A rapid measurement method for microalgae cell number based on seawater salinity

Abstract

The present invention discloses a method for rapidly measuring the number of microalgae cells based on seawater salinity, and relates to the field of microalgae cell number measurement. The present invention establishes a set of function relationship curves for different types of microalgae cells corresponding to each growth stage, selects a corresponding number of microalgae cell densities as a training set, uses a hemocytometer to accurately count, and combines a seawater salinity meter to measure the salinity value of the algae solution, and integrates the data to establish a set of function relationship curves. The present invention takes into account the differences in types of microalgae cells and changes in growth stages, enhances the applicability and versatility of the measurement method for different microalgae, conducts a comprehensive analysis of the set of function relationship curves to select the optimal function relationship curve, substitutes the microalgae cell density in the verification set into the curve equation to calculate the predicted value, compares and analyzes with the measured value, and selects the curve with the lowest deviation evaluation index as the optimal function relationship curve based on the analysis result, thereby improving the measurement accuracy.

Description

Technical Field

[0001] The invention relates to the field of microalgae cell quantity measurement, and in particular to a method for quickly measuring the microalgae cell quantity based on seawater salinity. Background Art

[0002] It is crucial to timely grasp the cell growth curve during the large-scale cultivation of microalgae cells, which is related to the measurement of microalgae cell quantity and production efficiency.

[0003] The existing methods for rapid measurement of microalgae cell number still have the following problems:

[0004] Traditional measurement methods, such as direct counting under a microscope, are cumbersome, time-consuming, and easily affected by human factors; while some instrument-based measurement methods do not adequately consider the specific growth environment and conditions of microalgae cells, and do not fully consider the complexity of seawater salinity and different growth stages, and are unable to quickly and accurately measure the relationship between the number of microalgae cells and seawater salinity.

[0005] Ambient temperature is one of the key factors affecting the growth and number of microalgae cells. In the actual measurement process, the ambient temperature will change the physiological characteristics and metabolic rate of microalgae cells, thereby affecting the cell number. However, the existing technology often fails to effectively combine the influence of ambient temperature for accurate measurement and correction when measuring the number of microalgae cells. Summary of the invention

[0006] The present invention provides a method for quickly measuring the number of microalgae cells based on seawater salinity to solve the problems raised by the above background technology.

[0007] The present invention is achieved through the following technical solutions.

[0008] A rapid method for measuring the number of microalgae cells based on seawater salinity, including:

[0009] Step 1: Based on different types of microalgae cells, analyze the growth stages of different types of microalgae cells respectively, and establish a set of function relationship curves corresponding to the growth stages of different types of microalgae cells;

[0010] Step 2: Comprehensively analyze the function relationship curve sets corresponding to different types of microalgae cells at different growth stages, and select the final function relationship curve based on the analysis results as the optimal function relationship curve corresponding to different types of microalgae cells at different growth stages;

[0011] Step 3: according to the type of microalgae cells currently measured and the corresponding growth stage, extract the corresponding optimal function relationship curve from the optimal function relationship curves of different types of microalgae cells corresponding to each growth stage, and measure the number of microalgae cells accordingly;

[0012] Step 4: After measuring the number of microalgae cells in step 3, determine the influence coefficient of the current ambient temperature on the number of microalgae cells based on the influence of the ambient temperature during the measurement process, and multiply the number of microalgae cells measured in step 3 by the determined influence coefficient to correct the measured number of microalgae cells.

[0013] In some embodiments, a set of functional relationship curves corresponding to different types of microalgae cells at different growth stages is established, specifically:

[0014] Select R microalgae cell densities from microalgae cells of the same type at the same growth stage as a training set, and use a hemocytometer to accurately count each microalgae cell density in the training set to obtain the accurate microalgae cell density value corresponding to each microalgae cell density in the training set;

[0015] Using a seawater salinity meter to measure the salinity value of the algae solution corresponding to each microalgae cell in the training set, integrating the precise microalgae cell density value of the measured microalgae cells with the corresponding algae solution salinity value, to obtain an integrated data set corresponding to each microalgae cell in the training set;

[0016] Based on the integrated data set corresponding to the same type of microalgae cells at the same growth stage, a functional relationship curve is established;

[0017] Construct a plane rectangular coordinate system, with the x-axis representing the salinity value of the algae solution and the y-axis representing the precise microalgae cell density value; plot the coordinate values ​​of each integrated data group in the plane rectangular coordinate system, and after plotting, use different methods to establish function relationship curves, and integrate the function relationship curves established by different methods as a set of function relationship curves corresponding to the same type of microalgae cells at the same growth stage.

[0018] In some embodiments, a comprehensive analysis is performed on a set of functional relationship curves corresponding to different types of microalgae cells at various growth stages, specifically:

[0019] Select E microalgae cell densities from the microalgae cells of the same type at the same growth stage as the validation set;

[0020] For each microalgae cell density value corresponding to each microalgae cell in the validation set, substitute it into the curve equation corresponding to each function relationship curve in the corresponding function relationship curve set for calculation, and obtain each group of algae solution salinity values ​​corresponding to each microalgae cell in the validation set as the prediction value set;

[0021] The salinity of the algae solution of each microalgae cell in the validation set is measured using a seawater salinity meter as a measured value;

[0022] Calculate the difference between each group of predicted values ​​and the corresponding measured values ​​and take the absolute value. After taking the absolute value, mark each group of values ​​obtained as deviation values ​​and classify them according to the curve equation substituted into it;

[0023] The number of curves in the corresponding function relationship curve set is represented by a number q, where q=1, 2, ..., k, where k represents the total number of curves in the corresponding function relationship curve set;

[0024] After the classification is completed, the mean of each group of deviation values ​​calculated by each functional relationship curve is calculated to obtain the deviation mean Saq; the maximum and minimum values ​​are respectively extracted from each group of deviation values ​​calculated by each functional relationship curve, and recorded as the deviation peak value Sbq and the deviation valley value Scq.

[0025] In some embodiments, the final functional relationship curve is selected as follows:

[0026] According to the formula , weighted calculation is performed on the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq corresponding to each functional relationship curve, so as to obtain the deviation evaluation index Fzq of each functional relationship curve; wherein c1, c2 and c3 are the influence weight factors of the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq respectively;

[0027] The functional relationship curve corresponding to the lowest deviation evaluation index Fzq is selected as the optimal functional relationship curve for the same type of microalgae cells at the same growth stage.

[0028] In some embodiments, the corresponding optimal functional relationship curve is extracted, and the number of microalgae cells is measured accordingly, specifically:

[0029] The current microalgae cells are measured using a seawater salinity meter. After the seawater salinity meter is stable, the salinity value of the algae solution corresponding to the current microalgae cells is read; the functional relationship curve corresponding to the current microalgae cells is extracted, and the read salinity value is converted into a microalgae cell density value, and then the cell number of the current microalgae cells is calculated according to the volume of the algae solution corresponding to the current microalgae cells; that is, the formula is obtained by microalgae number = microalgae cell density value × algae solution volume.

[0030] In some embodiments, the influence coefficient of the current ambient temperature on the number of microalgae cells is determined as follows:

[0031] According to the pre-constructed temperature correction formula corresponding to various types of microalgae cells at different growth stages, when measuring the number of microalgae cells, the temperature value of the environment during the measurement process is recorded;

[0032] The temperature values ​​of the environment at each time point during the measurement process were extracted, and after the average value was calculated, the average value was substituted into the pre-constructed temperature correction formula corresponding to the type and growth stage of the microalgae cells to calculate, so as to obtain the influence coefficient of the current environmental temperature on the number of microalgae cells.

[0033] In some embodiments, the pre-constructed temperature correction formulas corresponding to various types of microalgae cells at different growth stages are obtained, specifically:

[0034] Different temperature gradient conditions are set for each growth stage of various types of microalgae cells;

[0035] After the setting is completed, the number of microalgae cells of various types of microalgae cells at each temperature point under the temperature step conditions corresponding to each growth stage is measured repeatedly m times;

[0036] After repeatedly measuring the number of microalgae cells m times at each temperature point, the number of microalgae cells in the m groups measured at each temperature point is calculated using the standard deviation formula to obtain the measurement deviation value;

[0037] The maximum allowable value of the measurement deviation of different types of microalgae cells at each temperature point in each growth stage is preset. If the measurement deviation calculated at a certain temperature point is greater than the corresponding preset maximum allowable value, the measurement is repeated m times. Otherwise, the average of the number of microalgae cells in each group at the temperature point is taken as the final number of microalgae cells at the corresponding temperature point;

[0038] Each temperature point is combined with the corresponding final number of microalgae cells in pairs to obtain groups of sorting packages of various types of microalgae cells at each growth stage.

[0039] In some embodiments, obtaining pre-constructed temperature correction formulas corresponding to various types of microalgae cells at different growth stages also includes:

[0040] For each group of sorting packages of various types of microalgae cells at each growth stage, the least square method is used to substitute each group of sorting packages into each preset function model for fitting; after the fitting is completed, the root mean square error and determination coefficient corresponding to each preset function model are obtained;

[0041] The root mean square error and determination coefficient corresponding to each preset function model are marked as V1 and V2 respectively; according to the formula The root mean square error V1 and the determination coefficient V2 corresponding to each preset function model are weightedly calculated to obtain the model evaluation index Vs of each type of microalgae cell corresponding to each preset function model at each growth stage; wherein g1 and g2 are the influence weight factors corresponding to the root mean square error V1 and the determination coefficient V2, respectively;

[0042] From the preset function models corresponding to various types of microalgae cells at each growth stage, the preset function models corresponding to the highest model evaluation index Vs are selected as the preferred function models of various types of microalgae cells at each growth stage;

[0043] The functional relationship formula corresponding to the optimal function model is used as the temperature correction formula for various types of microalgae cells in each growth stage.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention establishes a set of function relationship curves for different types of microalgae cells corresponding to each growth stage, selects a corresponding number of microalgae cell densities as a training set, uses a blood cell counting plate to accurately count, and combines a seawater salinity meter to measure the salinity value of the algae solution, and integrates the data to establish a set of function relationship curves, taking into account the type differences and growth stage changes of microalgae cells, thereby enhancing the applicability and versatility of the measurement method for different microalgae.

[0046] The present invention selects the optimal function relationship curve by comprehensively analyzing the function relationship curve set, substitutes the microalgae cell density in the verification set into the curve equation to calculate the predicted value, compares it with the measured value to obtain the deviation value, calculates the deviation mean, peak value and valley value and weights them to obtain the deviation evaluation index, selects the curve with the lowest deviation evaluation index as the optimal function relationship curve, and improves the accuracy of measurement;

[0047] The present invention sets different temperature step conditions for each growth stage of various types of microalgae cells, repeatedly measures and calculates the measurement deviation value, obtains an accurate data combination of the number of microalgae cells and the temperature to obtain a sorting package, uses the least squares method to substitute the sorting package into a preset function model for fitting, calculates the model evaluation index weighted by the root mean square error and the determination coefficient, selects the optimal function model as the temperature correction formula, records the ambient temperature value in the actual measurement and calculates the average value, substitutes it into the temperature correction formula to obtain the influence coefficient, corrects the measured number of microalgae cells, and effectively combines the influence of the ambient temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the present invention;

[0049] Figure 2 It is a schematic diagram of establishing a functional relationship curve corresponding to the integrated data set in the present invention. DETAILED DESCRIPTION

[0050] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0051] See also Figure 1As shown, a rapid method for measuring the number of microalgae cells based on seawater salinity includes:

[0052] Step 1: Based on different types of microalgae cells, analyze the growth stages of different types of microalgae cells respectively, and establish a set of function relationship curves corresponding to the growth stages of different types of microalgae cells;

[0053] Specifically:

[0054] Select R microalgae cell densities from microalgae cells of the same type at the same growth stage as a training set; the specific value of R is preset by a technician, and R ≥ 5; and the R microalgae cell densities are from low to high, and for each microalgae cell density in the training set, a hemocytometer is used to accurately count, and the accurate microalgae cell density value corresponding to each microalgae cell density in the training set is obtained;

[0055] Using a seawater salinity meter to measure the salinity value of the algae solution corresponding to each microalgae cell in the training set, integrating the precise microalgae cell density value of the measured microalgae cells with the corresponding algae solution salinity value, to obtain an integrated data set corresponding to each microalgae cell in the training set;

[0056] Based on the integrated data set corresponding to the same type of microalgae cells at the same growth stage, a functional relationship curve is established;

[0057] Construct a plane rectangular coordinate system, where the x-axis represents the algae solution salinity value and the y-axis represents the precise microalgae cell density value; draw the coordinate values ​​of each integrated data group in the plane rectangular coordinate system, and after drawing, use different methods to establish function relationship curves, and integrate the function relationship curves established by different methods as a set of function relationship curves corresponding to the same type of microalgae cells at the same growth stage; different methods include linear regression method, nonlinear regression method and polynomial regression, etc.;

[0058] It should be noted that functional relationship curves are established for different types of microalgae cells corresponding to each growth stage, taking into account the type differences and growth stage changes of microalgae cells, thereby enhancing the applicability and versatility of the measurement method for different microalgae.

[0059] Step 1 In the specific implementation process, for example:

[0060] The technicians preset R = 8 algae cell densities. For Chlorella in the logarithmic growth period, the selected algae cell densities are: 2.00, 4.00, 6.00, 8.00, 10.00, 12.00, 14.00, 16.00, and these density values ​​are arranged from low to high;

[0061] For each selected algal cell density, a hemocytometer was used for accurate counting. Assuming that the actual counting result is consistent with the selected density value, there may be slight differences in the actual operation, that is, 8 accurate algal cell density values ​​were obtained: 2.00, 4.00, 6.00, 8.00, 10.00, 12.00, 14.00, and 16.00;

[0062] The salinity values ​​of the algae solution corresponding to each algae cell density were measured using a seawater salinity meter, which were 1.0165, 1.0168, 1.0170, 1.0172, 1.0174, 1.0176, 1.0178, and 1.0180 respectively;

[0063] The measured precise algae cell density values ​​were integrated with the corresponding algae solution salinity values ​​to obtain 8 integrated data sets as follows:

[0064] (1.0165, 2.00), (1.0168, 4.00), (1.0170, 6.00), (1.0172, 8.00), (1.0174,10.00), (1.0176, 12.00), (1.0178, 14.00), (1.0180, 16.00);

[0065] Construct a plane rectangular coordinate system, where the x-axis represents the algae liquid salinity value, and the y-axis represents the precise algae cell density value; plot the coordinate values ​​of the above 8 integrated data sets in the plane rectangular coordinate system, and use the linear regression method to establish a functional relationship curve. The linear equation obtained by calculation is (this equation is only an example, and the actual equation is calculated based on the data) y=1000x-1004.35. The curve represented by the linear equation is the functional relationship curve corresponding to Chlorella in the logarithmic growth period;

[0066] Step 2: Comprehensively analyze the function relationship curve sets corresponding to different types of microalgae cells at different growth stages, and select the final function relationship curve based on the analysis results as the optimal function relationship curve corresponding to different types of microalgae cells at different growth stages;

[0067] Specifically:

[0068] Select E microalgae cell densities from microalgae cells of the same type at the same growth stage as a validation set; the specific value of E is preset by a technician, and E≥8;

[0069] For each microalgae cell density value corresponding to each microalgae cell in the validation set, substitute it into the curve equation corresponding to each function relationship curve in the corresponding function relationship curve set for calculation, and obtain each group of algae solution salinity values ​​corresponding to each microalgae cell in the validation set as the prediction value set;

[0070] The salinity of the algae solution of each microalgae cell in the validation set is measured using a seawater salinity meter as a measured value;

[0071] Calculate the difference between each group of predicted values ​​and the corresponding measured values ​​and take the absolute value. After taking the absolute value, mark each group of values ​​obtained as deviation values ​​and classify them according to the curve equation substituted into it;

[0072] The number of curves in the corresponding function relationship curve set is represented by a number q, where q=1, 2, ..., k, where k represents the total number of curves in the corresponding function relationship curve set;

[0073] After the classification is completed, the mean of each group of deviation values ​​calculated by each functional relationship curve is calculated to obtain the deviation mean Saq; the maximum value and the minimum value are respectively extracted from each group of deviation values ​​calculated by each functional relationship curve, and recorded as the deviation peak value Sbq and the deviation valley value Scq;

[0074] According to the formula , weighted calculation is performed on the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq corresponding to each functional relationship curve, so as to obtain the deviation evaluation index Fzq of each functional relationship curve; wherein c1, c2 and c3 are the influence weight factors of the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq respectively;

[0075] The function relationship curve corresponding to the lowest deviation evaluation index Fzq is selected as the optimal function relationship curve for the same type of microalgae cells at the same growth stage;

[0076] It should be noted that, taking into account the differences in microalgae cells at different growth stages and types, by selecting a sufficient number of validation set microalgae cell densities and conducting a comprehensive analysis of the set of functional relationship curves, the accuracy of each curve under different density conditions can be more comprehensively evaluated, and the optimal curve can be selected from multiple functional relationship curves, thereby improving the accuracy and pertinence of subsequent measurements.

[0077] Step 3: according to the type of microalgae cells currently measured and the corresponding growth stage, extract the corresponding optimal function relationship curve from the optimal function relationship curves of different types of microalgae cells corresponding to each growth stage, and measure the number of microalgae cells accordingly;

[0078] Specifically:

[0079] The current microalgae cells are measured using a seawater salinity meter. After the seawater salinity meter is stable, the salinity value of the algae solution corresponding to the current microalgae cells is read; the function relationship curve corresponding to the current microalgae cells is extracted, and the read salinity value is converted into a microalgae cell density value, and then the number of the current microalgae cells is calculated according to the volume of the algae solution corresponding to the current microalgae cells; that is, the formula is obtained by the number of microalgae = microalgae cell density value × algae solution volume;

[0080] Step 3: During the specific implementation process, for example:

[0081] First, slowly and evenly pour the algae solution to be measured into a 100ml measuring cylinder, making sure that the measuring cylinder is clean, free of impurities and bubbles;

[0082] Then, gently place the seawater salinometer into the liquid in the measuring cylinder so that the salinometer can float freely and not touch the wall of the measuring cylinder to avoid affecting the measurement accuracy;

[0083] After the salinometer is stable, read the scale value of the liquid level. When reading, the line of sight should be kept level with the liquid level scale line to ensure the accuracy of the reading;

[0084] Finally, the salinity value is converted into the microalgae cell density value through the pre-established function equation, and then the number of microalgae is calculated according to the volume of the algae solution in the measuring cylinder (100 ml) according to the formula: number of microalgae = cell density value × volume of algae solution; after the measurement is completed, the salinity meter and measuring cylinder are cleaned and properly stored for next use.

[0085] In summary, by establishing a scientific functional relationship, subjective misjudgment caused by microscope angle problems can be avoided, and the error in measuring high-concentration microalgae cells can also be reduced;

[0086] Step 4: after measuring the number of microalgae cells in step 3, based on the influence of the ambient temperature during the measurement process, determine the influence coefficient of the current ambient temperature on the number of microalgae cells, and multiply the number of microalgae cells measured in step 3 by the determined influence coefficient, thereby correcting the measured number of microalgae cells;

[0087] Different types of microalgae show different relationships between temperature and microalgae cell number at different growth stages;

[0088] Specifically:

[0089] According to the pre-constructed temperature correction formula corresponding to various types of microalgae cells at different growth stages, when measuring the number of microalgae cells, the temperature value of the environment during the measurement process is recorded; the measurement is performed through the pre-deployed temperature sensor;

[0090] The temperature values ​​of the environment at each time point during the measurement process are extracted, and after the average value is calculated, the average value is substituted into the pre-constructed temperature correction formula corresponding to the type and growth stage of the microalgae cells to be calculated, so as to obtain the influence coefficient of the current environmental temperature on the number of microalgae cells;

[0091] The pre-constructed temperature correction formulas corresponding to various types of microalgae cells at different growth stages are obtained, specifically:

[0092] Different temperature gradient conditions are set for each growth stage of various types of microalgae cells; the specific settings are made by technicians according to the types of microalgae cells;

[0093] After the setting is completed, the number of microalgae cells of various types of microalgae cells at each temperature point under the temperature step conditions corresponding to each growth stage is measured repeatedly m times; the specific value of m is preset by the technicians, and m≥3;

[0094] After repeatedly measuring the number of microalgae cells m times at each temperature point, the number of microalgae cells in the m groups measured at each temperature point is calculated using the standard deviation formula to obtain the measurement deviation value;

[0095] The maximum allowable value of the measurement deviation of different types of microalgae cells at each temperature point in each growth stage is preset. If the measurement deviation calculated at a certain temperature point is greater than the corresponding preset maximum allowable value, the measurement is repeated m times. Otherwise, the average of the number of microalgae cells in each group at the temperature point is taken as the final number of microalgae cells at the corresponding temperature point;

[0096] By repeatedly measuring the number of microalgae cells (m times and m ≥ 3) and calculating the measurement deviation, the influence of random errors can be effectively reduced. For temperature points that exceed the maximum allowable deviation value, measure again to ensure that the number of microalgae cells at each temperature point is more accurate and reliable;

[0097] The mean of the number of microalgae cells in each group at the temperature point that meets the requirements is taken as the final number, which further improves the stability and accuracy of the data;

[0098] Combining each temperature point with the corresponding final number of microalgae cells in pairs, obtaining groups of sorted packages of various types of microalgae cells at each growth stage;

[0099] For each group of finishing packages of various types of microalgae cells at each growth stage, the least square method is used to substitute each group of finishing packages into each preset function model for fitting;

[0100] After fitting is completed, the root mean square error and determination coefficient corresponding to each preset function model are obtained;

[0101] It should be noted that the expression formula of the root mean square error is , where i represents the number of the data point, n is the number of data points, d1 is the actual number of microalgae cells, and d2 is the number of microalgae cells predicted by the model;

[0102] The formula for expressing the coefficient of determination , where SSR and SST represent the total sum of squares and regression sum of squares, respectively;

[0103] The root mean square error and determination coefficient corresponding to each preset function model are marked as V1 and V2 respectively;

[0104] According to the formula The root mean square error V1 and the determination coefficient V2 corresponding to each preset function model are weightedly calculated to obtain the model evaluation index Vs of each type of microalgae cell corresponding to each preset function model at each growth stage; wherein g1 and g2 are the influence weight factors corresponding to the root mean square error V1 and the determination coefficient V2, respectively;

[0105] The least squares method is used to substitute the sorted package data into each preset function model for fitting, and then the fitting effect and explanatory ability of the model are evaluated based on the root mean square error (V1) and the determination coefficient (V2); the smaller the root mean square error, the better the model fits the data, and the closer the determination coefficient is to 1, the stronger the model's explanatory ability for the data.

[0106] The model evaluation index is obtained by weighted calculation of the root mean square error and the coefficient of determination, and the preset function model corresponding to the highest model evaluation index is selected as the preferred function model, which can find the function model that best describes the relationship between the number of microalgae cells and temperature to the greatest extent, thereby improving the accuracy of correcting the number of microalgae cells based on the temperature correction formula;

[0107] From the preset function models corresponding to various types of microalgae cells at each growth stage, the preset function models corresponding to the highest model evaluation index Vs are selected as the preferred function models of various types of microalgae cells at each growth stage;

[0108] The functional relationship formula corresponding to the optimal functional model is used as the temperature correction formula for various types of microalgae cells at each growth stage;

[0109] The obtained temperature correction formulas for various types of microalgae cells at each growth stage can make more accurate corrections to the number of microalgae cells in actual measurements, providing a reliable method for measuring the number of microalgae cells.

[0110] It should be noted that the seawater salinity meter used in the above process has a measurement accuracy of 0.001, a measurement range of 1.000-1.060, and a length of 28 cm.

[0111] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the specific implementation methods of the invention. Obviously, many modifications and changes can be made based on the content of this specification. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A method for rapidly measuring the number of microalgae cells based on seawater salinity, characterized in that: include: Step 1: Based on different types of microalgae cells, analyze the growth stages of different types of microalgae cells respectively, and establish a set of function relationship curves corresponding to the growth stages of different types of microalgae cells; A set of functional relationship curves corresponding to different types of microalgae cells at different growth stages is established, specifically: Select R microalgae cell densities from microalgae cells of the same type at the same growth stage as a training set, and use a hemocytometer to accurately count each microalgae cell density in the training set to obtain the accurate microalgae cell density value corresponding to each microalgae cell density in the training set; Using a seawater salinity meter to measure the salinity value of the algae solution corresponding to each microalgae cell in the training set, integrating the precise microalgae cell density value of the measured microalgae cells with the corresponding algae solution salinity value, to obtain an integrated data set corresponding to each microalgae cell in the training set; Based on the integrated data set corresponding to the same type of microalgae cells at the same growth stage, a functional relationship curve is established; Construct a plane rectangular coordinate system, where the x-axis represents the algae solution salinity value and the y-axis represents the precise microalgae cell density value; plot the coordinate values ​​of each integrated data group in the plane rectangular coordinate system, and after plotting, use different methods to establish function relationship curves, and integrate the function relationship curves established by different methods as a set of function relationship curves corresponding to the same type of microalgae cells at the same growth stage; Step 2: Comprehensively analyze the function relationship curve sets corresponding to different types of microalgae cells at different growth stages, and select the final function relationship curve from the function relationship curve sets corresponding to different types of microalgae cells at different growth stages based on the analysis results as the optimal function relationship curves corresponding to different types of microalgae cells at different growth stages; Step 3: according to the type of microalgae cells currently measured and the corresponding growth stage, extract the corresponding optimal function relationship curve from the optimal function relationship curves of different types of microalgae cells corresponding to each growth stage, and measure the number of microalgae cells accordingly; Step 4: After measuring the number of microalgae cells in step 3, determine the influence coefficient of the current ambient temperature on the number of microalgae cells based on the influence of the ambient temperature during the measurement process, and multiply the number of microalgae cells measured in step 3 by the determined influence coefficient to correct the measured number of microalgae cells.

2. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 1, characterized in that: A comprehensive analysis of the functional relationship curves of different types of microalgae cells corresponding to each growth stage is conducted, specifically: Select E microalgae cell densities from the microalgae cells of the same type at the same growth stage as the validation set; For each microalgae cell density value corresponding to each microalgae cell in the validation set, substitute it into the curve equation corresponding to each function relationship curve in the corresponding function relationship curve set for calculation, and obtain each group of algae solution salinity values ​​corresponding to each microalgae cell in the validation set as the prediction value set; The salinity of the algae solution of each microalgae cell in the validation set is measured using a seawater salinity meter as a measured value; Calculate the difference between each group of predicted values ​​and the corresponding measured values ​​and take the absolute value. After taking the absolute value, mark each group of values ​​obtained as deviation values ​​and classify them according to the curve equation substituted into it; The number of curves in the corresponding function relationship curve set is represented by a number q, where q=1, 2, ..., k, where k represents the total number of curves in the corresponding function relationship curve set; After the classification is completed, the mean of each group of deviation values ​​calculated by each functional relationship curve is calculated to obtain the deviation mean Saq; the maximum and minimum values ​​are respectively extracted from each group of deviation values ​​calculated by each functional relationship curve, and recorded as the deviation peak value Sbq and the deviation valley value Scq.

3. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 2, characterized in that: Select the final functional relationship curve, specifically: According to the formula , weighted calculation is performed on the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq corresponding to each functional relationship curve, so as to obtain the deviation evaluation index Fzq of each functional relationship curve; wherein c1, c2 and c3 are the influence weight factors of the deviation mean value Saq, deviation peak value Sbq and deviation valley value Scq respectively; The functional relationship curve corresponding to the lowest deviation evaluation index Fzq is selected as the optimal functional relationship curve for the same type of microalgae cells at the same growth stage.

4. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 3, characterized in that: The corresponding optimal function relationship curve is extracted, and the number of microalgae cells is measured accordingly, specifically: The current microalgae cells are measured using a seawater salinity meter. After the seawater salinity meter is stable, the salinity value of the algae solution corresponding to the current microalgae cells is read; the functional relationship curve corresponding to the current microalgae cells is extracted, and the read salinity value is converted into a microalgae cell density value, and then the cell number of the current microalgae cells is calculated according to the volume of the algae solution corresponding to the current microalgae cells; that is, the formula is obtained by microalgae number = microalgae cell density value × algae solution volume.

5. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 4, characterized in that: Determine the influence coefficient of the current ambient temperature on the number of microalgae cells, specifically: According to the pre-constructed temperature correction formula corresponding to various types of microalgae cells at different growth stages, when measuring the number of microalgae cells, the temperature value of the environment during the measurement process is recorded; The temperature values ​​of the environment at each time point during the measurement process were extracted, and after the average value was calculated, the average value was substituted into the pre-constructed temperature correction formula corresponding to the type and growth stage of the microalgae cells to calculate, so as to obtain the influence coefficient of the current environmental temperature on the number of microalgae cells.

6. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 5, characterized in that: The pre-constructed temperature correction formulas corresponding to various types of microalgae cells at different growth stages are obtained, specifically: Different temperature gradient conditions are set for each growth stage of various types of microalgae cells; After the setting is completed, the number of microalgae cells of various types of microalgae cells at each temperature point under the temperature step conditions corresponding to each growth stage is measured repeatedly m times; After repeatedly measuring the number of microalgae cells m times at each temperature point, the number of m groups of microalgae cells measured at each temperature point is calculated using the standard deviation formula to obtain the measurement deviation value; The maximum allowable value of the measurement deviation of different types of microalgae cells at each temperature point in each growth stage is preset. If the measurement deviation calculated at a certain temperature point is greater than the corresponding preset maximum allowable value, the measurement is repeated m times. Otherwise, the average of the number of microalgae cells in each group at the temperature point is taken as the final number of microalgae cells at the corresponding temperature point; Each temperature point is combined with the corresponding final number of microalgae cells in pairs to obtain groups of sorting packages of various types of microalgae cells at each growth stage.

7. The method for rapid measurement of microalgae cell number based on seawater salinity according to claim 6, characterized in that: The pre-constructed temperature correction formulas corresponding to various types of microalgae cells at different growth stages are obtained, including: For each group of sorting packages of various types of microalgae cells at each growth stage, the least square method is used to substitute each group of sorting packages into each preset function model for fitting; after the fitting is completed, the root mean square error and determination coefficient corresponding to each preset function model are obtained; The root mean square error and determination coefficient corresponding to each preset function model are marked as V1 and V2 respectively; according to the formula The root mean square error V1 and the determination coefficient V2 corresponding to each preset function model are weightedly calculated to obtain the model evaluation index Vs of each type of microalgae cell corresponding to each preset function model at each growth stage; wherein g1 and g2 are the influence weight factors corresponding to the root mean square error V1 and the determination coefficient V2, respectively; From the preset function models corresponding to various types of microalgae cells at each growth stage, the preset function models corresponding to the highest model evaluation index Vs are selected as the preferred function models of various types of microalgae cells at each growth stage; The functional relationship formula corresponding to the optimal function model is used as the temperature correction formula for various types of microalgae cells in each growth stage.

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