Lake ecological restoration effect evaluation method, device, equipment and storage medium

Abstract

This invention relates to the field of computer technology and discloses a method, apparatus, equipment, and storage medium for evaluating the effectiveness of lake ecological restoration. The evaluation method includes: constructing a lake model based on the state variables of the target lake and restoration measures; adjusting the nutrient load parameters of the lake model until the lake model reaches a steady state, obtaining a bifurcation analysis diagram; determining the first nutrient threshold before restoration and the second nutrient threshold after restoration based on the bifurcation analysis diagram; determining the improvement effect of the lake model's self-purification capacity based on the first and second nutrient thresholds; performing scenario simulations on the lake model under various conditions to obtain simulation results for multiple target scenarios; and evaluating the effectiveness of lake ecological restoration based on the improvement effect of self-purification capacity and the simulation results for multiple target scenarios. This invention evaluates the effectiveness of lake ecological restoration to prevent lake ecosystems from remaining in a long-term eutrophic state.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a method and device for evaluating ecological restoration effect of a lake, equipment and a storage medium. BACKGROUND

[0002] At present, many lakes in cities are facing serious eutrophication problems. Eutrophication is a water pollution phenomenon caused by excessive content of plant nutrients such as nitrogen and phosphorus. Under natural conditions, with the continuous deposition and siltation of alluvium and aquatic organism debris on the lake bottom, the lake will transition from oligotrophic lake to eutrophic lake, and then evolve into marsh and land, which is a very slow process. However, after a large amount of industrial wastewater, domestic sewage and plant nutrients in farmland runoff are discharged into slow-flowing water bodies such as lakes, reservoirs, estuaries and bays, aquatic organisms, especially algae, will reproduce in large numbers, changing the population and quantity of biomass, and destroying the ecological balance of the water body. Therefore, eutrophication needs to be repaired.

[0003] The existing ecological problems of shallow lakes are usually repaired by typical measures such as dredging, submerged plant repair and biological manipulation. However, after the lake ecosystem is repaired, it is difficult to judge the effect of the repair. If the repair effect is not good, the lake ecosystem will be in a long-term eutrophication state, which will cause damage to the ecology. SUMMARY

[0004] Therefore, the present application provides a method for evaluating the ecological restoration effect of a lake to solve the problem of being unable to judge the repair effect.

[0005] In a first aspect, the present application provides a method for evaluating the ecological restoration effect of a lake. A lake model is constructed according to the state variables of a target lake and repair measures, the lake model is used to simulate the ecological restoration process of the target lake, the nutrient salt load parameter of the lake model is adjusted until the lake model is in a steady state, and a bifurcation analysis diagram is obtained. Based on the bifurcation analysis diagram, a first nutrient salt threshold before repair and a second nutrient salt threshold after repair of the lake model are determined. According to the first nutrient salt threshold and the second nutrient salt threshold, the self-purification ability improvement effect of the lake model is determined. The lake model is simulated under multiple scenarios to obtain multiple target scenario simulation results. According to the self-purification ability improvement effect and the multiple target scenario simulation results, the ecological restoration effect of the lake is evaluated.

[0006] Beneficial effects: The embodiment of the application constructs a lake model according to state variables of a target lake and a restoration measure, the lake model is used to simulate an ecological restoration process of the target lake, the nutrient salt load parameter of the lake model is adjusted until the lake model is in a steady state, a bifurcation analysis diagram is obtained, the embodiment of the application adjusts the nutrient salt load parameter of the lake model, which is used to make the lake model tend to be in a steady state under the restoration measure, based on the bifurcation analysis diagram, a first nutrient salt threshold value before restoration of the lake model and a second nutrient salt threshold value after restoration are determined, according to the first nutrient salt threshold value and the second nutrient salt threshold value, a self-purification capacity improvement effect of the lake model is determined, according to the bifurcation analysis diagram, the self-purification capacity improvement effect of the lake model is determined, the self-purification capacity improvement effect is quantified, which is convenient for intuitively determining how the self-purification capacity improvement effect is, situation simulation of the lake model under multiple situations is performed, multiple target situation simulation results are obtained, different situations correspond to different restoration measures, and the ecological restoration effect of the lake is evaluated according to the self-purification capacity improvement effect and the multiple target situation simulation results. The embodiment of the application quantifies the lake ecological restoration process, intuitively evaluates the lake ecological restoration effect, timely evaluates the lake restoration effect, prevents the situation that the restoration effect is unknown and the lake is in a eutrophication state for a long time, and protects the ecological environment.

[0007] In an optional embodiment, constructing a lake model according to state variables of a target lake and a restoration measure comprises: determining multiple state variables of the target lake; and constructing the lake model under different restoration measures by changing a state variable simulation equation, the state variable simulation equation being used to represent a restoration process under the restoration measure.

[0008] Beneficial effects: The restoration process under different restoration measures is simulated by changing the state variable simulation equation, so as to evaluate the restoration effect subsequently.

[0009] In an optional embodiment, the multiple state variables comprise an area, a water depth, initial conditions and the restoration measure of the target lake, and the initial conditions comprise a clear grass type and a turbid algal type.

[0010] In an optional embodiment, adjusting the nutrient salt load parameter of the lake model until the lake model is in a steady state, and obtaining a bifurcation analysis diagram comprises: adjusting the nutrient salt load parameter under different initial conditions until the lake model is in a steady state; extracting a chlorophyll concentration under the steady state from the lake model as a response variable value; and forming the bifurcation analysis diagram according to the nutrient salt load parameter and the response variable value.

[0011] Beneficial effects: Under different initial conditions, adjust the nutrient salt load parameters until the lake model is in a steady state, according to the response variable values and the nutrient salt load parameters in the steady state, obtain the bifurcation analysis diagram, determine the change of the nutrient salt threshold value before and after the repair according to the bifurcation analysis diagram, calculate the self-purification ability improvement effect through the change of the nutrient salt threshold value before and after the repair, and evaluate which repair measure has stronger self-purification ability.

[0012] In an optional implementation, determining the first nutrient salt threshold value before the repair of the lake model and the second nutrient salt threshold value after the repair includes: determining a mutation point of the response variable value according to the bifurcation analysis diagram; and determining two nutrient salt load parameters corresponding to the mutation point as the first nutrient salt threshold value and the second nutrient salt threshold value.

[0013] In an optional implementation, determining the self-purification ability improvement effect of the lake model according to the first nutrient salt threshold value and the second nutrient salt threshold value includes: subtracting the first nutrient salt threshold value from the second nutrient salt threshold value to obtain a difference value; and dividing the difference value by the first nutrient salt threshold value to obtain the self-purification ability improvement effect.

[0014] In an optional implementation, the lake model is simulated under multiple scenarios to obtain multiple target scenario simulation results, including: simulating an un-repaired scenario and a repaired scenario of the target lake, and the multiple scenarios include the un-repaired scenario and the repaired scenario; and comparing the water quality parameters and the water ecological parameters in the un-repaired scenario and the repaired scenario in time series to obtain the target scenario simulation results.

[0015] Beneficial effects: The repair effects of different repair measures can be evaluated through the target scenario simulation results, and the repair effect of how to repair is determined to be the best.

[0016] In an optional implementation, the water quality parameters and the water ecological parameters in the un-repaired scenario and the repaired scenario are compared in time series to obtain the target scenario simulation results, including: arranging the water quality parameters and the water ecological parameters in the un-repaired scenario and the repaired scenario in time order; comparing the water quality parameters in the un-repaired scenario and the repaired scenario according to the time order to obtain a first scenario simulation result; comparing the water ecological parameters in the un-repaired scenario and the repaired scenario according to the time order to obtain a second scenario simulation result; and determining the target scenario simulation result according to the first scenario simulation result and the second scenario simulation result.

[0017] In an optional implementation, the target lake includes a shallow lake.

[0018] In a second aspect, the present application provides a device for evaluating the effect of lake ecological restoration, comprising: a model construction module, configured to construct a lake model according to state variables of a target lake and restoration measures, the lake model being used to simulate a lake ecological restoration process; a bifurcation simulation module, configured to adjust a nutrient salt load parameter of the lake model until the lake model is in a steady state, and obtain a bifurcation analysis diagram; a nutrient salt threshold value determination module, configured to determine a first nutrient salt threshold value before restoration and a second nutrient salt threshold value after restoration of the lake model based on the bifurcation analysis diagram; a self-purification capacity improvement effect calculation module, configured to determine a self-purification capacity improvement effect of the lake model according to the first nutrient salt threshold value and the second nutrient salt threshold value; a scenario simulation module, configured to perform scenario simulation of the lake model under multiple scenarios to obtain multiple target scenario simulation results; and an evaluation module, configured to evaluate the effect of lake ecological restoration according to the self-purification capacity improvement effect and the multiple target scenario simulation results.

[0019] In an optional implementation, the model construction module, configured to construct a lake model according to state variables of a target lake and restoration measures, comprises: a state variable determination unit, configured to determine multiple state variables of the target lake; and a lake model construction unit, configured to construct the lake model under different restoration measures by changing a state variable simulation equation, the state variable simulation equation being used to represent a restoration process under the restoration measures.

[0020] In an optional implementation, the bifurcation simulation module, configured to adjust a nutrient salt load parameter of the lake model until the lake model is in a steady state, and obtain a bifurcation analysis diagram, comprises: an adjustment unit, configured to adjust the nutrient salt load parameter under different initial conditions until the lake model is in the steady state; a response variable value extraction unit, configured to extract a chlorophyll concentration under the steady state from the lake model as a response variable value; and a bifurcation analysis diagram formation unit, configured to form the bifurcation analysis diagram according to the nutrient salt load parameter and the response variable value.

[0021] In an optional implementation, the self-purification capacity improvement effect calculation module, configured to determine a self-purification capacity improvement effect of the lake model according to a first nutrient salt threshold value and a second nutrient salt threshold value, comprises: a difference unit, configured to subtract the first nutrient salt threshold value from the second nutrient salt threshold value to obtain a difference value; and a division unit, configured to divide the difference value by the first nutrient salt threshold value to obtain the self-purification capacity improvement effect.

[0022] In a third aspect, the present application provides a computer device, comprising: a memory and a processor, which are in communication connection with each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the lake ecological restoration effect evaluation method of the first aspect or any of the corresponding embodiments thereof.

[0023] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the lake ecological restoration effect evaluation method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a method for evaluating the effectiveness of lake ecological restoration according to an embodiment of the present invention;

[0026] Figure 2 This is a flowchart illustrating another method for evaluating the ecological restoration effect of a lake according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of bifurcation analysis according to an embodiment of the present invention;

[0028] Figure 4 This is a flowchart illustrating another method for evaluating the ecological restoration effect of a lake according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram illustrating the effect of improved self-purification capacity under different submerged plant restoration measures according to embodiments of the present invention;

[0030] Figure 6(a) is a schematic diagram of the steady-state transition process of a lake under the submerged plant restoration measures according to an embodiment of the present invention;

[0031] Figure 6(b) is a schematic diagram of the steady-state transition process of a lake under biomanipulation remediation measures according to an embodiment of the present invention;

[0032] Figure 6(c) is a schematic diagram of the steady-state transition process of a lake under the restoration measures combining submerged plants and biomanipulation according to an embodiment of the present invention;

[0033] Figure 7 This is a structural block diagram of an evaluation device for the ecological restoration effect of lakes according to an embodiment of the present invention;

[0034] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This invention provides a method for evaluating the ecological restoration effect of lakes by constructing a lake model to assess the ecological restoration effect of lakes.

[0037] According to an embodiment of the present invention, an embodiment of a method for evaluating the effect of lake ecological restoration is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] This embodiment provides a method for evaluating the ecological restoration effect of lakes, which can be used for the above-mentioned ecological restoration effect evaluation. Figure 1 This is a flowchart of a method for evaluating the ecological restoration effect of lakes according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0039] Step S101: Construct a lake model based on the state variables of the target lake and the restoration measures. The lake model is used to simulate the ecological restoration process of the target lake.

[0040] The state variables include the area or wind distance of the target lake, its water depth, and initial conditions. The initial conditions include the type of the target lake, which is either clear grass type or turbid algae type. The restoration measures in this embodiment may include typical lake ecological restoration measures such as submerged plant restoration information and biomanipulation information. The restoration measures may also be a combination of restoration information containing multiple restoration measures.

[0041] In some alternative implementations, the target lake includes shallow lakes.

[0042] In this embodiment of the invention, by modifying the state variable simulation equation, a simulation process of typical lake ecological restoration measures such as submerged plant restoration information and biological manipulation information is constructed. For the target lake, relevant parameters are calibrated, and a shallow lake eutrophication (PCLake) model that can simulate the ecosystem process of the target lake is constructed.

[0043] Step S102: Adjust the nutrient loading parameters of the lake model until the lake model is in a steady state and obtain the bifurcation analysis diagram.

[0044] The bifurcation analysis diagram is used to represent the relationship between the response variable value and the nutrient load during the repair process.

[0045] In this embodiment of the invention, based on the constructed PCLake model, a bifurcation analysis simulation is performed. This analysis requires the use of initial conditions for clear grass and turbid algae, respectively, and a series of different nutrient loads as stress variables. The simulation is then performed until a steady state is reached. The mean chlorophyll concentration in summer under steady state is then extracted as the response variable, forming a fitting curve between the stress variable and the response variable, which is the bifurcation analysis diagram.

[0046] Step S103: Based on the bifurcation analysis diagram, determine the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration.

[0047] In some alternative implementations, the first nutrient threshold and the second nutrient threshold are determined based on the abrupt change points of the curves in the bifurcation analysis graph.

[0048] Step S104: Determine the self-purification capacity enhancement effect of the lake model based on the first nutrient threshold and the second nutrient threshold.

[0049] In some alternative implementations, the method for determining the self-purification capacity improvement effect of the lake model is to subtract the first nutrient threshold from the second nutrient threshold to obtain the difference; and to divide the difference by the first nutrient threshold to obtain the self-purification capacity improvement effect.

[0050] Step S105: Simulate various scenarios for the lake model to obtain simulation results for multiple target scenarios.

[0051] In this embodiment of the invention, scenario analysis is conducted on the changes to the ecosystem in the target lake caused by various restoration measures. This analysis requires the actual situation of the target lake as the initial condition, which is generally a turbid algal type. By changing the equations and parameter settings, simulations are performed on different scenarios such as no restoration, restoration with submerged plants, and restoration with biological manipulation.

[0052] For example, the simulation results of the target scenario can be the impact of ecological restoration measures on ecosystem elements such as water quality parameters, chlorophyll concentration parameters, submerged plant biomass parameters, and fish biomass parameters, and the results may be an increase or a decrease.

[0053] Step S106: Evaluate the effect of lake ecological restoration based on the improvement of self-purification capacity and the simulation results of various target scenarios.

[0054] The lake ecological restoration effect evaluation method provided in this embodiment constructs a lake model based on the state variables of the target lake and restoration measures. The lake model simulates the ecological restoration process of the target lake. The nutrient loading parameters of the lake model are adjusted until the lake model reaches a steady state, resulting in a bifurcation analysis diagram. This embodiment adjusts the nutrient loading parameters of the lake model to make the lake model tend to a steady state under restoration measures. Based on the bifurcation analysis diagram, the first nutrient threshold before restoration and the second nutrient threshold after restoration are determined. Based on the first and second nutrient thresholds, the self-purification capacity improvement effect of the lake model is determined. The self-purification capacity improvement effect is quantified according to the bifurcation analysis diagram, making it easier to intuitively determine how effective the self-purification capacity improvement is. The lake model is simulated under various scenarios to obtain simulation results for multiple target scenarios. Different scenarios correspond to different restoration measures. Based on the self-purification capacity improvement effect and the simulation results of multiple target scenarios, the lake ecological restoration effect is evaluated. This invention quantifies the lake ecological restoration process, intuitively assesses the lake ecological restoration effect, and promptly evaluates the lake restoration effect to prevent situations where the restoration effect is unknown and the lake remains in a state of eutrophication for a long time, thus protecting the ecological environment.

[0055] This embodiment provides a method for evaluating the ecological restoration effect of lakes, which can be used for the above-mentioned ecological restoration effect evaluation. Figure 2 This is a flowchart of another method for evaluating the ecological restoration effect of lakes according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0056] Step S201: Construct a lake model based on the state variables of the target lake and the restoration measures. The lake model is used to simulate the ecological restoration process of the target lake. For details, please refer to [link to details]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0057] Specifically, step S201 includes:

[0058] Step S2011: Determine multiple state variables of the target lake.

[0059] In some alternative implementations, multiple state variables include the area of ​​the target lake, water depth, initial conditions, and remediation measures, with initial conditions including clear grass type and turbid algae type.

[0060] The target lake is a shallow lake.

[0061] Step S2012: By changing the state variable simulation equation, lake models under different restoration measures are constructed. The state variable simulation equation is used to characterize the restoration process under the restoration measures.

[0062] In this embodiment of the invention, the repair process under different repair measures is simulated by changing the state variable simulation equation, so as to provide a basis for subsequent evaluation of the repair effect.

[0063] Step S202: Adjust the nutrient loading parameters of the lake model until the lake model reaches a steady state, obtaining the bifurcation analysis diagram. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0064] Specifically, step S202 includes:

[0065] Step S2021: Adjust the nutrient loading parameters under different initial conditions until the lake model reaches a steady state.

[0066] Step S2022: Extract the steady-state chlorophyll concentration from the lake model as the response variable value.

[0067] Step S2023: Based on the nutrient load parameters and response variable values, a bifurcation analysis diagram is generated.

[0068] like Figure 3 The diagram shown is a schematic of bifurcation analysis. The dashed line represents the relationship between chlorophyll concentration and nutrient load when the initial condition is clear grass type. It is an upward curve, indicating the transition from clear water steady state to turbid water steady state in a lake with clear grass type as the initial condition, as the nutrient load gradually increases. The solid line represents the relationship between chlorophyll concentration and nutrient load when the initial condition is turbid algae type as the initial condition. It is a downward curve, indicating the transition from turbid water steady state to clear water steady state in a lake with turbid algae type as the initial condition, as the nutrient load gradually decreases.

[0069] The initial conditions are set by establishing initial values ​​for various variables of the target lake under either a clear, grassy steady-state or a turbid, algal steady-state. In this embodiment, the initial state utilizes a database of state variables calibrated by Janse for multiple lakes. The single bifurcation analysis in this embodiment includes multiple simulation examples, half of which are used to plot upward curves, and half to plot downward curves. The phosphorus load range used is selected as 0.0001–0.005 gPm. -2 day -1 Multiple phosphorus load values ​​were calculated at equal intervals within this range. Nitrogen load was calculated based on a nitrogen-to-phosphorus ratio of 10. Each load corresponds to a single case, and each case simulates the multi-year evolution of the lake ecosystem until steady state. The bifurcation analysis results are based on the annual average chlorophyll concentration of the water body under steady-state conditions using hyperspectral data.

[0070] In this embodiment of the invention, under different initial conditions, the nutrient loading parameters are adjusted until the lake model reaches a steady state. Based on the steady-state response variable values ​​and nutrient loading parameters, a bifurcation analysis diagram is obtained. The changes in nutrient thresholds before and after restoration are determined based on the bifurcation analysis diagram. The self-purification capacity improvement effect can be calculated by the changes in nutrient thresholds before and after restoration, and which restoration measure has a stronger self-purification capacity can be evaluated.

[0071] Step S203: Based on the bifurcation analysis diagram, determine the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0072] Specifically, step S203 includes:

[0073] Step S2031: Determine the abrupt change point of the response variable value based on the bifurcation analysis diagram.

[0074] Step S2032: Determine the two nutrient loading parameters corresponding to the mutation point as the first nutrient threshold and the second nutrient threshold.

[0075] like Figure 3 As shown, the nutrient load parameters corresponding to the mutation points of the dashed lines and the mutation points of the solid lines are respectively determined as the first nutrient threshold and the second nutrient threshold.

[0076] Step S204: Determine the self-purification capacity improvement effect of the lake model based on the first nutrient threshold and the second nutrient threshold. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0077] Step S205 involves performing scenario simulations on the lake model under various conditions to obtain simulation results for multiple target scenarios. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0078] Specifically, step S205 includes:

[0079] Step S2051: Simulate both the unrepaired and repaired scenarios for the target lake. Multiple scenarios include both unrepaired and repaired scenarios.

[0080] Step S2052: Compare the water quality parameters and aquatic ecological parameters in the unrestored and restored scenarios over time to obtain the simulation results of the target scenario.

[0081] Specifically, step S2052 includes:

[0082] Step a1: Arrange the water quality parameters and aquatic ecological parameters in the unrestored and restored scenarios in chronological order.

[0083] Among them, water quality parameters include total nitrogen (TN) and total phosphorus (TP) and other water quality indicators, while aquatic ecological parameters include chlorophyll concentration parameters, submerged plant biomass parameters, and fish biomass parameters.

[0084] Step a2: Compare the water quality parameters in the unrepaired scenario and the repaired scenario according to the time sequence to obtain the simulation results of the first scenario.

[0085] Step a3: Compare the aquatic ecological parameters in the unrestored and restored scenarios according to the time sequence to obtain the simulation results of the second scenario.

[0086] Step a4: Determine the target scenario simulation result based on the first scenario simulation result and the second scenario simulation result.

[0087] In this embodiment of the invention, the repair effects of different repair measures can be evaluated based on the results of target scenario simulation, and the best repair effect can be determined by how to repair.

[0088] Step S206: Evaluate the lake's ecological restoration effect based on the improved self-purification capacity and simulation results of various target scenarios. For details, please refer to [link to relevant documentation]. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0089] The lake ecological restoration effect evaluation method provided in this embodiment constructs a lake model based on the state variables of the target lake and restoration measures. The lake model simulates the ecological restoration process of the target lake. The nutrient loading parameters of the lake model are adjusted until the lake model reaches a steady state, resulting in a bifurcation analysis diagram. This embodiment adjusts the nutrient loading parameters of the lake model to make the lake model tend to a steady state under restoration measures. Based on the bifurcation analysis diagram, the first nutrient threshold before restoration and the second nutrient threshold after restoration are determined. Based on the first and second nutrient thresholds, the self-purification capacity improvement effect of the lake model is determined. The self-purification capacity improvement effect is quantified according to the bifurcation analysis diagram, making it easier to intuitively determine how effective the self-purification capacity improvement is. The lake model is simulated under various scenarios to obtain simulation results for multiple target scenarios. Different scenarios correspond to different restoration measures. Based on the self-purification capacity improvement effect and the simulation results of multiple target scenarios, the lake ecological restoration effect is evaluated. This invention quantifies the lake ecological restoration process, intuitively assesses the lake ecological restoration effect, and promptly evaluates the lake restoration effect to prevent situations where the restoration effect is unknown and the lake remains in a state of eutrophication for a long time, thus protecting the ecological environment.

[0090] This embodiment provides a method for evaluating the ecological restoration effect of lakes, which can be used for the above-mentioned ecological restoration effect evaluation. Figure 4 This is a flowchart of another method for evaluating the ecological restoration effect of a lake according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes:

[0091] The basic parameters of the lake are set, including its area or windwardness and water depth. Initial conditions are set, including clear grass type and turbid algae type. Lake ecological restoration measures are simulated based on the basic parameters and initial conditions of the target lake. These simulations include biological manipulation information and submerged plant restoration information. Bifurcation analysis is performed on the lake model, adjusting nutrient loading parameters under different initial conditions until the lake model reaches a steady state. The steady-state chlorophyll concentration parameter is extracted from the lake model as the response variable value. A bifurcation analysis diagram is formed based on the nutrient loading parameters and the response variable value. The first and second nutrient thresholds are obtained from the abrupt change points of the curves in the bifurcation analysis diagram. The ecosystem self-purification capacity is calculated based on the first and second nutrient thresholds.

[0092] The calculation of the ecosystem's self-purification capacity effect includes subtracting the first nutrient threshold from the second nutrient threshold to obtain the difference; dividing the difference by the first nutrient threshold to obtain the self-purification capacity improvement effect; and evaluating the ecological restoration based on the self-purification capacity improvement effect.

[0093] For example, such as Figure 5 The diagram shows the effect of different submerged plant remediation measures on improving self-purification capacity. Figure 5 This includes upper and lower thresholds for phosphorus load in cases of no remediation, submerged phytoremediation, biomanipulation, and a combination of submerged phytoremediation and biomanipulation information. Figure 5 Calculations show that in this embodiment, the improvement effects of submerged plant restoration information, biomanipulation information, and the combination of submerged plant information and biomanipulation information on the water body's self-purification capacity are 11%, 42%, and 53%, respectively. It can be seen that the combination of multiple ecological restoration measures is more effective for lake ecological restoration than a single measure. Furthermore, based on the analysis of nutrient thresholds in the target lake ecosystem before and after restoration, it is known that the ecological restoration measures increased the nutrient threshold of the shallow lake ecosystem, making the lake, which was initially clear and grassy, ​​have a stronger self-purification capacity.

[0094] This invention also includes scenario analysis of the lake model, time-series comparison of water quality parameters and aquatic ecological parameters, and evaluation of the improvement effects of water quality parameters and aquatic ecological parameters.

[0095] Figure 6(a) illustrates the steady-state transition process of a lake under submerged plant restoration measures. Under these measures, the phosphorus load increases slightly from unrestored to restored. Figure 6(b) illustrates the steady-state transition process of a lake under biomanipulation restoration measures. Under these measures, the phosphorus load increases moderately from unrestored to restored. Figure 6(c) illustrates the steady-state transition process of a lake under a combination of submerged plant and biomanipulation restoration measures. Under these combined measures, the phosphorus load increases significantly from unrestored to restored. This demonstrates that ecological restoration measures have an integrated effect. Combining multiple ecological restoration measures is more effective for lake ecological restoration than using a single measure. Furthermore, lakes initially in a turbid algal state are more easily transformed into clear, grass-dominated lakes through nutrient reduction.

[0096] The embodiments of the present invention provide a comprehensive evaluation of the effects of water ecological restoration. The effectiveness of ecological restoration measures can be assessed by evaluating the impact of ecological restoration measures on the nutrient threshold of the lake ecosystem.

[0097] This embodiment also provides an evaluation device for the ecological restoration effect of lakes. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0098] This embodiment provides a device for evaluating the effectiveness of lake ecological restoration, such as...Figure 7 As shown, it includes:

[0099] The model building module 701 is used to build a lake model based on the state variables of the target lake and the restoration measures. The lake model is used to simulate the ecological restoration process of the target lake.

[0100] The bifurcation simulation module 702 is used to adjust the nutrient load parameters of the lake model until the lake model reaches a steady state, thus obtaining the bifurcation analysis diagram.

[0101] The nutrient threshold determination module 703 is used to determine the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration based on the bifurcation analysis diagram.

[0102] The self-purification capacity improvement calculation module 704 is used to determine the self-purification capacity improvement effect of the lake model based on the first nutrient threshold and the second nutrient threshold.

[0103] The scenario simulation module 705 is used to perform scenario simulations on the lake model under various scenarios to obtain simulation results for multiple target scenarios.

[0104] The assessment module 706 is used to assess the effectiveness of lake ecological restoration based on the improvement of self-purification capacity and the simulation results of various target scenarios.

[0105] Furthermore, the aforementioned model building module 701 includes:

[0106] The state variable determination unit is used to determine multiple state variables of the target lake.

[0107] The lake model building unit is used to construct lake models under different remediation measures by changing the state variable simulation equations. The state variable simulation equations are used to characterize the remediation process under the remediation measures.

[0108] Specifically, the bifurcation simulation module 702 mentioned above includes:

[0109] The adjustment unit is used to adjust the nutrient loading parameters under different initial conditions until the lake model reaches a steady state.

[0110] The response variable value extraction unit is used to extract the steady-state chlorophyll concentration as the response variable value from the lake model.

[0111] The bifurcation analysis diagram forming unit is used to generate a bifurcation analysis diagram based on nutrient load parameters and response variable values.

[0112] Specifically, the nutrient threshold determination module 703 includes:

[0113] The mutation point identification unit is used to determine the mutation point of the response variable value based on the bifurcation analysis diagram.

[0114] The nutrient threshold confirmation unit is used to determine the two nutrient load parameters corresponding to the mutation point as the first nutrient threshold and the second nutrient threshold.

[0115] Specifically, the self-cleaning capacity enhancement effect calculation module 704 includes:

[0116] The difference unit is used to subtract the first nutrient threshold from the second nutrient threshold to obtain the difference.

[0117] The division unit is used to divide the difference by the first nutrient threshold to obtain the self-cleaning capacity improvement effect.

[0118] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0119] In this embodiment, the lake ecological restoration effect evaluation device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0120] This invention also provides a computer device having the above-described features. Figure 7 The device shown is for evaluating the effectiveness of lake ecological restoration.

[0121] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0122] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0123] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0124] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0125] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0126] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0127] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0128] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the effectiveness of lake ecological restoration, characterized in that, The method includes: A lake model is constructed based on the state variables of the target lake and the restoration measures. The lake model is used to simulate the ecological restoration process of the target lake. The state variables include the area or wind distance of the target lake, water depth, and initial conditions. The initial conditions include the type of the target lake, namely clear grass type and turbid algae type. The restoration measures include submerged plant restoration information, biological manipulation information, and co-restoration information. The nutrient loading parameters of the lake model are adjusted until the lake model reaches a steady state, and a bifurcation analysis diagram is obtained. This adjustment includes: adjusting the nutrient loading parameters under different initial conditions until the lake model reaches a steady state; extracting the steady-state chlorophyll concentration from the lake model as the response variable value; and forming a bifurcation analysis diagram based on the nutrient loading parameters and the response variable value. Based on the bifurcation analysis diagram, the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration are determined; the determination of the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration based on the bifurcation analysis diagram includes: determining the mutation point of the response variable value according to the bifurcation analysis diagram; and determining the two nutrient load parameters corresponding to the mutation point as the first nutrient threshold and the second nutrient threshold. The self-purification capacity enhancement effect of the lake model is determined based on the first nutrient threshold and the second nutrient threshold. The lake model was simulated under various scenarios to obtain simulation results for multiple target scenarios. Based on the self-purification capacity enhancement effect and the simulation results of various target scenarios, the lake ecological restoration effect is evaluated; scenario analysis is performed on the lake model, and water quality parameters and aquatic ecological parameters are compared over time to evaluate the improvement effect of water quality parameters and aquatic ecological parameters.

2. The method according to claim 1, characterized in that, The process of constructing a lake model based on the state variables of the target lake and restoration measures includes: Determine multiple state variables of the target lake; By changing the state variable simulation equation, lake models under different restoration measures are constructed. The state variable simulation equation is used to characterize the restoration process under the restoration measures.

3. The method according to claim 1 or 2, characterized in that, The step of determining the self-purification capacity improvement effect of the lake model based on the first nutrient threshold and the second nutrient threshold includes: Subtract the first nutrient threshold from the second nutrient threshold to obtain the difference; Dividing the difference by the first nutrient threshold yields the self-cleaning capacity enhancement effect.

4. The method according to claim 1 or 2, characterized in that, The lake model is subjected to scenario simulations under various conditions to obtain simulation results for multiple target scenarios, including: Simulations were performed on the target lake in both unrepaired and repaired scenarios, where the scenarios included both the unrepaired and repaired scenarios. The water quality parameters and aquatic ecological parameters in the unrestored scenario and the restored scenario are compared over time to obtain the simulation results of the target scenario.

5. The method according to claim 4, characterized in that, The step of comparing water quality parameters and aquatic ecological parameters in the unrestored and restored scenarios over time to obtain the simulation results of the target scenario includes: The water quality parameters and aquatic ecological parameters in the unrestored and restored scenarios are arranged in chronological order. The water quality parameters in the unrepaired scenario and the repaired scenario are compared according to the time sequence to obtain the simulation results of the first scenario; The aquatic ecological parameters in the unrestored scenario and the restored scenario are compared according to the time sequence to obtain the simulation results of the second scenario; The target scenario simulation result is determined based on the first scenario simulation result and the second scenario simulation result.

6. The method according to claim 1 or 2, characterized in that, The target lakes include shallow lakes.

7. A device for evaluating the effectiveness of lake ecological restoration, characterized in that, The device includes: The model building module is used to construct a lake model based on the state variables of the target lake and the restoration measures. The lake model is used to simulate the ecological restoration process of the target lake. The state variables include the area or wind distance of the target lake, water depth, and initial conditions. The initial conditions include the type of the target lake, which is either clear grass type or turbid algae type. The restoration measures include submerged plant restoration information, biological manipulation information, and co-restoration information. The bifurcation simulation module is used to adjust the nutrient loading parameters of the lake model until the lake model reaches a steady state, thereby obtaining a bifurcation analysis diagram. The module includes: an adjustment unit for adjusting the nutrient loading parameters under different initial conditions until the lake model reaches a steady state; a response variable value extraction unit for extracting the steady-state chlorophyll concentration as a response variable value from the lake model; and a bifurcation analysis diagram forming unit for forming a bifurcation analysis diagram based on the nutrient loading parameters and the response variable value. A nutrient threshold determination module is used to determine, based on the bifurcation analysis diagram, a first nutrient threshold before lake model restoration and a second nutrient threshold after restoration; the determination of the first nutrient threshold before lake model restoration and the second nutrient threshold after restoration based on the bifurcation analysis diagram includes: determining the mutation point of the response variable value according to the bifurcation analysis diagram; and determining the two nutrient load parameters corresponding to the mutation point as the first nutrient threshold and the second nutrient threshold. The self-purification capacity improvement effect calculation module is used to determine the self-purification capacity improvement effect of the lake model based on the first nutrient threshold and the second nutrient threshold. The scenario simulation module is used to perform scenario simulations on the lake model under various scenarios to obtain simulation results for multiple target scenarios; The evaluation module is used to evaluate the lake ecological restoration effect based on the self-purification capacity improvement effect and the simulation results of multiple target scenarios; to perform scenario analysis on the lake model, compare water quality parameters and aquatic ecological parameters over time, and evaluate the improvement effect of water quality parameters and aquatic ecological parameters.

8. The apparatus according to claim 7, characterized in that, The model building module is used to build a lake model based on the state variables of the target lake and the remediation measures, including: A state variable determination unit is used to determine multiple state variables of the target lake; The lake model building unit is used to construct lake models under different restoration measures by changing the state variable simulation equations, wherein the state variable simulation equations are used to characterize the restoration process under the restoration measures.

9. The apparatus according to claim 7 or 8, characterized in that, The self-purification capacity improvement calculation module is used to determine the self-purification capacity improvement effect of the lake model based on the first nutrient threshold and the second nutrient threshold, including: The difference unit is used to subtract the first nutrient threshold from the second nutrient threshold to obtain the difference; The division unit is used to divide the difference by the first nutrient threshold to obtain the self-cleaning capacity improvement effect.

10. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the lake ecological restoration effect assessment method according to any one of claims 1 to 6.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the lake ecological restoration effect assessment method according to any one of claims 1 to 6.

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