Method for predicting survival condition of shellfish in pipeline

Abstract

The invention discloses a method for predicting the survival condition of shellfishes in a pipeline, which comprises the following steps of: acquiring indexes such as salinity, temperature, pH value and dissolved oxygen of seawater in the pipeline through a seawater quality sensor, and constructing a prediction model for predicting the survival condition of shellfishes in the pipeline based on seawater quality parameters; the survival rate of the shellfish in the pipeline is predicted through the model so that the shellfish in the pipeline can be cleaned in time, operation is easy and convenient, practicability is high, and the detection cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of biological monitoring environment, and in particular to a method for predicting the survival status of shellfish in a pipeline. Background Art

[0002] With the rapid development of society, more and more industrial facilities are being built near the sea or on the ocean. Many of these facilities typically pump seawater directly from the sea to meet their needs for cooling, desalination, and other industrial applications. However, this pumped seawater may contain mollusks.

[0003] Shellfish have strong reproductive capacities and can accumulate densely. They cluster densely on pipe walls and in cracks, reducing the effective pipe diameter. The shells shed by dead shellfish can easily accumulate at pipe junctions and water diversion points, causing blockages, impacting the flow of seawater, and leading to equipment failure or downtime. Furthermore, some shellfish may produce corrosive substances, which can corrode pipe materials, reduce their strength and lifespan, and increase maintenance costs. Therefore, it is necessary to monitor the survival of shellfish in pipes so that any accumulated shellfish can be removed promptly.

[0004] At present, there are few technologies for detecting the survival status of shellfish in pipes, mainly chemical methods for detecting shellfish secretions or DNA, and image methods using cameras for detection. However, these methods are cumbersome and costly. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical problems in the above-mentioned existing technology that the method for detecting the survival status of shellfish in pipelines is cumbersome and costly, and to provide a method for predicting the survival status of shellfish in pipelines, which can predict the survival status of shellfish in pipelines so as to clean the shellfish in the pipelines in time, while having lower cost and simple operation.

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] The present invention provides a method for predicting the survival status of shellfish in a pipeline, the method comprising:

[0008] Step 100: Acquire water quality parameter data of seawater in the pipeline through a sensor, wherein the water quality parameter data includes salinity data, pH value, temperature data, and dissolved oxygen concentration;

[0009] Step 200: constructing a prediction model for predicting the survival of shellfish in the pipeline based on seawater quality parameters;

[0010] Step 300: Calculate coefficient values ​​and constant values ​​of the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline;

[0011] Step 400: When the dissolved oxygen concentration obtained in step 100 is greater than or equal to 4 mg / L, the salinity data, pH value, and temperature data obtained in step 100 are substituted into the prediction algorithm to obtain the survival rate of the shellfish in the pipeline at this time;

[0012] Step 500: Classify the survival rate of the shellfish in the pipeline. When the survival rate calculated in step 400 is greater than 40%, the shellfish in the pipeline need to be cleaned.

[0013] Preferably, in step 200, the prediction model is Y=aT+bS+cP+eT 2 +fS 2 +gP 2 +C, where Y is the survival rate of shellfish in the pipe, T is the temperature of the seawater in the pipe, S is the salinity of the seawater in the pipe, P is the pH value of the seawater in the pipe, a, b, c, e, f and g are coefficients, and C is a constant.

[0014] Preferably, in step 300, the step of calculating the coefficient values ​​and constant values ​​of the prediction model includes: based on the water quality parameter data obtained in step 100, calculating the values ​​of a, b, c, e, f, g and C in the prediction model by nonlinear fitting, and substituting the values ​​of a, b, c, e, f, g and C into the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline.

[0015] Preferably, in step 500, the method of dividing the survival rate levels of shellfish in the pipeline is: when the survival rate of shellfish in the pipeline is [0, 20%], the survival rate level of shellfish in the pipeline is L1; when the survival rate of shellfish in the pipeline is (20%, 40%], the survival rate level of shellfish in the pipeline is L2; ​​when the survival rate of shellfish in the pipeline is (40%, 60%], the survival rate level of shellfish in the pipeline is L3; when the survival rate of shellfish in the pipeline is (60%, 80%], the survival rate level of shellfish in the pipeline is L4; when the survival rate of shellfish in the pipeline is (80%, 100%], the survival rate level of shellfish in the pipeline is L5.

[0016] Preferably, in step 500, the shellfish in the pipeline are cleaned by regulating the temperature, pH value and salinity of the seawater in the pipeline.

[0017] Preferably, based on the conditions that the seawater temperature in the pipeline is 25.65°C and the salinity is 23.35‰, when the pH value is controlled to be ≤5 or the pH value is controlled to be ≥12, the survival rate of the shellfish in the pipeline is ≤40%.

[0018] Preferably, based on the conditions that the seawater temperature in the pipeline is 25.65°C and the pH value is 8.5, when the salinity is controlled to be ≤6.5‰ or the salinity is controlled to be ≥40.4‰, the survival rate of the shellfish in the pipeline is ≤40%.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention provides a method for predicting the survival status of shellfish in pipelines. The method obtains indicators such as the salinity, temperature, pH value, and dissolved oxygen of seawater in the pipeline through a seawater quality sensor, and constructs a prediction model for predicting the survival status of shellfish in the pipeline based on the seawater quality parameters. The survival rate of shellfish in the pipeline is predicted by the model so that the shellfish in the pipeline can be cleaned in time. The method is simple to operate, highly practical, and greatly reduces the detection cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0022] Figure 1 This is a principle structure diagram of a method for predicting the survival status of shellfish in a pipeline according to the present invention;

[0023] Figure 2 This is a flow chart of an implementation of an embodiment of a method for predicting the survival status of shellfish in a pipeline according to the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 should fall within the scope of protection of the present invention.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. Unless otherwise defined, the technical or scientific terms used in this specification should have the common meaning understood by people with ordinary skills in the field to which this application belongs. The terms "first", "second" and similar terms used in this specification and claims do not indicate any order, quantity or importance, but are only used to distinguish different technical features.

[0026] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0027] The present invention provides a method for predicting the survival status of shellfish in a pipeline, the method comprising the following steps:

[0028] Step 100: Acquire water quality parameter data of seawater in the pipeline through a sensor, wherein the water quality parameter data includes salinity data, pH value, temperature data, and dissolved oxygen concentration;

[0029] In this embodiment, water quality parameter data of seawater in the pipeline is obtained by sensors. These water quality parameter data must first be cleaned, and the cleaned data are used in subsequent steps.

[0030] Step 200: Construct a prediction model for predicting the survival of shellfish in the pipeline based on seawater quality parameters.

[0031] Specifically, the prediction model is Y = aT + bS + cP + eT 2 +fS 2 +gP 2 +C, where Y is the survival rate of shellfish in the pipe, T is the temperature of the seawater in the pipe, S is the salinity of the seawater in the pipe, P is the pH value of the seawater in the pipe, a, b, c, e, f and g are coefficients, and C is a constant.

[0032] Step 300: Calculate the coefficient values ​​and constant values ​​of the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline.

[0033] Specifically, the step of calculating the coefficient values ​​and constant values ​​of the prediction model includes: based on the water quality parameter data obtained in step 100, calculating the values ​​of a, b, c, e, f, g and C in the prediction model by nonlinear fitting, and substituting the values ​​of a, b, c, e, f, g and C into the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline.

[0034] Step 400: When the dissolved oxygen concentration obtained in step 100 is greater than or equal to 4 mg / L, the salinity data, pH value and temperature data obtained in step 100 are substituted into the prediction algorithm to obtain the survival rate of the shellfish in the pipeline at this time.

[0035] It should be noted that according to the national standard "GB 3097-1997 Seawater Quality Standard," when the dissolved oxygen concentration in seawater is less than 4 mg / L, the seawater is classified as Class IV seawater, indicating poor water quality and unsuitable for the survival of marine organisms such as shellfish. Therefore, it can be determined that no shellfish can survive or reproduce in the pipeline. Therefore, when the dissolved oxygen concentration obtained in step 100 is greater than or equal to 4 mg / L, it is necessary to predict the survival rate of shellfish in the pipeline so that the predicted survival rate can be used to determine whether to remove the shellfish in the pipeline.

[0036] Step 500: Classify the survival rate of the shellfish in the pipeline. When the survival rate calculated in step 400 is greater than 40%, the shellfish in the pipeline need to be cleaned.

[0037] Specifically, in step 500, the method of dividing the survival rate levels of shellfish in the pipeline is: when the survival rate of shellfish in the pipeline is [0, 20%], the survival rate level of shellfish in the pipeline is L1; when the survival rate of shellfish in the pipeline is (20%, 40%], the survival rate level of shellfish in the pipeline is L2; ​​when the survival rate of shellfish in the pipeline is (40%, 60%], the survival rate level of shellfish in the pipeline is L3; when the survival rate of shellfish in the pipeline is (60%, 80%], the survival rate level of shellfish in the pipeline is L4; when the survival rate of shellfish in the pipeline is (80%, 100%], the survival rate level of shellfish in the pipeline is L5.

[0038] Preferably, the shellfish in the pipeline are cleaned by regulating the temperature, pH value and salinity of the seawater in the pipeline.

[0039] In a preferred embodiment, based on the conditions that the seawater temperature in the pipeline is 25.65°C and the salinity is 23.35‰, when the pH value is controlled to be ≤5 or the pH value is controlled to be ≥12, the survival rate of the shellfish in the pipeline is ≤40%.

[0040] In another preferred embodiment, based on the conditions that the seawater temperature in the pipeline is 25.65°C and the pH value is 8.5, when the salinity is controlled to be ≤6.5‰ or the salinity is controlled to be ≥40.4‰, the survival rate of the shellfish in the pipeline is ≤40%.

[0041] Therefore, the method for predicting the survival status of shellfish in pipelines described in the present invention obtains indicators such as the salinity, temperature, pH value, dissolved oxygen, etc. of seawater in the pipeline through a seawater quality sensor, and constructs a prediction model for predicting the survival status of shellfish in pipelines based on seawater quality parameters. The survival rate of shellfish in the pipeline is predicted by this model so that the shellfish in the pipeline can be cleaned in time. The method is simple to operate, highly practical, and greatly reduces the detection cost.

[0042] In a specific embodiment, (1) suitable seawater quality sensors are selected, mainly including online seawater salinity sensor, online seawater pH sensor, online seawater temperature sensor, and online seawater dissolved oxygen sensor. The sensors used shall at least meet the following performance requirements: online seawater salinity sensor, range 0-70 PSU, resolution 0.1 PSU, minimum detection limit 0.1 PSU, accuracy ±1%, with automatic temperature compensation function, protection grade IP68; online seawater pH sensor, range 0-14, resolution 0.01, minimum detection limit 0.1, accuracy ±0.01, with automatic temperature compensation function, protection grade IP68; online seawater temperature sensor, range 0-50°C, resolution 0.1°C, accuracy ±0.3°C, protection grade IP68; online seawater dissolved oxygen sensor, range 0-20 mg / L, resolution 0.01 mg / L, minimum detection limit 0.08 mg / L, accuracy ±2%, with automatic temperature compensation function, protection grade IP68. (2) All seawater quality sensors are installed on the wall of the seawater pipeline. The installation interface is welded to the pipe wall and fixed. The sensor is perpendicular to the installation surface. Ensure that the probe at the front end of the sensor is completely immersed in the seawater in the pipeline. Keep a certain distance between each sensor to prevent collision and interference. The sensor cable has a certain corrosion resistance, and all connection points must be waterproofed. (3) Determine the data transmission method between the sensor and the data center. When the sensor is far away from the data center, RS485 communication or 4-20mA current communication can be adopted; when the sensor is extremely far away from the data center, optical fiber communication is required to avoid data distortion caused by long-distance communication. (4) The data center conducts preliminary analysis and saves the received pipeline seawater salinity, pH, temperature, and dissolved oxygen data. Data with large differences are identified as invalid data and eliminated. The data is saved and the time information is saved. (5) The prediction model Y=aT+bS+cP+eT is calculated by nonlinear fitting based on the seawater salinity, pH, temperature and dissolved oxygen data received by the data center. 2 +fS 2 +gP 2 The values ​​of a, b, c, e, f, g and C in +C are as follows: the optimal value of a is 4.496, the optimal value of b is 9.76, the optimal value of c is 81.2, the optimal value of e is -0.088, the optimal value of f is -0.208, the optimal value of g is -4.8, and the optimal value of C is -415.4. After substituting the coefficients, the model relationship is Y=4.496T+9.76S+81.2P-0.088T 2 -0.208S 2 -4.8P 2-415.4. (6) When the dissolved oxygen concentration obtained by the data center is greater than or equal to 4 mg / L, the salinity data, pH value, and temperature data obtained at this time are substituted into the prediction algorithm to obtain the survival rate of the shellfish in the pipeline at this time. (7) The survival rate of the shellfish in the pipeline is classified as shown in the following table. When the survival rate calculated in step (6) is greater than 40%, the shellfish in the pipeline are cleared by controlling the pH value or salinity.

[0043]

[0044] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Therefore, any modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the survival of shellfish in a pipeline, characterized in that: The method comprises: Step 100: Acquire water quality parameter data of seawater in the pipeline through a sensor, wherein the water quality parameter data includes salinity data, pH value, temperature data, and dissolved oxygen concentration; Step 200: constructing a prediction model for predicting the survival of shellfish in the pipeline based on seawater quality parameters; Step 300: Calculate coefficient values ​​and constant values ​​of the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline; Step 400: When the dissolved oxygen concentration obtained in step 100 is greater than or equal to 4 mg / L, the salinity data, pH value, and temperature data obtained in step 100 are substituted into the prediction algorithm to obtain the survival rate of the shellfish in the pipeline at this time; Step 500: Classify the survival rate of the shellfish in the pipeline. When the survival rate calculated in step 400 is greater than 40%, the shellfish in the pipeline need to be cleaned.

2. The method for predicting the survival of shellfish in a pipeline according to claim 1, characterized in that: In step 200, the prediction model is Y=aT+bS+cP+eT 2 +fS 2 +gP 2 +C, where Y is the survival rate of shellfish in the pipe, T is the temperature of the seawater in the pipe, S is the salinity of the seawater in the pipe, P is the pH value of the seawater in the pipe, a, b, c, e, f and g are coefficients, and C is a constant.

3. The method for predicting the survival of shellfish in a pipeline according to claim 1, characterized in that: In step 300, the step of calculating the coefficient values ​​and constant values ​​of the prediction model includes: Based on the water quality parameter data obtained in step 100, the values ​​of a, b, c, e, f, g and C in the prediction model are calculated by nonlinear fitting, and the values ​​of a, b, c, e, f, g and C are substituted into the prediction model to obtain a prediction algorithm that can be used to calculate the survival rate of shellfish in the pipeline.

4. The method for predicting the survival of shellfish in a pipeline according to claim 1, characterized in that: In step 500, the survival rate of the shellfish in the pipeline is divided into the following levels: When the survival rate of shellfish in the pipe is [0, 20%], the survival rate level of shellfish in the pipe is L1; When the survival rate of shellfish in the pipe is (20%, 40%], the survival rate level of shellfish in the pipe is L2; When the survival rate of shellfish in the pipe is (40%, 60%], the survival rate level of shellfish in the pipe is L3; When the survival rate of shellfish in the pipe is (60%, 80%], the survival rate level of shellfish in the pipe is L4; When the survival rate of the shellfish in the pipe is (80%, 100%], the survival rate level of the shellfish in the pipe is L5.

5. The method for predicting the survival of shellfish in a pipeline according to claim 4, characterized in that: In step 500, the shellfish in the pipeline are cleaned by: The shellfish in the pipeline are cleaned by regulating the temperature, pH value and salinity of the seawater in the pipeline.

6. The method for predicting the survival status of shellfish in a pipeline according to claim 5, characterized in that: Under the conditions that the seawater temperature in the pipeline is 25.65°C and the salinity is 23.35‰, when the pH value is controlled to be ≤5 or the pH value is controlled to be ≥12, the survival rate of the shellfish in the pipeline is ≤40%.

7. The method for predicting the survival status of shellfish in a pipeline according to claim 5, characterized in that: Under the conditions that the seawater temperature in the pipeline is 25.65°C and the pH value is 8.5, when the salinity is controlled to be ≤6.5‰ or the salinity is controlled to be ≥40.4‰, the survival rate of the shellfish in the pipeline is ≤40%.

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