CN111800444B - Control system and control method capable of being dynamically adjusted - Google Patents
Control system and control method capable of being dynamically adjusted Download PDFInfo
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- CN111800444B CN111800444B CN201910281134.8A CN201910281134A CN111800444B CN 111800444 B CN111800444 B CN 111800444B CN 201910281134 A CN201910281134 A CN 201910281134A CN 111800444 B CN111800444 B CN 111800444B
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Abstract
The application discloses a dynamically adjustable control system, which comprises a cloud platform and at least one networking system connected to the cloud platform through the Internet, wherein the networking system comprises at least one first type of equipment, at least one second type of equipment and a first central node equipment selected by all the second type of equipment according to decision conditions; the second type of equipment and the first center node equipment are matched and triggered by the event multicast by the first type of equipment according to the execution rule strategy in the current rule data, and when the matching is successful, an execution instruction is sent to the first type of equipment, and the execution effect data from the first type of equipment is forwarded to the cloud platform; the cloud platform adjusts the execution rule strategy in the rule data based on the big data at least comprising the execution effect data, and synchronizes the adjusted rule data to the networking system.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to a control system and a control method capable of being dynamically adjusted.
Background
The intelligent control is a control mode with intelligent information processing, intelligent information feedback and intelligent control decision, and is widely applied to the fields of production processes, electric power systems, automobile driving, home control and the like.
Taking the control of the smart home as an example, in the aspect of the control of the smart home, the most used by the user is a scenario formed by an automated linkage function and a set of actions (controls) executed by a plurality of devices according to the linkage function, but after the configuration of the conventional scenario or the smart home automation control system, only a central control device performs centralized control on each execution device, and sends instructions to each execution device in a simple sequence manner, that is, the order sending sequence is taken as a rule.
Referring to fig. 1, fig. 1 shows an architecture diagram of a conventional centralized control system. A first type of device, such as a door sensor, which has only an event triggering or instruction executing function, and which triggers a door opening or closing event as a triggering device; such as a motorized window treatment, as an execution device that performs an action based on a received control command; the first type of equipment establishes wired/wireless Ethernet connection with a central node through a router, or directly establishes wireless connection with the central node, and the central node, the first type of equipment establishing connection with the same central node and the router form a networking system; in a networking system, all messages are processed through central node equipment, for example, intelligent home gateway equipment; each networking system is accessed to the cloud platform through the Internet, and the Application (APP) of a user is accessed to the cloud platform.
Referring to fig. 2A and 2B, fig. 2A shows a control process timing diagram based on a centralized control system, and fig. 2B shows a flow diagram of event-triggered linkage of the centralized control system. The method comprises the following steps:
the user sends the configured automation rule data (linkage rules and/or scene rules) to the cloud platform through the APP, and the cloud platform feeds back a success confirmation to the user;
the cloud platform sends the received rule data to the central node equipment, and the node equipment feeds back a confirmation to the cloud platform after receiving the rule data;
when the first-class equipment reports the event trigger to the central node, the central node performs linkage rule matching, and sends an execution instruction to the first-class equipment executing the instruction according to a matching result (when the matching is successful).
In the centralized control system, after the rule data is configured, the rules executed by the central control equipment are all static and unchanged, so that the execution effect is the same every time; when there are many rules, the pressure of rule execution is concentrated on the central device, which results in slow response time of the central device and prolonged time for sending execution instructions, and affects the user experience. If the control equipment is down, the whole control system is crashed.
Disclosure of Invention
In view of this, the present invention provides a control system and method capable of dynamic adjustment to improve the overall control efficiency of the control system.
The invention provides a dynamically adjustable control system, which comprises a cloud platform for receiving rule data configured by intelligent terminal application and at least one networking system connected with the cloud platform through the Internet,
the networking system comprises at least one first type device with an event triggering or instruction executing function, at least one second type device with a rule analyzing and matching function and connected to the same local area network, and a first central node device selected by all the second type devices from all the second type devices according to a decision condition;
the first type of equipment is directly connected to the local area network or connected to second type of equipment; the local area network is accessed to the Internet;
the second type of equipment and the first center node equipment are matched and triggered by the event multicast by the first type of equipment according to the execution rule strategy in the current rule data, and when the matching is successful, an execution instruction is sent to the first type of equipment, and the execution effect data from the first type of equipment is forwarded to the cloud platform;
and the cloud platform adjusts the execution rule strategy in the rule data based on the big data at least comprising the execution effect data, and synchronizes the adjusted rule data to the first central node equipment, so that the first central node equipment synchronizes the rule data to the second equipment.
Preferably, the second type of device is connected with the internet through a router;
the first type of equipment is connected with the Internet through a router or is connected with the second type of equipment in a wireless or low-speed wired mode.
The rule data further comprises scene rule data and linkage rule data; the big data also comprises equipment association relation data, linkage effect data, equipment types and equipment residual performance data;
the cloud platform further comprises an execution device main body which triggers the adjustment of the rule execution strategy when the optimization triggering condition is met, and adjusts the scene rule and/or the linkage rule according to the optimization strategy based on the big data.
Wherein the optimization triggering condition comprises triggering at fixed period timing, or triggering when the quantity of data in the execution effect data larger than the execution time threshold exceeds a first threshold,
the optimization strategy may include the steps of,
preferentially selecting second-class equipment with parent-child association relation based on the equipment association relation;
if the device has a parent-child association relationship, selecting a second type of device with optimal linkage effect data based on the linkage effect data;
if the linkage effect data are the same, selecting second equipment connected with the first equipment with the optimal execution effect data based on the execution effect data;
if the execution effect data are the same, selecting second equipment with the optimal equipment type based on the second equipment type;
and if the types of the equipment are the same, selecting the second type equipment with the optimal residual performance data and the minimum current execution rule number based on the residual performance data and the execution rule number of the second type equipment.
Preferably, the first central node device further includes, when the first central node device does not meet the central node requirement, re-initiating the central node decision process, and deciding out the second central node device; the first central node device synchronizes the current rule data to the second central node, so that the second central node synchronizes the current rule data to the second type of device; the first central node device is changed to a second type of device.
Preferably, the second type of device further includes, after detecting that the heartbeat is disconnected, re-initiating a decision process to decide out a second central node device;
and if the first central node equipment is recovered, selecting one of the first central node equipment and the second central node equipment as the central node equipment according to the equipment information, and recovering the other one as second-class equipment. 7. The control system according to claim 5 or 6, wherein the decision process includes that all the second-type devices in the local area network multicast their respective device information through the local area network, and each second-type device respectively compares the obtained device information of all the second-type devices, and selects a second-type device as the first central node device according to the same decision condition; and reporting the selected first center node equipment to the cloud platform, and authenticating the second type of equipment in the local area network.
The decision condition comprises selecting a second type of equipment with the highest priority according to an equipment model priority list;
if the equipment model priorities of the second type of equipment are the same, selecting the second type of equipment with the highest version number according to the equipment version number;
if the highest version numbers are the same, selecting second equipment with the longest running time according to the running time of the equipment;
and if the running times of the equipment are the same, selecting the second type of equipment with the largest serial number according to the serial number of the equipment.
The invention provides a control method capable of dynamic adjustment, which comprises the steps that on the cloud platform side,
receiving execution effect data from a first type of device having an event trigger or instruction execution function in the networking system through the internet,
adjusting an enforcement rule policy in the rule data based on big data including at least the enforcement effect data,
synchronizing the adjusted rule data to a first central node device selected from all second devices by all the second devices which have rule analysis and matching functions and are connected to the same local area network according to decision conditions in the networking system so as to ensure that: the first central node device synchronizes the rule data to the second class device, the second class device and the first central node device are matched and triggered by the event multicast by the first class device according to the execution rule strategy in the current rule data, and when the matching is successful, an execution instruction is sent to the first class device;
and the execution effect data is reported to the cloud platform by the first type of equipment through the local area network or a second type of equipment connected with the first type of equipment through the accessed Internet.
The rule data further comprises scene rule data and linkage rule data; the big data also comprises equipment association relation data, linkage effect data, equipment types and equipment residual performance data;
the method further comprises the step of triggering the regulation of the rule execution strategy when the optimization triggering condition is met, and regulating the execution equipment main body of the scene rule and/or the linkage rule according to the optimization strategy based on the big data.
Preferably, the optimization triggering condition includes triggering at fixed period timing, or triggering when the number of data in the execution effect data larger than the execution time threshold exceeds a first threshold,
the optimization strategy comprises the steps of, in combination,
based on the association relationship of the equipment, preferentially selecting second equipment with parent-child association relationship;
if the device has a parent-child association relationship, selecting a second type of device with optimal linkage effect data based on the linkage effect data;
if the linkage effect data are the same, selecting second equipment connected with the first equipment with the optimal execution effect data based on the execution effect data;
if the execution effect data are the same, selecting second equipment with the optimal equipment type based on the second equipment type;
and if the types of the equipment are the same, selecting the second type equipment with the optimal residual performance data and the minimum current execution rule number based on the residual performance data and the execution rule number of the second type equipment.
Preferably, the method further comprises, when the first central node device does not meet the central node requirement, re-initiating the central node decision process, and deciding out a second central node device; the first central node device synchronizes the current rule data to the second central node, so that the second central node synchronizes the current rule data to the second type device; the first central node device is changed to a second type of device.
Preferably, the method further includes that the second type of device which detects the heartbeat loss initiates the decision process again to decide out the second center node device;
and if the first central node equipment is recovered, the first central node equipment and the second central node equipment select one of the first central node equipment and the second central node equipment as the central node equipment according to the equipment information, and the other one of the first central node equipment and the second central node equipment is recovered as second-class equipment.
The decision process comprises that all second-class devices in the local area network multicast respective device information through the local area network, all second-class devices respectively compare the obtained device information of all second-class devices, and a second-class device is selected as the first central node device according to the same decision condition; and the selected first center node equipment reports to the cloud platform and authenticates the second type of equipment in the local area network.
The decision conditions comprise that a second type of equipment with the highest priority is selected according to the equipment model priority list;
if the equipment model priorities of the second type of equipment are the same, selecting the second type of equipment with the highest version number according to the equipment version number;
if the highest version numbers are the same, selecting second equipment with the shortest running time according to the running time of the equipment;
and if the running times of the equipment are the same, selecting the second type of equipment with the highest serial number according to the serial number of the equipment.
The invention avoids the defect of centralized control in a centralized control mode by adding at least one second type device with rule analysis and matching functions in a networking system and by using a central node device decided by all the second type devices on line; the cloud platform adjusts the execution rule strategy in the rule data based on the big data comprising the execution effect data, and synchronizes the adjusted rule data to the networking system, so that the execution rule is not static and is optimized according to the big data such as the execution effect data after the user triggers or the equipment triggers the event, the optimal control effect is dynamically adjusted, the single centralized control weakness is solved, certain equipment processing load balance is realized, the equipment control efficiency is improved, the linkage response speed is higher, and the user experience effect is better.
Drawings
FIG. 1 is a schematic diagram of an architecture of a prior art centralized control system;
fig. 2A shows a control process timing diagram based on a centralized control system, and fig. 2B shows a flow diagram of event-triggered linkage of the centralized control system.
Fig. 3 is a schematic structural diagram of a dynamically adjustable control system according to an embodiment of the present invention.
Fig. 4 is a schematic flow chart of a process for determining the current central node device by all the second devices connected to the same router.
Fig. 5A is a schematic timing diagram of synchronization data performed by the central node device and a timing diagram of a change of the central node device, and fig. 5B is a schematic timing diagram of synchronization of rule data from the central node device to the cloud platform.
Fig. 6A is a timing diagram of a dynamically adjustable control process according to an embodiment of the present invention, and fig. 6B is a flowchart illustrating processing of a first type of device event trigger in a networking system.
Fig. 7 is a schematic diagram of device association relationship.
Fig. 8 is a schematic structural diagram of an intelligent home control system.
Detailed Description
For the purpose of making the objects, technical means and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings.
The conception of the invention is as follows: adding at least one second type device which has rule analysis and matching functions and uploads execution effect data from the first type device, such as gateway device and the like, in the networking system, wherein the second type device can integrate richer functions relative to the first type device; deciding a second type device from all second type devices connected to the same local area network as a central node device; the cloud platform executes the strategy data according to a big data updating rule formed by the reported execution effect data, the equipment association relation and other parameters, and synchronizes the strategy data to the central node equipment; the central node device synchronizes the updated rule execution policy data to the current second-class device in the local area network, so that the second-class device executes according to the updated rule execution policy, and thus, the execution rules of the second-class devices can be dynamically adjusted, and the technical effect of load balancing is achieved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a dynamically adjustable control system according to an embodiment of the present invention.
The second type of equipment connected to the same local area network is connected with an external network (Internet) through a router, wherein one current central node equipment decided by the second type of equipment connected to the same local area network is used for synchronizing rule data with a cloud platform connected to the Internet, and the rule data at least comprises a scene rule, a linkage rule and a rule execution strategy; at the same time, the rule data are synchronized with the current second type of device, and the central node contains the functionality of all second type devices.
The linkage rule is an association rule of event-triggered linkage, the scene rule is an association rule of a set of a series of actions executed by a plurality of devices, the set of the linkage rules can form a scene rule, and the rule execution strategy is associated data information executed by the scene rule and the linkage rule. For example:
rule 1, when the door lock is opened, the electric curtain is closed, and the rule 1 is a linkage rule;
rule 2: when the door lock is opened, the electric curtain is closed, the switch panel controls the lighting lamp to be opened, the air conditioner controller controls the air conditioner to be opened, and the rule 2 is a scene rule;
the second type device can be directly connected with the first type device in a low-speed wired connection mode or a wireless connection mode, and can also be connected with the first type device through the mounted second type device, so that the second type device is used for matching the linkage rule and the scene rule according to the rule execution strategy, sending the execution instruction to the first type device according to the matching result, and uploading the execution effect data reported by the first type device to the cloud platform. The second type of equipment is different from the central node equipment in that the second type of equipment cannot directly synchronize rule data with the cloud platform and can only synchronize the rule data through the central node equipment.
The first type of equipment is equipment with event triggering or execution and does not have the capability of processing rule data, such as a door magnet, and is used as triggering equipment of the first type of equipment and only triggers door opening or closing events; such as a motorized window treatment, as an execution device for a first type of device, performs an action only in response to a received execution instruction. The first type of equipment can be connected with an external network through a router so as to report the execution effect data to the cloud platform.
The cloud platform is used as an intermediate communication device between the user APP and each device in the networking system, the user APP can control each device in the networking system or perform data communication through the cloud platform, and the cloud platform calculates an optimal rule execution strategy by taking reported execution effect data as big data.
The second type of device, the current central node device, the first type of device connected to the same router, and the first type of device directly or indirectly connected to the second type of device form a local area network control networking system.
Referring to fig. 4, fig. 4 is a schematic flow chart of all the second-class devices connected to the same router to determine the current central node device.
All the second-type devices connected to the same router acquire respective device information, wherein the device information at least comprises a device model priority list, a device version number, a device running time and a device serial number,
all the second-class devices exchange respective device information through the local area network, namely, all the second-class devices multicast respective device information through the local area network so as to select a central device node based on the device information;
and each second type device respectively compares the obtained device information of all second type devices, and selects a second type device as a central node device according to a decision condition, wherein the decision condition is that the device information of all second type devices is arranged according to the priority from high to low: device model priority list, device version number, device runtime, and device serial number, i.e.:
selecting a second type of equipment with the highest priority according to the equipment model priority list; if the equipment model priorities of the second type of equipment are the same, selecting the second type of equipment with the highest version number according to the equipment version number; and if the highest version numbers are the same, selecting the second type of equipment with the longest running time according to the running time of the equipment, and if the running times of the equipment are the same, selecting the second type of equipment with the largest serial number according to the serial number of the equipment. Because each second type of equipment is selected according to the same decision condition based on the same equipment information, the decision results are the same.
After a second type of equipment serving as central node equipment is selected, the selected second type of equipment reports to the cloud platform and authenticates other second type of equipment to ensure the validity of the central node equipment, and after the second type of equipment passes the authentication, the central node equipment stores IP addresses and corresponding rule data of all the second type of equipment; thereby, the selected second type of equipment is converted into central node equipment.
Referring to fig. 5A and 5B, fig. 5A is a schematic timing diagram of synchronization data performed by a central node device and a timing diagram of a change of the central node device, and fig. 5B is a schematic diagram of synchronization of rule data from the central node device to a cloud platform.
And the decided central node equipment and cloud platform synchronization rule data specifically comprise that the central node equipment sends a rule data synchronization request to a cloud platform, and the cloud platform responds to the synchronization request and sends the latest rule data to the central node equipment.
After the synchronization of the rule data of the cloud platform is completed, the synchronization of the rule data between the central node device and all the second devices in the networking system specifically includes that the central node device sends the rule data corresponding to each second device through the local area network according to the IP address of each second device in the local area network.
In the control system, the central node device is not invariable, and when the central node device cannot meet the requirement of serving as the central node, the central node will reinitiate the decision process and decide a new central node device. The change of the central node equipment has two conditions, one is that the central node equipment is disconnected with the external network and the central node is abandoned; the other is that the central node is abandoned because the residual performance of the central node equipment cannot meet the requirement of being the central node. Whether or not it is an initiative to relinquish as a central node depends on the device's own attributes, e.g., gateway class devices typically do not proactively relinquish as a central node.
For the first condition, when the first central node equipment is disconnected and powered off, the first central node equipment is disconnected with the second type of equipment in a heartbeat loss manner, and the second type of equipment which detects the heartbeat loss initiates a decision making process again to make a decision on the second central node equipment; and if the first central node equipment is recovered, selecting one of the first central node equipment and the second central node equipment as the central node equipment according to the equipment information, and recovering the other one as second-class equipment.
For the second situation, when the first central node device (old central node device) re-initiates the decision process and decides a new central node device (second central node device), the first central node device synchronizes the current latest rule data to the second central node device (new central node device), and then changes the current latest rule data into the second type of device; the second central node device then synchronizes the latest rule data to all second class devices.
Referring to fig. 6A and fig. 6B, fig. 6A is a timing diagram of a dynamically adjustable control process according to an embodiment of the present invention, and fig. 6B is a flowchart illustrating a process for handling a first type of device event trigger in a networking system.
After all the second-class devices in the networking system select the central node device through the decision process and the central node device, the cloud platform and the second-class devices synchronize rule data, the initial configuration of the control system is equivalently completed, and then the execution of the control execution instruction can be performed.
When the first-class device detects a trigger event, multicasting the trigger event to all second-class devices and central node devices in the networking system through the local area network, matching the trigger event by each device receiving the trigger event according to the execution rule strategy data, if the matching is successful, sending an execution instruction to the first-class device by the device, and meanwhile, receiving execution effect data reported by the first-class device by the device.
The equipment receiving the execution effect data sends the execution effect data to the cloud platform through the router, the cloud platform takes the data as one of big data for carrying out rule execution strategy adjustment optimization, and in addition, the cloud platform also takes the linkage effect data, the association relation of parent and child equipment, the equipment type of the second type of equipment (including central node equipment) and the residual performance data of the second type of equipment (including central node equipment) as the big data for carrying out rule execution strategy adjustment optimization.
The execution effect data is time data from the time when the first-class equipment receives the execution instruction to the time when the execution action is completed; the linkage effect data is the time from the time when the event is triggered to the time when all the associated actions are executed, and the cloud platform can obtain the linkage effect data by accumulating all the execution effect data; the device association relationship is a relationship of network topology structures between devices, and in a networking system, the device association relationship is usually a parent-child association relationship, a child device is a device that cannot be directly connected to a cloud platform and only performs data communication with the parent device, and needs to be registered to the cloud platform through the parent device (e.g., a gateway), for example, as shown in fig. 7, fig. 7 is a schematic diagram of the device association relationship, wherein the child devices such as a door sensor, an infrared sensor, a switch panel and the like are accessed to the cloud platform through the gateway device, and topology structures formed by the child devices and the gateway are the parent-child association relationship.
When an optimization triggering condition of the rule execution policy is met, for example, the optimization triggering condition is triggered at fixed periods, or when the number of data in the execution effect data, which is greater than the execution time threshold, exceeds a first threshold, the cloud platform triggers the optimization of the rule execution policy: and adjusting the scene rules and the linkage rules to be executed in which second class of equipment based on the big data according to the optimization strategy, namely adjusting the executing equipment main bodies of the scene rules and the linkage rules, thereby realizing the load balance of rule execution.
One embodiment of the optimization strategy may be: the priority levels are ranked from high to low as follows: and selecting at least one full-function execution device or central node as an execution main body of the corresponding rule for the scene rule or the linkage rule according to the association relation of the parent and child devices, the linkage effect data, the execution effect data, the device types of the second type of devices and the central node device and the residual performance data of the second type of devices and the central node device, so that a rule execution strategy with the best effect is matched for the scene rule and the linkage rule. The method specifically comprises the following steps:
for a scene rule or a linkage rule, the cloud platform preferentially selects a second type of equipment with a parent-child association relation based on the parent-child association relation; if the device has a parent-child association relationship, selecting a second type of device with optimal linkage effect data based on the linkage effect data; if the linkage effect data are the same, selecting second equipment connected with the first equipment with the optimal execution effect data based on the execution effect data; if the execution effect data are the same, selecting second equipment with the optimal equipment type based on the second equipment type; and if the types of the equipment are the same, selecting the second type equipment with the optimal residual performance data and the minimum current execution rule number based on the residual performance data and the execution rule number of the second type equipment. In the above preferred procedure, the central node device has the function of the second type device, and therefore can be selected as the second type device.
After the rule execution strategy is adjusted, the cloud platform synchronizes the updated rule execution strategy to the central node equipment; the central node equipment synchronizes the received rule execution strategy to all second-class equipment in the networking system; and executing the second class of equipment and the central node equipment according to the updated rule execution policy data.
In the embodiment of the invention, the cloud platform obtains the optimal rule execution strategy based on the execution effect data reported by each first-class device and the device association relation big data, and synchronizes the rule execution strategy data to all second-class devices in the networking system through the central node device, so that the main devices for executing the control effect, the scene rule and the linkage rule are not constant but change according to the change of the second-class device load, the optimal execution effect is dynamically achieved, and the whole control system achieves self-optimized load balance.
The following description takes smart home control as an example.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an intelligent home control system. The door lock, the electric curtain and the switch panel are first-class equipment, the gateway 1-bit central node equipment and the gateway 2 is second-class equipment.
A user sets the following two linkage rules through an APP and uploads the linkage rules to a cloud platform.
Rule 1: when the door lock 1 is opened, the motorized window treatment performs closing.
Rule 2: when the door lock 2 is opened, the switch panel turns on the light.
When the rule is configured as an initial rule, since the gateway 2 is a second type device and is in a parent-child association relationship with the switch panel, the motorized window shades, the door lock 1, the gateway 1 and the gateway 2 have no association relationship, and both the rule 1 and the rule 2 are executed on the gateway 2 according to the device association relationship regardless of the device type, the execution effect and other factors.
After the operation is carried out for a period of time, if the number of the rules which need to be executed currently by the gateway 1 is 0, or the linkage efficiency data of 2 devices which are simultaneously controlled by the gateway 2 exceeds 1 second (the data can be customized according to experience values), or the residual performance of the gateway 2 is greatly lower than that of the gateway 1, an optimization strategy is triggered; according to the optimization strategy, the rule 1 is adjusted to be executed on the gateway 1, thereby achieving the purpose of load balancing control.
This is further explained below in connection with the control system shown in fig. 3.
Assume user configured rule 1: and executing the first type equipment B-2 after the first type equipment D is triggered by the event.
When a multicast event is triggered in a local area network by a first type device D, the central node device A successfully executes the strategy matching according to the rule, the central node device A sends an execution command to a second type device B, and the second type device B controls the first type device B-2 to execute an action; the second equipment B returns the action execution result (execution effect data) of the first equipment B-2 to the central equipment A, and the central equipment A reports the result to the cloud platform, so that one trigger execution rule is completed.
When the cloud platform obtains the second type device B to execute the rule 1 more appropriately according to the execution effect data reported by the central node device A, the device association relation and other factors, and the time for the central node device A to send the execution command to the second type device B is saved, so that the first type device B-2 responds more timely.
After optimization and adjustment, the first type device D triggers a multicast event in the local area network, according to the optimized rule execution strategy, the central node device A is unsuccessfully matched, the central node device A does not process the triggering event of the first type device D, after the second type device B receives the triggering event of the first type device D, the rule 1 is found to be successfully matched, the second type device B sends an execution instruction to the first type device B-2, and the execution result is reported to the cloud platform, so that the trigger execution rule is completed once.
Obviously, the response time of the optimized first type device B-2 is shorter, and the user experience is better.
For another example, if rule 2 is triggered by a first-class device D event, and then executes a first-class device B-2 and a first-class device C-2, the executing first-class device B-2 may be assigned to a second-class device B, and the executing first-class device C-2 may be assigned to a second-class device C, and these assignment rules may all be defined in a rule execution policy; if the rule execution policy is not optimized, the central node device a needs to control the execution of the first type device B-2 and the first type device C-2, respectively.
For another example, if the rule 3 is triggered by the event of the first-type device B-1, the first-type device B-2 and the first-type device B-3 are executed, and the rule triggering and the execution are optimized and then executed in the second-type device B without passing through the central node a, so that the execution efficiency is optimized.
The same applies to the fact that the executing device may be in a networking system 2 or a networking system N.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (15)
1. A dynamically adjustable control system is characterized by comprising a cloud platform for receiving rule data configured by an intelligent terminal application and at least one networking system connected with the cloud platform through the Internet,
the networking system comprises at least one first type device with an event triggering or instruction executing function, at least one second type device with a rule analyzing and matching function and connected to the same local area network, and a first central node device selected by all the second type devices from all the second type devices according to a decision condition;
the first type of equipment is directly connected to the local area network or connected to second type of equipment; the local area network is accessed to the Internet;
the second type of equipment and the first center node equipment are matched and triggered by the event multicast by the first type of equipment according to the execution rule strategy in the current rule data, and when the matching is successful, an execution instruction is sent to the first type of equipment, and the execution effect data from the first type of equipment is forwarded to the cloud platform;
and the cloud platform adjusts the execution rule strategy in the rule data based on the big data at least comprising the execution effect data, and synchronizes the adjusted rule data to the first central node equipment, so that the first central node equipment synchronizes the rule data to the second equipment.
2. The control system of claim 1, wherein the second type of device is connected to the internet via a router;
the first type of equipment is connected with the Internet through a router or is connected with the second type of equipment in a wireless or low-speed wired mode.
3. The control system of claim 1, wherein the rule data further comprises scene rule data, linkage rule data; the big data also comprises equipment association relation data, linkage effect data, equipment types and equipment residual performance data;
the cloud platform further comprises an execution device main body which triggers the adjustment of the rule execution strategy when the optimization triggering condition is met, and adjusts the scene rule and/or the linkage rule according to the optimization strategy based on the big data.
4. The control system according to claim 3, wherein the optimization triggering condition includes triggering at a fixed period of timing or triggering when the number of data of the execution effect data larger than the execution time threshold exceeds a first threshold,
the optimization strategy may include the steps of,
preferentially selecting second-class equipment with parent-child association relation based on the equipment association relation;
if the device has a parent-child association relationship, selecting a second type of device with optimal linkage effect data based on the linkage effect data;
if the linkage effect data are the same, selecting second equipment connected with the first equipment with the optimal execution effect data based on the execution effect data;
if the execution effect data are the same, selecting second equipment with the optimal equipment type based on the second equipment type;
and if the types of the equipment are the same, selecting the second type equipment with the optimal residual performance data and the minimum current execution rule number based on the residual performance data and the execution rule number of the second type equipment.
5. The control system of claim 1, wherein the first central node device further comprises, when the first central node device does not meet the central node requirements, reinitiating a central node decision process and deciding a second central node device; the first central node device synchronizes the current rule data to the second central node, so that the second central node synchronizes the current rule data to the second type device; the first central node device is changed to a second type of device.
6. The control system of claim 1, wherein the second class of devices further comprises, after detecting a heartbeat loss, re-initiating a decision process to decide out a second central node device;
and if the first central node equipment is recovered, the first central node equipment and the second central node equipment select one of the first central node equipment and the second central node equipment as the central node equipment according to the equipment information, and the other one of the first central node equipment and the second central node equipment is recovered as second-class equipment.
7. The control system according to claim 5 or 6, wherein the decision process includes that all the second-type devices in the local area network multicast their respective device information through the local area network, and each second-type device respectively compares the obtained device information of all the second-type devices, and selects a second-type device as the first central node device according to the same decision condition; and the selected first center node equipment reports to the cloud platform and authenticates the second type of equipment in the local area network.
8. The control system of claim 7, wherein the decision condition includes selecting a second type of device having a highest priority according to a device model priority list;
if the equipment model priorities of the second type of equipment are the same, selecting the second type of equipment with the highest version number according to the equipment version number;
if the highest version numbers are the same, selecting second equipment with the longest running time according to the running time of the equipment;
and if the running times of the equipment are the same, selecting the second type of equipment with the largest serial number according to the serial number of the equipment.
9. A dynamically adjustable control method is characterized in that the method comprises the steps of, on the cloud platform side,
receiving execution effect data from a first type of device having an event trigger or instruction execution function in the networking system through the internet,
adjusting an enforcement rule policy in the rule data based on big data including at least the enforcement effect data,
synchronizing the adjusted rule data to a first central node device selected from all second devices by all the second devices which have rule analysis and matching functions and are connected to the same local area network according to decision conditions in the networking system so as to ensure that: the first central node device synchronizes the rule data to the second class device, the second class device and the first central node device are matched and triggered by the event multicast by the first class device according to the execution rule strategy in the current rule data, and when the matching is successful, an execution instruction is sent to the first class device;
and the execution effect data is reported to the cloud platform by the first type of equipment through the local area network or a second type of equipment connected with the first type of equipment through the accessed Internet.
10. The control method according to claim 9, wherein the rule data further includes scene rule data, linkage rule data; the big data also comprises equipment association relation data, linkage effect data, equipment types and equipment residual performance data;
the method further comprises the step of triggering the regulation of the rule execution strategy when the optimization triggering condition is met, and regulating the execution equipment main body of the scene rule and/or the linkage rule according to the optimization strategy based on the big data.
11. The control method according to claim 10, wherein the optimization triggering condition includes triggering at a fixed period of timing or triggering when the number of data of the execution effect data larger than the execution time threshold exceeds a first threshold,
the optimization strategy comprises the steps of, in combination,
preferentially selecting second-class equipment with parent-child association relation based on the equipment association relation;
if the device has a parent-child association relationship, selecting a second type of device with optimal linkage effect data based on the linkage effect data;
if the linkage effect data are the same, selecting second equipment connected with the first equipment with the optimal execution effect data based on the execution effect data;
if the execution effect data are the same, selecting second equipment with the optimal equipment type based on the second equipment type;
and if the types of the equipment are the same, selecting the second type equipment with the optimal residual performance data and the minimum current execution rule number based on the residual performance data and the execution rule number of the second type equipment.
12. The method of claim 9, further comprising, when the first central node device does not meet the central node requirements, reinitiating the central node decision process and deciding a second central node device; the first central node device synchronizes the current rule data to the second central node, so that the second central node synchronizes the current rule data to the second type device; the first central node device is changed to a second type of device.
13. The method of claim 9, further comprising the step of the second type of device detecting the heart beat loss initiates a decision process again to decide a second central node device;
and if the first central node equipment is recovered, the first central node equipment and the second central node equipment select one of the first central node equipment and the second central node equipment as the central node equipment according to the equipment information, and the other one of the first central node equipment and the second central node equipment is recovered as second-class equipment.
14. The method according to claim 12 or 13, wherein the decision process includes that all second-type devices in the local area network multicast respective device information through the local area network, and each second-type device respectively compares the obtained device information of all second-type devices, and selects a second-type device as the first central node device according to the same decision condition; and the selected first center node equipment reports to the cloud platform and authenticates the second type of equipment in the local area network.
15. The control method of claim 14, wherein the decision condition includes selecting a second type of device having a highest priority according to a device model priority list;
if the equipment model priorities of the second type of equipment are the same, selecting the second type of equipment with the highest version number according to the equipment version number;
if the highest version numbers are the same, selecting second equipment with the shortest running time according to the running time of the equipment;
and if the running times of the equipment are the same, selecting the second type of equipment with the highest serial number according to the serial number of the equipment.
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