Standard text parameter correlation structured representation method and system

Abstract

The invention relates to the technical field of standard digitization, and provides a standard text parameter correlation structured representation method and model, through steps S1-S4, technical parameters and predicates are extracted from a standard text, the technical parameters are sorted according to word frequencies, and a first vector X is obtained; performing correlation analysis on the ith element and the jth element one by one, eliminating irrelevant technical parameter combinations, performing logical calculation on each element, performing logical calculation on relevant technical parameters, performing causal reasoning operation on the vector rij to obtain a causal graph, and generating a structured representation sequence. The invention further discloses a system, the method and the system can establish the minimum relation for the technical parameters, sequence expression can be performed on the relation among all the technical parameters, comprehensive mining and expression can be performed on the relation among the implicit technical parameters, the expression of the logic relation among the entities can be improved, and the method and the system can be applied to the field of engineering engineering. And the readability of the machine is obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of standard digitization, and in particular to a method and system for structured representation of standard text parameter correlation. Background Art

[0002] The International Organization for Standardization (ISO, IEC, ITU), regional standardization organizations (CEN, CENELEC) and developed countries are actively planning and promoting the process of standard digitization. ISO defines the level of standard digitization at the "machine-readable document" stage, that is, the structuring of standard content is achieved through XML to quickly identify and retrieve standard content. With the profound impact of digital technology on industrial development, daily life, government management and services, standard digitization has become an inevitable trend in economic and social development. It can upgrade the digital level and capabilities of the National Quality Infrastructure (NQI) and is a basic requirement for realizing the Digital China strategy. Standard digitization makes standards more open, more shared, and more intelligent, forming a standardized "open source" model and affecting the entire life cycle of standards. It enables all stakeholders to collaborate on the development of standards and conduct real-time interactive sharing of information, changing the entire working mode of standardization. With the development of standard digitization, the popularization and use of standards will become more extensive, and the influence of open source communities and standardization organizations will be further enhanced.

[0003] CN119201889A discloses a standard digital modeling method and system, the standard digital modeling method comprises: step S1, obtaining different activity stages of the technical standard in the time dimension during the life cycle to obtain first dimension data, the first dimension data at least including standard pre-research, standard drafting, standard solicitation of opinions, standard review, standard application, standard evaluation, standard revision, and standard abandonment; obtaining different resource types of the technical standard in the spatial dimension to obtain second dimension data, the second dimension data including knowledge, personnel, information, and associated standards; step S2, establishing a label layer according to the second dimension data, the label layer including multiple labels, The second dimensional data is associated and constrained with the labels in the label layer to form a first associated constraint set; the first dimensional data is associated and constrained with the labels in the label layer to form a second associated constraint set; step S3, different categories of data in the second dimensional data are separately established into a first data packet for storage and push, wherein the first data packet at least includes a knowledge data packet, a personnel data packet, an information data packet, and an associated standard data packet; step S4, if the data in the first data packet is updated, the label in the label layer is updated, and different versions of the second data packet are saved; the first associated constraint set and the second associated constraint set are updated, and the version data is retained.

[0004] CN118981540A A method and system for digital management of standards based on artificial intelligence, including: collecting standardized data sets for the power industry; navigating through the standard system, classifying and navigating the standards according to business sectors, technical standard systems and equipment classification systems; searching for standards through document services and knowledge service models; and implementing indicator comparison and standard statistics through digital reading.

[0005] At present, standard text structuring is the process of converting unstructured text data into structured or semi-structured data, which is crucial for computers to understand and process text information more efficiently. However, the parameter relationships in standard text are difficult to structure and semantically process, making it difficult for machines to recognize and understand the relationships between these parameters. Summary of the invention

[0006] Long-term practice has found that in the process of text structuring, information extraction technologies such as named entity recognition (NER) are used to extract key information from text, such as names of people, places, and organizations. Based on the extracted information, structured data models such as JSON and XML are constructed to facilitate computer processing and analysis. However, using information extraction to represent the association relationship in the text often cannot fully express the logical relationship between entities. In the process of artificial intelligence processing, weakly related logical relationships are often even ignored.

[0007] In view of this, the present invention aims to propose a standard text parameter correlation structured representation method, comprising:

[0008] Step S1, extracting technical parameters and predicates from the standard text, sorting the technical parameters according to word frequency to obtain a first vector X; standardizing the predicates to form an operator set Y;

[0009] Step S2: Perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that is not 0 is R ij ;

[0010] Step S3: R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ;

[0011] Step S4, according to the vector r ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

[0012] Preferably, in step S1, adverbs in the standard text are extracted, and adverbs of the same type are graded and quantified.

[0013] Preferably, in step S4, according to the causal graph G(V, E), operators with E≠0 are retained to form a first operator set y∈Y.

[0014] Preferably, the causal graph G(V, E) is a directed acyclic graph.

[0015] Preferably, each element in the first vector X is displayed in layers according to relative coordinates in the image data.

[0016] The present invention also discloses a system for the above-mentioned standard text parameter correlation structured representation method, the system comprising:

[0017] An extraction module is used to extract technical parameters and predicates from the standard text, sort the technical parameters according to word frequency to obtain a first vector X; and standardize the predicates to form an operator set Y;

[0018] The correlation calculation module is used to perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that is not 0 is R ij ;

[0019] Logical operation module, used to convert R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ;

[0020] The causal inference module is used to ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

[0021] Preferably, the system further comprises an updating module, wherein the updating module is used for updating the element V and the relationship edge E in the causal graph G(V, E) to generate an updated structured sequence.

[0022] Preferably, the system further comprises an image module, and the image module is used for relative coordinate mapping of each element in the first vector X.

[0023] The present invention also discloses an electronic device, at least one processor; and

[0024] a memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned standard text parameter correlation structured representation method.

[0026] The present invention also discloses a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the standard text parameter correlation structured representation method of the present invention as described above.

[0027] The standard text parameter correlation structured representation method disclosed in the present invention extracts technical parameters and predicates from the standard text through steps S1-S4, and sorts the technical parameters according to word frequency to obtain a first vector X. The i-th element and the j-th element in the first vector X are subjected to Pearson correlation analysis one by one, and irrelevant technical parameter combinations are eliminated to reduce the amount of calculation. ij The i-th element in the combination is logically calculated with the j-th element, and the technical parameters with correlation are logically operated to realize the calculation of various predicate connections and traverse the strong and weak relationships between all elements. ij A causal reasoning operation is performed on each pair of the combination of the ith element and the jth element to obtain a causal graph G(V, E), wherein V is an element and E is a relationship edge, and a structured representation sequence is generated according to the causal graph G(V, E). The present invention also discloses a system, which can establish a minimum relationship between technical parameters, that is, a correlation between elements, narrow the scope of logical relationships through correlation analysis, and then use logical operations and causal reasoning to associate the relationships between elements, and finally can express the relationships between all technical parameters in sequence, comprehensively mine and represent the relationships between implicit technical parameters, improve the expression of logical relationships between entities, and thus significantly improve machine readability.

[0028] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and the description thereof are used to explain the present invention and do not constitute improper limitations on the present invention.

[0030] In the attached picture:

[0031] Figure 1 A schematic diagram of a method for structurally representing standard text parameter correlation according to an embodiment of the present invention.

[0032] Figure 2 A schematic diagram of a system for executing a standard text parameter correlation structured representation method in one embodiment of the present invention. DETAILED DESCRIPTION

[0033] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", "third", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] In order to solve the existing technology, information extraction technology such as named entity recognition (NER) is used to extract key information from text, such as names of people, places, and organizations. Based on the extracted information, a structured data model such as JSON and XML is constructed to facilitate computer processing and analysis. However, the use of information extraction and representation of association relationships in texts often cannot fully express the logical relationship between entities. In the process of artificial intelligence processing, weakly related logical relationships are often even ignored. The present invention provides a standard text parameter correlation structured representation method, such as Figure 1 As shown, a schematic diagram of a standard text parameter correlation structured representation method according to an embodiment of the present invention, the standard text parameter correlation structured representation method includes:

[0037] Step S1, extracting technical parameters and predicates from the standard text, sorting the technical parameters according to word frequency to obtain a first vector X; standardizing the predicates to form an operator set Y;

[0038] Step S2: Perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that is not 0 is R ij ;

[0039] Step S3: R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ;

[0040] Step S4, according to the vector r ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

[0041] The standard text parameter correlation structured representation method disclosed in the present invention extracts technical parameters and predicates from the standard text through steps S1-S4, and sorts the technical parameters according to word frequency to obtain a first vector X. The i-th element and the j-th element in the first vector X are subjected to Pearson correlation analysis one by one, and irrelevant technical parameter combinations are eliminated to reduce the amount of calculation. ij The i-th element in the combination is logically calculated with the j-th element, and the technical parameters with correlation are logically operated to realize the calculation of various predicate connections and traverse the strong and weak relationships between all elements. ij Perform causal reasoning operations on each pair of the ith element and the jth element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E). Establish a minimum relationship between technical parameters, that is, the correlation between elements, narrow the scope of logical relationships through correlation analysis, and then use logical operations and causal reasoning to associate the relationships between elements. Finally, the relationships between all technical parameters can be expressed in sequence, the implicit relationships between technical parameters can be fully mined and represented, and the expression of logical relationships between entities can be improved, thereby significantly improving machine readability.

[0042] In order to further improve the expression of the logical relationship between technical parameters in the text, adverbs are usually used in sentences to express semantic information such as degree, manner, time, etc. Quantifying adverbs can help the machine better understand the meaning and context of sentences. Quantifying adverbs can make the technical parameters in the standard text better obtain relevance judgment. In the more preferred case of the present invention, in step S1, adverbs in the standard text are extracted, and adverbs of the same type are graded and quantified. Use natural language processing (NLP) tools, such as part-of-speech taggers (POS taggers), to identify adverbs in the text. The identified adverbs are classified according to their semantic functions, such as degree adverbs, frequency adverbs, manner adverbs, time adverbs, etc. A quantitative standard is established for each adverb category. For example, a scale is created, and for degree adverbs, quantification is performed from "very" (high quantitative value) to "slightly" (low quantitative value). The quantitative standard is numerical, for example, using a value between 0 and 1, or an ordered level, such as {low, medium, high}. According to the meaning of the adverb and the established quantitative standard, a quantitative value is assigned to each type of adverb. This process is done automatically by algorithms, for example, by analyzing the use of adverbs in a large number of standard texts and counting their associations with different degrees or frequencies. More preferably, the context of the adverbs is analyzed, because the same adverb may have different quantitative values ​​in different contexts, using syntactic and semantic analysis to determine the words modified by the adverbs and their role in the sentence.

[0043] A causal graph G(V, E) is a graphical tool for representing causal relationships between variables. G represents a graph, V represents a set of vertices (Variables) in the graph, and E represents a set of edges (Edges) in the graph. To mine the constraint relationships between technical parameters, each vertex in the vertex set V represents a technical parameter, and each edge in the edge set E represents a causal relationship between two technical parameters. A causal structure learning algorithm (such as a search-based algorithm, a scoring-based algorithm, or a Bayesian network-based algorithm) is used to construct a causal graph. In a more preferred embodiment of the present invention, in step S4, according to the causal graph G(V, E), operators with E≠0 are retained to form a first operator set y∈Y. In order to further improve the efficiency of constructing the causal graph G(V, E), the knowledge and experience of domain experts are required to explain and guide the inference of causal relationships. Retaining E≠0 means retaining the relationship between technical parameters with non-zero causal effects. Edges representing E being zero are removed from the causal graph, and the causal graph is updated to reflect the remaining non-zero causal relationships, e.g., the causal relationship between technical parameter A and technical parameter B, the structured sequence A→B(E1), where E1 is the edge.

[0044] Directed Acyclic Graph (DAG), in the more preferred case of the present invention, the causal graph G (V, E) is a directed acyclic graph. Each edge has a direction, indicating a causal relationship, that is, pointing from one node (technical parameter) to another node (technical parameter). The direction of the edge indicates that one node (technical parameter) has a direct impact on another node (technical parameter). Causal inference methods, such as structural equation models (Structural Equation Models, SEM) or latent variable models. In order to exclude the situation where a technical parameter is completely unrelated to another technical parameter, those edges that may be zero are identified and excluded through data analysis, statistical tests or domain knowledge. When processing a directed acyclic causal graph, specific algorithms, such as the PC algorithm (Peter-Clark algorithm) or the FCI algorithm (Fast Causal Inference algorithm), can also be used to learn or infer causal structures from data.

[0045] In order to better visualize the different technical parameters in the causal graph G(V, E), in a more preferred embodiment of the present invention, each element in the first vector X is displayed in layers according to the relative coordinates in the image data.

[0046] The present invention also discloses a system for the above-mentioned standard text parameter correlation structured representation method, such as Figure 2 As shown, the system comprises:

[0047] An extraction module is used to extract technical parameters and predicates from the standard text, sort the technical parameters according to word frequency to obtain a first vector X; and standardize the predicates to form an operator set Y;

[0048] The correlation calculation module is used to perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that is not 0 is R ij ;

[0049] Logical operation module, used to convert R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ;

[0050] The causal inference module is used to ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

[0051] The system for the standard text parameter correlation structured representation method disclosed in the present invention extracts technical parameters and predicates from the standard text through an extraction module, sorts the technical parameters according to word frequency, and obtains a first vector X. The correlation calculation module performs Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one, removes irrelevant technical parameter combinations, and reduces the amount of calculation. The logical operation module calculates the R ij The ith element in the combination is logically calculated with the jth element, and the technical parameters with correlation are logically operated to realize the calculation of various predicate connections and traverse the strong and weak relationships between all elements. The causal reasoning module targets the vector r ij Perform causal reasoning operations on each pair of the ith element and the jth element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E). Establish a minimum relationship between technical parameters, that is, the correlation between elements, narrow the scope of logical relationships through correlation analysis, and then use logical operations and causal reasoning to associate the relationships between elements. Finally, the relationships between all technical parameters can be expressed in sequence, the implicit relationships between technical parameters can be fully mined and represented, and the expression of logical relationships between entities can be improved, thereby significantly improving machine readability.

[0052] As the technical parameters in the standard text are updated and the standard text version changes, it is necessary to add new variables or entities to the causal graph, remove variables or entities that are no longer relevant or do not exist from the causal graph, and update the properties of the nodes, for example, changing the weight or category of the node. Add new causal relationships in the causal graph and update the weights of the edges to reflect the changes in the intensity of the causal effect. For nodes, perform add, remove or modify operations. For edges, perform add, remove or modify weight operations. In a more preferred embodiment of the present invention, the system also includes an update module, which is used to update the element V and the relationship edge E in the causal graph G(V, E) to generate an updated structured sequence. Convert the causal graph into a structured sequence representation, such as XML, JSON or a custom format. Store the structured sequence in a file or database, or transfer the structured sequence to other systems or services for further processing.

[0053] The image module converts the abstract data of the vector into coordinates represented on the image plane. In order to visualize the data, the elements of the vector are mapped to points on the image to help users or algorithms intuitively understand the relationship and pattern between the elements. In a more preferred embodiment of the present invention, the system further includes an image module, which is used for relative coordinate mapping of each element in the first vector X.

[0054] The present invention also discloses an electronic device, at least one processor; and

[0055] a memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned standard text parameter correlation structured representation method.

[0057] The present invention also discloses a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the standard text parameter correlation structured representation method of the present invention as described above.

[0058] The present invention also discloses an electronic device, at least one processor; and

[0059] a memory communicatively connected to the at least one processor; wherein,

[0060] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned standard text parameter correlation structured representation method.

[0061] The present invention also discloses a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the standard text parameter correlation structured representation method of the present invention as described above.

[0062] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0063] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0064] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0065] The method and device for providing service information provided by the present invention are described in detail above. The present invention uses specific examples to illustrate the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the method and concept of the present invention. At the same time, for those skilled in the art, according to the concept of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for structurally representing the correlation of standard text parameters, characterized in that: The standard text parameter correlation structured representation method includes: Step S1, extracting technical parameters and predicates from the standard text, sorting the technical parameters according to word frequency to obtain a first vector X; standardizing the predicates to form an operator set Y; Step S2: Perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that are not 0 is R ij ; Step S3: R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ; Step S4, according to the vector r ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

2. The method for structurally representing the correlation between standard text parameters according to claim 1, characterized in that: In step S1, adverbs in the standard text are extracted, and adverbs of the same type are graded and quantified.

3. The method for structurally representing the correlation between standard text parameters according to claim 1, characterized in that: In step S4, according to the causal graph G(V, E), operators with E≠0 are retained to form a first operator set y∈Y.

4. The method for structurally representing the correlation between standard text parameters according to claim 1, characterized in that: The causal graph G(V, E) is a directed acyclic graph.

5. The method for structurally representing the correlation between standard text parameters according to any one of claims 1 to 4, characterized in that: Each element of the first vector X is displayed in layers according to the relative coordinates in the image data.

6. A system for the method for structurally representing the standard text parameter correlation as claimed in any one of claims 1 to 5, characterized in that: The system comprises, An extraction module is used to extract technical parameters and predicates from the standard text, sort the technical parameters according to word frequency to obtain a first vector X; and standardize the predicates to form an operator set Y; The correlation calculation module is used to perform Pearson correlation analysis on the i-th element and the j-th element in the first vector X one by one to obtain the value P ij , keep P ij The combination of the i-th element and the j-th element that is not 0 is R ij ; Logical operation module, used to convert R ij The i-th element and the j-th element in the combination are logically operated one by one, and the logical operation type belongs to the operator set Y; the vector r is obtained ij ; The causal inference module is used to ij Perform causal reasoning on each pair of the i-th element and the j-th element to obtain a causal graph G(V, E), where V is an element and E is a relationship edge. Generate a structured representation sequence based on the causal graph G(V, E).

7. The system according to claim 6, characterized in that The system also includes an updating module, which is used to update the element V and the relationship edge E in the causal graph G(V, E) to generate an updated structured sequence.

8. The system according to claim 7, characterized in that The system further comprises an image module, wherein the image module is used for relative coordinate mapping of each element in the first vector X.

9. An electronic device, characterized in that: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the standard text parameter correlation structured representation method described in any one of claims 1-5.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for enabling a machine to execute the standard text parameter correlation structured representation method as described in any one of claims 1 to 5 of the present invention.

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