Ocean temperature-salinity-depth profile data automatic detection method

Abstract

The invention discloses an automatic detection method for ocean temperature-salinity-depth profile data, and particularly relates to the technical field of ocean monitoring, comprising the following steps: setting a target profile area of an ocean, placing an ocean temperature-salinity-depth sensor, and then performing data collection, processing and classification operations; the method comprises the following steps: acquiring a first type of profile data set and a second type of profile data set, then drawing a visual graph, acquiring profile closed areas with different gradients, performing volatility analysis and mobility analysis, and then performing comprehensive analysis. The comprehensive disorder value is compared with a preset disorder threshold value of the section closed area of the target gradient, and whether an early warning signal is sent out or not is determined according to a comparison result; according to the method, the dynamic change of the ocean profile can be comprehensively known, quantitative evaluation on the stability of the ocean environment is further provided through calculation of the comprehensive disorder value, an early warning signal can be sent out in time when an early warning mechanism is triggered, and the safety of ocean management is ensured.

Description

Technical Field

[0001] The present invention relates to the field of ocean monitoring technology, and more specifically, to an automatic detection method for ocean temperature-salinity-depth profile data. Background Art

[0002] Ocean temperature, salinity, and depth profile data are important basic data for marine scientific research and marine environmental monitoring, and are widely used in marine meteorology, marine ecology, marine resource development, and other fields. The traditional ocean temperature, salinity, and depth profile data acquisition method requires manual sensor deployment, data acquisition, and recording, which is labor-intensive and easily affected by human factors, resulting in poor data accuracy and consistency. In addition, it is limited by manual operation and the number of equipment, and the monitoring range and frequency are limited. It is difficult to achieve high-frequency monitoring of large sea areas, and it is impossible to capture the dynamic changes of the marine environment in a timely manner. Moreover, the collected data are often analyzed after the fact, and it is impossible to monitor and warn of abnormal changes in the marine environment in real time, resulting in delayed marine disaster prevention and emergency response. There is an urgent need for an ocean temperature, salinity, and depth profile data detection method with high automation, high monitoring efficiency, timely data processing, and early warning function 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: A method for automatically detecting ocean temperature-salinity-depth profile data comprises the following steps: 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 the temperature, salinity and depth information of the target profile area to obtain an observation subset, and then all observation subsets are summarized 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 volatility analysis values ​​of profile closed areas 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 profile of each gradient; Step 6: Obtain the volatility analysis value and the mobility analysis value of the profile closed area of ​​the target gradient, and then conduct a comprehensive analysis to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient; Step 7: 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 according to the comparison result.

[0004] 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.

[0005] In a preferred embodiment, placing 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, and also include h fixed monitoring ocean temperature, salinity and depth sensors.

[0006] In a preferred embodiment, classifying the profile data processing set to obtain a first type of profile data set and a second type of profile data set means: The monitoring data of the mobile monitoring ocean temperature, salinity and depth sensors are classified into the first type of profile data set, and the monitoring data of the fixed monitoring ocean temperature, salinity and depth sensors are classified into the second type of profile data set.

[0007] In a preferred embodiment, step 4, drawing a visualization graph based on the first type of profile data set to obtain profile closed areas of different gradients refers to: The monitoring positions of the mobile monitoring ocean temperature, salinity and depth sensors in the first type of profile data set are marked with coordinate points in the rectangular area in the established rectangular coordinate system, and then the coordinate points of 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.

[0008] 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 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 side lines of the rectangular area.

[0009] 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.

[0010] 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: 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 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. 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 volatility analysis value of the closed area of ​​the gradient profile.

[0011] In a preferred embodiment, the visualization graph is drawn n times in a preset time window and the mobility analysis is performed to obtain the mobility analysis value of the cross-section closed area of ​​each gradient, which means: Draw n visualization graphs, obtain the area difference of the closed area of ​​the profile with the same gradient in two adjacent visualization graphs and mark it as JDi, and then calculate the mobility analysis value: ; QYi is the mobility analysis value of the gradient.

[0012] According to the volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, and then 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 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 value and mobility standard value respectively, and WLi is the comprehensive disorder value.

[0013] In a preferred embodiment, comparing the comprehensive disorder value of the cross-sectional closed area of ​​the target gradient with the preset disorder threshold of the cross-sectional closed area of ​​the target gradient, and deciding whether to issue or not to issue a warning signal according to 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 is marked 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, 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, the target gradient is marked as ti, then .

[0014] Technical effects and advantages of the present invention: The present invention can comprehensively and accurately obtain the temperature, salinity and depth data of the ocean profile by placing multiple mobile and fixed monitoring ocean temperature, salinity and depth sensors in the profile area. The combination of different types of sensors increases the coverage and accuracy of the data. The data is automatically collected, summarized and processed, which reduces the errors and workload of human operations. Automatic classification and visual mapping make data analysis more intuitive and efficient, and can quickly identify changes in the ocean profile.

[0015] The present invention obtains volatility analysis values ​​and mobility analysis values ​​of the closed area of ​​the profile by comprehensive analysis of temperature, salinity and depth data, which helps to fully understand the dynamic changes of the ocean profile. 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 the preset disorder threshold, an early warning signal is issued in time. When an abnormality occurs in the 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.

[0016] The sensor placement and data processing steps in the present invention are highly flexible and can be adjusted according to different monitoring requirements 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

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of a method for automatically detecting ocean temperature, salinity and depth profile data in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 The following embodiments are obtained:

[0020] Embodiment 1: A method for automatically detecting ocean temperature-salinity-depth profile data comprises the following steps: Step 1: Set the target profile area of ​​the ocean, and place the 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 of the ocean as the target profile area, place m groups of mobile monitoring ocean temperature, salinity and depth sensors in the profile area, each group consists of k sensors, and the standard detection values ​​of each group of sensors are different, and also include h fixed monitoring ocean temperature, salinity and depth sensors, distributed in different positions within the profile area.

[0021] 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.

[0022] 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.

[0023] Step 4. Draw a visualization graph based on the first type of profile data set to obtain profile closed areas of different gradients, and 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.

[0024] Step 5. Draw n visualization graphics in a preset time window and perform mobility analysis to obtain the mobility analysis value of the profile closed area of ​​each gradient; specifically: draw n visualization graphics in 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.

[0025] Step 6: Based on 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.

[0026] 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.

[0027] 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 as [XB1, XB2], setting the boundary value 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 accurate 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 lateral range of the area, determining the boundary values ​​on the Y-axis, setting them as YB1 and YB2, indicating the longitudinal 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 a target profile area is to be set at a specified location in a certain sea area. The specific operation is as follows: Select a coastal area for monitoring, which is considered to be of great significance for research or monitoring purposes. Take a point in the target sea area as the coordinate origin (0,0), and then set it as the X-axis and Y-axis. Assume that the X-axis range of the profile area to be monitored is from 100 meters to 500 meters, and the Y-axis range is from 200 meters to 700 meters. Set the boundary value of the X-axis to [100, 500] and the boundary value of the Y-axis to [200, 700]. According to the set boundary values, a rectangular area is delineated in the rectangular coordinate system. The 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-depth profile data are obtained in the predetermined area.

[0028] Placing ocean temperature, salinity and depth sensors according to the profile area means: placing m groups of mobile monitoring ocean temperature, salinity and depth sensors in the profile area, and the mobile monitoring ocean temperature, salinity and depth sensors are used to capture dynamically changing ocean data. The standard detection value of each group of sensors is different, and different depths, temperatures and salinity ranges can be covered. The position can be adjusted as needed to adapt to different monitoring needs. 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. The purpose of different standard detection values ​​of sensors in different groups is to cover different measurement ranges and accuracy requirements, and the mobile monitoring ocean temperature, salinity and depth sensors can be moved to find coordinate points corresponding to specific standard detection values. It also includes h fixed monitoring ocean temperature, salinity and depth sensors, which are fixed in specific locations to provide stable long-term data monitoring.

[0029] 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, which can be used to analyze the dynamic changes of the marine 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, which can be used to analyze the changes in ocean parameters at a fixed location.

[0030] 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. Different gradients are assigned to all closed areas according to the preset assignment strategy 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 (150, 250), (250, 350), (350, 450), (450, 550) These line segments are connected to the edge of the rectangular area by a fitting method to form a curve. The first and second group of line segments and the edge of the rectangular area together enclose a closed area. Similarly, the second and third group of line segments 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.

[0031] 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 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 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, 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.

[0032] 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. It 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. The average temperature data and salinity data 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. 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 is 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 each gradient profile closed area can be obtained, which reflects the fluctuation of temperature and salinity in a specific time period. The stability of the environment in the ocean profile area can be evaluated through the volatility analysis value. 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.

[0033] Draw n visualization graphics in a preset time window and perform mobility analysis to obtain the mobility analysis value of the cross-section closed area of ​​each gradient. This means: 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: 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.

[0034] 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 indicates the volatility degree of the profile closed area of ​​the target gradient, and the mobility analysis value QYi, which indicates the mobility degree 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 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 ​​the 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 ​​the 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.

[0035] Comparing 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 deciding whether to issue or not an early warning signal according to the comparison result means: 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 instability of the environment. 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 abnormal situation 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, a warning signal is issued to remind relevant personnel that there may be environmental abnormalities and further monitoring or corresponding measures are required. When the comprehensive disorder value does not exceed the preset disorder threshold, no warning signal is issued, indicating that the environment is in a normal state. For example: assuming that 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 warning signal will be issued, indicating that the environment may be abnormal and further observation or processing is required.

[0036] Embodiment 2: It is necessary to explain: 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, wherein, 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 value is pre-set and meets the following conditions: ; BTi is the standard detection value, j1, j2, j3 are all preset detection coefficients and are all greater than zero, and the sum of j1, j2, j3 is a certain value, G1, G2, 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.

[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0038] 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.

[0039] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope 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 the temperature, salinity and depth information of the target profile area to obtain an observation subset, and then all observation subsets are summarized 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 volatility analysis values ​​of profile closed areas 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 profile of each gradient; Step 6: Obtain the volatility analysis value and the mobility analysis value of the profile closed area of ​​the target gradient, and then conduct a comprehensive analysis to obtain the comprehensive disorder value of the profile closed area of ​​the target gradient; Step 7: 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 according to the comparison result.

2. The method for automatically detecting ocean temperature-salinity-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-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, and also include h fixed monitoring ocean temperature, salinity and depth sensors.

4. The method for automatically detecting ocean temperature-salinity-depth profile data according to claim 3, characterized in that: The profile data processing set is classified to obtain the first type of profile data set and the second type of profile data set, which refers to: The monitoring data of the mobile monitoring ocean temperature, salinity and depth sensors are classified into the first type of profile data set, and the monitoring data of the 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-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, and obtain the profile closed areas 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 with coordinate points in the rectangular area in the established rectangular coordinate system, and then the coordinate points of 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-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 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 sides of the rectangular area.

7. The method for automatically detecting ocean temperature-salinity-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, 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.

8. The method for automatically detecting ocean temperature-salinity-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, which refers to: 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 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. 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 area of ​​the gradient profile; Draw n visualization graphs in the preset time window and perform mobility analysis to obtain the mobility analysis value of the closed area of ​​the profile of each gradient, which means: Draw n visualization graphs, obtain the area difference of the closed area of ​​the profile with the same gradient in two adjacent visualization graphs 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-depth profile data according to claim 8, characterized in that: According to the volatility analysis value and mobility analysis value of the profile closed area of ​​the target gradient, and then 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 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 value and mobility standard value respectively, and WLi is the comprehensive disorder value.

10. The method for automatically detecting ocean temperature-salinity-depth profile data according to claim 9, characterized in that: Comparing 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 deciding whether to issue or not an early warning signal according to 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 is marked 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, 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, the target gradient is marked as ti, then .

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