CN112907107A - Fishery accident emergency processing system and method based on multi-source information fusion - Google Patents
Fishery accident emergency processing system and method based on multi-source information fusion Download PDFInfo
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Abstract
The invention relates to a fishery accident emergency processing system and method based on multi-source information fusion, comprising the following steps: establishing a safety early warning model of the culture facility through big data; acquiring historical data of a water area to generate fishery breeding information; acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate; if the deviation rate is larger than a first threshold and smaller than a second threshold, generating early warning information, and inputting the early warning information into a safety early warning model to obtain a maintenance strategy; if the deviation rate is larger than a second threshold value, generating correction information; correcting the fishery breeding parameters according to the correction information to obtain result information; transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area; the first threshold is less than the second threshold.
Description
Technical Field
The invention relates to a fishery accident emergency processing system, in particular to a fishery accident emergency processing system and method based on multi-source information fusion.
Background
The traditional aquaculture mode mainly adopts an aquaculture mode which depends on experience and extensive operation, pursues yield and economic benefit one by one, is easy to realize ultra-capacity and high-density aquaculture, is unscientific and unreasonable in delivering, applying and fertilizing, causes serious self-pollution of aquaculture water quality and ecological imbalance, greatly deteriorates the growth environment of aquatic products, and brings great aquaculture risk to huge aquaculture industry.
In order to ensure the safe operation of fishery water culture, a system matched with the fishery water culture needs to be developed for control, and a culture facility safety early warning model is established through big data; acquiring historical data of a water area to generate fishery breeding information; acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a maintenance strategy; if the fishery environment information deviation is large, generating correction information to correct fishery breeding parameters; in addition, the correction information can also perform reverse correction and update of the historical data of the water area; how to realize accurate control on a fishery breeding facility safety early warning system is a problem to be solved urgently.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a fishery accident emergency processing system and method based on multi-source information fusion.
In order to achieve the purpose, the invention adopts the technical scheme that: a fishery accident emergency processing method based on multi-source information fusion is characterized by comprising the following steps:
establishing a safety early warning model of the culture facility through big data;
acquiring historical data of a water area to generate fishery breeding information;
acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
if the deviation rate is larger than a first threshold value and smaller than a second threshold value, generating early warning information,
inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
if the deviation rate is larger than a second threshold value, generating correction information;
correcting the fishery breeding parameters according to the correction information to obtain result information;
transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
In a preferred embodiment of the invention, a safety early warning model of the culture facility is established through big data; further comprising:
acquiring mass fault diagnosis data, and establishing a safety early warning model;
collecting the operation data of the culture facilities, generating the operation state curve of the culture facilities,
the operating state curve of the cultivation facility is smoothed, abnormal data is removed,
generating early warning information of the faults of the culture facilities according to the safety early warning model,
establishing a maintenance decision according to the early warning information of the faults of the culture facilities,
and transmitting the maintenance decision to the terminal according to a preset mode.
In a preferred embodiment of the invention, the fishery breeding parameters are corrected according to the correction information to obtain result information; the method specifically comprises the following steps:
obtaining historical culture environment data, and denoising the culture environment data by a wavelet analysis method to obtain result information;
extracting result information characteristic values, and carrying out vector decomposition on the characteristic values;
establishing a vector weight adaptive function, and carrying out weighted calculation on the characteristic value vector;
and fusing the weighted and calculated multi-scale feature vectors to generate new environment data.
In a preferred embodiment of the present invention, the method further comprises: acquiring water level information, and dividing the equipment into an above-water area and an underwater area;
acquiring equipment operation data of the water area, comparing the equipment operation data with a first threshold value, and if the equipment operation data is larger than the first threshold value, adjusting the ratio of the water area to the underwater area of the equipment;
obtaining the operation data of the underwater regional equipment, comparing the operation data of the equipment with a second threshold value,
and if so, adjusting the equipment operation data and generating early warning information.
In a preferred embodiment of the invention, the fishery environment information comprises one or more of aquaculture water quality, aquaculture water environment dissolved oxygen, water environment pH value, water temperature, aquaculture water area wind speed information, aquaculture water area wind direction change information and aquaculture water area water flow information.
In a preferred embodiment of the present invention, the method further comprises:
obtaining a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, transforming by utilizing an autoregressive model, fitting out the pulsating wind time course, and obtaining a change curve graph of time and the course;
forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
In a preferred embodiment of the invention, an autoregressive model is used for transformation, and a pulsating wind time course is fitted to obtain a change curve graph of time and the course; the method specifically comprises the following steps:
setting sampling intervals, sequentially collecting the fluctuating wind speeds of the culture water areas of different time nodes of the culture water areas,
sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
comparing longitudinal components of the pulsating wind speed of the adjacent time nodes to obtain a first deviation ratio;
comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation ratio;
if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
if the second deviation rate is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weight value;
carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and predicting the pulsating wind speed of the next time node according to the weight basis value, and transmitting the prediction result to the terminal.
The invention also provides a fishery accident emergency treatment system based on multi-source information fusion, which comprises: the fishery accident emergency processing method based on the multi-source information fusion comprises a storage and a processor, wherein the storage comprises a fishery accident emergency processing method program based on the multi-source information fusion, and the fishery accident emergency processing method program based on the multi-source information fusion realizes the following steps when being executed by the processor:
establishing a safety early warning model of the culture facility through big data;
acquiring historical data of a water area to generate fishery breeding information;
acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
if the deviation rate is larger than a first threshold value and smaller than a second threshold value, generating early warning information,
inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
if the deviation rate is larger than a second threshold value, generating correction information;
correcting the fishery breeding parameters according to the correction information to obtain result information;
transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
In a preferred embodiment of the present invention, the method further comprises:
obtaining a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, transforming by utilizing an autoregressive model, fitting out the pulsating wind time course, and obtaining a change curve graph of time and the course;
forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
In a preferred embodiment of the invention, an autoregressive model is used for transformation, and a pulsating wind time course is fitted to obtain a change curve graph of time and the course; the method specifically comprises the following steps:
setting sampling intervals, sequentially collecting the fluctuating wind speeds of the culture water areas of different time nodes of the culture water areas,
sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
comparing longitudinal components of the pulsating wind speed of the adjacent time nodes to obtain a first deviation ratio;
comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation ratio;
if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
if the second deviation rate is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weight value;
carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and predicting the pulsating wind speed of the next time node according to the weight basis value, and transmitting the prediction result to the terminal.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) historical aquaculture environment data are obtained through big data, equipment operation safety early warning is carried out through combining an equipment early warning model, a corresponding maintenance decision is established, the safe and efficient operation of the aquatic aquaculture facility is guaranteed, the facility safety early warning process is carried out, data redundancy is reduced through abnormal data in the process of rejecting the equipment operation, and the early warning accuracy is improved.
(2) The method comprises the steps of obtaining a culture water area strong wind data sample, extracting a culture water area strong wind parameter characteristic value, establishing a fluctuating wind speed field according to the culture water area strong wind parameter characteristic value, processing a fluctuating wind speed time course into a series of white noises by adopting linear filtering, converting by utilizing an autoregressive model, fitting a fluctuating wind time course, obtaining a change curve chart of time and a course to predict culture water area weather change information, early warning the culture water area according to the weather change information, and improving the water area wind change prediction precision by decomposing the fluctuating wind speed, so that the culture facility safety early warning is closer to an actual value.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 shows a flow chart of a fishery accident emergency processing method based on multi-source information fusion according to the invention;
FIG. 2 shows a flow chart of a method for establishing a safety precaution model of a farming facility;
FIG. 3 shows a flow diagram of a fishery farming environment data processing method;
FIG. 4 illustrates a flow diagram of an early warning policy generation method;
FIG. 5 illustrates a flow chart of a pulsating wind speed prediction method;
FIG. 6 shows a block diagram of a fishery accident emergency processing system based on multi-source information fusion;
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a fishery accident emergency treatment method based on multi-source information fusion.
As shown in fig. 1, a first aspect of the present invention provides a fishery accident emergency processing method based on multi-source information fusion, including:
s102, establishing a safety early warning model of the culture facility through big data;
s104, acquiring historical data of a water area to generate fishery breeding information;
s106, acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
s108, if the deviation rate is larger than the first threshold value and smaller than the second threshold value, generating early warning information,
s110, inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
s112, if the deviation rate is larger than a second threshold value, generating correction information;
s114, correcting the fishery breeding parameters according to the correction information to obtain result information;
s116, transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
It should be noted that historical aquaculture environment data are obtained through big data, equipment operation safety early warning is carried out by combining an equipment early warning model, a corresponding maintenance decision is established, safe and efficient operation of aquaculture facilities on water is guaranteed, and in the facility safety early warning process, data redundancy is reduced and early warning accuracy is improved by rejecting abnormal data in the equipment operation process.
As shown in fig. 2, the invention discloses a flow chart of a method for establishing a safety early warning model of a culture facility;
according to the embodiment of the invention, a safety early warning model of the culture facility is established through big data; further comprising:
s202, acquiring mass fault diagnosis data and establishing a safety early warning model;
s204, collecting the operation data of the culture facility, generating an operation state curve of the culture facility,
s206, smoothing the operating state curve of the cultivation facility, eliminating abnormal data,
s208, generating early warning information of the faults of the culture facilities according to the safety early warning model,
s210, establishing a maintenance decision according to the early warning information of the faults of the culture facilities,
and S212, transmitting the maintenance decision to the terminal according to a preset mode.
As shown in FIG. 3, the invention discloses a flow chart of a fishery aquaculture environment data processing method;
according to the embodiment of the invention, fishery breeding parameters are corrected according to the correction information to obtain result information; the method specifically comprises the following steps:
s302, obtaining historical culture environment data, and denoising the culture environment data by a wavelet analysis method to obtain result information;
s304, extracting a result information characteristic value, and performing vector decomposition on the characteristic value;
s306, establishing a vector weight adaptive function, and carrying out weighted calculation on the characteristic value vector;
and S308, fusing the weighted and calculated multi-scale feature vectors to generate new environment data.
It should be noted that, wavelet analysis is a new technology of time scale analysis and multiresolution analysis, can describe non-stationarity description of dynamic signals, simultaneously extract local information of frequency domain and time domain of signals, realize separation and extraction of characteristic frequency of water quality signals, has multiscale analysis and mathematical microscopic characteristics, and is a noise reduction and data cleaning tool for realizing high efficiency and convenience of water quality.
According to the embodiment of the invention, the method further comprises the following steps: acquiring water level information, and dividing the equipment into an above-water area and an underwater area;
acquiring equipment operation data of the water area, comparing the equipment operation data with a first threshold value, and if the equipment operation data is larger than the first threshold value, adjusting the ratio of the water area to the underwater area of the equipment;
obtaining the operation data of the underwater regional equipment, comparing the operation data of the equipment with a second threshold value,
and if so, adjusting the equipment operation data and generating early warning information.
According to the embodiment of the invention, the fishery environment information comprises one or more of aquaculture water quality, aquaculture water environment dissolved oxygen, water environment pH value, water temperature, aquaculture water area wind speed information, aquaculture water area wind direction change information and aquaculture water area water flow information.
As shown in fig. 4, the present invention discloses a flow chart of an early warning policy generation method;
according to the embodiment of the invention, the method further comprises the following steps:
s402, acquiring a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
s404, establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
s406, processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, converting by utilizing an autoregressive model, fitting out a pulsating wind time course, and obtaining a change curve graph of time and the course;
s408, forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and S410, establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
As shown in FIG. 5, the present invention discloses a flow chart of a method for predicting a pulsating wind speed;
according to the embodiment of the invention, an autoregressive model is used for transformation, and a pulsating wind time course is fitted to obtain a change curve graph of time and the course; the method specifically comprises the following steps:
s502, setting sampling intervals, sequentially collecting the fluctuating wind speeds of the aquaculture water areas at different time nodes of the aquaculture water areas,
s504, sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
s506, comparing longitudinal components of the pulsating wind speeds of the adjacent time nodes to obtain a first deviation rate;
s508, comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation rate;
s510, if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
s512, if the second deviation ratio is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weighted value;
s514, carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and S516, predicting the pulsating wind speed of the next time node according to the weight base value, and transmitting the prediction result to the terminal.
As shown in FIG. 6, the invention discloses a fishery accident emergency treatment system block diagram based on multi-source information fusion;
the invention also provides a fishery accident emergency treatment system based on multi-source information fusion, and the system 6 comprises: the fishery accident emergency processing system comprises a memory 61 and a processor 62, wherein the memory comprises a fishery accident emergency processing method program based on multi-source information fusion, and the fishery accident emergency processing method program based on the multi-source information fusion realizes the following steps when being executed by the processor:
establishing a safety early warning model of the culture facility through big data;
acquiring historical data of a water area to generate fishery breeding information;
acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
if the deviation rate is larger than the first threshold value and smaller than the second threshold value, generating early warning information,
inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
if the deviation rate is larger than a second threshold value, generating correction information;
correcting the fishery breeding parameters according to the correction information to obtain result information;
transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
According to the embodiment of the invention, the method further comprises the following steps:
obtaining a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, transforming by utilizing an autoregressive model, fitting out the pulsating wind time course, and obtaining a change curve graph of time and the course;
forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
According to the embodiment of the invention, an autoregressive model is used for transformation, and a pulsating wind time course is fitted to obtain a change curve graph of time and the course; the method specifically comprises the following steps:
setting sampling intervals, sequentially collecting the fluctuating wind speeds of the culture water areas of different time nodes of the culture water areas,
sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
comparing longitudinal components of the pulsating wind speed of the adjacent time nodes to obtain a first deviation ratio;
comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation ratio;
if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
if the second deviation rate is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weight value;
carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and predicting the pulsating wind speed of the next time node according to the weight basis value, and transmitting the prediction result to the terminal.
According to the embodiment of the invention, a safety early warning model of the culture facility is established through big data; further comprising:
acquiring mass fault diagnosis data, and establishing a safety early warning model;
collecting the operation data of the culture facilities, generating the operation state curve of the culture facilities,
the operating state curve of the cultivation facility is smoothed, abnormal data is removed,
generating early warning information of the faults of the culture facilities according to the safety early warning model,
establishing a maintenance decision according to the early warning information of the faults of the culture facilities,
and transmitting the maintenance decision to the terminal according to a preset mode.
According to the embodiment of the invention, fishery breeding parameters are corrected according to the correction information to obtain result information; the method specifically comprises the following steps:
obtaining historical culture environment data, and denoising the culture environment data by a wavelet analysis method to obtain result information;
extracting result information characteristic values, and carrying out vector decomposition on the characteristic values;
establishing a vector weight adaptive function, and carrying out weighted calculation on the characteristic value vector;
and fusing the weighted and calculated multi-scale feature vectors to generate new environment data.
It should be noted that, wavelet analysis is a new technology of time scale analysis and multiresolution analysis, can describe non-stationarity description of dynamic signals, simultaneously extract local information of frequency domain and time domain of signals, realize separation and extraction of characteristic frequency of water quality signals, has multiscale analysis and mathematical microscopic characteristics, and is a noise reduction and data cleaning tool for realizing high efficiency and convenience of water quality.
According to the embodiment of the invention, the method further comprises the following steps: acquiring water level information, and dividing the equipment into an above-water area and an underwater area;
acquiring equipment operation data of the water area, comparing the equipment operation data with a first threshold value, and if the equipment operation data is larger than the first threshold value, adjusting the ratio of the water area to the underwater area of the equipment;
obtaining the operation data of the underwater regional equipment, comparing the operation data of the equipment with a second threshold value,
and if so, adjusting the equipment operation data and generating early warning information.
According to the embodiment of the invention, the fishery environment information comprises one or more of aquaculture water quality, aquaculture water environment dissolved oxygen, water environment pH value, water temperature, aquaculture water area wind speed information, aquaculture water area wind direction change information and aquaculture water area water flow information.
In conclusion, historical aquaculture environment data is obtained through big data, equipment operation safety early warning is carried out by combining an equipment early warning model, a corresponding maintenance decision is established, safe and efficient operation of a water aquaculture facility is guaranteed, abnormal data in the operation process of the equipment are eliminated in the process of carrying out the facility safety early warning, data redundancy is reduced, the early warning precision is improved, a strong wind parameter characteristic value of an aquaculture water area is extracted by obtaining a strong wind data sample of the aquaculture water area, a pulsating wind speed time course is processed into a series of white noises by adopting linear filtering according to the strong wind parameter characteristic value of the aquaculture water area, an autoregressive model is used for transformation, a pulsating wind time course is fitted, a change curve graph of time and the course is obtained to predict aquaculture weather change information, and early warning can be carried out on the aquaculture water area according to the weather change information, and the method decomposes the pulsating wind speed, improves the prediction precision of the wind power change in the water area, and enables the safety early warning of the culture facility to be closer to the actual value.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A fishery accident emergency processing method based on multi-source information fusion is characterized by comprising the following steps:
establishing a safety early warning model of the culture facility through big data;
acquiring historical data of a water area to generate fishery breeding information;
acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
if the deviation rate is larger than a first threshold value and smaller than a second threshold value, generating early warning information,
inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
if the deviation rate is larger than a second threshold value, generating correction information;
correcting the fishery breeding parameters according to the correction information to obtain result information;
transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
2. The fishery accident emergency processing method based on multi-source information fusion of claim 1, characterized in that a breeding facility safety early warning model is established through big data; further comprising:
acquiring mass fault diagnosis data, and establishing a safety early warning model;
collecting the operation data of the culture facilities, generating the operation state curve of the culture facilities,
the operating state curve of the cultivation facility is smoothed, abnormal data is removed,
generating early warning information of the faults of the culture facilities according to the safety early warning model,
establishing a maintenance decision according to the early warning information of the faults of the culture facilities,
and transmitting the maintenance decision to the terminal according to a preset mode.
3. The fishery accident emergency processing method based on multi-source information fusion of claim 1, wherein fishery breeding parameters are corrected according to correction information to obtain result information; the method specifically comprises the following steps:
obtaining historical culture environment data, and denoising the culture environment data by a wavelet analysis method to obtain result information;
extracting result information characteristic values, and carrying out vector decomposition on the characteristic values;
establishing a vector weight adaptive function, and carrying out weighted calculation on the characteristic value vector;
and fusing the weighted and calculated multi-scale feature vectors to generate new environment data.
4. The fishery accident emergency processing method based on multi-source information fusion according to claim 3, further comprising: acquiring water level information, and dividing the equipment into an above-water area and an underwater area;
acquiring the operation data of the equipment in the water area, comparing the operation data of the equipment with a first threshold value,
if the ratio of the water area to the underwater area of the equipment is larger than the preset value, adjusting the ratio of the water area to the underwater area of the equipment;
obtaining the operation data of the underwater regional equipment, comparing the operation data of the equipment with a second threshold value,
and if so, adjusting the equipment operation data and generating early warning information.
5. The fishery accident emergency processing method based on multi-source information fusion of claim 1, wherein the fishery environment information comprises one or more of aquaculture water quality, aquaculture water environment dissolved oxygen, water environment pH value, water temperature, aquaculture water area wind speed information, aquaculture water area wind direction change information and aquaculture water area water flow information.
6. The fishery accident emergency processing method based on multi-source information fusion according to claim 1, further comprising:
obtaining a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, transforming by utilizing an autoregressive model, fitting out the pulsating wind time course, and obtaining a change curve graph of time and the course;
forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
7. The fishery accident emergency processing method based on multi-source information fusion of claim 6, wherein an autoregressive model is used for transformation, a pulsating wind time course is fitted, and a change curve graph of time and the course is obtained; the method specifically comprises the following steps:
setting sampling intervals, sequentially collecting the fluctuating wind speeds of the culture water areas of different time nodes of the culture water areas,
sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
comparing longitudinal components of the pulsating wind speed of the adjacent time nodes to obtain a first deviation ratio;
comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation ratio;
if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
if the second deviation rate is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weight value;
carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and predicting the pulsating wind speed of the next time node according to the weight basis value, and transmitting the prediction result to the terminal.
8. The utility model provides a fishery accident emergency processing system based on multisource information fusion which characterized in that, this system includes: the fishery accident emergency processing method based on the multi-source information fusion comprises a storage and a processor, wherein the storage comprises a fishery accident emergency processing method program based on the multi-source information fusion, and the fishery accident emergency processing method program based on the multi-source information fusion realizes the following steps when being executed by the processor:
establishing a safety early warning model of the culture facility through big data;
acquiring historical data of a water area to generate fishery breeding information;
acquiring fishery environment information, and comparing the fishery environment information with preset information to obtain a deviation rate;
if the deviation rate is larger than a first threshold value and smaller than a second threshold value, generating early warning information,
inputting the early warning information into a safety early warning model to obtain a maintenance strategy;
if the deviation rate is larger than a second threshold value, generating correction information;
correcting the fishery breeding parameters according to the correction information to obtain result information;
transmitting the result information to the terminal according to a preset mode, and updating the historical data of the water area;
the first threshold is less than the second threshold.
9. The fishery accident emergency processing system based on multi-source information fusion of claim 7, further comprising:
obtaining a strong wind data sample of the culture water area, extracting a strong wind parameter characteristic value of the culture water area,
establishing a fluctuating wind speed field according to the characteristic value of the strong wind parameter of the culture water area, and acquiring the fluctuating wind speed of the culture water area;
processing the pulsating wind speed time course into a series of white noises by adopting linear filtering, transforming by utilizing an autoregressive model, fitting out the pulsating wind time course, and obtaining a change curve graph of time and the course;
forecasting weather change information of the culture water area according to a change curve graph of pulsating wind time and a process;
and establishing early warning information according to the weather change information, and generating a corresponding early warning strategy.
10. The fishery accident emergency processing system based on multi-source information fusion of claim 9, wherein an autoregressive model is used for transformation, a pulsating wind time course is fitted, and a change curve graph of time and the course is obtained; the method specifically comprises the following steps:
setting sampling intervals, sequentially collecting the fluctuating wind speeds of the culture water areas of different time nodes of the culture water areas,
sequentially decomposing the pulsating wind speeds of different time nodes into longitudinal components and transverse components of the different time nodes;
comparing longitudinal components of the pulsating wind speed of the adjacent time nodes to obtain a first deviation ratio;
comparing the transverse components of the pulsating wind speeds of the adjacent time nodes to obtain a second deviation ratio;
if the first deviation rate is larger than a preset longitudinal component threshold value, generating a first fluctuating wind speed weight value;
if the second deviation rate is larger than a preset transverse component threshold value, generating a second fluctuating wind speed weight value;
carrying out weight reconstruction on the first fluctuating wind speed weight value and the second fluctuating wind speed weight value to obtain a weight base value,
and predicting the pulsating wind speed of the next time node according to the weight basis value, and transmitting the prediction result to the terminal.
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