Aquatic habitat quality evaluation method and device and computer equipment

Abstract

The invention relates to an aquatic habitat quality evaluation method and device and computer equipment, and relates to the technical field of environment evaluation. The method comprises the following steps: determining each aquatic habitat quality evaluation index corresponding to a target aquatic region in a target time interval and a target hydrological scene; according to the aquatic data of the target aquatic area in the target time interval and the target hydrological scene, determining an index score of the target aquatic area corresponding to each aquatic habitat quality evaluation index; inputting the position information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene and the score of each index into a weight determination model obtained by pre-training to obtain a weight corresponding to each aquatic habitat quality evaluation index; and according to the index score and the weight corresponding to each aquatic habitat quality evaluation index, determining an aquatic habitat quality evaluation result of the target aquatic area. By adopting the method, the reliability of an aquatic habitat quality evaluation result can be improved.

Description

Technical Field

[0001] This application relates to the field of environmental assessment technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the quality of aquatic habitats. Background Technology

[0002] Aquatic habitats refer to the natural habitat structure of water. Their quality directly affects the maintenance of aquatic biodiversity and the functioning of water resource ecosystem services. Conducting scientific and accurate aquatic habitat quality assessments is a key prerequisite for the construction and implementation of water conservancy projects.

[0003] Currently, the industry generally adopts the ecological multi-indicator comprehensive evaluation method for assessing aquatic habitats. This method has become the most widely used assessment technology path due to its comprehensive coverage and clear operational logic.

[0004] In related technologies, aquatic habitat quality is assessed using a multi-index comprehensive evaluation method. In this method, the scores of various indicators are usually integrated based on fixed weights to obtain the aquatic habitat quality assessment results. However, aquatic habitats are affected by a variety of factors, and the reliability of the aquatic habitat quality assessment results obtained based on fixed weights is low. Summary of the Invention

[0005] Therefore, it is necessary to address the technical problem of low reliability of the above-mentioned aquatic habitat quality assessment results by providing an aquatic habitat quality assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the reliability of aquatic habitat quality assessment results.

[0006] Firstly, this application provides a method for assessing the quality of aquatic habitats, including:

[0007] Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario;

[0008] Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario;

[0009] The location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each of the aquatic habitat quality assessment indicators are input into the pre-trained weight determination model to obtain the weights corresponding to each of the aquatic habitat quality assessment indicators.

[0010] The aquatic habitat quality assessment score of the target aquatic area is determined based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators.

[0011] Based on the aquatic habitat quality assessment score, the aquatic habitat quality assessment result of the target aquatic area is determined.

[0012] In one embodiment, the weight determination model is trained in the following manner:

[0013] Multiple sample data points are acquired; each sample data point corresponds to a sample aquatic area, a sample time interval, a sample hydrological scenario, and multiple sample aquatic habitat quality assessment indicators. The sample data at least includes the location information of the sample aquatic area, the time information of the sample time interval, the scenario information of the sample hydrological scenario, and the sample indicator scores of the sample aquatic area corresponding to each of the sample aquatic habitat quality assessment indicators under the sample time interval and the sample hydrological scenario.

[0014] The sample data are input into the weight determination model to be trained to obtain the predicted aquatic habitat quality assessment score corresponding to each sample data.

[0015] With the goal of reducing the difference between the sample aquatic habitat quality assessment score corresponding to each of the sample data and the predicted aquatic habitat quality assessment score, the weight determination model to be trained is iteratively trained until a preset training stopping condition is reached, thereby obtaining the weight determination model.

[0016] In one embodiment, determining the index score of the target aquatic area for each aquatic habitat quality assessment indicator based on aquatic data of the target aquatic area in the target time interval and the target hydrological scenario includes:

[0017] Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index value of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario;

[0018] Obtain the score mapping rule corresponding to each of the aquatic habitat quality assessment indicators;

[0019] Based on the score mapping rule corresponding to each of the aquatic habitat quality assessment indicators, the index value of the target aquatic area under each of the aquatic habitat quality assessment indicators is mapped to the corresponding score, thereby obtaining the index score of the target aquatic area under each of the aquatic habitat quality assessment indicators in the target time interval and the target hydrological scenario.

[0020] In one embodiment, determining the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score includes:

[0021] Obtain the correspondence between assessment scores and quality levels;

[0022] Based on the correspondence between the assessment scores and quality levels, the aquatic habitat quality assessment scores are mapped to the corresponding aquatic habitat quality levels;

[0023] The aquatic habitat quality assessment results are obtained based on the aquatic habitat quality level.

[0024] In one embodiment, each aquatic habitat quality assessment index includes a plurality of first assessment indices and at least one second assessment index under each first assessment index; the index score corresponding to each aquatic habitat quality assessment index includes the index score corresponding to each second assessment index, and the weight corresponding to each aquatic habitat quality assessment index includes the weight corresponding to each second assessment index.

[0025] The step of determining the aquatic habitat quality assessment score of the target aquatic area based on the index scores and weights corresponding to each of the aquatic habitat quality assessment indicators includes:

[0026] For each first evaluation indicator, the index scores corresponding to each second evaluation indicator are weighted and fused according to the weights corresponding to each second evaluation indicator of the first evaluation indicator to obtain the comprehensive index score corresponding to the first evaluation indicator.

[0027] The aquatic habitat quality assessment score is obtained by averaging the scores of each comprehensive index.

[0028] In one embodiment, the plurality of first assessment indicators include at least two of hydrological indicators, water quality indicators, food organism indicators, fish indicators, and habitat indicators; the second assessment indicators under the hydrological indicators include at least two of vertical connectivity, horizontal connectivity, ecological flow discharge, and water area change; the second assessment indicators under the water quality indicators include at least a water quality category proportion type, which is used to characterize the proportional distribution of each water quality category existing in the target aquatic area; the second assessment indicators under the food organism indicators include at least two of phytoplankton diversity, zooplankton diversity, and benthic animal diversity; the second assessment indicators under the fish indicators include at least two of fish species quantity change, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, and fish diversity; the second assessment indicators under the habitat indicators include at least two of fish spawning ground area proportion, fish foraging ground area proportion, and fish overwintering ground area proportion.

[0029] Secondly, this application also provides an aquatic habitat quality assessment device, comprising:

[0030] The assessment index determination module is used to determine the aquatic habitat quality assessment indexes corresponding to the target aquatic area to be assessed under the target time interval and target hydrological scenario.

[0031] The indicator score determination module is used to determine the indicator score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators in the target time interval and the target hydrological scenario, based on the aquatic data of the target aquatic area in the target time interval and the target hydrological scenario.

[0032] The weight dynamic determination module is used to input the location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each aquatic habitat quality assessment index into the pre-trained weight determination model to obtain the weights corresponding to each aquatic habitat quality assessment index.

[0033] The assessment score determination module is used to determine the aquatic habitat quality assessment score of the target aquatic area based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators.

[0034] The assessment result determination module is used to determine the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario;

[0037] Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario;

[0038] The location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each of the aquatic habitat quality assessment indicators are input into the pre-trained weight determination model to obtain the weights corresponding to each of the aquatic habitat quality assessment indicators.

[0039] The aquatic habitat quality assessment score of the target aquatic area is determined based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators.

[0040] Based on the aquatic habitat quality assessment score, the aquatic habitat quality assessment result of the target aquatic area is determined.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario;

[0043] Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario;

[0044] The location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each of the aquatic habitat quality assessment indicators are input into the pre-trained weight determination model to obtain the weights corresponding to each of the aquatic habitat quality assessment indicators.

[0045] The aquatic habitat quality assessment score of the target aquatic area is determined based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators.

[0046] Based on the aquatic habitat quality assessment score, the aquatic habitat quality assessment result of the target aquatic area is determined.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario;

[0049] Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario;

[0050] The location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each of the aquatic habitat quality assessment indicators are input into the pre-trained weight determination model to obtain the weights corresponding to each of the aquatic habitat quality assessment indicators.

[0051] The aquatic habitat quality assessment score of the target aquatic area is determined based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators.

[0052] Based on the aquatic habitat quality assessment score, the aquatic habitat quality assessment result of the target aquatic area is determined.

[0053] The aforementioned aquatic habitat quality assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product, based on the location information of the target aquatic area to be assessed, the time information of the target time interval corresponding to the acquired aquatic data, the scene information of the target hydrological scene, and the index scores corresponding to each aquatic habitat quality assessment indicator, can dynamically determine the corresponding weights for each aquatic habitat quality assessment indicator. Based on the index scores corresponding to each aquatic habitat quality assessment indicator and the dynamically determined weights, the aquatic habitat quality assessment score of the target aquatic area can be obtained, thereby obtaining the aquatic habitat quality assessment result of the target aquatic area. Compared with fixed weights, the dynamically determined weights can reflect the impact of location information, time information, scene information, and index scores on the aquatic habitat of the target aquatic area, thus improving the reliability of the obtained aquatic habitat quality assessment result. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is an application environment diagram of the aquatic habitat quality assessment method in one embodiment;

[0056] Figure 2 This is a flowchart illustrating a method for assessing the quality of aquatic habitats in one embodiment;

[0057] Figure 3 This is a flowchart illustrating the steps involved in training a weight determination model in one embodiment.

[0058] Figure 4 This is a flowchart illustrating the aquatic habitat quality assessment method in another embodiment;

[0059] Figure 5 This is a structural block diagram of an aquatic habitat quality assessment device in one embodiment;

[0060] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0063] The aquatic habitat quality assessment method provided in this application can be applied to, for example... Figure 1The application environment shown is as follows. In this application environment, server 102 communicates with terminal 104 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated on server 102, or it can be located on the cloud or other network servers. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Terminal 104 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc.

[0064] For example, firstly, server 102 determines the aquatic habitat quality assessment indicators corresponding to the target aquatic area under the target time interval and target hydrological scenario; then, based on the aquatic data of the target aquatic area under the target time interval and target hydrological scenario, server 102 determines the indicator score of the target aquatic area for each aquatic habitat quality assessment indicator under the target time interval and target hydrological scenario; next, server 102 inputs the location information of the target aquatic area, the time information of the target time interval, the scenario information of the target hydrological scenario, and the indicator scores corresponding to each aquatic habitat quality assessment indicator into a pre-trained weight determination model to obtain the weights corresponding to each aquatic habitat quality assessment indicator; then, server 102 determines the aquatic habitat quality assessment score of the target aquatic area based on the indicator scores and weights corresponding to each aquatic habitat quality assessment indicator; finally, server 102 determines the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score.

[0065] In this embodiment, terminal 104 includes a data acquisition terminal deployed in the target aquatic area and a terminal used by quality assessment personnel; server 102 collects aquatic data based on the data acquisition terminal and sends the aquatic habitat quality assessment results to the terminal used by the quality assessment personnel.

[0066] In some embodiments, such as Figure 2 As shown, a method for assessing the quality of aquatic habitats is provided, and this method is applied to... Figure 1 Taking a server as an example, it can be understood that this method can also be applied to a terminal, and can also be applied to a system that includes both a server and a terminal, and is implemented through the interaction between the server and the terminal. The method includes the following steps:

[0067] Step S202: Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be assessed under the target time interval and target hydrological scenario.

[0068] The aquatic habitat quality assessment indicators include multiple primary assessment indicators and at least one secondary assessment indicator under each primary assessment indicator; the multiple primary assessment indicators include at least two of the following: hydrological indicators, water quality indicators, food organism indicators, fish indicators, and habitat indicators.

[0069] Among them, the second assessment indicators under the hydrological indicators include at least two of the following: vertical connectivity, horizontal connectivity, ecological flow discharge, and water area change.

[0070] Among them, the second assessment indicator under the water quality index includes at least the water quality category proportion type, which is used to characterize the proportion distribution of each water quality category in the aquatic area; in practical applications, a proportion distribution of water quality categories in each sub-region of the aquatic area corresponds to a water quality category proportion type.

[0071] The second assessment indicator under the bait organism indicator includes at least two of the following: phytoplankton diversity, zooplankton diversity, and benthic animal diversity.

[0072] Among them, the second assessment indicator under the fish index includes at least two of the following: change in the number of fish species, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, and fish diversity. In practical applications, the proportion of fish (proportion of rare fish, proportion of carnivorous fish, and proportion of fish that lay drifting eggs) can be the ratio between the number of the corresponding fish and the total number of all fish, or it can be the ratio between the number of fish under the corresponding fish and the total number of all fish.

[0073] Among them, the second assessment indicator under the habitat indicator includes at least two of the following: the proportion of fish spawning grounds, the proportion of fish feeding grounds, and the proportion of fish overwintering grounds. In practical applications, the proportion of the area (the proportion of fish spawning grounds, the proportion of fish feeding grounds, and the proportion of fish overwintering grounds) can be the ratio between the watershed area of ​​the corresponding area and the total watershed area of ​​the aquatic environment, or it can be the ratio between the watershed length of the corresponding area and the total watershed length of the aquatic environment.

[0074] Different hydrological scenarios are used to describe different hydrological characteristics, such as natural conditions like climate, precipitation intensity, and topography, as well as human factors like water conservancy project scheduling, land use change, and water intake and use activities.

[0075] In this step, the evaluator initiates an aquatic habitat quality assessment request to the server via the terminal. The server responds to the aquatic habitat quality assessment request by determining the target aquatic area to be assessed, as well as the corresponding target time interval and target hydrological scenario. Based on the target aquatic area, target time interval, and target hydrological scenario, the server selects the corresponding aquatic habitat quality assessment indicator for the target aquatic area from multiple preset aquatic habitat quality assessment indicators.

[0076] In practical applications, the aquatic habitat quality assessment indicators may differ for different target aquatic areas, target time intervals, and target hydrological scenarios.

[0077] Step S204: Based on the aquatic data of the target aquatic area in the target time interval and target hydrological scenario, determine the index score of each aquatic habitat quality assessment indicator for the target aquatic area in the target time interval and target hydrological scenario.

[0078] In this step, the server collects aquatic data of the target aquatic area within the target time interval and target hydrological scenario. Based on the aquatic data of the target aquatic area within the target time interval and target hydrological scenario, the server determines the index value of each aquatic habitat quality assessment indicator for the target aquatic area within the target time interval and target hydrological scenario, and then determines the index score corresponding to each aquatic habitat quality assessment indicator.

[0079] Step S206: Input the location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each aquatic habitat quality assessment index into the pre-trained weight determination model to obtain the weights corresponding to each aquatic habitat quality assessment index.

[0080] Among them, the location information of the target aquatic area is used to characterize the geographical location information.

[0081] The time information of the target time interval is used to characterize the season to which the target time interval belongs.

[0082] Among them, the scene information of the target hydrological scene is used to characterize the scene identifier of the target hydrological scene.

[0083] The weight determination model is a model trained based on an artificial intelligence model, machine learning model, neural network model, or deep learning model.

[0084] In this step, the server inputs the location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores of each aquatic habitat quality assessment index of the target aquatic area under the target time interval and the target hydrological scene into the pre-trained weight determination model. The weight determination model extracts and learns features from the input data to determine the weights corresponding to each aquatic habitat quality assessment index.

[0085] Step S208: Determine the aquatic habitat quality assessment score of the target aquatic area based on the index scores and weights corresponding to each aquatic habitat quality assessment index.

[0086] In this step, the server integrates the scores of each aquatic habitat quality assessment indicator based on the weights corresponding to each aquatic habitat quality assessment indicator, and obtains the aquatic habitat quality assessment score of the target aquatic area.

[0087] Step S210: Based on the aquatic habitat quality assessment score, determine the aquatic habitat quality assessment result of the target aquatic area.

[0088] In this step, the server maps the aquatic habitat quality assessment score of the target aquatic area to the corresponding aquatic habitat quality level, thus obtaining the aquatic habitat quality assessment result of the target aquatic area.

[0089] In the aforementioned aquatic habitat quality assessment method, the server dynamically determines the corresponding weights for each aquatic habitat quality assessment indicator based on the location information of the target aquatic area to be assessed, the time information of the target time interval corresponding to the acquired aquatic data, the scene information of the target hydrological scene, and the indicator scores corresponding to each aquatic habitat quality assessment indicator. Based on the indicator scores corresponding to each aquatic habitat quality assessment indicator and the dynamically determined weights, the server can obtain the aquatic habitat quality assessment score of the target aquatic area, and thus obtain the aquatic habitat quality assessment result of the target aquatic area. Compared with fixed weights, the dynamically determined weights can reflect the impact of location information, time information, scene information, and indicator scores on the aquatic habitat of the target aquatic area, thus improving the reliability of the obtained aquatic habitat quality assessment result.

[0090] In some embodiments, such as Figure 3 As shown, the aquatic habitat quality assessment method provided in this application further includes the following steps for training a weight determination model:

[0091] Step S302: Obtain multiple sample data.

[0092] Step S304: Input the data of each sample into the weight determination model to be trained, and obtain the predicted aquatic habitat quality assessment score corresponding to each sample data.

[0093] Step S306: With the goal of reducing the difference between the sample aquatic habitat quality assessment score and the predicted aquatic habitat quality assessment score corresponding to each sample data, the weight determination model to be trained is iteratively trained until the preset training stopping condition is reached, and the weight determination model is obtained.

[0094] Each sample data point corresponds to a sample aquatic area, a sample time interval, a sample hydrological scenario, and multiple sample aquatic habitat quality assessment indicators. The sample aquatic habitat quality assessment indicators may be some or all of the preset aquatic habitat quality assessment indicators. Different sample data points may correspond to different sample aquatic habitat quality assessment indicators.

[0095] Each sample data point includes at least the location information of the corresponding aquatic area, the time information of the corresponding time interval, the scene information of the corresponding hydrological scenario, and the sample index scores of the corresponding aquatic habitat quality assessment indicators for the corresponding aquatic area in the corresponding time interval and the corresponding hydrological scenario.

[0096] Each sample data also corresponds to a sample aquatic habitat quality assessment score, which is a manually labeled aquatic habitat quality assessment score.

[0097] In this embodiment, firstly, the server obtains the sample index scores of different aquatic areas under different time intervals and hydrological scenarios, corresponding to different aquatic habitat quality assessment indicators. It then combines the location information of the aquatic areas, the time information of the time intervals, the scenario information of the hydrological scenarios, and the sample index scores to obtain multiple sample data. Next, the server inputs each sample data into the weight determination model to obtain the predicted weights corresponding to each aquatic habitat quality assessment indicator. Then, for each sample data, the server determines the predicted aquatic habitat quality assessment score based on the sample index scores and predicted weights corresponding to each aquatic habitat quality assessment indicator. Finally, the server... The system calculates the difference between the sample aquatic habitat quality assessment score and the predicted aquatic habitat quality assessment score for each sample data point. With the training objective of reducing this difference, the system repeatedly performs the steps described above: inputting each sample data point into the weight determination model to be trained; obtaining the predicted weights corresponding to each sample aquatic habitat quality assessment index; determining the predicted aquatic habitat quality assessment score for each sample data point; and calculating the difference between the sample aquatic habitat quality assessment score and the predicted aquatic habitat quality assessment score. This iterative training of the weight determination model continues until a preset training stopping condition is reached. The model that reaches the preset training stopping condition is then used as the weight determination model.

[0098] In practical applications, the preset training stopping condition can be that the number of training sessions reaches a preset number, or that the difference between the sample aquatic habitat quality assessment score and the predicted aquatic habitat quality assessment score is less than a preset difference threshold.

[0099] In practical applications, the structure of the model for determining the weights to be trained is a fusion structure of a multi-layer feedforward neural network (BP neural network) and a long short-term memory network (LSTM neural network). Furthermore, the input layer of the model to be trained for weight determination includes several nodes, each node receiving at least one of the following: location information, time information, scene information, and sample index scores of each sample aquatic habitat quality assessment index. The first hidden layer of the model to be trained for weight determination uses a ReLU activation function with several nodes to extract the nonlinear relationship between location information, time information, scene information, and sample index scores of each sample aquatic habitat quality assessment index. The second hidden layer of the model to be trained for weight determination introduces an LSTM unit to monitor and capture the temporal characteristics of the sample index scores of each sample aquatic habitat quality assessment index, enabling the model to consider seasonal changes and historical evolution trends of aquatic areas when dynamically adjusting weights. The output layer of the model to be trained for weight determination includes several nodes, each node outputting the weight corresponding to a sample aquatic habitat quality assessment index, as shown in Equation 1. The output layer uses a Softmax activation function to ensure that each weight is non-negative and satisfies normalization constraints.

[0100] (Formula 1)

[0101] in, The weights output by the output node corresponding to the aquatic habitat quality assessment index of the i-th sample; is the linear combination result of the output nodes corresponding to the aquatic habitat quality assessment indicators of the i-th sample; J is the total number of output nodes.

[0102] In practical applications, the weights to be trained determine the sample index scores corresponding to each aquatic habitat quality assessment index for each sample data point, and a standardization preprocessing as shown in Formula 2 is also performed:

[0103] (Formula 2)

[0104] in, The standardized index score of the nth sample data under the aquatic habitat quality assessment index of the ith sample; Let be the sample index score of the nth sample data under the aquatic habitat quality assessment index of the ith sample; The average score of the sample index under the aquatic habitat quality assessment index for the i-th sample is given by all sample data. Let be the standard deviation of the sample index scores for all sample data under the aquatic habitat quality assessment index for the i-th sample.

[0105] In practical applications, when the training weights determination model is used for monitoring points with missing or outlier values, it can employ interpolation based on nearest-neighbor time series and / or multiple interpolation based on similar points and / or boundary substitution based on physical constraints to ensure the integrity of the input.

[0106] In practical applications, to take into account the impact of seasonal and watershed flow changes on the weights, the model to be trained can use a sliding window of the most recent T days / times (e.g., T=30) to expand the aquatic habitat quality assessment index of each sample into a time-series feature vector, and input it into the subsequent LSTM unit.

[0107] In this embodiment, the server can train a weight determination model that can dynamically output the weights corresponding to the aquatic habitat quality assessment indicators based on different sample aquatic areas, different sample time intervals, different sample hydrological scenarios, and different sample aquatic habitat quality assessment indicators. Compared with fixed weights, the dynamically determined weights can reflect the influence of location information, time information, scenario information, and indicator scores on the aquatic habitat of the target aquatic area, thus improving the reliability of the obtained aquatic habitat quality assessment results.

[0108] In some embodiments, step S204, which determines the index score of the target aquatic area for each aquatic habitat quality assessment indicator in the target time period and target hydrological scenario based on the aquatic data of the target aquatic area in the target time period and target hydrological scenario, includes the following steps: determining the index value of the target aquatic area for each aquatic habitat quality assessment indicator in the target time period and target hydrological scenario based on the aquatic data of the target aquatic area in the target time period and target hydrological scenario; obtaining the score mapping rule corresponding to each aquatic habitat quality assessment indicator; mapping the index value of the target aquatic area for each aquatic habitat quality assessment indicator to the corresponding score according to the score mapping rule corresponding to each aquatic habitat quality assessment indicator, thereby obtaining the index score of the target aquatic area for each aquatic habitat quality assessment indicator in the target time period and target hydrological scenario.

[0109] In this embodiment, firstly, the server determines the index value of each aquatic habitat quality assessment indicator for the target aquatic area under the target time interval and target hydrological scenario based on the aquatic data of the target aquatic area under the target time interval and target hydrological scenario. Then, the server obtains the pre-constructed score mapping rule for each aquatic habitat quality assessment indicator. Next, for each aquatic habitat quality assessment indicator, the server maps the index value of the target aquatic area under the aquatic habitat quality assessment indicator to the corresponding score according to the score mapping rule corresponding to the aquatic habitat quality assessment indicator, thereby obtaining the index score of the target aquatic area under the target time interval and target hydrological scenario for the aquatic habitat quality assessment indicator.

[0110] In this embodiment, the server can determine the corresponding indicator value based on aquatic data, and based on the indicator value, it can map it to the corresponding indicator score.

[0111] In some embodiments, step S210 above, which determines the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score, includes the following steps: obtaining the correspondence between the assessment score and the quality level; mapping the aquatic habitat quality assessment score to the corresponding aquatic habitat quality level based on the correspondence between the assessment score and the quality level; and obtaining the aquatic habitat quality assessment result according to the aquatic habitat quality level.

[0112] In this embodiment, the server obtains the pre-constructed correspondence between assessment scores and quality levels. Then, based on the correspondence between assessment scores and quality levels, it maps the aquatic habitat quality assessment scores to the corresponding aquatic habitat quality levels. Finally, it uses the aquatic habitat quality level as the aquatic habitat quality assessment result.

[0113] In practical applications, the correspondence between assessment scores and quality levels is shown in Table 1:

[0114] Table 1. Correspondence between assessment scores and quality levels

[0115]

[0116] In this embodiment, the server can map the aquatic habitat quality assessment score to the corresponding aquatic habitat quality level, thereby obtaining the aquatic habitat quality assessment result.

[0117] In some embodiments, each aquatic habitat quality assessment indicator includes a plurality of first assessment indicators and at least one second assessment indicator under each first assessment indicator; the indicator score corresponding to each aquatic habitat quality assessment indicator includes the indicator score corresponding to each second assessment indicator, and the weight corresponding to each aquatic habitat quality assessment indicator includes the weight corresponding to each second assessment indicator.

[0118] Step S208 above, which determines the aquatic habitat quality assessment score of the target aquatic area based on the index scores and weights corresponding to each aquatic habitat quality assessment index, includes the following steps: for each first assessment index, the index scores corresponding to each second assessment index are weighted and integrated according to the weights corresponding to each second assessment index of the first assessment index to obtain the comprehensive index score corresponding to the first assessment index; and the aquatic habitat quality assessment score is obtained based on the average value of each comprehensive index score.

[0119] In this embodiment, firstly, the server performs weighted fusion of the index scores corresponding to each second evaluation index for each first evaluation index, based on the weights corresponding to each second evaluation index of the first evaluation index, such as weighted summation, to obtain the comprehensive index score corresponding to the first evaluation index; then, the server calculates the average value of the comprehensive index scores corresponding to each first evaluation index to obtain the aquatic habitat quality evaluation score.

[0120] In this embodiment, based on the weight and index score of each second evaluation index under each first evaluation index, a comprehensive index score for each first evaluation index is obtained. The server can comprehensively consider the corresponding second evaluation index to obtain a comprehensive index score. By taking the average of the comprehensive index scores corresponding to each first evaluation index as the aquatic habitat quality evaluation score, the server can comprehensively consider each first evaluation index to obtain an aquatic habitat quality evaluation score.

[0121] In some embodiments, the multiple first assessment indicators include at least two of hydrological indicators, water quality indicators, food organism indicators, fish indicators, and habitat indicators; the second assessment indicators under the hydrological indicators include at least two of vertical connectivity, horizontal connectivity, ecological flow discharge, and water area change; the second assessment indicators under the water quality indicators include at least a water quality category proportion type, used to characterize the proportional distribution of each water quality category present in the target aquatic area; the second assessment indicators under the food organism indicators include at least two of phytoplankton diversity, zooplankton diversity, and benthic diversity; the second assessment indicators under the fish indicators include at least two of fish species abundance change, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, and fish diversity; the second assessment indicators under the habitat indicators include at least two of the proportion of fish spawning grounds, proportion of fish feeding grounds, and proportion of fish overwintering grounds.

[0122] In this embodiment, the meaning, calculation rules, and score mapping rules of each evaluation indicator are as follows:

[0123] (1) Vertical connectivity. Vertical connectivity is used to evaluate the connectivity of water bodies within an aquatic region, including their own connectivity and their connectivity with surrounding tributaries.

[0124] The rules for calculating vertical connectivity are as follows:

[0125] Longitudinal connectivity = Length of continuous flow within the aquatic region / Total length of the aquatic region's catchment area, or...

[0126] Vertical connectivity = area of ​​continuous flow within the aquatic region / total area of ​​the aquatic region.

[0127] The fractional mapping rules for vertical connectivity are shown in Table 2:

[0128] Table 2. Score Mapping Rules for Vertical Connectivity

[0129]

[0130] (2) Lateral connectivity. Under human interference, the construction land along the shoreline of aquatic areas is often hardened, which is not conducive to the lateral movement of water. Hardening refers to the paving of the waterbed of the natural shoreline with concrete slabs or boulders, creating an artificial hard bed or artificial hard bank, which hinders the flow or connection of materials between the land and the water, and changes the natural characteristics of the aquatic habitat. This indicator reflects the degree to which the shoreline of aquatic areas is affected by human activities.

[0131] The rules for calculating lateral connectivity are as follows:

[0132] Lateral connectivity = length of hardened shoreline within the aquatic area / total shoreline length of the aquatic area.

[0133] The fractional mapping rules for lateral connectivity are shown in Table 3:

[0134] Table 3. Fractional mapping rules for lateral connectivity

[0135]

[0136] (3) Ecological flow release. Ecological flow release refers to a flow regulation mechanism set up in water conservancy facilities such as reservoirs or hydropower stations to protect the downstream ecological environment. By rationally controlling the flow release, the water demand of the downstream river section is supported, the living environment of aquatic organisms is maintained, the river ecological environment is protected, and the river ecological balance is maintained.

[0137] The calculation rules for ecological flow release are as follows:

[0138] Ecological flow release volume = released ecological flow / average natural flow of aquatic areas over many years.

[0139] The fractional mapping rules for ecological flow release are shown in Table 4:

[0140] Table 4. Fractional Mapping Rules for Ecological Flow Release

[0141]

[0142] (4) Changes in water area. This indicator reflects the degree to which changes in the water area of ​​aquatic regions are affected by human activities.

[0143] The calculation rules for changes in water body area are as follows:

[0144] Change in water area = Change in existing water area of ​​aquatic region / Change in natural water area of ​​aquatic region.

[0145] The fractional mapping rules for changes in water area are shown in Table 5:

[0146] Table 5. Fractional mapping rules for changes in water area

[0147]

[0148] (5) Water quality category proportion type. The water quality category proportion type is used to characterize the proportion distribution of each water quality category in the aquatic area. In practical applications, a proportion distribution of water quality categories in each sub-region of the aquatic area corresponds to a water quality category proportion type. In practical applications, the water quality category is determined based on the total nitrogen, total phosphorus, ammonia nitrogen, water temperature, and dissolved oxygen in the aquatic area.

[0149] The calculation rules for the proportion of water quality categories are as follows:

[0150] The proportion of each water quality category = the number of sub-regions corresponding to that water quality category / the total number of sub-regions.

[0151] The score mapping rules for water quality category proportions are shown in Table 6:

[0152] Table 6. Score Mapping Rules for Water Quality Category Proportion Types

[0153]

[0154] (6) Phytoplankton diversity. Phytoplankton diversity can reflect the abundance of phytoplankton species and the evenness of their distribution among species.

[0155] The rules for calculating phytoplankton diversity are as follows:

[0156] Phytoplankton diversity = Shannon-Wiener diversity index of phytoplankton / baseline value of phytoplankton diversity.

[0157] The Shannon-Wiener diversity index for phytoplankton is calculated according to Formula 3:

[0158] (Formula 3)

[0159] Where S is the total number of phytoplankton species in the aquatic region; i is the species ordinal number of the phytoplankton in the aquatic region; P i denoted as the proportion of individuals of the i-th phytoplankton species in the aquatic region.

[0160] The fractional mapping rules for phytoplankton diversity are shown in Table 7:

[0161] Table 7. Fractional mapping rules for phytoplankton diversity

[0162]

[0163] (7) Zooplankton diversity. Zooplankton diversity can reflect the species abundance and evenness of interspecific distribution of zooplankton.

[0164] The rules for calculating zooplankton diversity are as follows:

[0165] Zooplankton diversity = Shannon-Wiener diversity index of zooplankton / baseline value of zooplankton diversity.

[0166] The calculation rules for the Shannon-Wiener diversity index of zooplankton are the same as those for the Shannon-Wiener diversity index of phytoplankton.

[0167] The fractional mapping rule for zooplankton diversity is based on the fractional mapping rule for phytoplankton diversity.

[0168] (8) Benthic diversity. Benthic diversity can reflect the species abundance and evenness of interspecific distribution of benthic animals.

[0169] The rules for calculating benthic animal diversity are as follows:

[0170] Benthic diversity = Shannon-Wiener diversity index of benthic animals / Benthic diversity baseline value.

[0171] The calculation rules for the Shannon-Wiener diversity index of benthic animals are the same as those for the Shannon-Wiener diversity index of phytoplankton.

[0172] The fractional mapping rule for benthic animal diversity is based on the fractional mapping rule for phytoplankton diversity.

[0173] (9) Changes in fish species abundance. Changes in fish species abundance are used to assess the abundance of native fish species other than introduced species in aquatic areas, representing fish abundance.

[0174] The rules for calculating changes in the number of fish species are as follows:

[0175] Change in the number of fish species = Current number of fish species in the aquatic area / Number of native fish species in the aquatic area.

[0176] The fractional mapping rules for changes in the number of fish species are shown in Table 8:

[0177] Table 8. Fractional mapping rules for changes in the number of fish species

[0178]

[0179] (10) Proportion of rare fish species. The proportion of rare fish species is used to assess the number of protected and endemic fish species in aquatic areas.

[0180] The calculation rules for the proportion of rare fish species are as follows:

[0181] The percentage of rare fish species = the number of rare fish species in the aquatic area / the total number of all fish species in the aquatic area, or...

[0182] The percentage of rare fish species = the number of rare fish species in the aquatic area / the total number of all fish in the aquatic area.

[0183] The fractional mapping rules for the proportion of rare fish species are shown in Table 9:

[0184] Table 9. Score Mapping Rules for the Proportion of Rare Fish Species

[0185]

[0186] (11) Proportion of carnivorous fish. The proportion of carnivorous fish is used to assess the proportion of carnivorous fish in an aquatic area, indicating the degree of trophic structure of the fish community in the aquatic area.

[0187] The rules for calculating the percentage of carnivorous fish are as follows:

[0188] Percentage of carnivorous fish = Number of carnivorous fish in the aquatic area / Total number of all fish in the aquatic area, or,

[0189] Percentage of carnivorous fish = Number of carnivorous fish in the aquatic area / Total number of all fish in the aquatic area.

[0190] The fractional mapping rules for the proportion of carnivorous fish are shown in Table 10:

[0191] Table 10. Fractional mapping rules for the proportion of carnivorous fish

[0192]

[0193] (12) Drifting-egg fish. The percentage of drifting-egg fish is used to assess the number of drifting-egg fish in aquatic areas.

[0194] The calculation rule for the percentage of fish that lay drifting eggs is as follows:

[0195] Percentage of fish that lay drifting eggs = Number of drifting egg-laying fish in the aquatic area / Total number of all fish in the aquatic area, or,

[0196] Percentage of fish that lay drifting eggs = Number of fish in the aquatic area that lay drifting eggs / Total number of fish in the aquatic area.

[0197] The fractional mapping rule for the proportion of fish that lay drifting eggs is the same as the fractional mapping rule for the proportion of carnivorous fish.

[0198] (13) Fish diversity.

[0199] The calculation rules for fish diversity are shown in Formula 4:

[0200] (Formula 4)

[0201] Where S represents the number of fish species at a given survey site; H represents the Shannon-Wiener diversity index of fish species at a given survey site; D represents the dominance index of fish species at a given survey site; and N represents the number of fish species at a given survey site. max Let N be the number of individuals of the dominant fish species at a certain survey site, and let N be the number of individuals of all fish species at a certain survey site. Q1 and Q2 are the minimum and maximum values ​​of the survey statistics of the corresponding indicators (S, H, D) across all survey sites, respectively.

[0202] The fractional mapping rules for fish diversity are shown in Table 11:

[0203] Table 11. Fractional mapping rules for fish diversity

[0204]

[0205] (14) Percentage of fish spawning grounds. Fish spawning grounds refer to the aquatic areas where fish reproduce and spawn. They are important places for fish survival and reproduction and play an important role in replenishing fishery resources.

[0206] The calculation rules for the proportion of fish spawning grounds are as follows:

[0207] The proportion of fish spawning grounds = the catchment area of ​​fish spawning grounds / the total catchment area of ​​aquatic environments, or,

[0208] The proportion of fish spawning grounds = the length of the catchment area of ​​spawning ground-like areas / the total catchment length of the aquatic environment.

[0209] The fractional mapping rules for the proportion of fish spawning grounds are shown in Table 12:

[0210] Table 12. Fractional mapping rules for the proportion of fish spawning grounds

[0211]

[0212] (15) Percentage of fish feeding grounds. Fish feeding grounds refer to the water areas where fish gather to feed.

[0213] The calculation rules for the proportion of fish feeding grounds are as follows:

[0214] The proportion of fish feeding grounds = the catchment area of ​​the fish feeding grounds / the total catchment area of ​​the aquatic environment, or,

[0215] The proportion of fish feeding grounds = the length of the catchment area of ​​spawning grounds / the total catchment length of the aquatic environment.

[0216] The fractional mapping rule for the proportion of fish feeding grounds is the same as the fractional mapping rule for the proportion of fish spawning grounds.

[0217] (16) Percentage of fish overwintering grounds. Fish overwintering grounds refer to aquatic areas where fish gather to feed.

[0218] The calculation rules for the regional proportion of fish overwintering grounds are as follows:

[0219] The proportion of fish overwintering grounds = the catchment area of ​​fish overwintering grounds / the total catchment area of ​​aquatic environments, or,

[0220] The proportion of fish overwintering grounds = length of the catchment area of ​​spawning grounds / total catchment length of the aquatic environment.

[0221] The fractional mapping rule for the proportion of fish overwintering areas refers to the fractional mapping rule for the proportion of fish spawning areas.

[0222] In this embodiment, the server can comprehensively assess the aquatic habitat quality of the target aquatic area by considering hydrological indicators, water quality indicators, food organism indicators, and habitat indicators, including vertical connectivity, horizontal connectivity, ecological flow release, water area change, water quality category ratio, fish species quantity change, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, fish diversity, proportion of fish spawning grounds, proportion of fish feeding grounds, and proportion of fish overwintering grounds.

[0223] To more clearly illustrate the aquatic habitat quality assessment method provided in the embodiments of this application, a specific embodiment is used below to describe the method in detail. However, it should be understood that the embodiments of this application are not limited thereto. Figure 4 As shown, in some embodiments, this application also provides another method for assessing the quality of aquatic habitats, specifically including the following steps:

[0224] 1. Based on the universality of the river section, five criteria indicators were selected, including hydrological indicators, water quality indicators, food organism indicators, and habitat indicators, as well as their specific indicators.

[0225] 2. Based on the characteristics of the river section, set up representative field monitoring points within the river section, conduct monitoring during appropriate time periods, and collect the data required for the above indicators.

[0226] 3. Determine the benchmark or reference values ​​for each indicator based on historical data of the river section and on-site investigation.

[0227] 4. Calculate the index value for each index by combining the data collected in the field survey with the benchmark or reference value of the index.

[0228] 5. Assign scores to each indicator based on the established scoring criteria.

[0229] 6. Input the geographical location information of the river section, the seasonal time information when the data was collected, the scene information of the hydrological scene, and the scoring results of each indicator into the trained dynamic weight determination model, and output the dynamic weight of each indicator.

[0230] 7. Based on the dynamic weights of each indicator, the weighted summation method is used to calculate the scores of the five criteria layers, namely hydrological indicators, water quality indicators, food organism indicators, and habitat indicators.

[0231] 8. Summarize the scores of each criterion level, calculate the comprehensive score of habitat quality, and determine the final evaluation level based on the set standards.

[0232] In this embodiment, a habitat fragmentation index is introduced, combined with habitat physical-chemical-biological indicators, to more comprehensively and accurately reflect the habitat quality of river sections, improve the river habitat assessment system, and enhance the accuracy of aquatic habitat evaluation. To further improve the adaptability of indicator weighting to different river section characteristics, a dynamic weight determination model based on artificial neural networks is introduced. This model enables dynamic adjustment of indicator weights according to local conditions, replacing the traditional fixed weighting method, enhancing the flexibility of the evaluation method, and making the evaluation results more objectively reflect the actual differences in habitat conditions across different river sections.

[0233] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0234] Based on the same inventive concept, this application also provides an aquatic habitat quality assessment device for implementing the aquatic habitat quality assessment method described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations of one or more aquatic habitat quality assessment device embodiments provided below can be found in the limitations of the aquatic habitat quality assessment method described above, and will not be repeated here.

[0235] In some embodiments, such as Figure 5 As shown, an aquatic habitat quality assessment device is provided, comprising: an assessment index determination module 502, an index score determination module 504, a weight dynamic determination module 506, an assessment score determination module 508, and an assessment result determination module 510, wherein:

[0236] The evaluation index determination module 502 is used to determine the various aquatic habitat quality evaluation indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario.

[0237] The indicator score determination module 504 is used to determine the indicator score of each aquatic habitat quality assessment indicator for the target aquatic area under the target time interval and target hydrological scenario, based on the aquatic data of the target aquatic area under the target time interval and target hydrological scenario.

[0238] The weight dynamic determination module 506 is used to input the location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each aquatic habitat quality assessment index into the pre-trained weight determination model to obtain the weights corresponding to each aquatic habitat quality assessment index.

[0239] The assessment score determination module 508 is used to determine the aquatic habitat quality assessment score of the target aquatic area based on the index scores and weights corresponding to each aquatic habitat quality assessment index.

[0240] The assessment result determination module 510 is used to determine the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score.

[0241] In one embodiment, the aquatic habitat quality assessment device further includes a weight model training module for acquiring multiple sample data. Each sample data corresponds to a sample aquatic area, a sample time interval, a sample hydrological scenario, and multiple sample aquatic habitat quality assessment indicators. The sample data includes at least the location information of the sample aquatic area, the time information of the sample time interval, the scenario information of the sample hydrological scenario, and the sample indicator scores of the sample aquatic area corresponding to each sample aquatic habitat quality assessment indicator under the sample time interval and sample hydrological scenario. Each sample data is input into the weight determination model to be trained to obtain the predicted aquatic habitat quality assessment score corresponding to each sample data. The training objective is to reduce the difference between the sample aquatic habitat quality assessment score corresponding to each sample data and the predicted aquatic habitat quality assessment score. The weight determination model to be trained is iteratively trained until a preset training stopping condition is reached to obtain the weight determination model.

[0242] In one embodiment, the index score determination module 504 is further configured to determine the index value of the target aquatic area corresponding to each aquatic habitat quality assessment index in the target time interval and target hydrological scenario based on the aquatic data of the target aquatic area in the target time interval and target hydrological scenario; obtain the score mapping rule corresponding to each aquatic habitat quality assessment index; and map the index value of the target aquatic area under each aquatic habitat quality assessment index to the corresponding score according to the score mapping rule corresponding to each aquatic habitat quality assessment index, thereby obtaining the index score of the target aquatic area corresponding to each aquatic habitat quality assessment index in the target time interval and target hydrological scenario.

[0243] In one embodiment, the assessment result determination module 510 is further configured to obtain the correspondence between the assessment score and the quality level; based on the correspondence between the assessment score and the quality level, map the aquatic habitat quality assessment score to the corresponding aquatic habitat quality level; and obtain the aquatic habitat quality assessment result according to the aquatic habitat quality level.

[0244] In one embodiment, each aquatic habitat quality assessment indicator includes multiple first assessment indicators and at least one second assessment indicator under each first assessment indicator; the indicator score corresponding to each aquatic habitat quality assessment indicator includes the indicator score corresponding to each second assessment indicator, and the weight corresponding to each aquatic habitat quality assessment indicator includes the weight corresponding to each second assessment indicator.

[0245] The evaluation score determination module 508 is also used to, for each first evaluation indicator, perform weighted fusion of the indicator scores corresponding to each second evaluation indicator according to the weights corresponding to each second evaluation indicator of the first evaluation indicator, to obtain the comprehensive indicator score corresponding to the first evaluation indicator; and obtain the aquatic habitat quality evaluation score based on the average of each comprehensive indicator score.

[0246] In one embodiment, the multiple first assessment indicators include at least two of the following: hydrological indicators, water quality indicators, food organism indicators, fish indicators, and habitat indicators; the second assessment indicators under the hydrological indicators include at least two of the following: vertical connectivity, horizontal connectivity, ecological flow discharge, and water area change; the second assessment indicators under the water quality indicators include at least a water quality category proportion type, used to characterize the proportional distribution of each water quality category existing in the target aquatic area; the second assessment indicators under the food organism indicators include at least two of the following: phytoplankton diversity, zooplankton diversity, and benthic diversity; the second assessment indicators under the fish indicators include at least two of the following: fish species quantity change, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, and fish diversity; the second assessment indicators under the habitat indicators include at least two of the following: proportion of fish spawning grounds, proportion of fish feeding grounds, and proportion of fish overwintering grounds.

[0247] Each module in the aforementioned aquatic habitat quality assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0248] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores relevant data required for aquatic habitat quality assessment. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an aquatic habitat quality assessment method.

[0249] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0250] In some embodiments, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0251] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0252] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0253] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0254] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0255] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the quality of aquatic habitats, characterized in that, The method includes: Determine the aquatic habitat quality assessment indicators corresponding to the target aquatic area to be evaluated under the target time interval and target hydrological scenario; Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario; The location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each of the aquatic habitat quality assessment indicators are input into the pre-trained weight determination model to obtain the weights corresponding to each of the aquatic habitat quality assessment indicators. The aquatic habitat quality assessment score of the target aquatic area is determined based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators. Based on the aquatic habitat quality assessment score, the aquatic habitat quality assessment result of the target aquatic area is determined.

2. The method according to claim 1, characterized in that, The weight determination model is trained in the following manner: Multiple sample data points are acquired; each sample data point corresponds to a sample aquatic area, a sample time interval, a sample hydrological scenario, and multiple sample aquatic habitat quality assessment indicators. The sample data at least includes the location information of the sample aquatic area, the time information of the sample time interval, the scenario information of the sample hydrological scenario, and the sample indicator scores of the sample aquatic area corresponding to each of the sample aquatic habitat quality assessment indicators under the sample time interval and the sample hydrological scenario. The sample data are input into the weight determination model to be trained to obtain the predicted aquatic habitat quality assessment score corresponding to each sample data. With the goal of reducing the difference between the sample aquatic habitat quality assessment score corresponding to each of the sample data and the predicted aquatic habitat quality assessment score, the weight determination model to be trained is iteratively trained until a preset training stopping condition is reached, thereby obtaining the weight determination model.

3. The method according to claim 1, characterized in that, The step of determining the index score of the target aquatic area for each aquatic habitat quality assessment indicator based on aquatic data of the target aquatic area in the target time interval and the target hydrological scenario includes: Based on the aquatic data of the target aquatic area under the target time interval and the target hydrological scenario, determine the index value of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators under the target time interval and the target hydrological scenario; Obtain the score mapping rule corresponding to each of the aquatic habitat quality assessment indicators; Based on the score mapping rule corresponding to each of the aquatic habitat quality assessment indicators, the index value of the target aquatic area under each of the aquatic habitat quality assessment indicators is mapped to the corresponding score, thereby obtaining the index score of the target aquatic area under each of the aquatic habitat quality assessment indicators in the target time interval and the target hydrological scenario.

4. The method according to claim 1, characterized in that, The determination of the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score includes: Obtain the correspondence between assessment scores and quality levels; Based on the correspondence between the assessment scores and quality levels, the aquatic habitat quality assessment scores are mapped to the corresponding aquatic habitat quality levels; The aquatic habitat quality assessment results are obtained based on the aquatic habitat quality level.

5. The method according to any one of claims 1 to 4, characterized in that, The aquatic habitat quality assessment indicators include multiple first assessment indicators and at least one second assessment indicator under each first assessment indicator; the indicator scores corresponding to each aquatic habitat quality assessment indicator include the indicator scores corresponding to each second assessment indicator; the weights corresponding to each aquatic habitat quality assessment indicator include the weights corresponding to each second assessment indicator. The step of determining the aquatic habitat quality assessment score of the target aquatic area based on the index scores and weights corresponding to each of the aquatic habitat quality assessment indicators includes: For each first evaluation indicator, the index scores corresponding to each second evaluation indicator are weighted and fused according to the weights corresponding to each second evaluation indicator of the first evaluation indicator to obtain the comprehensive index score corresponding to the first evaluation indicator. The aquatic habitat quality assessment score is obtained by averaging the scores of each comprehensive index.

6. The method according to claim 5, characterized in that, The plurality of first assessment indicators include at least two of the following: hydrological indicators, water quality indicators, food organism indicators, fish indicators, and habitat indicators; the second assessment indicators under the hydrological indicators include at least two of the following: vertical connectivity, horizontal connectivity, ecological flow discharge, and water area change; the second assessment indicators under the water quality indicators include at least one water quality category proportion type, which is used to characterize the proportional distribution of each water quality category existing in the target aquatic area; the second assessment indicators under the food organism indicators include at least two of the following: phytoplankton diversity, zooplankton diversity, and benthic animal diversity; the second assessment indicators under the fish indicators include at least two of the following: fish species quantity change, proportion of rare fish, proportion of carnivorous fish, proportion of fish that lay drifting eggs, and fish diversity; the second assessment indicators under the habitat indicators include at least two of the following: proportion of fish spawning grounds, proportion of fish foraging grounds, and proportion of fish overwintering grounds.

7. A device for assessing the quality of aquatic habitats, characterized in that, The device includes: The assessment index determination module is used to determine the aquatic habitat quality assessment indexes corresponding to the target aquatic area to be assessed under the target time interval and target hydrological scenario. The indicator score determination module is used to determine the indicator score of the target aquatic area corresponding to each of the aquatic habitat quality assessment indicators in the target time interval and the target hydrological scenario, based on the aquatic data of the target aquatic area in the target time interval and the target hydrological scenario. The weight dynamic determination module is used to input the location information of the target aquatic area, the time information of the target time interval, the scene information of the target hydrological scene, and the index scores corresponding to each aquatic habitat quality assessment index into the pre-trained weight determination model to obtain the weights corresponding to each aquatic habitat quality assessment index. The assessment score determination module is used to determine the aquatic habitat quality assessment score of the target aquatic area based on the index score and weight corresponding to each of the aquatic habitat quality assessment indicators. The assessment result determination module is used to determine the aquatic habitat quality assessment result of the target aquatic area based on the aquatic habitat quality assessment score.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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