CN112905862A - Data processing method and device based on table function and computer storage medium - Google Patents
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Abstract
The invention relates to a data processing method and device based on a table function and a computer storage medium. The method comprises the steps of classifying and characterizing cognitive content based on a disciplinary classification table to form different characterization classes and codes; classifying different characterization classes and codes by adopting different calculation methods; constructing a data storage module according to the characterization category and the coding and classification result; according to the characterization category, the coding and classification calculation method, generating output results corresponding to different input information in an off-line mode, and generating an input and output truth value mapping relation table based on a preset table function template; and inquiring in the input and output truth value mapping relation table by adopting a multistage mode search algorithm of the self-adaptive resonance network according to the input information, and outputting a data processing result based on a mode similarity threshold calculation method. The method and the device can quickly and accurately find the content required by the user and meet the requirements of the user.
Description
Technical Field
The present invention relates to the field of information computation, and more particularly, to a data processing method and apparatus based on a table function, and a computer storage medium.
Background
At present, with the rapid development of information network technologies, especially the development of cloud computing, 5G, internet of things and the like, applications of big data, remote management, real-time control, AI and the like become possible. With the continuous progress of informatization of various industries, the informatization application of various industries faces a mass information processing problem in large-scale business while the information processing is rapid and convenient due to informatization. Aiming at the problem that the current large data and large-scale information processing needs large computing power, the conventional method adopts high-energy computing or searches more advanced computing methods such as quantum computing, brain-like computing and the like. However. The problem of the stacking of computing resources in high-performance computing is limited by the limit of hardware, and the computing capacity is not improved any more after the computing resources reach a certain amount. For advanced methods such as quantum computation and brain-like computation, no mature and formed computing system is available at present.
Therefore, a method capable of quickly and accurately finding the content required by the user in the mass data information to meet the user requirement is needed.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a data processing method, device and computer storage medium based on table function, which can quickly and accurately find the content required by the user, and meet the requirements of the user.
One technical solution adopted by the present invention to solve the technical problem is to construct a data processing method based on a table function, including:
s1, classifying and characterizing the cognitive content based on the discipline classification table to form different characterization categories and codes;
s2, classifying different characterization categories and codes by adopting different calculation methods;
s3, constructing a data storage module according to the characterization categories and the coding and classification results;
s4, generating output results corresponding to different input information in an off-line manner according to the characterization category and the coding and classification calculation method, and generating an input and output truth value mapping relation table based on a preset table function template;
and S5, inquiring in the input and output truth value mapping relation table by adopting a multistage mode search algorithm of the self-adaptive resonance network according to the input information, and outputting a data processing result based on a mode similarity threshold calculation method.
Further, the step S1 further includes,
S11, classifying and characterizing the cognitive content of the physical world or the problem space based on the human brain cognitive function structure, and establishing a knowledge characterization system corresponding to the human brain cognitive function category;
s12, establishing corresponding data structure according to different attribute characteristics of different characterization categories to form different codes of different characterization categories.
Further, the step S12 further includes,
S121, defining different data structures aiming at different attribute characteristics of different characterization categories to establish attribute characteristic data structures corresponding to brain function partitions;
and S122, coding the attribute characteristic data structures corresponding to the brain function partitions, wherein different codes correspond to different data structures.
Further, the step S2 further includes,
S21, constructing corresponding algorithm libraries aiming at different characterization categories and codes;
and S22, calling different algorithms for calculation and processing according to different characterization types and coding input information.
Further, the step S3 further includes,
S31, respectively modeling data of different characterization types by adopting an extensible storage model;
and S32, storing the data of different characterization categories by adopting different data compression modes.
Further, the step S4 further includes:
s41, taking the characterization information sets of different characterization categories as input sets;
s42, according to the characterization category and the coding and classification calculation method, a gridding method is adopted to perform off-line calculation on each input value of grid division to generate a corresponding output result, and the input values of the whole input set are traversed to generate a corresponding output result set;
s43, generating an input and output truth value mapping relation table based on a preset table function template;
and S44, generating a database for storing the calculation body.
Further, the mapping relation table includes: one-to-one, one-to-many, many-to-one mapping templates.
Further, the step S5 further includes:
s51, receiving a calculation task, and starting the calculation task;
s52, searching in an input space of the input and output truth value mapping table by adopting a multi-stage mode search algorithm of an adaptive resonance network in a parallel distribution mode, judging the matching degree of the input information and an input mode in the input and output truth value mapping table by adopting a mode similarity threshold value calculation method, and taking an input value corresponding to the input information meeting the matching as an input truth value of a problem needing to be calculated;
and S53, according to the input truth value search result, searching in an output space of the input and output truth value mapping relation table through a mapping relation representation table, directly outputting an output truth value in the input and output truth value mapping relation table corresponding to the input truth value, and obtaining a data processing result.
Another technical solution adopted by the present invention to solve the technical problem is to construct a data processing apparatus based on a table function, including:
the characterization module is used for classifying and characterizing the cognitive content based on the discipline classification table to form different characterization categories and codes;
the classification calculation module is used for performing classification processing on different characterization classes and codes by adopting different calculation methods;
the data storage module is used for constructing the data storage module according to the characterization category, the coding and the classification result;
the search matching module is used for generating output results corresponding to different input information in an off-line mode according to the characterization category, the coding and the classification calculation method, and generating an input and output truth value mapping relation table based on a preset table function template;
and the output module is used for inquiring in the input and output truth value mapping relation table according to the input information by adopting a multi-stage mode search algorithm of the self-adaptive resonance network and outputting a data processing result based on a mode similarity threshold calculation method.
In order to solve the technical problem, a further technical solution of the present invention is to configure a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the data processing method based on a table function.
By implementing the data processing method and device based on the table function and the computer storage medium, the content required by the user can be quickly and accurately searched, and the requirement of the user is met.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which,
FIG. 1 is a flow chart of a method of data processing based on table functions in accordance with a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of an infinite-depth-side potential well data storage model employed by the table-function-based data processing method according to the preferred embodiment of the present invention;
fig. 3 is a functional block diagram of a data processing apparatus based on a table function according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention relates to a data processing method, a device and a computer storage medium based on table functions, which classify, characterize (or describe) cognitive contents of a physical world (or problem space) based on a human brain cognitive function structure, establish a knowledge characterization system corresponding to the human brain cognitive function class, construct a corresponding data storage model by combining the characteristics of classification characterization and attribute characteristic data, adopt a one-to-one mapping template, namely a mode that one input corresponds to one output to form a mapping relation table, or adopt a one-to-many or many-to-one mode to establish the mapping relation table according to the corresponding relation of output result values corresponding to input values, inquire in an output space of a mapping table by the mapping relation characterization table, directly output the output values in the table corresponding to the input values, and can quickly and accurately search contents required by a user, the requirements of users are met.
Fig. 1 is a flowchart of a data processing method based on a table function according to a preferred embodiment of the present invention. As shown in fig. 1, in step S1, the cognitive content is classified and characterized based on the discipline classification table to form different characterization classes and codes.
In a preferred embodiment of the present invention, the step further comprises classifying and characterizing the cognitive content of the physical world or the problem space based on the cognitive function structure of the human brain, and establishing a knowledge characterization system corresponding to the cognitive function category of the human brain; and establishing corresponding data structures according to different attribute characteristics of different characterization categories to form different codes of the different characterization categories.
Preferably, a formal description method is adopted based on the cognitive function structure of the human brain to classify and characterize the cognitive content of the physical world or the problem space, and a knowledge characterization system corresponding to the cognitive function category of the human brain is established. The physical attribute classification of the basic classes refers to that attributes and connection relations of information classification are formed by mapping different attributes of corresponding basic classes according to 66 partitions of human brain functions and inheriting connection relations between classes of brain function structures according to the division of the basic classes. The classification and characterization by using a formal description method means that a certain formal method is used to characterize basic category and attribute characteristics, including motion, color, spatial topological structure, time sequence, language, heat, sound, light, point, magnetism, energy and the like, to form characterization results such as numerical values, symbols, images, voice, video and the like. Overlay knowledge graph related information, and related knowledge systems.
Preferably, the establishing of the corresponding data structure according to the different attribute features of the different characterization classes, and the forming of the different codes of the different characterization classes may further include: defining different data structures aiming at different attribute characteristics of different characterization categories to establish attribute characteristic data structures corresponding to brain function partitions; and coding the attribute characteristic data structure corresponding to the brain function partition, wherein different codes correspond to different data structures.
Specifically, different data structures are defined for different attribute feature classes represented by the classification, such as a data structure of a spatial topological structure, a data structure of a language, a data structure of a sound, and the like, so as to form corresponding attribute class feature data structures of brain function partitions. And coding the attribute class characteristic data structure corresponding to the established brain function partition, wherein different codes correspond to different data structures.
In step S2, a classification process is performed using different calculation methods for different characterization classes and codes. Namely, different calculation algorithms are adopted for different characterization categories to carry out calculation and processing according to different characterization categories and codes. Specifically, aiming at different characterization categories and codes, constructing corresponding algorithm libraries; and calling different algorithms for calculation and processing aiming at different characterization categories and coding input information.
In a further preferred embodiment of the present invention, firstly, an algorithm library is constructed, and a corresponding processing algorithm is constructed for the characterization category and the attribute feature code to calculate the data. Such as numerical class processing algorithms, symbolic class processing algorithms, speech class processing algorithms, image class processing algorithms, and the like. And calling different algorithms for calculation and processing aiming at different characterization categories and coding input information.
In step S3, a data storage module is constructed according to the characterization categories, the encoding and classification results. Preferably, the corresponding data can be compressed and stored based on a preset data storage model according to the characterization category and the encoding and classification result. For example, an extensible storage model is adopted to respectively model data of different characterization categories; and storing the data of different characterization types by adopting different data compression modes.
In the preferred embodiment of the present invention, a data storage model is first constructed, which is based on the storage and computation integration and the requirement of fast access, and combines the characteristics of the classification characterization and the attribute feature data to construct a corresponding data storage model. The method is mainly characterized in that according to different types of numerical values, symbols, images, voice, videos and the like and different types of characteristic attribute data such as motion, color, space topological structure, time sequence, language, heat, sound, light, points, magnetism, energy and the like, an extensible storage model such as a one-dimensional infinite-depth potential well model is adopted to respectively model the data of different characteristic classified attribute characteristics. And the data storage model satisfies the high compression ratio index storage model of the discretized Schrodinger equation, such as an infinite depth square potential well shown in FIG. 2, U represents potential energy, a region II is an infinite depth potential well, the bottom potential energy is 0, and the top potential energy can be infinitely expanded. The data is stored by the model, different potential energy levels represent database tables with different coding types, and the overall storable data type and size can be flexibly expanded. And aiming at the data of different representation types, different data compression modes are adopted to store the representation data. The data compression mode adopts different compression algorithms according to different attribute characteristics of the representation type data, mainly comprises text data compression, image data compression, audio data compression, video data compression algorithms and the like, and adopts a lossless compression algorithm instead of a lossy compression algorithm. Optionally, the text compression data may adopt algorithms such as run length coding and arithmetic coding; the Image data adopts algorithms such as TIFF (tagged Image File Format), PNG (Portable Network Graphic Format), GIF (graphics exchange Format) and the like; the Audio data adopts algorithms such as FLAC (Fee Lossless Audio codec) format, TAK (Tom's Audio Kompresor) format and the like; the video data adopts H.264 and other algorithms.
In step S4, according to the characterization category and the encoding and classification calculation method, output results corresponding to different input information are generated offline, and an input/output truth mapping table is generated based on a preset table function template. Preferably, the characterization information sets of different characterization categories are taken as input sets; according to the characterization category, the coding and classification calculation method, a gridding method is adopted, for each input value of grid division, a corresponding output result is generated through off-line calculation, the input values of the whole input set are traversed, and a corresponding output result set is generated; generating an input and output truth value mapping relation table based on a preset table function template; and generating a database for storing the calculation body.
In a further preferred embodiment of the present invention, firstly, the characterization information sets of different categories are used as input sets, the input sets are determined, then the output sets are determined, and according to the classification characterization, coding and classification calculation algorithm, a gridding method is adopted to perform off-line calculation on each input value of grid division to generate corresponding output information result values. Then, the grid input values of the whole input set are traversed, and a corresponding result value output set is generated. And forming an input and output truth value mapping relation table of corresponding results based on the unified table function template. Optionally, a mapping relation table is formed by adopting a one-to-one mapping template and a mode that one input corresponds to one output. Or a one-to-many or many-to-one mode is adopted to establish a mapping relation table according to the corresponding relation of the output result values corresponding to the input values. The table function template can be simply realized by a two-dimensional table or a multi-dimensional table, and is specifically designed in actual realization according to the data type and the mapping relation. Those skilled in the art can make corresponding designs based on the teachings of the present invention and common general knowledge in the field.
In step S5, a query is performed in the true input/output mapping table according to the input information by using a multi-stage pattern search algorithm of the adaptive resonance network, and a data processing result is output based on a pattern similarity threshold calculation method. Further, receiving a calculation task, and starting the calculation task; searching in an input space of the input and output truth value mapping relation table by adopting a multistage pattern search algorithm of an adaptive resonance network and a parallel distribution mode, judging the matching degree of the input information and an input pattern in the input and output truth value mapping relation table by adopting a pattern similarity threshold value calculation method, and taking an input value corresponding to the input information meeting the matching as an input truth value of a problem to be calculated; and according to the input truth value search result, inquiring in an output space of the input and output truth value mapping relation table through a mapping relation representation table, directly outputting an output truth value in the input and output truth value mapping relation table corresponding to the input truth value, and obtaining a data processing result.
In a further preferred embodiment of the invention, the multi-stage pattern search algorithm of the Adaptive Resonance network, such as the Adaptive Resonance network ART3(Adaptive Resonance Theory 3), is an ad-hoc neural network model proposed by Carpenter and Grossberg, consisting of a comparison layer, a recognition threshold and a reset module. The comparison layer is responsible for receiving input samples and transmitting the input samples to the neurons of the recognition layer, each neuron of the recognition layer corresponds to one mode class, and the number of the neurons can be dynamically increased in the training process to add new mode classes. Belongs to a teacher-free learning network, and does not need to determine how many neurons exist initially. There are 3 types, which are ART1, ART2 and ART3, respectively. Wherein, ART1 contains a master-slave algorithm (leader-follower algorithm) with parallel structure, which applies set operation in the activation and matching function of the algorithm and mainly processes the image (i.e. black and white) identification problem/binary signal only containing 0 and 1; ART2 may handle grayscale (i.e., analog value) inputs for handling continuous analog signals; ART3 has a multi-level search architecture that fuses the functions of the first two structures and extends the two-layer neural network into any multi-layered neural network.
The mode similarity threshold calculation method mainly comprises a text similarity calculation method and a vector space cosine similarity calculation method. Moreover, the text similarity calculation method mainly adopts sif (smooth Inverse frequency) to calculate, and the process is as follows:
weighting: SIF takes the average weight of word embedding in sentences. Each word embedding is weighted by a/(a + p (w)), where the value of a is often set to 0.01 and p (w) is the frequency with which words are expected to appear in the corpus.
Deletion of common elements: next, SIF calculates the most important elements in the embedding of the sentence. It then subtracts the main components of these sentence embeddings and can delete the variables related to frequency and syntax.
Finally, SIF weights down some unimportant words, such as but, just, etc., while retaining information that contributes more to semantic meaning.
The similarity calculation method of the cosine of the vector space uses the cosine value of the included angle of two vectors in the vector space as the measure of the difference between two individuals. The cosine value is closer to 1, which indicates that the included angle is closer to 0 degree, namely the two vectors are more similar, which is called cosine similarity. And the similarity judgment can be completed by setting a similarity threshold.
Likewise, in other preferred embodiments of the present invention, other adaptive resonant network and/or mode similarity threshold calculation methods may be used, and any adaptive resonant network and/or mode similarity threshold calculation method known in the art may be used by one skilled in the art to implement the present invention.
The invention relates to a data processing method based on table functions, which is based on a human brain cognitive function structure, carries out classification representation (or description) on cognitive contents of a physical world (or problem space), establishes a knowledge representation system corresponding to the human brain cognitive function class, constructs a corresponding data storage model by combining the characteristics of classification representation and attribute characteristic data, forms a mapping relation table by adopting a one-to-one mapping template, namely a mode that one input corresponds to one output, or establishes the mapping relation table by adopting a one-to-many or many-to-one mode according to the corresponding relation of output result values corresponding to input values, queries in an output space of the mapping table by representing and looking up a table through the mapping relation, directly outputs the output values in the table corresponding to the input values, can quickly and accurately find the contents required by a user, and meets the requirements of the user.
Fig. 3 is a functional block diagram of a data processing apparatus based on a table function according to a preferred embodiment of the present invention. As shown in fig. 3, the data processing apparatus 100 based on table function of the present invention includes a characterization module 110, a classification calculation module 120, a data storage module 130, a search matching module 140, and an output module 150. The characterization module 110 is configured to classify and characterize the cognitive content based on the discipline classification table to form different characterization categories and codes. The classification calculation module 120 is configured to perform classification processing on different characterization classes and codes by using different calculation methods. The data storage module 130 is configured to construct a data storage module according to the characterization category and the encoding and classification result. The search matching module 140 is configured to generate output results corresponding to different input information offline according to the characterization category and the coding and classification calculation method, and generate an input/output truth mapping table based on a preset table function template. The output module 150 is configured to query the input/output truth mapping table according to the input information through a multi-stage mode search algorithm using an adaptive resonance network, and output a data processing result based on a mode similarity threshold calculation method. Preferably, the data storage module 130 is specifically configured to store the characterization class data in different data compression manners for data of different characterization types.
Those skilled in the art will appreciate that the characterization module 110, the classification calculation module 120, the data storage module 130, the search matching module 140 and the output module 150 may be any hardware module, software module, or software and hardware module, which can perform the corresponding steps of the aforementioned table function-based data processing method in a one-to-one correspondence. For example, the characterization module 110 may be further configured to classify and characterize the cognitive content of the physical world or the problem space based on the cognitive function structure of the human brain, and establish a knowledge characterization system corresponding to the cognitive function category of the human brain; and establishing corresponding data structures according to different attribute characteristics of different characterization categories to form different codes of the different characterization categories. The classification calculation module 120 may be further configured to construct a corresponding algorithm library for different characterization categories and codes; and calling different algorithms for calculation and processing aiming at different characterization categories and coding input information. The data storage module 130 may be further configured to employ an extensible storage model to respectively model data of different characterization categories; and storing the data of different characterization types by adopting different data compression modes. The search matching module 140 may be further configured to take as an input set a set of token information for different token categories; according to the characterization category, the coding and classification calculation method, a gridding method is adopted, for each input value of grid division, a corresponding output result is generated through off-line calculation, the input values of the whole input set are traversed, and a corresponding output result set is generated; generating an input and output truth value mapping relation table based on a preset table function template; and generating a database for storing the calculation body. The output module 150 may be further configured to receive a computing task, the computing task starting; searching in an input space of the input and output truth value mapping relation table by adopting a multistage pattern search algorithm of an adaptive resonance network and a parallel distribution mode, judging the matching degree of the input information and an input pattern in the input and output truth value mapping relation table by adopting a pattern similarity threshold value calculation method, and taking an input value corresponding to the input information meeting the matching as an input truth value of a problem to be calculated; and according to the input truth value search result, inquiring in an output space of the input and output truth value mapping relation table through a mapping relation representation table, directly outputting an output truth value in the input and output truth value mapping relation table corresponding to the input truth value, and obtaining a data processing result.
Based on the teaching of the present invention, those skilled in the art can implement various data processing apparatuses based on table functions corresponding to the above data processing methods based on table functions, and the description thereof will not be repeated here.
Further, the present invention also relates to a computer-readable storage medium having stored thereon a computer program having all the features enabling the implementation of the method of the present invention, when installed in a computer system. Computer program in this document refers to any expression, in any programming language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following steps, a) conversion to another language, code or notation; b) reproduced in a different format.
The computer readable medium includes, but is not limited to, various media that can store program code, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrically erasable programmable ROM, a register, a hard disk, a removable magnetic disk, a CD-ROM, a magnetic or optical disk, and the like.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A method for processing data based on a table function, comprising:
s1, classifying and characterizing the cognitive content based on the discipline classification table to form different characterization categories and codes;
s2, classifying different characterization categories and codes by adopting different calculation methods;
s3, constructing a data storage module according to the characterization categories and the coding and classification results;
s4, generating output results corresponding to different input information in an off-line manner according to the characterization category and the coding and classification calculation method, and generating an input and output truth value mapping relation table based on a preset table function template;
and S5, inquiring in the input and output truth value mapping relation table by adopting a multistage mode search algorithm of the self-adaptive resonance network according to the input information, and outputting a data processing result based on a mode similarity threshold calculation method.
2. The data processing method according to claim 1, wherein the step S1 further comprises,
S11, classifying and characterizing the cognitive content of the physical world or the problem space based on the human brain cognitive function structure, and establishing a knowledge characterization system corresponding to the human brain cognitive function category;
s12, establishing corresponding data structure according to different attribute characteristics of different characterization categories to form different codes of different characterization categories.
3. The data processing method according to claim 2, wherein the step S12 further comprises,
S121, defining different data structures aiming at different attribute characteristics of different characterization categories to establish attribute characteristic data structures corresponding to brain function partitions;
and S122, coding the attribute characteristic data structures corresponding to the brain function partitions, wherein different codes correspond to different data structures.
4. The data processing method according to claim 1, wherein the step S2 further comprises,
S21, constructing corresponding algorithm libraries aiming at different characterization categories and codes;
and S22, calling different algorithms for calculation and processing according to different characterization types and coding input information.
5. The data processing method according to claim 1, wherein the step S3 further comprises,
S31, respectively modeling data of different characterization types by adopting an extensible storage model;
and S32, storing the data of different characterization categories by adopting different data compression modes.
6. The data processing method based on table function according to claim 1, wherein the step S4 further comprises:
s41, taking the characterization information sets of different characterization categories as input sets;
s42, according to the characterization category and the coding and classification calculation method, a gridding method is adopted to perform off-line calculation on each input value of grid division to generate a corresponding output result, and the input values of the whole input set are traversed to generate a corresponding output result set;
s43, generating an input and output truth value mapping relation table based on a preset table function template;
and S44, generating a database for storing the calculation body.
7. The method of claim 6, wherein the mapping table comprises: one-to-one, one-to-many, many-to-one mapping templates.
8. The table function-based data processing method according to any one of claims 1 to 7, wherein the step S5 further comprises:
s51, receiving a calculation task, and starting the calculation task;
s52, searching in the input space of the input and output truth value mapping table by adopting a multi-stage mode search algorithm of a self-adaptive resonance network in a parallel distribution mode, and judging the matching degree of the input information and the input mode in the input and output truth value mapping table by adopting a mode similarity threshold value calculation method; taking the input value corresponding to the input information meeting the matching as an input true value of a solution problem needing to be calculated;
and S53, according to the input truth value search result, searching in an output space of the input and output truth value mapping relation table through a mapping relation representation table, directly outputting an output truth value in the input and output truth value mapping relation table corresponding to the input truth value, and obtaining a data processing result.
9. A data processing apparatus based on table functions, comprising:
the characterization module is used for classifying and characterizing the cognitive content based on the discipline classification table to form different characterization categories and codes;
the classification calculation module is used for performing classification processing on different characterization classes and codes by adopting different calculation methods;
the data storage module is used for constructing the data storage module according to the characterization category, the coding and the classification result;
the search matching module is used for generating output results corresponding to different input information in an off-line mode according to the characterization category, the coding and the classification calculation method, and generating an input and output truth value mapping relation table based on a preset table function template;
and the output module is used for inquiring in the input and output truth value mapping relation table according to the input information by adopting a multi-stage mode search algorithm of the self-adaptive resonance network and outputting a data processing result based on a mode similarity threshold calculation method.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for data processing based on table functions according to any one of claims 1 to 8.
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