Evaluation method and evaluation device

Abstract

Provided is a method for evaluating the persistence rate of an algal body. An estimation method comprises: a step for estimating, on the basis of ocean current data, the arrival position of an algal body discharged from a seaweed bed; a step for acquiring the water depth of the arrival position of the algal body discharged from the seaweed bed; and a step for evaluating the persistence rate of the algal body on the basis of the acquired water depth.

Description

Evaluation method and evaluation apparatus

[0001] This disclosure relates to a method and apparatus for evaluating the survival rate of seaweed. This disclosure claims priority under Japanese Patent Application No. 2024-182603, filed in Japan on October 18, 2024, the contents of which are incorporated herein by reference.

[0002] Amid efforts toward decarbonization, blue carbon is attracting attention. Blue carbon refers to "carbon absorbed and stored in marine ecosystems such as seagrass, mangroves, and salt marshes, after CO2 from the atmosphere has been taken up by marine organisms." Compared to green carbon (carbon taken up by terrestrial organisms such as trees and grass), blue carbon has the following characteristics: (1) High capacity to absorb CO2 from the atmosphere (storing carbon up to approximately 40 times faster). (2) High sustainability of carbon sequestration (while green carbon has a storage period of several decades, blue carbon has a storage period of several hundred to several thousand years). In Japan, JBE (Japan Blue Economy Association) issues blue carbon credits (J Blue Credits).

[0003] The JBE manual states that the amount of CO2 absorbed by aquaculture facilities can be calculated using the following formula (1): CO2 absorbed = Area of ​​aquaculture facility (or length of cultivation rope) × Wet weight per unit area (or length of rope) × (1 - water content) × Carbon content × 44 / 12 × P / B ratio × Retention rate ... (1) Sample analysis values ​​or literature values ​​should be applied to the water content and carbon content in formula (1), and literature values ​​should be applied to the P / B ratio. The wet weight should be the value measured by harvesting the target kelp, etc. Literature values ​​(e.g., 4.72%) are also listed in the JBE manual for the retention rate. However, this value is set considering the phenomenon in which cultured algae are produced by photosynthesis in the near-surface area at a depth of about 10 m, and the CO2 absorbed by the algae is recirculated into the atmosphere due to CO2 generation by decomposition by microorganisms, etc., and the release of dissolved CO2 in the sea into the atmosphere. In reality, after releasing cultured algae, they may sink to a depth where decomposition by microorganisms is difficult, and in this case, the survival rate can be assumed to be 100%. Considering this phenomenon, the survival rate may be higher than the literature value mentioned above. Applying a more accurate survival rate to equation (1) will allow for a more accurate calculation of CO2 absorption.

[0004] J-BlueCredit (Registered Trademark) Certification Application Guide Ver. 2.4, [online], March 2024, Japan Blue Economy Technology Research Association, [Accessed September 17, 2024], Internet <https: / / www.blueeconomy.jp / wp-content / uploads / jbc2024 / 20240312_J-BlueCredit_Guidline_v.2.4.pdf>

[0005] A method is needed to evaluate the survival rate of algal bodies.

[0006] This disclosure provides an evaluation method and an evaluation apparatus that can solve the above-mentioned problems.

[0007] According to one aspect of the present disclosure, the estimation method includes: estimating the arrival position of the algal thalli released from the algal bed based on ocean current data; obtaining the water depth at the arrival position of the algal thalli; and evaluating the survival rate of the algal thalli based on the obtained water depth.

[0008] According to one aspect of the present disclosure, the evaluation device includes: an estimation unit that estimates the arrival position of the algal thalli released from the algal bed based on ocean current data; an acquisition unit that obtains the water depth at the arrival position of the algal thalli; and an evaluation unit that evaluates the survival rate of the algal thalli based on the obtained water depth.

[0009] According to the above-described estimation method and evaluation device, the survival rate of the algal thalli can be evaluated.

[0010] It is a block diagram showing an example of the evaluation device according to the embodiment. It is a diagram showing an example of the physical model according to the embodiment. It is a diagram for explaining a method of calculating lift and drag according to the embodiment. It is a diagram for explaining a correction term according to the embodiment. It is FIG. 1 showing an example of the motion equation of the algal thalli according to the embodiment. It is FIG. 2 showing an example of the motion equation of the algal thalli according to the embodiment. It is FIG. 3 showing an example of the motion equation of the algal thalli according to the embodiment. It is FIG. 1 showing an example of the simulation result according to the embodiment. It is FIG. 2 showing an example of the simulation result according to the embodiment. It is FIG. 1 showing an example of the algal thalli arrival depth map according to the embodiment. It is FIG. 2 showing an example of the algal thalli arrival depth map according to the embodiment. It is a diagram showing an example of the survival rate table according to the embodiment. It is a diagram showing an example of the relationship between the arrival depth and the survival rate according to the embodiment. It is a diagram showing an example of the change in algal thalli density according to the embodiment. It is a diagram for explaining the calculation of the expected value of the survival rate according to the embodiment. It is a flowchart showing an example of the evaluation process of the survival rate according to the embodiment. It is a diagram showing an example of the hardware configuration of the evaluation device according to each embodiment.

[0011] <Embodiment> The following describes a method for evaluating the remaining rate of thallus according to the present disclosure with reference to the drawings. Hereinafter, cultivated kelp will be taken as an example of the thallus for explanation. FIG. 1 is a block diagram showing an example of an evaluation apparatus according to an embodiment. The evaluation apparatus 10 simulates the drift and movement of kelp (kelp that has absorbed C02) after releasing it in a cultivation field (seaweed bed), and finally estimates the remaining rate according to the water depth where the kelp arrives. The remaining rate is a ratio indicating the degree to which CO2 absorbed by kelp or the like is stored.

[0012] The evaluation apparatus 10 includes an input reception unit 11, a position estimation unit 12, a remaining rate evaluation unit 13, a CO2 absorption amount calculation unit 14, and a storage unit 15. The input reception unit 11 acquires various information input from input devices such as a keyboard, a mouse, and a touch panel. For example, the input reception unit 11 acquires information (such as ocean current data and seabed terrain data) necessary for simulating the movement and sedimentation of kelp after release. The position estimation unit 12 simulates the movement of kelp after release and estimates the position (three-dimensional coordinate information) of the finally reached kelp. The remaining rate evaluation unit 13 determines the remaining rate according to the position of the kelp (position in the water depth direction). For example, when the kelp sinks to a depth of a predetermined threshold value or more, the remaining rate of the kelp is determined to be 100%. The CO2 absorption amount calculation unit 14 calculates the CO2 absorption amount by applying the remaining rate determined by the remaining rate evaluation unit 13 to the formula (1) in the JBE manual. The storage unit 15 stores various information. For example, the storage unit 15 stores information necessary for simulating the movement and sedimentation of kelp after release.

[0013] Fig. 2 shows an example of a physical model for the simulation of the movement of kelp after release. The physical model 100 is a model that represents kelp as three flat plates 101 to 103 and frictionless hinges connecting the flat plate 101 and the flat plate 102, and the flat plate 102 and the flat plate 103 in order to express the movement of the thallus (kelp) in the sea. Gravity, buoyancy, lift force from the ocean current, and drag act on the flat plates 101 to 103, and equations of motion are established assuming that the action-reaction forces and moments through the hinges act between the respective flat plates. By establishing the equation of motion of the thallus from the forces acting on the thallus in the sea, the movement of the thallus due to the ocean current and density difference is evaluated. Let the length of the long side of the flat plates 101 to 103 be L １ , L ２ , L ３ (m), let the length of the short side of the flat plates 101 to 103 be W (m), let the thickness of the flat plates 101 to 103 be t (m), and let the weights of the flat plates 101 to 103 be M １ , M ２ , M ３ (kg), let the densities of the flat plates 101 to 103 be ρ ｃ１ , ρ ｃ２ , ρ ｃ３ (kg / m3), and let the moment of inertia of the flat plates 101 to 103 be I １ , I ２ , I ３ (kg·m2). When the vertical direction (the up and down direction of the paper surface in Fig. 2) is the z-axis, the horizontal direction (the left and right direction of the paper surface in Fig. 2) is the x-axis, and the depth direction of the paper surface in Fig. 2 is the y-axis, the center of gravity of the flat plate 101 is (x １ （ｔ） , y １ （ｔ） , z １ （ｔ） ), the center of gravity of the flat plate 102 is (x ２ （ｔ） , y ２ （ｔ） , z ２ （ｔ） ), and the center of gravity of the flat plate 103 is (x ３ （ｔ） , y ３ （ｔ） , z ３ （ｔ） ). Let the angles formed by the long sides of the flat plates 101 to 103 and the z-axis be θ １ （ｔ） , θ２ （ｔ） , θ ３ （ｔ） Let v be the horizontal velocity of the ocean current. ｘ (m / s), the velocity of the ocean current in the vertical direction is v ｚ (m / s), drag coefficient C ｄ , the lift coefficient is C ｌ ρ, the density of seawater ｗ Let g (m / s²) be the acceleration due to gravity. t represents the time (calculation step).

[0014] The deformation due to ocean currents and the changes in lift and drag are taken into consideration. When calculating lift and drag as shown in Figure 3, the kelp is rotated so that its short side is perpendicular to the horizontal direction of the ocean current. Drag F experienced by the i-th plate ｄｉ It can be calculated using the following formula (2).

[0015]

[0016] The normal force F acting on the i-th plate ｌｉ It can be calculated using the following formula (3).

[0017]

[0018] Gravitational force F acting on the i-th plate ｇｉ F can be calculated using the following formula (4). ｇｉ = -M ｉ g ... (4)

[0019] Buoyancy F acting on the i-th plate ｂｉ F can be calculated using the following formula (5). ｂｉ = -F ｇｉ (ρ ｗ / ρ ｃｉ ) ... (5)

[0020] If only equations (2) to (5) are considered, the terminal velocity of the kelp will coincide with the velocity of the ocean current. However, in reality, the terminal velocity of the kelp does not coincide with the velocity of the ocean current. Therefore, in order to reproduce the actual terminal velocity, a hypothetical force whose magnitude depends on the velocity of the kelp itself is added as a correction term. Figure 4 shows an image of the correction term. Vector 41 is the velocity vector of the i-th plate. The hypothetical force added as a correction term is in the opposite direction to the velocity vector 41, and the x component of the hypothetical force is F. ｄａｍｐｘ，ｉ , z component is F ｄａｍｐｚ，ｉ Let's assume that the x-component of the correction term for the i-th plate is calculated using the following equation (6). C ｘ This is the correction factor in the x-direction.

[0021]

[0022] The z component of the correction term for the i-th plate is calculated using the following equation (7). C ｚ This is the correction coefficient in the z direction.

[0023]

[0024] Correction factor C ｘ , C ｚ This is determined by conducting drift tests of kelp in the ocean and adjusting the results of the drift tests (the actual arrival location of the kelp after release) to be as close as possible to the simulation results (calculated arrival location of the kelp after release) as described below. This verifies the validity of the model and reduces the error in the arrival location of the algae.

[0025] Based on the above considerations, we can formulate the nine-variable linear equations shown in Figures 5A, 5B, and 5C. The three equations in Figure 5A are the equations of motion (I × α = N) for the rotation of the flat plates 101 to 103, respectively. In Figure 5A, the left side of each equation is the product of the moment of inertia (I) and the angular acceleration (α), and the right side is the torque (N). The two equations in Figure 5B are the equations of motion (ma = F) for the center of mass of the physical model 100. In Figure 5B, the left side is the product of the mass (m) and the acceleration (a), and the right side is the resultant force (F) acting on the flat plate. However, it is assumed that the flat plates 101 to 103, which are modeled after kelp, are oriented perpendicular to the velocity vector in the horizontal plane, that is, oriented in the direction that receives the most resistance force, and therefore only the two directions of translational movement, x and z, are considered. In the simulation, as the kelp moves due to the evolution of time, when the cell in which the kelp resides (for example, Figures 6A, 7A-7B described later) changes, an algorithm is applied that changes the orientation of the flat plate in the direction of the ocean current velocity vector of the cell to which it has been switched. The equations of motion in Figures 5A and 5B are equations of motion that calculate the position and orientation of each of the multiple flat plates at time t+1, based on the position and orientation of each of the multiple flat plates at time t and the gravitational force, buoyancy, lift, and drag force acting on each of the multiple flat plates at time t, assuming that the algal body is a configuration in which multiple flat plates are each linked so as to be able to swing. The four equations in Figure 5C are equations of motion derived from the positional relationship of each flat plate. Since the flat plates are connected by hinges, a geometric relationship holds between the position and angle of each center of gravity. In the equations in Figure 5C, the positional relationship of each flat plate at time t+1 is constrained from the positional relationship of each flat plate at time t based on this geometric relationship. These equations are formulated by the user and recorded in the memory unit 15. The position estimation unit 12 solves the system of nine linear equations shown in Figures 5A to 5C based on predetermined initial conditions, thereby determining the acceleration of the flat plates 101 to 103 at the next time t=1 (for example, x for flat plate 101). １ ~z １ (The second derivative of θ with respect to time) and angular acceleration (for example, θ for a flat plate 101) １The second derivative with respect to time is calculated, and the calculated acceleration and angular acceleration are integrated to calculate the moving velocity of the plates 101 to 103 at time t=1 and their positions after movement. The position estimation unit 12 performs these calculations sequentially, one calculation step (= one time) at a time, from time t=0 to a predetermined end time. For example, if ocean current data including seawater velocity, temperature, and density in a three-dimensional space of latitude, longitude, and water depth in the sea area targeted for blue credit calculation, seabed topography data, and kelp specifications (dimensions, density) are given as initial conditions, the movement of kelp over time is simulated by solving the nine-variable linear equations shown in Figures 5A to 5C, and the final destination position of the kelp (latitude, longitude, water depth) is calculated.

[0026] Figures 6A and 6B show an example of the simulation results. Figure 6A shows the movement of kelp in the xy plane, where the y-axis is in the vertical direction of the paper and the x-axis is in the horizontal direction. If we represent each cell in Figure 6A as (x, y), with the top left cell being (1, A) and the bottom right cell being (6, D), then cells (1, A), (2, A), (3, A), (4, A), (5, A), (6, A), (4, B), (5, B), and (6, B) represent land, and the other cells represent the sea. 1 to 5 represent seaweed beds where kelp was cultivated. The lines for seaweed beds 1 to 5 show the movement paths of the kelp obtained from the simulation. For example, the left end of the line for seaweed bed 1 shows the release point where the kelp was released, and the right end shows the final destination. The same applies to seaweed beds 2 to 5. For example, the kelp from seaweed bed 5 released at (4, C) is carried to near the left end of (6, C). In the simulation, conditions such as ocean current speed are set for each cell, and when kelp moves between cells, the ocean current conditions are switched to simulate the movement of the kelp.

[0027] Figure 6B shows the sinking of kelp. Lines 1 to 5 represent the depth positions of kelp released in seaweed beds 1 to 5 at different time points, as obtained from simulations. The vertical axis of the graph in Figure 6B represents depth, with lower values ​​indicating closer to the seabed. The horizontal axis of the graph in Figure 6B represents time. In this example, the kelp released in seaweed bed 3 sank to the deepest depth, followed by the kelp released in seaweed bed 4.

[0028] When the position estimation unit 12 calculates the kelp's arrival location (latitude, longitude, water depth), the survival rate evaluation unit 13 evaluates the survival rate according to the water depth to which the kelp reached. Figure 7A shows an example of a map of kelp arrival depth. If we take the x and y axes as in Figure 6A and represent each cell as (x, y), then (1, A) to (8, A), (3, B) to (8, B), and (1, H) to (4, H) are land, and the rest is the sea. The × marks in Figure 7A indicate the fisheries cooperative that manages the kelp bed. Multiple cultivation lines are set up in the seaweed bed, and multiple kelp plants are cultivated on each cultivation line. The release is carried out in units (for example, by cultivation line or by individual plants). Therefore, even in the same seaweed bed, the arrival location after release will differ depending on the timing of the release, the ocean current conditions at the time, the initial position, size, and weight of the released kelp, etc. The algal body depth map in Figure 7A displays the depth to which kelp ultimately reached in the sea area where it was released, using numerical values ​​on the xy plane. Each cell displaying a number from 1 to 3 indicates that when the kelp released from that cell reached, for example, the following depth ranges, a flag corresponding to that range was assigned to that cell. A cell displaying 1 indicates that the kelp released from that cell reached a depth in the range of 0 to 100 m, a cell displaying 2 indicates that the kelp released from that cell reached a depth in the range of 100 to 200 m, and a cell displaying 3 indicates that the kelp released from that cell reached a position deeper than 200 m. The survival rate evaluation unit 13 creates the algal body depth map based on the kelp's arrival positions included in the simulation results.

[0029] Furthermore, the above simulation may be performed multiple times using multiple ocean current data points from the same seaweed bed at different dates and times. The position estimation unit 12 acquires multiple past ocean current data (current velocity, temperature, density) for the sea area and release time planned as the aquaculture site (release site), provides one of them as an initial condition, and simulates the movement of kelp over time by solving the nine-variable linear equations shown in Figures 5A to 5C, and calculates the final arrival position of the kelp (latitude, longitude, water depth). The position estimation unit 12 changes the ocean current data provided as an initial condition and performs the same calculation for all of the acquired past ocean current data. For example, if the release is to be on July 30, 2025, ocean current data for 10 days before and after the previous year (2024) is input to create a seaweed body arrival depth map (10 data points in total). Figure 7B shows an example of a seaweed body arrival depth map for 10 data points. The interpretation of the algal depth map is the same as in Figure 7A, but in Figure 7B, cells displaying 0 indicate that the kelp released in that cell reached a depth of 0 to less than 100m, and cells displaying 1 indicate that the kelp released in that cell reached a depth of 100m or more but less than 200m. Similarly, for cells displaying 2 to 9, as the number increases, each cell indicates that the kelp reached a depth of 100m or more. Cells displaying 10 indicate that the kelp released in that cell reached a depth of 1000m or more.

[0030] Next, the survival rate evaluation unit 13 evaluates the survival rate for each kelp arrival location by referring to the survival rate table illustrated in Figure 8A. When the water depth is 200m or more, the amount of dissolved oxygen and light is such that algal decomposition is not expected to occur, so the survival rate in this case is set to 100% in the survival rate table. When the water depth is between 100m and 200m, the amount of light decreases, so it is thought that algal decomposition is also suppressed, and a value larger than the literature value (for example, 30%) is set. Based on the algal arrival depth map and the survival rate table, the survival rate evaluation unit 13 sets the survival rate to the literature value for cell 1. Similarly, the survival rate evaluation unit 13 sets the survival rate to 30% for cell 2 and to 100% for cell 3. The survival rate table in Figure 8A is just one example. The survival rate may be set by further subdividing the water depth, or the system may be set to uniformly apply the literature value if the water depth is less than 200m.

[0031] Alternatively, the survival rate evaluation unit 13 may set the survival rate using an approximation formula or map that shows the relationship between depth and survival rate. Figure 8B shows an example of an approximation formula or map that shows the relationship between the depth reached by the algae and the survival rate. In the map in Figure 8B, the vertical axis is the survival rate and the horizontal axis is the depth reached by the algae. For example, suppose that the survival rate of algae over 100 years for each water depth is obtained from literature, etc., as approximately 50% for a water depth of 500m and approximately 12% for a water depth of 200m. By plotting these two points on a graph with the survival rate on the vertical axis and the depth reached on the horizontal axis, and calculating an approximation line that passes near the two plotted points, a map showing the relationship between depth and survival rate as exemplified in Figure 8B is created and stored in the storage unit 15. Based on the map in Figure 8B, the survival rate evaluation unit 13 estimates the survival rate with respect to the depth reached by the algae. For example, the survival rate evaluation unit 13 estimates the survival rate to be 80% when the reachable depth is 800m, and estimates the survival rate to be 100% when the reachable depth is 1000m or more.

[0032] The CO2 absorption calculation unit 14 calculates the amount of CO2 absorbed by applying the survival rate evaluated by the survival rate evaluation unit 13 to formula (1) in the JBE manual. This allows for a more accurate calculation of CO2 absorption compared to calculating the amount of CO2 absorbed by kelp by uniformly applying literature values ​​to the survival rate, regardless of the water depth to which the released kelp reached. For example, for algal bodies that have sunk to a depth above which decomposition does not occur, the amount of CO2 absorbed can be calculated by assuming a survival rate of 100% and applied for as blue credits.

[0033] When the position estimation unit 12 performs a simulation, it sets the density of the kelp as an initial condition. However, it is conceivable that the kelp will decompose during drift, causing its density to change. To calculate the kelp's destination more accurately, the change in kelp density may be incorporated into the movement simulation. For example, appropriate values ​​can be set for the frequency factor, oxygen concentration, reaction order, activation energy, seawater temperature, and reaction rate constant, and the rate of kelp decomposition can be calculated from the Arrhenius equation. The position estimation unit 12 calculates the density of the kelp, taking into account the rate of kelp decomposition, according to the elapsed time from the simulation start time t=0, and then calculates the weight M of the flat plates 101-103 based on the kelp density at that time. １ ~M ３ Calculate the calculated M １ ~M ３Applying this, we solve the nine-variable linear equations in Figures 5A and 5B. Figure 9 shows an example of the change in kelp density over time, calculated considering the decomposition reaction rate of kelp. Since decomposition is inactivated when light does not reach the kelp, the decrease in the decomposition reaction rate of kelp based on the photon quantum density distribution according to water depth may be considered, and the decomposition reaction rate may be relaxed by a value corresponding to the position of the kelp in the z-axis direction (water depth). For example, the position estimation unit 12 may refer to a table that defines the relationship between water depth and photon quantum density (photon quantum density decreases as water depth increases) and a table that defines the relationship between photon quantum density and decomposition reaction rate (decomposition reaction rate decreases as photon quantum density decreases) to estimate the decrease in the decomposition reaction rate according to water depth from the decomposition reaction rate at a certain water depth, and calculate the decomposition reaction rate by subtracting this decrease from the decomposition reaction rate obtained by the Arrhenius equation. By considering the density change of kelp while it is drifting, it becomes possible to calculate the kelp's arrival position with higher accuracy.

[0034] Furthermore, the density may be calculated taking into account the effect of kelp growth through photosynthesis after release (the density will increase). For example, the position estimation unit 12 may estimate the growth rate at a certain water depth by referring to a table that defines the relationship between water depth and photon quantum density (photon quantum density decreases as water depth increases) and a table that defines the relationship between photon quantum density and growth rate (growth rate decreases as photon quantum density decreases), and then calculate the decomposition reaction rate by subtracting this growth rate from the above decomposition reaction rate. By considering the growth process of kelp while it is drifting, it becomes possible to calculate the arrival position of the kelp more accurately, not only when releasing kelp in the summer when it has fully grown, but also when releasing it in colder seasons.

[0035] Furthermore, the CO2 absorption calculation unit 14 may calculate the expected survival rate for each cell in the algal body depth map obtained by performing multiple simulations as illustrated in Figure 7B. If a certain cell is called point X, the depth to which the algal bodies reached point X as a result of 10 simulations can be summarized, for example, as shown in Figure 10. In the table data shown in Figure 10, "Data No." indicates which simulation was performed, "Depth Reached" is the depth reached calculated by the position estimation unit 12, and "Survival Rate X" is the expected survival rate. ｉ" is the survival rate calculated by the survival rate evaluation unit 13 using the survival rate table in Figure 8A or the map in Figure 8B, and "percentage P ｉ " represents the probability (1 / number of data sets) of the remaining rate. The CO2 absorption amount calculation unit 14 calculates the proportion P in each data. ｉ And the aforementioned survival rate X ｉ Therefore, the expected value of the survival rate is ΣX ｉ P ｉ The expected survival rate is calculated as follows: In the example shown in Figure 10, the expected survival rate is 4 + 5 + 6 + 8 + 9 + 10 + 10 + 10 + 10 + 10 = 82. The expected survival rate can be calculated by performing multiple simulations using ocean current data from different dates and times.

[0036] (Operation) Next, the flow of the survival rate evaluation process will be explained with reference to Figure 11. Figure 11 is a flowchart showing an example of the survival rate evaluation process according to the embodiment. First, a physical model is created (step S1). The user creates a physical model 100 as illustrated in Figure 2, and for this physical model 100, considering the gravity, buoyancy, drag, lift force acting on each element of the alga (plates 101 to 103), the action-reaction forces between the elements, and the moment of inertia, the user formulates the equations of motion shown in Figures 5A to 5C. The user inputs the formulated 9-variable linear simultaneous equations into the evaluation device 10. The input receiving unit 11 receives the input of the 9-variable linear simultaneous equations and records them in the storage unit 15.

[0037] Next, the position estimation unit 12 estimates the arrival position of the algae (step S2). The user specifies the initial calculation time, the upper limit of the calculation time, the initial position of the kelp, the size and density of the flat plates 101-103, and the orientation (θ) of the flat plates 101-103. １ ~θ ３Initial conditions such as ocean current data (3D current velocity, temperature, and density) and topographic data (obstacles and seabed depth) for each time and cell from the initial calculation time to the upper limit of the calculation time are input, and the evaluation device 10 is instructed to execute the algal movement simulation. The position estimation unit 12 solves a system of nine linear equations based on the initial conditions to calculate the acceleration and angular velocity of the plates 101 to 103, and calculates the position and orientation of the plates 101 to 103 at time t=1 from the calculated acceleration and angular velocity. The position estimation unit 12 applies the position and orientation of the plates 101 to 103 at time t=1 and the ocean current data, solves the system of nine linear equations again, and calculates the position and orientation of the plates 101 to 103 at time t=2. At this time, as explained with reference to Figure 9, the position estimation unit 12 may also calculate the density change due to the decomposition of the algal body in parallel, and solve the system of nine linear equations by applying the calculated density. The position estimation unit 12 repeatedly performs the same calculation until time t reaches the upper limit of the calculation time (time evolution method). When the upper limit of the calculation time is reached, the migration path and final destination of the algae are calculated (Figures 6A and 6B). The position estimation unit 12 records the migration path and final destination of the simulation results in the storage unit 15. The position estimation unit 12 may perform the above simulation multiple times using multiple ocean current data at different dates and times. The position estimation unit 12 may output the migration path of the algae (Figure 6A) and the sinking status of the algae according to time (Figure 6B) to a display device or electronic file (not shown).

[0038] Next, the survival rate evaluation unit 13 evaluates the survival rate (step S3). The survival rate evaluation unit 13 calculates the survival rate of the algae based on the water depth (value in the z-axis direction) of the position where the algae finally reached and the survival rate table exemplified in Figure 8A or the map showing the relationship between the depth reached and the survival rate exemplified in Figure 8B. Alternatively, the survival rate evaluation unit 13 may calculate the expected value of the survival rate from the survival rate of the algae ("survival rate" in Figure 10) and the ratio of the depth reached ("ratio" in Figure 10) calculated based on the depth reached by the algae calculated by multiple simulations ("depth reached" in Figure 10) and the survival rate table exemplified in Figure 8A or the map showing the relationship between the depth reached and the survival rate exemplified in Figure 8B. Depending on the water depth of the position where the algae finally reached, the survival rate evaluation unit 13 may create an algae reach depth map exemplified in Figure 7A or Figure 7B and output the algae reach depth map to a display device or electronic file (not shown).

[0039] Next, the CO2 absorption amount calculation unit 14 calculates the amount of CO2 absorbed (step S4). For example, the CO2 absorption amount calculation unit 14 substitutes the measured values ​​for the area of ​​the aquaculture facility or the length of the aquaculture rope, the wet weight per unit area or the wet weight per unit rope length in formula (1) of the JBE manual, substitutes the literature values ​​for the moisture content, carbon content, and P / B ratio, and substitutes the survival rate or the expected value of the survival rate evaluated in step S3 for the survival rate to calculate the amount of CO2 absorbed. The CO2 absorption amount calculation unit 14 may output the calculated amount of CO2 absorbed to a display device or electronic file (not shown).

[0040] (Effects) As described above, according to this embodiment, the arrival position of the algae after release is estimated, and the survival rate is evaluated according to the water depth at the arrival position of the algae. This allows the survival rate to be set to 100% when the released algae sink to a depth where they are not affected by decomposition, and the amount of CO2 absorbed can be calculated.

[0041] Figure 12 shows an example of the hardware configuration of an evaluation device according to each embodiment. The computer 900 includes a CPU 901, main memory 902, auxiliary storage device 903, input / output interface 904, and communication interface 905. The evaluation device 10 described above is implemented in the computer 900. The functions described above are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, expands it in the main memory device 902, and executes the above processing according to the program. The CPU 901 allocates a storage area in the main memory device 902 according to the program. The CPU 901 allocates a storage area in the auxiliary storage device 903 to store the data being processed according to the program.

[0042] A program to implement all or part of the functions of the evaluation device 10 may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into a computer system and executed to perform processing by each functional unit. Here, "computer system" includes hardware such as the OS and peripheral devices. If a WWW system is used, "computer system" also includes the homepage provisioning environment (or display environment). "Computer-readable recording medium" refers to portable media such as CDs, DVDs, USBs, and storage devices such as hard disks built into the computer system. If this program is distributed to the computer 900 via a communication line, the computer 900 that receives the program may load it into the main memory 902 and execute the above processing. The above program may be for implementing part of the functions described above, and may also be for implementing the above functions in combination with programs already recorded in the computer system.

[0043] As described above, several embodiments relating to this disclosure have been explained, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be carried out in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0044] <Note> The evaluation methods and evaluation apparatus described in each embodiment can be understood, for example, as follows.

[0045] (1) The estimation method according to the first embodiment includes the steps of: estimating the arrival location of seaweed released from a seaweed bed based on ocean current data; obtaining the water depth at the arrival location of the seaweed; and evaluating the survival rate of the seaweed based on the obtained water depth. This makes it possible to evaluate the survival rate of the seaweed.

[0046] (2) The estimation method according to the second embodiment further comprises the step of performing the estimation method of (1) on each of the multiple algal bodies and displaying the arrival positions of each of the multiple algal bodies in a list. This makes it possible to understand the arrival positions of multiple algal bodies.

[0047] (3) The estimation method relating to the third embodiment is the estimation method of (1) to (2), wherein in the estimation step, the destination position of the alga is estimated using a physical model that considers the alga as having a configuration in which a plurality of flat plates are each connected so as to be swingable. This makes it possible to simulate the movement of the alga.

[0048] (4) The estimation method relating to the fourth aspect is the estimation method of (1) to (3), wherein in the estimation step, the algae is considered to be a configuration in which a plurality of flat plates are each connected so as to be swingable, and the acceleration and angular acceleration of each of the plurality of flat plates are calculated by solving the equations of motion that calculate the position and orientation of each of the plurality of flat plates at time t+1 based on the position and orientation of each of the plurality of flat plates at time t and the gravity, buoyancy, lift, and drag force acting on each of the plurality of flat plates at time t, and an equation that constrains the positional relationship of each of the plurality of flat plates, and the arrival position of the algae is estimated by integrating the acceleration and angular acceleration, thereby simulating the movement of the algae.

[0049] (5) The estimation method relating to the fifth aspect is the estimation method of (1) to (4), wherein in the estimation step, in addition to the ocean current data, the arrival position of the algae is estimated based on the change in the state of the algae after release. This makes it possible to estimate the arrival position with higher accuracy.

[0050] (6) The estimation method relating to the sixth aspect is the estimation method of (1) to (5), wherein in the evaluation step, the survival rate of the algal bodies is evaluated based on information relating water depth and the survival rate of the algal bodies (Figures 8A and 8B). This makes it possible to estimate the survival rate according to the water depth.

[0051] (7) The estimation method relating to the seventh aspect is the estimation method of (1) to (6), wherein the estimation step outputs the horizontal movement path of the algae and the sedimentation status of the algae over time. This makes it possible to understand the state of drift of the algae.

[0052] (8) The estimation method according to the eighth aspect is the estimation method of (1) to (7), further comprising the step of estimating the amount of CO2 absorbed by the algae based on the remaining rate of the algae obtained in the evaluation step. This makes it possible to calculate the amount of CO2 absorbed by the algae.

[0053] (9) The estimation method relating to the ninth aspect is the estimation method of (1) to (8), further comprising the step of performing one of the evaluation methods of (1) to (8) multiple times using multiple ocean current data of the seaweed bed at different dates and times, and calculating the expected value of the survival rate using the survival rates of multiple seaweed bodies obtained from the multiple performances. This makes it possible to calculate the expected value of the survival rate.

[0054] (10) An evaluation device according to the tenth embodiment includes an estimation unit that estimates the arrival position of algal bodies released from a seaweed bed based on ocean current data, an acquisition unit that acquires the water depth of the arrival position of the algal bodies, and an evaluation unit that evaluates the survival rate of the algal bodies based on the acquired water depth.

[0055] This disclosure provides an evaluation method and an evaluation apparatus that can solve the above-mentioned problems.

[0056] 10...Evaluation device 11...Input reception unit 12...Position estimation unit 13...Remaining rate evaluation unit 14...CO2 absorption amount calculation unit 15...Storage unit 100...Physical model 101...Platform 102...Platform 103...Platform 900...Computer 901...CPU 902...Main memory 903...Auxiliary memory 904...Input / output interface 905...Communication interface

Claims

1. An evaluation method comprising the steps of: estimating the arrival location of algal bodies released from a seaweed bed based on ocean current data; obtaining the water depth at the arrival location of the algal bodies; and evaluating the survival rate of the algal bodies based on the obtained water depth.

2. The evaluation method according to claim 1, further comprising the step of performing the evaluation method of claim 1 for each of the multiple algal bodies and displaying the reached positions of each of the multiple algal bodies in a list.

3. The evaluation method according to claim 1 or 2, wherein in the estimation step, the arrival position of the algae is estimated using a physical model that considers the algae to be a configuration in which a plurality of flat plates are each connected in a swingable manner.

4. The evaluation method according to claim 1 or 2, wherein in the estimation step, the algae is considered to be a configuration in which a plurality of flat plates are each swingably connected, and the acceleration and angular acceleration of each of the plurality of flat plates are calculated by solving the equations of motion that calculate the position and orientation of each of the plurality of flat plates at time t+1 based on the position and orientation of each of the plurality of flat plates at time t and the gravitational force, buoyancy, lift, and drag force acting on each of the plurality of flat plates at time t, and the equations that constrain the positional relationship of each of the plurality of flat plates, and the arrival position of the algae is estimated by integrating the acceleration and angular acceleration.

5. The evaluation method according to claim 1 or 2, wherein the estimation step involves estimating the arrival position of the algae based on the change in the state of the algae after release, in addition to the ocean current data.

6. The evaluation method according to claim 1 or claim 2, wherein the evaluation step involves evaluating the survival rate of the algal bodies based on information relating water depth and the survival rate of the algal bodies.

7. The evaluation method according to claim 1 or claim 2, wherein the estimation step outputs the horizontal movement path of the algae and the sedimentation status of the algae over time.

8. The evaluation method according to claim 1 or 2, further comprising the step of estimating the amount of CO2 absorbed by the algae based on the remaining rate of the algae obtained in the evaluation step.

9. The evaluation method according to claim 1 or claim 2, further comprising the step of performing the evaluation method of claim 1 multiple times using multiple ocean current data for different dates and times of the seaweed bed, and calculating the expected value of the survival rate using the survival rates of multiple seaweed bodies obtained by the multiple performances.

10. An evaluation device comprising: an estimation unit that estimates the arrival location of algal bodies released from a seaweed bed based on ocean current data; an acquisition unit that acquires the water depth of the arrival location of the algal bodies; and an evaluation unit that evaluates the survival rate of the algal bodies based on the acquired water depth.

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