The completeness of the complex model is an important index to evaluate the pros and cons of the complex model. It is of great significance to study the objective evaluation under different indicators and construct an indicator system to ensure the integrity of the complex. The construction of complex system indicators requires instructors who can fully describe the functions of complex models. This study designs a complex index
Keywords
 modelling method
 index system
 complex model
 completeness index
MSC 2010
 76U05
Research results related to completeness in the field of mathematics and physics. Under the condition of locally convex space, the completeness theorem of open mapping can be expressed as when the domain space
In a knowledge expression system [3], Information System
In the completeness calculation method of the modelling field, the researcher uses the bottom event weight analysis method to complete the completeness calculation of the emergency plan based on the Bayesian Network [5]. By calculating the weight of each basic event in the standard Bayesian network, that is, the degree of influence of the event on the top event, the proof of all nodes is completed, thereby completing the complete calculation [6]. For nondiscrete problems, it is possible to expand multiple levels with explanatory relations, analyse and make decisions at the target level according to the pros and cons of decision indicators and convert difficulttoassign comparison relations into mathematical methods for calculations to complete system decisionmaking—completeness calculation.
In this study, the expression of the target problem is set, and it is verified that the parameter set of the target problem can construct the locally convex space
There is no research on the general expression method of modelling completeness in the simulation field. Usually, based on specific tasks and typical scenarios, the completion of the target task, the fidelity of the simulation system and the similarity of the results between different systems are used as the basis for judging the completeness of the system. Researchers have proposed a series of specific completeness methods. For example, for the damage effect simulation degree index, there are studies on the electromagnetic echo change after consistent deduction so as to find the weak point of the equipment model entropy. This indirect evaluation method is an effective system simulation degree evaluation technology [7]. At the same time, some researchers have proposed a method of spectrum comparison to verify the simulation results. Aiming at the comparison of the similarity of multiple sets of similar data, the researcher proposed a direct comparison method to evaluate the similarity of the two sets of data. By comparing the similarity of energy parameters, the amplitude, The weight of the, and the weight of the frequency spectrum, so as to complete the comparison process of the similarity between the simulated data and the measured data.
In addition, researchers extract different types of environmental information through the feature selection test method and use the numerical change method to amplify the feature differences so as to evaluate the indicators that are not obviously quantified. The specific process of this method is shown in Figure 1. The verification results of various levels of differences can be calculated. The specific evaluation method is to collect the calculated data and measured data of the model and use the difference calculation method to separate the elements such as amplitude and frequency. The separation values are compared in turn, and the global difference estimate is finally obtained. The similarity grading template is used to complete the similarity grading evaluation process.
In the comparison between the real environment and the simulation system, the US Air Force Research Office has been conducting research. The organisation layer proposes a uniform matching coefficient to measure the similarity of the measured and predicted images [8]. The National University of Defense Technology has also used this method to compare the simulated and measured SAR images of a military unit and set a specific distance for the uniformity matching index. The advantage of this method is to use the uniformity matching sparse index to calculate the quality description and credibility description of the single point of the image. At the same time, the index is versatile and can be compared with the results of expert visual evaluation.
These methods are relatively complete in terms of indicators and can support the verification of the completeness of modelling under specific businesses. Facing the more general intelligent blue square modelling, a complete distance method and error characterisation form are constructed, as much as possible to ensure the completeness of the intelligent blue model in the modelling process.
The introduction section explores the completeness of the proof process. At the same time, in the complex model principle, the basic principle of the complex model target problem is explained in symbolic form. is the general form of the target problem,
From the perspective of domain theory,
Therefore, when
The complexity of the game problem, i.e., the type of problem, is called a complex blue square problem, also known as a complex domain problem. When the complex model problem
Suppose there is a correct solution
It can be obtained that is the complete distance of the input function
The complete distance
Taking the actual problem of crowd movement as an example, measuring crowd pressure is the main task of the social emergency system. Crowd pressure is determined by crowd flow rate. It uses crowd movement presentation media as input for multisource information, physical modelling of roads and road use. The ratio of the width to the number of recognised people is used as the basic characteristic of the crowd flow rate so as to determine the current road crowd pressure characteristics and possible emergency challenges, as shown in formula (1).
However, the real crowd is composed of participants with height and weight, and they are affected by personal movement characteristics during the actual exercise. This is quite different from the motion model that is simply abstracted as a mass point during the simulation environment and simulation object based on the mass point. Once the logic model is formed, it is difficult for the system to improve its fidelity. This is a selfconstraint, which is determined by the selfclosed loop characteristics of the information source, simulation environment and simulation object as shown in formula (2).
This process of abstracting from the prototype system of the real environment to the typical simulation system based on the mass point has produced cumulative errors, although some researchers try to use complete context logic, rich constraint functions and more detailed simulation object structures. There is an improvement, but this error still exists, that is,
Since
The algorithm sets a transfer function
For E_{at}, the physical meaning in the real world is clear. For the same problem, the results are obtained by observing the real world and the simulation model, but the physical meaning is not limited to this. Take the observation orbit data and system as an example to calculate
Obtain the trajectory of the output elements in the
Define
If the effects of
The ratio between {
Ideally, if there is no error in the simulation deduction of the output element characterisation state, that is, the effects of
The simulation model is constructed and compared with the actual performance results, the presentation process of the output elements is simulated, and the presentation process of the target problem is recorded at the same time.
In this research, a simulation system and peripheral components capable of observing environmental changes are constructed. It can model and store the physical characteristics, position and trajectory parameters of the scene environment. The control simulation of the system can obtain the physical trajectory and output simulation data.
The simulation model relies on the characteristics of the elements established by the simulation pipeline to simulate the characterisation state of the elements. For example, in the visualisation system, such as colour, position, volume characterisation, the continuous performance of the element characterisation at different times is selected. The characterisation state and vector characteristics in the trajectory running time interval.
According to the interpolation process at the continuous time, the characterisation process of the element is constructed, in which the threedimensional space coordinates and vector characteristics of the entity element in the complex environment at a specific time
At this time, the set of threedimensional space coordinates and vector features of all entity elements i={1, 2, 3,..., n} is shown in formula (7).
In the running time interval o t = {
In the data acquisition stage, by comparing the simulation data with the real source orbit data obtained by processing the imaging presentation media captured by the presentation media monitoring, it is determined that the data modelling corresponding to the simulation is required.
In the data comparison stage, the twodimensional position coordinates of a single entity element
In the orbit time period
In the calculation stage, the position of the simulated entity group is compared with the position of the characterising entity group, and the formalisation of
The actual problem trajectory tracking and simulation process involve a huge multisource data fusion process, which has the characteristics of huge calculation data volume, relatively fuzzy evaluation results and unclear end points of the problem. Continuous improvement of evaluation methods in the simulation process has the characteristics of fuzzy calculation boundaries. It is a typical complex practical problem, which is used as an experimental environment. Compared with the image comparison task, the Go game task is relatively simple in the data source, but the evaluation of the comparison result of the midgame is very vague, and its game behaviour is sequential and continuous, and the learning data cited is diverse, so in Go, the disc task has the characteristics of fuzzy computing boundaries and unclear endpoints. It is also a small atypical game problem. In this section, multiple sets of scenario experiments will be used to apply the complete distance calculation process. Including the incomplete verification of the difference between the distance
The actual problem trajectory tracking and simulation process is a typical complex actual problem. Use this as an experimental environment to verify the effect of the completeness distance on actual problems. The video data of the real orbit system and the simulation vector data of the virtual simulation system are input into the complex model, and the completeness distance
This experiment studies the completeness of the target problem of the complex model using domain theory to prove several conditions for the completeness of the complex model, one of which is the expression of the distance
This experiment constructs the first set of experimental Table 1 to test the performance of the blue square model
Model
Testing purposes  The performance of the blue square model 
Test function 

Test environment  Random Go, Windows7, VS2010 
Contrast variable  
Data set  A complete game record of 2500 nineway Go in the StoneBase game record 
Data instance 

The experiment uses the Random Go nineway Go system to compare the distance
This experiment constructs the second set of experimental Tables 2 Test track simulation and observation system distance
Orbit simulation and observation system observation distance
Testing purposes  Orbit simulation and observation system observation distance 
Test function 

Test environment  Random Go, Windows7, VS2010 
Contrast variable  Frame 100, 200, 250, 275 
Data set  A track simulation system 
Data instance 

This experiment constructs the third set of experimental Table 3 using the complex model cognitive network to generate video and real video for observation distance
Experimental information table of the relationship between generated knowledge and generated video
Testing purposes  Use the complex model cognitive network to generate video and real video for observation distance 
Test function 

Test environment  TensorFlow, Cuda, Windows7, VS2010 
Contrast variable  Frame 100, 200, 250, 275 
Data set  600 video framed images of the Cityscape dataset 
Data instance 

The experimental result in Table 4 shows that the placement order of the model is inconsistent with the placement order of StoneBase. This inconsistency is one of the characteristics of the difference between the learning network and the natural person. However, as the number of moves increases, the completeness of the distance keeps
Table of experimental results of completeness distance index for
0.72  0.28  0.23  0.18  0.13  
0.83  0.18  0.14  0.12  0.06 
In addition, it is found that the algorithm based on pattern matching is more random when there are a few moves, that is, the distance
Experimental results in Tables 5 and 6 are the synchronisation position gap between a certain track system simulation data and video acquisition data. Through the data, it can be found that the completeness distance difference of different entities is different. The value X_{E} for each entity that does not exceed 1% is relatively stable, which shows that the system simulation process has high completeness. From another perspective, the distance X_{E} may be caused by the error of the lens shake, the optical effect of the collecting lens and the signal noise. This shows that any complete system is a high degree of coordination between software and hardware, and neither is indispensable.
Track system entity simulation twodimensional position coordinate experiment result table
1  (377, 241)  (280, 283)  (296, 317)  (329, 329) 
2  (386, 241)  (288, 284)  (313, 317)  (346, 323) 
3  (401, 239)  (297, 286)  (322, 322)  (369, 326) 
Experimental result table of physical pixel distance of track system
1  400, 696  505, 801  510, 795  490, 768 
2  392, 688  498, 793  496, 779  473, 750 
3  379, 673  492, 785  491, 772  457, 729 
The completeness distance index
1  0.1562  0.1587  0.1546  0.1565 
2  0.1495  0.1492  0.1457  0.1684 
3  0.1874  0.1854  0.1871  0.1824 
Table of experimental results 7. Figure of experimental results 5.1 The observation distance
From the overall effect of distance
The experiment shows that as time increases, the distance
Two sets of experiments show that
This paper studies the completeness problems faced by complex models and defines the completeness index and its physical meaning through the study of the definition of the target problem. At the same time, the study derives the calculation process for each indicator. These indicators reflect the intelligence, complexity and completeness characteristics of the complex model from different sides. At the same time, the effectiveness of these indicators is tested by using Go information and image information data. Among them, the complex model clarifies its fidelity through
Model P1 and model P2 completeness distance index EX experimental information table
Testing purposes  The performance of the blue square model 
Test function 

Test environment  Random Go, Windows7, VS2010 
Contrast variable  
Data set  A complete game record of 2500 nineway Go in the StoneBase game record 
Data instance 

Experimental information table of the relationship between generated knowledge and generated video
Testing purposes  Use the complex model cognitive network to generate video and real video for observation distance 
Test function 

Test environment  TensorFlow, Cuda, Windows7, VS2010 
Contrast variable  Frame 100, 200, 250, 275 
Data set  600 video framed images of the Cityscape dataset 
Data instance 

Orbit simulation and observation system observation distance EX experimental information table
Testing purposes  Orbit simulation and observation system observation distance 
Test function 

Test environment  Random Go, Windows7, VS2010 
Contrast variable  Frame 100, 200, 250, 275 
Data set  A track simulation system 
Data instance 

Experimental result table of physical pixel distance of track system
1  400, 696  505, 801  510, 795  490, 768 
2  392, 688  498, 793  496, 779  473, 750 
3  379, 673  492, 785  491, 772  457, 729 
The completeness distance index EX experiment result table of track system simulation and acquisition source
1  0.1562  0.1587  0.1546  0.1565 
2  0.1495  0.1492  0.1457  0.1684 
3  0.1874  0.1854  0.1871  0.1824 
Table of experimental results of completeness distance index for EX model and P1 model
0.72  0.28  0.23  0.18  0.13  
0.83  0.18  0.14  0.12  0.06 
Track system entity simulation twodimensional position coordinate experiment result table
1  (377, 241)  (280, 283)  (296, 317)  (329, 329) 
2  (386, 241)  (288, 284)  (313, 317)  (346, 323) 
3  (401, 239)  (297, 286)  (322, 322)  (369, 326) 
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