CN112202830A - Early warning decision system model based on block chain internet technology - Google Patents
Early warning decision system model based on block chain internet technology Download PDFInfo
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- CN112202830A CN112202830A CN202010181469.5A CN202010181469A CN112202830A CN 112202830 A CN112202830 A CN 112202830A CN 202010181469 A CN202010181469 A CN 202010181469A CN 112202830 A CN112202830 A CN 112202830A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
Abstract
The invention provides an early warning decision system model based on a block chain internet technology, namely a wild goose model, which has a basic structure divided into three layers and can be expanded: (1) a collection center chain comprised of a plurality of data collection centers; (2) an information analysis chain composed of a plurality of information analysis centers; (3) a chain of decision centers consisting of a plurality of decision centers. Cross-link nodes may exist between different links between the same levels, and cross-link nodes may exist between cross-levels. Whether data transmission is carried out between the upper layer and the lower layer or not is determined according to the following principle: each node on the chain analyzes the data by using the same algorithm to obtain whether the data needs to be transmitted or not, and common identification is carried out; if the consensus result is that the transmission is needed, sending data to the corresponding hierarchy; the consensus result is that the data transmission is not needed, but a certain node obtains the result needing to be transmitted through a local algorithm, and the node can still carry out the data transmission by itself. Bidirectional data transmission and supervision exist between the upper layer and the lower layer, real nodes with correct and timely data transmission are rewarded, and nodes which falsely hide delayed transmission are punished.
Description
Technical Field
The invention belongs to the technical field of a block chain technology and a block chain Internet, and particularly relates to a software system architecture design and a related technology for applying block chain characteristics to a software system.
Background
Conventional warning and reporting systems are generally non-penetrating and require human intervention to advance the process. After the data collection is completed, the early warning information or the decision is checked and transmitted by the relevant responsible person, and if any responsible person or operation and maintenance person with authority in the process modifies the data or interrupts the business process and does not continue to transmit and report, the early warning information or the decision may not be transmitted to the receiving end to be transmitted due to human intervention.
The block chain technology can enable information to be more ' true ', enable data transmission to be faster ', enable decision to have ' data ' and enable execution to be more ' efficient ', so that an early warning decision system for the important and special events is designed based on the block chain technology, and information collection, decision analysis and trusted execution of resources for the important and special events can be effectively improved. The core of the extra-large event early warning decision platform is the construction of block chain infrastructure.
Disclosure of Invention
The invention provides an early warning decision system model based on a block chain Internet technology, which is named as a 'wild goose model', and innovates a treatment system based on reliable block chain infrastructure, so that important information can be timely and really transmitted, and the early warning decision system model is suitable for scenes such as an early warning or decision system.
As shown in fig. 1, the basic structure of the system model provided by the present invention is divided into three layers, which can be increased or decreased according to the needs in the specific implementation:
(1) a collection center chain comprised of a plurality of data collection centers;
(2) an information analysis chain composed of a plurality of information analysis centers;
(3) a chain of decision centers consisting of a plurality of decision centers.
Further, each node in the collection center chain is a data collection center. The method comprises the steps that original data can rapidly arrive at a plurality of data collection centers at the same time, the data collection center nodes store the original data and obtain whether the data need to be reported or not through a certain algorithm, wherein the algorithm of each collection center node is the same. And the data collection central node on the same chain performs consensus on the data and whether the data is reported or not, and the consensus result is stored in the collection central chain.
In one embodiment, the two data collection center chains use different data collection center nodes.
In one embodiment, there is a common data collection center node for both data collection center chains.
And a plurality of data collection center nodes of the same collection center chain use a consensus algorithm to determine whether certain data is reported, and the data report is transmitted to one or more nodes in the corresponding upper-layer information analysis chain.
If the consensus result of each node in a certain collection center chain for certain data is that the data is not required to be reported, but a certain data collection center node obtains that the data is required to be reported according to a local algorithm, the node can still report the data to an upper-layer information analysis chain.
Further, each node in the information analysis chain is an information analysis center. And each information analysis center receives data reported by one or more collection center chains at the lower layer and processes the data.
In one embodiment, two information analysis center chains use different information analysis center nodes.
In one embodiment, there is a common information analysis hub node for both information analysis hub chains.
The information analysis center processes and analyzes the received data by using a certain algorithm to form corresponding early warning information of each grade, and the early warning information is stored in an information analysis chain. The analysis algorithms used by the various information analysis centers should be the same. And when the early warning level reaches a certain level, the corresponding decision center chain is automatically reported.
All nodes on each information analysis chain obtain the same information within a certain delay time, the same calculation conclusion is obtained by using the same algorithm, any node mechanism has the right to initiate information reporting, if all nodes agree to report (or agree with a certain proportion of nodes), the system automatically defines the event as a serious event, and simultaneously carries out early warning on all nodes of a decision center chain; responsibility for non-response or negative vote on this consensus event is required.
If the consensus result of certain data is that the data does not need to be reported to the decision center chain, but a certain data analysis node obtains that the data should be reported according to a local algorithm, the node can still report the data to the upper-layer decision center chain.
In one embodiment, the information analysis center uses big data technology to process and analyze the collected data.
In one embodiment, the information analysis center uses artificial intelligence techniques to perform process analysis on the collected data.
In one embodiment, the information analysis center performs process analysis on the collected data using cloud computing technology.
Further, each node of the chain of decision centers is a decision center. And the decision center node receives the early warning information reported by the information analysis chain or the information analysis center node and makes a decision. And storing the related data and the decision result into the chain after consensus.
Furthermore, the lower layer chain can transmit data upwards step by step and can also transmit data from top to bottom. And the decision center chain analyzes and processes the received early warning information, or processes the early warning information through an algorithm, or submits tasks or instructions through a person with system authority to obtain feedback data of the early warning information. The analysis basis of the data, the experience of the workers, the response information and the like need to be stored in the chain through the consensus of the decision center chain and transmitted to the lower level. And after receiving the data downloaded by the decision center chain, the lower layer information analysis chain carries out analysis processing, identifies the content and identifies whether the downloading needs to be continued or not, and simultaneously stores the key information into the uplink. Data that needs to be further downloaded will be sent to the lower collection hub chain.
Further, a chain crossing point or a shared node exists between the upper-layer chain and the lower-layer chain, that is, a certain node is a node of the upper-layer chain and a node of the lower-layer chain. The framework can allow an upper-layer chain to collect and supervise information of a lower-layer chain, and meanwhile, the lower-layer chain can also carry out certain synchronization and supervision on upper-layer data. The upper and lower layer chains of the model can be mutually monitored to a certain degree.
Because the chain-crossing point exists, if a significant message is generated, even if the lower-layer chain does not report in time, the chain-crossing point can detect the message to initiate reporting, the data transmission is not influenced, and the upper-layer chain can analyze the data and make relevant decision and early warning. Meanwhile, the feedback or corresponding information of the upper-layer chain can not be issued due to the problems of certain nodes,
furthermore, in the aspect of the design of the metric, a reward system is established. The real nodes with timely data reporting can be rewarded, and nodes which are hidden and not reported, falsely reported and delayed to report are punished. The real nodes which issue data in time can be rewarded, and punishment can be carried out on the nodes which intentionally cut off data not to be issued.
Preferably, the reward and punishment mechanism system can be realized by using an intelligent contract, and automatic reward or automatic punishment is carried out according to the behavior data of each node through the intelligent contract on the chain, and uploading and issuing bidirectional behaviors are restrained. The reward and penalty mechanism may be accomplished using a mnemonic, bonus award, or other means. The intelligent contract mechanism can be automatically executed to ensure that there is always a reward as long as the system contributes, and there is always a penalty if there is a mistake.
Further, technically, based on a consensus mechanism of a block chain, related nodes can receive the same information within a certain delay time range, nodes with reporting rights and obligations are configured, and if a certain node has unexpected faults, intentionally hides, does not act as and the like, other nodes can report, so that smooth, real and timely information is ensured. On the other hand, based on the consensus mechanism of the block chain, the related nodes can receive the same feedback information within a certain delay time range, the nodes with the downloading right and obligation are configured, and if some node has unexpected faults, intentionally hides, does not act as and the like, other nodes can issue messages to ensure that the information is smooth, real and timely.
The early warning decision system model provided by the invention is an internet (block chain internet, inter-link network for short) framework based on a block chain, is a novel management model, is a comprehensive and spread reporting early warning system, and can be applied to a plurality of scenes. Various important information including but not limited to flood, storm, earthquake, epidemic situation, locust, electricity, natural gas, crowd activities can be reported and collected by using the block chain infrastructure. All kinds of information can be quickly collected, quickly analyzed and reported to the corresponding office units where the upper nodes are located, such as public security, hospitals, armed police, central and local government units. And the upper office unit analyzes and feeds back the reported data, takes corresponding measures and issues instructions. And issuing the issued commands layer by layer until the commands are transmitted to the nodes needing to take measures.
On the other hand, the early warning decision system model provided by the invention can also be used for establishing a public sentiment analysis system, the information analysis chain is provided with public sentiment analysis nodes, important information which is issued by individuals, self media and other non-official parties on the network is collected through keywords, the information is broadcasted to each node of the information analysis chain, and real information is screened out through various algorithms or means such as artificial intelligence data analysis and the like and can be directly and automatically reported to the decision center chain.
Compared with the traditional early warning decision system, the system model provided by the invention combines the related technology of the block chain, so that data can be held in multiple ways and monitored mutually, and the condition that data is lost, tampered or the data transmission is interrupted due to human factors, single machine faults and the like in each link of each node of the system is ensured not to occur.
Compared with the common blockchain system, the system model provided by the invention is more flexible and easy to expand, and is a network formed by connecting a plurality of blockchains. The system model can be widely spread, is not only applied to the early warning decision-making system in a small range, but also can be deployed nationwide or globally after being combined with block chain Internet models such as panda models and golden monkey models, and is suitable for the extremely large-scale early warning decision-making system.
The model provided by the invention consists of multi-level block chains and nodes which cooperate with each other and support each other, and is consistent with the characteristic that the wild goose team members of the wild goose cooperate with each other to help each other, because the model is named as a 'wild goose model'.
Drawings
FIG. 1 is a schematic diagram of a system model according to the present invention;
fig. 2 is a schematic diagram of an epidemic early warning decision-making system in an embodiment of the invention.
Detailed description of the preferred embodiments
The invention will be further described below, by way of example, with reference to fig. 2, without in any way limiting the scope of the invention.
This embodiment is a nationwide epidemic situation early warning decision making system.
And each public hospital in each city is provided with a data collection central node to form a data collection chain in the city. And deploying information analysis center nodes by each city health committee and city CDC. And (5) deploying the decision center node by the health department of the state department.
Meanwhile, in order to realize up-down bidirectional supervision, part of nodes need to be deployed across chains. A city selects 1 or more hospitals which have deployed data collection center nodes, and simultaneously joins an information analysis chain, so that the hospitals can supervise data of an upper information analysis chain while collecting the data. The city health committee and the city CDC are added into a plurality of lower-layer collection center chains while deploying the information analysis nodes, and the purpose of monitoring and managing the data of the collection center chains is achieved. Similarly, the information analysis chain of each city is added with the health department node of the state department, and the decision center chain comprises the health commission or CDC node of each city.
When one or more hospitals have infection outbreaks and suspected infection outbreak epidemic situations, a data collection central node of the hospital acquires original epidemic situation data and stores the original epidemic situation data on a collection central chain through consensus. When the number of cases reaches the number corresponding to the requirement of epidemic situation grading reporting, reporting to city health committee and city CDC is needed. At the moment, the collection center chain automatically triggers reporting to an upper information analysis chain according to the data condition. And each node on the information analysis chain analyzes and processes the reported hospital epidemic situation data according to the infection outbreak epidemic situation grading algorithm to obtain an epidemic situation grading result and store the chain. And if the epidemic situation analysis result with higher grade appears, the information analysis chain automatically sends early warning data to an upper-grade decision center chain, including the nodes of the health departments of the state department.
If a certain hospital reports epidemic situation data, the epidemic situation is serious, and the individual data analysis center node does not report the epidemic situation data for some reasons, the municipal health committee or the municipal CDC can identify the hospital which is not reported according to the regulations and dispose the hospital while acquiring the epidemic situation data through cross-level supervision. Similarly, if the data is reported to the information analysis chain and the municipal health committee or the municipal CDC is not reported, the health department of the national institute can acquire and process the deputy node through cross-chain supervision.
The information transmission mechanism can ensure that serious epidemic situation occurring in the hospital can be reported to a municipal administration unit or a health department of a national institute at the first time, and the condition that similar epidemic situation outbreak events do not reach a decision-making department is avoided to the greatest extent.
And after receiving the early warning data, the health department of the state department identifies and analyzes the early warning data, and makes corresponding measures and makes consensus. And storing the corresponding measure instruction data which should be taken by each lower stage into a decision center chain and then transmitting the decision center chain. And the information analysis link performs data identification after receiving an instruction issued by a superior level, and identifies cochain in common. And transmitting the data to be continuously transmitted to a lower-layer collection center chain.
The above embodiments describe a nationwide deployment of early warning system models. The three-layer structure model can be expanded to form a multi-layer upper and lower level transmission structure, and can even be applied to a global early warning decision system.
While the present invention has been described with reference to the particular illustrative embodiments, it is not to be restricted by the embodiments but only by the appended claims. It will be understood by those skilled in the art that variations and modifications of the embodiments of the present invention can be made without departing from the scope and spirit of the invention.
Claims (5)
1. The wild goose model comprises: a block chain Internet technology-based early warning decision system model is characterized in that the basic structure is divided into three layers:
(1) a collection center chain comprised of a plurality of data collection centers;
(2) an information analysis chain composed of a plurality of information analysis centers;
(3) a chain of decision centers consisting of a plurality of decision centers;
(4) and the expansion can be carried out according to the number of the layers of the actual service scene.
2. The wide goose model of claim 1, wherein: different chains between the same levels can have cross-chain nodes, cross-chain nodes can exist between the chains of the cross-level, and bidirectional data transmission and supervision exist between the upper level and the lower level.
3. The wide goose model of claim 1, wherein: whether data transmission is carried out between the two chains of the upper layer and the lower layer is determined according to the following principle: each node on the chain analyzes the data by using the same algorithm to obtain whether the data needs to be transmitted or not, and common identification is carried out; if the consensus result is that the transmission is needed, sending data to the corresponding hierarchy; the consensus result is that the data transmission is not needed, but a certain node obtains the result needing to be transmitted through a local algorithm, and the node can still carry out the data transmission by itself.
4. The wide goose model of claim 1, wherein: each level has a decision mechanism and algorithm to decide whether the data is to be communicated across layers.
5. The wide goose model of claim 1, wherein: a reward and punishment mechanism is provided, timely and real nodes of data transmission are rewarded, and nodes which are false, concealed and delayed in transmission are punished.
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