Sea cucumber oviposition environment data monitoring method and system and medium

Abstract

The invention relates to the technical field of water temperature monitoring, in particular to a sea cucumber spawning environment data monitoring method and system and a medium. Collecting sea cucumber oviposition historical water temperature time sequence data in the target water area, water area depth and area, and historical temperature time sequence data in a preset historical time period; analyzing the historical water temperature time sequence data, determining a water temperature abnormal data segment, analyzing the temperature difference in the historical temperature time sequence data, and quantifying the influence of the external temperature on the water temperature to obtain a temperature abnormal value; considering different depths and areas of water areas, and determining an influence lag degree value; the length of the water temperature data is calculated based on the indexes so as to collect real-time water temperature time sequence data, so that the hysteresis influence is reduced to the maximum extent, and the water temperature change is reflected in time; calculating an abnormal characteristic value by combining the temperature abnormal value, the fluctuation characteristic of the real-time water temperature time sequence data and the similarity between the fluctuation characteristic and the water temperature abnormal data segment; finally, water temperature anomaly monitoring is performed based on the anomaly characteristic value, and the accuracy of water temperature anomaly monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water temperature monitoring, and specifically relates to a method, a system and a medium for monitoring sea cucumber spawning environment data. Background Art

[0002] Sea cucumbers are a precious marine biological resource, and their breeding and reproduction are of great significance to the development of the marine economy. During the reproduction process of sea cucumbers, water temperature is a crucial environmental factor. The change of water temperature not only affects the growth and development of sea cucumbers, but also directly relates to the spawning success rate and the hatching quality of eggs. Therefore, accurately monitoring and evaluating the water temperature environment during sea cucumber spawning is of great significance for improving the breeding efficiency of sea cucumbers and protecting sea cucumber resources.

[0003] When the existing technology monitors the water temperature data during sea cucumber spawning, it usually counts the change fluctuations of water temperature values within a fixed time length to reflect the water temperature anomalies in the water area; however, due to the large specific heat capacity of water, the influence of the decrease or increase of the external temperature on the water temperature of the water area has a lag. Therefore, if the change fluctuations of water temperature values within a fixed time length are analyzed, actual abnormal events may be missed, reducing the accuracy and timeliness of the monitoring results. Summary of the Invention

[0004] In order to solve the technical problem that due to the large specific heat capacity of water, the influence of the decrease or increase of the external temperature on the water temperature of the water area has a lag, so if the change fluctuations of water temperature values within a fixed time length are analyzed, actual abnormal events may be missed, reducing the accuracy and timeliness of the monitoring results, the purpose of the present invention is to provide a method, a system and a medium for monitoring sea cucumber spawning environment data, and the specific technical solutions adopted are as follows: A method for monitoring sea cucumber spawning environment data includes: Obtaining historical water temperature time series data when sea cucumbers spawn in a target water area; obtaining the water depth and area of the target water area; obtaining historical temperature time series data of the environment where the target water area is located in a preset historical period; the target water area is the water area where sea cucumbers are located; In the historical water temperature time series data, determine water temperature abnormal data segments according to the numerical distribution and fluctuation changes of water temperature values; based on the water depth and area of the target water area, determine the influence lag degree value; in the historical temperature time series data, analyze the difference situation between temperature values to determine temperature abnormal values; based on the influence lag degree value, temperature abnormal values and the length characteristics of the water temperature abnormal data segments, collect real-time water temperature time series data of the target water area; Based on the fluctuation characteristics of the water temperature values in the real-time water temperature time series data, the similarity between the real-time water temperature time series data and the water temperature abnormal data segments, and the temperature abnormal values, determine the abnormal characteristic values of the real-time water temperature time series data; based on the abnormal characteristic values, conduct real-time abnormal monitoring on the water temperature during sea cucumber spawning.

[0005] Further, the method for obtaining the water temperature abnormal data segments includes: In the historical water temperature time series data, obtain all the extreme points of the water temperature values, and segment the historical water temperature time series data based on the extreme points to obtain subsequences; Take the normalized value of the ratio of the range to the length of the water temperature values in each subsequence as the fluctuation factor of each subsequence, and take the subsequences with the fluctuation factor greater than the preset fluctuation threshold as the fluctuation data segments; In terms of time sequence, if there are adjacent fluctuation data segments, merge the adjacent fluctuation data segments as the water temperature abnormal data segments; if the fluctuation data segments exist alone, take the fluctuation data segments as the water temperature abnormal data segments.

[0006] Further, the method for obtaining the influence lag degree value includes: Take the average value of the water depth values at all monitoring positions in the target water area as the average depth of the target water area; Take the normalized value of the product of the average depth and the area of the target water area as the influence lag degree value.

[0007] Further, the method for obtaining the temperature abnormal values includes: In the historical temperature time series data, take the difference between the maximum temperature value and the minimum temperature value as the temperature change factor; Take the variance of all the temperature values in the historical temperature time series data as the temperature fluctuation factor; Take the normalized value of the sum of the temperature change factor and the temperature fluctuation factor as the temperature abnormal value.

[0008] Further, the method for obtaining the real-time water temperature time series data includes: Take the average value of the lengths of all the water temperature abnormal data segments as the initial data length value; Take the normalized value of the sum of the influence lag degree value and the temperature abnormal value as the adjustment weight, take the sum of the adjustment weight and the preset constant as the length weight, and take the rounded-up value of the product of the length weight and the initial data length value as the length of the real-time water temperature time series data of the target water area, so as to collect and obtain the real-time water temperature time series data.

[0009] Further, the method for obtaining the abnormal characteristic values includes: Calculate the DTW values between the real-time water temperature time series data and each water temperature anomaly data segment, and use the value obtained by negatively correlating and normalizing the DTW values as the similarity factor; Take the mean of all the similarity factors of the real-time water temperature time series data as the first anomaly factor of the real-time water temperature time series data; Analyze the fluctuation characteristics of the data values in the real-time water temperature time series data to determine the second anomaly factor of the real-time water temperature time series data; Take the value obtained by normalizing the sum of the first anomaly factor, the second anomaly factor, and the temperature anomaly value of the historical temperature time series data as the anomaly characteristic value of the real-time water temperature time series data.

[0010] Further, the method for obtaining the second anomaly factor includes: Take the mean of all the water temperature values in the real-time water temperature time series data as the mean characteristic value, and take the absolute value of the difference between each water temperature value and the mean characteristic value as the deviation factor; Take the value obtained by normalizing the sum of the deviation factors corresponding to all the water temperature values in the real-time water temperature time series data as the second anomaly factor.

[0011] Further, the real-time anomaly monitoring of the water temperature during sea cucumber spawning based on the anomaly characteristic value includes: When the anomaly characteristic value of the real-time water temperature time series data is greater than or equal to the preset anomaly threshold, it is determined that the water temperature is abnormal and a water temperature anomaly warning needs to be issued; When the anomaly characteristic value of the real-time water temperature time series data is less than the preset anomaly threshold, it is determined that the water temperature is normal and no water temperature anomaly warning is required.

[0012] A sea cucumber spawning environment data monitoring system includes a memory and a processor. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. When at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor, the steps of the sea cucumber spawning environment data monitoring method are implemented.

[0013] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the sea cucumber spawning environment data monitoring method are implemented.

[0014] The present invention has the following beneficial effects: By obtaining the historical water temperature time series data when sea cucumbers spawn in the target water area, the water depth and area of the target water area, and the historical temperature time series data of the environment where the target water area is located in the preset historical period, a comprehensive and accurate data basis is provided for subsequent anomaly monitoring. Abnormal water temperature fluctuations are likely to hinder the spawning of sea cucumbers and affect the normal reproduction of sea cucumbers. Therefore, analyzing the numerical distribution and fluctuation changes of the water temperature values in the historical water temperature time series data to determine the abnormal water temperature data segments can provide a reference for subsequent real-time water temperature monitoring. The abnormality of water temperature data is affected by the external temperature. Therefore, the differences between the temperature values in the historical temperature time series data are analyzed to quantify the influence of the external temperature on the water temperature and obtain the temperature anomaly values. Since the specific heat capacity of water is relatively large, the impact of sudden changes in the external temperature on the water temperature data will have a certain lag; and the degree of lag generated by different water depths and areas under the influence of the external environment is different. Therefore, the influence lag degree value is determined based on the water depth and area of the target water area. Based on the influence lag degree value, the temperature anomaly value, and the length of the abnormal water temperature data segment, real-time water temperature time series data are collected, so that the data can minimize the influence of lag and more timely reflect the water temperature changes in the target water area. Combining the similarity between the real-time water temperature time series data and the abnormal water temperature data segments, the temperature anomaly value, and the fluctuation characteristics of the real-time water temperature time series data itself, the abnormal characteristic value of the real-time water temperature time series data is calculated. Finally, based on the abnormal characteristic value, the water temperature during sea cucumber spawning is monitored for anomalies, effectively enhancing the ability to identify abnormal water temperature conditions, ensuring timely discovery of problems, and providing timely and accurate early warning information for sea cucumber reproduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the accompanying drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a method flow chart of a method for monitoring sea cucumber spawning environment data provided by an embodiment of the present invention; Figure 2 It is a method flow chart of a method for obtaining an abnormal characteristic value provided by an embodiment of the present invention; Figure 3 It is a system block diagram of a system for monitoring sea cucumber spawning environment data provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the system structure of a system for monitoring sea cucumber spawning environment data provided by an embodiment of the present invention; Figure 5Schematic diagram of a computer-readable storage medium provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a sea cucumber spawning environment data monitoring method, system and medium proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solutions of a sea cucumber spawning environment data monitoring method, system and medium provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of a method for monitoring sea cucumber spawning environment data provided by an embodiment of the present invention. The method includes the following steps: Step S1: Obtain historical water temperature time series data when sea cucumbers spawn in the target water area; obtain the water depth and area of the target water area; obtain historical temperature time series data of the environment where the target water area is located in a preset historical period; the target water area is the water area where the sea cucumbers are located.

[0021] As a precious seafood product, the environmental conditions during the spawning process are crucial for sea cucumber farming. Water temperature is one of the key factors affecting the growth, development and reproduction of sea cucumbers. During the spawning period of sea cucumbers, even slight changes in water temperature may lead to a decrease or even failure in the reproduction efficiency of sea cucumbers. Therefore, accurately monitoring the water temperature environment during sea cucumber spawning and promptly detecting and handling abnormal water temperatures are of great significance for improving the efficiency of sea cucumber farming.

[0022] In the first step of implementing sea cucumber spawning environment data monitoring, it is necessary to comprehensively and accurately collect relevant data of the target water area. These data are crucial for subsequent abnormal water temperature monitoring.

[0023] First, for the target water area (i.e., the water area where sea cucumbers are located), it is necessary to obtain its historical water temperature time series data. These data record the changes in water temperature over time in the water area during historical sea cucumber spawning. These data can be collected through water temperature sensors installed in the water area. The sensors will regularly record the water temperature values and transmit the data to the data collection system. In addition, these data can also be obtained from the historical database, but attention should be paid to the accuracy and timeliness of the data. In this embodiment of the present invention, the historical water temperature time series data is the water temperature time series data at the time of the most recent historical sea cucumber spawning closest to the current time, and the length of the data is the time length required for the historical sea cucumber spawning process, usually lasting about 20 days.

[0024] Second, it is also necessary to obtain the water depth and area information of the target water area. These information are crucial for understanding the physical characteristics of the water area and evaluating the influence degree of the external temperature on the water temperature. The water depth and area can be obtained through remote sensing technology, geographic information system (GIS), or on-site measurement, etc. Remote sensing technology can use satellites or drones to take aerial photos of the water area, and extract the boundaries of the target water area and the water depth values at each monitoring location through image processing technology; GIS can use existing topographic maps and hydrological data, and combine spatial analysis technology to obtain the area of the water area and the water depth values at each monitoring location; on-site measurement requires the use of professional measurement equipment, such as depth sounders, GPS, etc., to conduct on-site measurement of the target water area. In the embodiment of the present invention, the monitoring locations are evenly distributed in the target water area when obtaining the water depth.

[0025] Finally, it is necessary to obtain the historical temperature time series data of the environment where the target water area is located under a preset historical period. These data record the changes in the external temperature during the historical period of the target water area, and are of great significance for quantifying the influence of the external temperature on the water temperature. These data can be obtained through temperature sensors set in the environment where the target water area is located. By collecting these historical temperature time series data, the relationship between the external temperature and the water temperature can be analyzed, providing an important reference for subsequent anomaly monitoring. In the embodiment of the present invention, the preset historical period is set to 3 hours before the current moment.

[0026] It should be noted that in this embodiment of the present invention, the collection intervals of the historical water temperature time series data and the historical temperature time series data are both set to once a minute, and the specific time interval and the length of the data can be adjusted according to the implementation scenario, and are not limited here.

[0027] Step S2: In the historical water temperature time series data, determine the water temperature abnormal data segments according to the numerical distribution and fluctuation changes of the water temperature values; determine the influence lag degree value based on the water depth and area of the target water area; analyze the difference situation between the temperature values in the historical temperature time series data to determine the temperature abnormal values; based on the influence lag degree value, the temperature abnormal values, and the length characteristics of the water temperature abnormal data segments, collect the real-time water temperature time series data of the target water area.

[0028] Abnormal water temperature fluctuations are likely to impede the spawning of sea cucumbers and affect the normal reproduction of sea cucumbers. Therefore, in the historical water temperature time series data, analyzing the numerical distribution and fluctuation changes of the water temperature values can identify abnormal data segments that do not conform to the normal water temperature change pattern. These abnormal data segments represent sudden increases or decreases in water temperature, posing a potential threat to the reproduction environment of sea cucumbers. It provides a reference standard for abnormal identification in subsequent real-time water temperature monitoring. The temperature change in water is also affected by the external temperature. Therefore, analyzing the differences in temperature values in the historical temperature time series data and quantifying the temperature abnormal values are used to reflect the changes in the external temperature; and because the specific heat capacity of water is relatively large, the influence of the decrease or increase in the external temperature on the water temperature of the target water area has a lag, and different water depths and areas have different degrees of lag after being affected by the external environment. Therefore, based on the water depth and area of the target water area, the influence lag degree value is determined. If the lag effect is large, then it is necessary to monitor a longer length of water temperature time series data to ensure that the abnormal changes in water temperature can be captured in a timely and accurate manner. Therefore, according to the length characteristics of the water temperature abnormal data segments in the historical water temperature time series data, combined with the influence lag degree value and the temperature abnormal values, the length of the real-time water temperature time series data for the current real-time monitoring is determined and the data is collected.

[0029] First, in the historical water temperature time series data, determine the water temperature abnormal data segments according to the numerical distribution and fluctuation changes of the water temperature values, which are used as a reference for subsequent collection of water temperature time series data.

[0030] Preferably, in an embodiment of the present invention, the method for obtaining the water temperature abnormal data segments includes: In a set of data, extreme points often represent the turning points of data changes. By analyzing the extreme points, extreme situations can be obtained, which helps in subsequent abnormal identification. Therefore, in the historical water temperature time series data, first obtain all the extreme points of the water temperature values, and segment the historical water temperature time series data based on the extreme points to obtain subsequences, that is, take the water temperature values between every two adjacent extreme points as a subsequence. At this time, each subsequence represents the water temperature fluctuation changes within a small range.

[0031] During the breeding season, when the water temperature is within a suitable temperature range and undergoes slight fluctuations, the adaptability of sea cucumbers can resist such slight fluctuations. However, excessive sudden changes in water temperature can exert pressure on sea cucumbers, potentially leading to abnormal reproductive behavior, that is, the sudden change in water temperature exceeds the adaptability of sea cucumbers. Excessive sudden changes in water temperature often feature a large amplitude of change and a rapid rate of change. Therefore, the value obtained by normalizing the ratio of the range of water temperature values to the length in each subsequence is used as the fluctuation factor for each subsequence. At this time, the larger the range of water temperature values in the subsequence, the greater the amplitude of water temperature change, and the smaller the length of the subsequence, the more rapid the change in water temperature. Thus, the larger the fluctuation factor, the greater the abnormal change in water temperature values in the subsequence, that is, the greater the degree of mutation. Among them, normalization is a well-known technical means to those skilled in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0032] Therefore, among all the subsequences, the subsequences with a fluctuation factor greater than the preset fluctuation threshold are regarded as the fluctuation data segments.

[0033] Finally, in view of the possible adjacent and continuous abnormal water temperature fluctuations in time series, so in time series, if there are adjacent fluctuation data segments, the adjacent fluctuation data segments are merged and used as a water temperature abnormal data segment; if the fluctuation data segments exist alone, the fluctuation data segment is directly used as a water temperature abnormal data segment. After merging, multiple water temperature abnormal data segments in the historical water temperature time series data can be obtained.

[0034] It should be noted that the preset fluctuation threshold is 0.65, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0035] The water temperature in the target water area is affected by the change of the external environmental temperature. That is, the change of the external air temperature usually directly affects the surface temperature of the water body. However, due to the relatively large specific heat capacity of water, the increase or decrease of the external temperature has a lag effect on the water temperature of the target water area, and the depth and area of the target water area are the key factors affecting the lag of water temperature change. Therefore, the depth and area of the target water area are analyzed to determine the value of the influence lag degree.

[0036] Preferably, in an embodiment of the present invention, the method for obtaining the value of the influence lag degree includes: The deeper the water area, the less the water temperature is affected by the change of the external temperature, and the more lagged the change is; the larger the water area, the greater the impact on the uniformity and lag of the water temperature change.

[0037] Therefore, the mean of the water depth values ​​at all monitoring locations in the target water area is taken as the average depth of the target water area. The average depth reflects the average level of water depth in the target water area, that is, the average level of the lagging effect of water depth on water temperature changes.

[0038] Finally, the product of the average depth and area of ​​the target water area is normalized to a value that is used as the hysteresis value. The larger the average depth value, the greater the hysteresis effect of the change. Similarly, the larger the area of ​​the target water area, the greater the hysteresis effect of the change. Therefore, the greater the hysteresis value, the greater the hysteresis effect of the change in the external temperature on the water temperature change in the target water area. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0039] Changes in water temperature will be affected by changes in the external ambient temperature. Therefore, when conducting real-time abnormal monitoring of the water temperature in the target water area, it is also necessary to analyze the abnormal conditions of the external ambient temperature. The temperature anomaly value can be obtained based on the difference between the temperature values ​​in the historical temperature time series data to quantify the abnormal changes in the external ambient temperature.

[0040] Preferably, in one embodiment of the present invention, the method for obtaining the temperature anomaly value includes: In the historical temperature time series data, the difference between the maximum temperature value and the minimum temperature value is used as the temperature change factor. The temperature change factor reflects the maximum change range of the temperature value in the historical temperature time series data, and intuitively reflects the extreme changes in the external temperature. The larger the value, the more obvious the change in the external temperature.

[0041] Then the variance of all temperature values ​​in the historical temperature time series data is used as the temperature fluctuation factor. The temperature fluctuation factor reflects the discreteness of the temperature value. The larger the value, the greater the difference between the temperature values, and the more drastic the temperature fluctuation will be, which also reflects that the change in the external temperature is more obvious.

[0042] Finally, the value after normalization of the sum of the temperature change factor and the temperature fluctuation factor is used as the temperature anomaly value. At this time, the larger the temperature anomaly value is, the more drastic the change in the external temperature is, and the greater the impact on the water temperature value in the target water area will be. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0043] In waters with a relatively large influence lag, in order to more accurately capture abnormal changes in water temperature, it is necessary to obtain water temperature data of a longer length. And when the external temperature changes more significantly, it is also necessary to appropriately increase the length of the water temperature data to facilitate a more complete acquisition of the water temperature data affected by the external temperature, improving the accuracy of subsequent analysis. Therefore, the data length required for the current water temperature monitoring of the target water area can be determined by combining the influence lag value, the temperature anomaly value, and the length characteristics of the water temperature anomaly data segment, so as to obtain real-time water temperature time series data.

[0044] Preferably, in an embodiment of the present invention, the method for obtaining real-time water temperature time series data includes: Taking the mean value of the lengths of all water temperature anomaly data segments as the initial data length value. The initial data length value represents the average level of the lengths of the water temperature anomaly data segments and can be used as a reference for the data length when obtaining real-time water temperature time series data.

[0045] Taking the normalized value of the sum of the influence lag value and the temperature anomaly value as the adjustment weight. The larger the influence lag value, the greater the influence lag degree of the change in the external temperature on the water temperature change in the target water area; the larger the temperature anomaly value, the more drastic the change in the external temperature. Therefore, the larger the adjustment weight, the greater the influence of the external temperature on the water temperature of the target water area, and then the more necessary it is to increase the length of the water temperature data to ensure the integrity of data acquisition.

[0046] Then, taking the sum of the adjustment weight and the preset constant as the length weight, and taking the ceiling value of the product of the length weight and the initial data length value as the length of the real-time water temperature time series data of the target water area; the larger the length weight, the stronger the lag of the water temperature change and the greater the influence of the external temperature, then it is necessary to increase the number of sampling points or extend the sampling time, that is, increase the length value of the real-time water temperature time series data, so as to more comprehensively understand the situation of water temperature anomalies. Therefore, taking the ceiling value of the product of the initial data length value and the length weight as the length of the real-time water temperature time series data of the target water area, and then collecting and obtaining real-time water temperature time series data based on this length.

[0047] It should be noted that in this embodiment of the present invention, to avoid the length of the real-time water temperature time series data from being too long, the preset constant is 1. In other embodiments of the present invention, it can also be adjusted according to the implementation scenario, which is not limited herein.

[0048] Step S3: Based on the fluctuation characteristics of the water temperature values in the real-time water temperature time series data, the similarity between the real-time water temperature time series data and the water temperature anomaly data segment, and the temperature anomaly value, determine the anomaly characteristic value of the real-time water temperature time series data; perform real-time anomaly monitoring on the water temperature during sea cucumber spawning based on the anomaly characteristic value.

[0049] If the water temperature value in the real-time water temperature time series data has abnormal fluctuations, the degree of abnormality of the real-time water temperature time series data will be higher. And considering that the water temperature values in the water temperature abnormal data segments in the historical water temperature time series data are characterized by large data fluctuations and short time, so if the real-time water temperature time series data shows data change characteristics similar to those of the water temperature abnormal data segments, then the possibility that the real-time water temperature time series data is abnormal is relatively large. Therefore, by integrating the fluctuation characteristics of the water temperature value in the real-time water temperature time series data, the similarity between the real-time water temperature time series data and the water temperature abnormal data segments, and the temperature abnormal value, the abnormal characteristic value of the real-time water temperature time series data is determined for use in the subsequent abnormal monitoring process.

[0050] Preferably, in an embodiment of the present invention, the method for obtaining the abnormal characteristic value includes: Please refer to Figure 2 , which shows the flowchart of the method for obtaining the abnormal characteristic value in an embodiment of the present invention. The method includes the following steps: Step S301: Analyze the similarity between the real-time water temperature time series data and the water temperature abnormal data segments to obtain the first abnormal factor of the real-time water temperature time series data.

[0051] Dynamic time warping is an algorithm for measuring the similarity between two time series. Therefore, calculate the DTW value between the real-time water temperature time series data and each water temperature abnormal data segment. The smaller the DTW value, the greater the similarity between the two series. So, perform negative correlation mapping and normalization processing on the DTW value to achieve logical relationship correction and obtain the similarity factor. At this time, the larger the similarity factor, the higher the similarity between the two series, and the higher the degree of abnormality of the real-time water temperature time series data. The negative correlation mapping and normalization processing here can be carried out using the formula , where represents the exponential function with the natural constant e as the base, and x represents the independent variable.

[0052] Finally, take the mean value of all similarity factors of the real-time water temperature time series data as the first abnormal factor of the real-time water temperature time series data. The first abnormal factor comprehensively reflects the similarity between the real-time water temperature time series data and all water temperature abnormal data segments, so it can more comprehensively reflect the abnormal fluctuation state of the real-time water temperature time series data, and the larger the value, the more obvious the fluctuation, that is, the more significant the abnormality.

[0053] It should be noted that the method for obtaining the DTW value is a well-known technology, and the specific process will not be elaborated here.

[0054] Step S302: Analyze the fluctuation characteristics of the data values in the real-time water temperature time series data to determine the second abnormal factor of the real-time water temperature time series data.

[0055] The mean of all water temperature values ​​in the real-time water temperature time series data is taken as the mean eigenvalue. The mean eigenvalue can be regarded as the average level of all water temperature values ​​in the real-time water temperature time series data. It is used as a benchmark, and then the absolute value of the difference between each water temperature value and the mean eigenvalue is taken as the deviation factor. The larger the deviation factor, the greater the degree of deviation of the water temperature value from the benchmark, the more obvious the fluctuation of the water temperature value will be, and the higher the possibility of abnormality will be.

[0056] Finally, the sum of the deviation factors corresponding to all water temperature values ​​in the real-time water temperature time series data is normalized as the second abnormal factor. The larger the second abnormal factor, the more drastic the fluctuation of the water temperature value in the real-time water temperature time series data, and the greater the degree of abnormality of the real-time water temperature time series data. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0057] Step S303: The temperature anomaly value, the first anomaly factor, and the second anomaly factor are integrated to determine the anomaly characteristic value of the real-time water temperature time series data.

[0058] The temperature anomaly value reflects the abnormal situation of the outside temperature before the current monitoring time, and the first anomaly factor and the second anomaly factor reflect the abnormal situation of the real-time water temperature time series data itself. Therefore, the integration of the three can more comprehensively and accurately measure the abnormal state of the real-time water temperature time series data during the current monitoring time. Based on the analysis in the above process, it can be seen that the larger the temperature anomaly value, the more drastic the change of the outside temperature, and the greater the impact on the water temperature value in the target water area; the larger the first anomaly factor, the more obvious the abnormal fluctuation state of the real-time water temperature time series data; the larger the second anomaly factor, the greater the degree of abnormality of the real-time water temperature time series data. Therefore, the sum of the first anomaly factor, the second anomaly factor and the temperature anomaly value of the historical temperature time series data is normalized as the abnormal characteristic value of the real-time water temperature time series data. At this time, the larger the abnormal characteristic value, the more significant the abnormal state of the real-time water temperature time series data. Wherein normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0059] Based on the above steps, the abnormal water temperature of the target water area during the current monitoring time can be obtained, that is, the abnormal characteristic value of the real-time water temperature time series data, so the real-time abnormal water temperature during sea cucumber spawning can be monitored based on this value.

[0060] Preferably, in one embodiment of the present invention, real-time abnormal monitoring of water temperature data during sea cucumber spawning is performed based on abnormal characteristic values ​​of real-time water temperature time series data, including: When the abnormal characteristic value of the real-time water temperature time series data is greater than or equal to the preset abnormal threshold, it is determined that the water temperature is abnormal, and water temperature abnormal warning is required to remind the farmer to take measures in time; when the abnormal characteristic value of the real-time water temperature time series data is less than the preset abnormal threshold, it is determined that the water temperature is normal and water temperature abnormal warning is not required.

[0061] It should be noted that in this embodiment of the present invention, the preset abnormal threshold is 0.6, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0062] Based on the steps in the foregoing process, real-time water temperature time series data with an adaptive length can be obtained each time the water temperature of the target water area is monitored, effectively reducing the influence degree of the external temperature change on the lag of the water temperature change in the target water area, thereby improving the accuracy of the water temperature monitoring in the sea cucumber spawning water area.

[0063] It should be noted that since the water temperature monitoring during sea cucumber spawning should be a long-term and continuous process, in the embodiment of the present invention, during the entire monitoring period, that is, the time length required for the sea cucumber spawning process, when the water temperature of the target water area is monitored, a section of real-time water temperature time series data is adaptively obtained each time for abnormal monitoring, and the monitoring stops until the monitoring period, that is, the time required for the sea cucumber spawning process ends.

[0064] In summary, by obtaining the historical water temperature time series data, water depth, and area of the target water area during sea cucumber spawning, as well as the historical temperature time series data in a preset historical period, a comprehensive and accurate data basis is provided for subsequent anomaly monitoring. Abnormal water temperature fluctuations are likely to hinder the spawning of sea cucumbers and affect the normal reproduction of sea cucumbers. Therefore, analyzing the numerical distribution and fluctuation changes of the water temperature values in the historical water temperature time series data to determine the water temperature anomaly data segment can provide a reference for subsequent real-time water temperature monitoring. The anomaly of water temperature data is affected by the external temperature. Therefore, the differences between the temperature values in the historical temperature time series data are analyzed to quantify the influence of the external temperature on the water temperature and obtain the temperature anomaly value. Since the specific heat capacity of water is relatively large, there will be a certain lag in the impact of sudden changes in the external temperature on the water temperature data; and the degree of lag generated by different water depths and areas under the influence of the external environment is different. Therefore, the influence lag degree value is determined based on the water depth and area of the target water area. Based on the influence lag degree value, temperature anomaly value, and the length of the water temperature anomaly data segment, real-time water temperature time series data is collected, enabling the data to minimize the impact of lag to the greatest extent and more timely reflect the water temperature changes in the target water area. Combining the similarity between the real-time water temperature time series data and the water temperature anomaly data segment, the temperature anomaly value, and the fluctuation characteristics of the real-time water temperature time series data itself, the anomaly characteristic value of the real-time water temperature time series data is calculated. Finally, based on the anomaly characteristic value, the water temperature during sea cucumber spawning is monitored for anomalies, effectively enhancing the ability to identify water temperature anomalies, ensuring timely detection of problems, and providing timely and accurate early warning information for sea cucumber reproduction.

[0065] The embodiment of the present invention also provides a monitoring system for sea cucumber spawning environment data. Please refer to Figure 3 , which shows a system block diagram, including a data acquisition module 301 for implementing step S1 in the above method embodiment; an impact analysis module 302 for implementing step S2 in the above method embodiment; and an anomaly monitoring module 303 for implementing step S3 in the above method embodiment.

[0066] It should be noted that for the system provided in the above embodiment, only the above division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a monitoring system for sea cucumber spawning environment data and an embodiment of a method for monitoring sea cucumber spawning environment data provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0067] Please refer to Figure 4, which shows a schematic structural diagram of a sea cucumber spawning environment data monitoring system provided by an embodiment of the present invention, including a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected through the bus 402. Among them, the memory 401 may include a high-speed random access memory. The bus 402 may be an ISA bus, a PCI bus, an EISA bus, etc. The processor 400 may be an integrated circuit chip with signal processing capabilities. The memory 401 stores at least one instruction, at least one program, a code set, or an instruction set. When at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor, the steps in a sea cucumber spawning environment data monitoring method are implemented.

[0068] An embodiment of the present invention also provides a computer-readable storage medium corresponding to the method provided in the foregoing embodiment. Please refer to Figure 5 , which shows that the storage medium is an optical disc, on which a computer program (i.e., a program product) is stored. When the computer program is run by the processor, it will execute the method provided in any of the foregoing embodiments. Examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), other optical and magnetic storage media, which will not be elaborated here one by one.

[0069] It should be noted that the above-mentioned sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring sea cucumber spawning environment data, characterized in that: The method comprises: Obtaining the historical water temperature time series data when sea cucumbers spawn in the target water area; obtaining the water depth and area of ​​the target water area; obtaining the historical temperature time series data of the environment in which the target water area is located in a preset historical period; the target water area is the water area where the sea cucumbers are located; In the historical water temperature time series data, according to the numerical distribution and fluctuation of the water temperature value, the water temperature abnormal data segment is determined; based on the water depth and area of ​​the target water area, the impact hysteresis value is determined; in the historical temperature time series data, the difference between the temperature values ​​is analyzed to determine the temperature abnormal value; based on the impact hysteresis value, the temperature abnormal value and the length characteristics of the water temperature abnormal data segment, the real-time water temperature time series data of the target water area is collected; Based on the fluctuation characteristics of water temperature values ​​in the real-time water temperature time series data, the similarities between the real-time water temperature time series data and the water temperature anomaly data segments, and the temperature anomaly values, the abnormal characteristic values ​​of the real-time water temperature time series data are determined; based on the abnormal characteristic values, real-time abnormal monitoring of the water temperature during sea cucumber spawning is performed.

2. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The method for obtaining the abnormal water temperature data segment includes: In the historical water temperature time series data, all extreme value points of the water temperature value are obtained, and the historical water temperature time series data is segmented based on the extreme value points to obtain subsequences; The value after normalizing the ratio of the range of the water temperature value to the length in each subsequence is used as the fluctuation factor of each subsequence, and the subsequence with a fluctuation factor greater than a preset fluctuation threshold is used as a fluctuation data segment; In terms of time series, if there are adjacent fluctuation data segments, the adjacent fluctuation data segments will be merged and used as the water temperature abnormality data segment; if the fluctuation data segment exists alone, the fluctuation data segment will be used as the water temperature abnormality data segment.

3. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The method for obtaining the influence hysteresis degree value comprises: The average of the water depth values ​​at all monitoring locations in the target water area is taken as the average depth of the target water area; The value obtained by normalizing the product of the average depth and area of ​​the target water area is used as the impact hysteresis degree value.

4. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The method for obtaining the temperature abnormality value comprises: In the historical temperature time series data, the difference between the maximum temperature value and the minimum temperature value is used as the temperature change factor; Taking the variance of all temperature values ​​in the historical temperature time series data as a temperature fluctuation factor; The normalized value of the sum of the temperature change factor and the temperature fluctuation factor is used as the temperature anomaly value.

5. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The method for acquiring the real-time water temperature time series data comprises: The average length of all water temperature abnormal data segments is taken as the initial data length value; The normalized value of the sum of the impact hysteresis value and the temperature anomaly value is used as the adjustment weight, the sum of the adjustment weight and a preset constant is used as the length weight, and the product of the length weight and the initial data length value is rounded up to an integer value as the length of the real-time water temperature time series data of the target water area, thereby collecting and acquiring the real-time water temperature time series data.

6. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The method for obtaining the abnormal characteristic value comprises: Calculate the DTW value between the real-time water temperature time series data and each water temperature abnormality data segment, and use the negative correlation mapping and normalized value of the DTW value as a similarity factor; The mean of all similar factors of the real-time water temperature time series data is taken as the first abnormal factor of the real-time water temperature time series data; Analyze the fluctuation characteristics of the data values ​​in the real-time water temperature time series data to determine a second abnormal factor of the real-time water temperature time series data; The normalized value of the sum of the first abnormal factor, the second abnormal factor and the temperature abnormal value of the historical temperature time series data is used as the abnormal characteristic value of the real-time water temperature time series data.

7. A method for monitoring sea cucumber spawning environment data according to claim 6, characterized in that: The method for obtaining the second abnormal factor includes: The mean of all water temperature values ​​in the real-time water temperature time series data is taken as the mean characteristic value, and the absolute value of the difference between each water temperature value and the mean characteristic value is taken as the deviation factor; The value obtained by normalizing the sum of the deviation factors corresponding to all water temperature values ​​in the real-time water temperature time series data is used as the second abnormal factor.

8. A method for monitoring sea cucumber spawning environment data according to claim 1, characterized in that: The real-time abnormal monitoring of the water temperature during sea cucumber spawning based on the abnormal characteristic value comprises: When the abnormal characteristic value of the real-time water temperature time series data is greater than or equal to the preset abnormal threshold, the water temperature is judged to be abnormal and a water temperature abnormality warning is required; When the abnormal characteristic value of the real-time water temperature time series data is less than the preset abnormal threshold, the water temperature is judged to be normal and no water temperature abnormality warning is required.

9. A sea cucumber spawning environment data monitoring system, characterized in that: It includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a method for monitoring sea cucumber spawning environment data as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of a method for monitoring sea cucumber spawning environment data as described in any one of claims 1 to 8 are implemented.

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