CN105678157B - A kind of data property right protection system and method based on application environment identification - Google Patents
A kind of data property right protection system and method based on application environment identification Download PDFInfo
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- CN105678157B CN105678157B CN201610013642.4A CN201610013642A CN105678157B CN 105678157 B CN105678157 B CN 105678157B CN 201610013642 A CN201610013642 A CN 201610013642A CN 105678157 B CN105678157 B CN 105678157B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/45—Structures or tools for the administration of authentication
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
Abstract
The invention discloses a kind of data property right protection system and method based on application environment identification, belong to software analysis technology field, it is broadly divided into environmental samples collection extraction module, Neural Network Optimization module, Data access module, sample to be tested extraction module, classification and six parts of identification module and data access processing module, environmental samples collection extraction module points out target signature while establishing the feature database of environmental samples collection, Neural Network Optimization module optimizes training according to feature database to neural network, Data access module points out the access mode of data, sample to be tested extraction module extracts facility environment sample to be detected, classification and identification module processing sample data obtain recognition result, data access processing module carries out recognition result to sentence fixed sum data matching.The present invention can be residing for automatic identification single machine and network environment, there is certain elasticity to solve the problems, such as a series of pain spots according to the result of Context awareness as certification.
Description
Technical field
The invention belongs to software analysis technology field, it is related to a kind of single machine used for data and Network Recognition technology.
Background technology
With the rapid development of information technology, network has been deep into our life, global IT application has become as people
The main trend of class development, the thing followed is the quickening of all trades and professions IT application process, and applications of computer network field is opened up rapidly
The success of width, enterprise is also increasingly dependent on the various network applications increased rapidly.However, flying along with this application
Speed development, a large amount of relevant information are dispersed in various distributed systems, to cause current and potential various safety
Problem.Go deep into the information age of huge numbers of families in computer and networks, information security has become global problem, ensures user
Safety when use, just by increasingly serious challenge.
Authentication refers to the process of that whether confirmation operation person's identity is legal in computer system, in computer systems body
Identity authentication method can be divided into three categories：The first kind is the legal identity of the information proof user known to user, such as
Password etc., the legal identity for the validation of information operator that this mode is known by verification active user；Second class be according to
The certificate that family is possessed proves the legal identity of user, such as identity card etc..This mode is shown accordingly by active user
Certificate verifies the identity of user；Third class be according to the physical trait of user prove user legal identity, such as palm and
Facial characteristics etc..
According to the condition for needing to verify in authentication procedures, authentication is divided into single-factor certification and double factor authentication.
Wherein, single-factor certification refers to that can prove the legal identity of active user by verifying a condition；Double factor authentication is
Refer to the legal identity that just can prove that active user by verifying two different conditions.It is according to user in authentication procedures
It is no to be divided into software authentication and hardware identification using hardware, static certification is divided into according to the required information of certification and dynamic is recognized
Card.The evolution of identity identifying technology is to develop to hardware identification from software authentication, and double factor is developed to from single-factor certification
Certification and develop to dynamic authentication from static certification.
Dual identity verification provides better mechanism than single-factor certification, our mailbox, social media, bank account
Etc. every aspect should all enable the verification of this dual identity.But enabling dual identity verification can bring verification excessively cumbersome
The problem of.Each user wants to log in a website, must dig out mobile phone, then unlock, and finds identifying code, then on website it is defeated
Enter into.If action is too slow, identifying code is expired, must come again one time.Many users are reluctant to enable due to above
This dual identity verification, no matter the account of oneself is in the desperate situation that may be attacked at any time.Either single-factor certification
Or double factor authentication, will obtain secret key, and data could be obtained by then inputting key, this undoubtedly causes prodigious trouble.
Invention content
The present invention provides a kind of data property right protection system and method based on application environment identification, it is intended to authentication
Single machine and network environment residing for automatic identification user in the process, simplifies authentication procedures, has certain elasticity, according to
The result of Context awareness solves the problems, such as a series of pain spots as certification.
The present invention in order to solve the above-mentioned technical problem, using following technical scheme：
A kind of data property right protection system based on application environment identification, which is characterized in that including：
Environmental samples collection extraction module：Sample stand-alone environment and the environmental samples collection of network environment are obtained, each environment is extracted
The characteristic value of target signature in sample, establishes the feature database of environmental samples collection；
Neural Network Optimization module：The ANN Evolutionary model for establishing part connection, according to the feature of environmental samples collection
Each connection in library, the ANN Evolutionary model connected to the part using genetic algorithm optimizes calculating, obtains most
The ANN Evolutionary model of excellent part connection；
Data access module：When device request to be detected accesses data-interface, judge whether measurement equipment to be checked successfully pacifies
Dress driving allows calling interface to access data if successfully installation driving, does not otherwise allow to use the interface；
Sample to be tested extraction module：The environmental samples of measurement equipment to be checked are obtained, and extract target signature in each environmental samples
Measurement equipment characteristic value to be checked；
Classification and identification module：Measurement equipment characteristic value to be checked is inputted to the ANN Evolutionary model of optimal part connection,
The ANN Evolutionary model treatment connected by optimal part obtains recognition result；
Data access processing module：The recognition result obtained in identification module is judged, if identifying satisfactory single machine
And network environment, that is, correct key is generated, allows to access data, if not can recognize that satisfactory single machine and network rings
Border, then denied access data.
A kind of data property right protection method based on application environment identification, which is characterized in that include the following steps：
Step 1：Sample stand-alone environment and the environmental samples collection of network environment are obtained, target signature in each environmental samples is extracted
Characteristic value, establish the feature database of environmental samples collection；
Step 2：The ANN Evolutionary model for establishing part connection utilizes heredity according to the feature database of environmental samples collection
Algorithm optimizes calculating to each connection for the ANN Evolutionary model that the part connects, and obtains optimal part connection
ANN Evolutionary model；
Step 3：When device request to be detected accesses data-interface, measurement equipment to be checked whether successfully installation driving is judged,
Allow calling interface to access data if successfully installation driving, does not otherwise allow to use the interface；
Step 4:The environmental samples of measurement equipment to be checked are obtained, and extract the measurement equipment to be checked of target signature in each environmental samples
Characteristic value；
Step 5:The ANN Evolutionary model that measurement equipment characteristic value to be checked is inputted to optimal part connection, by optimal
The ANN Evolutionary model treatment of part connection obtains recognition result；
Step 6：The recognition result that judgment step 5 obtains generates if identifying satisfactory single machine and network environment
Correct key allows to access data, if not can recognize that satisfactory single machine and network environment, denied access data.
In said program, the step 1 includes following steps：
Step 1.1：Test is built in LAN, obtains sample stand-alone environment and the environmental samples collection of network environment, institute
Environmental samples collection is stated to be made of M satisfactory environmental samples and N number of undesirable environmental samples；
Step 1.2：Preset H target signature；
Step 1.3：Characteristic value of the target signature in each environmental samples is extracted, the feature database of environmental samples collection is established.
In said program, the step 2 includes following steps：
Step 2.1：Establish the ANN Evolutionary model of part connection, wherein the neural network of part connection into
The input layer for changing model is equipped with H neuron, and each neuron of the input layer may be coupled to 2 nerves of middle layer
Member；The middle layer of the ANN Evolutionary model of the part connection is equipped with 10 neurons, and the middle layer is each neural
Member is connected to 2 neurons of non-input layer；The output layer of the ANN Evolutionary model of the part connection is equipped with 1 nerve
Member, the neuron of the output layer are connect with the neuron of 2 middle layers；
Step 2.2：All characteristic values of each environmental samples are concentrated to input the god that the part connects environmental samples respectively
Through network evolution model；
Step 2.3：Calculate the fitness value of the ANN Evolutionary model of first generation part connection：
Wherein, F is fitness, and Mi (t) is the reality output of satisfactory environmental samples, and Nj (t) is undesirable
Environmental samples reality output, i and j are integer variable, and M is satisfactory environmental samples number, and N is undesirable
Environmental samples number, p are the desired output of positive example, and q is the desired output of counter-example, and T is the sum of " tick ", and T=10, α, β
To balance the factor of positive example and counter-example, wherein α M=β N；
Step 2.4：The fitness threshold value for the ANN Evolutionary model for stopping the connection of optimization part is set；
Step 2.5：By changing weights, cut-out connection and the foundation connection of connection come the neural network for part connection of evolving
Evolution Model；
Step 2.6：Calculate the fitness value by the step 2.5 ANN Evolutionary model that treated partly connects；
Step 2.7：If less than the fitness threshold value in step 2.4, repeatedly step 2.5-2.6, otherwise enters step
2.8；
Step 2.8：The ANN Evolutionary model of fitness the best part connection is chosen as optimal solution, is obtained optimal
The ANN Evolutionary model of part connection.
It is Software Development Kit to be provided for measurement equipment to be checked in said program, in the step 3 and access data-interface
SDK。
In said program, the step 6 includes following steps：
Step 6.1：The recognition result that judgment step 5 obtains defines currently detected if recognition result OP >=0.1
The stand-alone environment and network environment of equipment are satisfactory environment, you can obtain specified data as key；If identification knot
Fruit OP≤- 0.1, then it is undesirable environment, refusal to define the stand-alone environment of currently detected equipment and network environment
Access data；If the recognition result OP acquired is between -0.1 and 0.1, i.e., -0.1<OP<0.1, then definition is current tested
Whether it is satisfactory environment that the stand-alone environment and network environment of the equipment of survey can not be identified, and enters step 6.2；
Step 6.2：Simplify the stand-alone environment of currently detected equipment and the quantity H of network environment characteristics value extraction, it will
The characteristic value of acquisition is identified as new input, and data are defined with the definition in step 6.1 further according to recognition result
Service condition.
In conclusion by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
The ANN Evolutionary model that the present invention will be connected based on part, it is intended to automatic identification user in authentication procedures
Residing single machine and network environment, simplifies authentication procedures, has certain elasticity, according to the result conduct of Context awareness
Certification solves the problems, such as a series of pain spots.
Description of the drawings
Fig. 1 is the single machine and network environment identification process schematic diagram of the present invention；
Fig. 2 is neural network structure schematic diagram；
Fig. 3 is the data structure of the ANN Evolutionary model of part connection.
Specific implementation mode
All features disclosed in this specification or disclosed all methods or in the process the step of, in addition to mutually exclusive
Feature and/or step other than, can combine in any way.
It elaborates to the present invention with reference to Fig. 1.
In order to describe the technical content, the structural feature, the achieved object and the effect of this invention in detail, below in conjunction with embodiment
And attached drawing is coordinated to be explained in detail.
The present invention proposes a kind of data property right protection system and method identified based on application environment, the system and method
Applied to the single machine and network environment residing for automatic identification user in authentication procedures.The flow diagram of entire algorithm is as schemed
1, which includes：
Environmental samples collection extraction module：Sample stand-alone environment and the environmental samples collection of network environment are obtained, each environment is extracted
The characteristic value of target signature in sample, establishes the feature database of environmental samples collection；
Neural Network Optimization module：The ANN Evolutionary model for establishing part connection, according to the feature of environmental samples collection
Each connection in library, the ANN Evolutionary model connected to the part using genetic algorithm optimizes calculating, obtains most
The ANN Evolutionary model of excellent part connection；
Data access module：When device request to be detected accesses data-interface, judge whether measurement equipment to be checked successfully pacifies
Dress driving allows calling interface to access data if successfully installation driving, does not otherwise allow to use the interface；
Sample to be tested extraction module：The environmental samples of measurement equipment to be checked are obtained, and extract target signature in each environmental samples
Measurement equipment characteristic value to be checked；
Classification and identification module：Measurement equipment characteristic value to be checked is inputted to the ANN Evolutionary model of optimal part connection,
The ANN Evolutionary model treatment connected by optimal part obtains recognition result；
Data access processing module：The recognition result obtained in identification module is judged, if identifying satisfactory single machine
And network environment, that is, correct key is generated, allows to access data, if not can recognize that satisfactory single machine and network rings
Border, then denied access data.
The specific implementation process of this system includes step：
Step 1：Sample stand-alone environment and the environmental samples collection of network environment are obtained, target signature in each environmental samples is extracted
Characteristic value, establish the feature database of environmental samples collection；
Step 1.1：Test is built in LAN, obtains sample stand-alone environment and the environmental samples collection of network environment, institute
Environmental samples collection is stated to be made of M satisfactory environmental samples and N number of undesirable environmental samples；
Step 1.2：Preset H target signature；
Step 1.3：Characteristic value of the target signature in each environmental samples is extracted, the feature database of environmental samples collection is established.
Step 2：The ANN Evolutionary model for establishing part connection utilizes heredity according to the feature database of environmental samples collection
Algorithm optimizes calculating to each connection for the ANN Evolutionary model that the part connects, and obtains optimal part connection
ANN Evolutionary model；
Step 2.1：With reference to neural network structure shown in Fig. 2, the ANN Evolutionary model of part connection is established,
In, the input layer of the ANN Evolutionary model of the part connection is equipped with H neuron, each neuron of the input layer
It may be coupled to 2 neurons of middle layer；The middle layer of the ANN Evolutionary model of the part connection is equipped with 10 god
Through member, and each neuron of the middle layer is connected to 2 neurons of non-input layer；The neural network of part connection into
The output layer for changing model is equipped with 1 neuron, and the neuron of the output layer is connect with the neuron of 2 middle layers；Part connects
The data structure of the ANN Evolutionary model connect is as shown in Figure 3.
Step 2.2：All characteristic values of each environmental samples are concentrated to input the god that the part connects environmental samples respectively
Through network evolution model；
Step 2.3：Calculate the fitness value of the ANN Evolutionary model of first generation part connection：
Wherein, F is fitness, and Mi (t) is the reality output of satisfactory environmental samples, and Nj (t) is undesirable
Environmental samples reality output, i and j are integer variable, and M is satisfactory environmental samples number, and N is undesirable
Environmental samples number, p are the desired output of positive example, and q is the desired output of counter-example, and T is the sum of " tick ", and T=10, α, β
To balance the factor of positive example and counter-example, wherein α M=β N；
Step 2.4：The fitness threshold value for the ANN Evolutionary model for stopping the connection of optimization part is set；
Step 2.5：By changing weights, cut-out connection and the foundation connection of connection come the neural network for part connection of evolving
Evolution Model；
Step 2.6：Calculate the fitness value by the step 2.5 ANN Evolutionary model that treated partly connects；
Step 2.7：If less than the fitness threshold value in step 2.4, repeatedly step 2.5-2.6, otherwise enters step
2.8；
Step 2.8：The ANN Evolutionary model of fitness the best part connection is chosen as optimal solution, is obtained optimal
The ANN Evolutionary model of part connection.
Step 3：When device request to be detected accesses data-interface, measurement equipment to be checked whether successfully installation driving is judged,
Allow calling interface to access data if successfully installation driving, does not otherwise allow to use the interface；
Step 4:The environmental samples of measurement equipment to be checked are obtained, and extract the measurement equipment to be checked of target signature in each environmental samples
Characteristic value；
Step 5：The ANN Evolutionary model that measurement equipment characteristic value to be checked is inputted to optimal part connection, by optimal
The ANN Evolutionary model treatment of part connection obtains recognition result；
Step 6：The recognition result that judgment step 5 obtains generates if identifying satisfactory single machine and network environment
Correct key allows to access data, if not can recognize that satisfactory single machine and network environment, denied access data.
Step 6.1：The recognition result that judgment step 5 obtains defines currently detected if recognition result OP >=0.1
The stand-alone environment and network environment of equipment are satisfactory environment, you can obtain specified data as key；If identification knot
Fruit OP≤- 0.1, then it is undesirable environment, refusal to define the stand-alone environment of currently detected equipment and network environment
Access data；If the recognition result OP acquired is between -0.1 and 0.1, i.e., -0.1<OP<0.1, then definition is current tested
Whether it is satisfactory environment that the stand-alone environment and network environment of the equipment of survey can not be identified, and enters step 6.2；
Step 6.2：Simplify the stand-alone environment of currently detected equipment and the quantity H of network environment characteristics value extraction, it will
The characteristic value of acquisition is identified as new input, and data are defined with the definition in step 6.1 further according to recognition result
Service condition.
Preferably, it is Software Development Tools to be provided for measurement equipment to be checked in the Data access module and access data-interface
Wrap SDK.
In present application example, preset H feature can be the M+N environmental samples that given environmental samples are concentrated
General detection feature quantity, preset target signature can be under stand-alone environment：The size of memory, the utilization rate of memory, operation
System, cpu types and efficiency, video card type and size, operating system and hard disk size；Preset target signature under network environment
Can be：Whether message transmission rate in the range of certain regulations, under network environment is in some area for the geographic range of covering
In,
The bit error rate of data transmission whether be less than some define value, network response time whether defined range,
The handling capacity of network whether meet the requirement that data use, the utilization rate of network whether within the limits prescribed, under network environment
Confidentiality whether meet the requirements, the IP residing for current network whether defined range, current network gateway address whether
Whether whether provided in the netmask of defined range, current network in defined range, the initial address of current network
Range, current network gateway MAC address and whether network neighbor's MAC Address group all complies with standard, current network conditions make
Whether agreement meets the requirements.In addition to features above, the extension of feature type can also be carried out according to actual conditions.
Claims (5)
1. a kind of data property right protection system based on application environment identification, which is characterized in that including：
Environmental samples collection extraction module：Sample stand-alone environment and the environmental samples collection of network environment are obtained, each environmental samples are extracted
The characteristic value of middle target signature establishes the feature database of environmental samples collection；
Neural Network Optimization module：The ANN Evolutionary model for establishing part connection, according to the feature database of environmental samples collection, profit
Each connection of the ANN Evolutionary model connected to the part with genetic algorithm optimizes calculating, obtains optimal portion
Divide the ANN Evolutionary model of connection；Including following steps：
Step 2.1：Establish the ANN Evolutionary model of part connection, wherein the ANN Evolutionary mould of the part connection
The input layer of type is equipped with H neuron, and each neuron of the input layer is connected to 2 neurons of middle layer；The portion
Point connection ANN Evolutionary model middle layer be equipped with 10 neurons, and each neuron of the middle layer be connected to it is non-
2 neurons of input layer；The output layer of the ANN Evolutionary model of the part connection is equipped with 1 neuron, described defeated
The neuron for going out layer is connect with the neuron of 2 middle layers；
Step 2.2：All characteristic values of each environmental samples are concentrated to input the nerve net that the part connects environmental samples respectively
Network evolution Model；
Step 2.3：Calculate the fitness value of the ANN Evolutionary model of first generation part connection：
Wherein, F is fitness, and Mi (t) is the reality output of satisfactory environmental samples, and Nj (t) is undesirable ring
The reality output of border sample, i and j are integer variable, and M is satisfactory environmental samples number, and N is undesirable environment
Number of samples, p are the desired output of positive example, and q is the desired output of counter-example, and T is the sum of " t ick ", and T=10, α, β are flat
The factor for the positive example and counter-example of weighing, wherein α M=β N；
Step 2.4：The fitness threshold value for the ANN Evolutionary model for stopping the connection of optimization part is set；
Step 2.5：By changing weights, cut-out connection and the foundation connection of connection come the ANN Evolutionary for part connection of evolving
Model；
Step 2.6：Calculate the fitness value by the step 2.5 ANN Evolutionary model that treated partly connects；
Step 2.7：If less than the fitness threshold value in step 2.4, repeatedly step 2.5-2.6, otherwise enters step 2.8；
Step 2.8：The ANN Evolutionary model of fitness the best part connection is chosen as optimal solution, obtains optimal part
The ANN Evolutionary model of connection；
Data access module：When device request to be detected accesses data-interface, judge whether successfully measurement equipment to be checked drive by installation
It is dynamic, allow calling interface to access data if successfully installation driving, does not otherwise allow to use the interface；
Sample to be tested extraction module：The environmental samples of measurement equipment to be checked are obtained, and extract waiting for for target signature in each environmental samples
Detection device characteristic value；
Classification and identification module：The ANN Evolutionary model that measurement equipment characteristic value to be checked is inputted to optimal part connection, passes through
The ANN Evolutionary model treatment of optimal part connection obtains recognition result；
Data access processing module：The recognition result obtained in identification module is judged, if identifying satisfactory single machine and net
Network environment generates correct key, allow to access data, if not can recognize that satisfactory single machine and network environment,
Denied access data.
2. a kind of data property right protection method based on application environment identification, which is characterized in that include the following steps：
Step 1：Sample stand-alone environment and the environmental samples collection of network environment are obtained, the spy of target signature in each environmental samples is extracted
Value indicative establishes the feature database of environmental samples collection；
Step 2：The ANN Evolutionary model for establishing part connection utilizes genetic algorithm according to the feature database of environmental samples collection
Calculating is optimized to each connection of the ANN Evolutionary model of part connection, obtains the god of optimal part connection
Through network evolution model；Including following steps：
Step 2.1：Establish the ANN Evolutionary model of part connection, wherein the ANN Evolutionary mould of the part connection
The input layer of type is equipped with H neuron, and each neuron of the input layer is connected to 2 neurons of middle layer；The portion
Point connection ANN Evolutionary model middle layer be equipped with 10 neurons, and each neuron of the middle layer be connected to it is non-
2 neurons of input layer；The output layer of the ANN Evolutionary model of the part connection is equipped with 1 neuron, described defeated
The neuron for going out layer is connect with the neuron of 2 middle layers；
Step 2.2：All characteristic values of each environmental samples are concentrated to input the nerve net that the part connects environmental samples respectively
Network evolution Model；
Step 2.3：Calculate the fitness value of the ANN Evolutionary model of first generation part connection：
Wherein, F is fitness, and M i (t) are the reality output of satisfactory environmental samples, and Nj (t) is undesirable ring
The reality output of border sample, i and j are integer variable, and M is satisfactory environmental samples number, and N is undesirable environment
Number of samples, p are the desired output of positive example, and q is the desired output of counter-example, and T is the sum of " t ick ", and T=10, α, β are flat
The factor for the positive example and counter-example of weighing, wherein α M=β N；
Step 2.4：The fitness threshold value for the ANN Evolutionary model for stopping the connection of optimization part is set；
Step 2.5：By changing weights, cut-out connection and the foundation connection of connection come the ANN Evolutionary for part connection of evolving
Model；
Step 2.6：Calculate the fitness value by the step 2.5 ANN Evolutionary model that treated partly connects；
Step 2.7：If less than the fitness threshold value in step 2.4, repeatedly step 2.5-2.6, otherwise enters step 2.8；
Step 2.8：The ANN Evolutionary model of fitness the best part connection is chosen as optimal solution, obtains optimal part
The ANN Evolutionary model of connection；
Step 3：When device request to be detected accesses data-interface, measurement equipment to be checked whether successfully installation driving is judged, if at
Work(installation driving then allows calling interface to access data, does not otherwise allow to use the interface；
Step 4:The environmental samples of measurement equipment to be checked are obtained, and extract the measurement equipment feature to be checked of target signature in each environmental samples
Value；
Step 5:The ANN Evolutionary model that measurement equipment characteristic value to be checked is inputted to optimal part connection, by optimal part
The ANN Evolutionary model treatment of connection obtains recognition result；
Step 6：The recognition result that judgment step 5 obtains generates correct if identifying satisfactory single machine and network environment
Key, allow access data, if not can recognize that satisfactory single machine and network environment, denied access data.
3. the data property right protection method according to claim 2 based on application environment identification, which is characterized in that the step
Rapid 1 includes following steps：
Step 1.1：Test is built in LAN, obtains sample stand-alone environment and the environmental samples collection of network environment, the ring
Border sample set is made of M satisfactory environmental samples and N number of undesirable environmental samples；
Step 1.2：Preset H target signature；
Step 1.3：Characteristic value of the target signature in each environmental samples is extracted, the feature database of environmental samples collection is established.
4. the data property right protection method according to claim 2 based on application environment identification, which is characterized in that the step
It is Software Development Kit SDK to be provided for measurement equipment to be checked in rapid 3 and access data-interface.
5. the data property right protection method according to claim 2 based on application environment identification, which is characterized in that the step
Rapid 6 include following steps：
Step 6.1：The recognition result that judgment step 5 obtains defines currently detected equipment if recognition result OP >=0.1
Stand-alone environment and network environment be satisfactory environment, you can as key obtain specified data；If recognition result OP
≤ -0.1, then it is undesirable environment, denied access to define the stand-alone environment of currently detected equipment and network environment
Data；If the recognition result OP acquired is between -0.1 and 0.1, i.e., -0.1<OP<0.1, then it defines currently detected
Whether it is satisfactory environment that the stand-alone environment and network environment of equipment can not be identified, and enters step 6.2；
Step 6.2：Simplify the stand-alone environment of currently detected equipment and the quantity H of network environment characteristics value extraction, will obtain
Characteristic value be identified as new input, define making for data with the definition in step 6.1 further according to recognition result
Use situation.
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| CN105678157A (en) | 2016-06-15 |
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