CN114782026A

CN114782026A - Configuration method and system based on workflow and IOT (input/output) middleware fusion

Info

Publication number: CN114782026A
Application number: CN202210682907.5A
Authority: CN
Inventors: 李其伦; 薄涛; 李元春; 马璐
Original assignee: Beijing Lekai Technology Co ltd
Current assignee: Beijing Lekai Technology Co ltd
Priority date: 2022-06-17
Filing date: 2022-06-17
Publication date: 2022-07-22
Anticipated expiration: 2042-06-17
Also published as: CN114782026B

Abstract

The invention discloses a configuration method and a configuration system based on workflow and IOT (input/output) middlebox fusion, which relate to the technical field of computers, and the method comprises the following steps: obtaining a first work category of a first scene; obtaining a first analysis result through state analysis, and determining a first distinguishing characteristic index; monitoring in real time by using an intelligent acquisition module to obtain first monitoring data; judging whether a first trigger instruction is obtained or not by using an intelligent analysis module; and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow. The technical problems that in the prior art, other requirements of a user cannot be intelligently pre-judged after the user requirements are obtained, the speed is low, the stability is poor, the overall working efficiency is influenced, and the user experience is reduced in response to the requirements through a rear-end platform are solved. Through effectively fusing the workflow and the Internet of things middle desk, the technical effects of intelligently monitoring, analyzing and executing the workflow and further improving the portability and the individuation degree of life and work of a user are achieved.

Description

Configuration method and system based on workflow and IOT (input/output) middleware fusion

Technical Field

The invention relates to the technical field of computers, in particular to a configuration method and a configuration system based on workflow and IOT (input/output) intermediate station fusion.

Background

The IOT (internet of things) middle platform is a flexible framework for connecting a front-end platform and a rear-end platform of a system by using the internet of things technology, is used for quickly realizing the user demand of the front-end platform, avoids the repeated construction of the rear-end platform and can realize the aim of improving the working efficiency of the system. The practical significance of the IOT middle station lies in that the Internet of things technology is utilized to quickly identify user requirements, and intelligent prejudgment is carried out on current operations and the like of users, so that response is advanced, and on the other hand, the safety and processing equivalence rate of rear-end data are improved by combining with the middle station architecture, namely, the corresponding speed of the system is increased, the corresponding period is shortened, and the quick and intelligent response of the user requirements is realized under the synergistic effect of the two. In the prior art, after user requirements are obtained, other requirements of a user cannot be intelligently pre-judged, namely, after a workflow cannot be automatically triggered, in addition, after the user requirements are obtained, a system back-end platform responds and processes, a series of requirements of a front-end platform user cannot be quickly and stably responded, and therefore the technical problem that the user experience sense of the user is reduced is solved. Therefore, the research effectively fuses the workflow and the IOT middle platform, realizes the intelligent prejudgment of user requirements, further effectively shortens the system response and processing period by utilizing the IOT middle platform, and has important significance.

However, in the prior art, other requirements of the user cannot be intelligently predicted after the user requirements are obtained, and the requirements are responded and processed through a system back-end platform, so that the technical problems of low speed and poor stability exist, the overall working efficiency is influenced, and the user experience is reduced.

Disclosure of Invention

The invention aims to provide a configuration method and a configuration system based on workflow and IOT (input/output) intermediate station fusion, which are used for solving the technical problems that other requirements of a user cannot be intelligently predicted after the user requirements are obtained in the prior art, and the requirements are responded and processed through a system back-end platform, so that the speed is low, the stability is poor, the integral working efficiency is influenced, and the user experience is reduced.

In view of the above problems, the present invention provides a configuration method and system based on workflow and IOT middleware fusion.

In a first aspect, the present invention provides a configuration method based on fusion of a workflow and an intermediate stage of an IOT, where the method is implemented by a configuration system based on fusion of a workflow and an intermediate stage of an IOT, and the method includes: obtaining a first work category of a first scene; performing state analysis on the first working category to obtain a first analysis result, wherein the first analysis result comprises a first starting-up state analysis result and a first shutdown state analysis result; determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result; monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to obtain first monitoring data; judging whether the first monitoring data meet a first preset trigger condition or not by using an intelligent analysis module; if the first monitoring data meet the first preset trigger condition, obtaining a first trigger instruction; and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow.

In another aspect, the present invention further provides a configuration system based on workflow and IOT intermediate station fusion, configured to execute the configuration method based on workflow and IOT intermediate station fusion according to the first aspect, where the system includes: a first obtaining unit: the first obtaining unit is used for obtaining a first work category of a first scene; a second obtaining unit: the second obtaining unit is configured to perform state analysis on the first work category to obtain a first analysis result, where the first analysis result includes a first power-on state analysis result and a first power-off state analysis result; a first determination unit: the first determining unit is used for determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result; a third obtaining unit: the third obtaining unit is used for monitoring and collecting the first distinguishing characteristic index in real time by using an intelligent collecting module to obtain first monitoring data; a first judgment unit: the first judging unit is used for judging whether the first monitoring data meet a first preset triggering condition by using an intelligent analysis module; a fourth obtaining unit: the fourth obtaining unit is configured to obtain a first trigger instruction if the first monitoring data meets the first preset trigger condition; a first execution unit: the first execution unit is used for starting and triggering a first preset workflow by the intelligent decision module according to the first trigger instruction.

In a third aspect, an electronic device, comprising a processor and a memory;

the memory is used for storing;

the processor is configured to execute the method according to any one of the first aspect above by calling.

In a fourth aspect, a computer program product comprises a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the first aspect described above.

One or more technical schemes provided by the invention at least have the following technical effects or advantages:

1. the method comprises the steps that through characteristic collection of the starting and shutdown states of a first working category, distinguishing characteristic points for distinguishing the starting and shutdown states of the first working category are determined and recorded as first distinguishing characteristic indexes; then, intelligent equipment in an intelligent acquisition module is used for carrying out real-time monitoring and real-time acquisition on the first distinguishing characteristic index, and an intelligent analysis module is used for analyzing the acquired monitoring data in real time; further, when the monitoring data meets the condition of triggering a first preset workflow, the system automatically sends out a first triggering instruction for automatically triggering the first preset workflow; and finally, automatically executing each work category based on the first preset workflow. Through real-time monitoring and analysis of the intelligent equipment, an intelligent decision-making target whether the preset workflow is triggered or not is achieved, the preset workflow is automatically executed after being triggered, the technical target that the workflow and the Internet of things center station are effectively integrated is achieved, the intelligent monitoring, analysis and execution of the workflow are achieved, and the technical effects of improving the portability and the individuation degree of life and work of a user are improved.

2. The real-time monitoring and analysis of the intelligent acquisition module in the system realize the monitoring of the real-time state of the work category, the intelligent analysis module is further utilized to judge the actual state of the work category, and finally, based on the judgment result, the intelligent decision module triggers and executes the subsequent preset workflow, thereby realizing the technical goal of effectively fusing the workflow and the middle platform of the Internet of things.

3. Through the visual presentation of the preset workflow, the technical goal that the user pays attention to and knows the follow-up work categories and the execution conditions of the follow-up work categories in real time is achieved, meanwhile, the situation that the user is scared by sudden automatic work category execution is avoided, and the technical effect that a visual operation interface is provided for the user to change the follow-up work categories in real time is achieved.

4. The first preset workflow is comprehensively analyzed and determined based on the requirements of the actual life working scene, the relevance of each working category, the continuity and the like, and the technical effect of improving the practicability of the working categories automatically triggered by the system is achieved. In addition, based on the living habits and direct requirements of the first user, the preset workflow of the system is adjusted in a personalized mode, and the technical effect of improving the use satisfaction of the user is achieved.

The above description is only an overview of the technical solutions of the present invention, and the present invention can be implemented in accordance with the content of the description so as to make the technical means of the present invention more clearly understood, and the above and other objects, features, and advantages of the present invention will be more clearly understood.

Drawings

In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.

FIG. 1 is a schematic flow chart of a configuration method based on workflow and IOT middleware fusion according to the present invention;

fig. 2 is a schematic flow chart illustrating real-time display of the first visual information in a configuration method based on workflow and IOT staging fusion according to the present invention;

fig. 3 is a schematic flow chart illustrating the determination of the first preset workflow in the configuration method based on the fusion of the workflow and the IOT intermediate station according to the present invention;

FIG. 4 is a schematic flow chart illustrating a configuration method for obtaining a second preset workflow based on the fusion of the workflow and the IOT middlebox according to the present invention;

FIG. 5 is a schematic structural diagram of a configuration system based on workflow and IOT middleware fusion according to the present invention;

FIG. 6 is a schematic diagram of an exemplary electronic device of the present invention;

description of the reference numerals:

a first obtaining unit 11, a second obtaining unit 12, a first determining unit 13, a third obtaining unit 14, a first judging unit 15, a fourth obtaining unit 16, a first executing unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.

Detailed Description

The invention provides a configuration method and a configuration system based on the fusion of a workflow and an IOT (input/output) middlebox, and solves the technical problems that other requirements of a user cannot be intelligently predicted after the user requirements are obtained in the prior art, and the requirements are responded and processed through a system back-end platform, so that the speed is low, the stability is poor, the overall working efficiency is further influenced, and the user experience is reduced. Through real-time monitoring and analysis of the intelligent equipment, an intelligent decision-making target whether the preset workflow is triggered or not is achieved, the preset workflow is automatically executed after being triggered, the technical target that the workflow and the Internet of things center station are effectively integrated is achieved, the intelligent monitoring, analysis and execution of the workflow are achieved, and the technical effects of improving the portability and the individuation degree of life and work of a user are improved.

In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.

In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the features relevant to the present invention are shown in the drawings.

The invention provides a configuration method based on the fusion of a workflow and an IOT (input/output) intermediate station, which is applied to a configuration system based on the fusion of the workflow and the IOT intermediate station, wherein the method comprises the following steps: obtaining a first work category of a first scene; performing state analysis on the first work category to obtain a first analysis result, wherein the first analysis result comprises a first power-on state analysis result and a first power-off state analysis result; determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result; monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to obtain first monitoring data; judging whether the first monitoring data meet a first preset trigger condition or not by using an intelligent analysis module; if the first monitoring data meet the first preset trigger condition, a first trigger instruction is obtained; and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow.

Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.

Example one

Referring to fig. 1, the present invention provides a configuration method based on workflow and IOT staging fusion, wherein the method is applied to a configuration system based on workflow and IOT staging fusion, the system includes an intelligent acquisition module, an intelligent analysis module and an intelligent decision module, and the method specifically includes the following steps:

step S100: obtaining a first work category of a first scene;

specifically, the configuration method based on the fusion of the workflow and the IOT middle stage is applied to the configuration system based on the fusion of the workflow and the IOT middle stage, and can utilize intelligent equipment to monitor and collect state data of each device and device in real time, further analyze and judge whether to trigger the workflow, namely, based on the conditions of one-user operation and the like, intelligently pre-judge other related requirements of a user, make an intelligent response, and simultaneously, organically fuse the workflow and the IOT middle stage, thereby further achieving the technical aim of improving the response efficiency of the system. The first scene refers to any scene of the internet of things, such as a smart home and a smart office, which are configured with the smart monitoring device. The first work category refers to any one of work categories such as operation requirements and the like which may be executed and used by a user in the first scene, for example, if the user has a need for lighting in an intelligent home scene, the lighting is a work category.

By acquiring the first work category of the first scene, any one intelligent work category under a preset intelligent scene is acquired, and a technical target of providing a judgment basis for making follow-up demand judgment and the like intelligently based on the follow-up existing demands and the like sent by the existing users is achieved.

Step S200: performing state analysis on the first working category to obtain a first analysis result, wherein the first analysis result comprises a first starting-up state analysis result and a first shutdown state analysis result;

specifically, aiming at the requirement sent by the user, namely the first work category, the system intelligently carries out acquisition of relevant data and analysis of the operation state on the first work category so as to determine the analysis result of the first work category. For example, in a home scene, data acquisition of the on-off state of a main lamp in a living room can intelligently identify whether a user presses a main lamp switch or not through a pressure sensor, or a brightness sensor is installed on the main lamp to monitor whether the main lamp is turned on or not in real time, so that whether the main lamp is turned on or off is determined according to monitoring data such as the main lamp switch or the current brightness of the main lamp. That is, the first power-on state analysis result and the first power-off state analysis result are determined based on a first analysis result of the state analysis.

By analyzing different characteristic data of the corresponding working category in the on state and the off state, detailed data information of the working category in different states is obtained, accurate and reliable data information is provided for a follow-up system to intelligently judge the actual state of the first working category, and the technical effect of improving the follow-up state judgment accuracy is achieved.

Step S300: determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result;

specifically, the first power-on state analysis result of the first working category obtained through analysis is automatically compared with the first power-off state analysis result, so that the main distinguishing characteristics of the first working category in two different states are obtained, and the first distinguishing characteristic index is established. For example, in a home scene, in a main light on state of a living room, a monitoring value of a brightness sensor at the main light is obviously greater than the brightness in a power off state, and the monitoring value of the brightness sensor at the main light is changed when a switch button is pressed, that is, a pressure value of a pressure sensor at a switch is suddenly increased, so that a system can use a pressure monitoring value of the pressure sensor at the switch and a brightness monitoring value of the brightness sensor at the main light as distinguishing characteristic indexes for monitoring the working category of the main light.

By comparing the monitoring data of the on-off state and the analysis result thereof, the characteristic index of the corresponding working category state is determined, namely the first distinguishing characteristic index is determined, so that the technical effect of providing a judgment basis for subsequently judging the first working category state and further improving the accuracy of the judgment result is realized.

Step S400: monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to obtain first monitoring data;

specifically, the intelligent acquisition module includes all intelligent devices, such as a pressure sensor and a brightness sensor, which can perform intelligent monitoring and data acquisition on all work categories in the first scene, and in addition, the intelligent acquisition module is embedded in a configuration system in which the workflow and the IOT center station are integrated, and can transmit data information acquired and monitored by each intelligent device in the module in real time to the system for subsequent processing and analysis of the system. And acquiring real-time data of the first distinguishing characteristic index of the first working category according to the intelligent acquisition module to obtain corresponding first monitoring data. The first monitoring data refer to real-time data of distinguishing characteristic indexes. Such as the real-time pressure value of the pressure sensor and the real-time brightness value of the brightness sensor.

By obtaining the first monitoring data, accurate and reliable real-time data are provided for the system to perform subsequent processing and analyze the real-time working state of the first working category, and therefore the technical effects of improving the validity of the system analysis result and intelligently predicting the reliability are achieved.

Step S500: judging whether the first monitoring data meet a first preset trigger condition or not by using an intelligent analysis module;

step S600: if the first monitoring data meet the first preset trigger condition, obtaining a first trigger instruction;

specifically, the intelligent analysis module is configured to perform intelligent analysis on the real-time data of the first distinguishing characteristic index acquired by the intelligent acquisition module in real time, that is, perform intelligent comparative analysis on the first monitoring data. And when the first monitoring data meet the first preset trigger condition, the system automatically sends out the first trigger instruction. That is to say, when the intelligent analysis module analyzes and finds that the first monitoring data is similar to or consistent with the relevant data of the first working category in the startup state, it indicates that the first working category is currently in the startup and working state, and at this time, the system automatically sends out the first trigger instruction. The first preset trigger condition is a condition for the system to send out a first trigger instruction in advance based on the actual situation of the first working category and analysis and judgment of related data under the on-off state of the system. That is, the first preset trigger condition, that is, the first monitoring data, is consistent with the first boot state analysis result of the first job class. By obtaining the first trigger instruction, the technical effect of providing a basis for the system to intelligently pre-judge other related requirements and automatically execute based on the existing requirements of the user is achieved.

Step S700: and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow.

Specifically, according to the first trigger instruction sent by the system, the intelligent decision module in the system automatically starts and triggers the first preset workflow. The first preset workflow is other work categories determined by the system after comprehensive analysis based on the actual situation of the first work category and in combination with the actual situation of the first scene, the use habits of the user and the like. That is, the first preset workflow includes a plurality of work categories related to the first work category and needing to be cooperatively matched in real life and work. For example, in a home scene, a user often performs operations such as turning on a light and turning off a curtain after opening a door lock of a bedroom. Through real-time monitoring and analysis of the intelligent equipment, an intelligent decision-making target whether the preset workflow is triggered or not is achieved, the preset workflow is triggered and then automatically executed, and the technical target that the workflow and the Internet of things middle station are effectively fused is achieved.

Further, the present invention further includes step S800:

step S810: obtaining a first work category list of the first preset workflow;

step S820: extracting a second working category and a third working category according to the first working category list, wherein the third working category is a first working category behind the second working category;

step S830: performing state analysis on the second working category to determine a second distinguishing characteristic index;

step S840: the intelligent acquisition module is used for carrying out real-time monitoring and acquisition on the second distinguishing characteristic index to obtain second monitoring data;

step S850: and starting and triggering the third working category according to the second monitoring data.

Specifically, after the system is started to trigger the first preset workflow, the work categories in the first preset workflow are automatically executed.

First, a first work category list of the first preset workflow is extracted, that is, all work categories in the first preset workflow are extracted, and all work categories are preset with a certain execution sequence. And then, aiming at the second working category and the third working category in the first working category list, carrying out targeted distinctive feature index data monitoring and acquisition by using an intelligent acquisition module in sequence. The third work category refers to a first work category after the second work category, that is, the second work category is executed before the third work category, and the third work category is executed after the second work category. The second work category refers to any one of the work categories in the first work category list. That is to say, the startup and shutdown state characteristics of each work category in the first work category list are respectively collected, and then are contrasted and analyzed, the distinguishing characteristic indexes of each work category are respectively determined, and the distinguishing characteristic indexes are matched with appropriate intelligent equipment to perform real-time monitoring on the distinguishing characteristic indexes. Further, the state of the second work category is analyzed, a second distinguishing characteristic index is determined, the intelligent acquisition module is used for carrying out real-time monitoring and acquisition on the second distinguishing characteristic index, and then the third work category is started, triggered, judged and executed based on second monitoring data. For example, if the second task category is to turn on a television in a living room, and the third task category is to turn on a television sound, the second task category, that is, whether the television is in a power-on state, is determined, and the television can be subjected to real-time image acquisition through an image sensor.

The real-time monitoring and analysis of the intelligent acquisition module in the system are adopted to realize the monitoring of the real-time state of the work category, the intelligent analysis module is further used for judging the actual state of the work category, and finally, based on the judgment result, the intelligent decision module triggers and executes the subsequent preset workflow, so that the technical goal of effectively fusing the workflow and the Internet of things middleboxes is realized.

Further, as shown in fig. 2, the present invention further includes step S860:

step S861: constructing a first layer model according to the first work category list;

step S862: acquiring a first model node set according to the first layer model, and acquiring relationship information of each model node in the first model node set to form a first model trend set;

step S863: constructing a second layer model by using the first model node set and the first model trend set;

step S864: reading information of the second layer model, converting to obtain a first model structure, and constructing a third layer model according to the first model structure;

step S865: and generating first visual information according to the first layer model, the second layer model and the third layer model, and displaying the first visual information in real time.

Specifically, after a system triggers and executes a first preset workflow, the execution conditions of the first preset workflow and each work category in the current first preset workflow are displayed in real time by using an intelligent display screen.

Firstly, constructing a first layer model according to the first work category list, wherein the first layer model is a flexible workflow model; and then determining a first model node set of the first preset workflow according to the first layer model, and further acquiring relationship information of each model node in the first model node set to form a first model trend set. The first model node set refers to each category in a first preset workflow, and the first model trend set refers to the precedence relationship between two adjacent work categories. Further, a second-layer model is constructed according to the first model node set and the first model trend set, wherein the second-layer model is a visual storage model; and finally, reading information of the second layer of model, namely the visual storage model, converting all the information into a picture form, namely obtaining a first model structure, and constructing a third layer of model according to the first model structure, wherein the third layer of model is a visual interface model. And generating first visual information of the first preset workflow and the specific execution condition of the first preset workflow through the first layer model, the second layer model and the third layer model, and displaying the first visual information in real time by using equipment such as an intelligent display screen.

By visually presenting the first preset workflow, the technical goal that the user pays attention to and knows the follow-up work categories and the execution conditions of the follow-up work categories in real time is achieved, meanwhile, the situation that the user is scared by the sudden automatic work category execution is avoided, and the technical effect of providing a visual operation interface for the user to change the follow-up work categories in real time is achieved.

Further, as shown in fig. 3, step S700 of the present invention further includes:

step S710: analyzing to obtain a first work category set according to the first scene, wherein the first work category set comprises a plurality of work categories;

step S720: performing relevance analysis on the plurality of working categories by utilizing a grey relevance analysis algorithm idea to obtain a first relevance analysis result;

step S730: analyzing the working conditions and the working effects of each working category of the plurality of working categories in sequence to respectively obtain a plurality of working conditions and a plurality of working effects;

step S740: and determining the first preset workflow according to the first association degree analysis result, the plurality of working conditions and the plurality of working effects.

Specifically, before the system automatically triggers and starts a first preset workflow based on a first trigger instruction, the first preset workflow is comprehensively analyzed and determined according to the actual situation, the application scene and the like of the first work category.

Firstly, a first work category set is obtained through analysis according to the actual life and working conditions of the first scene, wherein the first work category set comprises a plurality of work categories. For example, a plurality of work categories such as lamps in different rooms and intelligent equipment lamps in different rooms are provided in a household intelligent scene. And then, performing relevance analysis on the plurality of working categories by using a grey relevance analysis algorithm idea, namely, respectively analyzing the relevance between each working category and all other working categories in the plurality of working categories, and obtaining a first relevance analysis result. Further, the working conditions and the working effects of the working categories are analyzed in sequence, and the working conditions and the working effects are obtained respectively. For example, in a home intelligent scene, a kitchen lamp needs to be turned on first when cooking is performed in a kitchen, and meanwhile, the range hood is turned on when cooking is performed, wherein the kitchen lamp is turned on when the cooking is performed under the working condition, and the range hood is turned on when the cooking is performed under the working effect. Therefore, the first preset workflow is determined according to the first relevance analysis result, the plurality of working conditions and the plurality of working effects.

The first preset workflow is comprehensively analyzed and determined based on the requirements of actual life working scenes, the relevance and the continuity of all the working categories and the like, and the technical effect of improving the practicability of the working categories automatically triggered by the system is achieved.

Further, as shown in fig. 4, the present invention further includes step S750:

step S751: constructing a first working time-conventional habit function of the first working category based on big data;

step S752: the first working time-conventional habit function refers to a first conventional habit of a user when the user executes the first working category at a first working time;

step S753: the first working time refers to the season and time for executing the first working category, and the first conventional habit refers to the general habit and the conventional habit of the user;

step S754: acquiring a first trigger time of the first trigger instruction;

step S755: analyzing and obtaining a first conventional habit of the first trigger time according to the first working time-conventional habit function;

step S756: and adjusting the first preset workflow according to the first conventional habit to obtain a second preset workflow.

Specifically, before a user does not use the configuration system with workflow merged with the IOT middleware, the system first performs routine workflow presets that are relatively consistent with most users based on historical usage data of most users.

Firstly, a first working time-conventional habit function of the first working category is constructed based on big data, that is, based on the use condition of a user who uses the configuration system with the workflow and the IOT middle station integrated in history, other conventional habits, that is, data such as other related requirements and the like when the first working category is executed in each history are analyzed, and the first working time-conventional habit function is constructed. For example, based on big data analysis, most users have a need to turn on room lights after turning on the room doors, from 6 pm to 2 pm. The first working time-routine habit function refers to a first routine habit of a user when the user executes the first working category at a first working time, the first working time refers to season and time when the first working category is executed, and the first routine habit refers to general and routine habits of the user. And then, acquiring the time for triggering the first trigger instruction by the system, namely acquiring the first trigger time, and analyzing other requirements which are often existed in the user when the first trigger time is analyzed according to the first working time-conventional habit function, namely the first conventional habit. And finally, adjusting the first preset workflow according to the first conventional habit to obtain a second preset workflow. For example, based on the first preset workflow, after a user opens a room door before 6 pm to 7 pm, the user has a need to turn on a room lamp, but according to the analysis of the use data of the historical user, the user turns on a room headlight before 6 pm to 2 pm, but turns on a room small lamp from 2 pm to 7 pm, and then the first preset workflow is adaptively adjusted according to the habit. For example, when a user turns on a television in the daytime, the user needs to turn on a sound box connected with the television, but after the television is turned on at night, in order to avoid disturbing the rest of a neighbor, the user does not turn on the sound box and only watches the sound box by using the original sound of the television.

By considering the user demand time into the actual demand, the personalized and intelligent adjustment target of the preset workflow is realized, the beneficial change of the preset workflow to the user life is improved, and the technical effect of improving the user experience is further improved.

Further, the present invention further includes step S757 a:

step S7571 a: obtaining a first habit record of a first user;

step S7572 a: in the screening of the first habit record, regarding the working habit of the first work category, recording as a first working habit;

step S7573 a: and adjusting the second preset workflow according to the first working habit.

Further, the present invention further includes step S757 b:

step S7571 b: obtaining a first user instruction of a first user;

step S7571 b: analyzing the first user instruction by using intelligent equipment to generate a first instruction;

step S7571 b: and adjusting the second preset workflow according to the first instruction.

Specifically, the first preset workflow is obtained by analyzing demand characteristics and the like in an actual scene, and the second preset workflow is obtained by optimizing the first preset workflow based on the influence of time on the same work category. The personalized adjustment of the second preset workflow is realized by collecting the relevant personal habits and the like of the user using the configuration system based on the fusion of the workflow and the IOT middlebox, namely the first user.

The method comprises the steps of firstly collecting a first habit record of a first user, screening the working habits of the first habit record about a first working category, recording the working habits as a first working habit, and then adjusting a second preset workflow according to the first working habit. For example, after a first user turns on a television, the habit of adjusting the room light to a specific brightness and performing immersive viewing is added to a workflow preset by the system. In addition, a first user instruction of a first user is collected, wherein the first user instruction comprises a voice instruction, a manual setting control instruction and the like, then the first user instruction is analyzed by using intelligent equipment to generate a first instruction, and a system adjusts the second preset workflow according to the first instruction. Wherein the first instruction is in an instruction form understandable by a computer language.

The first user living habits are intelligently acquired through the system, the workflow preset by the system is automatically and individually adjusted, the goal of improving the individual degree of the workflow preset by the system is achieved, meanwhile, the first user can adjust each workflow in real time, the setting with higher individual degree is achieved, and the technical effect of further improving the user satisfaction degree is further improved.

In summary, the configuration method based on the fusion of the workflow and the IOT has the following technical effects:

1. the method comprises the steps that through characteristic collection of the starting and shutdown states of a first working category, distinguishing characteristic points for distinguishing the starting and shutdown states of the first working category are determined and recorded as first distinguishing characteristic indexes; then, intelligent equipment in an intelligent acquisition module is used for carrying out real-time monitoring and real-time acquisition on the first distinguishing characteristic index, and an intelligent analysis module is used for analyzing the acquired monitoring data in real time; further, when the monitoring data meets the condition of triggering a first preset workflow, the system automatically sends out a first trigger instruction for automatically triggering the first preset workflow; and finally, automatically executing each work category based on the first preset workflow. Through real-time monitoring and analysis of the intelligent equipment, an intelligent decision-making target of whether the preset workflow is triggered or not is achieved, the preset workflow is automatically executed after being triggered, the technical target of effectively fusing the workflow and the Internet of things middle station is achieved, the technical effects of intelligently monitoring, analyzing and executing the workflow are achieved, and then the portability and the personalization degree of life and work of a user are improved.

2. The real-time monitoring and analysis of the intelligent acquisition module in the system are adopted to realize the monitoring of the real-time state of the work category, the intelligent analysis module is further used for judging the actual state of the work category, and finally, based on the judgment result, the intelligent decision module triggers and executes the subsequent preset workflow, so that the technical goal of effectively fusing the workflow and the Internet of things middleboxes is realized.

3. Through the visual presentation of the preset workflow, the technical goal that the user pays attention to and knows the follow-up work categories and the execution conditions of the follow-up work categories in real time is achieved, meanwhile, the situation that the user is scared by the sudden automatic work category execution is avoided, and the technical effect of providing a visual operation interface for the user to change the follow-up work categories and the like in real time is achieved.

4. The first preset workflow is comprehensively analyzed and determined based on the requirements of actual life working scenes, the relevance and the continuity of all the working categories and the like, and the technical effect of improving the practicability of the working categories automatically triggered by the system is achieved. In addition, based on the living habits and direct requirements of the first user, the preset workflow of the system is adjusted in a personalized mode, and the technical effect of improving the using satisfaction degree of the user is achieved.

Example two

Based on the configuration method based on the fusion of the workflow and the intermediate stage of the IOT in the foregoing embodiment, the present invention also provides a configuration system based on the fusion of the workflow and the intermediate stage of the IOT, referring to fig. 5, where the system includes:

a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain a first work category of a first scene;

a second obtaining unit 12, where the second obtaining unit 12 is configured to perform state analysis on the first work category to obtain a first analysis result, where the first analysis result includes a first power-on state analysis result and a first power-off state analysis result;

a first determining unit 13, where the first determining unit 13 is configured to determine a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result;

a third obtaining unit 14, where the third obtaining unit 14 is configured to perform real-time monitoring and collection on the first distinguishing characteristic index by using an intelligent collection module, so as to obtain first monitoring data;

the first judging unit 15 is configured to judge, by using an intelligent analysis module, whether the first monitoring data meets a first preset trigger condition;

a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to obtain a first trigger instruction if the first monitoring data meets the first preset trigger condition;

and the first execution unit 17, where the first execution unit 17 is configured to, according to the first trigger instruction, start and trigger a first preset workflow by an intelligent decision module.

Further, the system further comprises:

a fifth obtaining unit, configured to obtain a first work category list of the first preset workflow;

the first extraction unit is used for extracting a second working category and a third working category according to the first working category list, wherein the third working category is a first working category behind the second working category;

the second determining unit is used for performing state analysis on the second work category and determining a second distinguishing characteristic index;

a sixth obtaining unit, configured to perform real-time monitoring and collection on the second distinguishing characteristic index by using the intelligent collection module, so as to obtain second monitoring data;

and the second execution unit is used for starting and triggering the third working category according to the second monitoring data.

Further, the system further comprises:

the first construction unit is used for constructing a first layer model according to the first work category list;

the first composition unit is used for obtaining a first model node set according to the first layer model and collecting the relationship information of each model node in the first model node set to form a first model trend set;

the second construction unit is used for constructing a second layer model by using the first model node set and the first model trend set;

the third construction unit is used for reading information of the second layer model, converting the information to obtain a first model structure, and constructing a third layer model according to the first model structure;

and the third execution unit is used for generating first visual information according to the first layer model, the second layer model and the third layer model and displaying the first visual information in real time.

Further, the system further comprises:

a seventh obtaining unit, configured to analyze and obtain a first work category set according to the first scenario, where the first work category set includes multiple work categories;

an eighth obtaining unit, configured to perform relevance analysis on the multiple work categories by using a thought of a grey relevance analysis algorithm, so as to obtain a first relevance analysis result;

a ninth obtaining unit, configured to analyze the working conditions and the working effects of each of the multiple working categories in sequence, and obtain multiple working conditions and multiple working effects respectively;

a third determining unit, configured to determine the first preset workflow according to the first association analysis result, the plurality of working conditions, and the plurality of working effects.

Further, the system further comprises:

a fourth construction unit, configured to construct a first working time-routine habit function of the first working category based on big data;

the first setting unit is used for setting the first working time-conventional habit function as the first conventional habit of the user when the user executes the first working category at the first working time;

the second setting unit is used for setting the first working time as the season and time for executing the first working category, and the first conventional habit refers to the general habit and the conventional habit of the user;

the first acquisition unit is used for acquiring first trigger time of the first trigger instruction;

a tenth obtaining unit, configured to analyze and obtain a first regular habit of the first trigger time according to the first working time-regular habit function;

an eleventh obtaining unit, configured to adjust the first preset workflow according to the first conventional habit, to obtain a second preset workflow.

Further, the system further comprises:

a twelfth obtaining unit, configured to obtain a first habit record of the first user;

a third setting unit, configured to filter the working habits of the first work category from the first habit records, and record the working habits as first working habits;

and the fourth execution unit is used for adjusting the second preset workflow according to the first working habit.

Further, the system further comprises:

a thirteenth obtaining unit, configured to obtain a first user instruction of a first user;

the first generation unit is used for analyzing the first user instruction by using intelligent equipment to generate a first instruction;

a fifth execution unit, configured to adjust the second preset workflow according to the first instruction.

In the present specification, each embodiment is described in a progressive manner, and the focus of each embodiment is on the difference from other embodiments, and the configuration method and specific example based on the fusion of the workflow and the IOT intermediate stage in the first embodiment of fig. 1 are also applicable to the configuration system based on the fusion of the workflow and the IOT intermediate stage in this embodiment. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Exemplary electronic device

The electronic device of the present invention is described below with reference to fig. 6.

Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present invention.

Based on the inventive concept of the configuration method based on the fusion of the workflow and the IOT middle platform in the foregoing embodiments, the present invention further provides a configuration system based on the fusion of the workflow and the IOT middle platform, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the foregoing configuration methods based on the fusion of the workflow and the IOT middle platform.

Wherein in fig. 6 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.

The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

The invention provides a configuration method based on the fusion of a workflow and an IOT (input/output) intermediate station, which is applied to a configuration system based on the fusion of the workflow and the IOT intermediate station, wherein the method comprises the following steps: obtaining a first work category of a first scene; performing state analysis on the first working category to obtain a first analysis result, wherein the first analysis result comprises a first starting-up state analysis result and a first shutdown state analysis result; determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result; monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to obtain first monitoring data; judging whether the first monitoring data meet a first preset trigger condition or not by using an intelligent analysis module; if the first monitoring data meet the first preset trigger condition, a first trigger instruction is obtained; and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow. The technical problems that in the prior art, other requirements of a user cannot be intelligently predicted after the user requirements are obtained, the requirements are responded and processed through a system rear-end platform, the speed is low, the stability is poor, the overall working efficiency is influenced, and the user experience is reduced are solved. Through real-time monitoring and analysis of the intelligent equipment, an intelligent decision-making target of whether the preset workflow is triggered or not is achieved, the preset workflow is automatically executed after being triggered, the technical target of effectively fusing the workflow and the Internet of things middle station is achieved, the technical effects of intelligently monitoring, analyzing and executing the workflow are achieved, and then the portability and the personalization degree of life and work of a user are improved.

The invention also provides an electronic device, which comprises a processor and a memory;

the memory is used for storing;

the processor is configured to execute the method according to any one of the first embodiment through calling.

The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, carry out the steps of the method of any one of the above embodiments.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also include such modifications and variations.

Claims

1. A configuration method based on fusion of a workflow and an IOT (input/output) intermediate station is applied to a configuration system based on fusion of the workflow and the IOT intermediate station, the system comprises an intelligent acquisition module, an intelligent analysis module and an intelligent decision module, and the method comprises the following steps:

obtaining a first work category of a first scene;

performing state analysis on the first work category to obtain a first analysis result, wherein the first analysis result comprises a first power-on state analysis result and a first power-off state analysis result;

determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result;

monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to obtain first monitoring data;

judging whether the first monitoring data meet a first preset trigger condition or not by using an intelligent analysis module;

if the first monitoring data meet the first preset trigger condition, a first trigger instruction is obtained;

and according to the first trigger instruction, the intelligent decision module starts and triggers a first preset workflow.

2. The method of claim 1, wherein the method further comprises:

obtaining a first work category list of the first preset workflow;

extracting a second working category and a third working category according to the first working category list, wherein the third working category is a first working category behind the second working category;

performing state analysis on the second working category to determine a second distinguishing characteristic index;

the intelligent acquisition module is used for carrying out real-time monitoring and acquisition on the second distinguishing characteristic index to obtain second monitoring data;

and starting and triggering the third working category according to the second monitoring data.

3. The method of claim 2, wherein the method further comprises:

constructing a first layer model according to the first work category list;

acquiring a first model node set according to the first layer model, and acquiring relationship information of each model node in the first model node set to form a first model trend set;

constructing a second layer model by using the first model node set and the first model trend set;

reading information of the second layer model, converting to obtain a first model structure, and constructing a third layer model according to the first model structure;

and generating first visual information according to the first layer model, the second layer model and the third layer model, and displaying the first visual information in real time.

4. The method of claim 1, wherein the triggering of the initiation of the first preset workflow by the intelligent decision making module according to the first triggering instruction is preceded by:

analyzing to obtain a first work category set according to the first scene, wherein the first work category set comprises a plurality of work categories;

performing association analysis on the plurality of working categories by using a grey association analysis algorithm idea to obtain a first association analysis result;

analyzing the working conditions and the working effects of each working category of the plurality of working categories in sequence to respectively obtain a plurality of working conditions and a plurality of working effects;

and determining the first preset workflow according to the first association degree analysis result, the plurality of working conditions and the plurality of working effects.

5. The method of claim 4, wherein the determining the first preset workflow further comprises:

constructing a first working time-conventional habit function of the first working category based on big data;

the first working time-conventional habit function refers to a first conventional habit of a user when the user executes the first working category at a first working time;

the first working time refers to the season and time for executing the first working category, and the first conventional habit refers to the general and conventional habits of the user;

acquiring a first trigger time of the first trigger instruction;

analyzing and obtaining a first conventional habit of the first trigger time according to the first working time-conventional habit function;

and adjusting the first preset workflow according to the first conventional habit to obtain a second preset workflow.

6. The method of claim 5, wherein the method further comprises:

obtaining a first habit record of a first user;

in the screening of the first habit record, regarding the working habit of the first work category, recording as a first working habit;

and adjusting the second preset workflow according to the first working habit.

7. The method of claim 5, wherein the method further comprises:

obtaining a first user instruction of a first user;

analyzing the first user instruction by using intelligent equipment to generate a first instruction;

and adjusting the second preset workflow according to the first instruction.

8. A configuration system based on workflow and IOT middleware fusion, which is applied to the method of any claim 1-7, and comprises:

a first obtaining unit: the first obtaining unit is used for obtaining a first work category of a first scene;

a second obtaining unit: the second obtaining unit is configured to perform state analysis on the first work category to obtain a first analysis result, where the first analysis result includes a first power-on state analysis result and a first power-off state analysis result;

a first determination unit: the first determining unit is used for determining a first distinguishing characteristic index according to the first power-on state analysis result and the first power-off state analysis result;

a third obtaining unit: the third acquisition unit is used for monitoring and acquiring the first distinguishing characteristic index in real time by using an intelligent acquisition module to acquire first monitoring data;

a first judgment unit: the first judging unit is used for judging whether the first monitoring data meet a first preset triggering condition by using an intelligent analysis module;

a fourth obtaining unit: the fourth obtaining unit is configured to obtain a first trigger instruction if the first monitoring data meets the first preset trigger condition;

a first execution unit: the first execution unit is used for triggering the intelligent decision module to start a first preset workflow according to the first trigger instruction.

9. An electronic device comprising a processor and a memory;

the memory is used for storing;

the processor is used for executing the method of any one of claims 1-7 through calling.

10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.