CN114398241A - Monitoring method and system for intelligent breeding environment data - Google Patents
Monitoring method and system for intelligent breeding environment data Download PDFInfo
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- CN114398241A CN114398241A CN202210105207.XA CN202210105207A CN114398241A CN 114398241 A CN114398241 A CN 114398241A CN 202210105207 A CN202210105207 A CN 202210105207A CN 114398241 A CN114398241 A CN 114398241A
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- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
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- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
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Abstract
The embodiment of the invention provides a method and a system for monitoring intelligent breeding environment data, wherein N monitoring results are obtained according to N monitoring parameter vectors A; adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector, so that the accuracy of the monitoring parameters of the intelligent culture environment data monitoring system can be effectively improved.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to a method and a system for monitoring intelligent breeding environment data.
Background
In the monitoring process of the data of the intelligent aquaculture environment, relevant data needs to be collected, the accuracy of the monitoring parameters of the data monitoring system of the intelligent aquaculture environment is improved, the stability of the intelligent aquaculture environment is judged, and based on the accuracy, the accuracy of the monitoring parameters of the data monitoring system of the intelligent aquaculture environment is effectively improved, so that the technical problem to be solved urgently is solved.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to improve the accuracy of monitoring parameters of an intelligent aquaculture environment data monitoring system.
In a first aspect, an embodiment of the present invention provides a method for monitoring intelligent aquaculture environment data, which is applied to a server, where the server is provided with N monitoring parameter vectors a, where N is an integer greater than zero, and the method includes:
obtaining N monitoring results according to the N monitoring parameter vectors A;
adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors;
obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector.
The method for obtaining N monitoring results according to N monitoring parameter vectors a includes:
monitoring x-class environmental data of the intelligent breeding environmental data according to the x sub-elements of the monitoring parameter vector A.
Adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C, and performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors, including:
setting an initialized monitoring weight vector, and comparing the initialized monitoring weight vector with x-type preset environment data of the intelligent breeding environment according to the N monitoring results to obtain a first comparison result; adjusting the first monitoring weight vector according to the first comparison result and a preset step length C;
obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
Obtaining N third weight vectors according to the N test error probabilities, obtaining a fifth weight vector according to the third weight vector, and obtaining a final monitoring parameter vector according to the fifth weight vector, where the obtaining includes:
the third vector weight increases with the decrease of the test error probability, and is calculated by
Wherein R ismIs the third vector weight, tmIs the test error probability; adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations;
and acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
According to another aspect of the embodiments of the present invention, there is provided a system for monitoring smart cultivation environment data, applied to a server, the system including:
the acquisition module is used for acquiring N monitoring results according to the N monitoring parameter vectors A;
the processing module is used for adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; acquiring a fifth weight vector according to the third weight vector, and acquiring a final monitoring parameter vector according to the fifth weight vector;
and the output module is used for outputting the final monitoring parameter vector.
Wherein the obtaining module is specifically configured to:
monitoring x-class environmental data of the intelligent breeding environmental data according to the x sub-elements of the monitoring parameter vector A.
Wherein the processing module is further configured to:
setting an initialized monitoring weight vector, and comparing the initialized monitoring weight vector with x-type preset environment data of the intelligent breeding environment according to the N monitoring results to obtain a first comparison result; adjusting the first monitoring weight vector according to the first comparison result and a preset step length C;
obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
Wherein the processing module is further configured to:
the third vector weight increases with the decrease of the test error probability, and is calculated by
Wherein R ismIs the third vector weight, tmIs the test error probability; adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations;
and acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
In summary, the method and the system for monitoring the intelligent aquaculture environment data provided by the embodiment of the invention obtain N monitoring results according to the N monitoring parameter vectors a; adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector, so that the accuracy of the monitoring parameters of the intelligent culture environment data monitoring system can be effectively improved.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
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 are only some embodiments of the present invention, and therefore should not be considered as limiting the scope, and it is obvious for those skilled in the art that other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart illustrating a method for monitoring intelligent aquaculture environment data according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a monitoring system for intelligent aquaculture environment data according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by the scholars in the technical field, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. 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 invention.
Fig. 1 is a schematic flow chart of a monitoring method of intelligent aquaculture environment data according to an embodiment of the present invention, which can be executed by a server.
A server may include one or more processors, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. A server may also include any storage medium for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, the storage medium may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of a server. In one case, the server may perform any of the operations of the associated instructions when the processor executes the associated instructions, which are stored in any storage medium or combination of storage media. The server also comprises one or more drive units for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server also includes input/output (I/O) for receiving various inputs (via the input unit) and for providing various outputs (via the output unit)). One particular output mechanism may include a presentation device and an associated Graphical User Interface (GUI). The server may also include one or more network interfaces for exchanging data with other devices via one or more communication units. One or more communication buses couple the above-described components together.
The communication unit may be implemented in any manner, e.g., over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication units may comprise any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
The detailed steps of the intelligent breeding environment data monitoring method are introduced as follows:
step S101, obtaining N monitoring results according to the N monitoring parameter vectors A.
Step S102, adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; and obtaining N test error probabilities according to the N second monitoring weight vectors.
Step S103, obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector.
According to the steps, the embodiment obtains N monitoring results according to the N monitoring parameter vectors A; adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector, so that the accuracy of the monitoring parameters of the intelligent culture environment data monitoring system can be effectively improved.
Wherein, about step S101, monitoring parameter vector A includes x subelements, according to N monitoring parameter vector A, acquire and acquire N monitoring results, this embodiment can be based on monitoring parameter vector A x subelements monitor the x class environmental data of wisdom farming environmental data.
In step S102, according to the N monitoring results, according to a preset step length C, adjusting a first monitoring weight vector, and according to a preset threshold M, performing M iterations to obtain N second monitoring weight vectors; acquiring N test error probabilities according to N second monitoring weight vectors, wherein the embodiment can set an initialization monitoring weight vector, and compare the N second monitoring weight vectors with x-type preset environmental data of the intelligent breeding environment according to N monitoring results to acquire a first comparison result; and adjusting the first monitoring weight vector according to the first comparison result and a preset step length C.
Obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
In step S103, N third weight vectors are obtained according to the N test error probabilities, a fifth weight vector is obtained according to the third weight vector, and a final monitor is obtained according to the fifth weight vectorMeasuring the parameter vector, the present embodiment may increase the weight of the third vector with the decrease of the test error probability according to the formula
Wherein R ismIs the third vector weight, tmIs the test error probability; and adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations.
And acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
Fig. 2 is a functional block diagram of a monitoring system for intelligent aquaculture environment data according to an embodiment of the present invention, wherein the functions implemented by the monitoring system for intelligent aquaculture environment data may correspond to the steps executed by the above method. The monitoring system of the intelligent aquaculture environment data can be understood as the server, or the processor of the server, or can be understood as a component which is independent from the server or the processor and realizes the functions of the invention under the control of the server, as shown in fig. 2, and the functions of each functional module of the monitoring system of the intelligent aquaculture environment data are explained in detail below.
The obtaining module 201 is configured to obtain N monitoring results according to the N monitoring parameter vectors a.
The processing module 202 is configured to adjust a first monitoring weight vector according to the N monitoring results and according to a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, and acquiring a final monitoring parameter vector according to the fifth weight vector.
And the output module 203 is used for outputting the final monitoring parameter vector.
The obtaining module 201 obtains N monitoring results according to the N monitoring parameter vectors a in the following manner:
monitoring x-class environmental data of the intelligent breeding environmental data according to the x sub-elements of the monitoring parameter vector A.
Wherein the processing module 202 is further configured to:
setting an initialized monitoring weight vector, and comparing the initialized monitoring weight vector with x-type preset environment data of the intelligent breeding environment according to the N monitoring results to obtain a first comparison result; and adjusting the first monitoring weight vector according to the first comparison result and a preset step length C.
Obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
Wherein the processing module 202 obtains N third weight vectors according to the N test error probabilities in the following manner; and acquiring a fifth weight vector according to the third weight vector, and acquiring a final monitoring parameter vector according to the fifth weight vector.
The third vector weight increases with the decrease of the test error probability, and is calculated by
Wherein R ismIs the third vector weight, tmIs the test error probability; and adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations.
And acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The functional modules in the embodiments of the present invention 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.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, or data center to another website site, computer, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention 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 invention 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 drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (8)
1. The monitoring method of the intelligent breeding environment data is applied to a server, the server is provided with N monitoring parameter vectors A, wherein N is an integer greater than zero, and the method comprises the following steps:
obtaining N monitoring results according to the N monitoring parameter vectors A;
adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors;
obtaining N third weight vectors according to the N test error probabilities; and acquiring a fifth weight vector according to the third weight vector, acquiring a final monitoring parameter vector according to the fifth weight vector, and outputting the final monitoring parameter vector.
2. The method of claim 1, wherein the monitoring parameter vector a comprises x sub-elements, and the obtaining N monitoring results according to the N monitoring parameter vectors a comprises:
monitoring x-class environmental data of the intelligent breeding environmental data according to the x sub-elements of the monitoring parameter vector A.
3. The method according to claim 1, wherein the first monitoring weight vector is adjusted according to the N monitoring results and according to a preset step length C, and according to a preset threshold M, M iterations are performed to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors, including:
setting an initialized monitoring weight vector, and comparing the initialized monitoring weight vector with x-type preset environment data of the intelligent breeding environment according to the N monitoring results to obtain a first comparison result; adjusting the first monitoring weight vector according to the first comparison result and a preset step length C;
obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
4. The method of claim 1, wherein obtaining N third weight vectors according to the N test error probabilities, obtaining a fifth weight vector according to the third weight vectors, and obtaining a final monitoring parameter vector according to the fifth weight vector comprises:
the third vector weight increases with the decrease of the test error probability, and is calculated by
Wherein R ismIs the third vector weight, tmIs the test error probability; adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations;
and acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
5. The utility model provides a monitoring system of wisdom farming environment data which characterized in that, is applied to the server, the system includes:
the acquisition module is used for acquiring N monitoring results according to the N monitoring parameter vectors A;
the processing module is used for adjusting a first monitoring weight vector according to the N monitoring results and a preset step length C; performing M iterations according to a preset threshold value M to obtain N second monitoring weight vectors; obtaining N test error probabilities according to the N second monitoring weight vectors; obtaining N third weight vectors according to the N test error probabilities; acquiring a fifth weight vector according to the third weight vector, and acquiring a final monitoring parameter vector according to the fifth weight vector;
and the output module is used for outputting the final monitoring parameter vector.
6. The system of claim 5, wherein the acquisition module is specifically configured to:
monitoring x-class environmental data of the intelligent breeding environmental data according to the x sub-elements of the monitoring parameter vector A.
7. The system of claim 5, wherein the processing module is further configured to:
setting an initialized monitoring weight vector, and comparing the initialized monitoring weight vector with x-type preset environment data of the intelligent breeding environment according to the N monitoring results to obtain a first comparison result; adjusting the first monitoring weight vector according to the first comparison result and a preset step length C;
obtaining N second comparison results according to the N second monitoring weight vectors; and obtaining N test error probabilities according to the error quantity of x sub-elements in each second comparison result.
8. The system of claim 5, wherein the processing module is further configured to:
the third vector weight increases with the decrease of the test error probability, and is calculated by
Wherein R ismIs the third vector weight, tmIs the test error probability; adjusting N second monitoring weight vectors according to the third vector weight, and performing Z iterations according to a preset threshold value Z to obtain a fourth weight vector, wherein the fourth weight vector is the third weight vector updated by the Z iterations;
and acquiring a final monitoring parameter vector according to the fourth weight vector and the monitoring parameter vector.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116738185A (en) * | 2023-07-18 | 2023-09-12 | 汇链通产业供应链数字科技(厦门)有限公司 | AI algorithm construction method for intelligent cultivation |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738185A (en) * | 2023-07-18 | 2023-09-12 | 汇链通产业供应链数字科技(厦门)有限公司 | AI algorithm construction method for intelligent cultivation |
| CN116738185B (en) * | 2023-07-18 | 2024-03-19 | 汇链通产业供应链数字科技(厦门)有限公司 | AI algorithm construction method for intelligent cultivation |
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