CN105677770A - Inshore oceanic environment data monitoring adaptive sampling method - Google Patents
Inshore oceanic environment data monitoring adaptive sampling method Download PDFInfo
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Abstract
The invention relates to an inshore oceanic environment data monitoring adaptive sampling method. Oceanic environment data actual measurement samples in several periods before the sampling this time, a next-time oceanic environment data forecast sample is obtained through an exponential smoothing method, the sample distance between the oceanic environment data forecast sample and an actual sample is calculated, the next-time sampling time interval is adjusted, and a correction function of the next-time sampling time interval is established in order to correct the sampling time interval of the next-period oceanic environment data forecast sample, the current oceanic environment data is acquired actually by means of the corrected sampling time interval, and the oceanic environment data sample which is actually acquired this time is stored; the sample distance is judged and compared with a preset sample distance threshold, and in dependence on the judgement result, corresponding data are respectively stored to an oceanic environment data detection sample database and an oceanic environment data outlier sample database in order to analyze abnormal conditions of the oceanic environment.
Description
Technical field
The present invention relates to marine environmental monitoring field, particularly relate to a kind of adaptively sampled method of coastal ocean monitoring environmental data.
Background technology
The nucleus of fishing ground, coastal area Dou Shi China sea fishery all the time, due to overfishing in recent years and environmental pollution, causing that China coastal seas fishery resources move towards exhausted gradually, it is main that the production model of fisherman is also gradually converted into seawater fishery from traditional going fishing on the sea. Along with people live the raising of life, the demand of marine product is increasing, quality requirements is also more and more higher, green, healthy, safe consciousness is increasingly stronger and the dependence degree of coastal ocean environment is constantly deepened by seawater fishery industry, utilize cloud computing, the sea fishery of Internet of Things cultivates and environmental monitoring technology, to promote marine products scientific culture and Environmental security level, there is important researching value and application prospect.
But, in current high-end mariculture process, still suffering from some problems: first, seawater fishery is gradually to Precision cultural1 transition, Precision cultural1 needs using marine environment data long-term, reliable as cultivation reference, but currently not yet forms the ocean monitoring technologytechnologies of maturation; Secondly, the marine environmental monitoring equipment of nearshore waters is easily subject to the impact of water source, land, the irregular current of offshore, stormy waves etc., cause in discrete Monitoring Data it is possible that singular data, and these singular datas can not react the general characteristic of the residing coastal ocean environment of cultivation, it is necessary to these singular datas are screened and removes. Therefore, coastal ocean environmental data is effectively monitored, and remove the singular data in Monitoring Data, thus seawater fishery is had important practical significance by the change in detail accurately catching important environmental index.
Summary of the invention
The technical problem to be solved is to provide one can either coastal ocean environmental data effectively be gathered for above-mentioned prior art, can screen again the adaptively sampled method of the coastal ocean monitoring environmental data of singular data in gathered data.
This invention address that the technical scheme that above-mentioned technical problem adopts is: the adaptively sampled method of coastal ocean monitoring environmental data, it is characterised in that comprise the steps:
(1) the marine environment data actual measurement sample in the some cycles before this sampling is obtained;Wherein, described marine environment data actual measurement sample labeling is St;
(2) according to the marine environment data actual measurement sample S obtainedt, utilization index smoothing techniques predicts the marine environment data forecast sample next time sampled, and obtains the marine environment data actual sample next time sampled; Wherein, the marine environment data forecast sample next time sampled obtained described in is labeled as A't+1, the marine environment data actual sample next time sampled of described acquisition is At+1;
(3) according to gained marine environment data forecast sample A't+1And the marine environment data actual sample A obtainedt+1, obtain the sample distance between said two marine environment data sample, and set up the correction function of sampling time interval next time; Wherein, obtained sample range mark be δ, δ=| A't+1-At+1|, the correction function of described sampling time interval is labeled as φ (A't+1,At+1);
(4) the correction function φ (A' according to the sampling time interval obtainedt+1,At+1), the sampling time interval of correction marine environment data forecast sample next time; Wherein, the sampling time interval of described marine environment data forecast sample next time is labeled as t, t=φ (A't+1,At+1)·tp;
(5) according to revised sampling time interval, current marine environment data is carried out actual acquisition, and the marine environment data sample of this actual acquisition is stored;
(6) with default sample distance threshold, the sample distance of gained marine environment data sample is carried out judgement to compare, and stores the marine environment data of correspondence respectively according to judged result to the detection sample database of marine environment data and unusual sample database:
When the sample distance of gained marine environment data sample is less than default sample distance threshold, then store this marine environment data sample detection sample database to marine environment data; Otherwise, this marine environment data sample of labelling is abnormal singular value occur, and stores the unusual sample database to marine environment data, for analyzing the abnormal conditions that marine environment occurs.
In order to more precisely predict the marine environment data forecast sample sampled next time, selectively, described exponential smoothing is Single Exponential Smoothing or Secondary Exponential Smoothing Method or third index flatness.
Further, the marine environment data being labeled as singular value in described step (6) includes the combination of the marine environment data of substantial variations or the marine environment data of noise data or substantial variations and noise data.
Compared with prior art, it is an advantage of the current invention that: survey sample by the marine environment data in some cycles before obtaining this sampling, utilization index smoothing techniques predicts marine environment data forecast sample next time, calculate the sample distance between marine environment data forecast sample and actual sample, set up the correction function of sampling time interval next time, and adjust the sampling time interval of next time; Utilize revised sampling time interval, to current marine environment data actual acquisition, store the marine environment data sample of this actual acquisition; Judge to compare by this sample distance and default sample distance threshold, store the data of correspondence respectively according to judged result to the detection sample database of marine environment data and unusual sample database. The method has only to the sampling time interval according to self-adaptative adjustment and gathers marine environment data, change severe degree according to gathering data gathers information with rational density, and effectively remove noise data produced by nature instantaneous interference, save electric energy and cost of equipment maintenance;Additionally, stable data in the marine environment data of collection and singular value can be made a distinction by the method, to pass through to analyze the detailed change of singular value seizure important marine environmental index, thus more accurately grasp coastal ocean ambient conditions, and in ocean development, take precautions against natural calamities and mitigation play a significant role.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the adaptively sampled method of coastal ocean monitoring environmental data in the embodiment of the present invention.
Detailed description of the invention
Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.
As it is shown in figure 1, the adaptively sampled method of the coastal ocean monitoring environmental data at the present embodiment, comprise the steps 1 to step 6:
Step 1, obtains the marine environment data actual measurement sample in the some cycles before this sampling; Wherein, marine environment data actual measurement sample labeling is St; Marine environment data actual measurement sample is actually measured marine environment data sample; Such as, the actual measurement of the marine environment data in the present embodiment sample can be the actually measured marine environment data in two years before this sampling;
Step 2, according to the marine environment data actual measurement sample S obtainedt, utilization index smoothing techniques predicts the marine environment data forecast sample next time sampled, and obtains the marine environment data actual sample next time sampled; Wherein, the marine environment data forecast sample next time sampled obtained is labeled as A't+1, the marine environment data actual sample next time sampled of acquisition is At+1; Marine environment data forecast sample A't+1With marine environment data actual sample At+1For one to one;
In this step 2, in order to more precisely predict the marine environment data forecast sample sampled next time, this exponential smoothing can as required, select to use Single Exponential Smoothing or Secondary Exponential Smoothing Method or third index flatness;
Step 3, according to gained marine environment data forecast sample A't+1And the marine environment data actual sample A obtainedt+1, obtain said two marine environment data sample A't+1And At+1Sample distance δ, and set up the correction function of sampling time interval next time; Wherein, sample distance δ=| A't+1-At+1|, the time complexity curve function of marine environment data is labeled as φ (A't+1,At+1);
Step 4, the correction function according to the sampling time interval obtained, revise the sampling time interval of the marine environment data forecast sample in next cycle; Wherein, sampling time interval is labeled as t, t=φ (A' next timet+1,At+1)·tp; By the next sampling time interval t after time complexity curve function and adjustmentp, it is achieved that it is adaptively adjusted next cycle sampling time interval to marine environment data forecast sample; Wherein, " next cycle " and " next time " that this step 1 relates to step 4 is the some cycles before sampling relative to this; Here " next cycle " and " next time " all refers to this current sampling period;
Step 5, according to revised sampling time interval, stores the marine environment data sample of this actual acquisition; Here the marine environment data sample stored is the marine environment data sample of this actual acquisition;
Step 6, carries out judgement by the sample distance of gained marine environment data sample with default sample distance threshold and compares, and stores the marine environment data of correspondence respectively according to judged result to the detection sample database of marine environment data and unusual sample database:
When the sample distance of gained marine environment data sample is less than default sample distance threshold, it was shown that this marine environment data is more steady, it does not have big deviation occur, then store this marine environment data sample detection sample database to marine environment data;Otherwise, show that fluctuation occurs in this marine environment data, occur in that big deviation, such as occur in that noise data produced by nature instantaneous interference, then this marine environment data sample of labelling is abnormal singular value occur, and store the unusual sample database to marine environment data, for analyzing the abnormal conditions that marine environment occurs. Wherein, the singular value being arranged in unusual sample database includes the combination of the marine environment data of substantial variations or the marine environment data of noise data or substantial variations and noise data. By to the odd value analysis in unusual sample database, it is possible to understand the detailed change of marine environment index accurately.
In the adaptively sampled method of the coastal ocean monitoring environmental data of the present invention, sample is surveyed by the marine environment data in the some cycles before obtaining this sampling, and utilization index smoothing techniques obtains sampling marine environment data forecast sample next time, calculate the sample distance between marine environment data forecast sample and actual sample, adjust the next time of the sampling time interval to marine environment data, and set up the correction function of sampling time interval next time, to revise the sampling time interval of the marine environment data forecast sample in next cycle, and utilize the sampling time interval after adjustment, to current marine environment data actual acquisition, and the marine environment data sample of this actual acquisition is stored, with default sample distance threshold, this sample distance is carried out judgement compare, storing the marine environment data of correspondence respectively according to judged result to the detection sample database of marine environment data and unusual sample database, unusual sample database is for analyzing the abnormal conditions that marine environment occurs.
The method has only to the sampling time interval according to self-adaptative adjustment and gathers marine environment data, change severe degree according to gathering data gathers information with rational density, and effectively remove noise data produced by nature instantaneous interference, save electric energy and cost of equipment maintenance; Additionally, stable data in the marine environment data of collection and singular value can be made a distinction by the method, to pass through to analyze the detailed change of singular value seizure important marine environmental index, thus more accurately grasp coastal ocean ambient conditions, and in ocean development, take precautions against natural calamities and mitigation play a significant role.
Claims (3)
1. the adaptively sampled method of coastal ocean monitoring environmental data, it is characterised in that comprise the steps:
(1) the marine environment data actual measurement sample in the some cycles before this sampling is obtained; Wherein, described marine environment data actual measurement sample labeling is St;
(2) according to the marine environment data actual measurement sample S obtainedt, utilization index smoothing techniques predicts the marine environment data forecast sample next time sampled, and obtains the marine environment data actual sample next time sampled; Wherein, the marine environment data forecast sample next time sampled obtained described in is labeled as A't+1, the marine environment data actual sample next time sampled of described acquisition is At+1;
(3) according to gained marine environment data forecast sample A't+1And the marine environment data actual sample A obtainedt+1, obtain the sample distance between said two marine environment data sample, and set up the correction function of sampling time interval next time; Wherein, obtained sample range mark be δ, δ=| A't+1-At+1|, the correction function of described sampling time interval is labeled as φ (A't+1,At+1);
(4) the correction function φ (A' according to the sampling time interval obtainedt+1,At+1), the sampling time interval of correction marine environment data forecast sample next time; Wherein, the sampling time interval of described marine environment data forecast sample next time is labeled as t, t=φ (A't+1,At+1)·tp;
(5) according to revised sampling time interval, current marine environment data is carried out actual acquisition, and the marine environment data sample of this actual acquisition is stored;
(6) with default sample distance threshold, the sample distance of gained marine environment data sample is carried out judgement to compare, and stores the marine environment data of correspondence respectively according to judged result to the detection sample database of marine environment data and unusual sample database:
When the sample distance of gained marine environment data sample is less than default sample distance threshold, then store this marine environment data sample detection sample database to marine environment data; Otherwise, this marine environment data sample of labelling is abnormal singular value occur, and stores the unusual sample database to marine environment data, for analyzing the abnormal conditions that marine environment occurs.
2. the adaptively sampled method of coastal ocean monitoring environmental data according to claim 1, it is characterised in that described exponential smoothing is Single Exponential Smoothing or Secondary Exponential Smoothing Method or third index flatness.
3. the adaptively sampled method of coastal ocean monitoring environmental data according to claim 1, it is characterized in that, the marine environment data being labeled as singular value in described step (6) includes the combination of the marine environment data of substantial variations or the marine environment data of noise data or substantial variations and noise data.
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| CN112834577A (en) * | 2019-11-22 | 2021-05-25 | 中国船舶重工集团公司第七六研究所 | Correlation method for judging marine environment magnetic field and seawater conductivity |
| CN117114453A (en) * | 2023-10-24 | 2023-11-24 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Marine carbon sink state evaluation system based on aquatic organism observation |
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