CN111830842B - Intelligent household noninductive control system - Google Patents
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application belongs to the field of intelligent home, and relates to an intelligent home noninductive control system. The three-terminal network architecture of the system comprises a local integration terminal, a server terminal and a mobile terminal. The three-layer management and control architecture comprises a monitoring layer, a decision layer and a control layer, wherein the monitoring layer is located at a server end, the decision layer is located at a local integrated end and the server end, and the control layer is located at the local integrated end. And modeling the collected user activity information by using a discrete event system theory by using a human center, and outputting a corresponding equipment control strategy to perform top-level control. When the state of the user is changed, the system equipment automatically adjusts the state to create a proper living environment, so that the comfort and the living convenience of the user are improved. Meanwhile, the home systems are decoupled in a layered manner, corresponding strategies are designed aiming at different layers, all home appliances are coordinated and operated comprehensively, people are intelligently helped to manage the home systems, and safer, worry-saving and comfortable home services and home experiences are provided for people.
Description
Technical Field
The application belongs to the field of intelligent home, in particular to discrete event dynamic system theory, logic control and the like, and provides a non-inductive control method for meeting personalized demands of users, which reduces the operation amount of regulating and controlling equipment for people, thereby better enjoying life.
Background
With the development of emerging technologies such as intelligent perception, internet of things, big data, cloud computing and the like, the heat of intelligent home is continuously increased, and the intelligent level of home appliances is also continuously improved. Although the intelligent level of the household appliances reaches a higher degree nowadays, for the intelligent household systems, users still need to manually set complex logic rules for the intelligent devices, and the control level of the whole household system cannot be matched with the intelligent household systems. For complex and flexible home systems, it is necessary to set and adjust all smart home devices by automatically sensing the needs of the user, thereby providing an ideal living and working environment. How to intelligently help people manage a home system and enable all devices to operate in a coordinated and comprehensive manner is a problem to be solved urgently.
Disclosure of Invention
In order to solve the technical problems, the application provides an intelligent household non-inductance control system.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
a three-terminal network architecture of the intelligent home non-inductance control system comprises a local integration terminal, a server terminal and a mobile terminal, wherein WebSocket communication is adopted among three terminals. The three-layer management and control architecture comprises a monitoring layer, a decision layer and a control layer, wherein the monitoring layer is located at a server end, the decision layer is located at a local integrated end and the server end, and the control layer is located at the local integrated end.
The monitoring layer tracks the state of the office according to the state change of the sensor and the equipment and the instruction sent by the user, and outputs the current state of the office to the decision layer. The monitoring layer is realized by adopting a discrete event system theory, event information of system occurrence is obtained through a multi-sensor data fusion method for a series of sensors and instruction events, and the current state of an office is estimated according to an established state observer.
The construction method of the intelligent home system monitoring layer comprises the following steps:
step 1, defining sensors and user instructions affecting office states:
the office state is mainly divided into two states of human use and long-time unmanned use, depending on d 0 (door and window sensor), m 0 ～m 3 (human body sensor), m r The (virtual body sensor) can be accurately judged.
Meanwhile, instructions for resting and reporting by the user are received by setting up virtual switches "break" and "report" at the local side.
Step 2, establishing an automaton model:
the present application divides offices into four states: sleep state (user leaves office for a long time), working state, resting state, and reporting state. An automaton model is built by defining states in an office and transition events between the states. The automaton model is a four-tuple, as shown in the following equation:
wherein Is a set of states; sigma is the set of events; />Is a transfer function; q 0 Is a subset of states, determined by the initial state of the system; />Is a set of marking states for the system.
Step 3: determining an observable event:
the process of defining each sensor to be triggered and outputting state change information is a sensor event S s The process of each man-machine interaction instruction being sent and received by the office is an instruction event S d 。
When any event sigma in the automaton event set sigma occurs, the trigger sensor event set S is connected s And instruction event set S d Extracting the least event of the one or several events that can uniquely determine the state transition, and referring to the sequence of such one or several least events as the considerable event of the event sigma. The specific correspondence table is as follows:
wherein, the symbol description in the table above is as follows:
by d 0 m 0 Namely theta 1 Indicating that a person enters a room after the door is opened, so that a door and window sensor event and a human body sensor event occur, and the door and window sensor and the human body sensor are opened;namely theta 4 Indicating that a person leaves a room after opening a door, causing a door and window sensor event and a human body sensor event to occur, and closing the door and window sensor and the human body sensor; RE and RP instructions are switches through virtual switches that force switching room state instructions.
Step 4: determining a sequence of observable events:
constructing a state tree containing all possible sensor event sequences in a breadth-first manner can convert the sensor and instruction event sequences into a viewable event sequence.
Initially, the state tree is in an initial state, when a sensor event arrives, the state tree enters a corresponding branch and arrives at a next state, if the current state is a black mark, a corresponding considerable event is formed, and meanwhile, the state tree returns to the initial state to wait for the formation of another considerable event; if the current state is a white mark, continuing to wait for the next sensor event until the reached state is a black mark state, then generating a corresponding considerable event, and returning the state tree to the initial state to wait for the formation of another considerable event. In either the initial state or the white marked state, upon occurrence of an instruction event, the state tree reaches the black marked state and then generates a corresponding visual event, the state tree returns to the initial state waiting for the occurrence of another visual event.
Step 5: building a state observer:
firstly, replacing all automaton events in the automaton model G with corresponding considerable events in a considerable event set theta, and obtaining another uncertain automaton G nd :
G nd ＝(Q,Θ,δ N ,q 0 )
wherein ,
next, an uncertainty automaton G nd Conversion to its corresponding deterministic automaton, i.e. its state observation
G device obs ：
G obs ＝(X,Θ,ξ,x 0 )＝AC(2 Q ,Θ,ξ,Q 0 )
wherein ,
x 0 ＝Q 0 ，
the decision layer judges the equipment to be regulated according to the output of the monitoring layer, designs a regulation and control sequence and an automatic control logic which meet the comfort level experience of the user, and sends a control instruction of the corresponding equipment to the control layer.
The control layer receives the subsequent tasks formulated by the decision layer, and the control work is completed by changing the state variables of the virtual components.
The application has the beneficial effects that: and taking an resident as a center, and respectively applying proper methods in a monitoring layer, a decision layer and a control layer to formulate corresponding control strategies so as to lead a user to get rid of complex setting work. The intelligent home system can set and adjust all intelligent home equipment by automatically sensing the demands of users, so that the interaction level of people and equipment is improved, the non-inductive control of intelligent home is effectively realized, and the demands of users are met.
Drawings
Fig. 1 is an overall layout of an office in an embodiment of the present application.
Fig. 2 is a sensor profile of an office in an embodiment of the present application.
Fig. 3 is a schematic diagram of an intelligent home management and control system according to an embodiment of the present application.
Fig. 4 is a three-layer control framework of the smart home system according to an embodiment of the present application.
Fig. 5 is an automaton of a monitoring layer of an intelligent home system according to an embodiment of the application.
Fig. 6 is a state observer of a monitoring layer of the smart home system according to an embodiment of the present application.
Fig. 7 is a state tree structure model of a monitoring layer of an intelligent home system according to an embodiment of the present application.
Fig. 8 is a diagram of an overall architecture for office state tracking in an embodiment of the present application.
Fig. 9 is a decision flow of working state of an office in an embodiment of the application.
Detailed Description
The technical scheme provided by the application is further described below with reference to specific embodiments and attached drawings. The advantages and features of the present application will become more apparent in conjunction with the following description.
The application particularly relates to an intelligent household non-inductance control system. The system fully utilizes the advantages of different platforms and software, deploys the realization of a monitoring layer, a decision layer and a control layer at different places of the intelligent home software system, and enables the monitoring layer, the decision layer and the control layer to operate in a coordinated manner so as to realize the goal of non-inductive control. The monitoring layer adopts a discrete event system theory to carry out top-level control, and details the change logic of the office state when a sensor event occurs or a user instruction arrives.
The following describes how the non-inductive control of the smart home system is implemented according to the technical scheme provided by the application. In this embodiment, a normal personal office is selected, and a complete intelligent home system is set up, and after a person enters the office, the office dome lamp is automatically turned on for example, and the non-inductive control description is performed through the system.
Fig. 1 is an overall layout diagram of an office of an embodiment, which is divided into an office learning area and a meeting rest area. Fig. 2 is a sensor distribution diagram of an office, including a human body sensor, a temperature sensor, a door and window sensor, an illuminance sensor, and the like. The office has four states, namely: sleep state, rest state, working state and reporting state. When the office is in a dormant state, no user activity is available in the office; when the office is in a working state, a user can work at any position in the office; when the office is in a resting state, the user can rest at any position in the office; when the office is in the reporting state, the user reports using the projector in the office.
The three-terminal network architecture of the office comprises a local integration terminal, a server terminal and a mobile terminal, wherein WebSocket communication is adopted among three terminals, as shown in FIG. 3. The three-layer management and control architecture comprises a monitoring layer, a decision layer and a control layer, wherein the monitoring layer tracks the state of an office according to the state change of a sensor and equipment and an instruction sent by a user, and outputs the current state of the office to the decision layer; the decision layer judges the equipment to be regulated according to the output of the monitoring layer, designs an automatic control logic and sends a control instruction of the corresponding equipment to the control layer; and the control layer controls the equipment according to the decision generated by the decision layer. As shown in fig. 4.
The monitoring layer is implemented in the service layer of the server side. The state of the office is discrete and the transition of the state is triggered by events (sensor events and man-machine interaction events), so the application utilizes the discrete event dynamic system theory to build a monitoring layer model. When a user is in indoor activities of various sensors and intelligent household single products, a series of sensor events can be triggered, meanwhile, the user can trigger a series of instruction events through man-machine interaction, and for the series of sensor and instruction events, the series of sensor and instruction events are converted into a considerable event sequence through a state tree structure based module, and then office state estimation is conducted through the considerable events according to an established state observer.
The monitoring layer is constructed as follows:
step 1, defining sensors and user instructions affecting office states:
the office state is mainly divided into two states of human use and long-time (more than half an hour) unmanned use, and depends on d 0 (door and window sensor), m 0 ～m 3 (human body sensor), m r (virtual human body sensor) can accurately judge, when m 0 ～m 3 Any one sensor is triggered, then m r Is triggered, and when the non-triggering time of any one sensor is less than 20 minutes, m r Keep on; when m is 0 ～m 3 When the non-triggering time of the four sensors is more than 20 minutes, m r Returning to off, so can be aided by m r To determine if someone is currently in the office.
Meanwhile, the user's intention to rest and report is received by setting up virtual switches "break" and "report" on the HASS platform. For example, when the break switch is turned on, it indicates that the user is about to rest, and otherwise indicates that the user is about to end the rest and start working.
Step 2, establishing an automaton model:
the office is divided into four states, namely a dormant state, a working state, a resting state and a reporting state. The states of the automaton are defined as: state 1: a dormant state; state 2: a working state; state 3: rest state; state 4: reporting the state. The state set of automata is:
since these four states of the office are final states in different situations of the system, the set of tag states of the automaton is:
the events of the automaton represent transitions of the office between different states, are located as follows:
b 12 office state is changed from 1 to 2; b 13 The office state is changed from 1 to 3; b 14 Office state is changed from 1 to 4;
c 21 the office state is changed from 2 to 1; c 23 The office state is changed from 2 to 3; c 24 Office state is changed from 2 to 4;
d 31 the office state is changed from 3 to 1; d, d 32 The office state is changed from 3 to 2; d, d 34 The office state is changed from 3 to 4;
e 42 office state is changed from 4 to 2; e, e 43 The office state is changed from 4 to 3;
thus, the event set:
∑＝{b 12 ,b 13 ,b 14 ,c 21 ,c 23 ,c 24 ,d 31 ,d 32 ,d 34 ,e 42 ,e 43 }
the dynamic transition process of office state is defined as a transition functionIt describes the change of state when an event in the event set Σ occurs. The transfer function is defined as follows:
δ(1，b 12 )＝2；δ(1，b 13 )＝3；δ(1，b 14 )＝4；
δ(2，c 21 )＝1；δ(2，c 23 )＝3；δ(2，c 24 )＝4；
δ(3，d 31 )＝1；δ(3，d 32 )＝2；δ(3，d 34 )＝4；
δ(4，e 42 )＝2；δ(4，e 43 )＝3；
since the initial state of the office is uncertain and all states are possible, it is assumed that The final automaton model is shown in fig. 5.
Step 3: determining an observable event:
the sensor events and command events of the present application are shown in the following table.
Thus, the set of sensor events:
instruction event set:
when any event sigma in the automaton event set sigma occurs, the trigger sensor event set S is connected s And instruction event set S d Extracting the least event of the one or several events that can uniquely determine the state transition, and referring to the sequence of such one or several least events as the considerable event of the event sigma.
All automaton events and their corresponding sensor and instruction event sequences and defined observable events are represented by the following table:
the set of observable events is defined as:
Θ＝{θ 1 ,θ 2 ,θ 3 ,θ 4 ,θ 5 ,θ 6 }
step 4: determining a sequence of observable events:
the present application builds a state tree structure model in breadth-first manner. Initial state q of system st Represented by grey, transitional stateRepresented by white, target state->Represented in black. The state transition diagram of the state tree structure model of the present application is shown in fig. 6 below.
Based on the state tree structure model of fig. 6, the sensor and instruction event sequences can be converted into a considerable event sequence. The office is in a dormant state, the user enters the door and works, and the automaton event occurring in the process is b 12 The corresponding sensor event sequence is d 0 m 0 . For sequence d 0 m 0 When transmittingSensor event d 0 m 0 Upon arrival, the state tree generates a considerable event θ 1 And returns to the original state.
Step 5: office state estimation based on state observer
Firstly, replacing all automaton events in the automaton model G with corresponding considerable events in a considerable event set theta, and obtaining another uncertain automaton G nd :
G nd ＝(Q,Θ,δ N ,q 0 )
wherein ,
next, an uncertainty automaton G nd Conversion to its corresponding deterministic automaton, i.e. its state observer G obs ：
G obs ＝(X,Θ,ξ,x 0 )＝AC(2 Q ,Θ,ξ,Q 0 )
wherein ,
x 0 ＝Q 0 ，
according to the method of the state observer shown in step 5, the state observer is established as shown in fig. 7. From this state observer, the user can determine the state of the office, and the triggering condition for the change of the state of the office. The structural framework of the overall office state tracking problem is shown in fig. 8.
After the person enters the office, when automaton event b 12 What happens, i.e. the user enters the office to change the office state from dormant to active, is first a door and window sensor event d 0 What follows is a body sensor event m 0 Last occurring is a door and window sensor eventHowever when d occurs 0 and m0 After the event, a change in office state may be determined.
After receiving the equipment state information transmitted by the local terminal, the server judges the office state according to the method by calling the monitoring layer interface because an event affecting the office state occurs, and the office is changed from the dormant state to the working state. And returning the result to the server side, and transmitting the result to the local integrated side HASS by utilizing Websocket communication.
The decision layer judges the equipment to be regulated according to the output of the monitoring layer, designs a regulation and control sequence and an automatic control logic which meet the comfort level experience of the user, and sends a control instruction of the corresponding equipment to the control layer. Implementation of the decision layer in the local integration end, various component operations when the office state is changed are implemented by using Script scripts in the HASS. Script is implemented as an entity using independent Script building means. The application realizes four scripts, namely a decision layer strategy in four office states, namely an on-sleep state, an on-working state, an on-rest state and an on-report state.
The decision of the device regulatory sequence needs to meet two requirements: firstly, logic between devices is to avoid contradiction and conflict; secondly, because the abrupt change of the equipment state brings uncomfortable feeling to the user, the equipment is optimized, and good experience is created for the user. Considering that the temperature and the humidity are in an automatic regulation mode when the office is in working, resting and reporting states and the situation of no mutation exists, the application mainly considers the influence of illumination change on a user. As the illumination of the sofa area is not the main illumination source of the office, but is only used as the complement light source of the sofa area, the switch floor lamp has small influence on the illumination of the office, and obvious influence can not be brought to the user. In summary, the main impact on user experience is the change in desk area illumination control. The decision layer policy when the office transitions to an operational state is shown in fig. 9.
After the office state is changed into the working state, the decision layer judges that the dome lamp of the working area should be turned on, and sends the result to the control layer.
The input to the control layer is the output of the decision layer. The purpose of the control layer is to ensure that the device is adjusted in place, so the control layer is made up of actuators of the various components. For the virtual component of the present application, the controller of its programming implementation accomplishes the control actions by changing the values of the variables in the component that represent the states. At the moment, the control layer receives a control instruction for switching on the office area ceiling lamp, and controls the office area ceiling lamp to be switched on.
Above, after the people gets into the office, through the office state estimation of monitoring layer, the decision-making layer carries out control decision and sends control command to the control layer, has realized the automatic opening of the dome lamp in office district, has liberated people's both hands, has realized the noninductive control of this office, has satisfied the user demand.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present application, and such modifications and variations should also be regarded as being within the scope of the application.
Claims (3)
1. An intelligent home non-inductance control system is characterized in that: the three-terminal network architecture of the system comprises a local integration terminal, a server terminal and a mobile terminal, wherein WebSocket communication is adopted among three terminals; the three-layer management and control architecture of the system comprises a monitoring layer, a decision layer and a control layer, wherein the monitoring layer is positioned at a server end, the decision layer is positioned at a local integrated end and the server end, and the control layer is positioned at the local integrated end;
the monitoring layer tracks the state of the office according to the state change of the sensor and the equipment and the instruction sent by the user, and outputs the current state of the office to the decision layer; the monitoring layer is realized by adopting a discrete event system theory, for a series of sensors and instruction events, event information of system occurrence is obtained by a multi-sensor data fusion method, and the current state of an office is estimated according to an established state observer;
the construction method of the monitoring layer comprises the following steps:
step 1, defining sensors and user instructions affecting office states:
the office state is divided into two states of using by people and using by no people for a long time, and depends on a door and window sensor d 0 Human body sensor m 0 ～m 3 Virtual human body sensor m r Accurately judging;
meanwhile, a virtual switch 'break' and a virtual switch 'report' are established at a local end to receive instructions of resting and reporting of a user;
step 2, establishing an automaton model:
the offices are divided into four states: sleep state, working state, resting state and reporting state; establishing an automaton model through defining each state in an office and transition events among the states; the automaton model is a four-tuple, as shown in the following equation:
wherein Is a set of states; Σ is the event set; />Is a transfer function; q 0 Is a subset of states, determined by the initial state of the system; />Is a set of marking states for the system;
step 3: determining an observable event:
the process of defining each sensor to be triggered and outputting state change information is a sensor event S s The process of each man-machine interaction instruction being sent and received by the office is an instruction event S d ；
When any event sigma in the automaton event set sigma occurs, the trigger sensor event set S is connected s And instruction event set S d Extracting one or several events of the one or several eventsThe minimum event of the state transition can be uniquely determined in the events, and the sequence formed by one or a plurality of the minimum events is called as an observable event of the event sigma;
step 4: determining a sequence of observable events:
constructing a state tree containing all possible sensor event sequences in a breadth-first manner, and converting the sensor event sequences and instruction event sequences into considerable event sequences;
the state tree is in an initial state, when a sensor event arrives, the state tree enters a corresponding branch and arrives at a next state, if the current state is a black mark, a corresponding considerable event is formed, and meanwhile, the state tree returns to the initial state to wait for the formation of another considerable event; if the current state is a white mark, continuing to wait for the next sensor event until the reached state is a black mark state, then generating a corresponding considerable event, and returning the state tree to the initial state to wait for the formation of another considerable event; in either the initial state or the white mark state, once an instruction event occurs, the state tree reaches the black mark state, then a corresponding considerable event is generated, and the state tree returns to the initial state to wait for the occurrence of another considerable event;
step 5: building a state observer:
firstly, replacing all automaton events in the automaton model G with corresponding considerable events in a considerable event set theta, and obtaining another uncertain automaton G nd :
G nd ＝(Q,Θ,δ N ,q 0 )
wherein ,
next, an uncertainty automaton G nd Conversion to its corresponding deterministic automaton, i.e. its state observer G obs ：
G obs ＝(X,Θ,ξ,x 0 )＝AC(2 Q ,Θ,ξ,Q 0 )
wherein ,
x 0 ＝Q 0 ，。
2. the smart home non-inductive control system of claim 1, wherein: the decision layer judges the equipment to be regulated according to the output of the monitoring layer, designs a regulation and control sequence and an automatic control logic which meet the comfort level experience of the user, and sends a control instruction of the corresponding equipment to the control layer.
3. The smart home non-inductive control system of claim 1, wherein: the control layer receives the subsequent tasks formulated by the decision layer, and the control work is completed by changing the state variables of the virtual components.
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| CN115453897A (en) * | 2022-08-18 | 2022-12-09 | 青岛海尔科技有限公司 | Method and device for determining intention instruction, storage medium and electronic device |
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