CN113791761B - Digital aircraft source code writing method based on complexity granularity - Google Patents
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Abstract
The invention discloses a digital aircraft source code writing method based on complexity granularity, which comprises the following steps: constructing a point flow field frame by using three anchor points of a point model, a flow model and a field model; defining digital aircrafts with respectively defined point, stream and field model granularity; dividing the digital aircraft model into different granularities according to complexity; dividing the complexity of the source code writing of the digital aircraft into different steps according to the growth rule of students; the granularity of the digital aircraft model is corresponding to the step of the complexity of the source code writing, and the digital aircraft model is used as a quantitative thinking granularity frame; embedding the quantitative thinking granularity frame into the point flow field granularity frame to form a granularity frame capable of freely crossing qualitative and quantitative thinking; the framework is applied by special artificial intelligence to write digital aircraft source code. The invention improves the efficiency of the intelligent structure of the digital aircraft, reduces the cost of the intelligent structure of the digital aircraft and reduces the development cost of the complex aircraft.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a digital aircraft source code writing method based on complexity granularity.
Background
The aircraft source code writing is a complex process, the aircraft and the environment where the aircraft is located form a complex system, the corresponding digital twin system is a specific system with multiple dynamic, multiple spatial dimensions and multiple physical fields coupling, and the aircraft source code writing is simply carried out by using a decomposition method, so that the workload is huge, and even the computing capacity of a computer can be exceeded. Some problems are that the solution with fine granularity is not better than the solution with coarse granularity, and even if the solution is finer, the more accurate solution cannot be obtained, and only the calculation force of the computer is wasted. And the intelligent degree of the current computer needs to be improved, and the manpower cost for decision making in the process of solving the complex problem is too high.
However, the current source code writing method mainly relates to classical methods of decomposition, reasoning and other reduction theory, and lacks macroscopic thought of the ensemble theory.
Therefore, how to provide a digital aircraft source code writing method with high computing power utilization rate based on a framework with uniform granularity of qualitative thinking and quantitative thinking is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a digital aircraft source code writing method based on a framework with uniform granularity of qualitative and quantitative thinking, which is used for writing the digital aircraft source code in the development process of a complex aircraft and constructing a digital twin system, thereby improving the intelligent construction efficiency of the digital aircraft, reducing the intelligent construction cost of the digital aircraft and reducing the development cost of the complex aircraft.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a digital aircraft source code writing method based on complexity granularity, comprising the steps of:
s1, constructing a point flow field frame by using three anchor points of a point model, a flow model and a field model as a qualitative thinking granularity frame written by a digital aircraft source code, and carrying out qualitative evaluation on any given digital aircraft;
s2, defining the definition of the digital aircrafts with the point model granularity, the definition of the digital aircrafts with the stream model granularity and the definition of the digital aircrafts with the field model granularity;
s3, dividing the digital aircraft model into different granularities according to the complexity;
s4, dividing the complexity of writing the source codes of the digital aircraft into different steps according to the growth rule of students;
s5, corresponding the granularity of the complexity of the digital aircraft model to the step of the writing complexity of the source code, and taking the granularity of the complexity of the digital aircraft model as a quantitative thinking granularity frame of the writing of the source code of the digital aircraft;
s5, embedding the quantitative thought granularity frame into the point flow field granularity frame to form a qualitative thought and quantitative thought unified granularity frame capable of freely crossing between the qualitative thought granularity frame and the quantitative thought granularity frame, namely a granularity frame capable of freely crossing qualitative and quantitative thought;
s6, the special artificial intelligence is used for writing the source codes of the digital aircraft by applying the granularity framework capable of freely crossing qualitative and quantitative thinking.
Preferably, the three anchor points of the point model, the flow model and the field model respectively correspond to digital satellites with different granularities, the dynamic problem is divided from a numerical angle, the point flow field frame is formed, and the model complexity is definitely divided.
Preferably, defining the point model as an assumed ideal satellite platform, idealizing the satellite platform into a particle, wherein the point model defines a model for establishing a load according to a real satellite component;
the flow model assumes that all subsystems and all components in the digital satellite model are not interfered with each other, satellite capacity is determined by loads, all subsystem components and cooperative relations thereof, parameters related to functional flows are not influenced by other surrounding components during operation, and the problem of satellite system level is simplified into the problem of capacity maximization under the given constraint of all subsystems;
the field model is a satellite capacity model for defining integrated orbit, attitude and micro-vibration multi-frequency spectrum dynamics, celestial body, whole satellite and part local fine-view multi-space scale, electromechanical, thermal, electromagnetic and radiation multi-physical field coupling by taking the multi-dynamic multi-scale multi-probability multi-physical field coupling characteristics of the digital twin system.
Preferably, the specific content of S3 is: according to the complexity of the digital aircraft, the complexity of the digital aircraft source code writing is determined, and the multi-granularity description of the decision complexity in the digital aircraft source code writing process is extracted.
Preferably, the student growth rule in S4 is a process of continuously deepening the learning and mastering knowledge and skill of the student step by step according to granularity, and the student growth rule is obtained by counting the learning progress of the student.
Preferably, the multi-granularity description of the decision complexity corresponds to the step of the source code writing complexity, three anchor points are embedded into the point flow field frame according to the point flow field, the granularity of the decision complexity in the digital aircraft source code writing process is smoothly transited according to the growth rule of the students, the point flow field frame is further refined, and a qualitative thinking and quantitative thinking uniform granularity frame capable of freely crossing between the qualitative thinking granularity frame and the quantitative thinking granularity frame is formed for the special artificial intelligent writing source code.
Preferably, the specific content of S6 includes:
s61, the special artificial intelligence acquires a digital aircraft model to be written, coarse-granularity adaptation point flow field frames of the digital aircraft model are defined according to three granularity models of the point model, the flow model and the field model, various granularity model schemes are established, and whether the schemes can solve the problems is judged;
if the point model and the stream model cannot be solved, the field model can solve the problem, and S62 is executed;
if the point model cannot solve the problem, the flow model and the field model can solve the problem, and S63 is executed;
if the point model, the flow model and the field model can be solved, directly solving by using the point model;
s62, at the moment, granularity of a model required for solving the problem is located between a stream and a field, a principle of space fake protection of a grain calculator is applied, granularity is subdivided from the stream, refinement is continuously executed until uncertainty is smaller than a preset threshold, and simulation is carried out according to corresponding granularity;
s63, the granularity of the model required for solving the problem is located between the point and the stream, the principle of space fake protection of the grain calculator is applied, the granularity is subdivided from the point, the refinement is continuously executed until the uncertainty is smaller than a preset threshold value, and finally the digital aircraft source code meeting the requirements is obtained.
Compared with the prior art, the invention discloses a digital aircraft source code writing method based on complexity granularity, in the invention, the uncertainty in the digital aircraft source code writing is fully considered by a digital aircraft point flow field multi-granularity qualitative and quantitative thinking unifying frame, a general frame is provided for writing the digital aircraft source code, the special artificial intelligence can reasonably simulate the abstract thinking capability of human beings to a certain extent, a method is provided for the thinking process of how the special artificial intelligence learns to process complex professional problems, the method is used for writing the digital aircraft source code and constructing a digital twin system in the development process of the complex aircraft, the intelligent construction efficiency of the digital aircraft is improved, the intelligent construction cost of the digital aircraft is reduced, and the development cost of the complex aircraft is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a digital aircraft source code writing method based on a qualitative and quantitative thought unification framework provided by the invention;
FIG. 2 is a schematic diagram of three anchor points of a digital aircraft in a digital aircraft source code writing method based on a qualitative and quantitative thought unification framework;
FIG. 3 is a schematic diagram of a flow field frame for embedding model complexity into points according to granularity in the method for writing digital aircraft source codes based on a qualitative and quantitative thought unification frame provided by the invention;
FIG. 4 is a schematic diagram showing the steps of dividing the complexity of a model into different steps according to the growth rule of students in the method for writing the source code of the digital aircraft based on the unified framework of qualitative and quantitative thinking;
fig. 5 is a schematic flow chart of a digital aircraft source code writing method S7 based on a qualitative and quantitative thought unification framework provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a digital aircraft source code writing method based on complexity granularity, which is shown in figures 1-4 and comprises the following steps: the method comprises the following steps:
s1, constructing a point flow field frame by using three anchor points of a point model, a flow model and a field model as a qualitative thinking granularity frame written by a digital aircraft source code, and carrying out qualitative evaluation on any given digital aircraft;
s2, defining the definition of the digital aircrafts with the point model granularity, the definition of the digital aircrafts with the stream model granularity and the definition of the digital aircrafts with the field model granularity;
s3, dividing the digital aircraft model into different granularities according to the complexity;
s4, dividing the complexity of writing the source codes of the digital aircraft into different steps according to the growth rule of students;
s5, corresponding the granularity of the complexity of the digital aircraft model to the step of the writing complexity of the source code, and taking the granularity of the complexity of the digital aircraft model as a quantitative thinking granularity frame of the writing of the source code of the digital aircraft;
s5, embedding the quantitative thought granularity frame into the point flow field granularity frame to form a qualitative thought and quantitative thought unified granularity frame capable of freely crossing between the qualitative thought granularity frame and the quantitative thought granularity frame, namely a granularity frame capable of freely crossing qualitative and quantitative thought;
s6, the special artificial intelligence application can freely span a granularity framework of qualitative and quantitative thinking to write the source codes of the digital aircraft.
In order to further implement the technical scheme, three anchor points of the point model, the flow model and the field model respectively correspond to digital satellites with different granularities, the dynamics problem is divided from the angle of numerical values, a point flow field frame is formed, and the model complexity is definitely divided.
It should be noted that:
the simulation with different granularities brings uncertainty with different sizes, the finer the granularity is, the smaller the uncertainty is, and when the granularity is fine to a certain degree, the uncertainty is small enough to solve the problem, and the cost is not required to be further refined. Granularity can be divided infinitely, which is not possible with computer-specific artificial intelligence. Three decision schemes of acceptance, rejection and delay decision are provided by the three decision theory, and three anchor points are provided according to the three decision schemes, so that granularity is reasonably divided, and the computer can execute the three decision schemes.
In order to further implement the above technical solution, the point model: the method is a model with the coarsest granularity, is an ideal point model with the simplest definition according to a satellite platform, and under the granularity of the point model, the capacity of a satellite only depends on load and orbit constraint, satellite fuel is limited, and the orbit change is a costly matter and cannot be easily implemented. The loading capacity determines the satellite's capacity, assuming that the satellite subsystems are all ideal, and can support perfectly loaded operations, after considering orbit constraints.
Flow model: the satellite subsystem capacity is determined by the cooperative work relationship of each component and each component, and the cooperative work is required to accord with a certain principle, and the working principle determines the structure of the system. Typically, such structures have an upstream-downstream relationship like flowing water. Such as a gesture control subsystem, signals are transmitted from the sensors to the controller and then to the actuators. The power supply system is used for generating electricity by the solar sailboard and supplying the electricity to the storage battery and each single unit component. Each single machine is defined as an ideal component, and the satellite model which does not affect other surrounding components except parameters related to the functional flow during operation is a stream model of a digital satellite. The flow model is closed loop in subsystem function, so that the accuracy of the flow model can be verified in ground inspection verification, such as desktop joint debugging, a kinematic dynamics turntable test, a thermal vacuum high-low temperature environment test and the like. The flow model is not essentially different from the current general subsystem design model, and all the flow model is assumed that besides the design constraint given by the overall system, the mutual coupling between the subsystems is not existed any more, so that the problem of the satellite system level is simplified to the problem of maximizing the capacity of the subsystems under the constraint given by the subsystems.
Field model: by taking the characteristics of multiple dynamic, multiple scale, multiple probability and multiple physical field coupling of a digital twin system, the satellite model integrating local microscopic multiple spatial scales of celestial bodies, whole satellites and parts, with orbit, attitude, micro-vibration multi-frequency spectrum dynamic and electromechanical thermo-optic-magnetic radiation multiple physical field coupling is defined as a field model of a digital satellite, and the interference degree of multiple physical fields is respectively defined on the whole satellites and the parts. The field model is a digital satellite model with the minimum granularity at present and is consistent with the modeling requirements of a digital twin and parallel system aiming at individual satellites and different satellites, wherein the characteristics of dynamic frequency, spatial scale and physical field coupling are different. The field model of the personalized satellite is at least compared with the remote measurement data of the satellite in-orbit operation, so that the uncertainty of the model can be quantified, repeated model and parameter correction can be carried out according to the difference, and the uncertainty of the model is reduced.
In order to further implement the above technical solution, the specific content of S3 is: according to the complexity of the digital aircraft, the complexity of the digital aircraft source code writing is determined, and the multi-granularity description of the decision complexity in the digital aircraft source code writing process is extracted.
It should be noted that:
for writing a complex decision of a digital aircraft source code, the human is at the cost of evaluating the complexity of the problem and self logic reasoning, simplifying the problem and avoiding endless thinking. From the perspective of grain computation, the abstract granularity of the concepts is adjusted, and the coupling relation between the concepts is simplified by amplifying the uncertainty of the concepts. In the human thinking process, the principle of space false protection of grain computing business is applied, and one problem is that no solution exists under the granularity defined by a certain business space, and no solution exists under the granularity finer than the certain business space, so that a scheme which is not feasible after simplification is definitely not feasible, and the granularity of each feasible scheme is refined after filtering until the uncertainty is small enough.
In order to further implement the technical scheme, the student growth rule in S4 is a process that the learning and mastering of knowledge and skills of students are deepened step by step according to granularity, and the learning progress of the students is counted to obtain the student growth rule.
It should be noted that:
different students are individuals who fluctuate over time, while the educational process essentially follows certain criteria. A large number of students learn and master the growth process of digital satellite modeling, namely master the complexity of the source code writing of the digital aircraft, so that an average growth rule is obtained, the complexity of the source code writing is defined in a grading manner according to the growth rule of the students, the complexity of decision making is divided into infinite steps, and the complexity of each step is increased step by step.
In order to further implement the technical scheme, the multi-granularity description of the decision complexity corresponds to the steps of the source code writing complexity, three anchor points are embedded into the point flow field frame according to the point flow field, the granularity of the decision complexity in the digital aircraft source code writing process is smoothly transited according to the growth rule of students, the point flow field frame is further refined, and a qualitative thinking and quantitative thinking uniform granularity frame capable of freely crossing between the qualitative thinking granularity frame and the quantitative thinking granularity frame is formed for the special artificial intelligent writing source code.
It should be noted that:
if only three granularities of points, flows and fields are used for switching the intelligent construction system of the digital aircraft, the intelligent construction system of the digital aircraft still has limited capability of solving the complex problem, the intelligent degree is not high enough, the calculation force still has high requirements, and even a large amount of calculation force is wasted.
In the human thinking process, the principle of space false protection of grain computing business is applied, and one problem is that no solution exists under the granularity defined by a certain business space, and no solution exists under the granularity finer than the certain business space, so that a scheme which is not feasible after simplification is definitely infeasible, and the granularity of each feasible scheme is refined after filtering until the uncertainty is small enough.
The granularity of the digital aircraft corresponds to the step of the writing complexity of the source code, the flow field reference of the comparison points is respectively embedded, the complexity of the digital aircraft is divided into limited changes, and a qualitative thinking and quantitative thinking unified granularity frame which can freely span between a qualitative thinking granularity frame and a quantitative thinking granularity frame is formed for special artificial intelligence to understand.
In order to further implement the above technical solution, as shown in fig. 5, specific contents of S6 include:
s61, acquiring a digital aircraft model to be written by special artificial intelligence, adapting the coarse granularity of the digital aircraft model to a point flow field frame, defining according to three granularity models of the point model, the flow model and the field model, establishing a scheme of each granularity model, and judging whether each scheme can solve the problem;
if the point model and the stream model cannot be solved, the field model can solve the problem, and S62 is executed;
if the point model cannot solve the problem, both the stream model and the field model can solve the problem, and S63 is executed;
if the point model, the stream model and the field model can be solved, directly solving the problems by using the point model;
s62, at the moment, granularity of a model required for solving the problem is located between a stream and a field, a principle of space fake protection of a grain calculator is applied, granularity is subdivided from the stream, refinement is continuously executed until uncertainty is smaller than a preset threshold, and simulation is carried out according to corresponding granularity;
s63, the granularity of the model required for solving the problem is located between the point and the stream, the principle of space fake protection of the grain calculator is applied, the granularity is subdivided from the point, the refinement is continuously executed until the uncertainty is smaller than a preset threshold value, and finally the digital aircraft source code meeting the requirements is obtained.
The invention firstly provides three anchor points of the point flow field as the standard of a general framework. The concept of granularity is introduced, the complexity of the digital aircraft is divided into different granularities, and from the education, the complexity of the source code writing of the digital aircraft is divided into different steps according to the growth rule of students. The granularity of the digital aircraft corresponds to the step of the source code writing complexity, and the digital aircraft is embedded into a frame taking a point flow field as a reference to form a qualitative thinking and quantitative thinking uniform granularity frame which can freely span between a qualitative thinking granularity frame and a quantitative thinking granularity frame, so that the digital aircraft is used for special artificial intelligence.
The multi-granularity qualitative and quantitative thinking unifying framework of the digital aircraft point flow field fully considers the uncertainty in the writing of the digital aircraft source code, provides a general framework for the writing of the digital aircraft source code, enables the special artificial intelligence to reasonably simulate the abstract thinking capability of human beings to a certain extent, provides a method for how the special artificial intelligence learns the thinking process of human beings to deal with complex professional problems, is used for the writing of the digital aircraft source code in the development process of the complex aircraft, and the structure of a digital twin system, improves the intelligent structure efficiency of the digital aircraft, and reduces the intelligent structure cost of the digital aircraft.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. A digital aircraft source code writing method based on complexity granularity, comprising the steps of:
s1, constructing a point flow field frame by using three anchor points of a point model, a flow model and a field model as a qualitative thinking granularity frame written by a digital aircraft source code, and carrying out qualitative evaluation on any given digital aircraft;
s2, defining the definition of the digital aircrafts with the point model granularity, the definition of the digital aircrafts with the stream model granularity and the definition of the digital aircrafts with the field model granularity;
s3, dividing the digital aircraft model into different granularities according to the complexity;
s4, dividing the complexity of writing the source codes of the digital aircraft into different steps according to the growth rule of students;
s5, corresponding the granularity of the complexity of the digital aircraft model to the step of the writing complexity of the source code, and taking the granularity of the complexity of the digital aircraft model as a quantitative thinking granularity frame of the writing of the source code of the digital aircraft;
embedding the quantitative thought granularity frame into the point flow field granularity frame to form a qualitative thought and quantitative thought unified granularity frame capable of freely crossing between the qualitative thought granularity frame and the quantitative thought granularity frame, namely a granularity frame capable of freely crossing qualitative and quantitative thought;
s6, the special artificial intelligence is used for writing the source codes of the digital aircraft by applying the granularity framework capable of freely crossing qualitative and quantitative thinking;
the point model, the flow model and the field model are respectively corresponding to digital satellites with different granularities, the dynamics problem is divided from the angle of numerical value, the point flow field frame is formed, and the model complexity is definitely divided;
defining the point model as an assumed ideal satellite platform, idealizing the satellite platform as a particle, and defining a model for establishing a load according to a real satellite component in the point model;
the flow model assumes that all subsystems and all components in the digital satellite model are not interfered with each other, satellite capacity is determined by loads, all subsystem components and cooperative relations thereof, parameters related to functional flows are not influenced by other surrounding components during operation, and the problem of satellite system level is simplified into the problem of capacity maximization under the given constraint of all subsystems;
the field model is a satellite capacity model for defining integrated orbit, attitude and micro-vibration multi-frequency spectrum dynamics, local microscale multi-space dimensions of celestial bodies, whole stars and parts and electromechanical thermal electromagnetic radiation multi-physical field coupling by taking the multi-dynamic multi-scale multi-probability multi-physical field coupling characteristics of the digital twin system;
the specific content of S3 is as follows: determining the complexity of digital aircraft source code writing according to the complexity of the digital aircraft, and extracting multi-granularity description of decision complexity in the digital aircraft source code writing process;
s4, the student growth rule is a process that the learning knowledge and skill of the student are deepened step by step according to granularity, and the learning progress of the student is counted to obtain the student growth rule;
the multi-granularity description of the decision complexity corresponds to the steps of the source code writing complexity, three anchor points according to the point flow field are embedded into the point flow field frame, the granularity of the decision complexity in the digital aircraft source code writing process is smoothly transited according to the growth rule of the students, the point flow field frame is further refined, and a qualitative thinking and quantitative thinking unified granularity frame which can freely span between the qualitative thinking granularity frame and the quantitative thinking granularity frame is formed and is used for special artificial intelligent writing source codes.
2. The digital aircraft source code writing method based on complexity granularity as claimed in claim 1, wherein the specific content of S6 comprises:
s61, the special artificial intelligence acquires a digital aircraft model to be written, coarse-granularity adaptation point flow field frames of the digital aircraft model are defined according to three granularity models of the point model, the flow model and the field model, various granularity model schemes are established, and whether the schemes can solve the problems is judged;
if the point model and the stream model cannot be solved, the field model can solve the problem, and S62 is executed;
if the point model cannot solve the problem, the flow model and the field model can solve the problem, and S63 is executed;
if the point model, the flow model and the field model can be solved, directly solving by using the point model;
s62, at the moment, granularity of a model required for solving the problem is located between a stream and a field, a principle of space fake protection of a grain calculator is applied, granularity is subdivided from the stream, refinement is continuously executed until uncertainty is smaller than a preset threshold, and simulation is carried out according to corresponding granularity;
s63, the granularity of the model required for solving the problem is located between the point and the stream, the principle of space fake protection of the grain calculator is applied, the granularity is subdivided from the point, the refinement is continuously executed until the uncertainty is smaller than a preset threshold value, and finally the digital aircraft source code meeting the requirements is obtained.
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