CN117971192B - Simulation model code generation method and device - Google Patents
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Abstract
The application discloses a simulation model code generation method and device, wherein the method comprises the following steps: obtaining simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information; code generation is carried out on the simulation model information to obtain initial simulation model code information; and checking the initial simulation model code information to obtain target simulation model code information. Therefore, the method is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for generating a simulation model code.
Background
The network experiment simulation model is constructed, and the network experiment simulation model about the contents of a network attack and defense experiment plot, network countermeasure resources, network attack and defense weapons/tools, network attack and defense behaviors and the like is required to be built based on the types and rules of different models. Considering the network experiment requirement, proper simulation model construction technology is needed to carry out project construction and code generation on simulation models with different characteristics and types. However, in the process of constructing the network experimental simulation model, the traditional mode of manually writing the network experimental simulation model code gradually reveals various defects, and the mode of manually changing the code is more and more difficult to adapt to the complexity of a modern network experimental system which is developed at a high speed, and the defects of low efficiency, high error rate and the like exist. Therefore, the simulation model code generation method and device are provided to solve the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improve the simulation model code generation efficiency and accuracy.
Disclosure of Invention
The invention aims to solve the technical problems of low efficiency, high error rate, high complexity of a network experiment system and the like of writing a network experiment simulation model code, so that the simulation model code generation efficiency and accuracy are improved.
In order to solve the technical problem, a first aspect of the embodiment of the present invention discloses a simulation model code generation method, which includes:
Obtaining simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information;
Code generation is carried out on the simulation model information to obtain initial simulation model code information;
and checking the initial simulation model code information to obtain target simulation model code information.
In a first aspect of the embodiment of the present invention, the generating the code for the simulation model information to obtain initial simulation model code information includes:
determining code model information based on the compiling information of the model to be simulated; the code model information comprises a plurality of template code information;
determining target simulation rule information based on the standard simulation rule information and the code model information; the target simulation rule information comprises a plurality of model simulation rule information;
and determining initial simulation model code information based on the compiling information of the model to be simulated, the code model information and the target simulation rule information.
In a first aspect of the embodiment of the present invention, the determining, based on the standard simulation rule information and the code model information, target simulation rule information includes:
Responding to the adjustment operation of the user on the model rule to obtain model generation rule information;
Performing feature extraction and matrix construction on the standard simulation rule information and the model generation rule information to obtain a rule feature matrix;
Performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information; the matrix characteristic parameter information comprises a target test value and a maximum characteristic root;
calculating the matrix characteristic parameter information by using a characteristic detection model to obtain a characteristic detection value;
Wherein, the feature detection model is:
Wherein A is the feature detection value; y is the matrix order of the rule feature matrix; x is the maximum feature root;
Calculating the target test value and the feature detection value by using a judgment criterion model to obtain a target judgment value;
wherein, the judgment criterion model is as follows:
Wherein Z is the target judgment value; b is the target test value;
Judging whether the target judgment value is smaller than a judgment threshold value or not to obtain a threshold value judgment result;
when the threshold judgment result is negative, triggering and executing the adjustment operation responding to the model rule by the user to obtain model generation rule information;
and when the threshold judgment result is yes, determining the model generation rule information as target simulation rule information.
In a first aspect of the embodiment of the present invention, the performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information includes:
carrying out maximum eigenvalue decomposition on the rule feature matrix to obtain the maximum eigenvalue;
acquiring detection test information; the detection test information comprises a plurality of detection test data pairs; each detection test data pair comprises a detection number and a test value;
Sequentially determining target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
Judging whether the detection number of the target detection test data pair is equal to the matrix order of the rule feature matrix or not, and obtaining a number judgment result;
when the number judgment result is negative, triggering and executing the detection numbers according to the detection test data pairs to sequentially determine target detection test data pairs from small to large;
and when the number judgment result is yes, determining the test value of the target detection test data pair as the target test value.
In a first aspect of the embodiment of the present invention, determining initial simulation model code information based on the to-be-simulated model compiling information, the code model information, and the target simulation rule information includes:
Screening template code information matched with the compiling information of the model to be simulated from the code model information as target code information for any piece of the model information to be compiled;
screening model simulation rule information matched with the compiling information of the model to be simulated from the target simulation rule information to obtain target model simulation rule information;
Judging whether the rule type corresponding to the target model simulation rule information is attribute revision or not to obtain a type judgment result;
When the type judgment result is yes, loading an attribute revision interface matched with the target simulation rule information;
responding to the selection operation of a user on the component elements in the object code information on the attribute revision interface, and obtaining the component element information to be revised; the component element information to be revised comprises a plurality of component elements to be revised;
Traversing the to-be-compiled model information for any component element to be repaired, and screening out all first effective model information matched with the component element to be repaired from the to-be-compiled model information;
Replacing and updating the element attribute information in the element of the component to be revised by the first effective model information;
Responding to the new operation processing of the user on the component elements to obtain new component element information;
responding to the data selection operation of the user on the model information to be compiled, and obtaining second effective model information;
Replacing and updating the element attribute information in the newly added component element information by the second effective model information;
compiling the updated element information of the to-be-modified assembly and the updated element information of the newly-added assembly to obtain initial simulation model code information;
When the type judgment result is negative, loading a component assembly interface matched with the target simulation rule information;
responding to the assembling and matching operation of the user on the target code information to obtain assembling model code information;
and carrying out parameter updating on the assembled model code information by utilizing the model information to be compiled to obtain the initial simulation model code information.
In a first aspect of the embodiment of the present invention, the verifying the initial simulation model code information to obtain target simulation model code information includes:
Carrying out validity check on the initial simulation model code information to obtain a validity check result;
when the legal inspection result is that the inspection is not qualified, triggering and executing the code generation of the simulation model information to obtain initial simulation model code information;
When the legal test result is qualified, loading a test program file to test and analyze the initial simulation model code information to obtain test data information;
comparing and analyzing the test data information and the standard simulation rule information to obtain different symbol information;
Displaying the different symbol information;
responding to the modification operation of the user on the dissimilar symbol information to obtain revised symbol information;
And replacing and updating the initial simulation model code information by using the revision symbol information to obtain target simulation model code information.
In a first aspect of the embodiment of the present invention, the performing a validity check on the initial simulation model code information to obtain a validity check result includes:
Judging whether the code label in the initial simulation model code information is complete or not to obtain a first check judgment result;
When the first inspection judging result is negative, determining that the legal inspection result is unqualified;
When the first test judging result is yes, judging whether code variables in the initial simulation code information accord with compiling rule conditions or not, and obtaining a second test judging result; the compiling rule condition comprises 4 code rule requirement information;
When the second test judging result is negative, determining that the legal test result is the test failure;
when the second test judging result is yes, judging whether the initial simulation model code information accords with a three-dimensional condition or not, and obtaining a third test judging result; the three-property condition comprises 3 pieces of performance requirement information;
when the third test judging result is negative, determining that the legal test result is the test failure;
And when the third test judging result is yes, determining that the legal test result is qualified.
The second aspect of the embodiment of the invention discloses a simulation model code generating device, which comprises:
the acquisition module is used for acquiring simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information;
the generation module is used for generating codes of the simulation model information to obtain initial simulation model code information;
and the processing module is used for checking the initial simulation model code information to obtain target simulation model code information.
The third aspect of the present invention discloses another simulation model code generating apparatus, which includes:
A memory storing executable program code;
a processor coupled to the memory;
The processor calls the executable program code stored in the memory to execute part or all of the steps in the simulation model code generation method disclosed in the first aspect of the embodiment of the present invention.
A fourth aspect of the present invention discloses a computer readable storage medium storing computer instructions for executing some or all of the steps in the simulation model code generation method disclosed in the first aspect of the embodiment of the present invention when the computer instructions are called.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
In the embodiment of the application, simulation model information is obtained; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information; code generation is carried out on the simulation model information to obtain initial simulation model code information; and checking the initial simulation model code information to obtain target simulation model code information. Therefore, the method is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a simulation model code generation method disclosed in an embodiment of the invention;
FIG. 2 is a schematic diagram of a simulation model code generating device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another simulation model code generating apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, 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 terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a simulation model code generation method and device, which are beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing a network experiment simulation model code, thereby improving the simulation model code generation efficiency and accuracy. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a simulation model code generating method according to an embodiment of the invention. The simulation model code generation method described in fig. 1 is applied to a code management system, such as a local server or a cloud server for simulation model code generation management, which is not limited in the embodiment of the present invention. As shown in fig. 1, the simulation model code generation method may include the following operations:
101. and obtaining simulation model information.
In the embodiment of the invention, the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information.
In the embodiment of the invention, the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information.
102. And generating codes of the simulation model information to obtain initial simulation model code information.
103. And checking the initial simulation model code information to obtain target simulation model code information.
The simulation model code generation method can analyze the original information of the modeling task according to the network attack and defense experiment modeling requirement, code-convert the intermediate key model information data, insert the intermediate key model information data into a source code template, and automatically generate relevant codes such as simulation model items, code frames and the like.
It should be noted that, the standard simulation rule information includes simulation attribute, simulation model type, and simulation keyword, and the embodiment of the present invention is not limited.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In an optional embodiment, the generating the code for the simulation model information to obtain initial simulation model code information includes:
Determining code model information based on compiling information of a model to be simulated; the code model information comprises a plurality of template code information;
Determining target simulation rule information based on the standard simulation rule information and the code model information; the target simulation rule information comprises a plurality of model simulation rule information;
And determining the code information of the initial simulation model based on the compiling information of the model to be simulated, the code model information and the target simulation rule information.
It should be noted that, based on the to-be-simulated model compiling information, determining the code model information is to perform list analysis on the to-be-simulated model compiling information to obtain basic simulation model information such as simulation types, simulation data and the like, then perform unified conversion processing (data denoising and duplication removal, language conversion, coding conversion and format unification) on the basic simulation model information, and screen the information to obtain standard simulation model information, and then perform model indexing in a model code template list through the standard simulation model information, so as to screen the code model information.
Further, the code model information includes scene model code information, tool model code information, resource model code information, and behavior model code information, which is not limited in the embodiment of the present invention.
The scene model code information includes general class model code information (network device model code information, internet of things device model code information, IT resource model code information, application system model code information), industrial control class model code information (power model code information, traffic model code information), which is not limited in the embodiment of the present invention.
The tool model code information includes objective class model code information, tactical class model code information, technical class model code information, the organization class model information, platform class model code information, execution mode class model code information, and test effect class model code information, which is not limited in the embodiment of the present invention.
It should be noted that, the resource model code information includes personnel capability model code information, virtual resource collaborative capability model code information and post capability model code information, which is not limited by the embodiment of the present invention.
It should be noted that, the behavior model code information includes test behavior model code information, protection behavior model code information and random event behavior model code information, which is not limited by the embodiment of the present invention.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In another alternative embodiment, determining target simulation rule information based on standard simulation rule information and code model information includes:
Responding to the adjustment operation of the user on the model rule to obtain model generation rule information;
Feature extraction and matrix construction are carried out on standard simulation rule information and model generation rule information, and a rule feature matrix is obtained;
parameter calculation is carried out on the rule feature matrix to obtain matrix feature parameter information; the matrix characteristic parameter information comprises a target test value and a maximum characteristic root;
calculating matrix characteristic parameter information by using a characteristic detection model to obtain a characteristic detection value;
wherein, the feature detection model is:
Wherein A is a feature detection value; y is the matrix order of the rule feature matrix; x is the maximum feature root;
Calculating the target test value and the feature detection value by using a judgment criterion model to obtain a target judgment value;
wherein, the judgment criterion model is:
wherein Z is a target judgment value; b is a target test value;
judging whether the target judgment value is smaller than a judgment threshold value or not to obtain a threshold value judgment result;
When the threshold judgment result is negative, triggering and executing the adjustment operation responding to the model rule by the user to obtain the model generation rule information;
And when the threshold value judging result is yes, determining the model generating rule information as target simulation rule information.
Preferably, the judgment threshold is a value between (0,0.1).
It should be noted that the above adjustment operation in response to the user's adjustment to the model rule includes adjustment to the template component and the attribute in the code model information, and adjustment to the model generation rule.
In this optional embodiment, as an optional implementation manner, the feature extraction and matrix construction are performed on the standard simulation rule information and the model generation rule information to obtain a rule feature matrix, which includes:
Extracting features of standard simulation rule information and model generation rule information to obtain a first feature vector and a second feature vector; the first feature vector includes D first feature elements; the second feature vector includes D second feature elements;
For any first characteristic element, carrying out quantitative evaluation on the first characteristic element and the second characteristic vector by utilizing a characteristic evaluation model to obtain rule characteristic value information corresponding to the first characteristic element; the rule characteristic value information comprises D rule characteristic values;
wherein, the characteristic evaluation model is:
Wherein T ij is a rule eigenvalue with a coordinate number of (i, j); d i is the first feature element with the sequence number i in the first feature vector; d j is the first feature element with the sequence number j in the first feature vector;
And sequentially filling the coordinate numbers of the rule characteristic values into the matrix from small to large to obtain the rule characteristic matrix.
It should be noted that, the level of importance may be set by the user, or may be determined by the system based on historical data, which is not limited by the embodiment of the present invention.
It should be noted that, the feature extraction of the standard simulation rule information and the model generation rule information may be implemented by a model based on a neural network, a model based on deep learning, or a model based on machine learning, which is not limited by the embodiment of the present invention.
The above-mentioned sequential filling of the coordinate numbers of the rule feature values into the matrix from small to large is performed by using i as a row and j as a column.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In yet another alternative embodiment, performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information, including:
Carrying out maximum eigenvalue decomposition on the rule feature matrix to obtain a maximum feature root;
acquiring detection test information; the detection test information comprises a plurality of detection test data pairs; each detection test data pair comprises a detection number and a test value;
sequentially determining target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
judging whether the detection number of the target detection test data pair is equal to the matrix order of the rule feature matrix or not, and obtaining a number judgment result;
when the number judgment result is negative, triggering and executing to sequentially determine target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
And when the number judgment result is yes, determining the test value of the target detection test data pair as a target test value.
It should be noted that, the maximum eigenvalue decomposition of the rule eigenvalue matrix is a product form of the eigenvector with the maximum eigenvalue and the corresponding eigenvalue, which can be implemented by a power iteration method, an inverse power iteration method, and the like.
It should be noted that the test values in the above-mentioned test data pair gradually increase with the increase of the test number. Further, the test values corresponding to the two adjacent detection numbers may be equal.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In yet another alternative embodiment, determining initial simulation model code information based on to-be-simulated model compilation information, code model information, and target simulation rule information includes:
For any piece of model information to be compiled, screening template code information matched with the model information to be simulated from code model information to be object code information;
screening model simulation rule information matched with the compiling information of the model to be simulated from the target simulation rule information to obtain target model simulation rule information;
Judging whether the rule type corresponding to the simulation rule information of the target model is attribute revision or not to obtain a type judgment result;
when the type judgment result is yes, loading an attribute revision interface matched with the target simulation rule information;
Responding to the selection operation of a user on the component elements in the object code information in the attribute revision interface, and obtaining the component element information to be revised; the component element information to be revised comprises a plurality of component elements to be revised;
traversing the to-be-compiled model information for any component element to be revised, and screening all first effective model information matched with the component element to be revised from the to-be-compiled model information;
replacing and updating element attribute information in the element of the component to be revised by the first effective model information;
responding to the new operation processing of the user on the component elements to obtain the information of the new component elements;
Responding to the data selection operation of the user on the model information to be compiled, and obtaining second effective model information;
Replacing and updating element attribute information in the element information of the newly added component by the second effective model information;
compiling the updated element information of the to-be-modified assembly and the updated element information of the newly-added assembly to obtain initial simulation model code information;
when the type judgment result is negative, loading a component assembly interface matched with the target simulation rule information;
Responding to the assembling and matching operation of a user on the target code information to obtain assembling model code information;
and carrying out parameter updating on the assembled model code information by utilizing the to-be-compiled model information to obtain initial simulation model code information.
The matching between the object code information and the object model simulation rule information is determined according to the simulation model type. Further, the simulation model categories include a scene category, a tool category, a resource category, and a behavior category, which are not limited in the embodiment of the present invention.
It should be noted that the rule types include attribute revision and component assembly, and embodiments of the present invention are not limited.
It should be noted that, the above component elements include a class template, a data attribute template, an object attribute template, a sub-class relationship template, and a data type template, which is not limited in the embodiment of the present invention.
It should be noted that, the assembling and matching operation of the user on the object code information includes assembling and matching simulation models of different types according to the use condition or the implementation effect, matching the assembled and matched models with the simulation task, and then performing imaging compiling and displaying to obtain the assembled model code information.
It should be noted that, the element attribute information includes scene attribute information, tool attribute information, resource attribute information and behavior attribute information, and the embodiment of the present invention is not limited.
Further, the above-mentioned scene attribute information includes creator, industry category, scene category, version information, scene model scale, model structure, device/software composition, control component information, and management mode information, which is not limited in the embodiment of the present invention.
Further, the tool attribute information includes a tool code number, a tool category, a tool level, a platform to which the tool belongs, a usage mode, an entity type, an application target, a capability description, an application condition, an application effect, an application direction, an application environment, complexity, a countermeasure technology for countermeasure, a hidden burst prevention capability, a control effect, a paralysis destruction effect, an influence range, and a success rate, which are not limited in the embodiment of the present invention.
Further, the resource attribute information includes a resource category, a resource code, a resource scale, a virtual resource, and an entity supporting force, which is not limited in the embodiment of the present invention.
Further, the behavior attribute information includes a matrix stage, a behavior attribute, a station technology, a platform-dependent, an execution mode, and an execution effect, which is not limited in the embodiment of the present invention.
It should be noted that, the parameter updating of the assembled model code information by using the to-be-compiled model information is automatically replaced and updated by the system according to the matching relation of the parameters.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In an optional embodiment, the verifying the initial simulation model code information to obtain the target simulation model code information includes:
carrying out validity check on the code information of the initial simulation model to obtain a validity check result;
When the legal test result is that the test is unqualified, triggering execution to generate codes for the simulation model information to obtain initial simulation model code information;
when the legal test result is qualified, loading a test program file to test and analyze the initial simulation model code information to obtain test data information;
comparing and analyzing the test data information with the standard simulation rule information to obtain different symbol information;
Displaying different symbol information;
responding to the modification operation of the user on the dissimilar symbol information to obtain revised symbol information;
And replacing and updating the initial simulation model code information by using the revised symbol information to obtain target simulation model code information.
It should be noted that, the test program file may be input by a user or may be preset in the system, and the embodiment of the present invention is not limited. Further, the test program files may be set according to different simulation models.
It should be noted that, the above-mentioned comparison analysis of the test data information and the standard simulation rule information identifies the data information which does not conform to the standard simulation rule in the test data information, so as to obtain the different symbol information. Further, the modification operation for the dissimilar symbol information in response to the user is to modify the data information which does not conform to the standard simulation rule. Further, the replacement updating of the initial simulation model code information by using the revision symbol information is to compile the revision symbol information and then replace the revision symbol information to a code at a corresponding position so as to obtain a trusted simulation model code.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
In another alternative embodiment, the method for verifying the validity of the initial simulation model code information to obtain a verification result includes:
judging whether the code label in the initial simulation model code information is complete or not to obtain a first check judgment result;
when the first test judging result is negative, determining that the legal test result is unqualified;
When the first checking judgment result is yes, judging whether the code variable in the initial simulation code information accords with the compiling rule condition or not, and obtaining a second checking judgment result; compiling rule conditions comprise 4 code rule requirement information;
When the second test judging result is negative, determining that the legal test result is unqualified;
when the second test judging result is yes, judging whether the code information of the initial simulation model accords with the three-dimensional condition or not, and obtaining a third test judging result; the three-property condition comprises 3 pieces of performance requirement information;
When the third test judging result is negative, determining that the legal test result is unqualified;
And when the third test judging result is yes, determining that the legal test result is qualified.
It should be noted that, the code rule requirement information includes code semantic no-error, code no-language error, code coding no-error and code english lexical no-error, which is not limited by the embodiment of the invention.
The code labels include object class words, characteristic words, expression words and qualifiers.
It should be noted that, the performance requirement information includes that the code security meets the use requirement, the code vulnerability meets the use requirement, and the code stability meets the use requirement, which is not limited by the embodiment of the present invention.
Furthermore, the code security meets the use requirement and characterizes the code without obvious BUG and security loopholes.
Furthermore, the code vulnerability meets the use requirement, characterizes the back door, resists reverse, and has no obvious abnormality in the running environment.
Further, the stability of the code meets the use requirement, and the stability and compatibility of the characterization function are not abnormal.
Therefore, the simulation model code generation method described by the embodiment of the invention is beneficial to solving the problems of low efficiency, high error rate, high complexity of a network experiment system and the like in writing the network experiment simulation model code, and further improves the simulation model code generation efficiency and accuracy.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a simulation model code generating apparatus according to an embodiment of the present invention. The device described in fig. 2 can be applied to a code management system, such as a local server or a cloud server for simulation model code generation management, and the embodiment of the invention is not limited. As shown in fig. 2, the apparatus may include:
An acquisition module 201, configured to acquire simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information;
the generating module 202 is configured to generate codes for the simulation model information, so as to obtain initial simulation model code information;
And the processing module 203 is configured to perform verification processing on the initial simulation model code information to obtain target simulation model code information.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In another alternative embodiment, as shown in fig. 2, the generating module 202 generates codes for the simulation model information to obtain initial simulation model code information, including:
Determining code model information based on compiling information of a model to be simulated; the code model information comprises a plurality of template code information;
Determining target simulation rule information based on the standard simulation rule information and the code model information; the target simulation rule information comprises a plurality of model simulation rule information;
And determining the code information of the initial simulation model based on the compiling information of the model to be simulated, the code model information and the target simulation rule information.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In yet another alternative embodiment, as shown in FIG. 2, the generating module 202 determines target simulation rule information based on standard simulation rule information and code model information, including:
Responding to the adjustment operation of the user on the model rule to obtain model generation rule information;
Feature extraction and matrix construction are carried out on standard simulation rule information and model generation rule information, and a rule feature matrix is obtained;
parameter calculation is carried out on the rule feature matrix to obtain matrix feature parameter information; the matrix characteristic parameter information comprises a target test value and a maximum characteristic root;
calculating matrix characteristic parameter information by using a characteristic detection model to obtain a characteristic detection value;
wherein, the feature detection model is:
Wherein A is a feature detection value; y is the matrix order of the rule feature matrix; x is the maximum feature root;
Calculating the target test value and the feature detection value by using a judgment criterion model to obtain a target judgment value;
wherein, the judgment criterion model is:
wherein Z is a target judgment value; b is a target test value;
judging whether the target judgment value is smaller than a judgment threshold value or not to obtain a threshold value judgment result;
When the threshold judgment result is negative, triggering and executing the adjustment operation responding to the model rule by the user to obtain the model generation rule information;
And when the threshold value judging result is yes, determining the model generating rule information as target simulation rule information.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In yet another alternative embodiment, as shown in fig. 2, the generating module 202 performs parameter calculation on the rule feature matrix to obtain matrix feature parameter information, including:
Carrying out maximum eigenvalue decomposition on the rule feature matrix to obtain a maximum feature root;
acquiring detection test information; the detection test information comprises a plurality of detection test data pairs; each detection test data pair comprises a detection number and a test value;
sequentially determining target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
judging whether the detection number of the target detection test data pair is equal to the matrix order of the rule feature matrix or not, and obtaining a number judgment result;
when the number judgment result is negative, triggering and executing to sequentially determine target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
And when the number judgment result is yes, determining the test value of the target detection test data pair as a target test value.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In yet another alternative embodiment, as shown in fig. 2, the generating module 202 determines initial simulation model code information based on the to-be-simulated model compiling information, the code model information, and the target simulation rule information, including:
For any piece of model information to be compiled, screening template code information matched with the model information to be simulated from code model information to be object code information;
screening model simulation rule information matched with the compiling information of the model to be simulated from the target simulation rule information to obtain target model simulation rule information;
Judging whether the rule type corresponding to the simulation rule information of the target model is attribute revision or not to obtain a type judgment result;
when the type judgment result is yes, loading an attribute revision interface matched with the target simulation rule information;
Responding to the selection operation of a user on the component elements in the object code information in the attribute revision interface, and obtaining the component element information to be revised; the component element information to be revised comprises a plurality of component elements to be revised;
traversing the to-be-compiled model information for any component element to be revised, and screening all first effective model information matched with the component element to be revised from the to-be-compiled model information;
replacing and updating element attribute information in the element of the component to be revised by the first effective model information;
responding to the new operation processing of the user on the component elements to obtain the information of the new component elements;
Responding to the data selection operation of the user on the model information to be compiled, and obtaining second effective model information;
Replacing and updating element attribute information in the element information of the newly added component by the second effective model information;
compiling the updated element information of the to-be-modified assembly and the updated element information of the newly-added assembly to obtain initial simulation model code information;
when the type judgment result is negative, loading a component assembly interface matched with the target simulation rule information;
Responding to the assembling and matching operation of a user on the target code information to obtain assembling model code information;
and carrying out parameter updating on the assembled model code information by utilizing the to-be-compiled model information to obtain initial simulation model code information.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In yet another alternative embodiment, as shown in fig. 2, the processing module 203 performs a verification process on the initial simulation model code information to obtain target simulation model code information, including:
carrying out validity check on the code information of the initial simulation model to obtain a validity check result;
When the legal test result is that the test is unqualified, triggering execution to generate codes for the simulation model information to obtain initial simulation model code information;
when the legal test result is qualified, loading a test program file to test and analyze the initial simulation model code information to obtain test data information;
comparing and analyzing the test data information with the standard simulation rule information to obtain different symbol information;
Displaying different symbol information;
responding to the modification operation of the user on the dissimilar symbol information to obtain revised symbol information;
And replacing and updating the initial simulation model code information by using the revised symbol information to obtain target simulation model code information.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
In yet another alternative embodiment, as shown in fig. 2, the processing module 203 performs validity check on the initial simulation model code information to obtain a validity check result, including:
judging whether the code label in the initial simulation model code information is complete or not to obtain a first check judgment result;
when the first test judging result is negative, determining that the legal test result is unqualified;
When the first checking judgment result is yes, judging whether the code variable in the initial simulation code information accords with the compiling rule condition or not, and obtaining a second checking judgment result; compiling rule conditions comprise 4 code rule requirement information;
When the second test judging result is negative, determining that the legal test result is unqualified;
when the second test judging result is yes, judging whether the code information of the initial simulation model accords with the three-dimensional condition or not, and obtaining a third test judging result; the three-property condition comprises 3 pieces of performance requirement information;
When the third test judging result is negative, determining that the legal test result is unqualified;
And when the third test judging result is yes, determining that the legal test result is qualified.
Therefore, implementing the simulation model code generating device described in fig. 2 is beneficial to solving the problems of low efficiency, high error rate, high complexity of the network experiment system and the like in writing the network experiment simulation model code, thereby improving the efficiency and accuracy of simulation model code generation.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another simulation model code generating apparatus according to an embodiment of the present invention. The device described in fig. 3 can be applied to a code management system, such as a local server or a cloud server for generating and managing simulation model codes, and the embodiment of the invention is not limited. As shown in fig. 3, the apparatus may include:
a memory 301 storing executable program code;
A processor 302 coupled with the memory 301;
The processor 302 invokes the executable program code stored in the memory 301 for performing the steps in the simulation model code generation method described in embodiment one.
Example IV
The embodiment of the invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the simulation model code generation method described in the embodiment one.
Example five
The present invention discloses a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the simulation model code generation method described in the embodiment.
The apparatus embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a simulation model code generation method and device, which are disclosed as preferred embodiments of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (6)
1. A method of generating a simulation model code, the method comprising:
Obtaining simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information;
Code generation is carried out on the simulation model information to obtain initial simulation model code information;
The step of generating the code of the simulation model information to obtain initial simulation model code information comprises the following steps:
determining code model information based on the compiling information of the model to be simulated; the code model information comprises a plurality of template code information;
determining target simulation rule information based on the standard simulation rule information and the code model information; the target simulation rule information comprises a plurality of model simulation rule information;
determining initial simulation model code information based on the compiling information of the model to be simulated, the code model information and the target simulation rule information;
Wherein the determining the target simulation rule information based on the standard simulation rule information and the code model information includes:
Responding to the adjustment operation of the user on the model rule to obtain model generation rule information;
Performing feature extraction and matrix construction on the standard simulation rule information and the model generation rule information to obtain a rule feature matrix;
Performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information; the matrix characteristic parameter information comprises a target test value and a maximum characteristic root;
calculating the matrix characteristic parameter information by using a characteristic detection model to obtain a characteristic detection value;
Wherein, the feature detection model is:
Wherein A is the feature detection value; y is the matrix order of the rule feature matrix; x is the maximum feature root;
Calculating the target test value and the feature detection value by using a judgment criterion model to obtain a target judgment value;
wherein, the judgment criterion model is as follows:
Wherein Z is the target judgment value; b is the target test value;
Judging whether the target judgment value is smaller than a judgment threshold value or not to obtain a threshold value judgment result;
when the threshold judgment result is negative, triggering and executing the adjustment operation responding to the model rule by the user to obtain model generation rule information;
When the threshold judgment result is yes, determining that the model generation rule information is target simulation rule information;
the step of performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information comprises the following steps:
carrying out maximum eigenvalue decomposition on the rule feature matrix to obtain the maximum eigenvalue;
acquiring detection test information; the detection test information comprises a plurality of detection test data pairs; each detection test data pair comprises a detection number and a test value;
Sequentially determining target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
Judging whether the detection number of the target detection test data pair is equal to the matrix order of the rule feature matrix or not, and obtaining a number judgment result;
when the number judgment result is negative, triggering and executing the detection numbers according to the detection test data pairs to sequentially determine target detection test data pairs from small to large;
when the number judgment result is yes, determining a test value of a target detection test data pair as the target test value;
Wherein the determining initial simulation model code information based on the to-be-simulated model compiling information, the code model information and the target simulation rule information includes:
Screening template code information matched with the compiling information of the model to be simulated from the code model information as target code information for any piece of the model information to be compiled;
screening model simulation rule information matched with the compiling information of the model to be simulated from the target simulation rule information to obtain target model simulation rule information;
Judging whether the rule type corresponding to the target model simulation rule information is attribute revision or not to obtain a type judgment result;
When the type judgment result is yes, loading an attribute revision interface matched with the target simulation rule information;
responding to the selection operation of a user on the component elements in the object code information on the attribute revision interface, and obtaining the component element information to be revised; the component element information to be revised comprises a plurality of component elements to be revised;
Traversing the to-be-compiled model information for any component element to be repaired, and screening out all first effective model information matched with the component element to be repaired from the to-be-compiled model information;
Replacing and updating the element attribute information in the element of the component to be revised by the first effective model information;
Responding to the new operation processing of the user on the component elements to obtain new component element information;
responding to the data selection operation of the user on the model information to be compiled, and obtaining second effective model information;
Replacing and updating the element attribute information in the newly added component element information by the second effective model information;
compiling the updated element information of the to-be-modified assembly and the updated element information of the newly-added assembly to obtain initial simulation model code information;
When the type judgment result is negative, loading a component assembly interface matched with the target simulation rule information;
responding to the assembling and matching operation of the user on the target code information to obtain assembling model code information;
carrying out parameter updating on the assembled model code information by utilizing the model information to be compiled to obtain the initial simulation model code information;
and checking the initial simulation model code information to obtain target simulation model code information.
2. The method for generating a simulation model code according to claim 1, wherein the verifying the initial simulation model code information to obtain target simulation model code information comprises:
Carrying out validity check on the initial simulation model code information to obtain a validity check result;
when the legal inspection result is that the inspection is not qualified, triggering and executing the code generation of the simulation model information to obtain initial simulation model code information;
When the legal test result is qualified, loading a test program file to test and analyze the initial simulation model code information to obtain test data information;
comparing and analyzing the test data information and the standard simulation rule information to obtain different symbol information;
Displaying the different symbol information;
responding to the modification operation of the user on the dissimilar symbol information to obtain revised symbol information;
And replacing and updating the initial simulation model code information by using the revision symbol information to obtain target simulation model code information.
3. The method for generating a simulation model code according to claim 2, wherein the performing a validity check on the initial simulation model code information to obtain a validity check result comprises:
Judging whether the code label in the initial simulation model code information is complete or not to obtain a first check judgment result;
When the first inspection judging result is negative, determining that the legal inspection result is unqualified;
When the first test judgment result is yes, judging whether code variables in the initial simulation model code information accord with compiling rule conditions or not, and obtaining a second test judgment result; the compiling rule condition comprises 4 code rule requirement information;
When the second test judging result is negative, determining that the legal test result is the test failure;
when the second test judging result is yes, judging whether the initial simulation model code information accords with a three-dimensional condition or not, and obtaining a third test judging result; the three-property condition comprises 3 pieces of performance requirement information;
when the third test judging result is negative, determining that the legal test result is the test failure;
And when the third test judging result is yes, determining that the legal test result is qualified.
4. A simulation model code generating apparatus, the apparatus comprising:
the acquisition module is used for acquiring simulation model information; the simulation model information comprises standard simulation rule information and to-be-simulated model compiling information; the to-be-simulated model compiling information comprises a plurality of to-be-compiled model information;
the generation module is used for generating codes of the simulation model information to obtain initial simulation model code information;
The step of generating the code of the simulation model information to obtain initial simulation model code information comprises the following steps:
determining code model information based on the compiling information of the model to be simulated; the code model information comprises a plurality of template code information;
determining target simulation rule information based on the standard simulation rule information and the code model information; the target simulation rule information comprises a plurality of model simulation rule information;
determining initial simulation model code information based on the compiling information of the model to be simulated, the code model information and the target simulation rule information;
Wherein the determining the target simulation rule information based on the standard simulation rule information and the code model information includes:
Responding to the adjustment operation of the user on the model rule to obtain model generation rule information;
Performing feature extraction and matrix construction on the standard simulation rule information and the model generation rule information to obtain a rule feature matrix;
Performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information; the matrix characteristic parameter information comprises a target test value and a maximum characteristic root;
calculating the matrix characteristic parameter information by using a characteristic detection model to obtain a characteristic detection value;
Wherein, the feature detection model is:
Wherein A is the feature detection value; y is the matrix order of the rule feature matrix; x is the maximum feature root;
Calculating the target test value and the feature detection value by using a judgment criterion model to obtain a target judgment value;
wherein, the judgment criterion model is as follows:
Wherein Z is the target judgment value; b is the target test value;
Judging whether the target judgment value is smaller than a judgment threshold value or not to obtain a threshold value judgment result;
when the threshold judgment result is negative, triggering and executing the adjustment operation responding to the model rule by the user to obtain model generation rule information;
When the threshold judgment result is yes, determining that the model generation rule information is target simulation rule information;
the step of performing parameter calculation on the rule feature matrix to obtain matrix feature parameter information comprises the following steps:
carrying out maximum eigenvalue decomposition on the rule feature matrix to obtain the maximum eigenvalue;
acquiring detection test information; the detection test information comprises a plurality of detection test data pairs; each detection test data pair comprises a detection number and a test value;
Sequentially determining target detection test data pairs from small to large according to the detection numbers of the detection test data pairs;
Judging whether the detection number of the target detection test data pair is equal to the matrix order of the rule feature matrix or not, and obtaining a number judgment result;
when the number judgment result is negative, triggering and executing the detection numbers according to the detection test data pairs to sequentially determine target detection test data pairs from small to large;
when the number judgment result is yes, determining a test value of a target detection test data pair as the target test value;
Wherein the determining initial simulation model code information based on the to-be-simulated model compiling information, the code model information and the target simulation rule information includes:
Screening template code information matched with the compiling information of the model to be simulated from the code model information as target code information for any piece of the model information to be compiled;
screening model simulation rule information matched with the compiling information of the model to be simulated from the target simulation rule information to obtain target model simulation rule information;
Judging whether the rule type corresponding to the target model simulation rule information is attribute revision or not to obtain a type judgment result;
When the type judgment result is yes, loading an attribute revision interface matched with the target simulation rule information;
responding to the selection operation of a user on the component elements in the object code information on the attribute revision interface, and obtaining the component element information to be revised; the component element information to be revised comprises a plurality of component elements to be revised;
Traversing the to-be-compiled model information for any component element to be repaired, and screening out all first effective model information matched with the component element to be repaired from the to-be-compiled model information;
Replacing and updating the element attribute information in the element of the component to be revised by the first effective model information;
Responding to the new operation processing of the user on the component elements to obtain new component element information;
responding to the data selection operation of the user on the model information to be compiled, and obtaining second effective model information;
Replacing and updating the element attribute information in the newly added component element information by the second effective model information;
compiling the updated element information of the to-be-modified assembly and the updated element information of the newly-added assembly to obtain initial simulation model code information;
When the type judgment result is negative, loading a component assembly interface matched with the target simulation rule information;
responding to the assembling and matching operation of the user on the target code information to obtain assembling model code information;
carrying out parameter updating on the assembled model code information by utilizing the model information to be compiled to obtain the initial simulation model code information;
and the processing module is used for checking the initial simulation model code information to obtain target simulation model code information.
5. A simulation model code generating apparatus, the apparatus comprising:
A memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the simulation model code generation method of any of claims 1-3.
6. A computer readable storage medium storing computer instructions which, when invoked, are adapted to perform the simulation model code generation method of any of claims 1-3.
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