CN110096739A - Model generating method, generating means and the terminal device of finite state machine - Google Patents
Model generating method, generating means and the terminal device of finite state machine Download PDFInfo
- Publication number
- CN110096739A CN110096739A CN201910227099.1A CN201910227099A CN110096739A CN 110096739 A CN110096739 A CN 110096739A CN 201910227099 A CN201910227099 A CN 201910227099A CN 110096739 A CN110096739 A CN 110096739A
- Authority
- CN
- China
- Prior art keywords
- event
- input
- attribute
- state machine
- output attribute
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Stored Programmes (AREA)
Abstract
The application is suitable for electronic technology field, provides model generating method, generating means and the terminal device of a kind of finite state machine, comprising: obtains the model parameter of finite state machine to be generated, the model parameter includes input property set and output attribute collection；At least one set of validity event is generated based on the input property set and the output attribute collection, every group of validity event includes an input attribute and an output attribute；Calculate separately the state description function of every group of validity event, and respectively each state description function setup state transition condition；Based on the model parameter and the state transition condition, the model of the finite state machine is generated.By the above method, finite state machine adaptively can be generated according to model parameter, avoid and manually calculate, substantially increase the modeling efficiency of finite state machine.
Description
Technical field
This application involves the model generating method of electronic technology field more particularly to finite state machine, generating means and ends
End equipment.
Background technique
Finite state machine is the number for the behaviors such as indicating limited state and shifted and acted between these states
Learn model.Finite state machine is widely used in the fields such as electronic engineering, linguistics, computer science, logistics.
The modeling method of existing finite state machine is by the state description of each event in manually computation model
Function, and state transition condition is set for each event manually.When the scale of model of finite state machine is larger, artificial calculating
Efficiency is lower.
Summary of the invention
In view of this, model generating method, generating means and terminal that the embodiment of the present application provides finite state machine are set
It is standby, to solve the problems, such as that the efficiency for manually establishing finite state machine model in the prior art is lower.
The first aspect of the embodiment of the present application provides a kind of model generating method of finite state machine, comprising:
The model parameter of finite state machine to be generated is obtained, the model parameter includes input property set and output attribute
Collection；
At least one set of validity event, every group of validity event packet are generated based on the input property set and the output attribute collection
Include an input attribute and an output attribute；
The state description function of every group of validity event is calculated separately, and respectively each state description function setup state turns
Change condition；
Based on the model parameter and the state transition condition, the model of the finite state machine is generated.
The second aspect of the embodiment of the present application provides a kind of model generating means of finite state machine, comprising:
Acquiring unit, for obtaining the model parameter of finite state machine to be generated, the model parameter includes that input belongs to
Property collection and output attribute collection；
Event generation unit, for generating at least one set of effective thing based on the input property set and the output attribute collection
Part, every group of validity event include an input attribute and an output attribute；
Computing unit, for calculating separately the state description function of every group of validity event, and respectively each state description
Function setup state transition condition；
Model generation unit generates the finite state for being based on the model parameter and the state transition condition
The model of machine.
The third aspect of the embodiment of the present application provides a kind of terminal device, including memory, processor and is stored in
In the memory and the computer program that can run on the processor, when the processor executes the computer program
The step of realizing the method that the embodiment of the present application first aspect provides.
The fourth aspect of the embodiment of the present application provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer program, and the computer program realizes the embodiment of the present application when being executed by one or more processors
On the one hand the step of the method provided.
Existing beneficial effect is the embodiment of the present application compared with prior art:
For the embodiment of the present application by the model parameter of acquisition finite state machine to be generated, model parameter includes input attribute
Collection and output attribute collection generate at least one set of validity event based on the input property set and the output attribute collection, and every group has
Effect event includes that model parameter can be freely combined to obtain by an input attribute and an output attribute by the above method
The all possible state of finite state machine；Calculate separately the state description function of every group of validity event, and respectively each shape
State transition condition, by the above method, state needed for capable of adaptively generating finite state machine is arranged in state described function
Switch condition；Based on the model parameter and the state transition condition, the model of the finite state machine is generated.By above-mentioned
Method adaptively can generate finite state machine according to model parameter, avoid and manually calculate, substantially increase limited
The modeling efficiency of state machine.
Detailed description of the invention
It in order to more clearly explain the technical solutions in the embodiments of the present application, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only some of the application
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is the implementation process schematic diagram of the model generating method of finite state machine provided by the embodiments of the present application；
Fig. 2 is the schematic diagram of the model generating means of finite state machine provided by the embodiments of the present application；
Fig. 3 is the schematic diagram of terminal device provided by the embodiments of the present application.
Specific embodiment
In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed
Body details, so as to provide a thorough understanding of the present application embodiment.However, it will be clear to one skilled in the art that there is no these specific
The application also may be implemented in the other embodiments of details.In other situations, it omits to well-known system, device, electricity
The detailed description of road and method, so as not to obscure the description of the present application with unnecessary details.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " instruction is described special
Sign, entirety, step, operation, the presence of element and/or component, but be not precluded one or more of the other feature, entirety, step,
Operation, the presence or addition of element, component and/or its set.
It is also understood that mesh of the term used in this present specification merely for the sake of description specific embodiment
And be not intended to limit the application.As present specification and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in present specification and the appended claims is
Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.
As used in this specification and in the appended claims, term " if " can be according to context quilt
Be construed to " when ... " or " once " or " in response to determination " or " in response to detecting ".Similarly, phrase " if it is determined that " or
" if detecting [described condition or event] " can be interpreted to mean according to context " once it is determined that " or " in response to true
It is fixed " or " once detecting [described condition or event] " or " in response to detecting [described condition or event] ".
In order to illustrate technical solution described herein, the following is a description of specific embodiments.
Fig. 1 is the implementation process schematic diagram of the model generating method of finite state machine provided by the embodiments of the present application, is such as schemed
Shown, the method may include following steps:
Step S101, obtains the model parameter of finite state machine to be generated, and the model parameter includes input property set
With output attribute collection.
Wherein, inputting includes multiple input attributes in property set, and it includes multiple output attributes that output attribute, which is concentrated,.Input belongs to
Property be for describe enter state when movement, i.e., progress state transfer before movement；Output attribute is for describing to exit shape
Movement when state, i.e. movement after progress state transfer.
In finite state machine, when reaching certain condition, input attribute carries out state conversion according to default rule
Obtain output attribute.State transition condition in above-mentioned condition, that is, finite state machine, in default rule, that is, finite state machine
State description function.
Step S102 generates at least one set validity event based on the input property set and the output attribute collection, and every group
Validity event includes an input attribute and an output attribute.
In one embodiment, described that at least one set is generated effectively based on the input property set and the output attribute collection
Event, comprising:
S1021 belongs to the output successively using each input attribute in the input property set as input to be combined
Property each output attribute for concentrating all bases for being combined into basic event with the input to be combined respectively, and combination being obtained
Event generates event sets.
Illustratively, it is assumed that input attribute is concentrated with two input attributes of A, B, and output attribute is concentrated with I, II, III tri-
Output attribute.First using A as input to be combined, I, II, III are combined into basic event with A respectively, then using B as to be combined
Input, is combined into basic event for I, II, III with B respectively, and combination obtains A-I, A-II, A-III, B-I, B-II, B-III altogether
This 6 basic events are generated event sets by this 6 basic events.
S1022, when the number of event basic in the event sets is equal to preset value, judge be in the event sets
It is no there are repeated events, the preset value be inputted in the input property set attribute number and output attribute concentration it is defeated
The product of the number of attribute out, the repeated events include at least two identical basic events.
After input attribute all in input property set is combined with all output attributes that output attribute is concentrated,
The number of basic event in event sets should be equal to preset value.It is in this example, defeated as shown in the example of step S1021
Enter attribute and be concentrated with 2 input attributes, output attribute is concentrated with 3 output attributes, therefore preset value is 2*3=6.
S1023, if there are repeated events in the event sets, by any one basic thing in the repeated events
Part deletes the basic event in the repeated events in addition to the reservation event as reservation event.
Wherein, identical basic event refers to, input attribute is identical and the identical basic event of output attribute.
Step S103 calculates separately the state description function of every group of validity event, and respectively each state description function
State transition condition is set.
In one embodiment, the state description function for calculating separately every group of validity event, comprising:
Using the input attribute in the validity event as independent variable, using the output attribute in the validity event as because
Variable.
The mapping relations between the independent variable and the dependent variable are calculated, and using the mapping relations as the event
State description function.
In practice, it is possible that emergency event, therefore there are also consider the case where emergency event occur.Emergency event pair
The attribute answered is burst profile.
In one embodiment, the state description function for calculating separately every group of validity event, further includes:
Obtain the corresponding burst profile of the validity event.
It regard the input attribute in the burst profile and the validity event as independent variable, it will be in the validity event
Output attribute as dependent variable.
The mapping relations between the independent variable and the dependent variable are calculated, and using the mapping relations as the event
State description function.
In one embodiment, described is respectively each state description function setup state transition condition, comprising:
Using the coefficient of independent variable in the state description function as state transition condition.
Illustratively, it is assumed that input attribute is X, output attribute Y.
In the case where no emergency event, calculated state description function is Y=kX+b, then using k as this shape
The state transition condition of state described function, i.e., when input attribute is X, meets condition k, state can be switched to output attribute Y.
In the case where there is emergency event, calculated state description function is Y=k1X1+k2X2+b(X1To input attribute,
X2For burst profile), then by k1、k2State transition condition as this state description function, i.e., when input attribute is
X1, meet condition k1And k2When, state can be switched to output attribute Y.
It should be noted that the above-mentioned example that state transition condition is only arranged, does not retouch state transition condition, state
The form etc. for stating function is specifically limited.
Step S104 is based on the model parameter and the state transition condition, generates the model of the finite state machine.
For the embodiment of the present application by the model parameter of acquisition finite state machine to be generated, model parameter includes input attribute
Collection and output attribute collection generate at least one set of validity event based on the input property set and the output attribute collection, and every group has
Effect event includes that model parameter can be freely combined to obtain by an input attribute and an output attribute by the above method
The all possible state of finite state machine；Calculate separately the state description function of every group of validity event, and respectively each shape
State transition condition, by the above method, state needed for capable of adaptively generating finite state machine is arranged in state described function
Switch condition；Based on the model parameter and the state transition condition, the model of the finite state machine is generated.By above-mentioned
Method adaptively can generate finite state machine according to model parameter, avoid and manually calculate, substantially increase limited
The modeling efficiency of state machine.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present application constitutes any limit
It is fixed.
Fig. 2 is the schematic diagram of the model generating means of finite state machine provided by the embodiments of the present application, for ease of description,
Part relevant to the embodiment of the present application is only shown.
The model generating means of finite state machine shown in Fig. 2 can be the software list being built in existing terminal device
Member, hardware cell or soft or hard combination unit, can also be used as independent pendant and be integrated into the terminal device, can be with
Exist as independent terminal device.
The model generating means 2 of the finite state machine include:
Acquiring unit 21, for obtaining the model parameter of finite state machine to be generated, the model parameter includes input
Property set and output attribute collection.
Event generation unit 22 is at least one set of effective for being generated based on the input property set and the output attribute collection
Event, every group of validity event include an input attribute and an output attribute.
Computing unit 23, for calculating separately the state description function of every group of validity event, and respectively each state is retouched
State function setup state transition condition.
Model generation unit 24 generates the limited shape for being based on the model parameter and the state transition condition
The model of state machine.
Optionally, the event generation unit 22 includes:
Composite module, for successively using it is described input property set in each input attribute as input to be combined, by institute
The each output attribute for stating output attribute concentration is combined into basic event with the input to be combined respectively, and combination is obtained
All basis events generate event sets.
Judgment module, for judging the event when the number of event basic in the event sets is equal to preset value
It whether there is repeated events in set, the preset value is that the number of input attribute and the output belong in the input property set
Property concentrate output attribute number product, the repeated events include at least two identical basic events.
Removing module, if for there are repeated events in the event sets, it will be any one in the repeated events
A basis event deletes the basic event in the repeated events in addition to the reservation event as reservation event.
Optionally, the computing unit 23 includes:
First mark module, for using the input attribute in the validity event as independent variable, by the validity event
In output attribute as dependent variable.
First computing module is reflected for calculating the mapping relations between the independent variable and the dependent variable, and by described
Penetrate state description function of the relationship as the event.
Optionally, the computing unit 23 further include:
Module is obtained, for obtaining the corresponding burst profile of the validity event.
Second mark module, for being used as the input attribute in the burst profile and the validity event from change
Amount, using the output attribute in the validity event as dependent variable.
Second computing module is reflected for calculating the mapping relations between the independent variable and the dependent variable, and by described
Penetrate state description function of the relationship as the event.
Optionally, the computing unit 23 further include:
Conversion module, for using the coefficient of independent variable in the state description function as state transition condition.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
Fig. 3 is the schematic diagram of terminal device provided by the embodiments of the present application.As shown in figure 3, the terminal device 3 of the embodiment
Include: processor 30, memory 31 and is stored in the calculating that can be run in the memory 31 and on the processor 30
Machine program 32.The processor 30 realizes the model generation side of above-mentioned each finite state machine when executing the computer program 32
Step in method embodiment, such as step S101 to S104 shown in FIG. 1.Alternatively, the processor 30 executes the computer
The function of each module/unit in above-mentioned each Installation practice, such as the function of module 21 to 24 shown in Fig. 2 are realized when program 32.
Illustratively, the computer program 32 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 31, and are executed by the processor 30, to complete the application.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 32 in the terminal device 3 is described.For example, the computer program 32 can be divided
It is cut into acquiring unit, event generation unit, computing unit, model generation unit, each unit concrete function is as follows:
Acquiring unit, for obtaining the model parameter of finite state machine to be generated, the model parameter includes that input belongs to
Property collection and output attribute collection.
Event generation unit, for generating at least one set of effective thing based on the input property set and the output attribute collection
Part, every group of validity event include an input attribute and an output attribute.
Computing unit, for calculating separately the state description function of every group of validity event, and respectively each state description
Function setup state transition condition.
Model generation unit generates the finite state for being based on the model parameter and the state transition condition
The model of machine.
Optionally, the event generation unit includes:
Composite module, for successively using it is described input property set in each input attribute as input to be combined, by institute
The each output attribute for stating output attribute concentration is combined into basic event with the input to be combined respectively, and combination is obtained
All basis events generate event sets.
Judgment module, for judging the event when the number of event basic in the event sets is equal to preset value
It whether there is repeated events in set, the preset value is that the number of input attribute and the output belong in the input property set
Property concentrate output attribute number product, the repeated events include at least two identical basic events.
Removing module, if for there are repeated events in the event sets, it will be any one in the repeated events
A basis event deletes the basic event in the repeated events in addition to the reservation event as reservation event.
Optionally, the computing unit includes:
First mark module, for using the input attribute in the validity event as independent variable, by the validity event
In output attribute as dependent variable.
First computing module is reflected for calculating the mapping relations between the independent variable and the dependent variable, and by described
Penetrate state description function of the relationship as the event.
Optionally, the computing unit further include:
Module is obtained, for obtaining the corresponding burst profile of the validity event.
Second mark module, for being used as the input attribute in the burst profile and the validity event from change
Amount, using the output attribute in the validity event as dependent variable.
Second computing module is reflected for calculating the mapping relations between the independent variable and the dependent variable, and by described
Penetrate state description function of the relationship as the event.
Optionally, the computing unit further include:
Conversion module, for using the coefficient of independent variable in the state description function as state transition condition.
The terminal device 3 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The terminal device may include, but be not limited only to, processor 30, memory 31.It will be understood by those skilled in the art that Fig. 3
The only example of terminal device 3 does not constitute the restriction to terminal device 3, may include than illustrating more or fewer portions
Part perhaps combines certain components or different components, such as the terminal device can also include input-output equipment, net
Network access device, bus etc..
Alleged processor 30 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 31 can be the internal storage unit of the terminal device 3, such as the hard disk or interior of terminal device 3
It deposits.The memory 31 is also possible to the External memory equipment of the terminal device 3, such as be equipped on the terminal device 3
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..Further, the memory 31 can also both include the storage inside list of the terminal device 3
Member also includes External memory equipment.The memory 31 is for storing needed for the computer program and the terminal device
Other programs and data.The memory 31 can be also used for temporarily storing the data that has exported or will export.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
Scope of the present application.
In embodiment provided herein, it should be understood that disclosed device/terminal device and method, it can be with
It realizes by another way.For example, device described above/terminal device embodiment is only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the application realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program generation
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..The computer-readable medium
It may include: any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic that can carry the computer program code
Dish, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as in certain jurisdictions, according to legislation and patent practice, computer-readable medium do not include be electric carrier signal and
Telecommunication signal.
Embodiment described above is only to illustrate the technical solution of the application, rather than its limitations；Although referring to aforementioned reality
Example is applied the application is described in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution should all
Comprising within the scope of protection of this application.
Claims (10)
1. a kind of model generating method of finite state machine characterized by comprising
The model parameter of finite state machine to be generated is obtained, the model parameter includes input property set and output attribute collection；
At least one set of validity event is generated based on the input property set and the output attribute collection, every group of validity event includes one
A input attribute and an output attribute；
Calculate separately the state description function of every group of validity event, and respectively each state description function setup state conversion stripes
Part；
Based on the model parameter and the state transition condition, the model of the finite state machine is generated.
2. the model generating method of finite state machine as described in claim 1, which is characterized in that described to be belonged to based on the input
Property collection and the output attribute collection generate at least one set of validity event, comprising:
Successively using each input attribute in the input property set as input to be combined, the output attribute is concentrated every
A output attribute is combined into basic event with the input to be combined respectively, and all basic events that combination is obtained generate thing
Part set；
When the number of event basic in the event sets is equal to preset value, judge in the event sets with the presence or absence of repetition
Event, the preset value are the number that attribute is inputted in the input property set and of output attribute concentration output attribute
Several products, the repeated events include at least two identical basic events；
If there are repeated events in the event sets, using any one basic event in the repeated events as reservation
Event, and delete the basic event in the repeated events in addition to the reservation event.
3. the model generating method of finite state machine as described in claim 1, which is characterized in that described to calculate separately every group and have
The state description function of effect event, comprising:
Using the input attribute in the validity event as independent variable, using the output attribute in the validity event as because becoming
Amount；
The mapping relations between the independent variable and the dependent variable are calculated, and using the mapping relations as the shape of the event
State described function.
4. the model generating method of finite state machine as described in claim 1, which is characterized in that described to calculate separately every group and have
The state description function of effect event, further includes:
Obtain the corresponding burst profile of the validity event；
It regard the input attribute in the burst profile and the validity event as independent variable, it will be defeated in the validity event
Attribute is as dependent variable out；
The mapping relations between the independent variable and the dependent variable are calculated, and using the mapping relations as the shape of the event
State described function.
5. such as the model generating method of the described in any item finite state machines of claim 3 or 4, which is characterized in that the difference
For each state description function setup state transition condition, comprising:
Using the coefficient of independent variable in the state description function as state transition condition.
6. a kind of model generating means of finite state machine characterized by comprising
Acquiring unit, for obtaining the model parameter of finite state machine to be generated, the model parameter includes input property set
With output attribute collection；
Event generation unit, for generating at least one set of validity event based on the input property set and the output attribute collection,
Every group of validity event includes an input attribute and an output attribute；
Computing unit, for calculating separately the state description function of every group of validity event, and respectively each state description function
State transition condition is set；
Model generation unit generates the finite state machine for being based on the model parameter and the state transition condition
Model.
7. the model generating means of finite state machine as claimed in claim 6, which is characterized in that the event generation unit packet
It includes:
Composite module, for successively using it is described input property set in each input attribute as input to be combined, will be described defeated
Each output attribute in property set is combined into basic event with the input to be combined respectively out, and owns what combination obtained
Basic event generates event sets；
Judgment module, for judging the event sets when the number of event basic in the event sets is equal to preset value
In whether there is repeated events, the preset value be it is described input property set in input attribute number and the output attribute collection
The product of the number of middle output attribute, the repeated events include at least two identical basic events；
Removing module, if for there are repeated events in the event sets, by any one base in the repeated events
Plinth event deletes the basic event in the repeated events in addition to the reservation event as reservation event.
8. the model generating method of finite state machine as claimed in claim 7, which is characterized in that the computing unit includes:
First mark module will be in the validity event for using the input attribute in the validity event as independent variable
Output attribute is as dependent variable；
First computing module is closed for calculating the mapping relations between the independent variable and the dependent variable, and by the mapping
It is the state description function as the event.
9. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 5 when executing the computer program
The step of any one the method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 5 of realization the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227099.1A CN110096739A (en) | 2019-03-25 | 2019-03-25 | Model generating method, generating means and the terminal device of finite state machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227099.1A CN110096739A (en) | 2019-03-25 | 2019-03-25 | Model generating method, generating means and the terminal device of finite state machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110096739A true CN110096739A (en) | 2019-08-06 |
Family
ID=67444024
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910227099.1A Pending CN110096739A (en) | 2019-03-25 | 2019-03-25 | Model generating method, generating means and the terminal device of finite state machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110096739A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114844711A (en) * | 2022-05-17 | 2022-08-02 | 北京经纬恒润科技股份有限公司 | Vehicle-mounted Ethernet safety state detection method and device |
-
2019
- 2019-03-25 CN CN201910227099.1A patent/CN110096739A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114844711A (en) * | 2022-05-17 | 2022-08-02 | 北京经纬恒润科技股份有限公司 | Vehicle-mounted Ethernet safety state detection method and device |
| CN114844711B (en) * | 2022-05-17 | 2024-04-09 | 北京经纬恒润科技股份有限公司 | Method and device for detecting safety state of vehicle-mounted Ethernet |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110020620A (en) | Face identification method, device and equipment under a kind of big posture | |
| Cator et al. | Susceptible-infected-susceptible epidemics on networks with general infection and cure times | |
| CN109740739A (en) | Neural computing device, neural computing method and Related product | |
| CN109062610A (en) | Processing with Neural Network device and its method for executing Givens rotation instruction | |
| CN109740754A (en) | Neural computing device, neural computing method and Related product | |
| CN110163362A (en) | A kind of computing device and method | |
| Ozfatura et al. | Gradient coding with clustering and multi-message communication | |
| Zhang et al. | Automatic design of deterministic and non-halting membrane systems by tuning syntactical ingredients | |
| CN108446758B (en) | Artificial intelligence calculation-oriented neural network data serial flow processing method | |
| CN110096739A (en) | Model generating method, generating means and the terminal device of finite state machine | |
| CN110505655A (en) | Data command processing method, storage medium and bluetooth shield | |
| CN109064049A (en) | A kind of dynamic divides the method, apparatus and terminal device of risk zones | |
| CN109214519A (en) | Data processing system, method and apparatus | |
| CN103888246A (en) | Low-energy-consumption small-area data processing method and data processing device thereof | |
| Wei et al. | Drl-deploy: adaptive service function chains deployment with deep reinforcement learning | |
| CN104123431A (en) | Element modular inversion calculation method and device | |
| CN108399455B (en) | Scheduling method and device based on genetic algorithm | |
| CN110211227A (en) | A kind of method for processing three-dimensional scene data, device and terminal device | |
| CN110071879A (en) | Method for message transmission, terminal device and storage medium | |
| CN109063926A (en) | Plan generated energy determines method, system and terminal device in period | |
| CN111125977B (en) | Design method and device of MMC simulation model and computer readable storage medium | |
| CN109716288A (en) | Network model compiler and Related product | |
| CN107818522A (en) | Custom power status inquiry method and terminal device | |
| CN109783569A (en) | A kind of account book recording method, device and terminal device based on block chain | |
| Drossel et al. | Critical Boolean networks with scale-free in-degree distribution |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190806 |
|
| WD01 | Invention patent application deemed withdrawn after publication |