Tropical aquaculture data analysis and processing method

Abstract

The invention relates to the technical field of data analysis, in particular to a tropical aquaculture data analysis and processing method, which comprises the following steps: acquiring the decrease intensity of dissolved oxygen according to real-time dissolved oxygen data, and when the decrease intensity is greater than a first threshold value, judging that the decrease intensity is a suspected abnormal dissolved oxygen data segment; according to the historical and real-time dissolved oxygen amount temperature change rate, obtaining a suspected alga dramatic increase influence degree, and when the suspected alga dramatic increase influence degree is greater than a second threshold value, determining that the suspected alga dramatic increase influence degree is a suspected alga abnormal data Acquiring a seaweed aggregation degree according to the suspected seaweed abnormal data segment, when the seaweed aggregation degree is greater than a third threshold value, acquiring the possibility of abnormal oxygen content data caused by sudden increase of the seaweed according to the suspected seaweed abnormal data segment, and when the seaweed aggregation degree is greater than a fourth threshold value, determining the data segment as a standard abnormal data segment; the similarity between the to-be-detected data segment and the standard abnormal data segment is calculated, and when the similarity is larger than a similarity threshold value, it is judged that the to-be-detected data segment is an abnormal oxygen content data segment influenced by the alga dramatic increase. According to the invention, whether the phenomenon of insufficient dissolved oxygen caused by sudden increase of algae occurs in the current water area or not can be accurately monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a tropical aquaculture data analysis and processing method. Background Art

[0002] In the field of aquaculture, accurate monitoring of dissolved oxygen data is essential to maintaining the health of farmed organisms. Abnormal fluctuations in dissolved oxygen data are often early warning signs of sudden environmental changes, equipment failures, or potential problems in the aquaculture system. Especially in tropical aquaculture environments, although short-term temperature fluctuations may cause instantaneous changes in dissolved oxygen levels, this effect is usually temporary due to the self-regulation ability of aquatic ecosystems. However, when algae surge abnormally, the abnormality of dissolved oxygen data will show an intensifying trend, and it will become more serious as the algae reproduce faster. Therefore, early identification and analysis of abnormalities in dissolved oxygen data and timely response measures are of great significance for preventing economic losses.

[0003] The existing technology uses a fixed threshold to determine whether an oxygen deficiency event caused by an algae surge has certain limitations, because drastic temperature fluctuations at night may also cause abnormal dissolved oxygen data. Therefore, this fixed threshold method is not accurate enough in determining nocturnal oxygen deficiency events caused by an algae surge. Summary of the invention

[0004] In order to solve the technical problem that the judgment of nocturnal hypoxia events caused by the surge of algae at night is not accurate enough in the prior art, the purpose of the present invention is to provide a method for analyzing and processing tropical aquaculture data. The technical solution adopted is as follows: Obtain historical data of dissolved oxygen, historical data of temperature, real-time data of dissolved oxygen and real-time data of temperature in aquaculture areas; Obtaining the severity of the drop in dissolved oxygen according to the real-time dissolved oxygen data within a preset time period, and marking the real-time dissolved oxygen data within the preset time period as a suspected abnormal dissolved oxygen data segment when the severity of the drop in dissolved oxygen is greater than a preset first threshold; Obtaining the real-time dissolved oxygen temperature change rate at each moment in the suspected abnormal dissolved oxygen data segment and the historical dissolved oxygen temperature change rate at each moment in the dissolved oxygen historical data segment corresponding to the suspected abnormal dissolved oxygen data segment, and obtaining the suspected degree of influence by the seaweed surge according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate; when the suspected degree of influence by the seaweed surge is greater than a preset second threshold, marking the suspected abnormal dissolved oxygen data segment as a suspected seaweed abnormal data segment; The degree of seaweed aggregation is obtained according to the suspected seaweed abnormal data segment. When the degree of seaweed aggregation is greater than a preset third threshold, the possibility of abnormal oxygen content data due to the rapid increase of seaweed is obtained according to the suspected seaweed abnormal data segment. When the possibility is greater than a preset fourth threshold, the suspected seaweed abnormal data segment is used as a standard abnormal data segment. A data segment to be detected having the same data length as the standard abnormal data segment is obtained, and the similarity between the data segment to be detected and the standard abnormal data segment is calculated. When the similarity is greater than a preset similarity threshold, the data segment to be detected is an abnormal oxygen content data segment affected by the sharp increase of algae.

[0005] Furthermore, obtaining the severity of the drop in dissolved oxygen according to the real-time data of dissolved oxygen within a preset time period includes: Obtaining a dissolved oxygen change at each monitoring point within a preset time period, wherein the dissolved oxygen change is equal to the real-time dissolved oxygen data at the current moment minus the real-time dissolved oxygen data at the moment before the current moment, wherein the real-time dissolved oxygen data is obtained by a dissolved oxygen sensor at a monitoring point set in the aquaculture area, and the number of the monitoring points is at least two; Calculate the average value of the dissolved oxygen content change at each moment in a preset time period, and obtain the number of times that the average value is greater than a preset change rate threshold and the total number of the average values; The severity of the drop in dissolved oxygen is obtained by dividing the number of average values ​​that are greater than a preset change rate threshold by the total number of average values.

[0006] Furthermore, the process of obtaining the real-time dissolved oxygen temperature change rate includes: Obtain the initial real-time dissolved oxygen temperature change rate at the initial moment; The number of time intervals between the nth moment and the initial moment is calculated, the reciprocal of the time interval number plus the preset first adjustment value is taken, and then multiplied by the initial real-time dissolved oxygen temperature change rate to obtain the real-time dissolved oxygen temperature change rate at the nth moment, wherein the value of n is 1 to the number of moments corresponding to the data in the suspected abnormal dissolved oxygen data segment.

[0007] Furthermore, the process of obtaining the initial real-time dissolved oxygen temperature change rate includes: The real-time data of dissolved oxygen at the initial moment is subtracted from the real-time data of dissolved oxygen at the moment before the initial moment to obtain the real-time change of dissolved oxygen; The real-time temperature data at the initial moment is subtracted from the real-time temperature data at the moment before the initial moment to obtain the real-time temperature change; The real-time change in dissolved oxygen is divided by the real-time change in temperature to obtain the initial real-time dissolved oxygen temperature change rate.

[0008] Further, obtaining the degree of suspected impact of algae surge according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate includes: After obtaining the historical dissolved oxygen temperature change rate minus the real-time dissolved oxygen temperature change rate at the corresponding moment, the absolute value is taken to obtain the absolute value of the dissolved oxygen temperature change rate; The absolute values ​​of the dissolved oxygen temperature change rates corresponding to each moment are averaged to obtain an average value of the dissolved oxygen temperature change rate; The average value of the dissolved oxygen temperature change rate is normalized to obtain the degree of suspected impact caused by the algae surge.

[0009] Further, obtaining the degree of seaweed aggregation according to the suspected seaweed abnormality data segment includes: Calculate the average value of the distances between the monitoring point to which the suspected seaweed abnormality data segment belongs and other monitoring points and record it as the average distance; Obtaining the absolute value of the difference between the initial time of the suspected seaweed abnormality data segment and the initial time of the suspected seaweed abnormality data segment corresponding to the remaining monitoring points, and averaging the absolute values ​​to obtain the degree of time synchronization; The average distance is added with a preset second adjustment value and the reciprocal is taken to obtain the distance reciprocal, and the time synchronization degree is added with a preset third adjustment value and the reciprocal is taken to obtain the time reciprocal; The reciprocal of the distance is multiplied by the reciprocal of the time and then normalized to obtain the seaweed aggregation degree.

[0010] Further, the possibility of obtaining the abnormal oxygen content data due to the rapid increase of algae according to the suspected algae abnormal data segment includes: Obtaining a curve with dissolved oxygen data as the vertical axis and time as the horizontal axis according to the suspected algae abnormal data segment; Calculating the slope of each data point in the curve, and recording the average value of the slopes as the slope average value; The slope average is normalized to obtain the possibility that the oxygen content data is abnormal due to the rapid growth of algae.

[0011] Further, calculating the similarity between the data segment to be detected and the standard abnormal data segment includes: A Pearson correlation coefficient analysis is performed on the data segment to be detected and the standard abnormal data segment, and the absolute value of the result of the Pearson correlation coefficient analysis is used as the similarity.

[0012] Furthermore, the method also includes: when the data segment to be detected is an abnormal oxygen content data segment affected by the sharp increase of algae, taking algae control measures, the algae control measures including: physical removal, removal using algae killers and introduction of algae natural enemies for removal.

[0013] Furthermore, the similarity threshold is set to 0.7.

[0014] The present invention has the following beneficial effects: First, obtain the historical data of dissolved oxygen, historical data of temperature, real-time data of dissolved oxygen and real-time data of temperature in the aquaculture area, which is the data basis for subsequent analysis.

[0015] Secondly, the severity of the drop in dissolved oxygen is obtained according to the real-time dissolved oxygen data within a preset time period, and when the severity of the drop in dissolved oxygen is greater than a preset first threshold, the real-time dissolved oxygen data within the preset time period is marked as a suspected abnormal dissolved oxygen data segment. The suspected abnormal dissolved oxygen data segment is the data basis for subsequent analysis of whether it is a suspected seaweed abnormal data segment.

[0016] Furthermore, the real-time dissolved oxygen temperature change rate at each moment in the suspected abnormal dissolved oxygen data segment and the historical dissolved oxygen temperature change rate at each moment in the dissolved oxygen historical data segment corresponding to the suspected abnormal dissolved oxygen data segment are obtained, and the suspected degree of influence by the seaweed surge is obtained according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate. When the suspected degree of influence by the seaweed surge is greater than a preset second threshold, the suspected abnormal dissolved oxygen data segment is marked as a suspected seaweed abnormal data segment. The suspected seaweed abnormal data segment excludes the situation where the abnormal dissolved oxygen is caused by night temperature changes.

[0017] Then, the degree of seaweed aggregation is obtained according to the suspected seaweed abnormal data segment. When the degree of seaweed aggregation is greater than a preset third threshold, the possibility of abnormal oxygen content data due to the rapid increase of seaweed is obtained according to the suspected seaweed abnormal data segment. When the possibility is greater than a preset fourth threshold, the suspected seaweed abnormal data segment is used as a standard abnormal data segment. The standard abnormal data segment is a data segment that is determined to be the data segment that causes abnormal dissolved oxygen data due to the rapid increase of seaweed.

[0018] Finally, a data segment to be detected with the same data length as the standard abnormal data segment is obtained, and the similarity between the data segment to be detected and the standard abnormal data segment is calculated. When the similarity is greater than a preset similarity threshold, the data segment to be detected is an abnormal oxygen content data segment affected by the rapid growth of seaweed. The higher the similarity with the standard abnormal data segment, the more likely it is that the abnormal dissolved oxygen data segment is caused by the rapid growth of seaweed.

[0019] In summary, the present invention can accurately monitor whether the current water area has insufficient dissolved oxygen caused by a sharp increase in seaweed, thereby preventing the death of cultured organisms due to lack of oxygen and thus avoiding economic losses for farmers. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a tropical aquaculture data analysis and processing method provided by the first embodiment of the present invention; Figure 2 A flow chart of a process for obtaining the severity of the drop in dissolved oxygen provided by the second embodiment of the present invention; Figure 3 A flowchart of a process for obtaining a real-time dissolved oxygen temperature change rate provided by a third embodiment of the present invention; Figure 4 A flowchart of a process for obtaining the initial real-time dissolved oxygen temperature change rate provided by the fourth embodiment of the present invention; Figure 5 A flowchart of a process for obtaining the degree of influence of suspected algae proliferation provided by the fifth embodiment of the present invention; Figure 6 A flow chart of a process for obtaining the degree of seaweed aggregation provided by a sixth embodiment of the present invention; Figure 7 This is a flow chart of a process for obtaining the possibility of abnormal oxygen content data provided by the seventh embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a tropical aquaculture data analysis and processing method proposed by the present invention, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The specific scheme of a tropical aquaculture data analysis and processing method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0025] See also Figure 1 , which shows a flow chart of a tropical aquaculture data analysis and processing method provided by a first embodiment of the present invention; S101. Obtain historical data on dissolved oxygen, historical data on temperature, real-time data on dissolved oxygen, and real-time data on temperature in an aquaculture area.

[0026] The dissolved oxygen historical data, the temperature historical data, the dissolved oxygen real-time data and the temperature real-time data all belong to the same day of the year, the difference is that the dissolved oxygen historical data and the temperature historical data are historical data of the previous year, and the dissolved oxygen real-time data and the temperature real-time data are current real-time data, and can all be obtained by sampling at fixed time intervals. The dissolved oxygen historical data, the temperature historical data, the dissolved oxygen real-time data and the temperature real-time data correspond in time.

[0027] The historical data of dissolved oxygen can be obtained from the World Hydrology Online Database, which is the dissolved oxygen data of the aquaculture waters at night.

[0028] The temperature history data can be obtained from the meteorological bureau of the area, which is the historical outside temperature data of the aquaculture water area at night.

[0029] The real-time dissolved oxygen data can be obtained by a dissolved oxygen sensor arranged in an aquaculture area.

[0030] The real-time temperature data can be obtained by a temperature sensor arranged outside the aquaculture area.

[0031] The dissolved oxygen historical data, the temperature historical data, the dissolved oxygen real-time data and the temperature real-time data may all be subjected to data preprocessing such as removing abnormal values ​​and duplicate values, and using interpolation to supplement unknown data.

[0032] S102. Obtain the severity of the drop in dissolved oxygen based on the real-time dissolved oxygen data within a preset time period. When the severity of the drop in dissolved oxygen is greater than a preset first threshold, mark the real-time dissolved oxygen data within the preset time period as a suspected abnormal dissolved oxygen data segment.

[0033] The process of obtaining the severity of the drop in dissolved oxygen will be described in detail in the second embodiment and will not be repeated here.

[0034] The preset time period can be set independently, such as 6:30 to 6:40 in the evening.

[0035] The first threshold can be set independently, preferably 0.8.

[0036] S103. Obtain the real-time dissolved oxygen temperature change rate at each moment in the suspected abnormal dissolved oxygen data segment and the historical dissolved oxygen temperature change rate at each moment in the dissolved oxygen historical data segment corresponding to the suspected abnormal dissolved oxygen data segment, and obtain the suspected degree of influence caused by the surge in seaweed according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate; when the suspected degree of influence caused by the surge in seaweed is greater than a preset second threshold, mark the suspected abnormal dissolved oxygen data segment as a suspected seaweed abnormality data segment.

[0037] The process of obtaining the real-time dissolved oxygen temperature change rate will be described in detail in the third and fourth embodiments, and will not be repeated here. The process of obtaining the historical dissolved oxygen temperature change rate is consistent with the process of obtaining the real-time dissolved oxygen temperature change rate, except that the real-time data of the real-time dissolved oxygen temperature change rate acquisition process is replaced with the corresponding historical data to obtain the historical dissolved oxygen temperature change rate.

[0038] The process of obtaining the degree of influence suspected by the algae proliferation will be described in detail in the fifth embodiment and will not be repeated here.

[0039] The second threshold can be set independently, preferably 0.6.

[0040] S104. Obtain the degree of seaweed aggregation according to the suspected seaweed abnormal data segment. When the degree of seaweed aggregation is greater than a preset third threshold, obtain the possibility that the oxygen content data is abnormal due to the sharp increase of seaweed according to the suspected seaweed abnormal data segment. When the possibility is greater than a preset fourth threshold, use the suspected seaweed abnormal data segment as a standard abnormal data segment.

[0041] The process of obtaining the seaweed aggregation degree will be described in detail in the sixth embodiment and will not be repeated here.

[0042] The third threshold can be set independently, preferably 0.8.

[0043] The process of obtaining the possibility of abnormal oxygen content data will be described in detail in the seventh embodiment and will not be repeated here.

[0044] The fourth threshold can be set independently, and is preferably 0.87.

[0045] S105. Acquire a data segment to be detected with the same data length as the standard abnormal data segment, calculate the similarity between the data segment to be detected and the standard abnormal data segment, and when the similarity is greater than a preset similarity threshold, the data segment to be detected is an abnormal oxygen content data segment affected by the rapid growth of algae.

[0046] Calculating the similarity between the data segment to be detected and the standard abnormal data segment includes: A Pearson correlation coefficient analysis is performed on the data segment to be detected and the standard abnormal data segment, and the absolute value of the result of the Pearson correlation coefficient analysis is used as the similarity.

[0047] The Pearson correlation coefficient analysis is a prior art and will not be described in detail here.

[0048] The method further includes: when the data segment to be detected is an abnormal oxygen content data segment affected by the sharp increase of algae, taking algae control measures, the algae control measures including: physical removal, removal using algae killers and removal by introducing natural enemies of algae.

[0049] The physical measures include: using floating grids, mesh devices, etc. to remove algae from the water.

[0050] The algae killer includes copper-sulfur mixture and the like.

[0051] The natural enemies of algae include aquatic animals, microorganisms and the like.

[0052] The similarity threshold can be set independently, and is preferably 0.7.

[0053] At night in tropical areas, although the high water temperature weakens the water's ability to dissolve oxygen, the high temperature also promotes an increase in the oxygen consumption rate of algae, microorganisms and farmed organisms, which can cause water hypoxia events. When photosynthesis stops at night, the respiration of algae and other organisms (including farmed organisms) continuously consumes oxygen, causing the dissolved oxygen level to drop sharply, and the consumption rate far exceeds the natural replenishment rate. In contrast, under normal conditions at night, the dissolved oxygen level also shows a downward trend, but it is relatively gentle; under abnormal hypoxic conditions, the dissolved oxygen level will drop significantly. Based on this feature, the real-time dissolved oxygen data of specific time periods and specific areas at night can be preliminarily analyzed: First, collect continuous dissolved oxygen data for a certain area during a specific period of time at night. Since a single data point is difficult to accurately reflect the trend of change, multiple continuous data points over a period of time are used to evaluate the rate of decline of dissolved oxygen. Given that plant photosynthesis weakens or even stops at night, and respiration dominates, the dissolved oxygen level in the water will decrease at night under normal circumstances, but this decrease is usually gradual and gentle. Therefore, by analyzing whether the rate of decline of dissolved oxygen data is gentle or sharp, it is possible to distinguish between normal decline and abnormal hypoxia. The core here is to judge whether the decline rate is abnormal based on the characteristics of the decline in dissolved oxygen data, so as to promptly identify abnormal dissolved oxygen in the water.

[0054] Figure 2The flowchart of the process of obtaining the severity of the drop in dissolved oxygen provided by the second embodiment of the present invention, wherein obtaining the severity of the drop in dissolved oxygen according to the real-time data of dissolved oxygen within a preset time period includes: S201. Obtain the dissolved oxygen change at each monitoring point at each moment within a preset time period, wherein the dissolved oxygen change is equal to the real-time data of dissolved oxygen at the current moment minus the real-time data of dissolved oxygen at the moment before the current moment, and the real-time data of dissolved oxygen is obtained by a dissolved oxygen sensor at a monitoring point set up in the aquaculture area, and the number of the monitoring points is at least two.

[0055] The dissolved oxygen content change can be expressed as: ; Among them, the represents the change in dissolved oxygen content, represents the real-time data of dissolved oxygen at time t, express The real-time data of dissolved oxygen at the moment.

[0056] The aquaculture area is provided with at least two monitoring points, each of which is provided with a dissolved oxygen sensor, and the real-time dissolved oxygen data is acquired by the dissolved oxygen sensor.

[0057] Each monitoring point can obtain the dissolved oxygen change, so the number of dissolved oxygen changes at time t is the same as the number of monitoring points.

[0058] S202. Calculate the average value of the dissolved oxygen content change at each moment in a preset time period, and obtain the number of average values ​​greater than a preset change rate threshold and the total number of average values.

[0059] The average value of the dissolved oxygen change at time t can be obtained by That is to say, the dissolved oxygen change amount obtained at each monitoring point at time t is added together and then divided by the amount of dissolved oxygen change amount at time t.

[0060] The preset time includes multiple moments, so there are multiple average values ​​of the dissolved oxygen change.

[0061] The preset change rate threshold can be set independently, and is preferably set to 0.1 mg / L per hour.

[0062] S203: Divide the number of average values ​​that are greater than a preset change rate threshold by the total number of average values ​​to obtain the severity of the drop in dissolved oxygen.

[0063] The severity of the drop in dissolved oxygen can be expressed as: ; Wherein, n represents the number of times the average value is greater than a preset change rate threshold, represents the total number of the average values, and b represents the severity of the decrease in dissolved oxygen.

[0064] The outside temperature at night will continue to drop over time until it reaches its lowest point near dawn. As the temperature drops, the dissolved oxygen content in the water also changes to a certain extent. There is a certain relationship between the changes in temperature and dissolved oxygen content in the water. This relationship feature is expressed in the form of a ratio, and the difference between the real-time data ratio of the aquaculture area and the data ratio under normal circumstances is analyzed to determine whether there is an abnormality.

[0065] Figure 3 The flowchart of the process of obtaining the real-time dissolved oxygen temperature change rate provided by the third embodiment of the present invention includes: S301. Obtain the initial real-time dissolved oxygen temperature change rate at the initial moment.

[0066] The initial real-time dissolved oxygen temperature change rate can be To indicate, the process of obtaining the initial real-time dissolved oxygen temperature change rate will be described in detail in the fourth embodiment and will not be repeated here.

[0067] S302. Calculate the time interval number between the nth moment and the initial moment, add the preset first adjustment value to the time interval number and take the reciprocal, and then multiply it by the initial real-time dissolved oxygen temperature change rate to obtain the real-time dissolved oxygen temperature change rate at the nth moment, wherein the value of n is 1 to the number of moments corresponding to the data in the suspected abnormal dissolved oxygen data segment.

[0068] The real-time dissolved oxygen temperature change rate at the nth moment can be expressed as: ; Among them, the represents the real-time dissolved oxygen temperature change rate at the nth moment, represents the number of time intervals, the represents the first adjustment value, the represents the initial real-time dissolved oxygen temperature change rate, the The purpose of setting is to make the denominator not equal to 0. The value can be set independently, preferably 1.

[0069] Figure 4The flowchart of the process of obtaining the initial real-time dissolved oxygen temperature change rate provided by the fourth embodiment of the present invention includes: S401. Subtract the real-time data of dissolved oxygen at the moment before the initial moment from the real-time data of dissolved oxygen at the initial moment to obtain the real-time change of dissolved oxygen.

[0070] The real-time variation of dissolved oxygen can be expressed as: ; Among them, the Indicates the real-time change of dissolved oxygen content, represents the real-time data of the dissolved oxygen at the initial moment, Represents the real-time data of the dissolved oxygen content at the moment before the initial moment.

[0071] S402. Subtract the real-time temperature data at the previous moment from the real-time temperature data at the initial moment to obtain the real-time temperature change.

[0072] The real-time temperature variation can be expressed as: ; Among them, the represents the real-time temperature change, represents the real-time temperature data at the initial moment, Represents the real-time temperature data at the moment before the initial moment.

[0073] S403. Divide the real-time change in dissolved oxygen by the real-time change in temperature to obtain the initial real-time dissolved oxygen temperature change rate.

[0074] The initial real-time dissolved oxygen temperature change rate can be expressed as: ; Among them, the Indicates the initial real-time dissolved oxygen temperature change rate.

[0075] It should be noted that when When the value is 0, the Can be replaced by , the w is a fixed value set independently.

[0076] High temperatures in summer or sudden rainstorms can quickly change water temperature, which can cause dramatic fluctuations in dissolved oxygen concentrations in a short period of time, leading to hypoxia. In addition, when algal blooms (overgrowth of algae) reach their peak, algae colonies can die on a large scale due to nutrient depletion, temperature fluctuations, or interference from external factors (such as heavy rains and strong sunlight). Fluctuations in outside temperature directly affect the efficiency of heat exchange between the water body and the atmosphere, which in turn causes the water temperature to rise and fall, and changes in water temperature profoundly affect the solubility of oxygen in the water, thereby changing the oxygen content in the water. On nights with normal dissolved oxygen, there is an interactive balance between the outside temperature and the dissolved oxygen data in the water. However, when the dissolved oxygen data at night in the water is abnormal due to a surge in algae, this normal relationship between temperature and dissolved oxygen will be disturbed.

[0077] Based on the above principles, by analyzing the degree of abnormality, we can further explore the real cause of the suspected abnormal dissolved oxygen data segments (located in specific areas) identified in the previous step. Specifically, it is to evaluate whether these abnormalities are more likely to be caused by algae activities (such as overgrowth or death). Through the following analysis, we can more accurately determine whether the abnormal dissolved oxygen data is related to algae activities, thereby providing a scientific basis for subsequent water quality management and ecological protection.

[0078] Figure 5 The flowchart of the process of obtaining the degree of suspected impact of algae proliferation provided by the fifth embodiment of the present invention, obtaining the degree of suspected impact of algae proliferation according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate includes: S501. Obtain the historical dissolved oxygen temperature change rate minus the real-time dissolved oxygen temperature change rate at the corresponding moment, and then take the absolute value to obtain the absolute value of the dissolved oxygen temperature change rate.

[0079] The absolute value of the dissolved oxygen temperature change rate can be expressed as: ; Among them, the represents the historical dissolved oxygen temperature change rate at the nth moment, Represents the real-time dissolved oxygen temperature change rate at the nth moment.

[0080] S502. Take the average value of the absolute value of the dissolved oxygen temperature change rate corresponding to each moment to obtain the average value of the dissolved oxygen temperature change rate.

[0081] The average value of the dissolved oxygen temperature change rate can be expressed as: ; The n represents the number of the historical dissolved oxygen temperature change rates or the real-time dissolved oxygen temperature change rates.

[0082] S503. Normalize the average value of the dissolved oxygen temperature change rate to obtain the degree of suspected impact caused by the algae surge.

[0083] If the dissolved oxygen data in the aquaculture waters is abnormal and is affected by temperature, then there is a certain relationship between the changes in dissolved oxygen and temperature. If the dissolved oxygen data in the waters is affected by other factors such as the sharp increase in seaweed, then the relationship between dissolved oxygen and temperature will also change. The severity of the impact of other factors can be judged by the degree of change in this relationship.

[0084] The degree of suspected impact by the algae surge can be expressed as: ; Among them, the Indicates the suspected degree of impact caused by the algae proliferation, is a normalization function, which can be a range normalization function.

[0085] For the suspected abnormal algae data segment, it cannot be completely considered that it is affected by the surge in algae, because the abnormal dissolved oxygen data caused by the surge in algae also has spatial characteristics, that is, algae and other microorganisms stop photosynthesis at night and only perform respiration, consuming a large amount of oxygen, especially in areas with dense algae blooms, the dissolved oxygen concentration will also drop sharply, causing hypoxia. If the abnormal dissolved oxygen data is related to the surge in algae, then these abnormal dissolved oxygen data are clustered in space, and the authenticity of the suspected abnormal algae data segment can be judged by this spatial clustering feature.

[0086] In a body of water, when algae appear at a certain location, they will gradually expand, and abnormal monitoring data will appear at multiple monitoring points. Therefore, by comparing the time series relationship between different monitoring points, it is possible to determine whether the abnormal data segment is caused by algae abnormalities.

[0087] Figure 6 The flowchart of the process of obtaining the seaweed aggregation degree provided by the sixth embodiment of the present invention, wherein obtaining the seaweed aggregation degree according to the suspected seaweed abnormal data segment includes: S601. Calculate the average value of the distances between the monitoring point to which the suspected seaweed abnormality data segment belongs and other monitoring points and record it as the average distance.

[0088] S602. Obtain the absolute value of the difference between the initial time of the suspected seaweed anomaly data segment and the initial time of the suspected seaweed anomaly data segment corresponding to the remaining monitoring points, and average the absolute values ​​to obtain the degree of time synchronization.

[0089] S603. Add a preset second adjustment value to the average distance and take the reciprocal to obtain a distance reciprocal, and add a preset third adjustment value to the time synchronization degree and take the reciprocal to obtain a time reciprocal.

[0090] The inverse distance can be expressed as: ; Among them, the represents the average distance, the represents the second adjustment value, the The purpose of setting is to make the denominator not equal to 0. The value can be set independently, preferably 1.

[0091] The time inverse can be expressed as: ; Among them, the Indicates the degree of time synchronization, the represents the second adjustment value, the The purpose of setting is to make the denominator not equal to 0. The value can be set independently, preferably 1.

[0092] S604: multiply the inverse of the distance by the inverse of the time and then perform normalization processing to obtain the seaweed aggregation degree.

[0093] The algae aggregation degree can be expressed as: ; Wherein, H represents the aggregation degree of the seaweed, is a normalization function, which can be a range normalization function.

[0094] The abnormal dissolved oxygen data segment suspected to be caused by seaweed, i.e., the abnormal dissolved oxygen data segment suspected to be caused by seaweed, is only a preliminary judgment, which is based on the assumption that the rapid growth of seaweed is the cause of the abnormal dissolved oxygen data. It is worth noting that the uneven spatial distribution of water surface temperature can also cause regional differences in dissolved oxygen, and this natural variation may be mistakenly included in the category of abnormalities. However, the abnormal dissolved oxygen content on the water surface caused by the massive reproduction of seaweed is unique: as the number of seaweed continues to increase and the distribution of algae on the water surface becomes increasingly dense, the clustering characteristics of this abnormal data will continue to intensify. Therefore, for the abnormal data segment suspected to be caused by seaweed, further in-depth analysis is needed to confirm its true cause.

[0095] Figure 7 The flowchart of the process of obtaining the possibility of abnormal oxygen content data provided by the seventh embodiment of the present invention, wherein obtaining the possibility of abnormal oxygen content data caused by the rapid growth of algae according to the suspected algae abnormal data segment includes: S701. Obtain a curve with dissolved oxygen data as the vertical axis and time as the horizontal axis according to the suspected seaweed abnormality data segment.

[0096] S702. Calculate the slope of each data point in the curve, and record the average value of the slopes as the slope average value.

[0097] S703. Normalize the slope average value to obtain the possibility that the oxygen content data is abnormal due to the rapid increase of algae.

[0098] The possibility of abnormal oxygen content data due to the rapid increase of algae can be expressed as: ; Among them, the It indicates the possibility that the oxygen content data is abnormal due to the rapid growth of algae, and R indicates the average value of the slope.

[0099] Said The larger the value is, the greater the possibility that the oxygen content data is abnormal due to the rapid growth of algae, that is, the greater the authenticity of the suspected abnormal algae data segment as the abnormal dissolved oxygen data segment affected by the rapid growth of algae.

[0100] The present invention has the following beneficial effects: First, obtain the historical data of dissolved oxygen, historical data of temperature, real-time data of dissolved oxygen and real-time data of temperature in the aquaculture area, which is the data basis for subsequent analysis.

[0101] Secondly, the severity of the drop in dissolved oxygen is obtained according to the real-time dissolved oxygen data within a preset time period, and when the severity of the drop in dissolved oxygen is greater than a preset first threshold, the real-time dissolved oxygen data within the preset time period is marked as a suspected abnormal dissolved oxygen data segment. The suspected abnormal dissolved oxygen data segment is the data basis for subsequent analysis of whether it is a suspected seaweed abnormal data segment.

[0102] Furthermore, the real-time dissolved oxygen temperature change rate at each moment in the suspected abnormal dissolved oxygen data segment and the historical dissolved oxygen temperature change rate at each moment in the dissolved oxygen historical data segment corresponding to the suspected abnormal dissolved oxygen data segment are obtained, and the suspected degree of influence by the seaweed surge is obtained according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate. When the suspected degree of influence by the seaweed surge is greater than a preset second threshold, the suspected abnormal dissolved oxygen data segment is marked as a suspected seaweed abnormal data segment. The suspected seaweed abnormal data segment excludes the situation where the abnormal dissolved oxygen is caused by night temperature changes.

[0103] Then, the degree of seaweed aggregation is obtained according to the suspected seaweed abnormal data segment. When the degree of seaweed aggregation is greater than a preset third threshold, the possibility of abnormal oxygen content data due to the rapid increase of seaweed is obtained according to the suspected seaweed abnormal data segment. When the possibility is greater than a preset fourth threshold, the suspected seaweed abnormal data segment is used as a standard abnormal data segment. The standard abnormal data segment is a data segment that is determined to be the data segment that causes abnormal dissolved oxygen data due to the rapid increase of seaweed.

[0104] Finally, a data segment to be detected with the same data length as the standard abnormal data segment is obtained, and the similarity between the data segment to be detected and the standard abnormal data segment is calculated. When the similarity is greater than a preset similarity threshold, the data segment to be detected is an abnormal oxygen content data segment affected by the rapid growth of seaweed. The higher the similarity with the standard abnormal data segment, the more likely it is that the abnormal dissolved oxygen data segment is caused by the rapid growth of seaweed.

[0105] In summary, the present invention can accurately monitor whether the current water area has insufficient dissolved oxygen caused by a sharp increase in seaweed, thereby preventing the death of cultured organisms due to lack of oxygen and thus avoiding economic losses for farmers.

[0106] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A tropical aquaculture data analysis and processing method, characterized in that: The method comprises: Obtain historical data of dissolved oxygen, historical data of temperature, real-time data of dissolved oxygen and real-time data of temperature in aquaculture areas; Obtaining the severity of the drop in dissolved oxygen according to the real-time dissolved oxygen data within a preset time period, and marking the real-time dissolved oxygen data within the preset time period as a suspected abnormal dissolved oxygen data segment when the severity of the drop in dissolved oxygen is greater than a preset first threshold; Obtaining the real-time dissolved oxygen temperature change rate at each moment in the suspected abnormal dissolved oxygen data segment and the historical dissolved oxygen temperature change rate at each moment in the dissolved oxygen historical data segment corresponding to the suspected abnormal dissolved oxygen data segment, and obtaining the suspected degree of influence by the seaweed surge according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate; when the suspected degree of influence by the seaweed surge is greater than a preset second threshold, marking the suspected abnormal dissolved oxygen data segment as a suspected seaweed abnormal data segment; The degree of seaweed aggregation is obtained according to the suspected seaweed abnormal data segment. When the degree of seaweed aggregation is greater than a preset third threshold, the possibility of abnormal oxygen content data due to the rapid increase of seaweed is obtained according to the suspected seaweed abnormal data segment. When the possibility is greater than a preset fourth threshold, the suspected seaweed abnormal data segment is used as a standard abnormal data segment. A data segment to be detected having the same data length as the standard abnormal data segment is obtained, and the similarity between the data segment to be detected and the standard abnormal data segment is calculated. When the similarity is greater than a preset similarity threshold, the data segment to be detected is an abnormal oxygen content data segment affected by the sharp increase of algae.

2. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: The step of obtaining the severity of the drop in dissolved oxygen according to the real-time data of dissolved oxygen within a preset time period includes: Obtaining a dissolved oxygen change at each monitoring point within a preset time period, wherein the dissolved oxygen change is equal to the real-time dissolved oxygen data at the current moment minus the real-time dissolved oxygen data at the moment before the current moment, wherein the real-time dissolved oxygen data is obtained by a dissolved oxygen sensor at a monitoring point set in the aquaculture area, and the number of the monitoring points is at least two; Calculate the average value of the dissolved oxygen content change at each moment in a preset time period, and obtain the number of times that the average value is greater than a preset change rate threshold and the total number of the average values; The severity of the drop in dissolved oxygen is obtained by dividing the number of average values ​​that are greater than a preset change rate threshold by the total number of average values.

3. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: The process of obtaining the real-time dissolved oxygen temperature change rate includes: Obtain the initial real-time dissolved oxygen temperature change rate at the initial moment; The number of time intervals between the nth moment and the initial moment is calculated, the reciprocal of the time interval number plus the preset first adjustment value is taken, and then multiplied by the initial real-time dissolved oxygen temperature change rate to obtain the real-time dissolved oxygen temperature change rate at the nth moment, wherein the value of n is 1 to the number of moments corresponding to the data in the suspected abnormal dissolved oxygen data segment.

4. The tropical aquaculture data analysis and processing method according to claim 3, characterized in that: The process of obtaining the initial real-time dissolved oxygen temperature change rate includes: The real-time data of dissolved oxygen at the initial moment is subtracted from the real-time data of dissolved oxygen at the moment before the initial moment to obtain the real-time change of dissolved oxygen; The real-time temperature data at the initial moment is subtracted from the real-time temperature data at the moment before the initial moment to obtain the real-time temperature change; The real-time change in dissolved oxygen is divided by the real-time change in temperature to obtain the initial real-time dissolved oxygen temperature change rate.

5. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: Obtaining the suspected degree of impact caused by the algae surge according to the historical dissolved oxygen temperature change rate and the real-time dissolved oxygen temperature change rate includes: After obtaining the historical dissolved oxygen temperature change rate minus the real-time dissolved oxygen temperature change rate at the corresponding moment, the absolute value is taken to obtain the absolute value of the dissolved oxygen temperature change rate; The absolute values ​​of the dissolved oxygen temperature change rates corresponding to each moment are averaged to obtain an average value of the dissolved oxygen temperature change rate; The average value of the dissolved oxygen temperature change rate is normalized to obtain the degree of suspected impact caused by the algae surge.

6. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: Obtaining the degree of seaweed aggregation according to the suspected seaweed abnormal data segment includes: Calculate the average value of the distances between the monitoring point to which the suspected seaweed abnormality data segment belongs and other monitoring points and record it as the average distance; Obtaining the absolute value of the difference between the initial time of the suspected seaweed abnormality data segment and the initial time of the suspected seaweed abnormality data segment corresponding to the remaining monitoring points, and averaging the absolute values ​​to obtain the degree of time synchronization; The average distance is added with a preset second adjustment value and the reciprocal is taken to obtain the distance reciprocal, and the time synchronization degree is added with a preset third adjustment value and the reciprocal is taken to obtain the time reciprocal; The reciprocal of the distance is multiplied by the reciprocal of the time and then normalized to obtain the seaweed aggregation degree.

7. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: The possibility of obtaining the abnormal oxygen content data due to the rapid increase of algae according to the suspected algae abnormal data segment includes: Obtaining a curve with dissolved oxygen data as the vertical axis and time as the horizontal axis according to the suspected algae abnormal data segment; Calculating the slope of each data point in the curve, and recording the average value of the slopes as the slope average value; The slope average is normalized to obtain the possibility that the oxygen content data is abnormal due to the rapid growth of algae.

8. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: Calculating the similarity between the data segment to be detected and the standard abnormal data segment includes: A Pearson correlation coefficient analysis is performed on the data segment to be detected and the standard abnormal data segment, and the absolute value of the result of the Pearson correlation coefficient analysis is used as the similarity.

9. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: The method further includes: when the data segment to be detected is an abnormal oxygen content data segment affected by the sharp increase of algae, taking algae control measures, the algae control measures including: physical removal, removal using algae killers and removal by introducing natural enemies of algae.

10. The tropical aquaculture data analysis and processing method according to claim 1, characterized in that: The similarity threshold is set to 0.7.

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