CN114372471A - Semantic solidification and derivation method based on public predicate logic - Google Patents

Abstract

The invention discloses a semantic solidification and derivation method based on public predicate logic, which comprises the following steps: 1) selecting or designing a semantic unit X for the automaton A to form a semantic unit set { X } and a corresponding data state set { D }; 2) digital feature acquisition is carried out on each semantic unit in the { X } and the index value of each semantic unit is calculated to obtain a semantic unit index set { H _ X } and a digital feature set { Sign _ X }; digital feature acquisition is carried out on each data state in the { D } and the index value of each data state is calculated to obtain a data state index set { H _ D } and a digital feature set { Sign _ D }; 3) respectively taking { H _ X } and { H _ D } as input to carry out a calculation mode to obtain a Root _ A of the automaton A; 4) a' starts the automata A through the Root _ A, and obtains all semantic units and data states of the automata A to drive, so that the requirements for the automata A are obtained.

Description

Semantic solidification and derivation method based on public predicate logic

Technical Field

The invention relates to the field of blockchain technology and distributed computing, in particular to a semantic solidification and derivation method based on public predicate logic in an internet environment.

Background

A system such as a block chain system and the like which operates in an open Internet environment has a large number of scenes that data states in a public script form using a contract automaton as a typical case are driven and processed by combining predicate logic, and the characteristics of distributed multi-nodes and the like need the unique accuracy of script semantics and multi-level granularity semantics are solidified. In the process of developing the public script, semantic and data state hierarchical granularity is solidified, derived and multiplexed, so that the convenience and the efficiency of script development are improved.

Due to the fact that the automata mechanism of the data state of various programs including scripts has the situations of service scene upgrading, complex structure, difficult control of safety and the like, risks such as program writing errors or overflow can be caused.

Therefore, the invention provides a semantic solidification and derivation method of network public predicate logic based on public semantic and public state multiplexing, thereby improving the convenience and the unique certainty of computer program development.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention aims to provide a computer semantic solidification and derivation method based on public semantic and public state multiplexing, which is used for realizing the granularity level encapsulation and solidification of semantics and states suitable for business needs by solidifying through predicate logic and proposition logic based on a common basic state, is suitable for application occasions of vast computer practitioners for program development including various public scripts including block chains, parallel block chains and distributed storage, and is a method with stronger convenience and certainty for developing network programs in public occasions.

The technical scheme of the invention is as follows:

a semantic solidification and derivation method based on common predicate logic comprises the following steps:

1) selecting or designing a plurality of semantic units X for program development for the automaton A to form a semantic unit set sigma (X); wherein, the semantic unit set { X } comprises semantic units X (1), X (2), …, X (N), and X (N) is the Nth semantic unit; different semantic units are designed according to different requirements to realize different logic functions; the data state set { D } corresponding to the semantic unit set { X } includes data states D (1), D (2), …, D (K); d (K) is a data state corresponding to the Kth data processed by the semantic unit X;

2) digital feature acquisition is carried out on each semantic unit in the semantic unit set { X } and the index value of each semantic unit is calculated, so that a semantic unit index set { H _ X } and a digital feature set { Sign _ X } are obtained; the set { Sign _ X } comprises Sign _ X (1), Sign _ X (2), … and Sign _ X (N), the semantic unit index set { H _ X } comprises H _ X (1), H _ X (2), … and H _ X (N), the semantic unit X (N) has the corresponding digital feature of Sign _ X (N), and the semantic unit X (N) has the corresponding index value of H _ X (N); digital feature acquisition is carried out on each data state in the data state set { D } and an index value of each data state is calculated, so that a data state index set { H _ D } and a digital feature set { Sign _ D } are obtained; the data state index set { H _ D } comprises data state index values H _ D (1), H _ D (2), … and H _ D (K), the digital feature set { Sign _ D } comprises Sign _ D (1), Sign _ D (2), … and Sign _ D (K), H _ D (K) is the index value of the data state D (K), and Sign _ D (K) is the digital feature of the data state D (K);

3) obtaining a semantic Root _ X through a calculation mode by taking the semantic unit index set { H _ X } as input, and obtaining a state Root _ D through the calculation mode by taking the data state index set { H _ D } as input; obtaining a Root _ A of the automaton A through a calculation mode by taking the semantic Root _ X and the state Root _ D value as input;

4) the automaton A' starts the automaton A through the automaton Root _ A, all semantic units of the automaton A are obtained through the semantic Root _ X, and a data state corresponding to the execution semantics of the automaton A is obtained through the state Root _ D; and then, driving the corresponding semantic unit by using the digital features corresponding to the semantic unit, driving the corresponding data by using the digital features corresponding to the data state, and processing the corresponding proposition logic by using predicate logic in the semantic unit, so that the automaton A' obtains the requirement on the automaton A.

Further, the digital feature may be a digital signature or other common digital feature.

Furthermore, the type and kind of the input parameter of the semantic unit X are set by a specific service scenario, and the input parameter is a service parameter or other codes or a constraint condition related to functional application.

Furthermore, the semantic unit is a programmable code which is subjected to security audit or long-term execution verification by professional institutions, can also be a programmable code which is subjected to long-term verification and has high execution efficiency, can also be a programmable code which is subjected to long-term verification and stably executes, or can be a code of both the codes.

Further, the data state D is set by a specific service scenario, and the data state is a data block or a data set, and is in a structured data form, an unstructured data form, or a semi-structured data form.

Further, the calculation mode may be a merk tree, a dictionary tree, or other various calculation modes, so as to obtain a data root of the input data.

Further, the automaton a is a program template, a function, a framework or a computer software program.

Further, automata a' is the same type or different type as automata a.

In a given public network system, various transactions are driven and executed by predicate logic including programs, the reliability, safety and the like of semantics (specific form corresponds to 'X series' in subsequent description) and data state (specific form corresponds to 'D series' in subsequent description) of a script construction process are the basis of unique determinacy of system operation, and the solidification and derivation of various verified or assessed public predicate logics are important means.

Aiming at the commonalities or basic requirements of business applications, a class of computer automata A for semantic solidification and derivation is invented, which specifically comprises a semantic unit X series and an associated data state D series, wherein the semantic unit series and the data series are respectively subjected to digital signature to form a set of a semantic unit set { X }, a semantic unit index set { H _ X } and a digital feature set { Sign _ X }, a data state set { D }, a data state index set { H _ D } and a digital feature set { Sign _ D }, a semantic Root _ X and a state Root _ D, and an automaton Root _ A, and is shown in FIG. 1. The specific form of automaton a can be program templates, functions, frameworks and other computer software modalities.

The automaton Root _ a of the present invention is obtained by calculating a pattern with the semantic Root _ X and the state Root _ D as inputs, as shown in fig. 2. The Root of the automaton, Root _ A, can be used as the semantic unit index of other automatons. The calculation mode may be a Merry tree, a dictionary tree, or other various calculation modes, so as to obtain a data root of the input data.

The semantic Root _ X of the present invention is a semantic Root _ X obtained by a calculation mode with all semantic unit X(s) index values { H _ X } as input, as shown in fig. 3. All semantic unit index values H _ X(s) are obtained by evaluating the numerical feature values of semantic units X(s) one by one, where s ∈ [1, N ], as shown in FIG. 5. Wherein, N is the total number of semantic units, and the digital feature evaluation may be hash calculation or other self-defined methods, and may be digital signature or other common digital features.

The semantic unit index value set { H _ X } of the present invention is specifically composed of semantic units H _ X (1), H _ X (2), …, and H _ X (n), and realizes identifiability and retrievability of the semantic units, where H _ X(s) is an index value of the s-th semantic unit X(s), as shown in fig. 6. The specific form of the semantic unit can be 'program statement and combination thereof', and the index value of the semantic unit is obtained by adopting a form of evaluating the digital characteristic value of the semantic unit.

The semantic unit set Σ (X) of the present invention is specifically composed of semantic units X (1), X (2), …, X (n), as shown in fig. 7, different semantic units are designed according to specific requirements to realize different logic functions; each statement corresponds to a digital feature, which is a { Sign _ X } identifier, specifically, Sign _ X (1), Sign _ X (2), …, Sign _ X (n), as shown in fig. 8, and semantic units correspond to digital features one to one, as shown in fig. 13. The specific semantic unit can be a programmable code which is subjected to security audit or long-term execution verification by professional institutions, can also be a programmable code which is subjected to long-term verification and has high execution efficiency, can also be a programmable code which is subjected to long-term verification and stably executes, or can be a code of both the codes. The input parameter types and kinds of the semantic units X (1), X (2), …, X (n) are set by specific service scenarios, and different parameters may be specific service parameters or other codes, or may be constraints related to function applications, such as version numbers, years, charging and service constraints thereof, where the constraints may be for the entire semantic complex, or for specific individual or parameter combinations.

The state Root _ D of the present invention is a state Root _ D obtained by a calculation mode with the index values of all data states D (t) as inputs, as shown in fig. 4. All data state index values H _ D (t) are obtained by evaluating the numerical characteristic values of the data states D (t) one by one, where t ∈ [1, K ], as shown in FIG. 9. Where K is the total number of data states, and the feature evaluation may be a hash calculation or other custom method.

The data state index value set { H _ D } of the present invention is specifically composed of data state index values H _ D (1), H _ D (2), …, H _ D (k), and realizes identifiability and retrievability of data states, as shown in fig. 10. The invention obtains searchability by solving the digital characteristics of the semantic unit and the data state unit and obtains identifiability by carrying out the digital characteristics.

The data state set { D } of the present invention is specifically composed of different data states D (1), D (2), …, D (k), as shown in fig. 11, different data are stored into different specific data structures according to the requirement, and each data forms a state; each data state corresponds to a digital feature, which is a { Sign _ D } identifier, specifically Sign _ D (1), Sign _ D (2), …, Sign _ D (k), as shown in fig. 12, and the digital features of the data states correspond to the data states one to one, as shown in fig. 14. The data states D (1), D (2), …, D (k) are set by specific service scenarios, and the data states are in the form of existence of data blocks or data sets, and may be in the form of structured data, or in the form of unstructured data, or in the form of semi-structured data.

Based on the foregoing, automata A ' is derived and calls a schematic skeleton diagram of A during run-time, as shown at 15, wherein automata A, A ' may be of the same type or different types, automata A ' is derived from automata A. The automaton A' starts the automaton A through the automaton Root _ A, all semantic units of the automaton A are obtained through the semantic Root _ X, and a data state corresponding to the execution semantics of the automaton A is obtained through the state Root _ D. And then, the semantic units and the data states are driven by using the authorized digital features, and the predicate logic is used for processing the corresponding proposition logic, namely the authorized semantic units operate the corresponding data, so that the requirement of the automaton A' on the automaton A is obtained. The drive specifically means that a user obtains an authorized semantic unit digital feature Sign _ X _ } and a data state { Sign _ D _ } by combining with the semantic unit digital feature { Sign _ X } and the data state { Sign _ D _ } of the automaton a based on a computer logic state, and then obtains a semantic unit and a data state corresponding to the authorized digital feature, wherein the digital feature set { Sign _ X _ } is not greater than the set { Sign _ X }, and the digital feature set { Sign _ D _ } is not greater than the set { Sign _ D }.

Compared with the prior art, the invention has the following beneficial effects:

the method is independent of specific service scenes and operation environments, safe in operation and better in diffusibility of the reuse set. A method for solidifying and constructing a contract automaton is provided, which is suitable for public computing environments, comprises methods for calling and deriving under the occasions of block chains, parallel block chains, distributed computing, distributed storage and the like, and is applied to the fields of public automatons, private automatons and multi-state automatons.

The parts which are not involved in the device are the same as or can be realized by adopting the prior art, and the device has the advantages of simple structure and convenient operation.

Drawings

FIG. 1 is a data structure diagram of an automaton according to the present invention;

FIG. 2 is a schematic diagram of the root generation of the automaton proposed by the present invention;

FIG. 3 is a schematic diagram of semantic root generation according to the present invention;

FIG. 4 is a diagram illustrating the generation of a status root according to the present invention;

FIG. 5 is a schematic diagram of different semantic unit index generation proposed by the present invention;

FIG. 6 is a table of different semantic unit index sets proposed by the present invention;

FIG. 7 is a set of different semantic units proposed by the present invention;

FIG. 8 is a set of digital features corresponding to different semantic units according to the present invention;

FIG. 9 is a schematic diagram of different data index generation proposed by the present invention;

FIG. 10 is a diagram of different data index sets proposed by the present invention;

FIG. 11 is a different data set proposed by the present invention;

FIG. 12 is a set of digital features corresponding to different data set proposed by the present invention;

FIG. 13 illustrates different semantic units and corresponding numerical features according to the present invention;

FIG. 14 illustrates various data and corresponding numerical characteristics set forth herein;

FIG. 15 is a schematic diagram of a derivative of the automaton proposed by the present invention;

FIG. 16 is a data structure diagram of a case script proposed by the present invention;

FIG. 17 is a schematic diagram of a root generation of a case script function proposed by the present invention;

fig. 18 is a schematic diagram of the derivation of the case script function proposed by the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

In a given network system, various transactions are driven to run by predicate logic including scripts, and the reliability, safety and the like of a program building process are basic conditions for stable running of the system.

Aiming at the common or basic requirements of the services, designing a function f of a computer program comprises the following steps: the series of instruction units X and the series of associated data D, all the series of instructions and data are digitally signed, all the sets of instructions { X } and signatures { Sign _ X }, all the sets of data { D } and signatures { Sign _ D }, all the semantic roots Root _ X generated by the set of instruction hash values { H _ X } quink tree, the state roots Root _ D generated by the set of data hash values { H _ D } quink tree, and the automaton roots Root _ f generated again by Root _ X and Root _ D in the quink tree algorithm, as shown in fig. 16.

All instruction sets { X } are specifically composed of semantic units X (1), X (2), …, X (n), as shown in fig. 7, different instructions are designed according to requirements to implement different executions; each instruction corresponds to a digital signature, which is a { Sign _ X } identifier, specifically Sign _ X (1), Sign _ X (2), …, Sign _ X (n), as shown in fig. 8, and the instructions correspond to the digital signatures one by one, as shown in fig. 13. The specific instruction can be a programmable code which is subject to security audit or long-term execution verification by professional organizations, a high-efficiency execution programmable code which is subject to long-term verification, a stable execution programmable code which is subject to long-term verification, or a code of both.

All data state sets { D } are specifically composed of different data blocks D (1), D (2), …, D (k), as shown in fig. 11, different data are stored into specific different data structures according to requirements, and each data forms a state; each data block corresponds to a digital signature, which is a { Sign _ D } identifier, specifically Sign _ D (1), Sign _ D (2), …, Sign _ D (k), as shown in fig. 12, and the digital signatures correspond to data states one to one, as shown in fig. 14. The data blocks D (1), D (2), …, D (k) are set by specific service scenarios, and each may be in a structured data form, an unstructured data form, or semi-structured data.

All instruction sets { X } are hashed, strip-by-strip, to obtain a corresponding set of hash values { H _ X }, as shown in FIGS. 5 and 6. On this basis, the semantic Root _ X of the merk tree of the instruction set is found with all hash values as leaves, as shown in fig. 3.

All sets of data states { D } are hashed piece-by-piece to obtain a corresponding set of hash values { H _ D }, as shown in FIGS. 9 and 10. In addition, the state Root _ D of the merk tree of the data state obtained with all hash values as leaves is shown in fig. 4.

The Root automaton Root _ f is obtained with the Root _ X and Root _ D values as the merk leaves, as shown in fig. 17. The automaton Root _ f of the function on this basis can exist as an instruction index of other functions.

Based on the foregoing, a schematic frame diagram of a script function f ' derived from a script function f is shown at 18, where the functions f, f ' may be of the same type or different types, and the function f ' is derived from the function f. All instructions of the function f are acquired through Root _ X, and data states corresponding to execution semantics of all the functions f are acquired through Root _ D. After the authorized statement digital signature { Sign _ X _ } and the data digital signature { Sign _ D _ } are used, the unit data signature { Sign _ X } and the data digital signature { Sign _ D _ } corresponding to the statement are compared so as to obtain the use right of the corresponding statement and data through comparison, and the statement is used for carrying out the operation of logic requirement on the corresponding data. Thereby obtaining the requirement of the function f' for the function f.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (8)

1. A semantic solidification and derivation method based on common predicate logic comprises the following steps:

1) selecting or designing a plurality of semantic units X for program development for the automaton A to form a semantic unit set { X }; wherein, the semantic unit set { X } comprises semantic units X (1), X (2), …, X (N), and X (N) is the Nth semantic unit; different semantic units are designed according to different requirements to realize different logic functions; the data state set { D } corresponding to the semantic unit set { X } includes data states D (1), D (2), …, D (K); d (K) is a data state corresponding to the Kth data processed by the semantic unit X;

2) digital feature acquisition is carried out on each semantic unit in the semantic unit set { X }, the index value of each semantic unit is calculated, and a semantic unit index set { H _ X } and a digital feature value set { Sign _ X } are obtained; the digital feature set { Sign _ X } comprises Sign _ X (1), Sign _ X (2), … and Sign _ X (N), the semantic unit index set { H _ X } comprises H _ X (1), H _ X (2), … and H _ X (N), the digital feature corresponding to the semantic unit X (N) is Sign _ X (N), and the index value corresponding to the semantic unit X (N) is H _ X (N); digital feature acquisition is carried out on each data state in the data state set { D } and an index value of each data state is calculated, so that a data state index set { H _ D } and a digital feature set { Sign _ D } are obtained; the data state index set { H _ D } comprises data state index values H _ D (1), H _ D (2), … and H _ D (K), the signature set { Sign _ D } comprises Sign _ D (1), Sign _ D (2), … and Sign _ D (K), H _ D (K) is the index value of the data state D (K), and Sign _ D (K) is the digital characteristic of the data state D (K);

3) obtaining a semantic Root _ X through a calculation mode by taking the semantic unit index set { H _ X } as input, and obtaining a state Root _ D through the calculation mode by taking the data state index set { H _ D } as input; obtaining a Root _ A of the automaton A through a calculation mode by taking the semantic Root _ X and the state Root _ D value as input;

4) the automaton A' starts the automaton A through the automaton Root _ A, all semantic units of the automaton A are obtained through the semantic Root _ X, and a data state corresponding to the execution semantics of the automaton A is obtained through the state Root _ D; and then, driving the corresponding semantic unit by using the digital features corresponding to the semantic unit, driving the corresponding data by using the digital features corresponding to the data state, and processing the corresponding proposition logic by using predicate logic in the semantic unit, so that the automaton A' obtains the requirement on the automaton A.

2. The method of claim 1, wherein the digital feature is a digital signature.

3. The method of claim 1, wherein the type and kind of input parameters of the semantic unit X are set by specific service scenarios, and the input parameters are service parameters or constraints related to function applications.

4. The method of claim 1, wherein the semantic unit is programmable code that is subject to security audit or long term execution verification by a set professional organization, or is highly efficient to execute via long term verification, or is stably executable via long term verification, or both.

5. The method of claim 1, wherein the data state D is set by a specific service scenario, the data state is a data block or a data set, and the data structure of the data state is a structured data form, an unstructured data form, or a semi-structured data form.

6. The method of claim 1, wherein the computation pattern is a Merke tree or a dictionary tree for obtaining a data root of the input data.

7. The method of claim 1, wherein automata a is a program template, a function, a framework, or a computer software program.

8. The method of claim 1, wherein automata a' is the same type or a different type than automata a.

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