CN116962087B - Auxiliary information distribution method, electronic equipment and storage medium - Google Patents
Auxiliary information distribution method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116962087B CN116962087B CN202311216836.0A CN202311216836A CN116962087B CN 116962087 B CN116962087 B CN 116962087B CN 202311216836 A CN202311216836 A CN 202311216836A CN 116962087 B CN116962087 B CN 116962087B
- Authority
- CN
- China
- Prior art keywords
- target
- auxiliary information
- virtual device
- associated virtual
- action list
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 73
- 230000009471 action Effects 0.000 claims abstract description 131
- 238000004891 communication Methods 0.000 claims description 21
- 230000000694 effects Effects 0.000 abstract description 11
- 230000006870 function Effects 0.000 description 13
- 230000008569 process Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1433—Vulnerability analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/40—Network security protocols
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention provides an auxiliary information distribution method, electronic equipment and a storage medium, and relates to the field of auxiliary information distribution, wherein the method comprises the following steps: acquiring a user action list A aiming at a target task and input by a user; acquiring a plurality of preset preferred action lists corresponding to a target task to obtain a preferred action list set B; according to A and B, obtaining the similarity of each preferred action list in A and B to obtain a similarity list C; similarity of targets C max The auxiliary information set corresponding to the corresponding preferred action list is determined to be a target auxiliary information set D; c (C) max MAX (C); acquiring the acquisition difficulty of each associated virtual device to obtain an acquisition difficulty list G; determining a designated virtual device according to the G; distributing each piece of target auxiliary information in the D to at least one appointed virtual device according to a preset rule; the task deduction method and device improve the task deduction effect.
Description
Technical Field
The present invention relates to the field of auxiliary information distribution, and in particular, to an auxiliary information distribution method, an electronic device, and a storage medium.
Background
Along with the rapid development of network technology, task deduction is widely applied to security analysis of a network security protection system, and in general, a plurality of clues are distributed in a plurality of virtual devices before task deduction starts so that a user can acquire hidden clues, and target tasks are implemented according to the acquired clues; however, the current task deduction is to distribute fixed clues according to preset rules, and users can complete target tasks more and more easily along with the improvement of the cracking capability of the users; therefore, the deduction effect of the task deduction performed in this way is poor.
Disclosure of Invention
Aiming at the technical problems, the application adopts the following technical scheme:
according to a first aspect of the present application, there is provided an auxiliary information distribution method comprising the steps of:
s100, acquiring a user action list A= (A) aiming at a target task and input by a user 1 ，A 2 ，…，A i ，…，A n ) I=1, 2, …, n; wherein A is i The method comprises the steps that (1) an ith action in a user action list aiming at a target task is input for a user, and n is the number of actions in the user action list aiming at the target task, which is input by the user; the target task is provided with a corresponding target network, and the target network comprises a target virtual device and a plurality of associated virtual devices; the completion mark of the target task is that a target file in target virtual equipment is obtained or the control authority of the target virtual equipment is obtained;
S200, acquiring a plurality of preset preferred action lists corresponding to the target task to obtain a preferred action list set B= (B) 1 ，B 2 ，…，B j ，…，B m ) J=1, 2, …, m; wherein B is j The j-th preferred action list corresponding to the target task is obtained, and m is the number of the preferred action lists corresponding to the target task; each preferred action list has a corresponding set of auxiliary information; each preferred action list comprises a plurality of preferred actions;
s300, according to A and B, obtaining the similarity of each preferred action list in A and B to obtain a similarity list C= (C) 1 ，C 2 ，…，C j ，…，C m ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is j Is A and B j The similarity of (2) Cj is more than or equal to 0 and less than or equal to 1;
s400, object similarity C max The auxiliary information set corresponding to the corresponding preferred action list is determined as the target auxiliary information set d= (D) 1 ，D 2 ，…，D x ，…，D y ) X=1, 2, …, y; wherein D is x The x-th target auxiliary information in the target auxiliary information set is used, and y is the number of the target auxiliary information in the target auxiliary information set; c (C) max MAX (C), MAX () is a preset maximum function;
s500, determining the information acquisition difficulty of each associated virtual device to obtain an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the p=1, 2, …, q, where G p The difficulty of acquiring auxiliary information from the p-th associated virtual equipment is treated by a user, and q is the number of the associated virtual equipment; the information acquisition difficulty of each associated virtual device is obtained according to the device information of the corresponding associated virtual device;
S600, traversing G, if G p ＞C max Determining the p-th associated virtual device as a designated virtual device;
s700, if Q is more than y, distributing each piece of target auxiliary information in the D to at least one appointed virtual device according to a preset rule; wherein Q is the determined number of designated virtual devices.
Optionally, step S500 includes the steps of:
s510, obtaining the device information of each associated virtual device to obtain a device information set E= (E) 1 ，E 2 ，…，E p ，…，E q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is p Device information for a p-th associated virtual device; e (E) p = (Lp, jp, fp); lp is the communication distance between the p-th associated virtual device and the target virtual device; jp is the number of associated virtual devices that the p-th associated virtual device can directly communicate with; fp is the protection capability parameter of the p-th associated virtual device;
s520, according to each piece of equipment information in E, obtaining an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein G is p = (Lp/lmax+ (Jmax-Jp)/jmax+fp/Fmax)/3; lmax is the maximum communication distance in all communication distances between each associated virtual device and the target virtual device in the target network, JMax is the maximum number in the number of associated virtual devices which each associated virtual device in the target network can directly communicate, and Fmax is the preset maximum protection capability parameter of the associated virtual device.
Optionally, step S700 includes the steps of:
s711, acquiring a preset auxiliary information acquisition success rate eta; wherein, 0 < eta < 1;
s712, determining the number of the target specified virtual devices according to eta and QThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information;
s713, D is allocated into each target specified virtual device.
Optionally, step S700 includes the steps of:
s721, acquiring a preset auxiliary information acquisition success rate eta; wherein, 0 < eta < 1;
s722, determining a plurality of target specified virtual devices corresponding to each target auxiliary information in D according to eta and D to obtain a target specified virtual device list set HD= (HD) corresponding to D 1 ，HD 2 ，…，HD x ，…，HD y ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein HD is an x For D x A corresponding target designation virtual device list; HD (HD) x =(HD x，1 ，HD x，2 ，…，HD x，a ，…，HD x，NQ )，a=1，2，…，NQ；HD x，a For D x The corresponding a-th target designates a virtual device with NQ as D x The corresponding target designates the number of virtual devices;，a preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information;
s723, D x Distribution to HD x Is assigned within the virtual device.
Optionally, after step S700, the method further comprises the steps of:
S811, ifQ is less than or equal to y, Q pieces of target auxiliary information are randomly determined from D; />A preset upward rounding function;
s812, randomly distributing the Q target auxiliary information randomly determined from the D into each appointed virtual device;
s813, determining y-Q appointed virtual devices from the Q appointed virtual devices;
s814, the remaining y-Q pieces of target auxiliary information in the D are randomly distributed into y-Q pieces of designated virtual devices determined from the Q pieces of designated virtual devices.
Optionally, after step S700, the method further comprises the steps of:
s821, if Q is less than or equal toAny target auxiliary information in the step D is not allocated; />Is a preset round-up function.
Alternatively, C j The method comprises the following steps of:
s310, acquiring a first target value α=1 and a second target value β=1;
s311, if beta is less than or equal to g (j), entering a step S312, otherwise, entering a step S314; g (j) is B j The number of preferred actions in (a);
s312, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S313 is entered, otherwise step S314 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A;
s313, α=λ+1, β=β+1 is acquired, and step S311 is entered;
s314, obtain C j =(β-1)/g(j)。
Alternatively, C j The method comprises the following steps of:
s320, acquiring a first target value α=1 and a second target value β=1;
s321, if beta is less than or equal to g (j), entering step S322, otherwise, entering step S324; g (j) is B j The number of preferred actions in (a);
s322, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S323 is entered, otherwise step S324 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A;
s323, α=λ+1, β=β+1 is obtained, and step S321 is entered;
s324, obtain C j =(β-1)/g(j)-|n-β+1|/n。
According to another aspect of the present application, there is also provided a non-transitory computer readable storage medium having stored therein at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the above auxiliary information allocation method.
According to another aspect of the present application, there is also provided an electronic device comprising a processor and the above-described non-transitory computer-readable storage medium.
The invention has at least the following beneficial effects:
according to the auxiliary information distribution method, firstly, the user action list of a user aiming at a target task is obtained, then the maximum similarity of the user action list and each preset preferred action list is obtained, the most similar preferred action list with the user action list can be determined, and further a target auxiliary information set corresponding to the user action list is obtained; and distributing the target auxiliary information in the target auxiliary information set corresponding to the user action list into the associated virtual equipment with higher acquisition difficulty, so that the difficulty of acquiring the corresponding target auxiliary information from the associated virtual equipment by the user is increased, the difficulty of completing the target task by the user with higher capability is increased, and the task deduction effect is improved.
Further, when determining the designated virtual device, the method is based on the maximum similarity C of the similarity between the user action list and each preset preferred action list max And the difficulty of acquisition of each associated virtual device, thereby, when C max When the number of the specified associated virtual devices and the overall acquisition difficulty are larger, when C is larger max When the number of the determined specified associated virtual devices and the overall acquisition difficulty are smaller, so that the number of the determined specified associated virtual devices and the overall acquisition difficulty are smaller than those of C max Dynamic association; and when Q is greater than y, the target auxiliary information in D is distributed according to a preset rule, so that the situation that most of the target auxiliary information in D is distributed into the same designated virtual equipment due to the fact that the number of the determined designated virtual equipment is too small can be avoided, the rationality of the distribution of the target auxiliary information is improved, and the effect of task deduction is further improved.
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 flowchart of an auxiliary information allocation method according to an embodiment of the present invention;
fig. 2 is an application scenario diagram of an auxiliary information distribution method provided by an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present 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 fall within the scope of the invention.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
An auxiliary information distribution method will be described with reference to a flowchart of the auxiliary information distribution method described in fig. 1.
The auxiliary information distribution method comprises the following steps:
s100, acquiring a user action list A= (A) aiming at a target task and input by a user 1 ，A 2 ，…，A i ，…，A n ) I=1, 2, …, n; wherein A is i The method comprises the steps that (1) an ith action in a user action list aiming at a target task is input for a user, and n is the number of actions in the user action list aiming at the target task, which is input by the user; the target task has a corresponding target network, and the target network comprises a target virtual device And a number of associated virtual devices; and the completion mark of the target task is that a target file in the target virtual equipment is acquired or the control authority of the target virtual equipment is acquired.
In this embodiment, the target network may be a local area network, in which there are one target virtual device and a plurality of associated virtual devices, where the target virtual device and the associated virtual devices may be host computers, and a target file is preset in the target virtual device, and the associated virtual device is used to store auxiliary information required for completing the target task, that is, thread information, and a plurality of thread information combinations, so that the target task can be completed; it should be noted that, in the target network, the target virtual device is communicatively connected to at least one associated virtual device, where the associated virtual device may be communicatively connected to only the target virtual device, may be communicatively connected to only at least one other associated virtual device, or may be communicatively connected to the target virtual device and at least one other associated virtual device; the two arbitrary associated virtual devices can not necessarily communicate directly; the user action list records each action in the attack plan planned by the user.
S200, acquiring a plurality of preset preferred action lists corresponding to the target task to obtain a preferred action list set B= (B) 1 ，B 2 ，…，B j ，…，B m ) J=1, 2, …, m; wherein B is j The j-th preferred action list corresponding to the target task is obtained, and m is the number of the preferred action lists corresponding to the target task; each preferred action list has a corresponding set of auxiliary information; each preferred action list includes a number of preferred actions therein.
In this embodiment, a plurality of preferred action lists are preset for a target task, and attack schemes corresponding to all preferred actions in each preferred action list can better complete the target task; it should be noted that, in this embodiment, a mapping relationship between the preferred action list and the auxiliary information sets is preset, that is, each preferred action list corresponds to one preset auxiliary information set, and the auxiliary information set may be understood as a clue information set required for completing the target task according to the corresponding preferred action list.
S300, according to A and B, obtaining the similarity of each preferred action list in A and B to obtain a similarity list C= (C) 1 ，C 2 ，…，C j ，…，C m ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is j Is A and B j Is equal to or greater than 0 and equal to or less than 1.
In this embodiment, a includes each action of the user for the target task, each preferred action list in B includes a plurality of preferred actions, and by comparing each action in a with each preferred action in each preferred action list in B, the number of the same actions can be determined, and then the ratio of the number of the same actions to the number of preferred actions in the corresponding preferred action list is obtained, so that the corresponding similarity can be obtained.
S400, object similarity C max The auxiliary information set corresponding to the corresponding preferred action list is determined as the target auxiliary information set d= (D) 1 ，D 2 ，…，D x ，…，D y ) X=1, 2, …, y; wherein D is x The x-th target auxiliary information in the target auxiliary information set is used, and y is the number of the target auxiliary information in the target auxiliary information set; c (C) max MAX (C), MAX () is a preset maximum function.
In this embodiment, it should be noted that, the greater the similarity of a certain preferred action list in a and B, the closer the attack scenario corresponding to the user action list is to the attack scenario corresponding to the preferred action list, and because the attack scenario corresponding to the preferred action list is a preferred attack scenario, the attack capability of the user is stronger; in order to improve the difficulty of completing the target task for the user with stronger attack capability, in this embodiment, the maximum similarity in C is the target similarity C max Determining an auxiliary information set corresponding to the corresponding preferred action list as a target auxiliary information set, and then distributing each target auxiliary information in the target auxiliary information set according to a preset rule, so that the difficulty of acquiring the target auxiliary information by the user is increased; in this embodiment, C max It is possible to be 1, i.e. a certain list of preferred actions in a and B is identical; if it isC max =1, which means that the user has very strong attack ability, so that the attack difficulty of the user needs to be increased, and the user is not attacked by C max Any auxiliary information in the auxiliary information set corresponding to the corresponding preferred action list is allocated.
S500, determining the information acquisition difficulty of each associated virtual device to obtain an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein p=1, 2, …, q, G p The difficulty of acquiring auxiliary information from the p-th associated virtual equipment is treated by a user, and q is the number of the associated virtual equipment; the information acquisition difficulty of each associated virtual device is obtained according to the device information of the corresponding associated virtual device.
In this embodiment, the device information of each associated virtual device is different, for example, the communication distance between the associated virtual device and the target virtual device in the target network is different, the number of other connected associated virtual devices is different, and the protection capability parameters are different, which results in different difficulty of the user obtaining the target auxiliary information from each associated virtual device; specifically, the difficulty of acquiring each associated virtual device is determined by the following steps:
s510, obtaining the device information of each associated virtual device to obtain a device information set E= (E) 1 ，E 2 ，…，E p ，…，E q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is p Device information for a p-th associated virtual device; e (E) p = (Lp, jp, fp); lp is the communication distance between the p-th associated virtual device and the target virtual device; jp is the number of associated virtual devices that the p-th associated virtual device can directly communicate with; fp is the protection capability parameter of the p-th associated virtual device.
In this embodiment, each associated virtual device in the target network has a corresponding communication distance between the associated virtual device and the target virtual device in the target network, where the communication distance is the number of other associated virtual devices that any associated virtual device needs to pass through for communicating with the target virtual device; for example, communication between the associated virtual device 1 and the target virtual device needs to pass through the associated virtual device 2 and the associated virtual device 3, and then the communication distance between the associated virtual device 1 and the target virtual device may be determined to be 2; direct communication can be understood as communication without the need for intermediation through other associated virtual devices; the protection capability is the capability of the associated virtual device to prevent the associated virtual device from being broken, and the protection capability parameters of the associated virtual device can be determined according to preset protection measures on the associated virtual device, such as protection software, firewall strength and the like; the protective capability parameter may be set in the range of 1-10.
S520, according to each piece of equipment information in E, obtaining an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein G is p = (Lp/lmax+ (Jmax-Jp)/jmax+fp/Fmax)/3; lmax is the maximum communication distance in all communication distances between each associated virtual device and the target virtual device in the target network, JMax is the maximum number in the number of associated virtual devices which each associated virtual device in the target network can directly communicate, and Fmax is the preset maximum protection capability parameter of the associated virtual device.
In this embodiment, a communication distance corresponds to each associated virtual device and a target virtual device, and the maximum communication distance among the communication distances between all the associated virtual devices and the target virtual device is obtained, so as to obtain Lmax; each associated virtual device corresponds to a plurality of associated virtual devices capable of directly communicating, and the maximum number of the associated virtual devices capable of directly communicating in each associated virtual device is obtained, so that the JMax can be obtained; fmax is a preset value, for example, the protection capability parameter of the associated virtual device is between 1 and 10, then fmax=10; and carrying out normalization processing on the acquisition difficulty of each associated virtual device, so that the acquisition difficulty of each associated virtual device is in the range of 0 to 1, and the acquisition difficulty and the similarity are in the same numerical range, thereby facilitating subsequent numerical comparison.
S600, traversing G, if G p ＞C max The p-th associated virtual device is determined to be the designated virtual device.
In the present embodiment, it is understood that when C max When the number of the specified associated virtual devices is larger, the determined number and the determined number of the specified associated virtual devicesThe overall acquisition difficulty is also higher, when C max And when the number of the determined appointed associated virtual devices is smaller, the overall acquisition difficulty is also smaller.
S700, if Q is more than y, distributing each piece of target auxiliary information in the D to at least one appointed virtual device according to a preset rule; wherein Q is the determined number of designated virtual devices.
In this embodiment, when the number of the designated virtual devices is greater than the number of the target auxiliary information in D, the target auxiliary information in D is allocated according to a preset rule; therefore, the situation that most of the target auxiliary information in the D is distributed to the same designated virtual equipment due to the fact that the number of the determined designated virtual equipment is too small can be avoided, and the rationality of target auxiliary information distribution is improved.
The auxiliary information distribution method comprises the steps of firstly obtaining a user action list of a user aiming at a target task, then obtaining the maximum similarity in the similarity between the user action list and each preset preferred action list, and determining the most similar preferred action list with the user action list, so as to obtain a target auxiliary information set corresponding to the user action list; and distributing the target auxiliary information in the target auxiliary information set corresponding to the user action list into the associated virtual equipment with higher acquisition difficulty, so that the difficulty of acquiring the corresponding target auxiliary information from the associated virtual equipment by the user is increased, the difficulty of completing the target task by the user with higher capability is increased, and the task deduction effect is improved.
Further, when the acquisition difficulty of the associated virtual equipment is determined, the communication distance between the associated virtual equipment and the target virtual equipment, the number of the associated virtual equipment capable of directly communicating with the associated virtual equipment and the protection capability parameters of the associated virtual equipment are comprehensively considered, the influence of mutation of a certain parameter on the acquisition difficulty of the associated virtual equipment is reduced, the accuracy of determining the acquisition difficulty of the associated virtual equipment is improved, and further the task deduction effect is improved.
Further, in determining the designated virtual device, the virtual device is determined based on the user action list and each of the pre-defined virtual devicesMaximum similarity C among the similarity of the set preferred action list max And the difficulty of acquisition of each associated virtual device, thereby, when C max When the number of the specified associated virtual devices and the overall acquisition difficulty are larger, when C is larger max When the number of the determined specified associated virtual devices and the overall acquisition difficulty are smaller, so that the number of the determined specified associated virtual devices and the overall acquisition difficulty are smaller than those of C max And (3) dynamically associating, and further improving the task deduction effect.
Optionally, step S700 includes the steps of:
S711, acquiring a preset auxiliary information acquisition success rate eta; wherein 0 < eta < 1.
S712, determining the number of the target specified virtual devices according to eta and QThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information.
S713, D is allocated into each target specified virtual device.
In this embodiment, the user may not acquire the auxiliary information successfully, so the auxiliary information acquisition success rate is preset, and the auxiliary information acquisition success rate may be a ratio of the number of associated virtual devices allocated with all the auxiliary information in D to the total number of associated virtual devices; for example, η=0.3, there are 10 associated virtual devices in the target network, and then all the target auxiliary information in D is allocated in the 3 associated virtual devices; therefore, the success rate of the user to acquire the target auxiliary information in the D from all the associated virtual devices is 0.3; the task deduction process is closer to the actual process, so that the task deduction effect is improved.
Optionally, step S700 includes the steps of:
s721, acquiring a preset auxiliary information acquisition success rate eta; wherein 0 < eta < 1.
S722, determining a plurality of target specified virtual devices corresponding to each target auxiliary information in D according to eta and D to obtain a target specified virtual device list set HD= (HD) corresponding to D 1 ，HD 2 ，…，HD x ，…，HD y ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein HD is an x For D x A corresponding target designation virtual device list; HD (HD) x =(HD x，1 ，HD x，2 ，…，HD x，a ，…，HD x，NQ )，a=1，2，…，NQ；HD x，a For D x The corresponding a-th target designates a virtual device with NQ as D x The corresponding target designates the number of virtual devices;，a preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information.
S723, D x Distribution to HD x Is assigned within the virtual device.
In this embodiment, the auxiliary information acquisition success rate may also be for each target auxiliary information in D, e.g., D in D 1 、D 2 And D 3 Three target auxiliary information, η=0.3, there are 100 associated virtual devices in the target network, then D 1 D in 30 associated virtual devices needing to be distributed to the device 2 D in 30 associated virtual devices needing to be distributed to the device 3 Also need to be distributed among 30 associated virtual devices, D 1 、D 2 And D 3 Can be combined, namely, D can be distributed in a certain associated virtual device 1 May also be assigned D 1 And D 2 Or is allocated with D 1 、D 2 And D 3 The method comprises the steps of carrying out a first treatment on the surface of the Therefore, the success rate of the user for acquiring each target auxiliary information in the D from all the associated virtual devices is 0.3; the task deduction process is comparatively practical And (5) approaching, thereby improving the task deduction effect.
Optionally, after step S700, the method further comprises the steps of:
s811, ifQ is less than or equal to y, Q pieces of target auxiliary information are randomly determined from D; />Is a preset round-up function.
And S812, randomly distributing the Q pieces of target auxiliary information randomly determined from the D into each designated virtual device.
In this embodiment, Q pieces of target auxiliary information determined from D are used in a random manner.
S813, determining y-Q specified virtual devices from the Q specified virtual devices.
In the embodiment, y-Q designated virtual devices are determined from Q designated virtual devices in a random manner; when y is an even number, and q=And if so, all the Q appointed virtual devices are determined.
S814, the remaining y-Q pieces of target auxiliary information in the D are randomly distributed into y-Q pieces of designated virtual devices determined from the Q pieces of designated virtual devices.
In the present embodiment, whenWhen Q < y, the number of the specified virtual devices determined is smaller than the number of the target auxiliary information in D, and at this time, the allocation method in the above embodiment cannot be used, and all the target auxiliary information in D needs to be uniformly allocated to Q specified virtual devices; the allocation method in this embodiment can ensure that +. >When Q < y, each target auxiliary information in D is allocated to the corresponding object auxiliary informationAnd such that the amount of target auxiliary information within each specified virtual device is no greater than 2.
Optionally, after step S700, the method further comprises the steps of:
s821 if Q <)Any target auxiliary information in the step D is not allocated; />Is a preset round-up function.
In the present embodiment, if Q < >If the target auxiliary information in the D is allocated, more than 2 target auxiliary information is allocated in at least one appointed virtual device, if a user breaks through the appointed associated virtual device, a large amount of target auxiliary information can be directly obtained, and the target auxiliary information is the target auxiliary information associated with the user action list corresponding to the user, so that the difficulty of completing a target task by the user is greatly reduced, and the task deduction effect is not facilitated; thus, Q <)>And when the auxiliary information is not distributed to any target auxiliary information in the step D.
Optionally, after step S400 and before step S500, the method further comprises the steps of:
s410, if C max =1, no target auxiliary information in D is assigned, and execution of the current step is terminated.
In the present embodiment, if C max =1, indicating that the user action list is identical to any one of the plurality of preferred action lists, in which case the specified virtual device cannot be specified, and therefore, if C max =1, no target auxiliary information in D is assigned, and execution of the current step is terminated, i.e., no subsequent step is performedTo improve the program execution efficiency; at the same time, C is not allocated max The target auxiliary information in the target auxiliary information set corresponding to the corresponding preferred action list is used for increasing the difficulty of completing the target task for the user with stronger breaking capability.
In an exemplary embodiment, C j The method comprises the following steps of:
s310, a first target value α=1 and a second target value β=1 are acquired.
S311, if beta is less than or equal to g (j), entering a step S312, otherwise, entering a step S314; g (j) is B j The number of preferred actions in (a) is determined.
It will be appreciated that since in the present embodiment the number of preferred actions within each preferred action list is different, in the present embodiment g (j) does not refer to a specific function or function result value, but rather to a value that is possible with a different specific value of j, e.g. when j=1, g (j) =3; when j=2, g (j) =4; j=3, g (j) =3.
S312, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S313 is entered, otherwise step S314 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A.
S313, α=λ+1, β=β+1 is acquired, and the process proceeds to step S311.
S314, obtain C j =(β-1)/g(j)。
In this embodiment, C j For action-in-action B in user action list A j The same number as B of preferred actions j The ratio of the number of preferred actions in (a), i.e. the list of user actions (a) to (B) without taking into account other unwanted actions j The more the number of preferred actions is the same, C j The larger.
Alternatively, C j The method comprises the following steps of:
s320, a first target value α=1 and a second target value β=1 are acquired.
S321, if beta is less than or equal to g (j), entering step S322, otherwise, entering step S324; g (j) is B j The number of preferred actions in (a) is determined.
S322, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S323 is entered, otherwise step S324 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A;
s323, α=λ+1, β=β+1 is obtained, and step S321 is entered;
s324, obtain C j =(β-1)/g(j)-|n-β+1|/n。
In this embodiment, C j Acquisition method of (C) and the above embodiment j The acquisition method of (C) is different in that j = (β -1)/g (j) - |n- β+1|/n, i.e. in determining the user action list a and the preferred action list B j When the similarity of (C) is higher than the similarity of (A), adding other redundant actions in A to C j Is calculated; thus, the more other redundant actions in A, the more it is with B j The smaller the similarity of (2); in an actual scene, the more other redundant actions of a user, the lower the efficiency of completing a target task; therefore, the similarity determination is more reasonable, and the task deduction effect is improved.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Embodiments of the present invention also provide a non-transitory computer readable storage medium that may be disposed in an electronic device to store at least one instruction or at least one program for implementing one of the methods embodiments, the at least one instruction or the at least one program being loaded and executed by the processor to implement the methods provided by the embodiments described above.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. Referring to FIG. 2, the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Referring to fig. 3, an embodiment of the present application also provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.
An electronic device according to this embodiment of the application. The electronic device is merely an example, and should not impose any limitations on the functionality and scope of use of embodiments of the present application.
The electronic device is in the form of a general purpose computing device. Components of an electronic device may include, but are not limited to: the at least one processor, the at least one memory, and a bus connecting the various system components, including the memory and the processor.
Wherein the memory stores program code that is executable by the processor to cause the processor to perform steps according to various exemplary embodiments of the application described in the "exemplary methods" section of this specification.
The storage may include readable media in the form of volatile storage, such as Random Access Memory (RAM) and/or cache memory, and may further include Read Only Memory (ROM).
The storage may also include a program/utility having a set (at least one) of program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The bus may be one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any device (e.g., router, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. And, the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Embodiments of the present invention also provide a computer program product comprising program code for causing an electronic device to carry out the steps of the method according to the various exemplary embodiments of the invention as described in the specification, when said program product is run on the electronic device.
While certain specific embodiments of the invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. Those skilled in the art will also appreciate that many modifications may be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (8)
1. A method of auxiliary information distribution, the method comprising the steps of:
s100, acquiring a user action list A= (A) aiming at a target task and input by a user 1 ，A 2 ，…，A i ，…，A n ) I=1, 2, …, n; wherein A is i The method comprises the steps that (1) an ith action in a user action list aiming at a target task is input for a user, and n is the number of actions in the user action list aiming at the target task, which is input by the user; the target task is provided with a corresponding target network, and the target network comprises a target virtual device and a plurality of associated virtual devices; the completion mark of the target task is that a target file in target virtual equipment is obtained or the control authority of the target virtual equipment is obtained;
s200, acquiring a plurality of preset preferred action lists corresponding to the target task to obtain a preferred action list set B= (B) 1 ，B 2 ，…，B j ，…，B m ) J=1, 2, …, m; wherein B is j The j-th preferred action list corresponding to the target task is obtained, and m is the number of the preferred action lists corresponding to the target task; each preferred action list has a corresponding set of auxiliary information; each preferred action list comprises a plurality of preferred actions;
s300, according to A and B, obtaining the similarity of each preferred action list in A and B to obtain a similarity list C= (C) 1 ，C 2 ，…，C j ，…，C m ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein C is j Is A and B j Similarity of 0.ltoreq.C j ≤1；
S400, object similarity C max The auxiliary information set corresponding to the corresponding preferred action list is determined as the target auxiliary information set d= (D) 1 ，D 2 ，…，D x ，…，D y ) X=1, 2, …, y; wherein D is x The x-th target auxiliary information in the target auxiliary information set is used, and y is the number of the target auxiliary information in the target auxiliary information set; c (C) max MAX (C), MAX () is a preset maximum function;
s500, determining the information acquisition difficulty of each associated virtual device to obtain an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein p=1, 2, …, q, G p The difficulty of acquiring auxiliary information from the p-th associated virtual equipment is treated by a user, and q is the number of the associated virtual equipment; the information acquisition difficulty of each associated virtual device is obtained according to the device information of the corresponding associated virtual device;
s600, traversing G, if G p ＞C max Determining the p-th associated virtual device as a designated virtual device;
s700, if Q is more than y, distributing each piece of target auxiliary information in the D to at least one appointed virtual device according to a preset rule; wherein Q is the determined number of the specified virtual devices;
s811, ifQ is less than or equal to y, Q pieces of target auxiliary information are randomly determined from D; / >A preset upward rounding function;
s812, randomly distributing the Q target auxiliary information randomly determined from the D into each appointed virtual device;
s813, determining y-Q appointed virtual devices from the Q appointed virtual devices;
s814, randomly distributing the y-Q target auxiliary information remained in the D into y-Q appointed virtual devices determined from the Q appointed virtual devices;
s821, ifAny target auxiliary information in the step D is not allocated; />Is a preset round-up function.
2. The auxiliary information distribution method according to claim 1, wherein the step S500 includes the steps of:
s510, obtaining the device information of each associated virtual device to obtain a device information set E= (E) 1 ，E 2 ，…，E p ，…，E q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein E is p Device information for a p-th associated virtual device; e (E) p = (Lp, jp, fp); lp is the communication distance between the p-th associated virtual device and the target virtual device; jp is the number of associated virtual devices that the p-th associated virtual device can directly communicate with; fp is the protection capability parameter of the p-th associated virtual device;
s520, according to each piece of equipment information in E, obtaining an information acquisition difficulty list G= (G) 1 ，G 2 ，…，G p ，…，G q ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein G is p = (Lp/lmax+ (Jmax-Jp)/jmax+fp/Fmax)/3; lmax is the maximum communication distance in all communication distances between each associated virtual device and the target virtual device in the target network, JMax is the maximum number in the number of associated virtual devices which each associated virtual device in the target network can directly communicate, and Fmax is the preset maximum protection capability parameter of the associated virtual device.
3. The auxiliary information distribution method according to claim 1, wherein the step S700 includes the steps of:
s711, acquiring a preset auxiliary information acquisition success rate eta; wherein, 0 < eta < 1;
s712, determining the target specified virtual device according to eta and QQuantity ofThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information;
s713, D is allocated into each target specified virtual device.
4. The auxiliary information distribution method according to claim 1, wherein the step S700 includes the steps of:
s721, acquiring a preset auxiliary information acquisition success rate eta; wherein, 0 < eta < 1;
s722, determining a plurality of target specified virtual devices corresponding to each target auxiliary information in D according to eta and D to obtain a target specified virtual device list set HD= (HD) corresponding to D 1 ，HD 2 ，…，HD x ，…，HD y ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein HD is an x For D x A corresponding target designation virtual device list; HD (HD) x =(HD x，1 ，HD x，2 ，…，HD x，a ，…，HD x，NQ )，a=1，2，…，NQ；HD x，a For D x The corresponding a-th target designates a virtual device with NQ as D x The corresponding target designates the number of virtual devices;，/>a preset downward rounding function; the target specified virtual device is a specified virtual device for distributing target auxiliary information;
S723, D x Distribution to HD x Is assigned within the virtual device.
5. Auxiliary information allocation according to claim 1The method is characterized in that C j The method comprises the following steps of:
s310, acquiring a first target value α=1 and a second target value β=1;
s311, if beta is less than or equal to g (j), entering a step S312, otherwise, entering a step S314; g (j) is B j The number of preferred actions in (a);
s312, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S313 is entered, otherwise step S314 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A;
s313, α=λ+1, β=β+1 is acquired, and step S311 is entered;
s314, obtain C j =(β-1)/g(j)。
6. The auxiliary information distribution method according to claim 1, wherein C j The method comprises the following steps of:
s320, acquiring a first target value α=1 and a second target value β=1;
s321, if beta is less than or equal to g (j), entering step S322, otherwise, entering step S324; g (j) is B j The number of preferred actions in (a);
s322, traversing the alpha-th intermediate user action list A' α =(A α ，A α+1 ，…，A λ ，…，A n ) If A λ =B j，β Step S323 is entered, otherwise step S324 is entered; wherein A is λ For the (lambda-alpha+1) th user action in the alpha-th intermediate user action list, B j，β Is B j The value range of lambda is alpha to n; a's' α Obtained according to A;
s323, α=λ+1, β=β+1 is obtained, and step S321 is entered;
s324, obtain C j =(β-1)/g(j)-|n-β+1|/n。
7. A non-transitory computer readable storage medium having stored therein at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the auxiliary information allocation method of any one of claims 1-6.
8. An electronic device comprising a processor and the non-transitory computer-readable storage medium of claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311216836.0A CN116962087B (en) | 2023-09-20 | 2023-09-20 | Auxiliary information distribution method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311216836.0A CN116962087B (en) | 2023-09-20 | 2023-09-20 | Auxiliary information distribution method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116962087A CN116962087A (en) | 2023-10-27 |
| CN116962087B true CN116962087B (en) | 2023-12-01 |
Family
ID=88442910
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311216836.0A Active CN116962087B (en) | 2023-09-20 | 2023-09-20 | Auxiliary information distribution method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116962087B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110116280A (en) * | 2010-04-19 | 2011-10-26 | (주)제이제이 유니언 | Word game simulation system based on grid and motion detection |
| CN109636699A (en) * | 2018-11-06 | 2019-04-16 | 中国电子科技集团公司第五十二研究所 | A kind of unsupervised intellectualized battle deduction system based on deeply study |
| CN112968882A (en) * | 2021-02-03 | 2021-06-15 | 南京华鹞信息科技有限公司 | System and method for deducing multi-domain network security policy based on network function virtualization |
| CN114240485A (en) * | 2021-12-06 | 2022-03-25 | 湖南天云软件技术有限公司 | Virtual resource allocation method and related equipment |
| WO2022121769A1 (en) * | 2020-12-07 | 2022-06-16 | 陈贺龄 | Training partner robot control method, system and device, and storage medium |
| CN115970289A (en) * | 2023-01-09 | 2023-04-18 | 深圳市人马互动科技有限公司 | Plot information generation, evaluation and optimization method based on artificial intelligence technology |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11720391B2 (en) * | 2020-11-10 | 2023-08-08 | National Technology & Engineering Solutions Of Sandia, Llc | Emulation automation and model checking |
-
2023
- 2023-09-20 CN CN202311216836.0A patent/CN116962087B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110116280A (en) * | 2010-04-19 | 2011-10-26 | (주)제이제이 유니언 | Word game simulation system based on grid and motion detection |
| CN109636699A (en) * | 2018-11-06 | 2019-04-16 | 中国电子科技集团公司第五十二研究所 | A kind of unsupervised intellectualized battle deduction system based on deeply study |
| WO2022121769A1 (en) * | 2020-12-07 | 2022-06-16 | 陈贺龄 | Training partner robot control method, system and device, and storage medium |
| CN112968882A (en) * | 2021-02-03 | 2021-06-15 | 南京华鹞信息科技有限公司 | System and method for deducing multi-domain network security policy based on network function virtualization |
| CN114240485A (en) * | 2021-12-06 | 2022-03-25 | 湖南天云软件技术有限公司 | Virtual resource allocation method and related equipment |
| CN115970289A (en) * | 2023-01-09 | 2023-04-18 | 深圳市人马互动科技有限公司 | Plot information generation, evaluation and optimization method based on artificial intelligence technology |
Non-Patent Citations (1)
| Title |
|---|
| 基于可视化分析技术的追踪溯源研究;原雅姣等;保密科学技术;第46-55页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116962087A (en) | 2023-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR20230006033A (en) | Federated calculation processing methods, devices, electronic devices and storage media | |
| CN111144784A (en) | Task allocation method and system for manned/unmanned cooperative formation system | |
| CN114202062A (en) | Network model training method, client and server | |
| CN116962087B (en) | Auxiliary information distribution method, electronic equipment and storage medium | |
| CN111756711A (en) | Flow control method, device, system and storage medium | |
| CN113409898B (en) | Molecular structure acquisition method and device, electronic equipment and storage medium | |
| Li et al. | AngClust: angle feature-based clustering for short time series gene expression profiles | |
| CN117014389A (en) | Computing network resource allocation method and system, electronic equipment and storage medium | |
| CN113139881B (en) | Method, device, equipment and storage medium for identifying main power supply of dual-power-supply user | |
| CN114240157A (en) | Robot scheduling method, system, device and storage medium | |
| CN109801676B (en) | Method and device for evaluating activation effect of compound on gene pathway | |
| CN117688342B (en) | Model-based equipment state prediction method, electronic equipment and storage medium | |
| WO2020047537A1 (en) | Dosevolume histogram and dose distribution based autoplanning | |
| CN116318872B (en) | Method for determining abnormal session through message, electronic equipment and storage medium | |
| CN114615144B (en) | Network optimization method and system | |
| CN116595529B (en) | Information security detection method, electronic equipment and storage medium | |
| WO2024070589A1 (en) | Off-target risk analysis method, off-target risk analysis system, program and recording medium | |
| CN116956296B (en) | Dynamic detection method for file, electronic equipment and storage medium | |
| KR102531826B1 (en) | Method and apparatus for acquiring a random number for blockchain, device and storage medium | |
| CN116962086B (en) | File security detection method and system | |
| KR102378038B1 (en) | Graph generating device and method for obtaining synthetic graphs having properties of target network | |
| CN117609598A (en) | House source information distribution method, medium and equipment | |
| CN118094160A (en) | False flight data determining method, electronic equipment and storage medium | |
| Panesar et al. | A Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique-Based Hybrid Optimisation Algorithm for Efficient Virtual Machine Migration and Task Scheduling | |
| Yao et al. | Knowledge-guided evolutionary algorithm for multi-satellite resource scheduling optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |