CN117349269B - Full-river-basin data resource management and exchange sharing method and system - Google Patents
Full-river-basin data resource management and exchange sharing method and system Download PDFInfo
- Publication number
- CN117349269B CN117349269B CN202311084111.0A CN202311084111A CN117349269B CN 117349269 B CN117349269 B CN 117349269B CN 202311084111 A CN202311084111 A CN 202311084111A CN 117349269 B CN117349269 B CN 117349269B
- Authority
- CN
- China
- Prior art keywords
- data set
- feature vector
- data
- normalized
- max
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 239000013598 vector Substances 0.000 claims abstract description 164
- 230000010354 integration Effects 0.000 claims abstract description 40
- 230000007613 environmental effect Effects 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims description 18
- 238000010606 normalization Methods 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 239000002689 soil Substances 0.000 claims description 4
- 238000005067 remediation Methods 0.000 claims 1
- 230000009467 reduction Effects 0.000 abstract description 8
- 238000007726 management method Methods 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013523 data management Methods 0.000 description 2
- 238000013467 fragmentation Methods 0.000 description 2
- 238000006062 fragmentation reaction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- 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
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a system for managing and exchanging sharing full-river basin data resources, wherein the method comprises the following steps: acquiring environmental data in a stream domain, wherein the environmental data comprises a data set X and a data set Y, and carrying out standardized integration on the data set X and the data set Y, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y; setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y; and exchanging and sharing the integrated feature vectors, setting a feature vector reduction model, and reducing the received integrated feature vectors to generate reduced feature vectors.
Description
Technical Field
The invention belongs to the technical field of data resource management and exchange sharing, and particularly relates to a full-basin data resource management and exchange sharing method and system.
Background
Data fragmentation and islanding problems: in some areas, various related departments and institutions may collect and manage respective data, resulting in data fragmentation and information islanding problems, which make data sharing and integration difficult.
Data acquisition and acquisition: the acquisition and collection of data relate to various devices, sensors, monitoring stations and the like, and some areas may have the problems of insufficient data acquisition, imperfect monitoring facilities and the like.
Technology and standard: data sharing requires a uniform technical standard and data format to ensure that the data can interoperate and integrate, and in some cases, the data formats and standards of different data sources may not be consistent.
Therefore, a technical solution is needed to solve the above technical problems.
Disclosure of Invention
In order to solve the technical characteristics, the invention provides a full-basin data resource management and exchange sharing method, which comprises the following steps:
Acquiring environmental data in a stream domain, wherein the environmental data comprises a data set X and a data set Y, and carrying out standardized integration on the data set X and the data set Y, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y;
Setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y;
and exchanging and sharing the integrated feature vectors, setting a feature vector reduction model, and reducing the received integrated feature vectors to generate reduced feature vectors.
Further, the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
FX＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y.
Further, the feature vector integration model is as follows:
Wherein IF is the integrated feature vector, W is the relevance weight, Is the eigenvector of the normalized data set X,/>Is the eigenvector of the normalized dataset Y.
Further, the feature vector reduction model is as follows:
D＝IF*(max(FX)-min(FX))+min(FX)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
Further, the method further comprises the following steps: and encrypting the integrated feature vector, and decrypting by a receiver through a secret key.
The invention also provides a full-river basin data resource management and exchange sharing system, which comprises:
The system comprises an extraction feature module, a normalization integration module and a data processing module, wherein the extraction feature module is used for acquiring environmental data in a stream domain, the environmental data comprises a data set X and a data set Y, and the data set X and the data set Y are subjected to the normalization integration, and the normalization integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y;
The integration module is used for setting a feature vector integration model and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y;
And the restoring module is used for exchanging and sharing the integrated feature vectors, setting a feature vector restoring model, restoring the received integrated feature vectors and generating restored feature vectors.
Further, the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
FX＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y.
Further, the feature vector integration model is as follows:
Wherein IF is the integrated feature vector, W is the relevance weight, Is the eigenvector of the normalized data set X,/>Is the eigenvector of the normalized dataset Y.
Further, the feature vector reduction model is as follows:
D＝IF*(max(FX)-min(FX))+min(FX)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
Further, the method further comprises the following steps: and encrypting the integrated feature vector, and decrypting by a receiver through a secret key.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
the invention acquires environmental data in a stream domain, wherein the environmental data comprises a data set X and a data set Y, and the data set X and the data set Y are subjected to standardized integration, and the standardized integration comprises the following steps: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y; setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y; the method and the device can integrate and share the environmental data in river and river domains, improve the usability of the data and provide a data basis for environmental research in the river domains.
Drawings
FIG. 1 is a flow chart of data normalization and integration according to embodiment 1 of the present invention;
FIG. 2 is a block diagram of the system of embodiment 2 of the present invention;
Fig. 3 is a flow chart of the method of embodiment 1 of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
All subscripts in the formula of the invention are only used for distinguishing parameters and have no practical meaning.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 3, an embodiment of the present invention provides a method for managing and exchanging sharing data resources in a full-drainage basin, including:
Step 1, data acquisition and monitoring network: a data acquisition and monitoring network is established to collect environmental data within the stream in real time. The environment data comprise a weather station, a water quality monitoring station, a soil monitoring point and the like, so that accurate data can be timely obtained;
step 2, data standardization and integration: and (3) formulating a unified data standard to ensure that data from different sources can be consistent in terms of format, units and the like. This helps to eliminate data inconsistencies, facilitating data integration and analysis;
as shown in fig. 1, the data normalization and integration includes the following steps:
Step 101, acquiring environmental data in a stream domain, wherein the environmental data comprises a data set X and a data set Y, and performing standardized integration on the data set X and the data set Y, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y;
specifically, the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
FX＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y.
Specifically, the normalization processing includes:
step 102, setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y;
Specifically, the feature vector integration model is as follows:
Wherein IF is the integrated feature vector, W is the relevance weight, Is the eigenvector of the normalized data set X,/>Is the eigenvector of the normalized dataset Y.
The calculating of the relevance weight W specifically comprises the following steps:
Wherein, For/>And/>Relevance function of/>Is the ith eigenvector of normalized dataset X,/>Is the ith eigenvector of the normalized data set Y,/>Is the average value of the eigenvectors of the normalized data set X,/>Is the average of the eigenvectors of the normalized dataset Y.
And 103, exchanging and sharing the integrated feature vectors, setting a feature vector reduction model, and reducing the received integrated feature vectors to generate reduced feature vectors.
Specifically, the feature vector reduction model is as follows:
D＝IF*(max(FX)-min(FX))+min(FX)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
Step 3, data sharing policy: formulating a data sharing policy in the scope of the river basin, and defining the acquisition, use, sharing and protection rules of the data, wherein the rules comprise open access, authority management and the like of the data;
step 4, information technology infrastructure: establishing an information technology infrastructure, wherein the information technology infrastructure comprises a database, a data management system and a data exchange platform so as to support the storage, processing and sharing of data;
Step 5, data security and privacy protection: ensuring data security and taking measures to prevent unauthorized access and data leakage. Meanwhile, personal privacy protection is also considered, especially when sensitive information is involved;
specifically, the integrated feature vector is encrypted, and the receiver decrypts the feature vector through the secret key.
Step 6, data sharing protocol: a data sharing protocol is formulated to clarify rights and responsibilities between the data provider and the user, including data usage scope, limitations, and lifetime, etc.
Example 2
As shown in fig. 2, the embodiment of the present invention further provides a full-basin data resource management and exchange sharing system, including:
Data acquisition and monitoring network: a data acquisition and monitoring network is established to collect environmental data within the stream in real time. The environment data comprise a weather station, a water quality monitoring station, a soil monitoring point and the like, so that accurate data can be timely obtained;
Data normalization and integration: and (3) formulating a unified data standard to ensure that data from different sources can be consistent in terms of format, units and the like. This helps to eliminate data inconsistencies, facilitating data integration and analysis;
As shown in fig. 2, includes:
The system comprises an acquisition sample module, a data processing module and a data processing module, wherein the acquisition sample module is used for acquiring environmental data in a stream domain, the environmental data comprises a data set X and a data set Y, and the data set X and the data set Y are subjected to standardized integration, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and carrying out standardization processing to generate the standardized feature vector of the data set X and the standardized feature vector of the data set Y;
specifically, the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
FX＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y.
Specifically, the normalization processing includes:
The feature extraction module is used for setting a feature vector integration model and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y;
Specifically, the feature vector integration model is as follows:
Wherein IF is the integrated feature vector, W is the relevance weight, Is the eigenvector of the normalized data set X,/>Is the eigenvector of the normalized dataset Y.
The calculating of the relevance weight W specifically comprises the following steps:
Wherein, For/>And/>Relevance function of/>Is the ith eigenvector of normalized dataset X,/>Is the ith eigenvector of the normalized data set Y,/>Is the average value of the eigenvectors of the normalized data set X,/>Is the average of the eigenvectors of the normalized dataset Y.
And the feature extraction module is used for exchanging and sharing the integrated feature vectors, setting a feature vector reduction model, and reducing the received integrated feature vectors to generate reduced feature vectors.
Specifically, the feature vector reduction model is as follows:
D＝IF*(max(FX)-min(FX))+min(FX)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
Data sharing policy: formulating a data sharing policy in the scope of the river basin, and defining the acquisition, use, sharing and protection rules of the data, wherein the rules comprise open access, authority management and the like of the data;
information technology infrastructure: establishing an information technology infrastructure, wherein the information technology infrastructure comprises a database, a data management system and a data exchange platform so as to support the storage, processing and sharing of data;
Data security and privacy protection: ensuring data security and taking measures to prevent unauthorized access and data leakage. Meanwhile, personal privacy protection is also considered, especially when sensitive information is involved;
specifically, the integrated feature vector is encrypted, and the receiver decrypts the feature vector through the secret key.
Data sharing protocol: a data sharing protocol is formulated to clarify rights and responsibilities between the data provider and the user, including data usage scope, limitations, and lifetime, etc.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the full-river-basin data resource management and exchange sharing method.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, a storage medium is provided to store program code for performing the method of embodiment 1;
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a full-river-basin data resource management and exchange sharing method.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium may be used to store a software program and a module, for example, a full-river-basin data resource management and exchange sharing method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software program and the module stored in the storage medium, that is, implements the full-river-basin data resource management and exchange sharing method. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may call the information stored in the storage medium and the application program through the transmission system to perform the method steps of embodiment 1;
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc., which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (4)
1. A full-river basin data resource management and exchange sharing method is characterized by comprising the following steps:
establishing a data acquisition and monitoring network, and collecting environmental data in a water flow area in real time through a weather station, a water quality monitoring station and a soil monitoring point in the data acquisition and monitoring network, wherein the environmental data comprises a data set X and a data set Y, and the data set X and the data set Y are subjected to standardized integration, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and performing normalization processing to generate the normalized feature vector of the data set X and the normalized feature vector of the data set Y, wherein the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
Fx＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y;
setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y, wherein the feature vector integration model is as follows:
wherein IF is the integrated feature vector, w is the relevance weight, For the feature vector of the normalized dataset X,Is the feature vector of the normalized data set Y;
The calculating of the relevance weight w specifically comprises the following steps:
Wherein, For/>And/>Relevance function of/>Is the ith eigenvector of normalized dataset X,/>Is the ith eigenvector of the normalized data set Y,/>Is the average value of the eigenvectors of the normalized data set X,/>Is the average value of the eigenvectors of the normalized data set Y;
Formulating a data sharing policy in a river basin range, defining acquisition, use, sharing and protection rules of environmental data, exchanging and sharing the integrated feature vectors according to the data sharing policy, setting a feature vector restoration model, restoring the received integrated feature vectors, and generating restored feature vectors, wherein the feature vector restoration model is as follows:
D＝IF*(max(FX)-min(Fx))+min(FX)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
2. The full-basin data resource governance and exchange sharing method as set forth in claim 1, further comprising: and encrypting the integrated feature vector, and decrypting by a receiver through a secret key.
3. A full-basin data resource governance and exchange sharing system, comprising:
The system comprises an extraction feature module, a data acquisition and monitoring network, a weather station, a water quality monitoring station and a soil monitoring point in the data acquisition and monitoring network, wherein environmental data in a current area is collected in real time, the environmental data comprises a data set X and a data set Y, the data set X and the data set Y are subjected to standardized integration, and the standardized integration comprises: extracting the feature vector of the data set X and the feature vector of the data set Y, and performing normalization processing to generate the normalized feature vector of the data set X and the normalized feature vector of the data set Y, wherein the extracting the feature vector of the data set X and the feature vector of the data set Y includes:
FX＝[avg(X)，std(X)，max(X)，min(X)]
Fy＝[avg(Y)，std(Y)，max(Y)，min(Y)]
Wherein, F X is the feature vector of the data set X, avg (X) is the mean value of the data set X, std (X) is the standard deviation of the data set X, max (X) is the maximum value of the data set X, min (X) is the minimum value of the data set X, F Y is the feature vector of the data set Y, avg (Y) is the mean value of the data set Y, std (Y) is the standard deviation of the data set Y, max (Y) is the maximum value of the data set Y, and min (Y) is the minimum value of the data set Y;
the integration module is used for setting a feature vector integration model, and calculating an integrated feature vector according to the normalized feature vector of the data set X and the normalized feature vector of the data set Y, wherein the feature vector integration model is as follows:
wherein IF is the integrated feature vector, w is the relevance weight, For the feature vector of the normalized dataset X,Is the feature vector of the normalized data set Y;
The calculating of the relevance weight w specifically comprises the following steps:
Wherein, For/>And/>Relevance function of/>Is the ith eigenvector of normalized dataset X,/>Is the ith eigenvector of the normalized data set Y,/>Is the average value of the eigenvectors of the normalized data set X,/>Is the average value of the eigenvectors of the normalized data set Y;
The restoration module is used for formulating a data sharing policy in a river basin range, defining the acquisition, use, sharing and protection rules of environmental data, exchanging and sharing the integrated feature vectors according to the data sharing policy, setting a feature vector restoration model, restoring the received integrated feature vectors, and generating restored feature vectors, wherein the feature vector restoration model is as follows:
D＝IF*(max(Fx)-min(Fx))+min(Fx)
Where D is the restored feature vector, max (F X) is the maximum value of the feature vector of dataset X, and min (F X) is the minimum value of the feature vector of dataset X.
4. A full-basin data resource remediation and exchange sharing system as claimed in claim 3 further comprising: and encrypting the integrated feature vector, and decrypting by a receiver through a secret key.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311084111.0A CN117349269B (en) | 2023-08-24 | 2023-08-24 | Full-river-basin data resource management and exchange sharing method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311084111.0A CN117349269B (en) | 2023-08-24 | 2023-08-24 | Full-river-basin data resource management and exchange sharing method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117349269A CN117349269A (en) | 2024-01-05 |
| CN117349269B true CN117349269B (en) | 2024-05-28 |
Family
ID=89369961
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311084111.0A Active CN117349269B (en) | 2023-08-24 | 2023-08-24 | Full-river-basin data resource management and exchange sharing method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117349269B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105760515A (en) * | 2016-02-24 | 2016-07-13 | 国家电网公司 | Fusion method for same object data of multiple data sources |
| CN111783831A (en) * | 2020-05-29 | 2020-10-16 | 河海大学 | Complex image accurate classification method based on multi-source multi-label shared subspace learning |
| CN112165165A (en) * | 2020-09-24 | 2021-01-01 | 贵州电网有限责任公司 | Multi-source information fusion method for detection data of distribution automation equipment |
| CN112667797A (en) * | 2021-01-06 | 2021-04-16 | 华南师范大学 | Question-answer matching method, system and storage medium for adaptive transfer learning |
| CN113051323A (en) * | 2021-03-11 | 2021-06-29 | 江苏省生态环境监控中心(江苏省环境信息中心) | Water environment big data exchange method |
| CN113138981A (en) * | 2021-05-14 | 2021-07-20 | 江苏方天电力技术有限公司 | Power distribution data fusion processing method based on edge computing technology |
| CN113807447A (en) * | 2021-09-23 | 2021-12-17 | 兰州理工大学 | Multi-source heterogeneous data fusion method based on FC-SAE |
| US11615430B1 (en) * | 2014-02-05 | 2023-03-28 | Videomining Corporation | Method and system for measuring in-store location effectiveness based on shopper response and behavior analysis |
| CN115935969A (en) * | 2023-01-10 | 2023-04-07 | 西安电子科技大学 | Heterogeneous data feature extraction method based on multi-mode information fusion |
| CN116090823A (en) * | 2023-01-16 | 2023-05-09 | 煤炭科学技术研究院有限公司 | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium |
| WO2023093355A1 (en) * | 2021-11-25 | 2023-06-01 | 支付宝(杭州)信息技术有限公司 | Data fusion method and apparatus for distributed graph learning |
| CN116297841A (en) * | 2023-03-15 | 2023-06-23 | 电子科技大学 | Railway track disease identification method based on optical fiber distributed vibration detection |
| CN116548970A (en) * | 2023-05-12 | 2023-08-08 | 中船人因工程研究院(青岛)有限公司 | State studying and judging method and device for deep-open sea operators |
-
2023
- 2023-08-24 CN CN202311084111.0A patent/CN117349269B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11615430B1 (en) * | 2014-02-05 | 2023-03-28 | Videomining Corporation | Method and system for measuring in-store location effectiveness based on shopper response and behavior analysis |
| CN105760515A (en) * | 2016-02-24 | 2016-07-13 | 国家电网公司 | Fusion method for same object data of multiple data sources |
| CN111783831A (en) * | 2020-05-29 | 2020-10-16 | 河海大学 | Complex image accurate classification method based on multi-source multi-label shared subspace learning |
| CN112165165A (en) * | 2020-09-24 | 2021-01-01 | 贵州电网有限责任公司 | Multi-source information fusion method for detection data of distribution automation equipment |
| CN112667797A (en) * | 2021-01-06 | 2021-04-16 | 华南师范大学 | Question-answer matching method, system and storage medium for adaptive transfer learning |
| CN113051323A (en) * | 2021-03-11 | 2021-06-29 | 江苏省生态环境监控中心(江苏省环境信息中心) | Water environment big data exchange method |
| CN113138981A (en) * | 2021-05-14 | 2021-07-20 | 江苏方天电力技术有限公司 | Power distribution data fusion processing method based on edge computing technology |
| CN113807447A (en) * | 2021-09-23 | 2021-12-17 | 兰州理工大学 | Multi-source heterogeneous data fusion method based on FC-SAE |
| WO2023093355A1 (en) * | 2021-11-25 | 2023-06-01 | 支付宝(杭州)信息技术有限公司 | Data fusion method and apparatus for distributed graph learning |
| CN115935969A (en) * | 2023-01-10 | 2023-04-07 | 西安电子科技大学 | Heterogeneous data feature extraction method based on multi-mode information fusion |
| CN116090823A (en) * | 2023-01-16 | 2023-05-09 | 煤炭科学技术研究院有限公司 | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium |
| CN116297841A (en) * | 2023-03-15 | 2023-06-23 | 电子科技大学 | Railway track disease identification method based on optical fiber distributed vibration detection |
| CN116548970A (en) * | 2023-05-12 | 2023-08-08 | 中船人因工程研究院(青岛)有限公司 | State studying and judging method and device for deep-open sea operators |
Non-Patent Citations (1)
| Title |
|---|
| 基于降水大数据的不同区域洪水灾害特征统计系统设计;王海英;;灾害学;20200930(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117349269A (en) | 2024-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111914277B (en) | Intersection data generation method and federal model training method based on intersection data | |
| CN109766195A (en) | The method and Related product of loss of data in supervisory messages queue | |
| CN110648241B (en) | Method and device for processing claims based on micro-service architecture | |
| CN102355374A (en) | Data acquisition method and equipment | |
| CN109743532B (en) | Doorbell control method, electronic equipment, doorbell system and storage medium | |
| CN108109275A (en) | A kind of control method, the device and system of shared bicycle | |
| CN116383753B (en) | Abnormal behavior prompting method, device, equipment and medium based on Internet of things | |
| CN104853354A (en) | Bluetooth authentication method and system thereof | |
| CN111224834A (en) | Simulation test method, simulation test device, server and storage medium | |
| CN111431841A (en) | Internet of things security sensing system and Internet of things data security transmission method | |
| CN117349269B (en) | Full-river-basin data resource management and exchange sharing method and system | |
| CN113343309B (en) | Natural person database privacy security protection method and device and terminal equipment | |
| CN102316428B (en) | Method for communication between mobile application client and intelligent card and device | |
| CN112995939B (en) | Wireless sensor network transmission and cloud service access control system | |
| CN107908732B (en) | Mutually isolated multi-source big data fusion analysis method and system | |
| CN113792890A (en) | Model training method based on federal learning and related equipment | |
| CN114844695B (en) | Business data circulation method, system and related equipment based on block chain | |
| CN116781425A (en) | Service data acquisition method, device, equipment and storage medium | |
| CN112488825B (en) | Object transaction method and device based on blockchain | |
| CN108289120A (en) | A kind of short message service method and apparatus based on the unified registration of real estate | |
| CN115085794A (en) | Block chain credible evidence storing method and system for Beidou short message | |
| CN110602665B (en) | Method for determining sharing service index based on communication certificate sharing service | |
| CN114880704A (en) | Data matching method and system, identification device and image acquisition device | |
| CN113032828A (en) | Improved binary system interaction information encryption method and device | |
| CN103873329A (en) | Conventional data interface system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |