CN112051776A - Ecological housekeeper management method and intelligent platform - Google Patents
Ecological housekeeper management method and intelligent platform Download PDFInfo
- Publication number
- CN112051776A CN112051776A CN202010957095.1A CN202010957095A CN112051776A CN 112051776 A CN112051776 A CN 112051776A CN 202010957095 A CN202010957095 A CN 202010957095A CN 112051776 A CN112051776 A CN 112051776A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- ecological
- management method
- internet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 31
- 238000003860 storage Methods 0.000 claims abstract description 23
- 238000013135 deep learning Methods 0.000 claims abstract description 12
- 238000007781 pre-processing Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 23
- 238000004891 communication Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000011161 development Methods 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 9
- 238000010276 construction Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000012806 monitoring device Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 239000010865 sewage Substances 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention provides an ecological housekeeper management method and an intelligent platform, and relates to the field of ecological environment management. An ecological housekeeper management method comprises the following steps: collecting ecological environment data and transmitting the ecological environment data to a block chain for storage; acquiring block chains and data in a database, and decomposing the data; respectively preprocessing the decomposed data nodes; connecting the preprocessed multiple data nodes with each other for cooperative calculation and outputting an optimal result; carrying out deep learning on the output optimal result to obtain a control instruction; and the Internet of things module receives the control instruction and performs machine control on various devices. The intelligent platform can form an ecological manager intelligent platform which enables the development of various fields of production and living ecology according to the big data, the Internet of things and the block chain. In addition, the invention also provides an intelligent platform for managing the ecological housekeeper, which comprises: the device comprises an acquisition and storage module, an acquisition and decomposition module, a processing module, an output module, an instruction module and a control module.
Description
Technical Field
The invention relates to the field of ecological environment management, in particular to an ecological housekeeper management method and an intelligent platform.
Background
The human living environment is comprehensively improved for a long time and the hardware construction is emphasized, and an effective scheme of long-acting management and protection is lacked, so that the waste rate of serial basic engineering of sewage treatment facilities and the like is high, the maintenance cost is high, and the hardware construction and the high-quality maintenance are lacked.
With the deep advancement of the garbage revolution, the sewage revolution and the toilet revolution, a lot of effective advancing means and achievements emerge, but the means usually focus on one aspect, lack of systematic attention to the mountains, rivers, forests and lakes, lack of a full-system concept, and importance on single-hand advancing and lack of system integration.
The current human-living environment comprehensive improvement works mostly depend on the traditional mode, and the combination with big data and an information platform is not enough, so that the high cost and the high investment of assessment and supervision are caused, the mode of a retransmission system is insufficient, and intelligent management is lacked.
Disclosure of Invention
The invention aims to provide an ecological manager management method which can form an ecological manager intelligent platform capable of accurately enabling the development of various fields of production, living and ecology by taking intelligent management as a breakthrough, taking high-level construction, high-color-value restoration and a high-quality operation and maintenance ecological system as cut-in points and taking the acquaintance and the happiness of common people as general targets according to core technologies such as big data, cloud computing, artificial intelligence, the Internet of things and block chains.
Another object of the present invention is to provide an intelligent platform for managing an ecology butler, which can implement an ecology butler management method.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides an ecological steward management method, which includes the following steps: collecting ecological environment data and transmitting the ecological environment data to a block chain for storage; acquiring block chains and data in a database, and decomposing the data; respectively preprocessing the decomposed data nodes; connecting the preprocessed multiple data nodes with each other for cooperative calculation and outputting an optimal result; carrying out deep learning on the output optimal result to obtain a control instruction; and the Internet of things module receives the control instruction and performs machine control on various devices.
In some embodiments of the present invention, the collecting the ecological environment data and further transferring the data to the blockchain for storage includes: and the data acquisition transmits information through a web configuration mode and the network access of APN and VPDN.
In some embodiments of the present invention, the foregoing further includes a consensus protocol using POW as a bottom layer of the blockchain; the POW node and the Smart Contract Server node correspond to each other.
In some embodiments of the invention, the method further comprises processing the user request at the top level of the block chain according to a Smart Contract Server node.
In some embodiments of the present invention, the preprocessing the decomposed data nodes respectively includes: and carrying out abnormal value processing and missing value completion on each data node in the plurality of data nodes.
In some embodiments of the present invention, the receiving, by the internet of things module, the control instruction and performing machine control on various types of equipment includes: and the Internet of things module exchanges and communicates information with corresponding equipment and the Internet according to various sensors.
In some embodiments of the present invention, the obtaining the control instruction by performing deep learning on the output optimal result includes: and processing the optimal result by adopting an ANI mode to obtain a control instruction.
In some embodiments of the present invention, the acquiring and decomposing data in the distributed file system and the database includes: the database adopts a cache technology to relieve the data access pressure; and the application server cluster is used for improving the concurrent processing capacity of the website.
In a second aspect, an embodiment of the present application provides an intelligent platform for managing an ecosystem, which includes an acquisition and storage module, configured to acquire and transmit ecosystem data to a block chain for storage; the acquisition and decomposition module is used for acquiring data in the distributed file system and the database and decomposing the data; the processing module is used for respectively preprocessing the decomposed data nodes; the output module is used for connecting the preprocessed data nodes with each other for cooperative calculation and outputting an optimal result; the instruction module is used for carrying out deep learning on the output optimal result to obtain a control instruction; and the control module is used for receiving the control instruction and carrying out machine control on various devices by the Internet of things module.
In some embodiments of the invention, the above includes at least one memory for storing computer instructions; at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the device comprises an acquisition and storage module, an acquisition and decomposition module, a processing module, an output module, an instruction module and a control module.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
according to the ecological manager intelligent platform, core technologies such as big data, cloud computing, artificial intelligence, the Internet of things and a block chain can be supported, intelligent management is used as a breakthrough, a high-level construction, high-color-value restoration and high-quality operation and maintenance ecosystem are used as entry points, the general goals of meeting the acquaintance and happiness of common people are met, and the ecological manager intelligent platform capable of accurately enabling the development of various fields of production and living ecology is formed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic step diagram of an ecological steward management method according to an embodiment of the present invention;
fig. 2 is a detailed step diagram of an ecology manager management method according to an embodiment of the present invention;
fig. 3 is a schematic module diagram of an intelligent platform for managing an ecosystem according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic step diagram of an ecological steward management method according to an embodiment of the present invention, as follows:
s100, collecting ecological environment data and transmitting the ecological environment data to a block chain for storage;
specifically, the ecological environment data can be collected by moving equipment such as a screen monitoring device, a sewage pipeline monitoring device, an environment real-time monitor, a water quality monitoring device and a noise measuring instrument, the ecological environment related data comprises digital information, character information, sensor collection data and switching value information, and the ecological environment data is transmitted to a block chain for storage.
In some embodiments, the ecosystem data is transferred to the blockchain for storage, in order to facilitate later traceability. And a large amount of ecological data is gathered, and data support, system analysis and application are provided for the modernization of ecological management capacity. The intelligent platform provides positive repair suggestions for ecological environment construction. Ecological monitoring, intelligent analysis, scientific suggestion and intelligent decision.
Step S110, acquiring block chains and data in a database, and decomposing the data;
specifically, ecological environment data in the block chain library and ecological environment data in the database are respectively obtained, the extracted ecological environment data are decomposed, and a large amount of ecological environment data are decomposed into countless data nodes.
In some embodiments, the big data is decomposed into countless data nodes through a distributed file system and a database, and each data node is connected with each other through a special network or a commercial general-purpose network and is cooperated with each other to provide services as a whole. The data structure is a linear structure, a tree structure, a heap and a graph, the basic units of the structures are nodes, and different connections of the nodes form different data structures. The connections between nodes are using pointers. Structures or classes may be used as nodes.
Step S120, respectively preprocessing the decomposed data nodes;
specifically, after decomposition, large-scale data is decomposed into countless data nodes, and processed and analyzed.
In some embodiments, in actual business processes, the data is typically dirty data. So-called dirty, index data may present several problems: data loss, data noise, data inconsistency, data redundancy, data set imbalance, outliers/outliers, data duplication; the preprocessing can be performed by data cleaning, data conversion, data description, feature extraction, feature selection, feature combination or the like.
Step S130, connecting the preprocessed multiple data nodes with each other, performing collaborative calculation, and outputting an optimal result;
specifically, each data node is connected to each other through a private network or a commercial general-purpose network, and cooperatively calculates with each other to provide a service as a whole.
In some embodiments, cloud computing relies on big data distribution, and makes overall computation by combining various data interfaces, such as weather, people flow, traffic flow, and the like, and gives an optimal result.
Step S140, carrying out deep learning on the output optimal result to obtain a control instruction;
specifically, the optimal result is deeply learned, so that manual intervention is gradually reduced, and finally intelligent control is achieved.
In some embodiments, the method may be embodied as a deep learner, and the corresponding set of design, training, and usage methods may be used for self-learning. The deep learner model is composed of a plurality of processing layers, each layer comprises at least one processing unit, the output of each layer is a representation of data, and the representation levels are increased along with the increase of the processing levels.
The definition of depth is relative. For a specific scene and a learning task, if the total number of processing units and the number of layers of the learner are M and N, respectively, the amount of information or task performance retained by the learner exceeds that of a learner with any number of layers smaller than N and the total number of units being M, the learner is a strict or narrow deep learner, and the corresponding design, training and use methods are combined into strict or narrow deep learning.
S150, the Internet of things module receives a control instruction and performs machine control on various devices;
specifically, the internet of things module receives a control instruction after well depth learning and controls various devices.
In some embodiments, the internet of things module connects any article with the internet according to an agreed protocol based on various sensors to perform information exchange and communication, so as to realize intelligent identification, positioning, tracking, monitoring and management of the article. Such as realizing water quality monitoring and treatment, road parking space monitoring and prompting, ecological monitoring and warning, and the like.
Example 2
Referring to fig. 2, fig. 2 is a detailed step diagram of an ecological steward management method according to an embodiment of the present invention, as follows:
step S200, data acquisition transmits information through a web configuration mode and network access of APN and VPDN;
in some embodiments, the data acquisition part transmits information through a data gateway and a cloud service platform, the data gateway supports Telnet, web configuration mode and APN and VPDN network access, and supports mobility, connectivity, telecommunication TD-LTE, FDD-LTE and network systems, and the IP application supports Ping, Trace, DHCPServer, Telnet, and the IP routing supports static routing and other network access for information transmission.
Step S210, using POW as the consensus protocol of the bottom layer of the block chain;
in some embodiments, Proof of Work (PoW) is a Proof method. The hash function is the thing that the computational difficulty was verified repeatedly in the cryptography, so it proves to be most effective to use it, every time an information upload block chain is sent out, it proves to pay certain calculation power that the evidence is the character string in the block, and after adding this character string, the hash value of the block is just less than a certain number. The characteristics of the hash function indicate that this can be done without any deliberate method, only one string of characters being tried. The image is that the system can give out a mathematical question every time in order to find out who has stronger computing power, and the computer which can solve the question most quickly can carry out the bookkeeping. The advantage of POW is complete decentralization, with nodes in and out.
Step S220, the POW node corresponds to a Smart Contract Server node;
in some embodiments, each POW node has a Smart Contract Server (SCS) node. The SCS identity is fully verified by the corresponding POW node.
Step S230, processing a user request at the top layer of the block chain according to a Smart Contract Server node;
in some embodiments, each SCS node will be able to process top-level user requests. The SCS processes the calling of the intelligent contract. All transactions at the top level are in the form of smart contract invocations. Not all SCS will process a single transaction at the same time. Instead, some selected SCS will process a particular transaction.
The SCS is selected by initializing an intelligent contract call or refresh call (init/flush). The init/flush call actually passes the transaction to the POW node and agrees at the bottom. The init/flush call will set the SCS how the contract is selected, and the percentage of processing nodes. The corresponding POW node of each SCS would then invoke the call on its SCS using the EHDRand algorithm. The SCS may decide whether to choose to process this intelligent contract. Note that this is a deterministic process, and the participation of SCS can be verified by anyone.
Once the SCS combinations of smart contracts are selected, they will communicate with each other and form a small consensus group. The group will handle all the calling procedures of the smart contract. Furthermore, the behavior of how they agree can be specified by init/flush. The SCS nodes form a chain of sub-blocks and perform a consensus based on a predetermined protocol or a user-defined protocol. Note that the consensus protocol is different from the actual intelligent contract code.
The state of the smart contract is saved in each SCS. However, this is not preserved in a complete blockchain system. To achieve a complete consensus, the contract state needs to be written into the underlying POW node periodically or on demand.
In the consensus mode, the SCS node will accept data storage requests from the underlying POW nodes when refreshing (flush). The current state will be written to the blockchain system and a corresponding HASH generated. Note that all POW nodes will perform the same operation. For SCS of smart contracts that do not participate in this refresh, they do nothing. The SCS participating in the intelligent contract will obtain the commit status and verify it with its own status. If the previously committed state can be proven to be incorrect, it will initiate a HASH that updates a data store request with the correct state and references the incorrect state. If the previously submitted state is an undisputed data storage request, the SCS node will eventually refresh the smart contract with the correct state HASH. While each POW node will also process transactions related to contract status. SCS nodes that issue incorrect states will be revoked.
Step S240, collecting ecological environment data and transmitting the ecological environment data to a block chain for storage;
specifically, the ecological environment data can be collected by moving equipment such as a screen monitoring device, a sewage pipeline monitoring device, an environment real-time monitor, a water quality monitoring device and a noise measuring instrument, the ecological environment related data comprises digital information, character information, sensor collection data and switching value information, and the ecological environment data is transmitted to a block chain for storage.
In some embodiments, the ecosystem data is transferred to the blockchain for storage, in order to facilitate later traceability. And a large amount of ecological data is gathered, and data support, system analysis and application are provided for the modernization of ecological management capacity. The intelligent platform provides positive repair suggestions for ecological environment construction. Ecological monitoring, intelligent analysis, scientific suggestion and intelligent decision.
Step S250, acquiring block chains and data in a database, and decomposing;
specifically, ecological environment data in the block chain library and ecological environment data in the database are respectively obtained, the extracted ecological environment data are decomposed, and a large amount of ecological environment data are decomposed into countless data nodes. The database adopts a cache technology to relieve the data access pressure;
and the application server cluster is used for improving the concurrent processing capacity of the website.
In some embodiments, the big data is decomposed into countless data nodes through a distributed file system and a database, and each data node is connected with each other through a special network or a commercial general-purpose network and is cooperated with each other to provide services as a whole. The data structure is a linear structure, a tree structure, a heap and a graph, the basic units of the structures are nodes, and different connections of the nodes form different data structures. The connections between nodes are using pointers. Structures or classes may be used as nodes.
Step S260, respectively preprocessing the decomposed data nodes;
specifically, after decomposition, large-scale data is decomposed into countless data nodes, and processed and analyzed.
In some embodiments, in actual business processes, the data is typically dirty data. So-called dirty, index data may present several problems: data loss, data noise, data inconsistency, data redundancy, data set imbalance, outliers/outliers, data duplication; the preprocessing can be performed by data cleaning, data conversion, data description, feature extraction, feature selection, feature combination or the like.
Step S270, abnormal value processing and missing value completion are carried out on each data node in the plurality of data nodes;
in some implementations, outlier processing of data nodes can employ a Laplace criterion or a Grabbs criterion for culling outliers. For example, using the Lauda criterion, when the number of repeated tests is much greater than 10, the Bessel formula is used to calculate the standard deviation s of the experiment, a certain suspicious value χαAnd average of n results
Difference of difference
When the absolute value of (A) is 3s or more, it is judged that x isαIs an abnormal value.
Step S280, connecting the preprocessed multiple data nodes with each other, performing collaborative calculation, and outputting an optimal result;
specifically, each data node is connected to each other through a private network or a commercial general-purpose network, and cooperatively calculates with each other to provide a service as a whole.
In some embodiments, cloud computing relies on big data distribution, and makes overall computation by combining various data interfaces, such as weather, people flow, traffic flow, and the like, and gives an optimal result.
Step S290, deep learning is carried out on the output optimal result to obtain a control instruction;
specifically, the optimal result is deeply learned, so that manual intervention is gradually reduced, and finally intelligent control is achieved. And processing the optimal result by adopting an ANI mode to obtain a control instruction.
In some embodiments, the method may be embodied as a deep learner, and the corresponding set of design, training, and usage methods may be used for self-learning. The deep learner model is composed of a plurality of processing layers, each layer comprises at least one processing unit, the output of each layer is a representation of data, and the representation levels are increased along with the increase of the processing levels.
The definition of depth is relative. For a specific scene and a learning task, if the total number of processing units and the number of layers of the learner are M and N, respectively, the amount of information or task performance retained by the learner exceeds that of a learner with any number of layers smaller than N and the total number of units being M, the learner is a strict or narrow deep learner, and the corresponding design, training and use methods are combined into strict or narrow deep learning.
Step S300, the Internet of things module receives a control instruction and performs machine control on various devices;
specifically, the internet of things module receives a control instruction after well depth learning and controls various devices.
In some embodiments, the internet of things module connects any article with the internet according to an agreed protocol based on various sensors to perform information exchange and communication, so as to realize intelligent identification, positioning, tracking, monitoring and management of the article. Such as realizing water quality monitoring and treatment, road parking space monitoring and prompting, ecological monitoring and warning, and the like.
Step S310, the Internet of things module exchanges and communicates information with corresponding equipment and the Internet according to various sensors;
in some embodiments, the internet of things module connects any article with the internet according to an agreed protocol based on various sensors to perform information exchange and communication, so as to realize intelligent identification, positioning, tracking, monitoring and management of the article. Such as realizing water quality monitoring and treatment, road parking space monitoring and prompting, ecological monitoring and warning, and the like.
Example 3
Referring to fig. 3, fig. 3 is a schematic module diagram of an intelligent platform for managing an ecosystem, according to an embodiment of the present invention, including an acquisition and storage module, configured to acquire and transmit ecosystem data to a block chain for storage; the acquisition and decomposition module is used for acquiring data in the distributed file system and the database and decomposing the data; the processing module is used for respectively preprocessing the decomposed data nodes; the output module is used for connecting the preprocessed data nodes with each other for cooperative calculation and outputting an optimal result; the instruction module is used for carrying out deep learning on the output optimal result to obtain a control instruction; and the control module is used for receiving the control instruction and carrying out machine control on various devices by the Internet of things module.
Also included are a memory, a processor, and a communication interface, which are electrically connected, directly or indirectly, to each other to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory may be used for storing software programs and modules, such as program instructions/modules corresponding to the embodiments of the present application, and the processor executes the software programs and modules stored in the memory, thereby executing various functional applications and data processing. The communication interface may be used for communicating signaling or data with other node devices.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the illustration of fig. 3 is merely exemplary, and that more or fewer components than those illustrated in fig. 3 may be included, or in a different configuration than that illustrated in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
To sum up, the ecological manager management method and the intelligent platform provided by the embodiment of the application can use intelligent management as a breakthrough according to core technologies such as big data, cloud computing, artificial intelligence, internet of things and block chains, use high-level construction, high-color-value restoration and high-quality operation and maintenance ecological systems as entry points, and use meeting the general goals of people's feelings of acquaintances and happiness to form the ecological manager intelligent platform capable of accurately enabling the development of various fields of production and living ecology.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An ecological housekeeper management method is characterized by comprising the following steps:
collecting ecological environment data and transmitting the ecological environment data to a block chain for storage;
acquiring block chains and data in a database, and decomposing the data;
respectively preprocessing the decomposed data nodes;
connecting the preprocessed multiple data nodes with each other for cooperative calculation and outputting an optimal result;
carrying out deep learning on the output optimal result to obtain a control instruction;
and the Internet of things module receives the control instruction and performs machine control on various devices.
2. The ecosystem manager management method of claim 1, wherein the collecting and transferring the ecosystem data to a blockchain for storage comprises:
and the data acquisition transmits information through a web configuration mode and the network access of APN and VPDN.
3. The ecology butler management method of claim 2, further comprising:
adopting POW as a consensus protocol of a block chain bottom layer;
the POW node and the Smart Contract Server node correspond to each other.
4. The ecology butler management method of claim 3, further comprising:
and processing the user request at the top layer of the block chain according to the Smart Contract Server node.
5. The ecological steward management method according to claim 1, wherein the preprocessing the decomposed data nodes respectively comprises:
and carrying out abnormal value processing and missing value completion on each data node in the plurality of data nodes.
6. The ecological steward management method of claim 1, wherein the receiving of the control instruction and the machine control of the various types of equipment by the internet of things module comprises:
and the Internet of things module exchanges and communicates information with corresponding equipment and the Internet according to various sensors.
7. The ecological steward management method of claim 1, wherein the deep learning of the output optimal result to obtain the control instruction comprises:
and processing the optimal result by adopting an ANI mode to obtain a control instruction.
8. The method as claimed in claim 1, wherein the obtaining and decomposing the data in the block chain and the database comprises:
the database adopts a cache technology to relieve the data access pressure;
and the application server cluster is used for improving the concurrent processing capacity of the website.
9. An intelligent platform for managing an ecological housekeeper, comprising:
the acquisition and storage module is used for acquiring ecological environment data and transmitting the ecological environment data to the block chain for storage;
the acquisition and decomposition module is used for acquiring data in the distributed file system and the database and decomposing the data;
the processing module is used for respectively preprocessing the decomposed data nodes;
the output module is used for connecting the preprocessed data nodes with each other for cooperative calculation and outputting an optimal result;
the instruction module is used for carrying out deep learning on the output optimal result to obtain a control instruction;
and the control module is used for receiving the control instruction and carrying out machine control on various devices by the Internet of things module.
10. The intelligent platform for ecology housekeeping management of claim 9, comprising:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the device comprises an acquisition and storage module, an acquisition and decomposition module, a processing module, an output module, an instruction module and a control module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010957095.1A CN112051776A (en) | 2020-09-12 | 2020-09-12 | Ecological housekeeper management method and intelligent platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010957095.1A CN112051776A (en) | 2020-09-12 | 2020-09-12 | Ecological housekeeper management method and intelligent platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112051776A true CN112051776A (en) | 2020-12-08 |
Family
ID=73611576
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010957095.1A Pending CN112051776A (en) | 2020-09-12 | 2020-09-12 | Ecological housekeeper management method and intelligent platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112051776A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109392676A (en) * | 2018-12-13 | 2019-03-01 | 黑龙江大学 | A kind of system of the cloud algorithm platform control fertigation based on block chain structure |
| CN109714429A (en) * | 2019-01-15 | 2019-05-03 | 南京菲迪尔电子科技有限公司 | The broadband automatic building control system and its automatic control method of the full distributed power line of Internet of Things |
| CN109922162A (en) * | 2019-04-26 | 2019-06-21 | 山东建筑大学 | A kind of flattening Architectural Equipment network monitoring system for things and method based on block chain |
| CN110099055A (en) * | 2019-04-29 | 2019-08-06 | 北京工业大学 | Internet of Things service architecture based on lightweight block chain node |
| CN110225075A (en) * | 2019-03-25 | 2019-09-10 | 北京快电科技有限公司 | A kind of building energy internet wisdom operation cloud operating system |
| US20190306232A1 (en) * | 2018-03-27 | 2019-10-03 | Bank Of America Corporation | Asset management block chain |
| CN111369415A (en) * | 2019-12-27 | 2020-07-03 | 河北科技大学 | Park block chain control system based on wisdom environment-friendly |
| CN111543070A (en) * | 2018-02-09 | 2020-08-14 | 英特尔公司 | Trusted IOT device configuration and loading |
-
2020
- 2020-09-12 CN CN202010957095.1A patent/CN112051776A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111543070A (en) * | 2018-02-09 | 2020-08-14 | 英特尔公司 | Trusted IOT device configuration and loading |
| US20190306232A1 (en) * | 2018-03-27 | 2019-10-03 | Bank Of America Corporation | Asset management block chain |
| CN109392676A (en) * | 2018-12-13 | 2019-03-01 | 黑龙江大学 | A kind of system of the cloud algorithm platform control fertigation based on block chain structure |
| CN109714429A (en) * | 2019-01-15 | 2019-05-03 | 南京菲迪尔电子科技有限公司 | The broadband automatic building control system and its automatic control method of the full distributed power line of Internet of Things |
| CN110225075A (en) * | 2019-03-25 | 2019-09-10 | 北京快电科技有限公司 | A kind of building energy internet wisdom operation cloud operating system |
| CN109922162A (en) * | 2019-04-26 | 2019-06-21 | 山东建筑大学 | A kind of flattening Architectural Equipment network monitoring system for things and method based on block chain |
| CN110099055A (en) * | 2019-04-29 | 2019-08-06 | 北京工业大学 | Internet of Things service architecture based on lightweight block chain node |
| CN111369415A (en) * | 2019-12-27 | 2020-07-03 | 河北科技大学 | Park block chain control system based on wisdom environment-friendly |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Water-energy-food nexus: Concepts, questions and methodologies | |
| Wilson et al. | Modeling and detecting change in temporal networks via the degree corrected stochastic block model | |
| Fu et al. | Multiple objective optimal control of integrated urban wastewater systems | |
| CN103559375B (en) | The numerical simulation of scheduling engineering water correction and visual simulation system | |
| Ketabchi et al. | Assessment of a parallel evolutionary optimization approach for efficient management of coastal aquifers | |
| Hadjimichael et al. | Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector | |
| Wang et al. | Tdefsi: Theory-guided deep learning-based epidemic forecasting with synthetic information | |
| Ageng et al. | A short-term household load forecasting framework using LSTM and data preparation | |
| CN105676814B (en) | The online Adding medicine control method in digitlization water island based on SFLA SVM | |
| Li et al. | Water-quality prediction using multimodal support vector regression: case study of Jialing River, China | |
| CN104008420A (en) | Distributed outlier detection method and system based on automatic coding machine | |
| CN104536831B (en) | A kind of multinuclear SoC software image methods based on multiple-objection optimization | |
| Kolditz et al. | Environmental information systems: paving the path for digitally facilitated water management (Water 4.0) | |
| CN112948123B (en) | Spark-based grid hydrological model distributed computing method | |
| Crnkić et al. | Swarms on the 3-sphere for online clustering of multivariate time series and data streams | |
| CN116432998A (en) | Big data internal feedback method and system for water quality anomaly monitoring | |
| CN112051776A (en) | Ecological housekeeper management method and intelligent platform | |
| Maroosi et al. | Accelerated simulation of membrane computing to solve the n-queens problem on multi-core | |
| CN107995278A (en) | A kind of scene intelligent analysis system and method based on metropolitan area level Internet of Things perception data | |
| CN116506305A (en) | Network traffic prediction method, model training method and device | |
| CN116051699A (en) | Dynamic capture data processing method, device, equipment and storage medium | |
| CN110092507A (en) | A kind of method and device of Industrial Wastewater Treatment | |
| Sloot et al. | Supercomputer simulation of critical phenomena in complex social systems | |
| CN109981361B (en) | Method and device for determining infection source in propagation network | |
| Dayal et al. | Deep learning for Multi-horizon Water levelForecasting in KRS reservoir, India |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201208 |
|
| RJ01 | Rejection of invention patent application after publication |