Environmental monitoring instrument tendency error analysis system

Abstract

The invention relates to the technical field of instrument error monitoring, and discloses an environment monitoring instrument tendency error analysis system which comprises a multi-dimensional acquisition module and an intelligent analysis module. According to the tendentiousness error analysis system for the temperature-salinity-depth instrument, conductivity data, temperature data and water depth data of a measured water body are obtained through a multi-dimensional acquisition module and classified to form a data set, an intelligent analysis module analyzes time differences among different physical parameters, generates a time difference data set, analyzes fluctuation ranges of the different physical parameters, generates a fluctuation data set, and analyzes the tendentiousness error of the temperature-salinity-depth instrument. The intelligent analysis module analyzes the authenticity of each measurement result, generates a credibility index, dynamically evaluates the credibility of single measurement, can adapt to the monitoring requirements of more complex environments, accurately diagnoses error sources, sets a threshold value of a fixed range, and can accurately diagnose the error sources; the influence factors causing the tendency error of the thermohaline depth gauge and the authenticity of the measurement result are evaluated, corresponding management suggestions are output, and the automatic operation and maintenance support practicability is high.

Description

Technical Field

[0001] The present invention relates to the technical field of instrument error monitoring, and in particular to a tendency error analysis system for an environmental monitoring instrument. Background Art

[0002] There are many types of environmental monitoring instruments, including water quality monitors, atmospheric environment monitors, and radiation monitors. Among them, a temperature, salinity, and depth meter is used to measure the physical parameters of water bodies. It can simultaneously measure seawater conductivity, temperature, and depth. This data can be used to calculate seawater salinity and other related physical parameters, such as sound velocity. A temperature, salinity, and depth meter primarily consists of an underwater probe, a recorder and display, and connecting cables. The probe contains thermosensitive and pressure-sensitive elements for sensing temperature and pressure. The recorder and display receives, processes, records, and displays the data from the probe, acting as the operator of the entire device. A temperature, salinity, and depth meter's biased error refers to long-term errors caused by inherent system factors. This error can be caused by a variety of factors, including but not limited to sensor failure, calibration errors, and debris buildup. These single-cause errors can severely impact data quality and potentially lead users to erroneous scientific conclusions or decision-making. To address biased errors in temperature, salinity, and depth meters, regular calibration, artificial intelligence recognition, and optimized sampling can be used. Regular calibration and maintenance of the TEM / HSD instrument can promptly detect and correct system errors, ensuring the accuracy and stability of the equipment. Artificial intelligence technology can also be used to dynamically constrain and automatically identify the data collected by the TEM / HSD instrument, effectively and automatically identifying various flying point conditions. A corresponding error knowledge base can then be established based on the flying point distribution matrix of the TEM / HSD instrument. When deploying a TEM / HSD instrument, the impact of biased errors can be reduced by optimizing the sampling scheme.

[0003] At present, the tendency error analysis system of traditional environmental monitoring instruments requires manual error diagnosis, and the calibration intensity is high and the efficiency is low. Since the calibration process involves the precise control of multiple factors, the entire calibration process is cumbersome and time-consuming, which is difficult to meet the actual needs of rapid diagnosis at sea and has poor practicality. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an environmental monitoring instrument tendency error analysis system, which has the advantages of accurate diagnosis of error sources, automated operation and maintenance support, and strong practicality. It solves the problem that traditional environmental monitoring instrument tendency error analysis systems require manual diagnosis of errors, are difficult to meet the actual needs of rapid diagnosis at sea, and have poor practicality.

[0006] (2) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a tendency error analysis system for environmental monitoring instruments, comprising a multi-dimensional acquisition module and an intelligent analysis module;

[0008] The multi-dimensional acquisition module consists of a conductivity measurement unit, a temperature measurement unit and a water depth measurement unit. The conductivity measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a conductivity data set. The temperature measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a temperature data set. The water depth measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a water depth data set.

[0009] The intelligent analysis module consists of a time difference analysis unit, a fluctuation analysis unit, a measurement analysis unit, and an error management unit. The time difference analysis unit analyzes the time difference data group Scsj between physical parameters based on the conductivity, temperature, and water depth datasets. The fluctuation analysis unit analyzes the fluctuation data group Bdsj of physical parameters based on the conductivity, temperature, and water depth datasets. The measurement analysis unit analyzes the credibility index Kxzs of each measurement result based on the conductivity, temperature, and water depth datasets. The error management unit is set with a fixed range of time difference thresholds SCY, conductivity threshold DBY, temperature threshold WBY, depth threshold HBY, and credibility threshold KXY. It then evaluates the factors affecting the tendency error of the temperature, salinity, and depth instrument, as well as the authenticity of the measurement results, and outputs corresponding management suggestions.

[0010] Preferably, the expression of the conductance data set is {D1 s 、D2 s 、D3 s 、...、Dn s}, D1 s To Dn s represents the conductivity from the first measurement to the nth measurement, and s represents the time point when the conductivity is obtained.

[0011] Preferably, the expression of the temperature data set is {W1 j 、W2 j 、W3 j 、...、Wn j}, W1 j To Wn j represents the temperature values ​​from the first measurement to the nth measurement, and j represents the time point when the temperature value is obtained.

[0012] Preferably, the expression of the water depth dataset is {H1 m 、H2 m 、H3 m 、...、Hn m}, H1 m To Hn mRepresents the depth values ​​from the first measurement to the nth measurement, and m represents the time point when the depth value is obtained.

[0013] Preferably, the calculation process of the time difference data set Scsj is as follows:

[0014] According to the conductivity dataset, temperature dataset and water depth dataset, the conductivity, temperature value and depth value measured for the i-th time are extracted, and the time point of the i-th conductivity acquisition is marked as i s , mark the time point of obtaining the temperature value for the i-th time as i j , mark the time point of obtaining the depth value for the i-th time as i m ;

[0015]

[0016] In the formula, Indicates that the time difference between conductivity and temperature values ​​is calculated in sequence during each measurement process. Indicates the calculation of the time difference between conductivity and depth values ​​in each measurement process. Indicates that the time difference between the depth value and the temperature value is calculated in sequence during each measurement process.

[0017] Preferably, the calculation process of the fluctuation data set Bdsj is as follows:

[0018]

[0019]

[0020] In the formula, Indicates the average conductivity of the measured water body, Indicates the average temperature of the measured water body. Indicates the average depth of the measured water body, Dk s Indicates the conductivity measured at the kth time, Wk j Indicates the temperature value measured for the kth time, Hk m Indicates the depth value measured for the kth time, Indicates that the fluctuation range of conductivity is calculated according to the standard deviation formula. Indicates that the fluctuation range of the temperature value is calculated according to the standard deviation formula. Indicates the fluctuation range of the depth value calculated according to the standard deviation formula.

[0021] Preferably, the calculation process of the credibility index Kxzs is as follows:

[0022]

[0023] In the formula, BD represents the standard value used to measure conductivity, β1 represents the weight for the ratio of the standard value to the conductivity, BW represents the standard value used to measure the temperature, β2 represents the weight for the ratio of the standard value to the temperature, BH represents the standard value used to measure the depth, β3 represents the weight for the ratio of the standard value to the depth, β1, β2, and β3 are all constants, and β1+β2+β3=1. Indicates that the credibility index Kxzs of the k-th measurement result is calculated according to the weights β1, β2 and β3 k .

[0024] Preferably, when any value in the time difference data group Scsj exceeds the time difference threshold SCY, it indicates that the response delay leads to a tendency error of the temperature-salinity-depth instrument, and it is recommended to correct the delayed time difference in time.

[0025] Preferably, in the fluctuation data group Bdsj, when the fluctuation range of conductivity exceeds the conductivity threshold DBY, the fluctuation range of temperature value exceeds the temperature threshold WBY, or the fluctuation range of depth value exceeds the depth threshold HBY, it indicates that the attachment causes the temperature-salinity-depth meter to have a tendency error, and it is recommended to clean the underwater probe in time.

[0026] Preferably, when the credibility index Kxzs exceeds the credibility threshold KXY, it indicates that there is an abnormality in the authenticity of the measurement result, and it is recommended to optimize the measurement plan.

[0027] Compared with the prior art, the present invention provides a system for analyzing the tendency error of environmental monitoring instruments, which has the following beneficial effects:

[0028] 1. The present invention connects the temperature-salinity-depth instrument through a multi-dimensional acquisition module network to obtain conductivity data, temperature data, and water depth data of the measured water body, and classifies them into conductivity data sets, temperature data sets, and water depth data sets. This ensures the time series integrity and traceability of the multi-dimensional data, avoids the errors that may be introduced by traditional manual recording, and improves data acquisition efficiency. The intelligent analysis module analyzes the time difference between different physical parameters based on the conductivity data sets, temperature data sets, and water depth data sets, and generates corresponding time difference data sets Scsj, which helps to eliminate systematic errors caused by sensor timing asynchrony. It then analyzes the fluctuation range of different physical parameters and generates corresponding fluctuation data sets Bdsj, which quickly locates measurement deviations caused by biological attachment or mechanical failure. The intelligent analysis module analyzes the authenticity of each measurement result and generates a corresponding credibility index Kxzs. It flexibly adjusts the weight coefficient and dynamically evaluates the credibility of a single measurement. It can adapt to the monitoring needs of more complex environments and accurately diagnose the source of errors.

[0029] 2. The present invention sets a fixed range of time difference threshold SCY, conductivity threshold DBY, temperature threshold WBY, depth threshold HBY and credible threshold KXY through the intelligent analysis module, and then combines the time difference data group Scsj, the fluctuation data group Bdsj and the credible index Kxzs to evaluate the factors affecting the tendency error of the temperature-salinity-depth instrument and the authenticity of the measurement results. When any value in the time difference data group Scsj exceeds the time difference threshold SCY, it means that the response delay causes the tendency error of the temperature-salinity-depth instrument, and it is recommended to correct the delayed time difference in time. The fluctuation data group Bdsj In j, if the conductivity fluctuation range exceeds the conductivity threshold DBY, the temperature fluctuation range exceeds the temperature threshold WBY, or the depth fluctuation range exceeds the depth threshold HBY, it indicates that attachments have caused a biased error in the temperature-salinity-depth instrument. It is recommended to clean the underwater probe promptly. If the credibility index Kxzs exceeds the credibility threshold KXY, it indicates that there is an anomaly in the authenticity of the measurement result. It is recommended to optimize the measurement plan and implement automated operation and maintenance support to reduce manual inspection costs and improve equipment maintenance efficiency. This is especially suitable for long-term unmanned ocean monitoring scenarios, and automated operation and maintenance support is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] The traditional environmental monitoring instrument tendency error analysis system requires manual error diagnosis, and the calibration intensity is high and the efficiency is low. Since the calibration process involves the precise control of multiple factors, the entire calibration process is cumbersome and time-consuming, which is difficult to meet the actual needs of rapid diagnosis at sea and has poor practicality. Therefore, a temperature, salinity and depth instrument tendency error analysis system is provided. Please refer to Figure 1 ,A tendency error analysis system for environmental monitoring instruments, including a multi-dimensional acquisition module and an intelligent analysis module;

[0033] The multi-dimensional acquisition module consists of a conductivity measurement unit, a temperature measurement unit, and a water depth measurement unit. The conductivity measurement unit is connected to the temperature-salinity-depth instrument through the network to collect the conductivity data set. The conductivity data set includes the conductivity data of the measured water body. The expression of the conductivity data set is {D1 s 、D2 s 、D3 s 、...、Dn s}, D1 s To Dns represents the conductivity from the first measurement to the nth measurement, and s represents the time point when the conductivity is obtained;

[0034] The temperature measurement unit is connected to the temperature-salinity-depth instrument through the network to collect the temperature data set. The temperature data set includes the temperature data of the measured water body. The expression of the temperature data set is {W1 j 、W2 j 、W3 j 、...、Wn j}, W1 j To Wn j represents the temperature values ​​from the first measurement to the nth measurement, and j represents the time point when the temperature value is obtained;

[0035] The water depth measurement unit is connected to the temperature-salinity-depth instrument through the network to collect the water depth data set. The water depth data set includes the water depth data of the measured water body. The expression of the water depth data set is {H1 m 、H2 m 、H3 m 、...、Hn m}, H1 m To Hn m Represents the depth values ​​from the first measurement to the nth measurement, and m represents the time point when the depth value was obtained. This ensures the time series integrity and traceability of multidimensional data, avoids the errors that may be introduced by traditional manual recording, and improves data collection efficiency;

[0036] The intelligent analysis module consists of a time difference analysis unit, a fluctuation analysis unit, a measurement analysis unit, and an error management unit. The time difference analysis unit analyzes the time difference between different physical parameters based on the conductivity data set, temperature data set, and water depth data set, and generates the corresponding time difference data set Scsj. The calculation process is as follows:

[0037] According to the conductivity dataset, temperature dataset and water depth dataset, the conductivity, temperature value and depth value measured for the i-th time are extracted, and the time point of the i-th conductivity acquisition is marked as i s , mark the time point of obtaining the temperature value for the i-th time as i j , mark the time point of obtaining the depth value for the i-th time as i m ;

[0038]

[0039] In the formula, Indicates that the time difference between conductivity and temperature values ​​is calculated in sequence during each measurement process. Indicates the calculation of the time difference between conductivity and depth values ​​in each measurement process. This function calculates the time difference between the depth and temperature values ​​obtained during each measurement, which helps eliminate systematic errors caused by sensor timing asynchrony.

[0040] The fluctuation analysis unit analyzes the fluctuation range of different physical parameters based on the conductivity data set, temperature data set, and water depth data set, and generates the corresponding fluctuation data set Bdsj. The calculation process is as follows:

[0041]

[0042] In the formula, Indicates the average conductivity of the measured water body, Indicates the average temperature of the measured water body. Indicates the average depth of the measured water body, Dk s Indicates the conductivity measured at the kth time, Wk j Indicates the temperature value measured for the kth time, Hk m Indicates the depth value measured for the kth time, Indicates that the fluctuation range of conductivity is calculated according to the standard deviation formula. Indicates that the fluctuation range of the temperature value is calculated according to the standard deviation formula. It indicates that the fluctuation range of the depth value is calculated according to the standard deviation formula, which can quickly locate the measurement deviation caused by biological attachment or mechanical failure;

[0043] The measurement analysis unit analyzes the authenticity of each measurement result based on the conductivity data set, temperature data set, and water depth data set, and generates the corresponding credibility index Kxzs. The calculation process is as follows:

[0044]

[0045] In the formula, BD represents the standard value used to measure conductivity, β1 represents the weight for the ratio of the standard value to the conductivity, BW represents the standard value used to measure the temperature, β2 represents the weight for the ratio of the standard value to the temperature, BH represents the standard value used to measure the depth, β3 represents the weight for the ratio of the standard value to the depth, β1, β2, and β3 are all constants, and β1+β2+β3=1. Indicates that the credibility index Kxzs of the k-th measurement result is calculated according to the weights β1, β2 and β3 k , flexibly adjust the weight coefficient and dynamically evaluate the credibility of a single measurement, which can adapt to the monitoring needs of more complex environments and accurately diagnose the source of errors;

[0046] The error management unit is configured with fixed-range time difference thresholds (SCY), conductivity thresholds (DBY), temperature thresholds (WBY), depth thresholds (HBT), and a trustworthy threshold (KXY). It then combines the time difference data set (Scsj), the fluctuation data set (Bdsj), and the trustworthy index (Kxzs) to assess factors influencing the trend error of the temperature-salinity-depth instrument and the authenticity of the measurement results. If any value in the time difference data set (Scsj) exceeds the time difference threshold (SCY), it indicates that response delay is causing a trend error in the temperature-salinity-depth instrument. Prompt correction of the delayed time difference is recommended. If the conductivity fluctuation range in the fluctuation data set (Bdsj) exceeds the conductivity threshold (DBY), the temperature fluctuation range exceeds the temperature threshold (WBY), or the depth fluctuation range exceeds the depth threshold (HBY), it indicates that debris is causing a trend error in the temperature-salinity-depth instrument. Prompt cleaning of the underwater probe is recommended. If the trustworthy index (Kxzs) exceeds the trustworthy threshold (KXY), it indicates an anomaly in the authenticity of the measurement results. It is recommended to optimize the measurement plan and implement automated operation and maintenance support to reduce manual inspection costs and improve equipment maintenance efficiency. This automated operation and maintenance support is particularly suitable for long-term unmanned ocean monitoring scenarios and is highly practical.

[0047] Example 1:

[0048] In this experiment, a sea area with a maximum depth of 30 meters was selected as the experimental object. Three measurements were conducted in this sea area, with measurement depths of 10 meters, 20 meters, and 30 meters respectively. According to statistics, the time point of the first conductivity measurement was 100 seconds, the time point of the first temperature measurement was 105 seconds, and the time point of the first depth measurement was 110 seconds. The time point of the second conductivity measurement was 200 seconds, the time point of the second temperature measurement was 202 seconds, and the time point of the second depth measurement was 208 seconds. The time point of the third conductivity measurement was 300 seconds, the time point of the third temperature measurement was 303 seconds, and the time point of the third depth measurement was 310 seconds. The calculation process of the three measurement time difference data set Scsj is as follows:

[0049]

[0050] In the formula, i s -i j =100-105 represents the time difference between conductivity and temperature during the first measurement. s -i j =200-202 represents the time difference between conductivity and temperature during the second measurement, i s -i j =300-303 represents the time difference between conductivity and temperature during the third measurement. s -i m =100-110 represents the time difference between conductivity and depth during the first measurement. s -i m=200-208 represents the time difference between conductivity and depth during the second measurement. s -i m =300-310 represents the time difference between conductivity and depth during the third measurement. m -i j =110-105 represents the time difference between the depth value and the temperature value during the first measurement, i m -i j =208-202 represents the time difference between the depth value and the temperature value during the second measurement, i m -i j =310-303 represents the time difference between the depth value and the temperature value during the third measurement. The time difference threshold SCY is set to -1 to 1 second. After judgment, each value in the time difference data group Scsj corresponding to the three measurements has exceeded the time difference threshold SCY, indicating that the response delay has caused a tendency error of the temperature, salinity and depth instrument. It is recommended to correct the delayed time difference in time.

[0051] Example 2:

[0052] In this experiment, the measurement data of the thermosalinity and depth instrument in June were selected as the experimental data. The measurement frequency was once a day. According to statistics, the measurement data on June 12th had a conductivity of 1.2mS / cm, a temperature of 22℃, and a depth of 16 meters. The calculation process of the reliability index Kxzs of this measurement result is as follows:

[0053]

[0054] In the formula, BD = 1.0 mS / cm represents the standard value for measuring conductivity, β1 = 0.5 represents the weight for the ratio of the standard value to conductivity, BW = 20 °C represents the standard value for measuring temperature, β2 = 0.3 represents the weight for the ratio of the standard value to temperature, BH = 15 meters represents the standard value for measuring depth, β3 = 0.2 represents the weight for the ratio of the standard value to depth, β1, β2, and β3 are all constants, and 0.5 + 0.3 + 0.2 = 1. According to the weights of β1, β2, and β3, the credibility index Kxzs of the measurement result is calculated. k is 0.8769, the credible threshold KXY is set to 0.5~1.2, and the credible index Kxzs of the measurement result is k The confidence threshold KXY is not exceeded, indicating that there is no abnormality in the authenticity of the measurement result and there is no need to optimize the measurement plan.

[0055] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A tendency error analysis system for environmental monitoring instruments, characterized by: Including multi-dimensional acquisition module and intelligent analysis module; The multi-dimensional acquisition module consists of a conductivity measurement unit, a temperature measurement unit and a water depth measurement unit. The conductivity measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a conductivity data set. The temperature measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a temperature data set. The water depth measurement unit is connected to the temperature-salinity-depth instrument via a network to collect a water depth data set. The intelligent analysis module consists of a time difference analysis unit, a fluctuation analysis unit, a measurement analysis unit, and an error management unit. The time difference analysis unit analyzes the time difference data group Scsj between physical parameters based on the conductivity, temperature, and water depth datasets. The fluctuation analysis unit analyzes the fluctuation data group Bdsj of physical parameters based on the conductivity, temperature, and water depth datasets. The measurement analysis unit analyzes the credibility index Kxzs of each measurement result based on the conductivity, temperature, and water depth datasets. The error management unit is set with a fixed range of time difference thresholds SCY, conductivity threshold DBY, temperature threshold WBY, depth threshold HBY, and credibility threshold KXY. It then evaluates the factors affecting the tendency error of the temperature, salinity, and depth instrument, as well as the authenticity of the measurement results, and outputs corresponding management suggestions.

2. The environmental monitoring instrument tendency error analysis system according to claim 1, characterized in that: The expression of the conductance data set is {D1 s 、D2 s 、D3 s 、...、Dn s }, D1 s To Dn s represents the conductivity from the first measurement to the nth measurement, and s represents the time point when the conductivity is obtained.

3. The environmental monitoring instrument tendency error analysis system according to claim 2, characterized in that: The expression of the temperature data set is {W1 j 、W2 j 、W3 j 、...、Wn j }, W1 j To Wn j represents the temperature values ​​from the first measurement to the nth measurement, and j represents the time point when the temperature value is obtained.

4. The environmental monitoring instrument tendency error analysis system according to claim 3, characterized in that: The expression of the water depth dataset is {H1 m 、H2 m 、H3 m 、...、Hn m }, H1 m To Hn m Represents the depth values ​​from the first measurement to the nth measurement, and m represents the time point when the depth value is obtained.

5. The environmental monitoring instrument tendency error analysis system according to claim 4, characterized in that: The calculation process of the time difference data set Scsj is as follows: According to the conductivity dataset, temperature dataset and water depth dataset, the conductivity, temperature value and depth value measured for the i-th time are extracted, and the time point of the i-th conductivity acquisition is marked as i s , mark the time point of obtaining the temperature value for the i-th time as i j , mark the time point of obtaining the depth value for the i-th time as i m ; In the formula, Indicates that the time difference between conductivity and temperature values ​​is calculated in sequence during each measurement process. Indicates the calculation of the time difference between conductivity and depth values ​​in each measurement process. Indicates that the time difference between the depth value and the temperature value is calculated in sequence during each measurement process.

6. The environmental monitoring instrument tendency error analysis system according to claim 5, characterized in that: The calculation process of the fluctuation data group Bdsj is as follows: In the formula, Indicates the average conductivity of the measured water body, Indicates the average temperature of the measured water body. Indicates the average depth of the measured water body, Dk s Indicates the conductivity measured at the kth time, Wk j Indicates the temperature value measured for the kth time, Hk m Indicates the depth value measured for the kth time, Indicates that the fluctuation range of conductivity is calculated according to the standard deviation formula. Indicates that the fluctuation range of the temperature value is calculated according to the standard deviation formula. Indicates the fluctuation range of the depth value calculated according to the standard deviation formula.

7. The environmental monitoring instrument tendency error analysis system according to claim 6, characterized in that: The calculation process of the credibility index Kxzs is as follows: In the formula, BD represents the standard value used to measure conductivity, β1 represents the weight for the ratio of the standard value to the conductivity, BW represents the standard value used to measure the temperature value, β2 represents the weight for the ratio of the standard value to the temperature value, BH represents the standard value used to measure the depth value, β3 represents the weight for the ratio of the standard value to the depth value, β1, β2 and β3 are all constants, and Indicates that the credibility index Kxzs of the k-th measurement result is calculated according to the weights β1, β2 and β3 k .

8. The environmental monitoring instrument tendency error analysis system according to claim 7, characterized in that: When any value in the time difference data set Scsj exceeds the time difference threshold SCY, it means that the response delay causes the temperature, salinity and depth instrument to have a tendency error, and it is recommended to correct the delayed time difference in time.

9. The environmental monitoring instrument tendency error analysis system according to claim 8, characterized in that: In the fluctuation data group Bdsj, if the conductivity fluctuation range exceeds the conductivity threshold DBY, the temperature fluctuation range exceeds the temperature threshold WBY, or the depth fluctuation range exceeds the depth threshold HBY, it means that attachments have caused a bias error in the temperature-salinity-depth meter. It is recommended to clean the underwater probe in a timely manner.

10. The environmental monitoring instrument tendency error analysis system according to claim 9, characterized in that: When the credibility index Kxzs exceeds the credibility threshold KXY, it indicates that there is an abnormality in the authenticity of the measurement result, and it is recommended to optimize the measurement plan.

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