River ecological environment monitoring method, device and equipment and storage medium

Abstract

The invention discloses a river ecological environment monitoring method, device and equipment and a storage medium. The method belongs to the technical field of river monitoring, and comprises the following steps: obtaining river monitoring data of a target river, the river monitoring data comprising pollutant change information and target ecological species; the river monitoring data are input into a preset river digital twinborn model, a water quality model of the preset river digital twinborn model carries out pollutant migration prediction based on pollutant change information, and a self-purification trend value of the target river is obtained; the ecological model of the preset river digital twinborn model performs environmental change simulation based on the target ecological species to obtain an environmental change value of the target river; based on the self-purification trend value and the environment change value, determining an environment monitoring value in a preset future time period; and determining a monitoring execution strategy based on the environment monitoring value and a preset early warning condition. According to the embodiment of the invention, the method can achieve the accurate evaluation of the ecological environment of the river and the prediction of the future trend, thereby facilitating the protection in advance.

Description

Technical Field

[0001] This application belongs to the technical field of river monitoring, and in particular relates to a method, device, equipment and storage medium for river ecological environment monitoring. Background Technology

[0002] With the continuous expansion of industrial production and the rapid development of urbanization, a large amount of pollutants flowing into rivers are causing pollution to the rivers, and the polluted soil around the rivers will damage the river ecological environment and affect individual organisms and populations in the rivers. Therefore, it is necessary and urgent to establish a river ecological environment monitoring system.

[0003] Currently, the monitoring of river ecological environment usually involves manually collecting river water samples periodically and then conducting laboratory tests to obtain ecological environment data of the river. This monitoring method is not only inefficient, but by the time the test results indicate that the river is seriously polluted, it is already too late to control the pollution in a timely and effective manner. Therefore, there is an urgent need for a new method for monitoring river ecological environment. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for monitoring the river's ecological environment, which can accurately assess the river's ecological environment and predict future trends, so as to facilitate early protection.

[0005] On the one hand, embodiments of this application provide a method for monitoring the ecological environment of a river, the method comprising: Acquire river monitoring data for the target river, including information on pollutant changes and target ecological species; The river monitoring data is input into a preset river digital twin model. The water quality model of the preset river digital twin model predicts pollutant migration based on the pollutant change information to obtain the self-purification trend value of the target river. The ecological model of the preset river digital twin model simulates environmental changes based on the target ecological species to obtain the environmental change values ​​of the target river; Based on the self-cleaning trend value and the environmental change value, determine the environmental monitoring values ​​for a preset future time period; Based on the environmental monitoring values ​​and preset early warning conditions, a monitoring execution strategy is determined.

[0006] Optionally, before inputting the river monitoring data into a preset river digital twin model, the method further includes: Obtain historical river ecological data of the target river, including hydrological data, water quality data, and pollutant data; Based on the historical river ecological data, pollutant transformation simulation is performed to obtain source-sink terms, which are used to characterize the process parameters of pollutant increase or decrease. Based on the source-sink term, the preset diffusion / dispersion term, and the preset convection term, the pollutant concentration change rate is determined; The water quality model is generated based on the rate of change of the pollutant concentration. The historical river ecological data is input into the water quality model, which then performs water quality simulation to obtain simulation results. Based on the simulation results and the historical river ecological data, the degradation coefficient and reoxygenation coefficient in the water quality model are adjusted until the training completion conditions are met.

[0007] Optionally, after obtaining source-sink terms through pollutant transformation simulation based on the historical river ecological data, the method further includes: Obtain the light intensity, plant species, and vegetation cover area of ​​the target river; Based on the light intensity, plant species, and vegetation coverage area, the oxygen production, oxygen consumption, and sediment oxygen consumption of each plant species are determined respectively. Based on the sum of the oxygen production, oxygen consumption, and sediment oxygen consumption of the plant species, the correction parameters are determined. The source and sink terms are modified based on the modification parameters to obtain the final source and sink terms, and the final source and sink terms are determined as the new source and sink terms.

[0008] Optionally, before inputting the river monitoring data into a preset river digital twin model, the method further includes: The ecological species of the target river are obtained, including algae and fish. For the algae, obtain the algal biomass of the algae; Based on the algal biomass, the preset maximum growth rate, and the preset constraint function, an algal ecological model is constructed. And / or, for the fish species, construct a fish ecological model using a pre-defined bioenergetics model; Based on the algae ecological model and the fish ecological model, a first ecological model is constructed.

[0009] Optionally, before constructing the first ecological model based on the algae ecological model and the fish ecological model, the method further includes: Obtain historical environmental factors and historical species status of the target environment; Using the historical environmental factors as input, a preset regression algorithm is used to determine the predicted species status; The model parameters of the first ecological model are corrected based on the predicted species status and the historical species status to obtain the second ecological model; Obtain the training dataset; The second ecological model is trained using the training dataset to obtain a third ecological model, and the third ecological model is determined as the final ecological model.

[0010] Optionally, determining the environmental monitoring values ​​for a preset future time period based on the self-cleaning trend value and the environmental change value includes: The target river is divided into multiple sections; Based on the river monitoring data for each section, the parameters of the preset river digital twin model are adjusted to obtain a simulated river environment; Based on the simulated river environment, determine the self-purification trend value and the amount of environmental change for each section within a preset future time period; The environmental monitoring value for each segment is determined based on the sum of the self-cleaning trend value and the environmental change value within each segment.

[0011] Optionally, based on the environmental monitoring values ​​and preset early warning conditions, a monitoring execution strategy is determined, including: If the environmental monitoring value exceeds a preset threshold, a threshold alarm will be triggered. If the posture trend value exceeds a second preset threshold in the preset future time period, a trend alarm will be triggered in advance. In the event of triggering the threshold alarm and / or the triggering trend alarm, the predicted pollution source area is determined based on the environmental detection value.

[0012] On the other hand, this application provides a river ecological environment monitoring device, the device comprising: The acquisition module is used to acquire river monitoring data of the target river, including pollutant change information and target ecological species; The input module is used to input the river monitoring data into a preset river digital twin model. The water quality model of the preset river digital twin model predicts pollutant migration based on the pollutant change information to obtain the self-purification trend value of the target river. The simulation module is used to simulate environmental changes of the target ecological species based on the ecological model of the preset river digital twin model, and obtain the environmental change values ​​of the target river. The determination module is used to determine environmental monitoring values ​​within a preset future time period based on the self-cleaning trend value and the environmental change value. The determination module is also used to determine the monitoring execution strategy based on the environmental monitoring values ​​and preset early warning conditions.

[0013] In another aspect, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the river ecological environment monitoring method as described in any one of the first aspects.

[0014] In another aspect, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the river ecological environment monitoring method as described in any one of the first aspects.

[0015] The river ecological environment monitoring method, apparatus, equipment, and storage medium of this application embodiment can simulate the operation of a target river based on river monitoring data using a preset river digital twin model. This allows for the acquisition of the target river's self-purification trend value and environmental change value. The self-purification trend value takes into account the target river's self-purification capacity, while the environmental change value considers the impact of the environment on the target river. By considering both the self-purification trend value and the environmental change value, the river's self-adaptation and purification process can be quantified, enabling accurate assessment of the ecological environment status and prediction of future trends. Based on environmental monitoring values ​​and preset early warning conditions, a monitoring execution strategy can be determined to predict the target river's behavior and provide early protection, ensuring the stability of the target river's ecological environment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a river ecological environment monitoring method provided in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of a river ecological environment monitoring device provided in another embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0020] To address the problems of existing technologies, embodiments of this application provide a method, apparatus, device, and storage medium for river ecological environment monitoring. In these embodiments, a simulated operation can be performed using a preset digital twin model of the river based on river monitoring data. This allows for the acquisition of the self-purification trend value and environmental change value of the target river. The self-purification trend value considers the self-purification capacity of the target river, while the environmental change value considers the impact of the environment on the target river. By considering both the self-purification trend value and the environmental change value, the river's self-adaptation and purification processes can be quantified, enabling accurate assessment of the ecological environment and prediction of future trends. Based on environmental monitoring values ​​and preset early warning conditions, a monitoring execution strategy can be determined to predict the performance of the target river, thereby providing early protection and ensuring the stability of the target river's ecological environment.

[0021] The following section first introduces the river ecological environment monitoring method provided in the embodiments of this application.

[0022] Figure 1 A schematic flowchart of a river ecological environment monitoring method according to an embodiment of this application is shown. Figure 1 As shown, river ecological environment monitoring methods may include S101-S105: S101, Obtain river monitoring data for the target river.

[0023] In this embodiment, the river monitoring data includes pollutant change information and target ecological species. The pollutant change information may include conventional pollutants, toxic substances, and eutrophication indicators. Conventional pollutants may include COD (chemical oxygen demand), BOD (biochemical oxygen demand), NH3-N (ammonia nitrogen), and DO (dissolved oxygen). Toxic substances may include heavy metals and persistent organic pollutants. Eutrophication indicators include total nitrogen and total phosphorus.

[0024] In this application embodiment, river monitoring data of the target river can be obtained in various ways, specifically, the following methods are possible: Fixed monitoring points: Multi-parameter water quality monitoring buoy stations are deployed on the river cross-section to monitor key indicators such as pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen (NH3-N), chemical oxygen demand (COD), total organic carbon (TOC), and temperature in real time.

[0025] Mobile monitoring unit: Using unmanned vessels equipped with mobile sensors to conduct patrols, it makes up for the lack of space at fixed points, especially for identifying pollution sources and mapping the spread of pollutants.

[0026] Remote sensing monitoring: Using UAV hyperspectral imaging or satellite remote sensing data, large-scale monitoring of chlorophyll a concentration, water transparency, thermal pollution, and shoreline soil erosion is conducted.

[0027] Ecological sensors: Deploy environmental DNA (eDNA) collectors to monitor the presence and changes of biological populations such as fish and microorganisms by analyzing DNA fragments in water bodies, and directly assess ecological health from the perspective of biodiversity.

[0028] In this embodiment of the application, after data is collected in the manner described above, it can be transmitted reliably to electronic devices in real time through wireless communication technologies such as 5G / 4G, LoRa, and NB-IoT, so as to monitor the ecological environment of the target river.

[0029] S102, input the river monitoring data into the preset river digital twin model. The water quality model of the preset river digital twin model predicts the migration of pollutants based on the pollutant change information and obtains the self-purification trend value of the target river.

[0030] In this embodiment of the application, when monitoring a target river, a preset digital twin model of the river can be used to simulate the actual operation of the target river, thereby identifying potential pollution locations during the simulation process and enabling early prediction.

[0031] Furthermore, to monitor the target river more accurately, the method may also include the following steps before S102: Obtain historical river ecology data for the target river; Based on historical river ecological data, pollutant transformation simulation is performed to obtain source and sink terms, which are used to characterize the process parameters of pollutant increase or decrease. The rate of change of pollutant concentration is determined based on source-sink terms, preset diffusion / dispersion terms, and preset convection terms; A water quality model is generated based on the rate of change of pollutant concentration. Historical river ecological data is input into the water quality model, which then performs water quality simulations to obtain simulation results. Based on simulation results and historical river ecological data, the degradation coefficient and reoxygenation coefficient in the water quality model are adjusted until the training completion conditions are met.

[0032] In this embodiment, the preset river digital twin model may include a water quality model. When using the preset river digital twin model for simulation, it is necessary to complete the construction of the preset river digital twin model in advance. The construction process of the water quality model in the preset river digital twin model will be described in detail below.

[0033] Specifically, the first step is to obtain historical river ecological data for the target river. This data includes hydrological data, water quality data, and pollutant data. Hydrological data can include flow rate and velocity at various river sections, water level elevation at various points in the river channel, river channel cross-section shape, riverbed slope, and roughness (Manning coefficient). Water quality data can include background concentrations and the concentrations of target pollutants monitored simultaneously at multiple river sections and at different time points. Background concentrations can be the baseline concentrations of various pollutants at the upstream boundary, while pollutant data can include the location of sewage outlets, discharge flow rates, pollutant concentrations, and pollution loads from rainfall runoff.

[0034] In this embodiment, a water quality model can be constructed using the partial differential equation of the law of conservation of mass, namely the convection-diffusion-reaction equation, as shown in the following formula: (1) in, The rate of change of pollutant concentration over time. This is the convection term, used to represent the migration of pollutants carried by water flow at a velocity μ along the X direction of the river channel. It is the main driving force for pollutant migration. For diffusion / dispersion, it is used to represent the diffusion and mixing process of pollutants caused by concentration gradient and water turbulence. D is the diffusion / dispersion coefficient, which is obtained experimentally. Source and sink items include all processes that increase (sources, such as sewage discharge) or decrease (sinks, such as degradation, adsorption, and precipitation) pollutants.

[0035] The water quality model can be constructed using the above formula (1), which facilitates the simulation of water quality, quantifies the pollutant transformation process, considers the self-purification capacity of the river, and ensures accurate monitoring of the target river.

[0036] As an example, the following example can be used for source and sink entries: The BOD (simulated degradation process) can be simulated using the following formula (2): (2) Where S represents the source and sink terms, and L represents the BOD concentration. This is the degradation rate constant; the negative sign indicates a decrease.

[0037] The DO (simulated reoxygenation and oxygen consumption process) can be simulated using the following formula (3): (3) in, For atmospheric reoxygenation (source, (where C is the saturated dissolved oxygen concentration and C is the current dissolved oxygen concentration). For source coefficients, BOD degradation requires oxygen.

[0038] By using the above formulas (2) and (3) to determine the source and sink terms, all processes that increase or decrease pollutants can be taken into account.

[0039] Furthermore, to obtain more accurate source-sink parameters, it is necessary to consider the processes of oxygen production through plant photosynthesis, oxygen consumption through respiration, and oxygen consumption in the sediment. Therefore, after obtaining source-sink parameters through pollutant transformation simulation based on historical river ecological data, the method also includes: Obtain the light intensity, plant species, and vegetation cover area of ​​the target river; Based on light intensity, plant species, and vegetation cover area, the oxygen production, oxygen consumption, and sediment oxygen consumption of each plant species were determined. The correction parameters are determined based on the sum of oxygen production and consumption of plant species and oxygen consumption of sediment. The source and sink terms are modified based on the correction parameters to obtain the final source and sink terms, and the final source and sink terms are determined as the new source and sink terms.

[0040] In this embodiment of the application, the correction parameter can be determined by the following formula (4): (4) in, For plants to produce oxygen through photosynthesis, Oxygen is consumed by plant respiration. It consumes oxygen in the bottom sediment.

[0041] Furthermore, let's take algae as an example to illustrate this.

[0042] In this embodiment, the oxygen production from plant photosynthesis can be determined using the following formula (5): (5) in, Maximum oxygen production rate [mgO2 / (mg algal biomass·day)]. This can usually be obtained through experiments or literature.

[0043] f(I): Illumination limitation function (0-1). It can be determined by the following formula (6): (6) Where I: actual light intensity [μE / (m²·s)], I_s: optimal light intensity (light intensity at which the photosynthetic rate reaches its maximum). The above formula describes the phenomenon of photoinhibition: when the light is too weak, photosynthesis is insufficient, and when it is too strong, photosynthesis will be inhibited.

[0044] f(N): Nutrient limitation function (0-1). It is usually described by the Michaelis-Menten equation (Equation 7 below): (7) Wherein, N: nutrient concentration (e.g., total nitrogen or total phosphorus) [mg / L], : Half-saturation constant (the concentration of nutrients when the oxygen production rate reaches half of its maximum).

[0045] f(T): Temperature limiting function (0-1). Commonly used exponential form (Q10) or Arrhenius equation formula (8): (8) Where θ: temperature coefficient (usually 1.02-1.09), T: actual water temperature [°C], Reference water temperature [°C].

[0046] [A]: Algal biomass concentration [mg / L] or [μg Chla / L].

[0047] Furthermore, the oxygen consumption of plant respiration can be expressed by the following formula (9): (9) in, θ_r is the algal respiration rate constant at 20℃, θ_r is the temperature coefficient of respiration, and T is the actual water temperature [°C]. Reference water temperature [°C], [A]: Algal biomass concentration [mg / L] or [μg Chla / L].

[0048] Furthermore, the oxygen consumption of the sediment can be expressed by the following formula (10): (10) in, The oxygen consumption rate of the sediment at 20℃ can be obtained through on-site measurement or instrument measurement. Here, T represents the temperature coefficient of oxygen consumption in the sediment, H represents the hydrological value, and H represents the water depth.

[0049] In this embodiment, the correction coefficient can be determined by the above formula, thereby correcting the source and sink terms to obtain the final source and sink terms, resulting in a more accurate water quality model.

[0050] Furthermore, to ensure that the preset river digital twin model is closer to the actual situation, the method may also include the following steps before S102: Obtain the ecological species of the target river; For algae, obtain the algal biomass; An algal ecological model is constructed based on algal biomass, a preset maximum growth rate, and a preset constraint function. And / or, for fish, construct a fish ecological model using a pre-defined bioenergetics model; Based on algae ecological models and fish ecological models, a first ecological model is constructed.

[0051] In this embodiment, since each river has a wide variety of ecosystems, it is necessary to obtain the ecosystems of the target river, which may include algae and fish.

[0052] For algae, the following formula (11) can be used to construct an algal ecological model: (11) in, This represents the maximum specific growth rate of algae [usually in units of 1 / day]. This is the fastest growth rate that algae can achieve under ideal conditions (optimal light, nutrients, and temperature). f(I) represents the light limitation function, ranging from 0 to 1. f(I) = 0 indicates complete darkness, where photosynthesis cannot occur; f(I) = 1 indicates optimal light intensity, where photosynthesis proceeds at maximum efficiency. g(N) represents the nutrient limitation function, also ranging from 0 to 1. g(N) = 0 indicates a lack of nutrients, preventing growth; g(N) = 1 indicates sufficient nutrients, allowing unrestricted growth. h(T): Temperature limitation function (0 to 1). Temperature regulates the rate of physiological processes by affecting enzyme activity. It is usually described using an exponential function (Q10) or an optimum temperature curve (such as the nome equation). A: Current algal biomass. This reflects the essence of population growth: the larger the current population, the more individuals are added per unit time under the same conditions (similar to compound interest); grazing: zooplankton predation rate (or feeding rate) [unit is usually L / (mg biomass·day)]. It indicates how much algae can be cleared per unit of zooplankton biomass per unit time; Z: zooplankton biomass concentration [mg / L]; mortality: algal mortality rate [unit is 1 / day]. This includes processes such as natural aging, cell lysis, viral infection, and settling (leaving the water surface); A: current algal biomass. The larger the current population, the more individuals die per unit time.

[0053] For fish, the pre-defined bioenergetics model can be an energy conservation model, which can be simply represented by the following formula (12): (12) Wherein, G: Growth Energy. Energy used for body tissue growth and gonadal development. This is directly related to the model's output—the increase in fish weight; C: Consumption. The total energy obtained by the fish through feeding. This is the main driver of the model; F: Egestion. The proportion of energy that is not absorbed but excreted in feces; (1-F): Assimilation. The energy actually absorbed by the body (A=C*(1-F)); R: Routine Metabolism. The energy consumed to maintain basic life activities (respiration, circulation, etc.). It is mainly affected by water temperature and fish weight; S: Specific Dynamic Action. The energy consumed in digesting and absorbing food itself, usually expressed as a proportion of assimilation (A); H: Excretion. The energy contained in metabolic waste products, mainly excreted in the form of ammonia nitrogen.

[0054] The above methods can simulate the survival process of ecological species, enabling precise monitoring of the ecological environment.

[0055] In this embodiment, to make the ecological model more realistic, before building the first ecological model based on the algae ecological model and the fish ecological model, the method further includes: Obtain historical environmental factors and historical species status of the target environment; Using historical environmental factors as input, a pre-defined regression algorithm is used to determine the predicted species status; The model parameters of the first ecological model are corrected based on the predicted species status and historical species status to obtain the second ecological model; Obtain the training dataset; The second ecological model was trained using the training dataset to obtain the third ecological model, and the third expression model was determined as the final ecological model.

[0056] In this embodiment, environmental factors can be used as input features and species status as output labels. A preset regression algorithm can be used to predict the species status so that the ecological model can be simulated according to the real environment.

[0057] As an example, the preset regression algorithm can be random forest / gradient boosting tree, support vector machine, or neural network for prediction. The training method for the second ecological model is similar to the related technologies and will not be elaborated further here.

[0058] It is worth noting that historical environmental data of the target river can be obtained as a dataset, which can be divided into training, validation, and test sets. The parameters of the preset river digital twin model can be continuously adjusted to ensure the accurate operation of the preset river data twin model and obtain more accurate prediction results.

[0059] S103, The ecological model of the preset river digital twin model is used to simulate environmental changes based on the target ecological species to obtain the environmental change value of the target river.

[0060] In this embodiment of the application, a pre-trained preset digital twin model of a river can be used to simulate the environment of target ecological species on the target river. The simulation is performed on an electronic device, and the environmental change values ​​are calculated through the various parts constructed by the above model. This allows attention to the changes of the target river over time, making it easier to find the source of pollution in the target river, accurately locate the pollution location, and provide a basis for decision-making for subsequent treatment.

[0061] S104. Based on the self-purification trend value and the environmental change value, determine the environmental monitoring value for a preset future time period.

[0062] In this embodiment, S104 may include: Divide the target river into multiple sections; Based on the river monitoring data of each section, the parameters of the preset river digital twin model are adjusted to obtain the simulated river environment; Based on the simulated river environment, determine the self-purification trend value and environmental change amount of each section within a preset future time period; The environmental monitoring value for each section is determined based on the sum of the self-purification trend value and the environmental change value within each section.

[0063] In this embodiment, when using a preset river digital twin model for monitoring, the parameters can be modified in real time according to the river monitoring data of the target river, continuously getting closer to the ecological environment of the actual target river, so as to ensure that the preset river digital twin model can accurately monitor the target river.

[0064] Of course, in the preset digital twin model of the river, the water quality changes in the future can be predicted based on the current state and the model. Once an anomaly is detected, it can be quickly simulated and reversed to accurately locate the possible pollution source.

[0065] In other embodiments, real-time data and model simulation results of each detection section can be visually measured through maps, charts, and dashboards so that users can quickly, conveniently, and intuitively understand the ecological status of the target river.

[0066] S105, Based on environmental monitoring values ​​and preset early warning conditions, determine the monitoring execution strategy.

[0067] In this embodiment, S105 may specifically include: A threshold alarm is triggered when environmental monitoring values ​​exceed a preset threshold. If the posture trend value exceeds the second preset threshold within a preset future time period, a trend alarm will be triggered in advance. In the event of triggering a threshold alarm and / or a trend alarm, the predicted pollution source area is determined based on environmental monitoring values.

[0068] Specifically, intelligent early warning platforms can be deployed in electronic devices to trigger threshold alarms when environmental monitoring values ​​exceed preset thresholds. If the posture trend value exceeds the second preset threshold within a preset future time period, a trend alarm will be triggered in advance. In the event of triggering a threshold alarm and / or a trend alarm, the predicted pollution source area is determined based on environmental monitoring values.

[0069] By triggering different alarms with different data, managers can be provided with simulated scenario analysis. For example, the effects of closing a sewage outlet or initiating ecological water replenishment can be simulated to determine whether the alarm can be resolved, thus assisting in scientific decision-making.

[0070] In this embodiment, a simulated operation can be performed using a pre-set digital twin model of the river based on river monitoring data. This allows for the acquisition of the self-purification trend value and environmental change value of the target river. The self-purification trend value takes into account the self-purification capacity of the target river, while the environmental change value considers the impact of the environment on the target river. By considering both the self-purification trend value and the environmental change value, the river's self-adaptation and purification process can be quantified, enabling an accurate assessment of the ecological environment and a prediction of future trends. Based on the environmental monitoring values ​​and pre-set early warning conditions, a monitoring execution strategy can be determined to predict the target river's performance and provide early protection, thereby ensuring the stability of the target river's ecological environment.

[0071] Based on the river ecological environment monitoring method provided in the above embodiments, this application also provides specific implementation methods for river ecological environment monitoring devices. Please refer to the following embodiments.

[0072] First see Figure 2 The river ecological environment monitoring device 200 provided in this application embodiment may include the following: The acquisition module 201 is used to acquire river monitoring data of the target river, including information on pollutant changes and target ecological species. The input module 202 is used to input river monitoring data into a preset river digital twin model. The water quality model of the preset river digital twin model predicts pollutant migration based on pollutant change information and obtains the self-purification trend value of the target river. Simulation module 203 is used to simulate environmental changes of the target river based on the ecological model of the preset river digital twin model to obtain the environmental change value of the target river; The determination module 204 is used to determine the environmental monitoring values ​​within a preset future time period based on the self-purification trend value and the environmental change value; The determination module 204 is also used to determine the monitoring execution strategy based on environmental monitoring values ​​and preset early warning conditions.

[0073] As an optional implementation, the input module 202 can also be used for: Obtain historical river ecological data for the target river, including hydrological data, water quality data, and pollutant data; Based on historical river ecological data, pollutant transformation simulation is performed to obtain source and sink terms, which are used to characterize the process parameters of pollutant increase or decrease. The rate of change of pollutant concentration is determined based on source-sink terms, preset diffusion / dispersion terms, and preset convection terms; A water quality model is generated based on the rate of change of pollutant concentration. Historical river ecological data is input into the water quality model, which then performs water quality simulations to obtain simulation results. Based on simulation results and historical river ecological data, the degradation coefficient and reoxygenation coefficient in the water quality model are adjusted until the training completion conditions are met.

[0074] As an optional implementation, the input module 202 can also be used for: Obtain the light intensity, plant species, and vegetation cover area of ​​the target river; Based on light intensity, plant species, and vegetation cover area, the oxygen production, oxygen consumption, and sediment oxygen consumption of each plant species were determined. The correction parameters are determined based on the sum of oxygen production and consumption of plant species and oxygen consumption of sediment. The source and sink terms are modified based on the correction parameters to obtain the final source and sink terms, and the final source and sink terms are determined as the new source and sink terms.

[0075] As an optional implementation, the input module 202 can also be used for: Obtain the ecological species of the target river, including algae and fish; For algae, obtain the algal biomass; An algal ecological model is constructed based on algal biomass, a preset maximum growth rate, and a preset constraint function. And / or, for fish, construct a fish ecological model using a pre-defined bioenergetics model; Based on algae ecological models and fish ecological models, a first ecological model is constructed.

[0076] As an optional implementation, the input module 202 can also be used for: Obtain historical environmental factors and historical species status of the target environment; Using historical environmental factors as input, a pre-defined regression algorithm is used to determine the predicted species status; The model parameters of the first ecological model are corrected based on the predicted species status and historical species status to obtain the second ecological model; Obtain the training dataset; The second ecological model was trained using the training dataset to obtain the third ecological model, and the third expression model was determined as the final ecological model.

[0077] As an alternative implementation, the determining module 204 can also be used for: Divide the target river into multiple sections; Based on the river monitoring data of each section, the parameters of the preset river digital twin model are adjusted to obtain the simulated river environment; Based on the simulated river environment, determine the self-purification trend value and environmental change amount of each section within a preset future time period; The environmental monitoring value for each section is determined based on the sum of the self-purification trend value and the environmental change value within each section.

[0078] As an alternative implementation, the determining module 204 can also be used for: A threshold alarm is triggered when environmental monitoring values ​​exceed a preset threshold. If the posture trend value exceeds the second preset threshold within a preset future time period, a trend alarm will be triggered in advance. In the event of triggering a threshold alarm and / or a trend alarm, the predicted pollution source area is determined based on environmental monitoring values.

[0079] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0080] An electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0081] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0082] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.

[0083] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0084] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the river ecological environment monitoring method according to the first aspect of this disclosure.

[0085] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 A method for monitoring the river's ecological environment is shown in the embodiment.

[0086] In one example, the electronic device may also include a communication interface 303 and a bus 304. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0087] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0088] Bus 304 includes hardware, software, or both, that couples components of an electronic device together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0089] This electronic device can execute the river ecological environment monitoring method in the embodiments of this application, thereby achieving a combination of Figures 1-2 The methods and devices for monitoring the river's ecological environment are described.

[0090] Furthermore, in conjunction with the river ecological environment monitoring methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the river ecological environment monitoring methods in the above embodiments.

[0091] In an optional embodiment, in conjunction with the river ecological environment monitoring methods in the above embodiments, this application embodiment can provide a computer program product to implement the method. The instructions in the computer program product are executed by the processor of an electronic device, enabling the electronic device to implement any of the river ecological environment monitoring methods in the above embodiments.

[0092] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0093] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0094] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0095] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0096] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0097] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0098] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0099] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A river ecological environment monitoring method, characterized in that, The method comprises the following steps: obtaining river monitoring data of a target river, the river monitoring data comprising pollutant change information and a target ecological species; inputting the river monitoring data into a preset river digital twin model, a water quality model of the preset river digital twin model predicting pollutant migration based on the pollutant change information to obtain a self-purification trend value of the target river; an ecological model of the preset river digital twin model simulating environmental changes based on the target ecological species to obtain an environmental change value of the target river; based on the self-purification trend value and the environmental change value, determining an environmental monitoring value in a preset future time period; based on the environmental monitoring value and a preset warning condition, determining a monitoring execution strategy.

2. The method of claim 1, wherein, Before the step of inputting the river monitoring data into the preset river digital twin model, the method further comprises the following steps: obtaining historical river ecological data of the target river, the historical river ecological data comprising hydrological data, water quality data and pollutant data; based on the historical river ecological data, simulating pollutant conversion to obtain source-sink items, the source-sink items being used to represent process parameters of pollutant increase or decrease; based on the source-sink items, a preset diffusion / dispersion item and a preset convection item, determining a pollutant concentration change rate; based on the pollutant concentration change rate, generating the water quality model; inputting the historical river ecological data into the water quality model, the water quality model simulating water quality to obtain a simulation result; based on the simulation result and the historical river ecological data, adjusting degradation coefficients and reoxygenation coefficients in the water quality model until a training completion condition is met.

3. The method of claim 2, wherein, After the step of simulating pollutant conversion based on the historical river ecological data to obtain source-sink items, the method further comprises the following steps: obtaining light intensity, plant species and vegetation coverage area of the target river; based on the light intensity, plant species and the vegetation coverage area, respectively determining oxygen production, oxygen consumption and sediment oxygen consumption of each plant species; based on the sum of the oxygen production, oxygen consumption and sediment oxygen consumption of the plant species, determining a correction parameter; based on the correction parameter, correcting the source-sink items to obtain final source-sink items, and determining the final source-sink items as new source-sink items.

4. The method according to any one of claims 1 to 3, characterized in that, Before the step of inputting the river monitoring data into the preset river digital twin model, the method further comprises the following steps: obtaining ecological species of the target river, the ecological species comprising algae and fish; for the algae, obtaining algae biomass of the algae; based on the algae biomass, a preset maximum growth rate and a preset limiting function, constructing an algae ecological model; and / or, for the fish, constructing a fish ecological model by using a preset bioenergetics model; based on the algae ecological model and the fish ecological model, building a first ecological model.

5. The method of claim 4, wherein, Before the step of building the first ecological model based on the algae ecological model and the fish ecological model, the method further comprises the following steps: obtaining historical environmental factors and historical species states of a target environment; using a preset regression algorithm, determining a predicted species state by taking the historical environmental factors as input. correcting model parameters of the first ecological model according to the predicted species state and the historical species state, to obtain a second ecological model; obtain a training data set; train the second ecological model using the training data set to obtain a third ecological model, and determine the third ecological model as a final ecological model.

6. The method of claim 1, wherein, determining an environmental monitoring value in a preset future time period based on the self-purification trend value and the environmental change value, including: dividing the target river into multiple sections; adjusting parameters of the preset river digital twin model according to the river monitoring data of each section to obtain a simulated river environment; determining a self-purification trend value and an environmental change value in a preset future time period for each section according to the simulated river environment; determining an environmental monitoring value for each section based on the sum of the self-purification trend value and the environmental change value in each section.

7. The method of claim 6, wherein, determining a monitoring execution strategy based on the environmental monitoring value and a preset warning condition, including: triggering a threshold alarm if the environmental monitoring value exceeds a preset threshold value; triggering a trend alarm in advance if the environmental monitoring value exceeds a second preset threshold value in the preset future time period; determining a predicted pollution source area based on the environmental monitoring value if the threshold alarm and / or the trend alarm is triggered.

8. A river ecological environment monitoring device, characterized in that, The device includes: an acquisition module for acquiring river monitoring data of a target river, the river monitoring data including pollution change information and target ecological species; an input module for inputting the river monitoring data into a preset river digital twin model, a water quality model of the preset river digital twin model predicting pollution migration based on the pollution change information to obtain a self-purification trend value of the target river; a simulation module for an ecological model of the preset river digital twin model to simulate environmental changes based on the target ecological species to obtain an environmental change value of the target river; a determination module for determining an environmental monitoring value in a preset future time period based on the self-purification trend value and the environmental change value; a determination module for determining a monitoring execution strategy based on the environmental monitoring value and a preset warning condition.

9. An electronic device, comprising: The device includes a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the river ecological environment monitoring method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the river ecological environment monitoring method of any one of claims 1-7.

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