An automatic detection method for ocean temperature, salinity and depth profile data

Abstract

The present invention discloses an automatic detection method for ocean temperature, salinity and depth profile data, which specifically relates to the field of ocean monitoring technology. The method comprises the following steps: setting a target profile area of ​​the ocean and placing ocean temperature, salinity and depth sensors; then performing data collection, processing and classification operations to obtain a first type of profile data set and a second type of profile data set; then drawing a visual graph to obtain profile closed areas of different gradients and performing fluctuation analysis and mobility analysis; then performing a comprehensive analysis to obtain a comprehensive disorder value of the profile closed area of ​​the target gradient and compare it with a preset disorder threshold of the profile closed area of ​​the target gradient; and deciding whether to issue or not an early warning signal based on the comparison result. The present invention helps to fully understand the dynamic changes of the ocean profile, and the calculation of the comprehensive disorder value further provides a quantitative assessment of the stability of the ocean environment. Moreover, an early warning signal can be issued in a timely manner when an early warning mechanism is triggered, thereby ensuring the safety of ocean management.

Description

Technical Field

[0001] The present invention relates to the field of ocean monitoring technology, and more particularly to a method for automatically detecting ocean temperature, salinity and depth profile data. Background Art

[0002] Ocean temperature, salinity, and depth profile data are important foundational data for marine scientific research and marine environmental monitoring, and are widely used in fields such as marine meteorology, marine ecology, and marine resource development. Traditional methods for collecting ocean temperature, salinity, and depth profile data require manual sensor deployment, data collection, and recording. This is labor-intensive and susceptible to human influence, resulting in poor data accuracy and consistency. Furthermore, due to limitations in manual operation and the number of devices, the monitoring range and frequency are limited, making it difficult to achieve high-frequency monitoring of large sea areas and unable to capture dynamic changes in the marine environment in a timely manner. Furthermore, the collected data is often analyzed post-hoc, making it impossible to monitor and warn of abnormal changes in the marine environment in real time, resulting in delayed marine disaster prevention and emergency response. A highly automated, efficient, and timely data processing method for detecting ocean temperature, salinity, and depth profile data with early warning capabilities is urgently needed to improve the overall level of marine environmental monitoring. Therefore, an automatic detection method for ocean temperature, salinity, and depth profile data is proposed. Summary of the Invention

[0003] To achieve the above object, the present invention provides the following technical solutions:

[0004] A method for automatically detecting ocean temperature, salinity and depth profile data comprises the following steps:

[0005] Step 1: Set the target ocean profile area and place ocean temperature, salinity and depth sensors according to the profile area;

[0006] Step 2: Each placed ocean temperature, salinity, and depth sensor collects temperature, salinity, and depth information of the target profile area to obtain an observation subset, and then all observation subsets are aggregated to obtain a profile observation information set;

[0007] Step 3: Processing the profile observation information set to obtain a profile data processing set, and then classifying the profile data processing set to obtain a first-category profile data set and a second-category profile data set;

[0008] Step 4: Draw a visualization graph based on the first type of profile data set to obtain profile closed areas of different gradients, then put the second type of profile data set into the visualization graph, and then perform volatility analysis to obtain the volatility analysis value of the profile closed area of ​​each gradient;

[0009] Step 5: Draw n visualization graphs in a preset time window and perform mobility analysis to obtain the mobility analysis value of the closed area of ​​the cross section of each gradient;

[0010] Step 6: Based on the obtained volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, a comprehensive analysis is performed to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient;

[0011] Step 7: Compare the comprehensive turbulence value of the profile closed area of ​​the target gradient with the preset turbulence threshold of the profile closed area of ​​the target gradient, and decide whether to issue a warning signal or not based on the comparison result.

[0012] Setting the target profile area of ​​the ocean means: establishing a rectangular coordinate system, setting the boundary value of the profile area on the X-axis to [XB1, XB2], setting the boundary value of the profile area on the Y-axis to [YB1, YB2], and setting the rectangular area in the rectangular coordinate system corresponding to the specified position of the ocean as the target profile area.

[0013] In a preferred embodiment, placing ocean temperature, salinity and depth sensors according to the profile area refers to:

[0014] m groups of mobile monitoring ocean temperature, salinity, and depth sensors are placed in the profile area. Each group of mobile monitoring ocean temperature, salinity, and depth sensors consists of k mobile monitoring ocean temperature, salinity, and depth sensors. The standard detection values ​​of the k mobile monitoring ocean temperature, salinity, and depth sensors in each group of mobile monitoring ocean temperature, salinity, and depth sensors are the same, and the standard detection values ​​of each group of mobile monitoring ocean temperature, salinity, and depth sensors are different. It also includes h fixed monitoring ocean temperature, salinity, and depth sensors.

[0015] In a preferred embodiment, classifying the processed profile data set to obtain a first type of profile data set and a second type of profile data set refers to:

[0016] The monitoring data of mobile monitoring ocean temperature, salinity and depth sensors are classified into the first type of profile data set, and the monitoring data of fixed monitoring ocean temperature, salinity and depth sensors are classified into the second type of profile data set.

[0017] In a preferred embodiment, step 4, drawing a visualization graph based on the first type of cross-section data set to obtain cross-section closed areas of different gradients refers to:

[0018] The monitoring positions of the mobile monitoring ocean temperature, salinity and depth sensors in the first type of profile data set are marked in the rectangular area of ​​the established rectangular coordinate system. Then, the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group are connected to obtain a line segment connected to the two sides of the rectangular area. The two adjacent line segments and the two sides of the rectangular area together form a closed area. Different gradients are assigned to all closed areas according to the preset assignment strategy to obtain profile closed areas with different gradients.

[0019] In a preferred embodiment, connecting the coordinate points of k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two side lines of the rectangular area means: using interpolation or fitting method to obtain a smooth curve passing through all the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group, and discarding the part exceeding the rectangular area to obtain a line segment connected to the two side lines of the rectangular area.

[0020] In a preferred embodiment, connecting the coordinate points of k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two side lines of the rectangular area means: using the least squares method to obtain a straight line, the sum of the distances from the straight line to all the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group is the smallest, and discarding the part exceeding the rectangular area to obtain a line segment connected to the two side lines of the rectangular area.

[0021] In a preferred embodiment, the second type of profile data set is put into a visualization graph, and then a volatility analysis is performed to obtain the volatility analysis value of the profile closed area of ​​each gradient, which refers to:

[0022] The coordinate points of the monitoring positions of the h fixed monitoring ocean temperature, salinity and depth sensors in the second type of profile data set are marked. The average temperature data and salinity data of all ocean temperature, salinity and depth sensors in the profile closed area of ​​the same gradient at the same time are obtained. They are sorted according to the observation time to obtain a group of combination 1 consisting of the average temperature data and a group of combination 2 consisting of the average salinity data. The standard deviation of combination 1 is then calculated and marked as S1, and the standard deviation of combination 2 is calculated and marked as S2. The volatility analysis value of the profile closed area of ​​the gradient is then calculated: ; f1 and f2 are both preset fluctuation coefficients, and BFi is the volatility analysis value of the closed area of ​​the gradient profile.

[0023] In a preferred embodiment, the mobility analysis value of the cross-sectional closed area of ​​each gradient is obtained by drawing n visualization graphs and performing mobility analysis in a preset time window:

[0024] Draw n visualization graphics, obtain the area difference of the cross-section closed area of ​​the same gradient in two adjacent visualization graphics and mark it as JDi, and then calculate the mobility analysis value: ; QYi is the mobility analysis value of the gradient.

[0025] Obtaining the volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient and then performing a comprehensive analysis to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient means: obtaining the volatility analysis value BFi and the mobility analysis value QYi of the profile closed area of ​​the target gradient and then calculating: ;w1 and W2 are the preset disorder coefficients, T1 and T2 are the preset volatility standard values ​​and migration standard values ​​respectively, and WLi is the comprehensive disorder value.

[0026] In a preferred embodiment, comparing the comprehensive disorder value of the cross-sectional closed area of ​​the target gradient with a preset disorder threshold value of the cross-sectional closed area of ​​the target gradient, and determining whether to issue or not an early warning signal based on the comparison result means:

[0027] Obtain the comprehensive disorder value WLi of the profile closed area of ​​the target gradient, the preset disorder threshold of the profile closed area of ​​the target gradient and mark it as YWi, when the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is greater than or equal to the preset disorder threshold YWi of the profile closed area of ​​the target gradient, issue a warning signal, when the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is less than the preset disorder threshold YWi of the profile closed area of ​​the target gradient, no warning signal is issued, the preset disorder threshold YWi of the profile closed area of ​​the target gradient meets the following requirements: a standard disorder threshold is preset and marked as WB, the target gradient is marked as ti, then .

[0028] Technical effects and advantages of the present invention:

[0029] By placing multiple mobile and fixed monitoring ocean temperature, salinity and depth sensors within the profile area, the present invention can comprehensively and accurately obtain temperature, salinity and depth data of the ocean profile. The combination of different types of sensors increases the coverage and accuracy of the data. The automatic collection, aggregation and processing of data reduces the errors and workload of manual operation. Automatic classification and visual mapping make data analysis more intuitive and efficient, and can quickly identify changes in the ocean profile.

[0030] The present invention obtains volatility analysis values ​​and mobility analysis values ​​for closed profile areas through comprehensive analysis of temperature, salinity, and depth data, which helps to fully understand the dynamic changes of ocean profiles. The calculation of the comprehensive disorder value further provides a quantitative assessment of the stability of the marine environment. By comparing the comprehensive disorder value with a preset disorder threshold, an early warning signal is issued in a timely manner. When anomalies occur in ocean profile data, a rapid response can be made to avoid or reduce the impact of marine disasters and improve the safety and reliability of marine monitoring and management.

[0031] The sensor placement and data processing steps in the present invention are highly flexible and can be adjusted according to different monitoring needs and marine environmental conditions. The preset detection coefficients and thresholds can also be optimized according to actual conditions to adapt to different application scenarios. The combined use of mobile and fixed monitoring sensors maximizes the utilization of monitoring equipment. Mobile sensors can cover larger areas, while fixed sensors provide stable long-term data references. The two complement each other to improve the overall efficiency of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0033] Figure 1 This is a schematic diagram of the principle of an automatic detection method for ocean temperature, salinity and depth profile data in the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] Reference Figure 1 The following examples were obtained:

[0036] Example 1:

[0037] A method for automatically detecting ocean temperature, salinity and depth profile data comprises the following steps:

[0038] Step 1: Set the target profile area of ​​the ocean and place ocean temperature, salinity, and depth sensors according to the profile area; specifically: establish a rectangular coordinate system, set the profile area boundary value on the X-axis as [XB1, XB2], set the profile area boundary value on the Y-axis as [YB1, YB2], set the rectangular area in the rectangular coordinate system corresponding to the specified position in the ocean as the target profile area, and place m groups of mobile monitoring ocean temperature, salinity, and depth sensors in the profile area, each group consisting of k sensors, and the standard detection value of each group of sensors is different, and also include h fixed monitoring ocean temperature, salinity, and depth sensors, distributed at different positions in the profile area.

[0039] Step 2: Each placed ocean temperature, salinity, and depth sensor collects the temperature, salinity, and depth information of the target profile area to obtain an observation subset, and then all the observation subsets are summarized to obtain a profile observation information set; specifically: each ocean temperature, salinity, and depth sensor collects the temperature, salinity, and depth information of the target profile area to obtain multiple observation subsets, and then all the observation subsets are summarized to form a complete profile observation information set, including the observation data of the entire target profile area.

[0040] Step three: perform processing operations on the profile observation information set to obtain a profile data processing set, and then classify the profile data processing set to obtain a first-category profile data set and a second-category profile data set; specifically: perform preprocessing operations on the profile observation information set, including noise removal, missing value filling, and data smoothing, and classify the processed profile data according to sensor type, classify the data of mobile monitoring sensors into the first-category profile data set, and classify the data of fixed monitoring sensors into the second-category profile data set.

[0041] Step 4. Draw a visualization graph based on the first type of profile data set to obtain profile closed areas of different gradients, then put the second type of profile data set into the visualization graph, and then perform volatility analysis to obtain the volatility analysis value of the profile closed area of ​​each gradient; specifically: in the rectangular area in the rectangular coordinate system, mark the coordinate points of the sensor monitoring positions in the first type of profile data set, use interpolation or least squares method to connect the coordinate points of the same group of sensors into curves or straight lines to form a closed area, and assign different gradients according to the preset strategy, put the second type of profile data set into the visualization graph, calculate the standard deviation of temperature and salinity for each gradient, and finally obtain the volatility analysis value.

[0042] Step 5. Draw n visualization graphics within a preset time window and perform mobility analysis to obtain the mobility analysis value of the cross-sectional closed area of ​​each gradient; specifically: draw n visualization graphics within a preset time window, record the area of ​​each gradient closed area in each graphic, calculate the area difference of the same gradient closed area in two adjacent graphics, perform mobility analysis, and obtain the mobility analysis value of each gradient.

[0043] Step 6: Based on the obtained volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, a comprehensive analysis is performed to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient.

[0044] Step seven: Compare the comprehensive disorder value of the profile closed area of ​​the target gradient with the preset disorder threshold of the profile closed area of ​​the target gradient, and decide whether to issue or not an early warning signal based on the comparison result. Specifically, compare the comprehensive disorder value of the target gradient with the preset disorder threshold. When the comprehensive disorder value is greater than or equal to the threshold, an early warning signal is issued; otherwise, no early warning signal is issued.

[0045] Setting the target profile area of ​​the ocean means: establishing a rectangular coordinate system, setting the boundary values ​​of the profile area on the X-axis as [XB1, XB2], setting the boundary values ​​of the profile area on the Y-axis as [YB1, YB2], and setting the rectangular area in the rectangular coordinate system corresponding to the specified position of the ocean as the target profile area; specifically: establishing a rectangular coordinate system: establishing a rectangular coordinate system in the target ocean area for precise positioning and analysis of ocean profile data; setting boundary values: determining the boundary values ​​on the X-axis, setting them as XB1 and XB2, indicating the horizontal range of the area, determining the boundary values ​​on the Y-axis, setting them as YB1 and YB2, indicating the vertical range of the area ; Target profile area setting: The rectangular area determined by the above boundary values ​​is used as the target profile area. In the subsequent steps, sensors will be placed in this area for data collection and analysis. For example, suppose you want to set a target profile area at a specified location in a certain sea area. The specific operations are as follows: Select a coastal area for monitoring. This area is considered to be of great significance for research or monitoring purposes. A point in the target sea area is used as the coordinate origin (0,0), and then set as the X-axis and Y-axis. Assume that the X-axis range of the profile area you want to monitor is from 100 meters to 500 meters, and the Y-axis range is from 200 meters to 700 meters. Set the X-axis boundary value to [100, 500] and the Y-axis boundary value to [200, 700]. According to the set boundary values, a rectangular area is delineated in the rectangular coordinate system. This rectangular area is the target profile area. By setting a clear target profile area, subsequent data collection and analysis can be more targeted, ensuring that high-quality temperature, salinity and depth profile data are obtained in the predetermined area.

[0046] Placing ocean temperature, salinity, and depth sensors based on the profile area involves placing m groups of mobile ocean temperature, salinity, and depth sensors within the profile area. These sensors are used to capture dynamically changing ocean data. Each group of sensors has different standard detection values, covering different depths, temperatures, and salinity ranges. They can be repositioned as needed to accommodate different monitoring requirements. Each group of mobile ocean temperature, salinity, and depth sensors consists of k mobile ocean temperature, salinity, and depth sensors. The k sensors in each group have the same standard detection value, and each group has different standard detection values. The different standard detection values ​​of different groups of sensors are intended to cover different measurement ranges and accuracy requirements. Furthermore, the mobile ocean temperature, salinity, and depth sensors can be moved to find coordinate points corresponding to specific standard detection values. The mobile ocean temperature, salinity, and depth sensors also include h fixed ocean temperature, salinity, and depth sensors, which are fixed in specific locations to provide stable, long-term data monitoring.

[0047] Classifying the profile data processing set to obtain the first type of profile data set and the second type of profile data set means: classifying the monitoring data of the mobile monitoring ocean temperature, salinity and depth sensor into the first type of profile data set. The first type of profile data set reflects the data changes of the mobile monitoring sensor at different locations and time points, and can be used to analyze the dynamic changes of the ocean environment; classifying the monitoring data of the fixed monitoring ocean temperature, salinity and depth sensor into the second type of profile data set. The second type of profile data set provides long-term data at a fixed location and can be used to analyze the changes in ocean parameters at a fixed location.

[0048] Step 4: Draw a visualization graph based on the first type of profile data set to obtain profile closed areas with different gradients. This means: mark the coordinate points of the monitoring positions of the mobile monitoring ocean temperature, salinity and depth sensors in the first type of profile data set in the rectangular area in the established rectangular coordinate system, and then connect the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two sides of the rectangular area. The two adjacent line segments and the two sides of the rectangular area together form a closed area. According to the preset assignment strategy, different gradients are assigned to all closed areas to obtain profile closed areas with different gradients. For example: assuming that the target profile area is a rectangular area with a coordinate range of [100, 500] meters on the X axis and [200, 700] meters on the Y axis, the coordinate points of the mobile monitoring ocean temperature, salinity and depth sensors obtained from the first type of profile data set are as follows: Mobile monitoring sensor data: Group 1: Sensor 1: Position (150, 250); Sensor 2: Position (250, , 350); sensor 3: position (350, 450); sensor 4: position (450, 550); second group: sensor 1: position (160, 260); sensor 2: position (260, 360); sensor 3: position (360, 460); sensor 4: position (460, 560); third group: sensor 1: position (170, 270); sensor 2: position (270, 370); sensor 3: position (370, 470); sensor 4: position (470, 570); mark the monitoring positions of the above sensors in the rectangular area, and then use interpolation or fitting method to connect the coordinate points in the same group to form a line segment, for example, the points of the first group of sensors are (150, 250), (250, 350), (350, 450), (450, 550) These line segments are connected to the edge of the rectangular area by fitting into a curve. The line segments of the first and second groups and the edge of the rectangular area together enclose a closed area. Similarly, the line segments of the second and third groups and the edge of the rectangular area together enclose another closed area. According to the preset assignment strategy such as temperature gradient, salinity gradient, etc., each closed area is assigned a different gradient. For example, the gradient value of the first group of areas is TT1, and the gradient value of the second group of areas is TT2. Different gradient areas reflect the changes in the marine environment at different locations, such as temperature gradient, salinity gradient, etc., which can be used to identify abnormal changes in the ocean, conduct environmental monitoring and assessment, and provide intuitive visualization graphics to help researchers and managers better understand and analyze marine environmental data.

[0049] Connecting the coordinate points of k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two sides of the rectangular area means: using interpolation or fitting method to obtain a smooth curve passing through all the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group, discarding the part exceeding the rectangular area to obtain a line segment connected to the two sides of the rectangular area; using least squares method to obtain a straight line, the sum of the distances from the straight line to all the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group is the smallest, discarding the part exceeding the rectangular area to obtain a line segment connected to the two sides of the rectangular area. The user can choose which method to use for closed area division according to actual conditions.

[0050] The second type of profile data set is put into the visualization graph, and then the volatility analysis is performed to obtain the volatility analysis value of the profile closed area of ​​each gradient. This means: the monitoring positions of the h fixed monitoring ocean temperature, salinity and depth sensors in the second type of profile data set are marked with coordinate points, and the average temperature data and salinity data of all ocean temperature, salinity and depth sensors located in the profile closed area of ​​the same gradient are obtained at the same time. They are sorted according to the observation time to obtain a group of combination 1 consisting of the average temperature data and a group of combination 1 consisting of the average salinity data. Then, the standard deviation of combination 1 is calculated and marked as S1, and the standard deviation of combination 2 is calculated and marked as S2. Then, the volatility analysis value of the profile closed area of ​​the gradient is calculated: ; f1 and f2 are both preset fluctuation coefficients, and BFi is the fluctuation analysis value of the closed section area of ​​the gradient; For example: Assume that in a certain ocean section area, a visualization graph based on the first type of section data set has been drawn, and the data of the fixed monitoring ocean temperature, salinity and depth sensor in the second type of section data set are put into the graph, fixed sensor 1: position (200, 300), temperature data: 16℃, salinity data: 34‰; fixed sensor 2: position (300, 400), temperature data: 15℃, salinity data: 35‰; fixed sensor 3: position (400, 500), temperature data: 17℃, salinity data: 33‰. The positions of these sensors are marked in the visualization graph, and it is found that they are all in the same gradient area. Then the average temperature and salinity data of all sensors in the gradient area are calculated. The average temperature data is 16℃, and the average salinity data is 34‰. By performing volatility analysis on the fixed sensor data of the second type of profile data set, the volatility analysis value of the closed area of ​​each gradient profile can be obtained, which reflects the fluctuation of temperature and salinity in a specific time period. The volatility analysis value can be used to evaluate the stability of the environment in the ocean profile area. If the volatility analysis value is high, it means that the environmental changes in the area are large, and further monitoring and analysis may be required. If the volatility analysis value suddenly increases within a certain period of time, it may mean that abnormal changes have occurred in the area, such as pollution incidents or natural disasters.

[0051] Draw n visualizations within a preset time window and perform mobility analysis to obtain the mobility analysis value of the cross-section enclosed area of ​​each gradient. This involves drawing n visualizations, obtaining the area difference of the cross-section enclosed area of ​​the same gradient in two adjacent visualizations and marking it as JDi, and then calculating the mobility analysis value: The time window of the mobility analysis is the same as that of combination one and combination two in the volatility analysis. QYi is the mobility analysis value of the gradient. The mobility analysis value reflects the changes in the closed area of ​​the profile over a period of time. If the mobility analysis value is large, it means that the environmental changes in the area are more drastic.

[0052] According to the volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, a comprehensive analysis is performed to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient, which means: obtaining the volatility analysis value BFi of the profile closed area of ​​the target gradient, which represents the degree of volatility of the profile closed area of ​​the target gradient, and the mobility analysis value QYi, which represents the degree of movement of the profile closed area of ​​the target gradient, and then calculating: ;w1 and W2 are both preset disorder coefficients, which are used to adjust the weights of volatility analysis value and mobility analysis value in the comprehensive disorder value. T1 and T2 are respectively the preset volatility standard value and mobility standard value. WLi is the comprehensive disorder value. The comprehensive consideration of volatility analysis value and mobility analysis value reflects the comprehensive dynamic stability of the closed area of ​​target gradient profile. The larger the value, the worse the comprehensive dynamic stability. The comprehensive disorder value can be used to judge the overall environmental stability of the closed area of ​​target gradient profile. A higher comprehensive disorder value means that the environment in this area has changed greatly and needs further monitoring. The comprehensive disorder value provides a basis for decision-making and helps to formulate relevant strategies such as environmental protection and pollution control.

[0053] Comparing the comprehensive disorder value of the profile enclosed area of ​​the target gradient with the preset disorder threshold of the profile enclosed area of ​​the target gradient, and deciding whether to issue a warning signal based on the comparison result means:

[0054] Obtain the comprehensive disorder value WLi of the profile closed area of ​​the target gradient, which is used to reflect the stability of the environmental state of the target gradient. The larger the value, the higher the degree of environmental instability. The preset disorder threshold of the profile closed area of ​​the target gradient is marked as YWi, which is a pre-set threshold used to judge the stability of the environment. Exceeding this threshold may indicate that an abnormality has occurred in the environment. When the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is greater than or equal to the preset disorder threshold YWi of the profile closed area of ​​the target gradient, an early warning signal is issued. When the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is less than the preset disorder threshold YWi of the profile closed area of ​​the target gradient, no early warning signal is issued. The preset disorder threshold YWi of the profile closed area of ​​the target gradient meets the following requirements: a standard disorder threshold is preset and marked as WB, and the target gradient is marked as ti, then In actual applications, when the comprehensive disorder value of the target gradient exceeds the preset disorder threshold, an early warning signal is issued to remind relevant personnel that there may be an environmental abnormality and further monitoring or corresponding measures are required. When the comprehensive disorder value does not exceed the preset disorder threshold, no early warning signal is issued, indicating that the environment is in a normal state. For example: suppose the comprehensive disorder value of a target gradient is 1.5, and the preset disorder threshold is set to 1.2. Since 1.5 is greater than 1.2, a early warning signal will be issued, indicating that the environment may be abnormal and further observation or processing is required.

[0055] Example 2:

[0056] It should be noted that:

[0057] Regarding the placement of m groups of mobile monitoring ocean temperature, salinity, and depth sensors in the profile area mentioned in the present invention, each group of mobile monitoring ocean temperature, salinity, and depth sensors consists of k mobile monitoring ocean temperature, salinity, and depth sensors, and the standard detection values ​​of the k mobile monitoring ocean temperature, salinity, and depth sensors in each group of mobile monitoring ocean temperature, salinity, and depth sensors are the same, and the standard detection values ​​of each group of mobile monitoring ocean temperature, salinity, and depth sensors are different, and also include h fixed monitoring ocean temperature, salinity, and depth sensors. Among them, taking the 12 mobile monitoring ocean temperature, salinity, and depth sensors in the third group of mobile monitoring ocean temperature, salinity, and depth sensors as an example, the standard detection values ​​are pre-set and meet the following conditions: ; BTi is the standard detection value, j1, j2, and j3 are all preset detection coefficients and are all greater than zero, and the sum of j1, j2, and j3 is a certain value. G1, G2, and G3 correspond to temperature, salinity, and depth data respectively. Based on the standard detection value, the mobile ocean temperature, salinity, and depth sensor can specifically determine the monitoring position, and better determine the profile closed area of ​​each gradient and the mobility analysis value of the target gradient.

[0058] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0059] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for automatically detecting ocean temperature, salinity and depth profile data, characterized in that: The following steps are involved: Step 1: Set the target ocean profile area and place ocean temperature, salinity and depth sensors according to the profile area; Step 2: Each placed ocean temperature, salinity, and depth sensor collects temperature, salinity, and depth information of the target profile area to obtain an observation subset, and then all observation subsets are aggregated to obtain a profile observation information set; Step 3: Processing the profile observation information set to obtain a profile data processing set, and then classifying the profile data processing set to obtain a first-category profile data set and a second-category profile data set; Step 4: Draw a visualization graph based on the first type of profile data set to obtain profile closed areas of different gradients, then put the second type of profile data set into the visualization graph, and then perform volatility analysis to obtain the volatility analysis value of the profile closed area of ​​each gradient; Step 5: Draw n visualization graphs in a preset time window and perform mobility analysis to obtain the mobility analysis value of the closed area of ​​the cross section of each gradient; Step 6: Based on the obtained volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, a comprehensive analysis is performed to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient; Step 7: Compare the comprehensive turbulence value of the profile closed area of ​​the target gradient with the preset turbulence threshold of the profile closed area of ​​the target gradient, and decide whether to issue a warning signal or not based on the comparison result.

2. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 1, characterized in that: Setting the target profile area of ​​the ocean means: establishing a rectangular coordinate system, setting the boundary value of the profile area on the X-axis to [XB1, XB2], setting the boundary value of the profile area on the Y-axis to [YB1, YB2], and setting the rectangular area in the rectangular coordinate system corresponding to the specified position of the ocean as the target profile area.

3. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 2, characterized in that: The placement of ocean temperature, salinity and depth sensors according to the profile area refers to: m groups of mobile monitoring ocean temperature, salinity, and depth sensors are placed in the profile area. Each group of mobile monitoring ocean temperature, salinity, and depth sensors consists of k mobile monitoring ocean temperature, salinity, and depth sensors. The standard detection values ​​of the k mobile monitoring ocean temperature, salinity, and depth sensors in each group of mobile monitoring ocean temperature, salinity, and depth sensors are the same, and the standard detection values ​​of each group of mobile monitoring ocean temperature, salinity, and depth sensors are different. It also includes h fixed monitoring ocean temperature, salinity, and depth sensors.

4. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 3, wherein: The profile data processing set is classified into the first type of profile data set and the second type of profile data set, which are: The monitoring data of mobile monitoring ocean temperature, salinity and depth sensors are classified into the first type of profile data set, and the monitoring data of fixed monitoring ocean temperature, salinity and depth sensors are classified into the second type of profile data set.

5. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 4, characterized in that: Step 4: Draw a visualization graph based on the first type of profile data set to obtain the closed areas of the profiles with different gradients: The monitoring positions of the mobile monitoring ocean temperature, salinity and depth sensors in the first type of profile data set are marked in the rectangular area of ​​the established rectangular coordinate system. Then, the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group are connected to obtain a line segment connected to the two sides of the rectangular area. The two adjacent line segments and the two sides of the rectangular area together form a closed area. Different gradients are assigned to all closed areas according to the preset assignment strategy to obtain profile closed areas with different gradients.

6. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 5, characterized in that: Connecting the coordinate points of k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two sides of the rectangular area means: using an interpolation method or a fitting method to obtain a smooth curve passing through all the coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group, and discarding the part exceeding the rectangular area to obtain a line segment connected to the two sides of the rectangular area.

7. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 6, characterized in that: Connecting the coordinate points of k mobile monitoring ocean temperature, salinity and depth sensors belonging to the same group to obtain a line segment connected to the two side lines of the rectangular area means: using the least squares method to obtain a straight line whose sum of distances to all coordinate points of the k mobile monitoring ocean temperature, salinity and depth sensors in the same group is the smallest, and discarding the part that exceeds the rectangular area to obtain a line segment connected to the two side lines of the rectangular area.

8. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 7, characterized in that: The second type of profile data set is put into the visualization graph, and then the volatility analysis is performed to obtain the volatility analysis value of the profile closed area of ​​each gradient: The coordinate points of the monitoring positions of the h fixed monitoring ocean temperature, salinity and depth sensors in the second type of profile data set are marked. The average temperature data and salinity data of all ocean temperature, salinity and depth sensors in the closed area of ​​the profile with the same gradient at the same time are obtained. They are sorted according to the observation time to obtain a group of combination 1 consisting of the average temperature data and a group of combination 2 consisting of the average salinity data. The standard deviation of combination 1 is then calculated and marked as S1, and the standard deviation of combination 2 is calculated and marked as S2. The volatility analysis value of the closed area of ​​the profile with this gradient is then calculated: ; f1 and f2 are both preset fluctuation coefficients, and BFi is the fluctuation analysis value of the closed area of ​​the gradient profile; Draw n visualization graphs in a preset time window and perform mobility analysis. The mobility analysis value of the closed area of ​​the cross section of each gradient is: Draw n visualization graphics, obtain the area difference of the cross-section closed area of ​​the same gradient in two adjacent visualization graphics and mark it as JDi, and then calculate the mobility analysis value: ; QYi is the mobility analysis value of the gradient.

9. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 8, characterized in that: Obtaining the volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient and then performing a comprehensive analysis to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient means: obtaining the volatility analysis value BFi and the mobility analysis value QYi of the profile closed area of ​​the target gradient and then calculating: ;w1 and w2 are the preset disorder coefficients, T1 and T2 are the preset volatility standard values ​​and migration standard values ​​respectively, and WLi is the comprehensive disorder value.

10. The method for automatically detecting ocean temperature, salinity and depth profile data according to claim 9, characterized in that: Comparing the comprehensive disorder value of the profile enclosed area of ​​the target gradient with the preset disorder threshold of the profile enclosed area of ​​the target gradient, and deciding whether to issue a warning signal based on the comparison result means: Obtain the comprehensive disorder value WLi of the profile closed area of ​​the target gradient, the preset disorder threshold of the profile closed area of ​​the target gradient and mark it as YWi, when the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is greater than or equal to the preset disorder threshold YWi of the profile closed area of ​​the target gradient, issue a warning signal, when the comprehensive disorder value WLi of the profile closed area of ​​the target gradient is less than the preset disorder threshold YWi of the profile closed area of ​​the target gradient, no warning signal is issued, the preset disorder threshold YWi of the profile closed area of ​​the target gradient meets the following requirements: a standard disorder threshold is preset and marked as WB, the target gradient is marked as Ti, then .

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