Intelligent numerical calculation method, device and system

Abstract

The invention discloses an intelligent numerical calculation method and system. The method comprises the following steps: symbolizing calculation data to obtain a symbolized calculation data sequence; performing knowledge assignment on the symbolized calculation data sequence by utilizing a knowledge engineering state machine, and establishing a corresponding relationship between the symbolized calculation data sequence and a solution space region division mode; performing parallel search in a knowledge engineering engine on the basis of a corresponding relationship between the symbolized calculation data sequence and a solution space region division mode to search a corresponding solution space region division mode; and obtaining a numerical calculation result according to the solution space region division mode obtained by searching. According to the method, mapping is carried out through a symbolic calculation data knowledge engineering state machine, and fusion of numerical calculation and symbolic calculation is completed. Through a high-performance parallel search process, information acquisition of numerical calculation is completed, and a numerical calculation result value is obtained. Therefore, the efficiency and the performance in a calculation task with relatively high complexity are improved.

Description

Technical Field

[0001] The present invention relates to the field of parallel computing technology in the field of information systems, and in particular to an intelligent numerical computing method, device and system. Background Art

[0002] Numerical computing is the method and process of effectively using digital computers to find approximate solutions to mathematical problems, as well as a discipline consisting of related theories. It mainly studies how to use computers to better solve various mathematical problems, including the discretization of continuous systems and the solution of discrete form equations, and considers issues such as error, convergence and stability.

[0003] Numerical calculation methods play an important role in modern scientific research and engineering technology calculations. Speed ​​and accuracy are two important indicators of the quality of numerical calculation methods. A good numerical calculation method is not only accurate but also fast. Although the advantage of fast speed is weakened by the power of computers for problems of appropriate scale, speed is still an important factor for large-scale problems. Slow numerical calculation methods are eliminated because they are not practical. The following methods are commonly used in traditional numerical calculations: such as finite element method, multi-grid method, finite difference method, finite volume method, error estimation method for approximate solution, multi-scale calculation method, etc. These numerical calculation methods can only use arithmetic and logical operations, otherwise the computer will not be able to calculate, which will directly affect whether the computer can be used for practical problems. Therefore, in the process of solving complex problems, it is inevitable to face the bottleneck of computing power, and there are shortcomings in efficiency and performance in high-complexity computing tasks.

[0004] Based on this, a new solution is needed. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent numerical calculation method, device and system to address the computing power bottleneck problem and the shortcomings of low performance and efficiency of traditional numerical calculation methods.

[0006] To achieve the above object, the present invention provides an intelligent numerical calculation method, comprising: Symbolize the calculation data to obtain a symbolized calculation data sequence; Using a knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associating the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establishing a corresponding relationship between the symbolic computing data sequence and the solution space region division mode; Based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern, a parallel search is performed in the knowledge engineering engine to search for the corresponding solution space region division pattern; According to the solution space region partitioning pattern obtained by searching, the numerical calculation results are obtained.

[0007] In the intelligent numerical calculation method provided by the present invention, before the step of using a knowledge engineering state machine to assign knowledge to the symbolic calculation data sequence, associating the symbolic calculation data sequence with the knowledge of the corresponding sequence in the knowledge base, and establishing the corresponding relationship between the symbolic calculation data sequence and the solution space area division mode, the method further includes: Based on the decomposition, identification and reconstruction of the numerical calculation system, a calculation process state machine is constructed, wherein the calculation process state machine includes elementary calculation steps and intermediate states; Based on the relationship between the computing process state machine and the knowledge attributes, the knowledge engineering state machine is constructed, and the knowledge engineering state machine is used to perform knowledge assignment on the symbolized sequence of data to obtain the corresponding solution space area division mode.

[0008] In the intelligent numerical calculation method provided by the present invention, before the step of performing parallel search in the knowledge engineering engine based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern and searching for the corresponding solution space region division pattern, the method further includes: Determine whether the partition granularity of the solution space region partition mode obtained by the current mapping meets the calculation accuracy requirements; When the division granularity of the solution space region division pattern obtained by the current mapping does not meet the calculation accuracy requirements, the solution space region obtained by the current mapping continues to be fine-grained to obtain fine-resolution sub-intervals that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

[0009] In addition, to achieve the above-mentioned purpose, the present invention also provides an intelligent numerical calculation device, comprising: A data processing module is used to symbolize the calculation data to obtain a symbolized calculation data sequence; A mapping module, used to use a knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associate the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establish a corresponding relationship between the symbolic computing data sequence and the solution space region division mode; A search module, for performing a parallel search in the knowledge engineering engine based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern, to search for the corresponding solution space region division pattern; The calculation module is used to obtain numerical calculation results according to the solution space area division mode obtained by searching.

[0010] The intelligent numerical computing device provided by the present invention also includes a knowledge engineering state machine building module and a computing process state machine generating module: A computing process state machine generation module, used to construct a computing process state machine based on the decomposition, identification and reconstruction of the numerical computing system, wherein the computing process state machine includes elementary computing steps and intermediate states; The knowledge engineering state machine construction module is used to construct the knowledge engineering state machine based on the relationship between the calculation process state machine and the knowledge attributes. The knowledge engineering state machine is used to assign knowledge to the symbolic sequence of data to obtain the corresponding solution space area division pattern.

[0011] The intelligent numerical computing device provided by the present invention also includes a knowledge expansion module, which is used to determine whether the division granularity of the solution space area division pattern currently mapped meets the calculation accuracy requirements. When the division granularity of the solution space area division pattern currently mapped does not meet the calculation accuracy requirements, the solution space area currently mapped continues to be fine-grained to obtain sub-intervals with fine resolution that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

[0012] The present invention also provides an intelligent numerical calculation device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the intelligent numerical calculation method described above are implemented.

[0013] The intelligent numerical calculation device and method provided by the present invention have the following beneficial effects: Based on the natural knowledge numerical characteristic that "natural number" data has "knowledge symbol" representation value, the present invention completes the fusion of data and symbolic calculation in numerical calculation. Based on the support of the complex pattern recognition algorithm system that integrates numerical calculation and intelligent calculation, the calculation workflow is decomposed, identified, and reconstructed through the knowledge engineering engine, and a knowledge engineering state machine is constructed with data as the center; the symbolic calculation data knowledge engineering state machine is mapped to complete the fusion of numerical calculation and symbolic calculation. Through a high-performance parallel search process, the information acquisition of numerical calculation is completed to obtain the numerical calculation result value. In this way, the efficiency and performance in computing tasks with higher complexity are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work: Figure 1 Shown is a flow chart of an intelligent numerical calculation method provided by an embodiment of the present invention; Figure 2The figure shows a composition diagram of an intelligent numerical calculation device provided by an embodiment of the present invention; Figure 3 FIG. 1 is a logical architecture diagram of an intelligent numerical computing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0017] Figure 1 FIG. 1 is a flow chart of an intelligent numerical calculation method provided by an embodiment of the present invention. Figure 1 As shown, the intelligent numerical calculation method includes the following steps: Step S1, symbolizing the calculated data to obtain a symbolized calculated data sequence; Specifically, in one embodiment of the present invention, the data of the computing task is first serialized by time to obtain a time series , the time series is symbolized by wavelet space method, and the symbolized sequence is obtained . The so-called symbolization is to transform the real number sequence into a symbol sequence. The research object of the knowledge engineering state machine is a finite symbol set. The time series obtained by sampling cannot be processed directly. The time series needs to be symbolized first, and then analyzed using the knowledge engineering state machine. Symbolization requires the removal of irrelevant redundant information while retaining the information of the key features of the original sequence. The wavelet transform can adopt related wavelet transform or wavelet fast transform algorithm, such as Mallat algorithm. Therefore, the wavelet space method for data symbolization processing includes the following steps: 1) For the preprocessed sequence Calculate the scale space of wavelet decomposition and determine the scale space for decomposing the signal , let the minimum decomposition scale , the maximum decomposition scale The calculation formula is as follows:

[0018] Where N is the time series to be analyzed Length.

[0019] 2) Use Mallat algorithm to Scale-space Perform discrete wavelet transform to obtain the scale space The specific calculation method of multiple groups of wavelet coefficients on is as follows: like is the discrete sampling data of the signal, if , the multi-scale Mallat algorithm calculates the wavelet coefficients through discrete convolution and second-order downsampling process, the formula is as follows:

[0020] Where N is the number of discrete sampling data; h, g are the filter impulse responses, i.e., the filter coefficients of each column; for exist The approximation at the resolution is called the low-frequency part coefficient of the signal, which is the approximate value coefficient at scale j and j+1 and needs to be further decomposed; for exist The discrete details at the resolution are called the high-frequency coefficients of the signal. This part, as the detail value coefficients at scales j and j+1, maintains the information we need in the sequence.

[0021] For example, starting from layer j=1, the wavelet coefficient scale sequence obtained after J layers of decomposition is .

[0022] 3) In scale space In the following example, assuming that N wavelet transforms are performed, the following N groups of wavelet coefficient scale sequences will be obtained:

[0023]

[0024]

[0025] , The combined scale sequence is: .

[0026] 4) Perform static division on the scale sequence to obtain the symbol sequence.

[0027] It needs to determine the threshold first, and the threshold remains unchanged during the processing. Therefore, the selection of the threshold is the most critical. The value that can fully reflect the trend of the real number sequence should be selected. The degree of detail of the division can also be selected by changing the number of thresholds. The division is as follows:

[0028] Where: s i Indicates the symbol, x t represents the amplitude of the time series at time t, x1~x N represents the set threshold, and N represents the size of the symbol set. The threshold can be selected using the equal interval method or the equal probability method.

[0029] Step S2: Use the knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associate the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establish a corresponding relationship between the symbolic computing data sequence and the solution space area division pattern.

[0030] Specifically, in one embodiment of the present invention, through symbolization, based on the natural knowledge numerical property that "natural number" data has "knowledge symbol" representation value, the fusion of data and symbolic calculation in numerical calculation is completed; then, the knowledge attribute is assigned to the symbol sequence, and the knowledge engineering state machine of calculation is constructed with data as the center; and then the knowledge engineering state machine of the symbolic calculation process is used to promote the underlying mapping of data and knowledge fusion. Therefore, before step S2, it also includes: Step S11, based on the decomposition, identification and reconstruction of the numerical calculation system, construct a calculation process state machine, wherein the calculation process state machine includes elementary calculation steps and intermediate states; Specifically, in one embodiment of the present invention, according to the requirements of a specific numerical calculation task, after the system selectively determines the corresponding algorithm, the calculation steps and processes are determined accordingly, and the calculation engineering flow can be determined. Therefore, by hierarchically decomposing, identifying, and reconstructing the numerical calculation workflow of the existing complex numerical calculation system through algorithms (including but not limited to the Euler method, the Runge-Kutta method, the Lagrange interpolation method, the Adams method, etc.), a calculation process state machine consisting of elementary calculation steps and intermediate states can be obtained. By deconstructing the numerical calculation system, the complex calculation process is converted into elementary calculation processes such as area approximation and polynomial approximation, and multiple solution space regions are obtained to facilitate the subsequent construction of the knowledge engineering state machine.

[0031] Step S12: construct the knowledge engineering state machine based on the relationship between the calculation process state machine and the knowledge attributes, and the knowledge engineering state machine is used to perform knowledge assignment on the symbolized sequence of data to obtain the corresponding solution space area division pattern.

[0032] Specifically, in one embodiment of the present invention, knowledge engineering is a discipline of knowledge information processing such as knowledge representation, expression, sharing, retrieval, and application, and mainly includes the construction and reasoning of ontology libraries and knowledge graphs. The computational workflow state machine is a state machine composed of elementary computational steps and intermediate states, and the knowledge engineering state machine is a state machine that assigns knowledge to the symbolic sequence of data. The assignment process is the mapping process between symbols and knowledge. The data symbolization result sequence is used to associate and correspond the corresponding sequence knowledge in the knowledge base, so that the symbolic sequence of data can be matched one-to-one with the solution space area division pattern.

[0033] Step S3: Based on the correspondence between the symbolized calculation data sequence and the solution space region division pattern, a parallel search is performed in the knowledge engineering engine to search for the corresponding solution space region division pattern; Specifically, in one embodiment of the present invention, the knowledge represented by the knowledge engineering engine is mapped with the sequence knowledge converted from data symbolization, thereby establishing a correspondence between the data sequence and the solution space region division pattern; thus, the solution process is transformed into a search process for the solution space region division pattern corresponding to the solution space.

[0034] Specifically, in one embodiment of the present invention, the parallel search algorithm can adopt the "roll call" method, and the specific implementation process is as follows: each knowledge storage unit has its own information comparator, which can only store one piece of knowledge, and obtain related information through the bus (search system), and also transmit the knowledge through this bus (search / broadcast system). A certain number of such storage units constitute a knowledge storage area. When the system needs to search for knowledge in this knowledge storage area, the corresponding information will be broadcast through the bus. At this time, each knowledge storage unit compares the transmitted information together. If it is related to the knowledge of its own unit, the knowledge will be uploaded to the bus, so that the system can quickly find the target knowledge.

[0035] Specifically, in one embodiment of the present invention, the parallel computing models adopted include a shared memory model, a message passing model, a data flow model and a data parallel model, wherein: Shared memory model: multiple processors share the same memory space and communicate and synchronize by reading and writing shared memory; Message passing model: Multiple processors communicate by sending and receiving messages. Each processor has its own local memory and uses message passing for synchronization and data transfer. Data flow model: The program is centered on data flow, and parallel computing is described by operating on data flow. The computing nodes in the data flow model can be executed in parallel and can be automatically scheduled according to the availability of input and output. This model is suitable for data-intensive computing and stream processing; Data parallel model: divide the data into multiple parts, and different processors perform parallel calculations on different data parts; the data parallel model is suitable for parallel image processing, matrix operations and other applications.

[0036] Step S4: Obtain numerical calculation results according to the solution space region division mode obtained by searching.

[0037] Specifically, in one embodiment of the present invention, the corresponding solution space area division pattern is searched based on the corresponding relationship, that is, the corresponding knowledge searched, that is, the required calculation result. For example, the 9X9 multiplication table is the knowledge, and the multiplication result of each number is to search in the multiplication table according to the position of the two multipliers, and the corresponding value is the required multiplication calculation result.

[0038] Furthermore, in one embodiment of the present invention, according to the calculation accuracy requirement, if the granularity of the solution space region obtained by the current mapping is not enough, further fine-grained division is performed to divide it into sub-intervals with finer resolution, and the corresponding knowledge graph relationship is further refined and expanded, and the calculation processing is performed on the further refined and expanded knowledge level. Therefore, before step S3, it also includes: Step S21, judging whether the division granularity of the solution space region division mode obtained by the current mapping meets the calculation accuracy requirement; Step S22: When the division granularity of the solution space region division pattern obtained by the current mapping does not meet the calculation accuracy requirements, continue to perform fine-grained division on the solution space region obtained by the current mapping to obtain sub-intervals with fine resolution that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

[0039] In this embodiment, by expanding the "content" and "extension" data of knowledge, knowledge attribute big data is created based on knowledge characteristics, and through multiple iterative data-knowledge-data-knowledge cycle processes, new knowledge reconstruction of deep and fine-grained knowledge attributes is performed.

[0040] The present invention is based on the natural knowledge numerical property that "natural number" data has "knowledge symbol" representation value, and completes the fusion of data and symbolic calculation in numerical calculation. Based on the support of the complex pattern recognition algorithm system that integrates numerical calculation and intelligent calculation, the computational workflow is decomposed, identified, and reconstructed through the knowledge engineering engine, and a knowledge engineering state machine is built with data as the center; the symbolic computing data knowledge engineering state machine is mapped to complete the fusion of numerical calculation and symbolic calculation. Through the high-performance parallel search process, the information acquisition of numerical calculation is completed, and the numerical calculation result value is obtained. In this way, the efficiency and performance in computing tasks with higher complexity are improved.

[0041] The present invention also provides an intelligent numerical calculation device, the composition of which is shown in the figure Figure 2 As shown, including: The data processing module includes two functional submodules, namely the symbolization module and the calculation process state machine generation module. The symbolization module is used to symbolize the calculation data to obtain a symbolic calculation data sequence; the calculation process state machine generation module is used to construct a calculation process state machine based on the decomposition, identification and reconstruction of the numerical calculation system, and the calculation process state machine includes elementary calculation steps and intermediate states.

[0042] The knowledge processing module is used for knowledge editing, knowledge engineering state machine generation and other processing, including a knowledge engineering state machine construction module and a knowledge expansion module. The knowledge engineering state machine construction module is used to construct the knowledge engineering state machine based on the relationship between the calculation process state machine and the knowledge attributes. The knowledge engineering state machine is used to perform knowledge assignment on the symbolic sequence of data to obtain the corresponding solution space area division pattern. The knowledge expansion module is used to determine whether the division granularity of the solution space area division pattern obtained by the current mapping meets the calculation accuracy requirements. When the division granularity of the solution space area division pattern obtained by the current mapping does not meet the calculation accuracy requirements, the solution space area obtained by the current mapping continues to be fine-grained to obtain fine-resolution sub-intervals that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

[0043] A mapping module, used to use a knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associate the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establish a corresponding relationship between the symbolic computing data sequence and the solution space region division mode; The parallel computing node module is used for parallel computing and processing, and mainly includes a multi-computer parallel search module and a multi-processor parallel computing module.

[0044] A multi-computer parallel search module is used to perform parallel search in the knowledge engineering engine based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern, and search for the corresponding solution space region division pattern; A multi-processor parallel computing module is used to obtain numerical calculation results according to the solution space region partitioning mode obtained by searching; The multi-computer parallel search module uses multiple von Neumann machines to form a multi-machine system and supports a node-level parallel computing model. However, the computing process fundamentally changes the control flow driving mode of the von Neumann machine and adopts a data flow computer that works in a data flow driving mode. As long as the data is ready, the relevant instructions can be executed in parallel, which is a truly non-Neumann computing process.

[0045] This module has four computing models, including shared memory model, message passing model, data flow model and data parallel model.

[0046] Shared memory model: Multiple processors share the same memory space and communicate and synchronize by reading and writing shared memory.

[0047] Message passing model: Multiple processors communicate by sending and receiving messages. Each processor has its own local memory and uses message passing for synchronization and data transfer.

[0048] Data flow model: The program is centered on data flow, and parallel computing is described by operations on data flow. The computing nodes in the data flow model can be executed in parallel and can be automatically scheduled based on the availability of input and output. This model is suitable for data-intensive computing and stream processing.

[0049] Data parallel model: Split the data into multiple parts, and different processors perform parallel calculations on different parts of the data. The data parallel model is suitable for parallel image processing, matrix operations and other applications.

[0050] The multi-computer parallel module adopts a computer cluster mode, and each computer is installed with a parallel network operating system. In the form of a network operating system, each high-performance computing node is distributed and aggregated for parallel tasks, thereby realizing cluster-level network parallel processing.

[0051] The computers in the computer cluster of the multi-computer parallel module are linked in the form of a high-speed network to form a highly reliable and high-bandwidth communication network. The high-speed network mainly adopts the form of a deterministic computing power network, with a deterministic network as the foundation and computing power as the center, integrating computing power with the network, realizing unified scheduling and coordination of computing and network, and providing a new infrastructure with low-cost, high-performance and high-security computing network integrated services. Including a computing network collaborative gateway, which processes service identification rather than traditional IP / port numbers. By perceiving the service identification, various computing network collaborative strategies (access control / load balancing / traffic engineering, etc.) can be implemented very accurately. At the same time, when the application is scheduled across regions by the computing network collaborative scheduling engine, the service identification can remain unchanged to the outside world, which is the basis for achieving the goals of "location transparency" and "migration transparency" of the distributed operating system. And high-speed routing and switching equipment, connecting all computing nodes, implementing high-speed communication between high-performance computing nodes.

[0052] The multi-processor parallel computing module is a modification of the traditional von Neumann machine, such as using multiple processing components to form pipeline processing, relying on time overlap to improve processing efficiency; or forming an array machine structure to form a single instruction stream and multiple data streams to improve processing speed.

[0053] The present invention also discloses an intelligent numerical calculation system, whose logical architecture is as follows: Figure 3 As shown. Figure 3 As shown in the figure, the overall architecture of the system consists of five layers: application layer, expert system layer (AIGC-ES), parallel operating system layer, high-speed network layer, and node layer. Among them, the application layer is responsible for the input and output of various computing tasks in various fields, and interacts with users; the expert system layer (AIGC-ES) is responsible for learning and unifying symbolic representation of computing data in various fields, and decomposing, identifying, and reconstructing computing workflows. Based on artificial intelligence technologies such as machine learning, computer vision, natural language processing, optimization algorithms, and large pre-trained models, by learning and pattern recognition of existing data, relevant knowledge attribute big data is automatically and intelligently generated with appropriate generalization capabilities; the parallel operating system layer is used to schedule and distribute computing tasks based on the management of parallel computing resources, and to summarize and report results; the high-speed network layer is used to provide high-speed network bandwidth and resources on demand and flexibly according to the needs of computing tasks to meet real-time computing requirements; the node layer is used to achieve high-performance numerical computing, including finite element computing, streaming computing, multi-scale computing, symbolic computing, etc.

[0054] Specifically, the node layer can realize high-performance node numerical calculation functions, including: Finite element calculation: providing finite element calculation functions, including integral equation construction, regional unit decomposition, unit basis function determination, unit analysis, overall synthesis, boundary condition processing, finite element equation solving and other functions; Finite difference calculation: providing forward difference, backward difference, central difference and other calculation functions; Data streaming calculation: data stream calculation driver, providing data streaming calculation function, supporting big data streaming calculation; Multiple grid division: providing grid division function according to different calculation accuracy and convergence speed; Multi-scale calculation: providing functions such as construction of operators and basis functions of different scales required for multi-resolution analysis; Communication function: local communication function, connecting multiple local processors; at the same time, providing high-speed network interface to support cluster communication of multiple computing nodes.

[0055] Furthermore, the node computing flow is as follows: the entire computing process consists of multiple global phases separated by a series of synchronization barriers; in each global phase, each processor runs its local program asynchronously; the last instruction in each local program is a synchronization barrier instruction; each processor can read and write shared memory asynchronously, but two processors are not allowed to access the same storage unit in the same global phase; classification of instructions within the program: global synchronization, global read / write, local operation.

[0056] Specifically, the intelligent expert system (AIGC-ES) can achieve the following functions: (1) AIGC learning engine: Based on artificial intelligence technologies such as machine learning, computer vision, natural language processing, optimization algorithms, and large pre-trained models, it automatically and intelligently generates knowledge attribute big data with appropriate generalization capabilities through learning and pattern recognition of existing data, supporting the reconstruction of new knowledge; (2) Knowledge engineering engine, which performs computational workflow decomposition, identification, and reconstruction based on the knowledge engineering of reflective general artificial intelligence (R-AGI), and computational knowledge engineering construction; (3) Knowledge graph ontology library, based on the AIGC engine, provides knowledge graphs for problem solving; (4) Expert System (ES), knowledge reasoning module: knowledge-based causal reasoning module; knowledge expert system: AGI knowledge modeling, storage, and query; Specifically, a parallel operating system can achieve the following functions: (1) Parallel computing resource management: register, activate, and deregister the high-performance computing node resources under its jurisdiction; (2) Parallel algorithm library, 1) General algorithm library: provides general algorithms required for calculation; 2) Special algorithm library: provides special parallel algorithms; partition algorithm, divide-and-conquer algorithm, balanced tree algorithm, doubling algorithm, pipeline algorithm parallel scheduling algorithm library; (3) Parallel search engine: multiple search algorithms: support high-performance indexing and parallel retrieval, multi-granularity indexing and retrieval, general retrieval, classification retrieval, quantum search algorithm, etc.; Search core components: abstract and componentize basic core capabilities to facilitate expansion, such as index calculation and retrieval core; Search basic services: Retrieval services based on layered packaging of search core components, mainly including offline indexing, online retrieval and retrieval access. Supports distributed indexing and retrieval environments including memory and disk indexing; Search object storage system: including offline index generation, online index rolling update, retrieval intervention, AB testing and other functions; Effect extension component: Search effects vary with business scenarios. We decouple scoring and sorting, built-in some basic relevance sorting functions, and can also customize sorting; Query analysis: In addition to basic word segmentation, it also has functions such as synonyms, error correction, timeliness calculation, intent recognition, component analysis, non-biology, and new word discovery. (Interfacing with the AIGC engine); API component: provides multiple access methods including SDK protocol access and HTTP RESTful interface.

[0057] (4) Parallel Scheduling Engine: Based on the busy and idle status of computing resources, computing tasks are scheduled and distributed on demand, and their structure is summarized and reported.

[0058] Specifically, high-speed networks can achieve the following functions: Collaborative scheduling: responsible for implementing control decisions on computing power and networks, including blueprint perception, computing network collaboration, hierarchical cross-domain, interface adaptation and other major capabilities; Closed-loop monitoring: responsible for collecting and responding to the status of computing power and network, including real-time collection, event triggering, scheduling feedback, log tracking and other major capabilities; Resource management: responsible for the full life cycle management of computing power and network resources, including resource registration, unified management, certificate authority, computing network topology and other major capabilities; Identity governance: responsible for implementing the full life cycle management of service identities, including identity registration, identity allocation, identity resolution, identity strategy and other key capabilities.

[0059] The embodiment of the present invention further provides an intelligent numerical calculation device, which may include: Memory for storing computer programs; The processor can implement the following steps when executing the computer program stored in the above memory: The calculation data is symbolized to obtain a symbolized calculation data sequence; the symbolized calculation data sequence is assigned knowledge using a knowledge engineering state machine, the symbolized calculation data sequence is associated with the knowledge of the corresponding sequence in the knowledge base, and a corresponding relationship between the symbolized calculation data sequence and the solution space region division pattern is established; based on the corresponding relationship between the symbolized calculation data sequence and the solution space region division pattern, a parallel search is performed in the knowledge engineering engine to search for the corresponding solution space region division pattern; and a numerical calculation result is obtained according to the solution space region division pattern obtained by the search.

[0060] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0061] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0062] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0063] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

Claims

1. An intelligent numerical calculation method, characterized in that: The following steps are involved: Symbolize the calculation data to obtain a symbolized calculation data sequence; Using a knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associating the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establishing a corresponding relationship between the symbolic computing data sequence and the solution space region division mode; Based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern, a parallel search is performed in the knowledge engineering engine to search for the corresponding solution space region division pattern; According to the solution space region partitioning pattern obtained by searching, the numerical calculation results are obtained.

2. The intelligent numerical calculation method according to claim 1, characterized in that: Before the step of using the knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associating the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establishing the corresponding relationship between the symbolic computing data sequence and the solution space area division mode, the method further includes: Based on the decomposition, identification and reconstruction of the numerical calculation system, a calculation process state machine is constructed, wherein the calculation process state machine includes elementary calculation steps and intermediate states; Based on the relationship between the computing process state machine and the knowledge attributes, the knowledge engineering state machine is constructed, and the knowledge engineering state machine is used to perform knowledge assignment on the symbolized sequence of data to obtain the corresponding solution space area division mode.

3. The intelligent numerical calculation method according to claim 2, characterized in that: Before the step of performing parallel search in the knowledge engineering engine based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern to search for the corresponding solution space region division pattern, the method further includes: Determine whether the partition granularity of the solution space region partition mode obtained by the current mapping meets the calculation accuracy requirements; When the division granularity of the solution space region division pattern obtained by the current mapping does not meet the calculation accuracy requirements, the solution space region obtained by the current mapping continues to be fine-grained to obtain fine-resolution sub-intervals that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

4. An intelligent numerical computing device, characterized in that: include: A data processing module is used to symbolize the calculation data to obtain a symbolized calculation data sequence; A mapping module, used to use a knowledge engineering state machine to assign knowledge to the symbolic computing data sequence, associate the symbolic computing data sequence with the knowledge of the corresponding sequence in the knowledge base, and establish a corresponding relationship between the symbolic computing data sequence and the solution space region division mode; A search module, for performing a parallel search in the knowledge engineering engine based on the correspondence between the symbolic calculation data sequence and the solution space region division pattern, to search for the corresponding solution space region division pattern; The calculation module is used to obtain numerical calculation results according to the solution space area division mode obtained by searching.

5. The intelligent numerical calculation device according to claim 4, characterized in that: It also includes knowledge engineering state machine building modules and computing process state machine generation modules: A computing process state machine generation module, used to construct a computing process state machine based on the decomposition, identification and reconstruction of the numerical computing system, wherein the computing process state machine includes elementary computing steps and intermediate states; The knowledge engineering state machine construction module is used to construct the knowledge engineering state machine based on the relationship between the calculation process state machine and the knowledge attributes. The knowledge engineering state machine is used to assign knowledge to the symbolic sequence of data to obtain the corresponding solution space area division pattern.

6. The intelligent numerical calculation device according to claim 4, characterized in that: It also includes a knowledge expansion module, which is used to determine whether the division granularity of the solution space area division pattern obtained by the current mapping meets the calculation accuracy requirements. When the division granularity of the solution space area division pattern obtained by the current mapping does not meet the calculation accuracy requirements, the solution space area obtained by the current mapping continues to be fine-grained to obtain sub-intervals with fine resolution that meet the calculation accuracy requirements and refine and expand the corresponding knowledge graph relationship in the knowledge engineering engine.

7. An intelligent numerical calculation system, characterized in that: The method is used to implement the steps of the intelligent numerical calculation method according to any one of claims 1 to 3, wherein the system architecture consists of five layers: application layer, expert system layer (AIGC-ES), parallel operating system layer, high-speed network layer, and node layer.

8. An intelligent numerical calculation device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the intelligent numerical calculation method according to any one of claims 1 to 3 are implemented.

Images