the following problems: (1) The local calculation mode relies on local data storage. Users need to download the complete dataset, which takes up a lot of storage space. In addition, the data format may be heterogeneous, which increases the complexity of data preprocessing. (2) Some gridded datasets and their calculation models are more streamlined and efficient. The calculation models of such datasets require some additional basic datasets (topography, surface type, slope and aspect, etc.) as support, which requires users to download more basic datasets. (3) The calculation process is usually carried out in a single-machine environment, which cannot fully utilize the distributed computing power. The computing efficiency is limited by hardware performance and it is difficult to meet large-scale computing needs. (4) The traditional local computing mode does not have online interactive capabilities and is difficult to support multi-user concurrent access and remote computing, which affects the efficiency of data sharing and collaborative analysis.

[0005] At present, on the Web platform, the online calculation application for non-uniform grid data is still in a blank state. Existing online calculation platforms are mostly based on raster data with a fixed resolution, making it difficult to flexibly adapt to different application scenarios. Moreover, the calling method of the calculation model is relatively single, lacking the management of multi-level data dependency relationships. In addition, the calculation of gridded data often requires external data as input, such as meteorological, geographical, and building parameters, which further increases the complexity of local calculation. Users need to download multiple data sources and perform format conversion. In contrast, the Web platform can help users avoid the problems of data download and format conversion, support the online selection of multiple data sources, automatically handle the problem of data homology and heterogeneity, and optimize the organization method of calculation tasks to make the calculation process more efficient. Therefore, there is an urgent need for an online calculation system that supports non-uniform gridded data and its calculation model, enabling users to directly query and calculate on the Web side, improving the application efficiency of building energy-saving data, and supporting larger-scale data processing and real-time calculation. Summary of the Invention

[0006] Aiming at the problems existing in the prior art, the present invention provides a non-uniform grid data calculation method and related device based on a hierarchical call rule, aiming to solve problems such as chaotic calculation task dependency relationships, high calculation redundancy, and low calculation efficiency, so as to improve the organization efficiency and calculation performance of building energy-saving calculations.

[0007] In order to solve the above technical problems, the present invention is realized through the following technical solutions: According to the first aspect of the present invention, there is provided a non-uniform grid data calculation method based on a hierarchical call rule, including: Analyze the input parameters of the calculation task, and select the corresponding calculation model based on the input parameter type; the calculation models are divided into type C models, type B models, and type A models according to the dependency relationship of the input parameters. Among them, type C models only depend on their own data sets, type B models depend on the calculation results of type C models and basic data, and type A models depend on the calculation results and basic data of type B models and / or type C models; Construct a directed acyclic graph according to the hierarchical call rule. The hierarchical call rule stipulates that type C models are executed prior to type B models, type B models are executed prior to type A models, and mutual calls between type A models are prohibited; the directed acyclic graph is used to represent the dependency relationship between calculation tasks and exclude circular calls; Generate a calculation task execution sequence based on the dependency relationship of the directed acyclic graph. The execution sequence is sorted according to the hierarchical call rule to ensure that calculation tasks without dependencies are executed first; Perform parallelization optimization on the task execution sequence according to the calculation resource status, and dynamically allocate calculation tasks to a distributed calculation engine for execution; Store the calculation results in the database, which supports subsequent calculation tasks to directly call historical calculation results for data reuse.

[0008] In a possible implementation of the first aspect, when constructing a directed acyclic graph, task sorting is performed in the following order: 1) Parse all calculation tasks and mark the C-class model as the first-priority task; 2) Mark the B-class model that depends on the results of the C-class model as the second-priority task; 3) Mark the A-class model that depends on the results of the B-class or C-class model as the third-priority task; The directed acyclic graph generates a task execution sequence without dependency conflicts through topological sorting.

[0009] In a possible implementation of the first aspect, the generation of the calculation task execution sequence based on the dependency relationship of the directed acyclic graph includes: Execute the same-level calculation tasks without dependency relationships in parallel; Dynamically allocate the number of task concurrencies according to the resource load of the distributed computing engine; Package multiple subtasks into a calculation job and submit it in batches to reduce task scheduling overhead.

[0010] In a possible implementation of the first aspect, all calculation models are implemented by inheriting a unified calculation base class. The calculation base class encapsulates the functions of data reading, preprocessing, calculation execution, and result storage. Among them, data reading is used to load a non-uniformly gridded data set from the database; preprocessing performs standardization processing on input parameters; calculation execution is implemented by subclasses for calculation algorithms; result storage writes the calculation results into the database.

[0011] In a possible implementation of the first aspect, the non-uniformly gridded data set is composed of discrete points with non-uniform distribution.

[0012] In a possible implementation of the first aspect, the calculation results stored in the database include intermediate calculation results and final calculation results, and support the following reuse mechanisms: In subsequent calculation tasks, directly call the stored intermediate results by querying the database to skip repeated calculations; When returning the specified calculation results according to the user request, preferentially retrieve the existing calculation results from the database.

[0013] According to the second aspect of the present invention, a non-uniform grid data calculation device based on a hierarchical call rule is provided, including: A parsing and selection module, configured to parse the input parameters of a computing task and select a corresponding computing model based on the input parameter type; the computing models are divided into type C models, type B models, and type A models according to the dependency relationship of the input parameters, where type C models only depend on their own data sets, type B models depend on the calculation results of type C models and basic data, and type A models depend on the calculation results and basic data of type B models and / or type C models; A construction module, configured to construct a directed acyclic graph according to a hierarchical call rule, where the hierarchical call rule stipulates that type C models are executed prior to type B models, type B models are executed prior to type A models, and mutual calls between type A models are prohibited; the directed acyclic graph is used to represent the dependency relationship between computing tasks and exclude circular calls; A generation module, configured to generate a computing task execution sequence based on the dependency relationship of the directed acyclic graph, and the execution sequence is sorted according to the hierarchical call rule to ensure that computing tasks without dependencies are executed first; An optimization and allocation module, configured to perform parallelization optimization on the task execution sequence according to the computing resource status, and dynamically allocate computing tasks to a distributed computing engine for execution; A storage module, configured to store the calculation results in a database, and the database supports subsequent computing tasks to directly call historical calculation results to reuse data.

[0014] According to a third aspect of the present invention, there is provided a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, the non-uniform grid data calculation method based on a hierarchical call rule as described above is implemented.

[0015] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, where when the computer program is executed by a processor, the non-uniform grid data calculation method based on a hierarchical call rule as described above is implemented.

[0016] According to a fifth aspect of the present invention, there is provided a computer program product, where when the computer program product is executed by a processor, the non-uniform grid data calculation method based on a hierarchical call rule as described above is implemented.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: A non-uniform grid data calculation method based on a hierarchical call rule provided by the present invention realizes effective classification of calculation tasks by parsing input parameters of a calculation task and selecting a corresponding calculation model according to the parameter type. At the same time, a directed acyclic graph is constructed using the hierarchical call rule, clearly representing the dependency relationships between calculation tasks, avoiding the problem of chaotic dependency relationships of calculation tasks, and thus optimizing the organization manner of calculation tasks. By dividing calculation models into Class C, Class B, and Class A, a structured division of calculation models is achieved. This division method enables the basic calculation model and the composite calculation model to perform their respective functions. Meanwhile, the calculation results are stored in a database to support subsequent calculation tasks to directly call historical calculation results, improving the reusability of calculation models and reducing redundant calculations. Based on the construction of the directed acyclic graph, the present invention generates an execution sequence of calculation tasks and performs parallelization optimization on the task execution sequence according to the calculation resource status, can dynamically allocate calculation tasks to a distributed calculation engine for execution, make full use of calculation resources, reduce calculation waiting time, and thus improve calculation efficiency. Since the present invention adopts a distributed calculation engine and a parallel calculation strategy, it can support large-scale data processing. Through an online calculation platform, users can directly query and calculate on the Web side without downloading and preprocessing data, realizing the requirement of real-time calculation.

[0018] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following will briefly introduce the drawings required for use in the description of the specific embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 is a flowchart of the non-uniform grid data calculation method based on the hierarchical call rule of the present invention; Figure 2 is a schematic diagram of the non-uniform grid data calculation method based on the hierarchical call rule; Figure 3 is a schematic diagram of the storage structure of a non-uniformly meshed data set; Figure 4 is a diagram of the hierarchical call rule and dependency relationships of calculation models; Figure 5 is a flowchart of the organization and execution of calculation tasks; Figure 6 is a UML class diagram of a calculation base class and calculation models. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0022] As Figure 1 shown, an embodiment of the present invention provides a non-uniform grid data calculation method based on a hierarchical call rule, specifically including the following steps: S1. Analyze the input parameters of the calculation task, and select the corresponding calculation model based on the input parameter type; the calculation models are divided into Class C models, Class B models, and Class A models according to the dependency relationship of the input parameters. Among them, Class C models only depend on their own data sets, Class B models depend on the calculation results and basic data of Class C models, and Class A models depend on the calculation results and basic data of Class B models and / or Class C models.

[0023] Specifically, as Figure 6 shown, the calculation models are all implemented by inheriting a unified calculation base class. The calculation base class encapsulates the functions of data reading, preprocessing, calculation execution, and result storage. Among them, data reading is used to load non-uniform grid datasets from the database; preprocessing performs standardization processing on the input parameters; calculation execution is implemented by subclasses to calculate algorithms; result storage writes the calculation results into the database.

[0024] The non-uniform grid dataset is composed of discrete points with non-uniform distribution. Specifically, the distribution density of discrete points in complex terrain areas is higher than that in flat terrain areas, and each discrete point stores geographical coordinates and at least one attribute value, and the attribute values include temperature, humidity, wind speed, solar radiation, or population data.

[0025] More specifically, the non-uniform grid dataset is a dataset calculated based on various factors such as terrain, surface type, meteorological data, etc. Its data essence is composed of a group of non-uniform discrete points, and each data point represents a certain area range, and the values in this area range are the same. These points are distributed more densely in mountainous areas with complex terrain to improve calculation accuracy; in flat terrain areas, the data points are distributed more sparsely to reduce redundant data storage. Each non-uniform discrete grid point has a unique geographical coordinate and stores the attribute value of this point, such as temperature, humidity, wind speed, solar radiation, and population data, etc. Since the data points are discrete, they do not form regular grids.

[0026] To calculate the data value at the target position, a calculation model corresponding to the non-uniform grid dataset needs to be used. The calculation model is used to define the methods of data query and calculation to ensure that the correct calculation results can be obtained from the non-uniform discrete grid point dataset. According to the different input parameters of the calculation model, the present invention divides the calculation model into three categories: A, B, and C.

[0027] As Figure 2 and Figure 3 shown, according to the different categories of input parameters it requires, the calculation model is divided into the following three categories: (1) Category C model: Only depends on the data of its own dataset as the input parameter; (2) Category B model: Not only depends on the data of its own dataset, but also requires basic data support (such as terrain, surface type, slope aspect, and population data, etc.) as the input parameter together; (3) Category A model: Not only depends on the data of its own dataset, but also requires the support of other Category B and C models and basic data. The input parameter requirements of this type of model are the highest.

[0028] S2. Construct a directed acyclic graph (DAG, Directed Acyclic Graph) according to the hierarchical call rule. The hierarchical call rule stipulates that the Category C model is executed prior to the Category B model, the Category B model is executed prior to the Category A model, and mutual calls between Category A models are prohibited; the directed acyclic graph is used to represent the dependency relationship between calculation tasks and exclude circular calls.

[0029] In one implementable manner, when constructing the directed acyclic graph, the task sorting is performed in the following order: 1) Parse all calculation tasks and mark the Category C model as the first-priority task; 2) Mark the Category B model that depends on the result of the Category C model as the second-priority task; 3) Mark the Category A model that depends on the result of the Category B or C model as the third-priority task; The directed acyclic graph generates a task execution sequence without dependency conflicts through topological sorting.

[0030] Specifically, as Figure 4 shown, the calculation and query of the non-uniform grid dataset depend on the calculation model. By constructing a reasonable calculation hierarchy and dependency relationship, efficient data query and calculation are achieved. The calculation model defines how to query or calculate the value of the target point from the non-uniform discrete grid point dataset to support building energy efficiency calculation and other environmental simulation applications.

[0031] To adapt to different types of calculation requirements, the present invention divides the calculation model into three categories: A, B, and C according to the different types of input parameters required by the calculation model. The call rules for each type of model are as follows: (1) Class C model: It relies only on its own data set, does not call other calculation models, and can be calculated independently; (2) Type B model: It not only relies on its own dataset, but also requires basic data (such as topography, surface type, slope and aspect, population data, etc.) as input parameters, and can call the calculation results of type C model; (3) Class A model: It not only depends on its own data set, but also needs to rely on the calculation results and basic data of other Class B and C models. It can call Class B and C models, but cannot call Class A models to each other to avoid circular dependencies.

[0032] This hierarchical rule forms a top-down calculation order, i.e., Class C → Class B → Class A, ensuring that the execution order of computing tasks conforms to data dependencies.

[0033] S3. Generate a computing task execution sequence based on the dependency relationship of the directed acyclic graph, and sort the execution sequence according to the hierarchical calling rules to ensure that computing tasks without dependencies are executed first.

[0034] In one achievable manner, generating a computing task execution sequence based on the dependency relationship of the directed acyclic graph includes: Parallel execution of non-dependent computing tasks at the same level; Dynamically allocate concurrent tasks based on the resource load of the distributed computing engine; Package multiple subtasks into computing jobs and submit them in batches to reduce task scheduling overhead.

[0035] S4. Parallelize and optimize the task execution sequence according to the computing resource status, and dynamically allocate computing tasks to the distributed computing engine for execution; S5. The calculation results are stored in a database, where the database supports subsequent calculation tasks to directly call historical calculation results to reuse data.

[0036] In one implementation, the calculation results stored in the database include intermediate calculation results and final calculation results, and support the following reuse mechanism: In subsequent computing tasks, the stored intermediate results are directly called by querying the database, skipping repeated calculations; When returning a specified calculation result based on a user request, the existing calculation results are retrieved from the database first.

[0037] In one embodiment, if Figure 5 As shown in the figure, the organization and execution of computing tasks are divided into five steps: computing task initialization, dependency analysis, task sorting, task execution, and computing result return and storage.

[0038] (1)Initialization of calculation tasks: Receive the user's calculation request, determine the data types and input parameters required for the calculation, and identify the calculation model used; (2)Dependency analysis: According to the calculation target, analyze the required calculation models and their dependencies, and construct a directed acyclic graph (DAG) to ensure the dependency order of the calculation tasks; (3)Task sorting: According to the hierarchical call rule, calculate the C-class model first, then the B-class model, and finally the A-class model. Through DAG task scheduling and dependency analysis, the calculation tasks can be reasonably split to ensure the efficiency and correctness of the calculation execution.

[0039] (4)Task execution: Execute the calculation tasks step by step according to the task sorting; the calculation tasks may involve the calculation results of multiple B and C-class models. First, execute all its dependencies, and then execute the current model; the calculation tasks are packaged into calculation jobs and submitted to the calculation engine by the calculation backend for calculation; (5)Return and storage of calculation results: After the calculation is completed, return the final result to the user and store the calculation result according to the requirements.

[0040] To improve the calculation efficiency and scalability, this embodiment adopts a calculation model encapsulation mechanism, enabling all calculation models to inherit from a unified calculation base class and implement the corresponding calculation methods.

[0041] (1)Calculation base class: Provide general functions such as data reading, preprocessing, calculation execution, and result storage to ensure that the calculation models can be flexibly extended and reused.

[0042] (2)Inheritance and extension of calculation methods: The calculation models can extend specific calculation methods according to needs to improve calculation flexibility.

[0043] (3)Automatic scheduling of calculation tasks: Automatically determine the calculation order according to the hierarchical structure of the calculation models to improve the calculation efficiency.

[0044] Through the hierarchical design, query optimization, and task scheduling strategy of the calculation models, the present invention realizes efficient calculation based on non-uniformly gridded datasets, providing fast and accurate data support for building energy consumption calculations.

[0045] In another embodiment of the present invention, a non-uniform grid data calculation device based on a hierarchical call rule is provided, including: An analysis and selection module, configured to analyze the input parameters of a computing task and select a corresponding computing model based on the input parameter type; the computing models are divided into type C models, type B models, and type A models according to the dependency relationship of the input parameters, where type C models only depend on their own data sets, type B models depend on the calculation results of type C models and basic data, and type A models depend on the calculation results and basic data of type B models and / or type C models.

[0046] A construction module, configured to construct a directed acyclic graph according to a hierarchical call rule, where the hierarchical call rule stipulates that type C models are executed prior to type B models, type B models are executed prior to type A models, and mutual calls between type A models are prohibited; the directed acyclic graph is used to represent the dependency relationship between computing tasks and exclude circular calls.

[0047] A generation module, configured to generate a computing task execution sequence based on the dependency relationship of the directed acyclic graph, where the execution sequence is sorted according to the hierarchical call rule to ensure that computing tasks without dependencies are executed first.

[0048] An optimization and allocation module, configured to perform parallelization optimization on the task execution sequence according to the computing resource status and dynamically allocate computing tasks to a distributed computing engine for execution.

[0049] A storage module, configured to store the calculation results in a database, and the database supports subsequent computing tasks to directly call historical calculation results for data reuse.

[0050] All relevant contents of each step involved in the embodiment of the foregoing non-uniform grid data calculation method based on a hierarchical call rule can be cited in the function description of the corresponding functional modules of a non-uniform grid data calculation device based on a hierarchical call rule in the embodiments of the present invention, and will not be elaborated herein. The division of modules in the embodiments of the present invention is illustrative, merely a logical function division, and there may be other division methods in actual implementation. In addition, in various embodiments of the present invention, the functional modules may be integrated in one processor, may exist separately physically, or two or more modules may be integrated in one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0051] In another embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of a non-uniform grid data calculation method based on a hierarchical call rule.

[0052] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the non-uniform grid data calculation method based on a hierarchical call rule in the above embodiment.

[0053] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0054] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0055] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 [[ID=2I]]one block or multiple blocks.

[0057] The present invention also provides a computer program product, since the computer program product is used to execute any one of the above-mentioned non-uniform grid data calculation methods based on a hierarchical call rule. Since the computer program product provided by the present invention and the above-mentioned non-uniform grid data calculation method based on a hierarchical call rule belong to the same inventive concept, the computer program product provided by the present invention has all the advantages of the above-mentioned non-uniform grid data calculation method based on a hierarchical call rule. Therefore, the beneficial effects of the computer program product provided by the present invention will not be elaborated one by one here.

[0058] In the present invention, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0059] Finally, it should be noted that the above-mentioned embodiments are only specific embodiments of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for calculating non-uniform grid data based on hierarchical calling rules, characterized in that: include: Analyze the input parameters of the computing task and select the corresponding computing model based on the input parameter type; The computing models are divided into Class C models, Class B models and Class A models according to the dependency relationship of the input parameters, wherein the Class C models only rely on their own data sets, the Class B models rely on the computing results and basic data of the Class C models, and the Class A models rely on the computing results and basic data of the Class B models and / or Class C models; Constructing a directed acyclic graph based on hierarchical calling rules, wherein the hierarchical calling rules stipulate that class C models take precedence over class B models, class B models take precedence over class A models, and class A models are prohibited from calling each other; the directed acyclic graph is used to represent the dependencies between computing tasks and exclude circular calls; Generate a computing task execution sequence based on the dependency relationship of the directed acyclic graph, wherein the execution sequence is sorted according to a hierarchical calling rule to ensure that computing tasks without dependencies are executed first; Parallelize and optimize the task execution sequence according to the computing resource status, and dynamically allocate computing tasks to the distributed computing engine for execution; The calculation results are stored in a database, which supports subsequent calculation tasks to directly call historical calculation results to reuse data.

2. The method for calculating non-uniform grid data based on hierarchical calling rules according to claim 1, characterized in that: When building a directed acyclic graph, tasks are sorted in the following order: 1) Analyze all computing tasks and mark the C-type model as the first priority task; 2) Mark the B-type model that depends on the results of the C-type model as the second priority task; 3) Mark the Class A model that depends on the results of the Class B or Class C model as the third priority task; The directed acyclic graph generates a task execution sequence without dependency conflicts through topological sorting.

3. The method for calculating non-uniform grid data based on hierarchical calling rules according to claim 1, characterized in that: The step of generating a computing task execution sequence based on the dependency relationship of the directed acyclic graph includes: Parallel execution of non-dependent computing tasks at the same level; Dynamically allocate concurrent tasks based on the resource load of the distributed computing engine; Package multiple subtasks into computing jobs and submit them in batches to reduce task scheduling overhead.

4. The method for calculating non-uniform grid data based on hierarchical calling rules according to claim 1, characterized in that: The calculation models all inherit the unified calculation base class implementation, which encapsulates the functions of data reading, preprocessing, calculation execution and result storage. Among them, data reading is used to load non-uniform gridded data sets from the database; preprocessing standardizes the input parameters; calculation execution is implemented by subclasses to implement the calculation algorithm; result storage writes the calculation results to the database.

5. The method for calculating non-uniform grid data based on hierarchical calling rules according to claim 4, characterized in that: The non-uniform gridded data set is composed of discrete points that are non-uniformly distributed.

6. The method for calculating non-uniform grid data based on hierarchical calling rules according to claim 1, characterized in that: The calculation results stored in the database include intermediate calculation results and final calculation results, and support the following reuse mechanisms: In subsequent computing tasks, the stored intermediate results are directly called by querying the database, skipping repeated calculations; When returning a specified calculation result based on a user request, the existing calculation results are retrieved from the database first.

7. A non-uniform grid data computing device based on hierarchical calling rules, characterized in that: include: The parsing and selection module is used to parse the input parameters of the computing task and select the corresponding computing model based on the input parameter type; The computing models are divided into Class C models, Class B models and Class A models according to the dependency relationship of the input parameters, wherein the Class C models only rely on their own data sets, the Class B models rely on the computing results and basic data of the Class C models, and the Class A models rely on the computing results and basic data of the Class B models and / or Class C models; A construction module is used to construct a directed acyclic graph according to a hierarchical calling rule, wherein the hierarchical calling rule stipulates that class C models are executed before class B models, class B models are executed before class A models, and class A models are prohibited from calling each other; the directed acyclic graph is used to represent the dependencies between computing tasks and exclude circular calls; A generation module, configured to generate a computing task execution sequence based on the dependency relationship of the directed acyclic graph, wherein the execution sequence is sorted according to a hierarchical calling rule to ensure that computing tasks without dependencies are executed first; An optimization allocation module is used to parallelize and optimize the task execution sequence according to the computing resource status, and dynamically allocate computing tasks to the distributed computing engine for execution; The storage module is used to store the calculation results in a database, and the database supports subsequent calculation tasks to directly call historical calculation results to reuse data.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for calculating non-uniform grid data based on hierarchical calling rules as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for calculating non-uniform grid data based on hierarchical calling rules as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the computer program product is executed by a processor, it implements the non-uniform grid data calculation method based on hierarchical calling rules as described in any one of claims 1 to 6.

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