Method and device for detecting water quality of artificial seawater and electronic equipment

Abstract

The embodiment of the invention provides a water quality detection method and device for artificial seawater and electronic equipment, and relates to the field of water quality detection. The artificial seawater water quality detection method comprises the steps that first artificial seawater is obtained, water quality detection is conducted on the first artificial seawater based on a plurality of preset water quality detection indexes, a first detection result is obtained, and the first detection result comprises first detection values corresponding to the water quality detection indexes; setting a detection time interval, and detecting the water quality of the first artificial seawater again based on a plurality of water quality detection indexes to obtain a second detection result; calculating to obtain a first comprehensive detection value according to the plurality of first detection values, and calculating to obtain a second comprehensive detection value according to the plurality of second detection values; analyzing the first comprehensive detection value and the second comprehensive detection value by using a preset statistical method to obtain an analysis result; and determining the quality of the first artificial seawater according to the analysis result. According to the embodiment of the invention, the detection result of the water quality detection of the artificial seawater is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of water quality detection, and in particular to a method, device and electronic equipment for detecting the water quality of artificial seawater. Background Art

[0002] At present, the water quality testing of artificial seawater refers to the testing and analysis of artificial seawater based on its water quality testing indicators through certain methods and technologies to understand its quality and sanitary conditions.

[0003] In the existing artificial seawater water quality detection technology, artificial seawater needs to be prepared first, and then various indicators in the artificial seawater are detected, and finally it is determined whether the artificial seawater can meet the transportation, storage and breeding requirements of live seafood. The problem with detecting various indicators in artificial seawater is that although there are many indicators to be detected, each indicator has a different impact on the quality of artificial seawater, which will lead to inaccurate test results of artificial seawater water quality detection, thereby affecting the breeding quality of live seafood. Summary of the invention

[0004] The present application provides a method, device and electronic equipment for detecting the water quality of artificial seawater, which can make the detection results of the water quality detection of artificial seawater more accurate, thereby improving the breeding quality of live seafood.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting water quality of artificial seawater, the method comprising: Acquire first artificial seawater, and perform water quality detection on the first artificial seawater based on a plurality of preset water quality detection indicators to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each water quality detection indicator, and the preset water quality detection indicators include at least an effective component indicator, a harmful component indicator, and a physical parameter indicator; Setting a detection time interval, and based on the multiple water quality detection indicators, performing water quality detection on the first artificial seawater again to obtain a second detection result, wherein the second detection result includes a second detection value corresponding to each water quality detection indicator; A first comprehensive detection value is calculated based on the plurality of first detection values, and a second comprehensive detection value is calculated based on the plurality of second detection values; The first comprehensive detection value and the second comprehensive detection value are analyzed using a preset statistical method to obtain an analysis result; and the quality of the first artificial seawater is determined based on the analysis result.

[0006] By adopting the above technical solution, the first artificial seawater is obtained, and the water quality of the first artificial seawater is tested based on multiple preset water quality test indicators to obtain a first test result, the first test result includes a first test value corresponding to each water quality test indicator, the preset water quality test indicator includes at least an effective component indicator, a harmful component indicator and a physical parameter indicator, and the influence of multiple indicators on the quality of the artificial seawater is considered, so as to improve the comprehensiveness of the water quality test; the test time interval is set, and the water quality of the first artificial seawater is tested again based on multiple water quality test indicators to obtain a second test result, the second test result includes a second test value corresponding to each water quality test indicator, and the test time interval is set and the secondary test is performed, so as to dynamically monitor the change of the quality of the artificial seawater; the first comprehensive test value is calculated according to the multiple first test values, and the second comprehensive test value is calculated according to the multiple second test values, so as to comprehensively consider the indicators affecting the quality of the artificial seawater and evaluate the quality of the artificial seawater; the first comprehensive test value and the second comprehensive test value are analyzed by using a preset statistical method to obtain an analysis result, and the comprehensive test value is further analyzed to determine the change of the quality of the first artificial seawater and obtain a more accurate analysis result; the quality of the first artificial seawater is determined according to the analysis result. The embodiment of the present application takes into account the influence of multiple indicators on the water quality detection of artificial seawater, comprehensively considers various indicators, can better reflect the quality changes of artificial seawater, can make the test results of the water quality detection of artificial seawater more accurate, thereby improving the breeding quality of live seafood.

[0007] In some embodiments of the present application, based on a plurality of preset water quality detection indicators, a water quality detection is performed on the first artificial seawater to obtain a first detection result, including: pre-treating the first artificial seawater to obtain second artificial seawater; Based on multiple water quality detection indicators, the second artificial seawater is tested to obtain a first test result.

[0008] By adopting the above technical solution, the first artificial seawater is pretreated to obtain the second artificial seawater, which can remove impurities and pollutants in the first artificial seawater and improve the accuracy of water quality detection; based on multiple water quality detection indicators, the second artificial seawater is detected to obtain the first detection result.

[0009] In some embodiments of the present application, the first comprehensive detection value is calculated based on multiple first detection values, including: according to the multiple first detection values, the first comprehensive detection value is calculated using a preset comprehensive detection value calculation method, wherein the preset comprehensive detection value calculation method is, P1 = Y1 × Q1 + Y2 × Q2 .... + Y i ×Q i , P1 is the first comprehensive detection value, Y iis the first detection value detected based on the i-th indicator, Q i is the preset weight value of the i-th indicator, and i is the serial number corresponding to the water quality detection indicator of the quality of the first artificial seawater.

[0010] By adopting the above technical solution, based on multiple first detection values ​​and the weight value of each water quality detection index, a first comprehensive detection value is calculated using a preset comprehensive detection value calculation method. By calculating the comprehensive detection value, the quality of artificial seawater can be comprehensively evaluated, making the detection result of artificial seawater more accurate.

[0011] In some embodiments of the present application, a preset statistical method is used to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result, including: Calculate the mean value, variance and change rate of the first comprehensive detection value and the second comprehensive detection value using a preset statistical method; The mean, variance and rate of change are analyzed to obtain the analysis results.

[0012] By adopting the above technical solution, the average value can be calculated to analyze whether the water quality change of the artificial seawater is good or bad, the variance can be calculated to analyze the degree of water quality change, and the rate of change can be calculated to evaluate the rate of change of the water quality of the artificial seawater. The change of the quality of the first artificial seawater can be determined to obtain a more accurate analysis result.

[0013] In some embodiments of the present application, determining the quality of the first artificial seawater according to the analysis result includes: comparing the analysis result with a preset first limit value and a preset second limit value; In the case where the analysis result is between a first limit value and a second limit value, determining that the quality of the first artificial seawater is good, wherein the first limit value is less than the second limit value; In case the analysis result is lower than the first limit value, determining the quality difference of the first artificial seawater; When the analysis result is higher than the second limit value, it is determined that the quality of the first artificial seawater exceeds the standard.

[0014] By adopting the above technical solution, by comparing the comprehensive analysis result with the preset first limit value and the second limit value, the quality status of the first artificial seawater can be accurately and quantitatively evaluated.

[0015] When the comprehensive analysis result is between the first limit and the second limit, it can be determined that the quality of the first artificial seawater is at a good level, indicating that various water quality indicators basically meet the standards and are suitable for the cultivation of live seafood. By setting reasonable first and second limits, and setting the interval range in a way that the first limit is less than the second limit, qualified artificial seawater samples can be effectively screened out.

[0016] When the comprehensive analysis result is lower than the first limit value, it can be determined that the quality of the first artificial seawater is poor, and some key water quality indicators may seriously deviate from the standard values, and cannot meet the basic requirements for live seafood farming. It is necessary to further find the causes and carry out water quality regulation and improvement.

[0017] When the comprehensive analysis result is higher than the second limit, it indicates that the quality of the first artificial seawater seriously exceeds the standard, some water quality indicators may be significantly beyond the normal range, there is serious water pollution or other quality problems, and emergency measures must be taken to avoid adverse effects on aquaculture activities.

[0018] Through the above analysis, comparison and judgment process, this method can quickly and accurately identify artificial seawater samples of different quality levels based on the comprehensive analysis results, providing farmers with an intuitive and reliable basis for water quality assessment. At the same time, by presetting scientific and reasonable dual limit standards, dividing the three typical situations of good quality, poor quality and excessive quality, it helps farmers to discover water quality problems in a timely manner, take corresponding countermeasures, and achieve refined and graded water quality control, thereby ensuring the safety and output quality of artificial seawater aquaculture.

[0019] In some embodiments of the present application, before inputting the real-time aquaculture seawater detection results, aquaculture production plan data and water purification equipment operation status data into a preset water quality development trend prediction model to obtain water quality prediction trend data, the following is also included: Acquire historical breeding sample data, and perform fitting processing on the historical breeding sample data by using the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting processing; Based on the historical aquaculture sample data after fitting processing, water quality prediction trend data is constructed. The historical aquaculture sample data includes historical aquaculture seawater test results, historical aquaculture production plan data and historical water quality purification equipment operation status data.

[0020] By adopting the above technical solution and obtaining historical aquaculture sample data including historical aquaculture seawater test results, historical aquaculture production plan data and historical water purification equipment operation status data, we can comprehensively and systematically grasp the changes in water quality in the aquaculture environment, the implementation of aquaculture production activities and the effectiveness of water quality management measures over the past period of time, providing rich and reliable data support for subsequent data analysis and model construction.

[0021] When processing historical aquaculture sample data, this method innovatively introduced the least squares method and polynomial regression algorithm. Through fitting processing, the noise and outliers in the historical data were removed, and the inherent trends and laws of the data were extracted, so that the data quality was effectively improved. The least squares method finds the optimal function fitting curve by minimizing the sum of squared errors, while the polynomial regression algorithm captures the nonlinear relationship in the data by constructing a high-order polynomial function. The combined application of the two algorithms can fully tap the potential value of historical aquaculture data and lay a good data foundation for subsequent water quality predictions.

[0022] In some embodiments of the present application, the historical breeding sample data is fitted by the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting, including: The historical breeding sample data are classified based on the equipment information, and the historical breeding sample data with the same equipment information are classified into one category; the historical breeding sample data with the same equipment information are fitted by the least squares method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting.

[0023] By adopting the above technical solution, in the actual artificial seawater aquaculture process, various types of aquaculture equipment are often involved, such as aquaculture ponds, water quality monitoring equipment, water quality regulation equipment, etc. The historical aquaculture data recorded by different equipment may have significant differences in numerical range, change pattern, etc. If all historical aquaculture sample data are directly fitted as a whole, it is easy to produce large fitting errors and affect data quality. By introducing a data classification method based on equipment information, the historical aquaculture data generated by different equipment can be distinguished to form several data subsets with the same equipment information. The data characteristics in each subset are more consistent, which is conducive to improving the accuracy of the fitting process.

[0024] When fitting the historical breeding sample data with the same equipment information for each type, this method makes comprehensive use of the least squares method and the polynomial regression algorithm. The least squares method seeks the optimal parameter combination in a given function type by minimizing the sum of squared errors, so that the distance between the fitting curve and the actual data points is minimized. It has the advantages of good stability and high computational efficiency. The polynomial regression algorithm can fit nonlinear data well and capture the complex changing trends of the data by constructing polynomial functions of different orders. The organic combination of the two algorithms can give full play to their respective advantages, improve the accuracy and robustness of the fitting process, and obtain a higher quality fitting result that is more in line with the actual laws.

[0025] In a second aspect, the present application provides a device for detecting water quality of artificial seawater, the device comprising: A first test result acquisition module, used to acquire the first artificial seawater, and perform water quality testing on the first artificial seawater based on a plurality of preset water quality testing indicators to obtain a first test result, wherein the first test result includes a first test value corresponding to each water quality testing indicator, and the preset water quality testing indicators include at least an effective component indicator, a harmful component indicator, and a physical parameter indicator; A second detection result acquisition module is used to set a detection time interval, and based on multiple water quality detection indicators, perform water quality detection on the first artificial seawater again to obtain a second detection result, wherein the second detection result includes a second detection value corresponding to each water quality detection indicator; A comprehensive detection value calculation module, used for calculating a first comprehensive detection value according to a plurality of first detection values, and calculating a second comprehensive detection value according to a plurality of second detection values; A comprehensive detection value analysis module, used to analyze the first comprehensive detection value and the second comprehensive detection value using a preset statistical method to obtain an analysis result; The artificial seawater quality determination module is used to determine the quality of the first artificial seawater according to the analysis result.

[0026] By adopting the above-mentioned device, the first detection result acquisition module is called to obtain the first artificial seawater, and the water quality detection of the first artificial seawater is performed based on multiple preset water quality detection indicators to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each water quality detection indicator, and the preset water quality detection indicators at least include sodium salt content, sulfate content, dissolved oxygen content and ammonia nitrogen content. Considering the influence of multiple indicators on the quality of artificial seawater, the comprehensiveness of water quality detection can be improved; the second detection result acquisition module is called to set the detection time interval, and the water quality detection of the first artificial seawater is performed again based on multiple water quality detection indicators to obtain a second detection result, wherein the second detection result includes a second detection value corresponding to each water quality detection indicator, and the second detection value is set. The detection time interval and secondary detection can dynamically monitor the quality changes of artificial seawater; the comprehensive detection result calculation module is called to calculate the first comprehensive detection value according to multiple first detection values, and the second comprehensive detection value is calculated according to multiple second detection values, which can comprehensively consider the indicators affecting the quality of artificial seawater and evaluate the quality of artificial seawater; the comprehensive detection value analysis module is called to use a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain the analysis result, and the comprehensive detection value is further analyzed to determine the change of the quality of the first artificial seawater and obtain a more accurate analysis result; the artificial seawater quality determination module is called to determine the quality of the first artificial seawater according to the analysis result. The embodiment of the present application considers the influence of multiple indicators on the water quality detection of artificial seawater, comprehensively considers various indicators, can better reflect the quality changes of artificial seawater, can make the detection results of the water quality detection of artificial seawater more accurate, thereby improving the breeding quality of live seafood.

[0027] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute instructions stored in the memory so that the electronic device executes any one of the methods provided in the first aspect above.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed, any one of the methods provided in the first aspect is executed.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain the first artificial seawater, and based on multiple preset water quality detection indicators, perform water quality detection on the first artificial seawater to obtain a first detection result; set a detection time interval, and based on multiple water quality detection indicators, perform water quality detection on the first artificial seawater again to obtain a second detection result; calculate a first comprehensive detection value based on multiple first detection values, and calculate a second comprehensive detection value based on multiple second detection values; use a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result; determine the quality of the first artificial seawater based on the analysis result. The embodiments of the present application, on the basis of considering the influence of multiple indicators on the water quality detection of artificial seawater, comprehensively consider various indicators, can better reflect the quality changes of artificial seawater, can make the detection results of the water quality detection of artificial seawater more accurate, thereby improving the breeding quality of live seafood.

[0030] 2. Pre-treating the first artificial seawater can remove impurities and pollutants in the first artificial seawater and improve the accuracy of the test results of water quality testing.

[0031] 3. According to the multiple first detection values ​​and the weight value of each water quality detection index, the first comprehensive detection value is calculated using a preset comprehensive detection value calculation method, which can comprehensively evaluate the quality of artificial seawater and make the detection result of artificial seawater more accurate.

[0032] 4. By comparing the analysis result with the preset first limit value and second limit value, the quality level of the first artificial seawater can be quickly determined, and the quality of the first artificial seawater can be adjusted to a good level when the water quality is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic flow chart of a method for detecting water quality of artificial seawater provided in an embodiment of the present application; Figure 2 yes Figure 1 A schematic flow chart of a sub-step of step S1; Figure 3 yes Figure 1 A schematic flow chart of a sub-step of step S4; Figure 4 yes Figure 1 A schematic flow chart of a sub-step of step S5; Figure 5 It is a structural schematic diagram of a water quality detection device for artificial seawater provided in an embodiment of the present application; Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0035] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0036] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0037] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0038] Artificial seawater (ASW), also known as artificial seawater, refers to a mixed solution of salts used to raise marine animals or preserve the organs and tissues of marine animals to keep them in a normal state.

[0039] The embodiment of the present application is applicable to application scenarios such as aquaculture, ocean monitoring, and marine scientific research, and aquaculture is used as an example for explanation. In the process of water quality detection of artificial seawater, the prepared artificial seawater is first obtained, and the artificial seawater is detected based on various water quality detection indicators to obtain multiple detection values, and then the artificial seawater is detected again based on a preset detection time interval, and the quality of the artificial seawater is comprehensively evaluated, which can make the results of the water quality detection of the artificial seawater more accurate, thereby improving the quality of the artificial seawater.

[0040] The embodiment of the present application is an interactive process between a user end and a detection device. The user end may include electronic devices such as mobile phones, computers, and terminals. The user triggers the water quality detection process on the user end according to his or her needs, and then the detection device automatically executes the technical solution provided in the embodiment of the present application to perform water quality detection on artificial seawater.

[0041] The technical solution provided in the embodiments of the present application is further described below in conjunction with the accompanying drawings.

[0042] Reference Figure 1 , Figure 1 The present invention provides a method for detecting the water quality of artificial seawater, which includes S1, S2, S3, S4 and S5.

[0043] S1, obtaining first artificial seawater, and performing water quality detection on the first artificial seawater based on a plurality of preset water quality detection indicators to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each water quality detection indicator, and the preset water quality detection indicators include at least an effective component indicator, a harmful component indicator, and a physical parameter indicator.

[0044] In one embodiment, the first artificial seawater needs to be obtained by artificial preparation. To prepare artificial seawater, various types of salts, such as sodium salt, potassium salt, sulfate, etc., need to be used and mixed in a certain proportion. The specific preparation steps are as follows: first, add an appropriate amount of water source to the water quality detection device, then calculate the total weight of the salt required for preparing the artificial seawater according to the weight of the water, and then calculate the weight of various types of salt according to a preset ratio and prepare them. Finally, the weighed artificial seawater salt is directly dissolved in the water source to complete the preparation of artificial seawater.

[0045] It should be noted that in the process of preparing artificial seawater, chemical reactions may occur due to the various salts used (such as sodium salts, sulfates, calcium salts, etc.), resulting in the formation of precipitations such as calcium sulfate, calcium carbonate and magnesium hydroxide, which will affect the composition and quality of artificial seawater. Therefore, when preparing, the addition process and dosage of these salts should be strictly controlled according to their chemical properties to ensure that the artificial seawater has good solubility and stability.

[0046] The prepared artificial seawater will be temporarily stored in a special storage tank as the object of subsequent water quality testing. When conducting water quality testing, the system will extract a certain amount of artificial seawater samples from the storage tank through automated equipment and conduct tests item by item according to the preset water quality test indicators. These preset water quality test indicators cover many key aspects of artificial seawater aquaculture, including at least three categories: effective component indicators, harmful component indicators and physical parameter indicators.

[0047] In terms of active ingredient indicators, the system focuses on detecting the content of minerals such as sodium and sulfate in artificial seawater, usually using precision instruments such as inductively coupled plasma atomic emission spectrometry (ICP-AES) for quantitative analysis. At the same time, the system will also measure the dissolved oxygen level of artificial seawater as an important indicator for evaluating its organic matter supply capacity.

[0048] In terms of harmful component indicators, the system mainly focuses on the content of pollutants such as ammonia nitrogen in artificial seawater. The ammonia nitrogen content is usually measured by salicylic acid spectrophotometry, Nessler's reagent colorimetry and other methods to assess the pollution level and safety risks of artificial seawater.

[0049] In terms of physical parameter indicators, the system will detect the pH value, salinity, turbidity, etc. of artificial seawater to evaluate its suitability for the aquaculture environment and biological growth. These indicators can be directly measured by conventional instruments such as pH meters, salinity meters, and turbidity meters.

[0050] By performing a comprehensive water quality test on the first artificial seawater sample, the system will eventually obtain a set of structured test results, namely the first test results. The first test results are composed of the measured values ​​of each preset water quality test index (i.e., the first test values), which quantitatively reflect the actual level of the artificial seawater sample in each key index, such as Figure 2 As shown, based on a plurality of preset water quality detection indicators, the first artificial seawater is subjected to water quality detection to obtain a first detection result, including but not limited to the following steps: S11, pre-treating the first artificial seawater to obtain second artificial seawater.

[0051] In a possible embodiment of the present application, since some ions have certain colors, such as ferric iron and ferrous iron, some ions may combine to produce precipitation, such as calcium ions and carbonate ions combined to produce calcium carbonate precipitation, which will affect water quality detection. Therefore, in order to make the water quality detection more accurate and reliable, the first artificial seawater can be pretreated to eliminate the influence of these factors on the water quality detection. Specifically, a filter is used to remove impurities, pollutants or suspended matter in the artificial seawater, and activated carbon, zeolite, fiber balls and other water purification materials are used to adsorb the heterochromatic, odor, and organic matter in the water, and filter the microorganisms and bacteria in the water.

[0052] After the pretreatment is completed, the second artificial seawater is obtained. The second artificial seawater removes impurities, organic matter and bacteria in the water, which not only makes subsequent water quality testing easier and the test results more accurate, but also is more suitable for the survival, breeding and transportation of seafood.

[0053] Possibly, the filters used here may include multiple types, but the main functions of each type of filter are different, and multiple filters can be used at the same time to achieve better treatment effects. For example, quartz sand filters are mainly used to remove suspended impurities in water, and activated carbon filters are mainly used to remove organic matter, color and residual chlorine in water.

[0054] S12, based on multiple water quality detection indicators, the second artificial seawater is tested to obtain a first test result.

[0055] In a possible embodiment of the present application, the second artificial seawater is tested based on the multiple water quality detection indicators proposed above. Specifically, the sodium salt and sulfuric acid contents of the second artificial seawater are determined by inductively coupled plasma atomic emission spectrometry, the dissolved oxygen content is determined by an optical method, and the ammonia nitrogen content is determined by a salicylic acid-hypochlorite method and a colorimetric method.

[0056] It should be noted that the method for detecting the second artificial seawater here is the same as the method for detecting the first artificial seawater, except that the detection result of the artificial seawater after S11 pretreatment will be more accurate. After the water quality detection is completed, the detection values ​​corresponding to the various water quality detection indicators are obtained. The detection value here refers to the first detection value. All the first detection values ​​are combined together to obtain the first detection result. The water quality detection considers the influence of various indicators on the quality of artificial seawater, so that the detection result is more comprehensive, and the obtained first detection result can be used for subsequent comparative analysis.

[0057] It should be noted that the detection indicators based on this step are tested according to the water quality detection indicators proposed in the above steps, but in fact, water quality detection indicators also include pH value, residual chlorine content, water hardness, etc., but due to limited space here, we will not describe them in detail, and actual testing is still required.

[0058] S2, setting a detection time interval, and performing a water quality test on the first artificial seawater again based on a plurality of water quality test indicators to obtain a second test result.

[0059] In one embodiment, a detection time interval is set. After the first detection result is obtained in S1, after a detection time interval, the first artificial seawater is tested again based on the multiple water quality detection indicators and the detection time interval in step S1. The specific detection method and steps are the same as the water quality detection in S1. After the water quality detection is completed, the detection values ​​corresponding to the multiple water quality detection indicators are obtained. The detection value here refers to the second detection value. All the second detection values ​​are combined together to obtain the second detection result. After a certain period of time, the water quality of the artificial seawater is tested again to monitor the change in the quality of the artificial seawater. The obtained second detection result can be used for subsequent comparative analysis with the first detection result.

[0060] Possibly, the setting of the detection time interval can be flexible, such as 1 day, 1 hour, etc., but if the detection time interval is 1 hour, 2 hours, etc., it is equivalent to that the detection device is always running, which will consume a lot of resources; if the detection time interval is 1 day, 2 days, etc., some detection data may be missed. In order to avoid the above problems, the embodiment of the present application sets the detection time interval to 12 hours or 13 hours.

[0061] Possibly, after two rounds of water quality testing, there were problems with the test values ​​of various water quality test indicators, such as low salinity, in which case a certain amount of salt was added according to the specific type of salt that was missing, and low dissolved oxygen, in which case oxygen was delivered to the water to increase the dissolved oxygen content.

[0062] S3, calculating a first comprehensive detection value according to the plurality of first detection values, and calculating a second comprehensive detection value according to the plurality of second detection values.

[0063] In one embodiment, a preset comprehensive detection value calculation method is used to calculate the first comprehensive detection value, wherein the preset comprehensive detection value calculation method is: P1 = Y1 × Q1 + Y2 × Q2 .... + Y i ×Q i , P1 is the first comprehensive detection value, Y i is the first detection value detected based on the i-th indicator, Q i is the preset weight value of the i-th indicator, and i is the serial number corresponding to the water quality detection indicator of the quality of the first artificial seawater.

[0064] Similarly, the calculation method of the second comprehensive detection value is the same as the first comprehensive detection value. It only needs to replace the detection value obtained based on the water quality detection index with the second detection value, that is, the calculation method of the second comprehensive detection value is, P2 = X1 × Q1 + X2 × Q2.... + X i ×Q i , P2 is the second comprehensive detection value, X i is the second detection value detected based on the i-th indicator, Q iis the preset weight value of the i-th indicator, and i is the serial number corresponding to the water quality detection indicator of the quality of the first artificial seawater.

[0065] Then, the detection value of each water quality detection index and the corresponding preset weight value are calculated. The first detection value of each water quality detection index can be obtained through the water quality detection in S1, and the second detection value of each water quality detection index can be obtained through the water quality detection in S2. The weight value of each water quality detection index can only be calculated by itself, and since there are multiple water quality detection indexes that affect the quality of the first artificial seawater, there are also multiple weight values ​​to be calculated and they correspond one to one with each water quality detection index. The weight value of each water quality detection index can be calculated using the analytic hierarchy process (AHP).

[0066] Moreover, since the importance of each water quality detection index to the quality of artificial seawater is not the same, the weight values ​​corresponding to each water quality detection index are also different. By assigning values ​​to each water quality detection index according to its importance, the weight values ​​of each water quality detection index can be calculated, wherein the value assignment range is 0-1, and the value assignment value is an integer or a decimal. Then, the detection values ​​corresponding to each water quality detection index obtained by S1 are multiplied by the weight values ​​corresponding to each water quality detection index, and then added to obtain the first comprehensive detection value. The calculation method of the second comprehensive detection value is the same as that of the first comprehensive detection value. The first comprehensive detection value and the second comprehensive detection value obtained are the performance of a comprehensive evaluation of each index, which can obtain a more accurate water quality detection result of artificial seawater, and can serve as the basis for subsequent comparative analysis of comprehensive detection values.

[0067] S4, using a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result.

[0068] In one embodiment, the statistical method refers to a method for collecting, organizing, analyzing and interpreting statistical data and drawing certain conclusions on the problems reflected by the statistical data, including the calculation of the mean value, variance and change rate. The mean value, variance and change rate of the first comprehensive detection value and the second comprehensive detection value are calculated and analyzed to determine the change in the quality of the first artificial seawater, and then the specific change is analyzed to obtain the analysis result, which can be used as the basis for determining the quality of the first artificial seawater.

[0069] like Figure 3 As shown, using a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result includes but is not limited to the following steps: S41, using a preset statistical method to calculate the average value, variance and change rate of the first comprehensive detection value and the second comprehensive detection value.

[0070] In a possible embodiment of the present application, the average value, variance, change rate and relative change amount of the first comprehensive detection value and the second comprehensive detection value are calculated. Specifically, the average value is calculated by adding the first comprehensive detection value and the second comprehensive detection value and dividing it by the number of detections. The variance is calculated, and the variance S2=∑(x i -μ) 2 / n, where x i are the first integrated detection value and the second integrated detection value, is the average value of the first integrated detection value and the second integrated detection value, n is the number of integrated detection values, and Σ is the summation symbol.

[0071] To calculate the rate of change, subtract the second comprehensive test value from the first comprehensive test value, and then divide it by the test time interval in S2. A positive result indicates that the water quality is improving, and a negative result indicates that the water quality is decreasing. To calculate the relative change, subtract the first comprehensive test value from the second comprehensive test value, and then divide it by the first comprehensive test value.

[0072] S42, analyzing the mean value, variance and change rate to obtain analysis results.

[0073] In a possible embodiment of the present application, the average value is a numerical expression of the change in the mass of the first artificial seawater, and the variance or the rate of change is a numerical expression of the fluctuation in the change in the mass of the first artificial seawater. The mass change of the first artificial seawater is determined according to the numerical value of the variance or the rate of change, and then the analysis result is determined. Considering the numerical values ​​of the two aspects here can avoid misjudgment due to numerical calculation errors or abnormalities in one of the aspects.

[0074] Specifically, if the values ​​of the variance or the rate of change are both large, it can be judged that the quality change fluctuation of the first artificial seawater is large, and the first comprehensive detection result and the second comprehensive detection result are both inaccurate, and the average value of the two can be used as the final detection basis, that is, the analysis result; if the values ​​of the variance or the rate of change are both small, it can be judged that the quality change fluctuation of the first artificial seawater is small, and one is selected from the first comprehensive detection result, the second comprehensive detection result and the average value of the two as the analysis result of this step.

[0075] Possibly, after analyzing the first comprehensive detection value and the second comprehensive detection value, if the difference between the two values ​​is large, in addition to taking the average value of the two as the final detection result, water quality detection can be performed again based on multiple water quality detection indicators and detection time intervals until the quality change of the first artificial seawater tends to be stable and then the analysis result is determined.

[0076] S5, determining the quality of the first artificial seawater according to the analysis result.

[0077] In one embodiment, the limit value refers to a value with a limiting effect, and the limit value here includes a first limit value and a second limit value, which respectively refer to the lowest value and the highest value of the range corresponding to good water quality. According to the analysis result obtained in S4, the first limit value and the second limit value are set, and the first limit value is less than the second limit value. In this way, three intervals can be determined by the first limit value and the second limit value, and different intervals correspond to different water qualities. Then, the analysis result is matched with the three intervals, and the interval corresponding to the analysis result is found, so that the quality of the first artificial seawater can be quickly determined.

[0078] like Figure 4 As shown, according to the analysis results, determining the quality of the first artificial seawater includes but is not limited to the following steps: S51, comparing the analysis result with a preset first limit value and a preset second limit value; In one embodiment, firstly, the first limit and the second limit need to be reasonably set according to the quality standards and empirical data of artificial seawater aquaculture. The first limit represents the lower limit threshold of the quality of artificial seawater. When the comprehensive analysis result is lower than the first limit, it indicates that the water quality is poor, and some key indicators may seriously deviate from the standard and fail to meet the aquaculture requirements. The second limit represents the upper limit threshold of the quality of artificial seawater. When the comprehensive analysis result is higher than the second limit, it indicates that the water quality exceeds the standard, some indicators may seriously exceed the normal range, and there are problems such as water pollution.

[0079] In actual operation, the first limit and the second limit can be set as two fixed values, or the limit standards can be dynamically adjusted according to factors such as different seasons and different breeding species to adapt to changes in the actual breeding environment. After obtaining scientific and reasonable first and second limit values ​​through manual setting or automatic calculation, they can be compared with the comprehensive analysis results.

[0080] S52, determining that the quality of the first artificial seawater is good when the analysis result is between a first limit value and a second limit value, wherein the first limit value is less than the second limit value; In one embodiment, after the comprehensive analysis result is compared with the preset limit value, the system will automatically determine whether the analysis result falls within the closed interval formed by the first limit value and the second limit value. The first limit value and the second limit value are preset according to the quality standards and practical experience of artificial seawater aquaculture, wherein the first limit value represents the lower limit threshold of qualified water quality, the second limit value represents the upper limit threshold of excellent water quality, and the first limit value is smaller than the second limit value.

[0081] When the comprehensive analysis result is greater than or equal to the first limit and less than or equal to the second limit, that is, it falls within the closed interval of [first limit, second limit], the system will automatically determine that the quality of the first artificial seawater is at a good level. This means that the key water quality indicators of this batch of artificial seawater are within the ideal range, which can well meet the growth needs of the aquaculture objects, there is no water pollution or other abnormal conditions, and it can be safely used for aquaculture production.

[0082] S53, when the analysis result is lower than the first limit value, determining the quality difference of the first artificial seawater; In one embodiment, after the comprehensive analysis result is compared with the preset limit, the system will automatically determine whether the analysis result is less than the first limit. The first limit here is pre-set according to the quality standards and risk control requirements of artificial seawater aquaculture, indicating the lower limit threshold of qualified water quality. When the water quality index is lower than the first limit, it means that some key parameters deviate seriously from the normal range and can no longer meet the basic growth needs of the aquaculture object. Continuing to use this batch of seawater for aquaculture may lead to increased aquaculture risks and serious negative effects.

[0083] S54: When the analysis result is higher than the second limit value, determine that the quality of the first artificial seawater exceeds the standard.

[0084] In one embodiment, after the comprehensive analysis result is compared with the preset limit, the system will automatically determine whether the analysis result is greater than the second limit. The second limit here is pre-set according to the quality standards and ecological safety requirements of artificial seawater aquaculture, indicating the upper limit threshold of excellent water quality. When the water quality index is higher than the second limit, it means that some key parameters are seriously beyond the normal range, such as excessive concentrations of harmful substances such as heavy metals and organic pollutants, which have already posed a threat to the growth and development of the aquaculture objects and the quality of the products. At the same time, it may also destroy the ecological balance of the aquaculture waters and produce a chain of negative effects.

[0085] In order to effectively deal with such water quality exceeding the standard, the system will automatically trigger the water quality pollution warning mechanism after determining that the comprehensive analysis result is greater than the second limit. The system will generate a water quality exceeding standard report, indicating the comprehensive analysis result and the specific value of the second limit, and specifically reminding the main types of pollutants and potential hazards of water quality exceeding the standard.

[0086] In a possible embodiment of the present application, before inputting the real-time aquaculture seawater detection results, aquaculture production plan data and water purification equipment operation status data into a preset water quality development trend prediction model to obtain water quality prediction trend data, the following is also included: S61, obtaining historical breeding sample data, and performing fitting processing on the historical breeding sample data by using the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting processing; In one embodiment, historical aquaculture sample data within a certain time range is obtained from the aquaculture management database. These data include historical water quality monitoring records, aquaculture input usage records, aquaculture object growth data, etc., reflecting the operating status of the artificial seawater aquaculture system and water quality changes in the past period of time.

[0087] After obtaining historical breeding sample data, the system will use the least squares method and polynomial regression algorithm to fit the data. The least squares method is a commonly used data fitting method. It determines the best fitting function by finding a curve with the smallest sum of squares of vertical distances from the sample data points. The polynomial regression algorithm uses a polynomial function to fit the sample data based on the least squares method in order to obtain a nonlinear model that is closer to the actual situation.

[0088] During the fitting process, the system will select a polynomial function of appropriate order as the fitting model according to the characteristics of the historical aquaculture sample data. The coefficients of the polynomial function are solved by the least squares method to minimize the sum of square errors between the fitting curve and the sample data points. After multiple iterations of optimization, the system will obtain a polynomial regression model that can better describe the trend of historical water quality changes, that is, the historical aquaculture sample data after fitting processing.

[0089] By fitting the historical aquaculture sample data, the noise interference in the sample data can be effectively removed, and the inherent laws and trend characteristics of water quality changes can be extracted. The historical aquaculture sample data after fitting will serve as an important input for the water quality development trend prediction model, providing high-quality sample support for model training. In the subsequent prediction process, the model can make full use of the water quality change laws contained in the historical data, combined with real-time monitoring data, production plans and other information, to make more accurate predictions on future water quality development trends, and provide a reliable basis for aquaculture decision-making and risk control.

[0090] S62, constructing water quality prediction trend data based on the historical aquaculture sample data after fitting processing, the historical aquaculture sample data including historical aquaculture seawater detection results, historical aquaculture production plan data and historical water quality purification equipment operation status data.

[0091] In one embodiment, key features are extracted from the historical aquaculture sample data after fitting. These features include historical aquaculture seawater detection results, historical aquaculture production plan data, and historical water purification equipment operation status data, which respectively reflect the water quality status, production operation plan, and purification equipment working condition of the artificial seawater aquaculture system in the past period. Through feature extraction, the original high-dimensional historical data can be converted into a low-dimensional feature vector with condensed information, providing a more refined and effective input for subsequent model training.

[0092] After extracting key features, the system will use machine learning algorithms to build a water quality prediction trend model using the historical aquaculture sample data after fitting as training samples. Common machine learning algorithms include support vector machines, random forests, neural networks, etc. You can select the appropriate algorithm for model training based on the characteristics of the sample data and the requirements of the prediction task.

[0093] During the model training process, the system divides the sample data into training set and validation set according to a certain ratio. The training set is used to learn and optimize the model parameters, and the validation set is used to evaluate the generalization performance of the model. Through multiple rounds of iterative training, the model structure and parameters are continuously adjusted to achieve a low level of fitting error on the training set and prediction error on the validation set, forming a stable water quality prediction trend model with strong generalization ability.

[0094] The water quality prediction trend model constructed based on the historical aquaculture sample data after fitting processing can better capture and learn the inherent laws and influencing factors of water quality changes. By analyzing the correlation between historical water quality monitoring results and aquaculture production plans and purification equipment operating status, the model can reveal the degree and mechanism of influence of different factors on water quality changes and form a set of quantitative prediction rules. In subsequent prediction applications, the model can quickly predict the development trend of artificial seawater quality in the future based on various data collected in real time, and generate intuitive water quality prediction trend data, providing a reference basis for the decision-making behavior of aquaculture managers, and facilitating the refined management and risk prevention of the aquaculture process.

[0095] In a possible embodiment of the present application, the historical breeding sample data is fitted by the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting, including: S71, classifying the historical breeding sample data based on the equipment information, and classifying the historical breeding sample data with the same equipment information into one category; In one embodiment, the device information is extracted from the historical breeding sample data. The device information includes identification information such as the model, number, and manufacturer of the data acquisition device, as well as relevant parameters of the data transmission and processing device. By parsing the metadata or additional information of the historical breeding sample data, the device information corresponding to each sample data can be obtained.

[0096] After extracting the equipment information, the system will classify and group the historical breeding sample data according to the similarity of the equipment information. For sample data with exactly the same key attributes such as equipment model and serial number, the system will classify them into the same category. For sample data with certain differences in equipment information, the system will classify them according to the preset similarity threshold. The similarity threshold can be set according to the technical characteristics of the equipment and the data quality requirements to balance the fineness of the classification and the coverage of the sample data.

[0097] By classifying historical breeding sample data based on equipment information, sample data from the same equipment or similar equipment can be brought together to form several sample subsets with the same equipment information. This classification process can effectively eliminate the systematic errors introduced by different equipment in the data collection, transmission and processing links, and improve the consistency and comparability of sample data. At the same time, for each sample subset with the same equipment information, the system can select the optimal fitting processing method and parameters according to its data characteristics, thereby improving the accuracy and reliability of the fitting results.

[0098] In the subsequent fitting process, the system will perform least squares and polynomial regression fitting on each sample subset with the same device information, and obtain a series of fitting results for different devices. These fitting results not only reflect the inherent laws of the historical sample data collected by different devices, but also reflect the data characteristics of the devices themselves. Through the comprehensive analysis of the fitting results of each subset, the overall trend and change mechanism of the historical breeding sample data can be more comprehensively and accurately grasped.

[0099] S72, performing fitting processing on the historical breeding sample data with the same equipment information by using the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting processing.

[0100] In one embodiment, a sample subset with the same device information is selected from the classified historical aquaculture sample data, which includes all aquaculture sample data collected and recorded by a specific device in the past period of time, reflecting the water quality status and change trend of the environment monitored by the device.

[0101] For the selected sample subsets with the same device information, the system will use the least squares method for data fitting. The least squares method is a commonly used parameter estimation method that determines the parameters of the best fitting curve by minimizing the sum of squared errors between the sample data and the fitting curve. When applying the least squares method, the system will select a suitable function form as the fitting model based on the data distribution characteristics of the sample subset, such as a linear function, exponential function, logarithmic function, etc. By constructing the objective function of the sum of squared errors and solving the minimum value, the optimal parameter estimate of the fitting function can be obtained.

[0102] After completing the least squares fitting, the system will further use the polynomial regression algorithm to fit the sample subset. Polynomial regression is a regression analysis method based on polynomial functions. By introducing higher-order terms, it can fit the complex changing trends of nonlinear data. When applying the polynomial regression algorithm, the system will select a polynomial function of appropriate order as the regression model based on the data characteristics of the sample subset and the fitting accuracy requirements. By estimating the coefficients of the polynomial function through the least squares method and using regularization technology to control the model complexity, a smooth fitting curve with good generalization performance can be obtained.

[0103] By combining the least squares method with the polynomial regression algorithm, the system can extract a fitting function that can accurately reflect the changing pattern of the environmental water quality monitored by the device from historical sample data with the same device information. This fitting function not only takes into account the overall trend of the sample data, but also takes into account the local nonlinear characteristics, which significantly improves the fitting accuracy of the sample data and the prediction ability of unknown data.

[0104] like Figure 5 As shown, an embodiment of the present application provides a water quality detection device for artificial seawater, wherein the water quality detection device for artificial seawater first calls a first detection result acquisition module 110 to obtain first artificial seawater, and based on a plurality of preset water quality detection indicators, performs water quality detection on the first artificial seawater to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each water quality detection indicator, and the preset water quality detection indicator includes at least an effective component indicator, a harmful component indicator, and a physical parameter indicator; the first detection result acquisition module 110 is connected to a second detection result acquisition module 120, and then calls the second detection result acquisition module 120 to set a detection time interval, and based on a plurality of water quality detection indicators, performs water quality detection on the first artificial seawater again to obtain a second detection result, wherein the second detection result includes The second detection value corresponding to each water quality detection index; the second detection result acquisition module 120 is connected to the comprehensive detection value calculation module 130, and then the comprehensive detection value calculation module 130 is called to calculate the first comprehensive detection value according to multiple first detection values, and the second comprehensive detection value according to multiple second detection values; the comprehensive detection value calculation module 130 is connected to the comprehensive detection value analysis module 140, and then the comprehensive detection value analysis module 140 is called to analyze the first comprehensive detection value and the second comprehensive detection value using a preset statistical method to obtain an analysis result; the comprehensive detection value analysis module 140 is connected to the artificial seawater quality determination module 150, and then the artificial seawater quality determination module 150 is called to determine the quality of the first artificial seawater according to the analysis result. The water quality detection device of artificial seawater is applied to the water quality detection method of artificial seawater.

[0105] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0106] The present application also discloses an electronic device. Figure 6 , Figure 6 The electronic device 600 may include: at least one processor 601 , at least one network interface 604 , a user interface 603 , a memory 605 , and at least one communication bus 602 .

[0107] The communication bus 602 is used to realize the connection and communication between these components.

[0108] The user interface 603 may include a display interface (Display Interface) and a camera interface (Camera Interface), and the optional user interface 603 may also include a standard wired interface and a wireless interface.

[0109] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0110] Among them, the processor 601 may include one or more processing cores. The processor 601 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 605, and calling data stored in the memory 605. Optionally, the processor 601 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 601 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 601, and it can be implemented by a single chip.

[0111] Among them, the memory 605 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 605 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 605 may optionally be at least one storage device located away from the aforementioned processor 601. Refer to Figure 6 , the memory 605 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a method for detecting water quality of artificial seawater.

[0112] exist Figure 6In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 601 can be used to call an application program of a water quality detection method for artificial seawater stored in the memory 605. When executed by one or more processors 601, the electronic device 600 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0113] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some service interface, device or unit, which can be electrical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0118] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0119] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for detecting the water quality of artificial seawater, characterized in that: The method comprises: Acquire first artificial seawater, and perform water quality detection on the first artificial seawater based on a plurality of preset water quality detection indicators to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each of the water quality detection indicators, and the preset water quality detection indicators include at least an effective component indicator, a harmful component indicator, and a physical parameter indicator; Setting a detection time interval, and based on the plurality of water quality detection indicators, performing water quality detection on the first artificial seawater again to obtain a second detection result, wherein the second detection result includes a second detection value corresponding to each of the water quality detection indicators; A first comprehensive detection value is calculated based on the plurality of the first detection values, and a second comprehensive detection value is calculated based on the plurality of the second detection values; Using a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result; The quality of the first artificial seawater is determined according to the analysis result.

2. The method according to claim 1, characterized in that The step of performing water quality testing on the first artificial seawater based on a plurality of preset water quality testing indicators to obtain a first testing result includes: pre-treating the first artificial seawater to obtain second artificial seawater; Based on the plurality of water quality detection indicators, the second artificial seawater is detected to obtain a first detection result.

3. The method according to claim 1, characterized in that The step of calculating a first comprehensive detection value according to the plurality of the first detection values ​​comprises: According to the first detection values, a first comprehensive detection value is calculated using a preset comprehensive detection value calculation method, wherein the preset comprehensive detection value calculation method is: P1 = Y1 × Q1 + Y2 × Q2 .... + Y i ×Q i , P1 is the first comprehensive detection value, Y i is the first detection value detected based on the i-th indicator, Q i is the preset weight value of the i-th indicator, and i is the serial number corresponding to the water quality detection indicator of the quality of the first artificial seawater.

4. The method according to claim 1, characterized in that The using of a preset statistical method to analyze the first comprehensive detection value and the second comprehensive detection value to obtain an analysis result includes: Using the preset statistical method to calculate the mean value, variance and change rate of the first comprehensive detection value and the second comprehensive detection value; The mean value, variance and change rate are analyzed to obtain analysis results.

5. The method according to claim 1, characterized in that Determining the quality of the first artificial seawater according to the analysis result includes: Comparing the analysis result with a preset first limit value and a preset second limit value; In a case where the analysis result is between the first limit value and the second limit value, determining that the quality of the first artificial seawater is good, wherein the first limit value is less than the second limit value; In a case where the analysis result is lower than the first limit value, determining a quality difference of the first artificial seawater; When the analysis result is higher than the second limit value, it is determined that the quality of the first artificial seawater exceeds the standard.

6. The method according to claim 1, characterized in that Before inputting the real-time aquaculture seawater detection result, the aquaculture production plan data and the water purification equipment operation status data into a preset water quality development trend prediction model to obtain water quality prediction trend data, the method further includes: Acquire historical breeding sample data, and perform fitting processing on the historical breeding sample data by using the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting processing; Based on the historical aquaculture sample data after the fitting process, water quality prediction trend data is constructed, wherein the historical aquaculture sample data includes historical aquaculture seawater detection results, historical aquaculture production plan data and historical water quality purification equipment operation status data.

7. The method according to claim 6, characterized in that The fitting process of the historical breeding sample data by the least square method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting process includes: The historical breeding sample data are classified based on the equipment information, and the historical breeding sample data with the same equipment information are classified into one category; the historical breeding sample data with the same equipment information are fitted by the least squares method and the polynomial regression algorithm to obtain the historical breeding sample data after fitting.

8. A water quality detection device for artificial seawater, characterized in that: The device comprises: A first detection result acquisition module (110) is used to acquire first artificial seawater, and based on a plurality of preset water quality detection indicators, perform water quality detection on the first artificial seawater to obtain a first detection result, wherein the first detection result includes a first detection value corresponding to each of the water quality detection indicators, and the preset water quality detection indicators include at least an effective component indicator, a harmful component indicator, and a physical parameter indicator; A second detection result acquisition module (120) is used to set a detection time interval, and based on the plurality of water quality detection indicators, perform water quality detection on the first artificial seawater again to obtain a second detection result, wherein the second detection result includes a second detection value corresponding to each of the water quality detection indicators; A comprehensive detection value calculation module (130), used for calculating a first comprehensive detection value based on a plurality of the first detection values, and calculating a second comprehensive detection value based on a plurality of the second detection values; A comprehensive detection value analysis module (140), used for analyzing the first comprehensive detection value and the second comprehensive detection value using a preset statistical method to obtain an analysis result; The artificial seawater quality determination module (150) is used to determine the quality of the first artificial seawater according to the analysis result.

9. An electronic device (600), characterized in that: The electronic device (600) comprises a processor (601), a memory (605), a user interface (603) and a network interface (604), wherein the memory (605) is used to store instructions, the user interface (603) and the network interface (604) are used to communicate with other devices, and the processor (601) is used to execute the instructions stored in the memory (605) so that the electronic device (600) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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