A method for calculating the probability and biomass of large drifting algae in the sea

Abstract

The present invention discloses a method for calculating the probability and biomass of large drift algae appearing in the sea area, including using monitoring means to obtain relevant data of environmental factors and the sea area where large drift algae are located, combining the basic principles of phenology, constructing a large drift algae multi-source time series database, based on the database, distinguishing volatile environmental factors and stable environmental factors, calculating the information entropy of large drift algae appearing in the sea area, and obtaining the probability of large drift algae appearing in the grid within a set time scale; using a causal analysis method to determine the key stability environmental limiting factors for the growth of large drift algae; analyzing the influence of key stability environmental limiting factors on the growth of large drift algae biomass, constructing a large drift algae growth model, and calculating the growth amount of large drift algae biomass in each grid within a set time scale. The present invention is helpful for the early comprehensive prevention and control of large algae blooms, and is helpful for improving the ability to respond to major marine disasters and marine environmental security.

Description

Technical Field

[0001] The present invention relates to the technical field of marine disaster prevention and control, and in particular to a method for calculating the probability and biomass of large drifting algae appearing in a sea area. Background Art

[0002] It is generally believed that the factors affecting the appearance and occurrence of large drift algae in the sea include nutrients, light, ocean currents, wind direction, wind speed, etc. In smaller time scales such as days, weeks, and ten days, seawater quality and light intensity can be considered stable, and the biomass growth of large drift algae can be calculated based on this; however, the time scale of changes in factors such as wind direction, wind speed, and sunshine time (affected by cloud cover) is smaller, the stability is very low, and the uncertainty is very high. Even factors such as ocean currents have considerable uncertainty. It is relatively difficult to estimate the sea area where large drift algae occur using deterministic methods such as dynamics, and the accuracy is not high. The variability of ocean current velocity and wind direction and wind force causes the variability of the sea area where large drift algae appear, as well as the variability of the corresponding biomass, and the scale of algal blooms and the degree of ecological disaster damage are also very variable. At present, there is no better measure to prevent the ecological disaster of large drift algae, and emergency salvage is still the main response. However, the relevant departments need to determine the scale and area of ​​salvage when organizing salvage operations.

[0003] The biomass of large drift algae is an important basis for decision-making in launching salvage operations, and the sea area where they occur is a prerequisite for arranging salvage operations. Although remote sensing inversion technology can be successfully used to estimate the distribution area of ​​floating green algae and its changes over time, the sea area and biomass where large drift algae will appear in the future, which are important parameters for assessing the scale of large drift algae disasters and making disaster prevention and mitigation decisions, cannot be determined by remote sensing methods. Although there are also studies that estimate the biomass of large drift algae through ecological dynamics models, such models have many parameters and are difficult to verify through experiments.

[0004] So far, there is no report on estimating the sea area and biomass of large drifting algae by distinguishing between variable environmental factors and stable environmental factors. Summary of the invention

[0005] The purpose of the present invention is to provide a method for calculating the probability and biomass of large drift algae appearing in the sea area. Based on a multi-source time series database of large drift algae, the probability distribution of unknown events such as the future sea area where large drift algae appear is inferred through the known information of the sea area where large drift algae appear. The causal analysis method is used to select key environmental factors, construct a large drift algae growth model, calculate the future biological growth of large drift algae, realize the calculation of the sea area where large drift algae appear and the biomass growth, and provide a basis for disaster reduction and prevention decision-making.

[0006] Technical solution of the present invention: The present invention proposes a method for calculating the probability and biomass of large drifting algae appearing in the sea area, comprising the following steps:

[0007] S1. Use multiple types of monitoring methods to obtain relevant data on environmental factors and sea areas where large drift algae appear, and combine the basic principles of phenology to build a multi-source time series database of large drift algae; environmental factors include seawater temperature, salinity, dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved organic nitrogen, dissolved organic phosphorus, light intensity, light duration, wind direction and wind speed.

[0008] S2. Based on the multi-source time series database of large drift algae, distinguish between volatile environmental factors and stable environmental factors, calculate the information entropy of the sea area where large drift algae appear, and obtain the probability of large drift algae appearing in the grid within the set time scale.

[0009] S3. Use causal analysis methods to determine the key stability environmental limiting factors for the growth of large drift algae.

[0010] S4. Analyze the impact of key stability environmental limiting factors on the biomass growth of large drift algae and construct a growth model of large drift algae.

[0011] S5. Use the large drift algae growth model to calculate the growth of large algae biomass in each grid within a set time scale, and complete the measurement of the sea area where large drift algae appear and the biomass growth.

[0012] Furthermore, in step S1, constructing a large-scale drift algae multi-source time series database includes the following contents:

[0013] According to the requirements for the distribution of large drift algae salvage work, the target sea area is gridded to obtain its longitude and latitude information, that is, the relevant data of the sea area where large drift algae appear, and environmental factors are collected online in real time by using pile foundation infrastructure, fixed-point observation buoys, water quality observation instruments (such as nutrient analyzers, algae analyzers, chlorophyll), channel fluorescence sensors and monitoring methods (such as video monitoring, satellite remote sensing, aerial telemetry, ship surveillance and monitoring, and volunteer on-site monitoring).

[0014] Environmental factors are interpolated at grid points, and the neighbor method is used to group the relevant data of the sea areas where large drift algae appear into the nearest grid, completing the construction of a multi-source time series database of large drift algae.

[0015] Among them, satellite remote sensing data has the advantages of large scale, wide coverage and high temporal resolution, while buoy observation data is continuous and easy to obtain.

[0016] Furthermore, in step S2, the probability of large drift algae appearing in the grid includes the following:

[0017] The CV (Coefficient of Variation) of environmental factors in the multi-source time series database within the set time scale is calculated for each grid, and environmental factors with CV < 50% are classified as stable environmental factors, and environmental factors with CV ≥ 50% are classified as volatile environmental factors.

[0018] The maximum entropy analysis method was used to determine the relationship between the probability of large drift algae appearing in the sea area and the stability environmental factors and the variability environmental factors. Combined with the spatial distribution database of marine variability environmental factors in the environmental factors, the contribution rate of environmental factors to the occurrence of large drift algae in the grid was calculated, and the probability of large drift algae appearing in each grid was obtained. The specific content is as follows:

[0019] Set the characteristic function f i (x ij ,y k ) represents the probability of large drift algae appearing in the jth grid when the kth environmental factor exists in the i-th grid where large drift algae appear. The characteristic function set is C, Among them, p represents the conditional probability, represents the expected value of the empirical distribution, E p (f i ) represents the expected value of the characteristic function, T represents seawater temperature, S represents salinity, DIN represents dissolved inorganic nitrogen, DIP represents dissolved inorganic phosphorus, DON represents dissolved organic nitrogen, DOP represents dissolved organic phosphorus, Lin represents light intensity, Lt represents light duration, Wd represents wind direction, and Ws represents wind speed.

[0020] when When it is equal to the expected value of the characteristic function, the maximum value of the information entropy of the conditional probability distribution p(y|x) is obtained, that is, the maximum entropy. The specific formula is:

[0021]

[0022] Among them, H(p) represents the maximum entropy, n x represents the number of occurrences of large drifting algae in a known sea area, n y Indicates the number of times environmental factors appear in a known sea area, represents the average probability of large drifting algae appearing in a known sea area, p(n y |n x ) represents the probability of the environmental factor appearing when large drift algae appear in a known sea area, a represents the minimum number of large drift algae appearing in a grid, b represents the maximum number of large drift algae appearing in a grid, and f i (x i ,ω k ) represents the probability of the existence of large drift algae and the kth environmental factor set in the i-th grid, xi Indicates the i-th grid where large drift algae appear, ω k Represents the set of k-th environmental factors in the ith grid.

[0023] In fact, each characteristic function is a constraint condition for solving the maximum entropy. The characteristic function set C is the set of all constraints that need to be satisfied when solving the maximum entropy. The problem of solving the maximum entropy here is essentially an optimization problem with constraints. Through the Lagrange duality, this problem can be transformed into an unconstrained optimization problem, and then the Lagrange function is used to introduce the Lagrange multiplier, and finally the Lagrange multiplier is obtained by solving the optimization problem.

[0024] The cumulative value of all environmental factors on the probability of large drift algae appearing in each grid is calculated and used as the contribution rate of environmental factors to the prediction of the appearance of large drift algae. The specific formula is:

[0025]

[0026] Among them, CB k Represents the contribution rate of the kth environmental factor.

[0027] When large drift algae appear in the i-th grid, the probability of large drift algae appearing in the j-th grid is predicted. The specific formula is:

[0028]

[0029] Among them, P ij represents the probability of large drift algae appearing in the jth grid when large drift algae appear in the i-th grid, λ0, λ n All represent Lagrange multipliers, n represents the total number of grids, f i (x ij ,ω k ) represents the probability of the existence of large drift algae and the kth environmental factor set in the i-th and j-th grids, x ij Indicates the i-th and j-th grids where large drift algae appear.

[0030] Furthermore, in step S3, determining the key stability environmental limiting factors includes the following:

[0031] Based on information entropy, the causal analysis method was used to analyze the dominant factors related to the biomass changes of large drift algae in environmental factors, and the maximum likelihood estimate was calculated. The specific formula is:

[0032]

[0033] Where M represents the biomass of large drifting algae, y represents environmental factors, and T y→MThe criterion for whether the change of the time series data of the biomass of large drifting algae and the change of the time series data of environmental factors have a causal relationship, C MM represents the covariance between the time series data of large drifting algae biomass, C My represents the covariance of the time series data of large drifting algae biomass and environmental factor time series data, C y,d1 It represents the covariance of the new series formed by the pre-difference of the environmental factor time series data and the large drifting algae biomass time series data. Represents C My The square of C M,d1 represents the covariance of the new series formed by the pre-difference between the time series data of large drifting algae biomass, Represents C MM The square of C yy Represents the covariance between environmental factor time series data.

[0034] According to the maximum likelihood estimation, when T y→M When ≠0, it indicates that the change of this environmental factor is the cause of the change in the biomass of large drift algae. At this time, this environmental factor is the key stability environmental limiting factor.

[0035] Furthermore, in step S4, constructing a large drift algae growth model includes the following contents:

[0036] The expression of the growth rate of large drift algae is:

[0037]

[0038] Where M represents the biomass of large drifting algae, represents the growth rate of large drift algae, t represents time, v s represents the assimilation rate of large drift algae, v' s represents the alienation rate of large drift algae, ω w represents the key stability environment limiting factor parameter set, and Q() represents the growth function.

[0039] Large drifting algae were collected from offshore, healthy algae were selected for pretreatment, and cultured with key stability environmental limiting factors. The level changes of the limiting factors were observed, and the relationship between the biomass growth of large drifting algae and the level of limiting factors was obtained. The specific expression is:

[0040] v s =Q1(N,α)

[0041] v' s =Q2(N,β)

[0042] Among them, N represents the total number of key stability environmental limiting factors, Q1() represents the growth function of the assimilation rate of large drift algae, Q2() represents the growth function of the alienation rate of large drift algae, α represents the growth function parameter set of the assimilation rate, and β represents the growth function parameter set of the alienation rate.

[0043] The growth model of large drift algae is obtained, and the specific expression is:

[0044]

[0045] Among them, ω represents the environmental factor parameter set.

[0046] Based on the experimental results of the effects of stability environmental limiting factors on the growth of large drift algae, the model parameters, namely α, β and ω, were obtained through model fitting.

[0047] Further, in step S5, calculating the growth amount of large drift algae biomass includes the following contents:

[0048] Combined with the spatial distribution database of key stability environmental limiting factors, the initial large drift algae biomass data of known grids are input into the large drift algae growth model, the growth rate of large drift algae in the jth grid is calculated, and the growth amount of large drift algae biomass in the grid within the set time scale is obtained. The specific formula is:

[0049]

[0050] Among them, M j represents the biomass of large drifting algae in the jth grid, M i represents the known large drifting algae biomass in the ith grid, and th represents the set time scale.

[0051] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for calculating the sea area and biomass of large drifting algae that cause marine disasters as described above are implemented.

[0052] Furthermore, the present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is run by a processor, it executes the method for calculating the sea area and biomass of large-scale drifting algae that cause marine disasters as described above.

[0053] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0054] The present invention distinguishes between volatile environmental factors and stable environmental factors, combines the basic principles of phenology, and uses volatile environmental factors to calculate the information entropy of the sea area where large drift algae appear, and calculates the probability of large drift algae appearing in each grid within one phenology (5 days); the key stability environmental limiting factors for the growth of large drift algae are determined by the causal analysis method, and the biomass of large drift algae in the marine environment and its growth scale are calculated using the key stability environmental limiting factor-large drift algae growth model, which not only solves the problem of measuring the sea area where large drift algae appear, but also solves the problem that the biomass of large drift algae that causes disasters is difficult to estimate, and provides a criterion for initiating large drift algae salvage measures at different levels, and provides a basis for environmental protection departments to make quick decisions. Environmental protection departments can determine the scale of mobilization and organization of salvage operations based on the sea area and biomass where large drift algae appear in the next 5 days, organize manpower and material resources in advance, arrive at the designated sea area, and enhance the ability of relevant departments to accurately prevent and reduce disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is an overall implementation flow chart of the present invention.

[0056] Figure 2 1. It is a schematic diagram of a grid for calculating the probability of occurrence and biomass of Enteromorpha in a target sea area in an embodiment of the present invention.

[0057] Figure 3 It is a schematic diagram of the growth rate of Enteromorpha in the gridded target sea area in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0059] To achieve the above object, the present invention proposes a method for calculating the probability and biomass of large drifting algae in the sea area, such as Figure 1 As shown, the specific steps are as follows:

[0060] S1. Use multiple types of monitoring methods to obtain relevant data on environmental factors and large drift algae occurrence areas, and combine the basic principles of phenology to build a multi-source time series database of large drift algae. The specific contents are as follows:

[0061] According to the requirements for the distribution of large drift algae salvage work, the target sea area is gridded to obtain its longitude and latitude information, that is, the relevant data of the sea area where large drift algae appear, and environmental factors are collected online in real time by using pile foundation infrastructure, fixed-point observation buoys, water quality observation instruments (such as nutrient analyzers, algae analyzers, chlorophyll), channel fluorescence sensors and monitoring methods (such as video monitoring, satellite remote sensing, aerial telemetry, ship surveillance and monitoring, and volunteer on-site monitoring).

[0062] Environmental factors include seawater temperature, salinity, dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved organic nitrogen, dissolved organic phosphorus, light intensity, light duration, wind direction and wind speed.

[0063] Environmental factors are interpolated at grid points, and the neighbor method is used to classify the relevant data of the sea areas where large drift algae appear into the nearest grid, thus completing the construction of a multi-source time series database of large drift algae.

[0064] Among them, satellite remote sensing data has the advantages of large scale, wide coverage and high temporal resolution, and buoy observation data is continuous and easy to obtain, which can supplement and improve the growth status and spatial distribution data of Ulva in the target sea area.

[0065] In this embodiment, the sea area west of E 123° in the South Yellow Sea was taken as the target sea area, and gridding (0.5 longitude × 0.5 latitude) was implemented. Enteromorpha was selected from the large drifting algae for measurement.

[0066] S2. Based on the multi-source time series database of large drifting algae, the volatile environmental factors and stable environmental factors are distinguished, the information entropy of the sea area where large drifting algae appear is calculated, and the probability of large drifting algae appearing in the grid within a pentad (5 days) time scale is obtained; the specific contents are as follows:

[0067] The CV (Coefficient of Variation) of environmental factors in the multi-source time series database within the one-pentad time scale of each grid was calculated, and environmental factors with CV < 50% were classified as stable environmental factors, such as dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved organic nitrogen, dissolved organic phosphorus, light intensity, seawater temperature and salinity; environmental factors with CV ≥ 50% were classified as variable environmental factors, such as wind direction and wind speed.

[0068] The maximum entropy analysis method is used to determine the relationship between the probability of large drift algae appearing in the sea area and the stability environmental factors and the variability environmental factors. Combined with the spatial distribution database of marine variability environmental factors in the environmental factors, the contribution rate of environmental factors to the occurrence of large drift algae in the grid is calculated, and the probability of large drift algae appearing in the grid is obtained. The specific content is:

[0069] Set the characteristic function f i (x ij ,y k ) represents the probability of large drift algae appearing in the jth grid when the kth environmental factor exists in the i-th grid where large drift algae appear. The characteristic function set is C, Among them, p represents the conditional probability, represents the expected value of the empirical distribution, E p (f i) represents the expected value of the characteristic function, T represents seawater temperature, S represents salinity, DIN represents dissolved inorganic nitrogen, DIP represents dissolved inorganic phosphorus, DON represents dissolved organic nitrogen, DOP represents dissolved organic phosphorus, Lin represents light intensity, Lt represents light duration, Wd represents wind direction, and Ws represents wind speed.

[0070] when When it is equal to the expected value of the characteristic function, the maximum value of the information entropy of the conditional probability distribution p(y|x) is obtained, that is, the maximum entropy. The specific formula is:

[0071]

[0072] Among them, H(p) represents the maximum entropy, n x represents the number of occurrences of large drifting algae in a known sea area, n y Indicates the number of times environmental factors appear in a known sea area, represents the average probability of large drifting algae appearing in a known sea area, p(n y |n x ) represents the probability of the environmental factor appearing when large drift algae appear in a known sea area, a represents the minimum number of large drift algae appearing in a grid, b represents the maximum number of large drift algae appearing in a grid, and f i (x i ,ω k ) represents the probability of the existence of large drift algae and the kth environmental factor set in the i-th grid, x i Indicates that large drift algae appear in the i-th grid, ω k Represents the set of k-th environmental factors in the ith grid.

[0073] The cumulative value of all environmental factors on the probability of large drift algae appearing in each grid is calculated and used as the contribution rate of environmental factors to the prediction of large drift algae appearance. The specific formula is:

[0074]

[0075] Among them, CB k Represents the contribution rate of the kth environmental factor.

[0076] When large drift algae appear in the i-th grid, the probability of large drift algae appearing in the j-th grid is predicted. The specific formula is:

[0077]

[0078] Among them, P ij represents the probability of large drift algae appearing in the jth grid when large drift algae appear in the i-th grid, λ0, λ n All represent Lagrange multipliers, n represents the total number of grids, fi (x ij ,ω k ) represents the probability of the existence of large drift algae and the kth environmental factor set in the i-th and j-th grids, x ij Indicates the i-th and j-th grids where large drift algae appear.

[0079] In the present embodiment, the maximum entropy principle is utilized, by known information (stability environmental factors and variability environmental factors of the Enteromorpha grid that have appeared), inferring unknown information in the future (the grid that the Enteromorpha will appear in a time scale of one pentad), the variability environmental factor is mainly an environmental dynamics factor, which has a control effect on the drift of the Enteromorpha, and contributes the greatest probability to the occurrence of the Enteromorpha in the grid. Wind force is the main forcing force that the Enteromorpha moves in the ocean, and the prevailing wind direction is the main environmental factor that affects the drift direction of the Enteromorpha. Strong and continuous easterly wind or southeasterly wind is the main external forcing force that the Enteromorpha path is westward and lands on the coast of Qingdao, and the southwesterly wind makes the Enteromorpha path eastward.

[0080] S3. The key stability environmental limiting factors of the growth of large drift algae were determined by using the causal analysis method, which reduced the factors to be considered in the calculation of the biomass growth of Enteromorpha, simplified the calculation process, and improved the accuracy of the calculation; the specific contents are as follows:

[0081] Based on information entropy, the causal analysis method was used to analyze the dominant factors of environmental factors and large drift algae biomass changes, and the maximum likelihood estimate was calculated. The specific formula is:

[0082]

[0083] Where M represents the biomass of large drifting algae, y represents environmental factors, and T y→M The criterion for whether the change of the time series data of the biomass of large drifting algae and the change of the time series data of environmental factors have a causal relationship, C MM represents the covariance between the time series data of large drifting algae biomass, C My represents the covariance of the time series data of large drifting algae biomass and environmental factor time series data, C y,d1 It represents the covariance of the new series formed by the pre-difference of the environmental factor time series data and the large drifting algae biomass time series data. Represents C My The square of C M,d1 represents the covariance of the new series formed by the pre-difference between the time series data of large drifting algae biomass, Represents C MM The square of C yy Represents the covariance between environmental factor time series data.

[0084] According to the maximum likelihood estimation, when T y→MWhen ≠0, it indicates that the change of this environmental factor is the cause of the change in the biomass of large drift algae. At this time, this environmental factor is the key stability environmental limiting factor.

[0085] In this embodiment, seawater temperature, dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved organic nitrogen, dissolved organic phosphorus, and light intensity are the leading environmental factors for the growth of Enteromorpha biomass. Dissolved inorganic nitrogen, dissolved organic nitrogen, and light intensity have a causal relationship with Enteromorpha biomass, and thus, dissolved inorganic nitrogen, dissolved organic nitrogen, and light intensity are determined to be the key stability environmental limiting factors for the growth of Enteromorpha biomass.

[0086] S4. Analyze the impact of key stability environmental limiting factors on the biomass growth of large drift algae and construct a growth model of large drift algae; the specific contents are as follows:

[0087] The expression of the growth rate of large drift algae is:

[0088]

[0089] Where M represents the biomass of large drifting algae, represents the growth rate of large drift algae, t represents time, v s represents the assimilation rate of large drift algae, v′ s represents the alienation rate of large drift algae, ω w represents the key stability environment limiting factor parameter set, and Q() represents the growth function.

[0090] Large drifting algae were collected from offshore, healthy algae were selected for pretreatment, and cultured with key stability environmental limiting factors. The level changes of the limiting factors were observed, and the relationship between the biomass growth of large drifting algae and the level of limiting factors was obtained. The specific expression is:

[0091] v s =Q1(N,α)

[0092] v' s =Q2(N,β)

[0093] Among them, N represents the total number of key stability environmental limiting factors, Q1() represents the growth function of the assimilation rate of large drift algae, Q2() represents the growth function of the alienation rate of large drift algae, α represents the growth function parameter set of the assimilation rate, and β represents the growth function parameter set of the alienation rate.

[0094] The growth model of large drift algae is obtained, and the specific expression is:

[0095]

[0096] Among them, ω represents the environmental factor parameter set.

[0097] Based on the experimental results of the effects of stability environmental limiting factors on the growth of large drift algae, the model parameters, namely α, β and ω, were obtained through model fitting.

[0098] In this embodiment, the key stability environmental limiting factors affecting the growth of Enteromorpha culture experiment are:

[0099] Step 1: Collecting Enteromorpha

[0100] Collect Enteromorpha near the target sea area, select healthy algae, remove surface attachments, and wash the algae repeatedly with filtered natural seawater. Place the clean algae in a sealed box filled with seawater, temporarily store it in a refrigerator, and bring it back to the laboratory.

[0101] Step 2: Expand the culture

[0102] The seawater used for the cultivation experiment was collected from the Yellow Sea where Enteromorpha grows. The seawater was filtered through a 0.45μm mixed fiber filter membrane and stored in a polyethylene barrel (5L ground-mouth bottle). Before the experiment, it was sterilized in a steam sterilizer at 125℃ for 15 minutes, then naturally cooled and fully shaken to restore the original amount of dissolved gas before use.

[0103] The culture medium is prepared by filtering high-pressure steam sterilized natural seawater and adding nutrient salts according to the f / 2 formula.

[0104] In the laboratory, the samples of Enteromorpha were selected and separated, and healthy algae were selected, and sterilized by soaking in 0.2% KI solution for 1-3 minutes, and then rinsed 3-4 times by filtered high-temperature sterilized seawater (mixed fiber filter membrane, sterilized at 125℃ for 15 minutes), and placed in a container containing 1L culture medium, and expanded in a light incubator, with a temperature of 15±1℃, a light intensity of 6000lux, and a light period of L:D=12h:12h. The culture medium was replaced every 3 days.

[0105] Step 3: Cultivation experiment

[0106] Measure 1100mL of autoclaved seawater and place it in a 2L beaker. Use nitrate ions to represent dissolved nitrogen. Add a certain concentration of sodium nitrate solution according to Table 1. Stir evenly. Measure 300mL and place it in a 500mL conical flask. Make 3 portions per group for later use. The remaining culture medium is used to determine the nitrogen concentration (the actual nitrogen concentration of the culture medium).

[0107] After 48 hours of nitrogen starvation culture, the Enteromorpha was taken out from the culture bottle, the surface water of the algae was dried with absorbent paper, 0.2g / portion of Enteromorpha was weighed with an electronic balance, washed with sterile seawater, and placed in a 500mL conical flask containing 300mL culture solution, with 3 parallel samples in each group. The experiment used nitrate concentration as a variable, and phosphate and light intensity were kept constant.

[0108] Nitrogen group experiment: Enteromorpha was cultured in a light incubator at a temperature of 18±1℃ and a light intensity of 90W / m 2 , the photoperiod was 12h:12h, the nitrate concentration gradient was as shown in Table 1, and the culture period was 14d. At 18:00 every day after inoculation, the Enteromorpha was taken out from the culture bottle, the water on the surface of the algae was dried with absorbent paper, the wet weight of the Enteromorpha was weighed with an electronic balance, the culture solution was renewed (the steps were as above), and the culture was returned to the conical flask for further culture. The original culture solution was filtered through a 0.45μm mixed fiber filter membrane, and the concentrations of NaNO2, NaNO3 and NaH2PO4 were determined respectively. Another 100mL of the filtered culture solution was taken, placed in a sample bottle, and stored at -20℃.

[0109] Light group experiment: Enteromorpha was cultured in a light incubator at 18±1℃. The light intensity gradient, nitrate and phosphate concentrations are shown in Table 1. The light period was 12h:12h and the culture period was 14d. Enteromorpha was taken out from the culture bottle at 18:00 every day after inoculation, the water on the surface of the algae was dried with absorbent paper, the wet weight of Enteromorpha was weighed with an electronic balance, the culture solution was renewed (the steps were as above), and the culture was returned to the conical flask for further cultivation. The original culture solution was filtered through a 0.45μm mixed fiber filter membrane, and the concentrations of NaNO2, NaNO3 and NaH2PO4 were determined respectively. Another 100mL of filtered culture solution was taken, placed in a sample bottle, and stored in a freezer at -20℃.

[0110] Table 1 Molar concentration of sodium nitrate added in the culture experiment (μmolL -1 ), light intensity (W m -2 ) and temperature (℃)

[0111] Nitrogen Group N P L T Light Group N P T L N0# 0 2 90 18 L0# 60 2 18 0 N1# 10 2 90 18 L1# 60 2 18 30 N2# 30 2 90 18 L2# 60 2 18 60 N3# 90 2 90 18 L3# 60 2 18 90 N4# 270 2 90 18 L4# 60 2 18 120 N5# 810 2 90 18 L5# 60 2 18 150

[0112] The data obtained from the cultivation experiment can provide experimental support for obtaining the growth model parameters of drifting Enteromorpha, a key stability environmental limiting factor.

[0113] In this embodiment, based on the single-layer algae structure of Enteromorpha and the results of the culture experiment, it is assumed that the way in which nitrate enters the cell membrane is active transport, and when the nitrate concentration is high, the intermediates have competitive inhibition. The factors affecting the absorption and assimilation of nitrate by large drift algae are mainly nitrate concentration and absorption area, as shown in formulas (1) and (2).

[0114]

[0115] Among them, E represents transmembrane transport protein, N represents nitrate, EN represents transport protein after binding to limiting factor, A represents assimilation product of nitrate in Enteromorpha, NEN represents competitive inhibitor produced at high concentration, k +1 , k -1 , k2, k +3 and k -3Both represent the corresponding rate coefficients.

[0116] Based on formula (1) and formula (2), a nitrate absorption model (Formula 3) was established, and the relationship between the assimilation rate, nitrate concentration and absorption area (Formula 4) was established. The specific content is:

[0117]

[0118] when We can get:

[0119]

[0120] x=μS

[0121]

[0122] Where, μ represents the absorption rate of nitrate, μ max Indicates the maximum absorption rate of nitrate, K M represents the stability constant of active transport conditions, K I represents the competitive inhibition condition stability constant, μ max =k2[E0], E0 represents the total amount of transmembrane transport protein, x represents the assimilation rate of Enteromorpha, S represents the area of ​​Enteromorpha absorbing nitrate, r1 and r2 represent the outer diameter and inner diameter of the tubular Enteromorpha respectively, m represents the specific surface parameter, and V represents the volume of Enteromorpha.

[0123] According to the relationship between the growth rate of Enteromorpha and the assimilation rate and dissimilation rate (Formula 5), ​​the nitrate absorption model was embedded in the assimilation rate, the environmental nitrate concentration was linked to the biomass growth of large drift algae, and a differential model of Enteromorpha growth mechanism was constructed (Formula 6).

[0124]

[0125] Where k represents the alienation rate coefficient of Enteromorpha, Represents the apparent constant of the maximum absorption rate of nitrate.

[0126] Through the analysis of the cultivation experiment results, it was found that under high concentration conditions, the active transport of nitrate across the membrane has a competitive inhibition effect. Under steady-state conditions, a nitrate absorption model was established. Using the relationship between the nitrate absorption model and the assimilation rate, the quantitative relationship between the environmental nitrate concentration and the assimilation rate was constructed, and the environmental nitrate concentration was linked to the changes in the Enteromorpha biomass.

[0127] Light provides energy for nutrient assimilation. Light intensity regulates the rate of nutrient assimilation by affecting the ratio of enzyme activation. The light-promoted enzyme activation process is as follows:

[0128]

[0129] Where R represents the concentration of the enzyme, L represents the light intensity, n represents the reaction coefficient, RL* represents the concentration of the activated enzyme, and K a represents the half-saturation light intensity, θ represents the ratio of enzyme activation, K d represents the reaction equilibrium constant.

[0130] Since the effects of nutrients and light on biological growth are usually multiplicative, the key stability environmental factor reflecting the effects of nutrients and light - the growth model of drifting Enteromorpha can be constructed in the following differential form:

[0131]

[0132] The key stability environmental factor-drift Enteromorpha growth model was applied to fit the Enteromorpha growth data in the cultivation experiment where the above key stability environmental factors affected Enteromorpha growth, and the model parameters were obtained, as shown in Table 2.

[0133] Table 2 Key stability environmental factors - drifting Enteromorpha growth model parameters

[0134]

[0135] Through the analysis of the results of the cultivation experiment, a nitrate absorption model was established. The effect of the light-promoted enzyme activation reaction on nitrate absorption was used to establish the relationship between light intensity and nitrate assimilation rate. A quantitative relationship was established between key stability environmental factors such as ambient light intensity and nitrate and the growth rate of Enteromorpha, and then a key stability environmental factor-drifting Enteromorpha growth model was established. Using the cultivation experiment data, the key stability environmental factor-drifting Enteromorpha growth model was fitted to obtain the model parameters, laying the foundation for the calculation of the per-season growth rate of Enteromorpha biomass through the key stability environmental factor-drifting Enteromorpha growth model using the survey data in the South Yellow Sea in the future.

[0136] S5. Using the large drift algae growth model, calculate the growth of large drift algae biomass in each grid within the set time scale, and complete the measurement of large drift algae appearance sea area and biomass growth; the specific contents are as follows:

[0137] Combined with the spatial distribution database of key stability environmental limiting factors, the initial large drift algae biomass data of known grids are input into the large drift algae growth model, and the growth rate of large drift algae in the jth grid is calculated to obtain the growth amount of large drift algae biomass in the grid within one pentad time scale. The specific formula is:

[0138]

[0139] Among them, M j represents the biomass of large drifting algae in the jth grid, M irepresents the known large drifting algae biomass in the ith grid, and th represents the length of one pentad.

[0140] Usually, there are some changes in the drift rate of Ulva in the South Yellow Sea from May to June, but long-term observations have found that the movement distance of Ulva within one pentad time scale is all within one grid length, and the growth rate of Ulva drifting across grids can be calculated by the geometric mean of the one-pentad growth rates of adjacent grids.

[0141] In this embodiment, Figure 2 As shown, large drift algae appear in grid D6, and M0 represents the biomass of large drift algae; the probability of large drift algae appearing in grid E6 is P1, and M1 represents the biomass of large drift algae; the probability of large drift algae appearing in grid E5 is P2, and M2 represents the biomass of large drift algae; the probability of large drift algae appearing in grid D5 is P3, and M3 represents the biomass of large drift algae; the probability of large drift algae appearing in grid C5 is P4, and M4 represents the biomass of large drift algae; the probability of large drift algae appearing in grid C6 is P5, and M5 represents the biomass of large drift algae; the probability of large drift algae appearing in grid C7 is P6, and M6 represents the biomass of large drift algae; the probability of large drift algae appearing in grid D7 is P7, and M7 represents the biomass of large drift algae; the probability of large drift algae appearing in grid E7 is P8, and M8 represents the biomass of large drift algae.

[0142] like Figure 3 As shown in the figure, the values ​​in the grid of 0.5 longitude × 0.5 latitude represent the average growth rate of Enteromorpha per season (5 days) in the grid. Every time Enteromorpha drifts through a grid, the biomass will increase according to the growth rate of the value displayed in the grid. Floating Enteromorpha appeared in the D5 sea area on May 24, 2017, and Enteromorpha appeared in the E5 and E4 sea areas on May 28, 2017; floating Enteromorpha appeared in the E9 sea area on May 24, 2018, and Enteromorpha appeared in the D8 sea area on May 29, 2018; floating Enteromorpha appeared in the D2 sea area on June 28, 2019, and Enteromorpha appeared in the C2 and C3 sea areas on July 2, 2019. At the same time, the parallel observation and parallel survey data of temperature, salinity, dissolved inorganic nitrogen, dissolved inorganic phosphorus, light intensity, scalar wind direction, etc. in the South Yellow Sea are gridded to construct a time series database of Enteromorpha multi-source observation data.

[0143] On May 24, 2021, Enteromorpha appeared in grid D6. Combined with the gridded mean of the statistical results of many years of water quality surveys in the South Yellow Sea and the wind direction and wind force weather forecast for the next period, the maximum entropy calculation model based on the maximum entropy principle was used to calculate the information entropy of the grids E6, E5, D5, C5, C6, C7, D7, and E7 around D6, which were 0.0495, 0.0507, 0.0505, 0.0504, 0.0467, 0.0455, 0.0465, and 0.0479, respectively. The probabilities of Enteromorpha appearing were 0.448, 0.454, 0.453, 0.452, 0.434, 0.427, 0.433, and 0.439, respectively. It is inferred that floating Enteromorpha will appear in E5, which has the largest information entropy, and it is also likely to appear in the D5 and C5 grids, which have the second and third largest information entropy. The calculated contribution rate of each environmental factor to the appearance of Enteromorpha in each grid is as high as 97% for the variable environmental factor - wind direction, and only 3% for other stable environmental factors such as temperature, salinity, dissolved nitrogen, light intensity, etc. The interpretation results of remote sensing images show that a large number of floating Enteromorpha appeared in the E5 and D5 grids on May 28, 2021, and sporadic floating Enteromorpha appeared in C5. In addition, according to the gridded mean of the statistical results of dissolved nitrogen, light intensity, and seawater extinction coefficient in the target sea area for many years, the floating Enteromorpha biomass calculated by the key stable environmental factor-drifting Enteromorpha growth model increased by 2.8 times, and the remote sensing image interpretation results showed that the Enteromorpha biomass increased by 3.6 times on May 28, 2021. The probability of Enteromorpha appearance and the growth multiple of drifting Enteromorpha in the adjacent sea area grid within the next one-term time scale are basically consistent with the remote sensing measurement results based on the information entropy of the sea area grid where Enteromorpha appears.

[0144] The method of the present invention is applied, based on the time series database of multi-source observation data of Enteromorpha and the weather forecast data of wind direction and wind force in the target sea area in the next one-term, and the information entropy calculation of each grid in the target sea area can be used to determine the probability of Enteromorpha appearance in each grid within the one-term time scale, and the drift Enteromorpha biomass growth multiple is calculated through the key stability environmental factor-drift Enteromorpha growth model. The grid Enteromorpha appearance probability and Enteromorpha biomass growth multiple are basically consistent with the results of remote sensing technology measurement, which further verifies the reliability of the method of the present invention, and also shows that the method of the present invention has a high application value in the disaster reduction and prevention of large-scale drifting algal blooms in actual sea areas.

[0145] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0146] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. It should be noted that when the computer program is executed by the processor, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0147] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating the probability and biomass of large drifting algae in the sea, characterized in that: include: S1. Use monitoring methods to obtain relevant data on environmental factors and the sea areas where large drift algae are located, and combine the basic principles of phenology to build a multi-source time series database of large drift algae; the environmental factors include seawater temperature, salinity, dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved organic nitrogen, dissolved organic phosphorus, light intensity, light duration, wind direction and wind speed; S2. Based on the multi-source time series database of large drifting algae, distinguish the volatile environmental factors from the stable environmental factors, calculate the information entropy of the large drifting algae in the sea area, and obtain the probability of large drifting algae appearing in the grid within the set time scale; the specific contents are as follows: Calculate the coefficient of variation of environmental factors in the multi-source time series database for each grid within the set time scale, classify environmental factors with a coefficient of variation less than 50% as stable environmental factors, and classify environmental factors with a coefficient of variation greater than or equal to 50% as volatile environmental factors; The maximum entropy analysis method is used to determine the relationship between the probability of large drift algae appearing in the sea area and the stability environmental factors and the variability environmental factors. Combined with the spatial distribution database of marine variability environmental factors in the environmental factors, the contribution rate of environmental factors to the appearance of large drift algae in the grid is calculated, and the probability of large drift algae appearing in the grid is obtained. The specific content is: Set the characteristic function f i (x ij ,y k ) represents the probability of large drift algae appearing in the jth grid when the kth environmental factor exists in the i-th grid where large drift algae appear. The characteristic function set is C, Among them, p represents the conditional probability, represents the expected value of the empirical distribution, E p (f i ) represents the expected value of the characteristic function, T represents seawater temperature, S represents salinity, DIN represents dissolved inorganic nitrogen, DIP represents dissolved inorganic phosphorus, DON represents dissolved organic nitrogen, DOP represents dissolved organic phosphorus, Lin represents light intensity, Lt represents light duration, Wd represents wind direction, and Ws represents wind speed; when When it is equal to the expected value of the characteristic function, the maximum value of the information entropy of the conditional probability distribution p(y|x) is obtained, that is, the maximum entropy. The specific formula is: Among them, H(p) represents the maximum entropy, n x represents the number of occurrences of large drifting algae in a known sea area, n y Indicates the number of times environmental factors appear in a known sea area, represents the average probability of large drifting algae appearing in a known sea area, p(n y |n x ) represents the probability of occurrence of environmental factors when large drifting algae appear in a known sea area; The cumulative value of all environmental factors on the probability of large drift algae appearing in each grid is calculated and used as the contribution rate of environmental factors to predicting the appearance of large drift algae. The specific formula is: Among them, CB k represents the contribution rate of the kth environmental factor; When large drift algae appear in the i-th grid, the probability of large drift algae appearing in the j-th grid is predicted. The specific formula is: Among them, P ij represents the probability of large drift algae appearing in the jth grid when large drift algae appear in the i-th grid, λ0, λ n All represent Lagrange multipliers, n represents the total number of grids, f i (x ij ,ω k ) represents the probability of the existence of large drift algae and the kth environmental factor set in the i-th and j-th grids, x ij Indicates the i-th and j-th grids where large drift algae appear; S3. Use causal analysis to determine the key stability environmental limiting factors for the growth of large drift algae; the specific contents are: Based on information entropy, the causal analysis method was used to analyze the dominant factors related to the biomass changes of large drift algae in environmental factors, and the maximum likelihood estimate was calculated. The specific formula is: Where M represents the biomass of large drifting algae, y represents environmental factors, and T y→M The criterion for whether the change of the time series data of the biomass of large drifting algae and the change of the time series data of environmental factors have a causal relationship, C MM represents the covariance between the time series data of large drifting algae biomass, C My represents the covariance of the time series data of large drifting algae biomass and environmental factor time series data, C y,d1 It represents the covariance of the new series formed by the pre-difference of the environmental factor time series data and the large drifting algae biomass time series data. Represents C My The square of C M,d1 represents the covariance of the new series formed by the pre-difference between the time series data of large drifting algae biomass, Represents C MM The square of C yy Represents the covariance between time series data of environmental factors; According to the maximum likelihood estimation, when T y→M The environmental factor when ≠0 is the key stability environmental limiting factor; S4. Analyze the impact of key stability environmental limiting factors on the biomass growth of large drift algae and construct a growth model of large drift algae; S5. Use the large drift algae growth model to calculate the growth of large drift algae biomass in each grid within a set time scale; complete the probability of large drift algae appearing in the sea area and the calculation of biomass growth.

2. The method for calculating the probability and biomass of large drifting algae in the sea area according to claim 1, characterized in that: In step S1, constructing a large-scale drift algae multi-source time series database includes the following contents: According to the requirements of the large drift algae salvage work, the target sea area is gridded to obtain the corresponding longitude and latitude information, that is, the relevant data of the sea area where the large drift algae appear, and the environmental factors are collected online in real time using pile foundation infrastructure, fixed-point observation buoys, water quality observation instruments, channel fluorescence sensors and monitoring methods; Environmental factors are interpolated at grid points, and the neighbor method is used to classify the relevant data of the sea areas where large drift algae appear into the nearest grid, thus completing the construction of a multi-source time series database of large drift algae.

3. The method for calculating the probability and biomass of large drifting algae in the sea area according to claim 1, characterized in that: In step S4, constructing a large drift algae growth model includes the following contents: The expression of the growth rate of large drift algae is: Where M represents the biomass of large drifting algae, represents the growth rate of large drift algae, t represents time, v s represents the assimilation rate of large drift algae, v' s represents the alienation rate of large drift algae, ω w represents the key stability environment limiting factor parameter set, Q() represents the growth function; Large drifting algae were collected from offshore, healthy algae were selected for pretreatment, and cultured with key stability environmental limiting factors. The level changes of the limiting factors were observed, and the relationship between the biomass growth of large drifting algae and the level of limiting factors was obtained. The specific expression is: vs=Q1(N,α) v's=Q2(N,β) Where N represents the total number of key stability environmental limiting factors, Q1() represents the growth function of the assimilation rate of large drift algae, Q2() represents the growth function of the alienation rate of large drift algae, α represents the growth function parameter set of the assimilation rate, and β represents the growth function parameter set of the alienation rate; The growth model of large drift algae is obtained, and the specific expression is: Among them, ω represents the environmental factor parameter set.

4. The method for calculating the probability and biomass of large drifting algae in the sea area according to claim 3, characterized in that: In step S5, calculating the growth amount of large drift algae biomass includes the following contents: Combined with the spatial distribution database of key stability environmental limiting factors, the initial large drift algae biomass of the known grid is input into the large drift algae growth model, the growth rate of the large drift algae in the jth grid is calculated, and the growth amount of the large drift algae biomass in the grid within the set time scale is obtained. The specific formula is: Among them, M j represents the biomass of large drifting algae in the jth grid, M i represents the known biomass of large drifting algae in the ith grid, and th represents the set time scale.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for calculating the probability and biomass of large drifting algae appearing in the sea area according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for calculating the probability and biomass of large drifting algae appearing in the sea area according to any one of claims 1 to 4 is executed.

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