CN110209716A - Intelligent internet of things water utilities big data processing method and system - Google Patents
Intelligent internet of things water utilities big data processing method and system Download PDFInfo
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Abstract
The present invention discloses Intelligent internet of things water utilities big data processing method and system, and method includes: to acquire water utilities data via the sensor connecting with fringe node；Water utilities data are standardized, and calculating task corresponding with treated water utilities data is generated based on preset need；The calculating task of system-level calculating task and user class is identified and is categorized into calculating task；System-level calculating task is transmitted to cloud to handle；The calculating task of user class is divided into the calculating task block of predefined size；Combined optimization is carried out to the computing resource and fringe node of fringe node and the communication resource of adjacent node using sparse optimization algorithm, to determine to stay in calculating task block into fringe node processing or branch to adjacent node processing.Method and system of the invention is shunted by edge calculations and optimization can lower network delay and network energy consumption.
Description
Technical field
The invention belongs to the Intelligent internet of things water utilities in Intelligent internet of things field, more particularly, to fringe node and cloud is big
Data processing method and system.
Background technique
Internet, Internet of Things rapid development, all things on earth Internet age have pulled open curtain.Edge calculations focus on all things on earth intelligence
Connection meets crucial requirement of industry digitlization in terms of connection, real-time, intelligent, data-optimized, security and privacy,
Have become the indispensable key element of industry digitlization transition, industry development enters important opportunity period.
In the implementation of the present invention, discovery in the prior art, at least has the disadvantage that inventor
The prior art can not adapt to different types of water utilities big data calculating task, the performance difficulty in real network；Together
When the prior art the high efficiency of the practice of algorithm complexity it is low, it is excellent when mixing combining of considering that the communication resource and calculating task shunt
Calculating task distributing strategy can not be provided when change problem.
The water utilities network environment of the complicated dynamic change of prior art reply cannot make in time Rational Decision to reach most
Big benefit；The data volume of the water utilities network of prior art perception simultaneously is extremely limited, and cannot make full use of according to big data point
It is not fine enough to make corresponding processing result aid decision suggestion for analysis processing.
Summary of the invention
The embodiment of the present invention provides a kind of Intelligent internet of things water utilities big data processing method for fringe node and cloud
And system, at least solving one of above-mentioned technical problem.
In a first aspect, the embodiment of the present invention provides a kind of Intelligent internet of things water utilities big data processing side for fringe node
Method, comprising: acquire water utilities data via the sensor connecting with fringe node；The water utilities data are standardized,
And calculating task corresponding with treated water utilities data is generated based on preset need；The calculating task is identified and divided
Class at system-level calculating task and user class calculating task；The system-level calculating task is transmitted at cloud
Reason；The calculating task of the user class is divided into the calculating task block of predefined size；And using sparse optimization algorithm to institute
The communication resource of the computing resource and the fringe node and adjacent node of stating fringe node carries out combined optimization, with determine by
The calculating task block stays in the fringe node processing or branches to the adjacent node processing.
Second aspect, the embodiment of the present invention provide a kind of Intelligent internet of things water utilities big data processing method for cloud,
It include: to receive calculating task and standardized water utilities data from each fringe node；Based on the calculating task to the water
Business data are handled to obtain calculated result；And the calculated result and preset multiple fault data models are compared
To obtain and the matched fault model of the prediction result thus anticipation failure corresponding with the prediction result.
The third aspect, the embodiment of the present invention provide a kind of Intelligent internet of things water utilities pipe network dispatching method for cloud, packet
It includes: being Markovian decision process by Intelligent internet of things water utilities pipe network scheduling modeling, to carry out intelligence based on deeply study
It can water utilities scheduling；Using the status information measured as the input of neural network, wherein the state space is real-time by sensor
The operating status for perceiving city water drainage-supply system determines；Using motion space as the output of neural network, wherein the movement is empty
Between include different zones are dispatched with different water consumptions, strategy decision mapping of the state space to the motion space；
And Utilization strategies gradient method training neural network, to update the strategy by intelligent body dynamic to update neural network ginseng
Number is to approach optimal strategy, wherein the intelligent body obtains the pipe network state in which, so by observation pipe network environment
Strategy described in decision takes corresponding actions to influence the pipe network environment afterwards, to realize water regulation.
Fourth aspect, the embodiment of the present invention provide a kind of Intelligent internet of things water utilities big data processing system for fringe node
System, comprising: acquisition module is configured to acquire water utilities data via the sensor connecting with fringe node；Processing module is configured to
The water utilities data are standardized, and corresponding calculate of water utilities data is appointed with treated based on preset need generation
Business；Categorization module is configured to identify the calculating task and be categorized into the meter of system-level calculating task and user class
Calculation task；System level tasks distribution module, is configured to the system-level calculating task being transmitted to cloud and handles；User
Grade task divides module, is configured to for the calculating task of the user class to be divided into the calculating task block of predefined size；And connection
Optimization diverter module is closed, is configured to using computing resource and the fringe node of the sparse optimization algorithm to the fringe node
Carry out combined optimization with the communication resource of adjacent node, with determine to stay in the calculating task block fringe node processing or
Branch to the adjacent node processing.
5th aspect, the embodiment of the present invention provide a kind of Intelligent internet of things water utilities big data processing system for cloud,
Include: receiving module, is configured to receive calculating task and standardized water utilities data from each fringe node；Computing module,
The calculating task is configured to handle to obtain calculated result the water utilities data；And failure predication module,
It is configured to for the calculated result being compared with preset multiple fault data models to obtain and match with the prediction result
Fault model to prejudge corresponding with prediction result failure.
6th aspect, the embodiment of the present invention provide a kind of Intelligent internet of things water utilities network scheduler system for cloud, wrap
Include: modeling module is configured to Intelligent internet of things water utilities pipe network scheduling modeling be Markovian decision process, to be based on depth
Intensified learning carries out intelligent water utilities scheduling；Input module, is configured to the status information that will be measured as the input of neural network,
Wherein, the state space is determined by the operating status of sensor real-time perception city water drainage-supply system；Output module is configured to
Using motion space as the output of neural network, wherein the motion space includes that different zones are dispatched with different water consumptions,
Strategy decision mapping of the state space to the motion space；And policy update module, it is configured to Utilization strategies ladder
Degree method trains neural network, optimal to approach to update neural network parameter to update the strategy by intelligent body dynamic
Strategy, wherein the intelligent body obtains the pipe network state in which, then strategy described in decision by observation pipe network environment
Corresponding actions are taken to influence the pipe network environment, to realize water regulation.
7th aspect, provides a kind of electronic equipment comprising: at least one processor, and with described at least one
Manage the memory of device communication connection, wherein the memory is stored with the instruction that can be executed by least one described processor, institute
It states instruction to be executed by least one described processor, so that at least one described processor is able to carry out any embodiment of the present invention
The Intelligent internet of things water utilities big data processing method for fringe node or cloud the step of.
Eighth aspect, the embodiment of the present invention also provide a kind of computer program product, and the computer program product includes
The computer program being stored on non-volatile computer readable storage medium storing program for executing, the computer program include program instruction, when
When described program instruction is computer-executed, make computer execution any embodiment of the present invention is used for fringe node or cloud
The step of Intelligent internet of things water utilities big data processing method at end.
Method and system of the invention will by the way that system level tasks are transmitted to cloud processing by above technical scheme
User-level task branches to closer fringe node and is handled, and can mitigate the burden in cloud to a certain extent, and can
Effectively to lower network delay and network energy consumption.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are some embodiments of the invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is a kind of Intelligent internet of things water utilities big data processing side for fringe node that one embodiment of the invention provides
The flow chart of method；
Fig. 2 is a kind of Intelligent internet of things water utilities big data processing method for cloud that one embodiment of the invention provides
Flow chart；
Fig. 3 is a kind of stream for Intelligent internet of things water utilities pipe network dispatching method for cloud that one embodiment of the invention provides
Cheng Tu；
Fig. 4 is a kind of Intelligent internet of things water utilities big data closed loop system based on feature modeling that one embodiment of the invention provides
The schematic diagram of system；
Fig. 5 is the water utilities big data deep learning frame that one embodiment of the invention provides；
Fig. 6 is the water utilities big data calculating task current-dividing network topological diagram that one embodiment of the invention provides；
Fig. 7 is the frame diagram for the wisdom water utilities platform that one embodiment of the invention provides；
Fig. 8 is a kind of Intelligent internet of things water utilities big data processing system for fringe node that one embodiment of the invention provides
The block diagram of system；
Fig. 9 is a kind of Intelligent internet of things water utilities big data processing system for cloud that one embodiment of the invention provides
Block diagram；
Figure 10 is a kind of Intelligent internet of things water utilities network scheduler system for cloud that one embodiment of the invention provides
Block diagram；
Figure 11 is the structural schematic diagram for the electronic equipment that one embodiment of the invention provides.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
In the following, first introducing presently filed embodiment, the scheme and existing skill of the application will be confirmed with experimental data later
Any beneficial effect be able to achieve compared to there is what difference for art.
Referring to FIG. 1, it illustrates a kind of Intelligent internet of things water utilities big data processing for fringe node of the invention
The flow chart of one embodiment of method, the present embodiment can be used for wisdom water utilities platform, which may include various for acquiring
It the end sensor of water utilities data, the fringe node for the data that sensor acquires to be allocated and handled and is used for
Handle the cloud data processing centre of the data transmitted by fringe node.
As shown in Figure 1, in a step 101, acquiring water utilities data via the sensor connecting with fringe node；
In a step 102, the water utilities data are standardized, and based on preset need generate with treated
The corresponding calculating task of water utilities data；
Later, in step 103, the calculating task and use of system level are identified and are categorized into the calculating task
The calculating task of family rank；
At step 104, the calculating task of the system level cloud is transmitted to handle；
Later, in step 105, the calculating task of the user class is divided into the calculating task block of predefined size；
Finally, in step 106, computing resource and the edge using sparse optimization algorithm to the fringe node
The communication resource of node and adjacent node carries out combined optimization, to determine to stay in the calculating task block at the fringe node
Manage or branch to the adjacent node processing.
In the present embodiment, for step 101, the fringe node of wisdom water utilities platform will be via connecting with fringe node
Sensor acquires water utilities data, so that the data of collecting sensor acquisition are further processed, such as is acquired by sensor
To the water data of each family resident.Later, for step 102, collected water utilities data are standardized, such as will
The unit of water utilities data is all converted into unified unit, then all inserts water utilities data in database, based on subsequent
Calculation task；And calculating task corresponding with treated water utilities data is generated based on preset need, such as to water consumption in region
Statistical analysis perhaps water regulation analysis or user water habits analysis and water-saving suggestions analyze.Using treated
Water utilities data call corresponding analytic function function, carry out analytical calculation task.Such as: the analysis of regional water use amount, Yong Huyong
Water habit analysis etc., can all carry out corresponding analytical calculation task.Such as: when carrying out the analysis of regional water use amount, utilize processing
Water utilities data afterwards call region water consumption statistics analytic function function, formation zone water consumption statistics analytical calculation task；Into
When the habit analysis of row user's water, using treated water utilities data, user's water habit analytic function function is called, production is used
It is accustomed to analytical calculation task dispatching etc. in family.Later, for step 103, the calculating task is identified and is categorized into system-level
The calculating task of other calculating task and user class, such as system-level calculating task, such as water regulation analysis, area
The reasons such as domain water per analysis etc. is huge because of required data volume, and calculating task is huge are not suitable for being diverted to each feature modeling,
And for calculating task of user class, such as the analysis of user's water habits, water-saving suggestion analysis etc., it is particularly suitable for being diverted to each
Feature modeling, so that network load, delay and power consumption be greatly lowered.Therefore, at step 104, by the calculating of system level
Multiplexed transport to cloud is handled, and later in step 105, the calculating task of user class is divided into the meter of predefined size
Calculate task block handled with being transmitted to different fringe nodes, the method for processing can be in step 106 using sparse excellent
Change algorithm and combined optimization is carried out to the computing resource and fringe node of fringe node and the communication resource of adjacent node, to determine
Calculating task block is stayed in into the fringe node processing or branches to adjacent node processing, because comprehensive using sparse optimization algorithm
Consider computing resource and the communication resource, it is possible to lower network delay and network energy consumption, and the method for sparse optimization can
To solve the optimization problem of this kind of highly irregularization.
It wherein, may include: firstly, obtaining current system using the specific steps that sparse optimization algorithm carries out combined optimization
Communication resource information, computing resource information, calculating task block message to be allocated after segmentation.Secondly, being limited according to the communication resource
Condition, computing resource restrictive condition construct optimization system.Finally, being optimized using sparse optimization algorithm to system, counted
It calculates task block and handles Facility location result.
In some alternative embodiments, above-mentioned steps 106 further comprise: each calculating will be indicated in system to be optimized
The calculating task distributing strategy of task block execution position is mapped to sparse spike of equal value, and by the sparse spike scaling Cheng Lian
Continuous optimized variable；Non-convex non-linear of the continuous optimized variable after solving scaling using Continuous Convex Function approximation technique
Optimization problem, specific steps may include: to be constructed firstly, checking according to communication resource restrictive condition, computing resource restrictive condition
System to be optimized whether be non-convex nonlinear.Then, if system to be optimized be it is non-convex nonlinear, utilize
Sequential chart function approximation technology goes to approach system to be optimized；And it is determined based on the result after optimization by the calculating task block
It stays in the fringe node processing or branches to the adjacent node processing.To use the method for discrete variable rarefaction can be with
This hybrid combining optimization problem of effective solution.
In other optional embodiments, the system-level calculating task includes historic analysis task and predictability
Analysis task；Wherein, the historic analysis task includes report to sensor battery dosage and to water utilities network failure
Diagnosis；The predictability analysis task includes user demand prediction, the prediction of water utilities network fault recovery times and water utilities scheduling
Requirement forecasting.
In other optional embodiments, the calculating task of the user class include user's water habits analysis and it is water-saving
It is recommended that analysis.
With further reference to Fig. 2, it illustrates a kind of Intelligent internet of things water utilities big data processing sides for cloud of the present invention
The flow chart of one embodiment of method, the present embodiment can be used for wisdom water utilities platform, which may include various for acquiring water
It is engaged in the end sensors of data, the fringe node for the data that sensor acquires to be allocated and handled and for locating
The cloud data processing centre for the data that reason fringe node transmits.
As shown in Fig. 2, in step 201, receiving calculating task and standardized water utilities data from each fringe node；
In step 202, standardized water utilities data are handled to obtain calculated result based on calculating task；
Finally, in step 203, the calculated result is compared with preset multiple fault data models to obtain
And the matched fault model of calculated result is to prejudge failure corresponding with the calculated result.
In the present embodiment, for step 201, cloud receives the calculating task transmitted via each fringe node, Zhi Hou
In step 202, calculating task is handled to obtain calculated result, then in step 203, by calculated result and preset more
A fault data model is compared, can so as to prejudge to show whether the calculated result matches any fault type
The failure that can occur models various fault datas specifically, can establish corresponding neural network model, is formed
Fault data model, then calculated result is input in fault data model it may determine that the calculated result whether match appoint
One failure solves relevant issues as early as possible, is as much as possible down to loss most to realize the failure for knowing to be likely to occur in advance
It is low.By the way that calculated result to be input in the specially designed neural network for being used for fault diagnosis, it is allowed to and preset multiple events
Barrier data model compare, according to the neural network of fault diagnosis output with each preset failure model matching scoring come
Prejudge the corresponding failure of the prediction result.
Referring to FIG. 3, it illustrates a kind of Intelligent internet of things water utilities pipe network dispatching method one for cloud of the present invention is real
The flow chart of example is applied, the present embodiment can be used for wisdom water utilities platform, which may include various for acquiring water utilities data
End sensor, the fringe node for the data that sensor acquires to be allocated and handled and for handle by side
The cloud data processing centre for the data that edge node-node transmission comes.
As shown in figure 3, be Markovian decision process by Intelligent internet of things water utilities pipe network scheduling modeling in step 301,
To carry out intelligent water utilities scheduling based on deeply study；
In step 302, using state space as the input of neural network, wherein the state space is by sensor reality
When perception city water drainage-supply system operating status determine；
Later, in step 303, using motion space as the output of neural network, wherein the motion space includes pair
Different zones dispatch different water consumptions, strategy decision mapping of the state space to the motion space；
Finally, in step 304, Utilization strategies gradient method training neural network model, to be updated by intelligent body dynamic
The strategy is to update neural network parameter to approach optimal strategy, wherein and the intelligent body passes through observation pipe network environment,
The pipe network state in which is obtained, then strategy described in decision takes corresponding actions to influence the pipe network environment, to realize water
Amount scheduling.
In the present embodiment, for step 301, system is dispatched by the intelligent water utilities big data using cloud, such as: it goes through
History regional water use amount, the data such as user's water demands forecasting, the process model building that Intelligent internet of things water utilities pipe network is dispatched can at Ma Er
Husband's decision process can introduce deeply study in intelligent water utilities platform preferably to carry out water utilities scheduling.For step
302, step 303 and step 304, respectively using the operating status of sensor real-time perception city water drainage-supply system as neural network
The state space of model inputs neural network, dispatches different water consumptions as the movement of neural network model for different zones
Space and output, the strategy by the mapping of state space to motion space as neural network model, more by intelligent body dynamic
The new strategy is to update neural network parameter to approach optimal strategy, wherein the intelligent body passes through observation pipe network ring
Border obtains the pipe network state in which, and then strategy described in decision takes corresponding actions to influence the pipe network environment, to realize
Water regulation.To which when environment changes, intelligent body state is shifted, while intelligent body is received and broken by hydraulic pressure, water pipe
The user experience quality that factors determine such as split as return feedback information.Intelligent body realizes intelligent water by dynamic more new strategy
Business pipe network scheduling, provides higher service quality for user.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Movement merge, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described because
According to the present invention, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, and related actions and modules is not necessarily of the invention
It is necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, reference can be made to the related descriptions of other embodiments.
Next, the present invention is discussed in detail with specific example with reference to the following drawings.
Wherein, Fig. 5 is the water utilities big data deep learning frame that one embodiment of the invention provides；Fig. 6 is that the present invention one is real
The water utilities big data calculating task current-dividing network topological diagram of example offer is provided；And Fig. 7 is the wisdom that one embodiment of the invention provides
The frame diagram of water utilities platform；
Wisdom water utilities big data management system, by possessing the gateway of internet of things of powerful edge calculations ability and open architecture,
Couple supply equipment and various kinds of sensors；Cloud management framework, the quick controller with million equipment management abilities, provides equipment
The functions such as management, managing computing resources and application management, and pass through open interface and wisdom water supplying pipe platform and system docking,
By acquiring the operation data of supply equipment in real time, in conjunction with cloud big data analysis platform, each portion of supply equipment can be fully understanded
' health indicator ' of part realizes the preventive maintenance to supply equipment, this will be substantially improved the supply equipment uptime, and allow
It supplies water safer.
Edge calculations can effectively reduce the requirement to bandwidth, be capable of providing and timely respond, and to the hidden of data
Private provide protection, therefore edge calculations can play the role of in the development process of Internet of Things from now on it is very important.Edge calculations
Essence, be to provide intelligent Service close to data source in network edge, be more to digitize transition for industry to inject new energy, be
Industrial application brings real-time data analysis and service response.The practice of innovation of Internet of Things is calculated in open edge, passes through opening
Edge calculations Internet of Things is adapted to supply equipment the old and new and deposits, various scene such as producer is more, interface and agreement are different, meets difference
The Edge intelligence data processing demand of water affairs management scene realizes that wisdom supplies water.The wisdom water utilities realized based on edge calculations is big
Data management system can predict equipment fault, power-assisted water with real-time monitoring equipment and water quality condition, processing locality mass data
It is engaged in enterprise or operating administration reduction fault time, reduces manpower maintenance cost, promote water supply security and quality, reduce and manage
Cost.
The big data system of edge calculations will provide strong support for Intelligent internet of things, based on a large amount of of mass data
Analysis demand is calculated, the demand with the intelligent decision based on analysis to network delay and reliability is solved using edge calculations
Water utilities big data calculating task shunts the problem of with intelligent water utilities decision, realizes big data system in Intelligent internet of things water utilities closed loop
Low delay, high reliability in system (please referring to Fig. 4).
It, as a result, can be to water utilities tune using existing big data analysis based on the intelligent decision of Internet of Things water utilities big data
Degree, price etc. do intelligent decision.System not only carries out real-time analysis decision to existing water utilities data in the process of running, also not
It is disconnected to there are new water utilities data to generate feedback into system.Closed loop configuration so can allow water utilities system more and more intelligent.
Water utilities big data calculating task based on sparse optimization, which shunts, is used for fringe node: this part technology is to calculating and leads to
Believe that resource and water utilities big data calculating task distributing strategy carry out combined optimization, calculating task prolongs in attenuating terminal device
When and energy consumption, network topology structure such as Fig. 6.The frame of the corresponding Intelligent internet of things water utilities big data closed-loop system based on feature modeling
Frame, we merge delivery network with analysis network, the calculating task of partial analysis network are diverted in delivery network
(computing resource is pushed toward edge), corresponding with delivery network is the edge device in Fig. 6 and base station.This technology is first big to water utilities
Data calculating task is split and models.For specific water utilities big data calculating task, it would be desirable to predefined which
Task be can carry out shunt processing which be unsuitable for shunt.For system-level calculating task, such as water regulation
Analysis, the analysis of regional water use amount etc. are because the reasons such as required data volume is huge, and calculating task is huge are not suitable for being diverted to each side
Boundary calculates.For calculating task of user class, such as the analysis of user's water habits, water-saving suggestion analysis etc., it is particularly suitable for shunting
To each feature modeling, so that network load, delay and power consumption be greatly lowered.I has been determined after the calculating task that can be shunted
Need to be split task.In this stage, we are divided into calculating task the calculating of predefined same size
Task block is used for the next step optimizing phase.We term it the mappings of calculating task calling figure for this process.Then this technology is to low
Power consumption calculation task shunting problems carry out combined calculation resource and communication resource optimization, to lower network delay and network energy
Consumption.This technology uses the method for sparse optimization to solve the optimization problem of this kind of highly irregularization.The first step, according to only
Some terminal device can shunt the fact that benefit from calculating task, and calculating task distributing strategy is mapped to the sparse of equivalence
Vector, thus further by its scaling at continuous optimized variable.Second step is solved using the technology that Continuous Convex Function approaches
Non-convex nonlinear optimal problem after scaling.In addition, this technology further derives calculating using higher-dimension Random Matrices Theory
Task distributing strategy, this method will only utilize channel statistic, to reinforce the practical feasibility of set calculating method.Difference
In the prior art, the calculating task calling figure of this technology will improve the feasibility of distributing strategy.Furthermore discrete variable rarefaction
Method can be with this hybrid combining optimization problem of effective solution.
Intelligent water utilities scheduling (being used for cloud data processing centre) based on deeply study: this technology uses extensive chemical
The tools such as habit infer the water consumption in each region of pipe network to analyze.It is intended to study under dynamic pipe network environment, by Internet of Things intelligent water
Business pipe network scheduling modeling is Markovian decision process (Markov Decision Process), to use intensified learning
(Reinforcement Learning) obtains preferably tactful, auxiliary dispatching personnel's decision use according to the status information of pipe network
Which kind of Optimized Operation scheme ensures user's water.Specifically, in intensified learning model intelligent body state space (State
Space the operation of the on-line monitoring equipments real-time perception such as instrument, wireless network, water quality water pressure gauge city water drainage-supply system) is adopted by number
Status information determines that motion space (Action space) is that different zones are dispatched with different water consumptions, tactful (Policy)
Determine state space to motion space mapping relations.Intelligent body (Agent) obtains locating shape by observation pipe network environment
Then state takes corresponding actions to influence environment, that is, chooses suitable water regulation according to decision strategy.When environment changes
When, intelligent body state shifts, while intelligent body receives the user experience quality determined by factors such as hydraulic pressure, pipe bursts
(QoE) as return (Reward) feedback information.Intelligent body realizes intelligent water utilities pipe network scheduling by dynamic more new strategy, is
User provides higher service quality.Since number adopts the on-line monitoring equipments real-time perception such as instrument, wireless network, water quality water pressure gauge city
The running state information parameter value of city's water drainage-supply system is mostly continuous real number, this causes water utilities scheduling problem in extensive chemical
Practising its state space and motion space under frame has high dimensional feature, and traditional policy update algorithm is due to memory complexity, meter
It calculates complexity and sample complex is excessively high and no longer applicable.Deep neural network can find the compact low-dimensional of high dimensional data automatically
It indicates (feature), therefore policy learning algorithm of this project proposed adoption based on deep learning, by deep neural network (Deep
Neural network) powerful function approximation property is to tactful function approximation.Specifically, the status information measured is made
For neural network input, motion space is neural network output, the methods of Utilization strategies gradient method training neural network, thus more
New neural network parameter approaches optimal policy, and the deeply learning system is as shown in Figure 5.Finally, the training stage in order to
It makes full use of motion space to obtain more dominant strategy, entropy regular terms optimisation strategy gradient method can be added in policy update step, and
It can be by training process asynchronous parallel to accelerate training speed.Compared to other schemes, the advantage of the design is can be abundant
Using big data come Optimal Decision-making network, so that system performance is obviously improved.
In one embodiment of wisdom water utilities big data management system, as shown in fig. 7, wisdom water utilities big data management system
System framework is made of following sections: Intellisense layer, data Server, cloud management platform and front end applications system.
(1) Intellisense layer: hydraulic pressure, valve, sum, electricity, communication, flow, surplus, number, state and attribute；
By sensors such as hydraulic pressure, valve, sum, electricity, communication, flow, surplus, number, state and attributes, realize to water
The complete perception and detection data of environment acquire.
Sensing layer equipment mainly includes on-line instrument instrument, production equipment, the robot control system(RCS) of each sewage treatment plant, pumping plant
Deng being the basis of technology of Internet of things framework, the construction of sensing layer is passed through powerful data protocol and turns using each factory's robot control system(RCS)
Function is changed, under the premise of not influencing each sewage treatment plant's production run, carries out the conversion of multiple communication interface, communication protocol,
It realizes the production run data of automatic collection various PLC and driver, and establishes between all kinds of PLC, driver, motor controller
Data communication, ultimately form perfect perception coating systems.
(2) data Server:Server interface, open interface, safe interface, data transmit-receive, data cleansing and data are deposited
It takes；
Using NB-IoT/WIFI multi-mode communication mode, by the collected real-time data transmission of sensor to network center.It adopts
With the wisdom water utilities big data management system based on edge calculations, the acquisition of water utilities data, the execution and interaction of decision are realized, together
When identification classification and simple pretreatment are carried out to original water utilities data.
(3) cloud management platform: equipment management, financial management, management, operation management, big data analysis and system pipes
Reason；
1. equipment management
Monitoring of tools-real-time monitoring data of water meter, provides basic data for software statistics；
Plant maintenance-strictly observes plant maintenance process, the maintenance record including water meter；
2. financial management
Order, statement export, application of withdrawing deposit, reimbursement audit and managing bill are supplemented in report form statistics and export with money；
3. management: water meter, pipeline, activity, operation cost typing, customer service management；
4. operation management: work order to be processed, work order, history O&M information；
5. big data analysis:
It is divided into historic and predictive analysis, wherein historic analysis includes the report to water utilities sensing equipment battery level
It accuses, diagnosis of water utilities network failure etc. is applied；Predictability analysis may include that the prediction of user demand, water utilities network failure are extensive
The applications such as multiple time prediction, the prediction of water utilities dispatching requirement.It embodies are as follows:
Data statistics: energy-saving and emission-reduction data, date operation data statistics, equipment operating data statistics and certain customers' number
According to statistics；
Statistical analysis-really realizes leakage loss analysis, and intelligence is with advanced function of statistic analysis such as tables；
Historical query-includes data of water meter inquiry and global warning message inquiry etc.；
6. system administration
Rights management, account distribution.
(4) front end applications system: Android-APP, ios-APP, small routine trade company platform, pipe network and public platform.
Wisdom water utilities big data management system can complete the access to water utilities industry Various types of data, filtering, cleaning, turn
The processes such as change, load, compared based on trigger, based on timestamp, based on full text, the different mode data based on log it is synchronous,
Batch extraction, the in real time various data pick-up implementation strategies such as extraction, timing extraction are provided, it is final to realize the organic whole of isomeric data
It closes.The shared platform of water utilities data is established, the convenient and efficient information resources for realizing each system are exchanged and shared.
The wisdom water utilities big data management system of use introduces edge calculations Internet of Things solution, realizes supply equipment
Connection and real-time detection to supply equipment operation conditions and water quality condition, greatly discharge the energy of edge calculations, automatically
Realize the intellectual analysis to water quality condition, greatly guarantee quality of water supply；Meanwhile automatic realization is to the pre- of supply equipment failure
Sentence, the bottom nonstandardized technique data of supply equipment are standardized, these data are delivered to cloud；Cloud is to these
Data are analysed in depth, and are compared by machine learning techniques with cloud mathematical model, to realize anticipation failure hair in advance
Raw effect；Mathematical model is adjusted and is optimized simultaneously.Reduce the management and maintenance of water enterprise and operating administration
Cost greatly improves the efficiency of management, can will shorten 70% failure detection time, and failure rate reduces by 60%, saves manpower maintenance
Cost about 60%.Accomplish centered on guarantee, is to solve the problems, such as that industry intelligent level is low, operation management is poor
Purpose realizes wisdom water supply personalized customization, standardized designs, optimal production using industry internet advanced technology, and leads to
Too far the application and APP, Web etc. such as range monitoring, mobile construction, fault pre-alarming, quick response at many levels, diversification user interface,
Realize that water supply industry " equipment-enterprise-society " three-level serviceization extends.
Above big data system based on edge calculations, advantage embody are as follows:
Scheme is shunted using the calculating task based on sparse optimization, one is established to water utilities big data calculating task shunting problems
A unified frame, i.e., to computing resource, (calculating task is performed locally, also for the communication resource and calculating task distributing strategy
It is to be diverted to neighbouring Wireless Access Unit), carry out combined optimization.Simultaneously, it is also contemplated that wherein several critical issues, including water
The segmentation for big data calculating task of being engaged in, it is irregular to optimize.The distributed optimization algorithm of expansibility is designed, is appointed to solve cross-layer
Business shunts the optimization problem of this highly irregularization.Computing resource can be solved how to push to network boundary, to meet low
Be delayed the key problem communicated.
Using the intelligent water utilities scheduling learnt based on deeply, intelligent water utilities scheduling problem can be modeled as Markov
Decision process obtains preferably scheduling strategy according to the status information of pipe network using intensified learning, and auxiliary dispatching personnel's decision is adopted
With which kind of Optimized Operation scheme, user's water is ensured.Meanwhile collected network state number is understood using deep neural network
According to by the powerful function approximation property of deep neural network to tactful function approximation.And the methods of Utilization strategies gradient method is instructed
Practice neural network, to update neural network parameter to approach optimal policy.
Referring to FIG. 8, it illustrates a kind of Intelligent internet of things water utilities big data processing system 800 for fringe node,
Including acquisition module 810, processing module 820, categorization module 830, system level tasks distribution module 840, user-level task segmentation
Module 850 and combined optimization diverter module 860.
Wherein, acquisition module 810 are configured to acquire water utilities data via the sensor connecting with fringe node；Handle mould
Block 820 is configured to be standardized the water utilities data, and is generated and treated water utilities data based on preset need
Corresponding calculating task；Categorization module 830 is configured to identify the calculating task and be categorized into system-level calculating times
The calculating task of business and user class；System level tasks distribution module 840 is configured to for the system-level calculating task being transmitted to
Cloud is handled；User-level task divides module 850, is configured to the calculating task of the user class being divided into predefined size
Calculating task block；And combined optimization diverter module 860, it is configured to the meter using sparse optimization algorithm to the fringe node
The communication resource for calculating resource and the fringe node and adjacent node carries out combined optimization, to determine the calculating task block
It stays in the fringe node processing or branches to the adjacent node processing.
Referring to FIG. 9, it illustrates a kind of Intelligent internet of things water utilities big data processing systems 900 for cloud, including
Receiving module 910, computing module 920 and failure predication module 930.
Wherein, receiving module 910 are configured to receive the calculating task from each fringe node and standardized water utilities number
According to；Computing module 920 is configured to the calculating task and is handled the water utilities data to obtain calculated result；With
And failure predication module 930, it is configured to for the calculated result being compared with preset multiple fault data models to obtain
And the matched fault model of prediction result is to prejudge failure corresponding with the prediction result.
Referring to FIG. 10, it illustrates a kind of Intelligent internet of things water utilities network scheduler systems 1000 for cloud, including
Modeling module 1010, input module 1020, output module 1030 and policy update module 1040.
Modeling module 1010 is configured to Intelligent internet of things water utilities pipe network scheduling modeling be Markovian decision process, from
And intelligent water utilities scheduling is carried out based on deeply study；Input module 1020, be configured to the status information that will be measured as
The input of neural network, wherein the state space is determined by the operating status of sensor real-time perception city water drainage-supply system；
Output module 1030 is configured to using motion space as the output of neural network, wherein the motion space includes to not same district
The different water consumption of domain scheduling, strategy decision mapping of the state space to the motion space；And policy update mould
Block 1040 is configured to Utilization strategies gradient method training neural network, to update the strategy by intelligent body dynamic to update
Neural network parameter approaches optimal strategy, wherein the intelligent body is obtained locating for the pipe network by observation pipe network environment
State, then strategy described in decision takes corresponding actions to influence the pipe network environment, to realize water regulation.
It should be appreciated that in all modules recorded in Fig. 8, Fig. 9 and Figure 10 and the method with reference to described in Fig. 1, Fig. 2 and Fig. 3
Each step it is corresponding.The operation above with respect to method description and feature and corresponding technical effect are equally applicable as a result,
All modules in Fig. 8, Fig. 9 and Figure 10, details are not described herein.
It is worth noting that, the module in embodiment of the disclosure is not limited to the scheme of the disclosure, such as receive
Module can be described as receiving the module of calculating task and standardized water utilities data from each fringe node.In addition, may be used also
It is no longer superfluous herein to realize that related function module, such as memory module can also be realized with processor by hardware processor
It states.
In further embodiments, the embodiment of the invention also provides a kind of nonvolatile computer storage medias, calculate
Machine storage medium is stored with computer executable instructions, which can be performed in above-mentioned any means embodiment
The Intelligent internet of things water utilities big data processing method for fringe node or cloud；
As an implementation, nonvolatile computer storage media of the invention is stored with the executable finger of computer
It enables, computer executable instructions setting are as follows:
Water utilities data are acquired via the sensor connecting with fringe node；
The water utilities data are standardized, and water utilities data are corresponding with treated based on preset need generation
Calculating task；
The calculating task of system-level calculating task and user class is identified and is categorized into the calculating task；
The system-level calculating task is transmitted to cloud to handle；
The calculating task of the user class is divided into the calculating task block of predefined size；
Using sparse optimization algorithm to the computing resource of the fringe node and the fringe node and adjacent node
The communication resource carries out combined optimization, to determine that the calculating task block is stayed in the fringe node handles or branch to the phase
Neighbors processing.
As a kind of non-volatile computer readable storage medium storing program for executing, it can be used for storing non-volatile software program, non-volatile
Property computer executable program and module, as in the embodiment of the present invention for fringe node or the Intelligent internet of things water in cloud
Corresponding program instruction/the module of big data processing method of being engaged in.One or more program instruction is stored in non-volatile computer
In readable storage medium storing program for executing, when being executed by a processor, executes in above-mentioned any means embodiment and be used for fringe node or cloud
Intelligent internet of things water utilities big data processing method.
Non-volatile computer readable storage medium storing program for executing may include storing program area and storage data area, wherein storage journey
It sequence area can application program required for storage program area, at least one function；Storage data area can be stored according to for edge
The Intelligent internet of things water utilities big data processing unit in node goods cloud uses created data etc..In addition, non-volatile meter
Calculation machine readable storage medium storing program for executing may include high-speed random access memory, can also include nonvolatile memory, for example, at least
One disk memory, flush memory device or other non-volatile solid state memory parts.In some embodiments, non-volatile
Optional computer readable storage medium includes the memory remotely located relative to processor, these remote memories can pass through
Network connection is extremely used for the Intelligent internet of things water utilities big data processing unit in fringe node goods cloud.The example of above-mentioned network includes
But be not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
The embodiment of the present invention also provides a kind of computer program product, and computer program product is non-volatile including being stored in
Computer program on computer readable storage medium, computer program include program instruction, when program instruction is held by computer
When row, computer is made to execute any of the above-described for fringe node or the Intelligent internet of things water utilities big data processing method in cloud.
Figure 11 is the structural schematic diagram of electronic equipment provided in an embodiment of the present invention, and as shown in figure 11, which includes: one
A or multiple processors 1110 and memory 1120, in Figure 11 by taking a processor 1110 as an example.For fringe node or cloud
The equipment of the Intelligent internet of things water utilities big data processing method at end can also include: input unit 1130 and output device 1140.
Processor 1110, memory 1120, input unit 1130 and output device 1140 can be connected by bus or other modes,
In Figure 11 for being connected by bus.Memory 1120 is above-mentioned non-volatile computer readable storage medium storing program for executing.Processor
1110 non-volatile software program, instruction and the modules being stored in memory 1120 by operation, thereby executing server
Various function application and data processing, i.e. Intelligent internet of things of the realization above method embodiment for fringe node or cloud
Water utilities big data processing method.Input unit 1130 can receive the number or character information of input, and generates and launch with information
The related key signals input of the user setting and function control of device.Output device 1140 may include that the displays such as display screen are set
It is standby.
Method provided by the embodiment of the present invention can be performed in the said goods, has the corresponding functional module of execution method and has
Beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to method provided by the embodiment of the present invention.
As an implementation, above-mentioned electronic apparatus application is in the Cloud Server of unattended physical stores, comprising:
At least one processor；And the memory being connect at least one processor communication；Wherein, be stored with can be by extremely for memory
The instruction that a few processor executes, instruction are executed by least one processor so that at least one processor can:
Water utilities data are acquired via the sensor connecting with fringe node；
The water utilities data are standardized, and water utilities data are corresponding with treated based on preset need generation
Calculating task；
The calculating task of system-level calculating task and user class is identified and is categorized into the calculating task；
The system-level calculating task is transmitted to cloud to handle；
The calculating task of the user class is divided into the calculating task block of predefined size；
Using sparse optimization algorithm to the computing resource of the fringe node and the fringe node and adjacent node
The communication resource carries out combined optimization, to determine that the calculating task block is stayed in the fringe node handles or branch to the phase
Neighbors processing.
The electronic equipment of the embodiment of the present application exists in a variety of forms, including but not limited to:
(1) mobile communication equipment: the characteristics of this kind of equipment is that have mobile communication function, and to provide speech, data
Communication is main target.This Terminal Type includes: smart phone (such as iPhone), multimedia handset, functional mobile phone and low
Hold mobile phone etc..
(2) super mobile personal computer equipment: this kind of equipment belongs to the scope of personal computer, there is calculating and processing function
Can, generally also have mobile Internet access characteristic.This Terminal Type includes: PDA, MID and UMPC equipment etc., such as iPad.
(3) portable entertainment device: this kind of equipment can show and play multimedia content.Such equipment include: audio,
Video player (such as iPod), handheld device, e-book and intelligent toy and portable car-mounted navigation equipment.
(4) server: providing the equipment of the service of calculating, and the composition of server includes that processor, hard disk, memory, system are total
Line etc., server is similar with general computer architecture, but due to needing to provide highly reliable service, in processing energy
Power, stability, reliability, safety, scalability, manageability etc. are more demanding.
(5) other electronic devices with data interaction function.
The apparatus embodiments described above are merely exemplary, wherein unit can be as illustrated by the separation member
Or may not be and be physically separated, component shown as a unit may or may not be physical unit, i.e.,
It can be located in one place, or may be distributed over multiple network units.It can select according to the actual needs therein
Some or all of the modules achieves the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creative labor
In the case where dynamic, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
The method of certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of Intelligent internet of things water utilities big data processing method for fringe node, comprising:
Water utilities data are acquired via the sensor connecting with fringe node；
The water utilities data are standardized, and by preset need generate it is corresponding with treated water utilities data based on
Calculation task；
The calculating task of system-level calculating task and user class is identified and is categorized into the calculating task；
The system-level calculating task is transmitted to cloud to handle；
The calculating task of the user class is divided into the calculating task block of predefined size；
Using sparse optimization algorithm to the computing resource of the fringe node and the communication of the fringe node and adjacent node
Resource carries out combined optimization, to determine that the calculating task block is stayed in the fringe node handles or branch to the adjacent segments
Point processing.
2. described to be provided using sparse optimization algorithm to the calculating of the fringe node according to the method described in claim 1, wherein
The communication resource of source and the fringe node and adjacent node carries out combined optimization, to determine to stay in the calculating task block
The fringe node handles or branches to the adjacent node processing
The calculating task distributing strategy of calculating task block execution position is mapped to sparse spike of equal value, and by the sparse arrow
Scaling is measured into continuous optimized variable；
Utilize the non-convex nonlinear optimal problem of the continuous optimized variable after Continuous Convex Function approximation technique solution scaling；
It determines to stay in the calculating task block into the fringe node processing or branch to described adjacent based on the result after optimization
Node processing.
3. according to the method described in claim 1, wherein, the system-level calculating task is including historic analysis task and in advance
The property surveyed analysis task；
Wherein, the historic analysis task includes the report to sensor battery dosage and the diagnosis to water utilities network failure；
The predictability analysis task includes that user demand prediction, the prediction of water utilities network fault recovery times and water utilities scheduling need
Ask prediction.
4. according to the method described in claim 1, wherein, the calculating task of the user class include the analysis of user's water habits and
Water-saving suggestion analysis.
5. a kind of Intelligent internet of things water utilities big data processing method for cloud, comprising:
Receive calculating task and standardized water utilities data from each fringe node；
The water utilities data are handled to obtain calculated result based on the calculating task；
The calculated result is compared with preset multiple fault data models matched with the prediction result to obtain
Fault model is to prejudge failure corresponding with the prediction result.
6. a kind of Intelligent internet of things water utilities pipe network dispatching method for cloud, comprising:
Intelligent internet of things water utilities pipe network scheduling modeling is Markov decisior process by the water utilities big data being collected into using cloud
Journey, to carry out intelligent water utilities scheduling based on deeply study；
Using state space as the input of neural network, wherein the state space is by sensor real-time perception city water supply and sewage
The operating status of system determines；
Using motion space as the output of neural network, wherein the motion space includes that different zones are dispatched with different use
Water, strategy decision mapping of the state space to the motion space；
Utilization strategies gradient method trains neural network, to update the strategy by intelligent body dynamic to update neural network ginseng
It counts to approach optimal strategy,
Wherein, the intelligent body obtains the pipe network state in which, then strategy described in decision is adopted by observation pipe network environment
Corresponding actions are taken to influence the pipe network environment, to realize water regulation.
7. a kind of Intelligent internet of things water utilities big data processing system for fringe node, comprising:
Acquisition module is configured to acquire water utilities data via the sensor connecting with fringe node；
Processing module is configured to be standardized the water utilities data, and based on preset need generate with treated
The corresponding calculating task of water utilities data；
Categorization module is configured to identify the calculating task and be categorized into the meter of system-level calculating task and user class
Calculation task；
System level tasks distribution module, is configured to the system-level calculating task being transmitted to cloud and handles；
User-level task divides module, is configured to for the calculating task of the user class to be divided into the calculating task of predefined size
Block；
Combined optimization diverter module is configured to using computing resource and the side of the sparse optimization algorithm to the fringe node
The communication resource of edge node and adjacent node carries out combined optimization, to determine the calculating task block staying in the fringe node
Handle or branch to the adjacent node processing.
8. a kind of Intelligent internet of things water utilities big data processing system for cloud, comprising:
Receiving module is configured to receive calculating task and standardized water utilities data from each fringe node；
Computing module is configured to the calculating task and is handled the water utilities data to obtain calculated result；
Failure predication module, be configured to for the calculated result to be compared with preset multiple fault data models with obtain with
The matched fault model of prediction result is to prejudge failure corresponding with the prediction result.
9. a kind of Intelligent internet of things water utilities network scheduler system for cloud, comprising:
Modeling module is configured to Intelligent internet of things water utilities pipe network scheduling modeling be Markovian decision process, thus based on deep
It spends intensified learning and carries out intelligent water utilities scheduling；
Input module is configured to the status information that will be measured as the input of neural network, wherein the state space is by passing
The operating status of sensor real-time perception city water drainage-supply system determines；
Output module is configured to using motion space as the output of neural network, wherein the motion space includes to not same district
The different water consumption of domain scheduling, strategy decision mapping of the state space to the motion space；
Policy update module is configured to Utilization strategies gradient method training neural network, to update the plan by intelligent body dynamic
Slightly to update neural network parameter to approach optimal strategy, wherein the intelligent body obtains institute by observation pipe network environment
Pipe network state in which is stated, then strategy described in decision takes corresponding actions to influence the pipe network environment, to realize water regulation.
10. a kind of electronic equipment comprising: at least one processor, and connect at least one described processor communication
Memory, wherein the memory be stored with can by least one described processor execute instruction, described instruction by it is described extremely
A few processor executes, so that at least one described processor is able to carry out the step of any one of claim 1 to 6 the method
Suddenly.
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| CN110687865A (en) * | 2019-10-21 | 2020-01-14 | 福建锋冠科技有限公司 | Wisdom water utilities management platform |
| CN110730245A (en) * | 2019-10-22 | 2020-01-24 | 青岛农业大学 | Neural network based edge computing system and method |
| CN110827172A (en) * | 2019-11-12 | 2020-02-21 | 河北建投水务投资有限公司 | Wisdom water affairs cloud service platform |
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