A data collection and analysis system for watershed ecological product monitoring

Abstract

The present invention relates to the field of data processing technology, and discloses a data collection and analysis system for monitoring ecological products in a watershed. Within a confidence period T, monitoring data of M types of ecological products in a target watershed and amplitude data of the corresponding monitoring data are collected at fixed time intervals t; wherein the monitoring data represents the dimensional value of the ecological product in a unit dimension; a first topological network is constructed based on the M types of ecological products in the target watershed, and a first verification model is constructed based on the first topological network; the target watershed is divided into K sub-areas from upstream to downstream at fixed distance intervals to construct a second topological network, and a second verification model is constructed based on the second topological network; within a target time period, monitoring data of the M types of ecological products in the target watershed are obtained, and the monitoring data are subjected to first verification and second verification respectively through the first verification model and the second verification model to obtain the result of human interference in the target watershed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a data collection and analysis system for monitoring watershed ecological products. Background Art

[0002] With the increasing emphasis on environmental protection and ecological civilization, river basin ecological protection is gradually shifting from simple pollution control to more proactive ecological conservation and restoration. Compared with the past, the current stage places greater emphasis on the ecosystem's inherent resilience and minimizes human intervention.

[0003] The existing watershed ecosystem measures ecological product data from target watersheds at regular intervals to analyze the value of the watershed ecosystem in evaluation systems such as GEP. However, existing methods for analyzing monitoring data make it difficult to determine whether there is human interference in the watershed ecosystem. If there is human interference, the GEP evaluation results for the target watershed will be inaccurate.

[0004] Therefore, there is an urgent need for a system for conducting confidence analysis and judgment on human intervention based on monitoring data. Summary of the Invention

[0005] The present invention provides a data collection and analysis system for monitoring watershed ecological products, which solves the technical problems raised in the background technology.

[0006] The present invention provides a data collection and analysis system for watershed ecological product monitoring, comprising:

[0007] Data acquisition module, used to collect data in the confidence period At fixed time intervals Collect monitoring data of M types of ecological products in the target watershed, as well as the corresponding amplitude data of the monitoring data; wherein the monitoring data represents the dimension value of the ecological product in unit dimension;

[0008] A first processing module is used to construct a first topological network based on the M types of ecological products in the target watershed, and to construct a first verification model based on the first topological network;

[0009] The second processing module is used to divide the target watershed into K sub-areas at fixed distance intervals from upstream to downstream to construct a second topological network, and to construct a second verification model based on the second topological network;

[0010] The interference analysis module is used to obtain the monitoring data of M kinds of ecological products in the target watershed within the target time period, and perform the first verification and the second verification on the monitoring data through the first verification model and the second verification model respectively to obtain the human intervention results of the target watershed.

[0011] Furthermore, the confidence period indicates that within a predetermined period of time, there is no human interference in the monitoring data collected for several ecological products in the target watershed at fixed time intervals.

[0012] Furthermore, the amplitude data includes: obtaining the monitoring data of the mth ecological product at adjacent moments in the target watershed within the confidence period, calculating the difference between the corresponding monitoring data to obtain the amplitude data of the corresponding monitoring data; wherein, , , and All are positive integers.

[0013] Furthermore, a topological network is constructed based on the M kinds of ecological products in the target watershed, including: classifying the M kinds of ecological products in the target watershed;

[0014] The characteristic types include: if the mth ecological product belongs to the biological category, it is classified as the first type; if the mth ecological product belongs to the environmental category, it is classified as the second type; if the mth ecological product belongs to the GEP system indicator category, it is classified as the third type;

[0015] Map the first type of ecological products to first topological nodes respectively. If any two ecological products corresponding to the first topological nodes have a predator-prey relationship or a symbiotic relationship, a topological edge is constructed between the corresponding first topological nodes.

[0016] Mapping the second type of ecological products into second topological nodes respectively, and building a topological edge between each second topological node and each first topological node;

[0017] The third type of ecological products are mapped to third topological nodes respectively, and topological edges are constructed between each third topological node and each first topological node and second topological node to obtain a first topological network.

[0018] Furthermore, a first verification model is constructed based on the first topology network, including:

[0019] Obtain the amplitude data of M types of ecological products at each adjacent moment within the confidence period, and build a first verification model based on the first topological network, specifically including:

[0020] The amplitude data of the ecological product corresponding to the topological node with a topological edge established with the topological node corresponding to the mth ecological product in the first topological network at the i-th moment is used as the first initial sample data; the amplitude data of the mth ecological product at the i-th moment is used as the first initial sample label, where, ,and is a positive integer;

[0021] After normalizing the first initial sample data and the first initial sample label, the first sample data and the first sample label are obtained; and a first verification model is obtained by training based on the first sample data and the first sample label; wherein the first verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

[0022] Furthermore, a second topology network is constructed, including:

[0023] Each sub-region is mapped to a fourth topological node, and topological edges are constructed between the fourth topological nodes corresponding to adjacent sub-regions to obtain a second topological network.

[0024] Furthermore, a second verification model is constructed, including:

[0025] Determine the common ecological products in the K sub-areas; where the common ecological products refer to ecological products that exist in the K sub-areas, and the monitoring data of each ecological product is greater than or equal to the preset monitoring threshold at any time within the confidence period;

[0026] Vectorize the amplitude data of the shared ecological products in the sub-region corresponding to the topological node that establishes a topological edge with the topological node corresponding to the k-th sub-region in the second topological network at the i-th moment to obtain the second initial sample data; vectorize the amplitude data of the shared ecological products in the k-th sub-region at the i-th moment to obtain the second initial sample label;

[0027] After normalizing the second initial sample data and the second initial sample label, the second sample data and the second sample label are obtained; and a second verification model is obtained by training based on the second sample data and the second sample label; wherein the second verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

[0028] Furthermore, the first verification includes:

[0029] In the target time period Within, the amplitude data of the mth ecological product at the jth moment is normalized and used as the first data to be matched; , and j is a positive integer;

[0030] In the first topological network, the amplitude data of the ecological product corresponding to the topological node with which the topological node corresponding to the mth ecological product has a topological edge is used as the first input data;

[0031] The first input data is normalized and input into the first verification model to obtain the output data of the first verification model; if the difference between the output data of the first verification model and the first data to be matched is less than or equal to the preset first matching threshold, the match is successful and the first verification is obtained, otherwise the match fails.

[0032] Further, the second verification includes:

[0033] In the target time period, the amplitude data of the common ecological products in the kth sub-region are normalized and used as the second data to be matched;

[0034] In the second topological network, the amplitude data of the sub-region corresponding to the topological node having the topological edge established with the topological node corresponding to the k-th sub-region is obtained as the second input data;

[0035] The second input data is normalized and input into the second verification model to obtain the output data of the second verification model; if the Euclidean distance between the output data of the second verification model and the second data to be matched is less than or equal to the preset second matching threshold, the match is successful and the second verification is obtained, otherwise the match fails.

[0036] Furthermore, the results of human intervention in the target watershed include:

[0037] During the target time period, if the amplitude data of any ecological product in the target basin at any time passes the first verification, and the amplitude data of the common ecological product in any sub-area in the target basin at any time passes the second verification, then there is no human interference in the target basin; otherwise, there is human interference in the target basin.

[0038] The beneficial effects of the present invention are: by constructing a dual verification model, the first verification model is constructed by utilizing the inherent data association between ecological products, and the second verification model is constructed by dividing the watershed into multiple sub-areas according to the direction of data change, which effectively distinguishes between natural ecological changes and changes in monitoring data caused by human interference, thereby greatly improving the credibility of data used for GEP assessment of watershed ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a module diagram of a data acquisition and analysis system for watershed ecological product monitoring according to the present invention. DETAILED DESCRIPTION

[0040] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0041] like Figure 1 As shown, a data collection and analysis system for watershed ecological product monitoring includes:

[0042] Data acquisition module, used to collect data in the confidence period At fixed time intervals Collect monitoring data of M types of ecological products in the target watershed, as well as the corresponding amplitude data of the monitoring data; wherein the monitoring data represents the dimension value of the ecological product in unit dimension;

[0043] A first processing module is used to construct a first topological network based on the M types of ecological products in the target watershed, and to construct a first verification model based on the first topological network;

[0044] The second processing module is used to divide the target watershed into K sub-areas at fixed distance intervals from upstream to downstream to construct a second topological network, and to construct a second verification model based on the second topological network;

[0045] The interference analysis module is used to obtain the monitoring data of M kinds of ecological products in the target watershed within the target time period, and perform the first verification and the second verification on the monitoring data through the first verification model and the second verification model respectively to obtain the human intervention results of the target watershed.

[0046] It should be noted that "monitoring data represents the dimensional value of ecological products in unit dimensions" means that a unified standardized or normalized measurement method is used for various ecological products in the target watershed to establish a comparable numerical system. The "unit dimension" can be determined based on the characteristics of the ecological products, monitoring objectives and industry standards. For example, aquatic organisms can be expressed by the number of individuals, biomass or density per cubic meter or per hectare, vegetation can be expressed by coverage or biomass per unit area, and water quality parameters can be expressed as concentration or percentage. This method ensures that data from different ecological products can be easily managed, compared and comprehensively analyzed under a unified measurement, thereby improving the accuracy of identifying the overall state of the ecosystem and potential human interference. Regardless of whether the data is numerical, proportional or concentration-type, as long as it is expressed in a unified dimension, it can meet the requirements of this application.

[0047] In one embodiment of the present invention, the confidence period indicates that within a predetermined period of time, there is no human interference in the monitoring data collected on several ecological products in the target watershed at fixed time intervals.

[0048] Specifically, during the confidence period, the data collected is considered to truly reflect the natural ecological state of the watershed, free from external human interference. This provides a reliable foundation for the subsequent construction of validation models based on this data. Human interference includes, for example, artificially releasing ecological products into the target watershed to obtain a false GEP value assessment.

[0049] In one embodiment of the present invention, the amplitude data includes: obtaining monitoring data of the mth ecological product at adjacent moments in the target watershed within the confidence period, and calculating the difference between the corresponding monitoring data to obtain amplitude data of the corresponding monitoring data; wherein, , , and All are positive integers.

[0050] Specifically, amplitude data is used to quantitatively describe the degree of change in ecological products over a short period of time. For example, if the monitoring data for the mth ecological product at the i-th moment is 10, and the monitoring data for the mth ecological product at the i-1-th moment is 13, then the amplitude data at the i-th moment is -3.

[0051] In one embodiment of the present invention, constructing a topological network based on M types of ecological products in a target watershed includes: classifying the M types of ecological products in the target watershed;

[0052] The characteristic types include: if the mth ecological product belongs to the biological category, it is classified as the first type; if the mth ecological product belongs to the environmental category, it is classified as the second type; if the mth ecological product belongs to the GEP system indicator category, it is classified as the third type;

[0053] Map the first type of ecological products to first topological nodes respectively. If any two ecological products corresponding to the first topological nodes have a predator-prey relationship or a symbiotic relationship, a topological edge is constructed between the corresponding first topological nodes.

[0054] Mapping the second type of ecological products into second topological nodes respectively, and building a topological edge between each second topological node and each first topological node;

[0055] The third type of ecological products are mapped to third topological nodes respectively, and topological edges are constructed between each third topological node and each first topological node and second topological node to obtain a first topological network.

[0056] It should be noted that the first topological network is used to reflect the inherent dependencies between various ecological products in the target watershed and the transmission mechanism of their data changes. Ecological products are divided into three categories: biological, inorganic, and evaluation indicators.

[0057] For biological ecological products, the topology is based on predator-prey relationships and symbiotic relationships. Predator-prey relationships refer to the ecological interaction in an ecosystem where one organism captures, ingests, and digests another organism to obtain energy and nutrients. Symbiotic relationships refer to the long-term, close interaction between different species, where at least one of the species benefits.

[0058] For example, consider four ecological products: algae 1, algae 2, herbivorous fish, and carnivorous fish. Algae 1 and algae 2 form a symbiotic relationship. The herbivorous fish consumes algae 1 and algae 2, establishing a predator-prey relationship with the herbivorous fish. Carnivorous fish consume the herbivorous fish, establishing a predator-prey relationship with the carnivorous fish. Accordingly, when the density per unit area (monitoring data) of algae 1 changes (amplitude data), the monitoring data for algae 2, herbivorous fish, and carnivorous fish will also change to varying degrees (amplitude data).

[0059] Environmental ecological products provide living space for biological ecological products. Therefore, when the monitoring data of each environmental ecological product changes (amplitude data), the monitoring data of biological ecological products also changes (amplitude data).

[0060] Ecological products in the evaluation indicator category are artificially defined concepts. Therefore, when the monitoring data (amplitude data) of each biological ecological product and environmental ecological product changes, the monitoring data of the corresponding ecological product in the evaluation indicator category also changes.

[0061] In one embodiment of the present invention, constructing a first verification model based on a first topology network includes:

[0062] Obtain the amplitude data of M types of ecological products at each adjacent moment within the confidence period, and build a first verification model based on the first topological network, specifically including:

[0063] The amplitude data of the ecological product corresponding to the topological node with a topological edge established with the topological node corresponding to the mth ecological product in the first topological network at the i-th moment is used as the first initial sample data; the amplitude data of the mth ecological product at the i-th moment is used as the first initial sample label, where, ,and is a positive integer;

[0064] After normalizing the first initial sample data and the first initial sample label, the first sample data and the first sample label are obtained; and a first verification model is obtained by training based on the first sample data and the first sample label; wherein the first verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

[0065] In one embodiment of the present invention, both the first and second validation models are built on a one-dimensional convolutional neural network architecture from deep learning. This architecture extracts characteristic patterns from ecological product monitoring data layer by layer, effectively improving the model's ability to learn nonlinear and heterogeneous data.

[0066] Deep learning models typically consist of multiple convolutional layers, activation layers (such as ReLU), pooling layers, and fully connected layers. In the first validation model, the amplitude data of the ecological product associated with each topological node is used as the input sequence. Multiple convolutional kernels are used for feature extraction, resulting in a high-dimensional, deep representation. This process uses a sliding window mechanism to scan the time series data, extracting local trends and modeling the dynamic characteristics of ecological products.

[0067] During training, the system uses the mean squared error (MSE) loss function as the optimization objective based on sample data and labels collected during the confidence period. It then iteratively updates model parameters using a gradient descent algorithm until the loss function converges. The entire training process utilizes an end-to-end supervised learning approach, ensuring the model achieves high accuracy in predicting target ecosystem products.

[0068] The model also includes batch normalization, a Dropout layer to prevent overfitting, and an Early Stopping mechanism during training to improve generalization performance and avoid underfitting or overfitting on small samples or highly volatile datasets.

[0069] It should be noted that the first topological network integrates the supporting effects of biological and environmental ecological products on biological products, as well as the relationship between evaluation indicator ecological products and the first two types of data. By obtaining the amplitude data of the mth ecological product and the amplitude data of its related ecological products, we construct sample data and sample labels, and then train the first verification model.

[0070] In one embodiment of the present invention, the first verification model includes M hidden units. The mth hidden unit is configured to input a normalized vector constructed by the amplitude data of the eco-product corresponding to the topological node that has an edge relationship with the topological node corresponding to the mth eco-product, and output the predicted amplitude data of the mth eco-product.

[0071] In one embodiment of the present invention, constructing the second topology network includes:

[0072] Each sub-region is mapped to a fourth topological node, and topological edges are constructed between the fourth topological nodes corresponding to adjacent sub-regions to obtain a second topological network.

[0073] It should be noted that the second topological network is used to reflect the spatial diffusion and changes in ecological product monitoring data from upstream to downstream within a watershed. Specifically, the watershed is divided into several sub-regions at fixed distances, each of which is mapped to a fourth topological node. Topological edges are constructed between adjacent sub-region nodes, forming the second topological network. The second topological network can capture how changes in ecological product monitoring data at any upstream location are gradually transmitted downstream along the watershed, thereby demonstrating the trend of data changes throughout the entire watershed.

[0074] In one embodiment of the present invention, constructing a second verification model includes:

[0075] Determine the common ecological products in the K sub-areas; where the common ecological products refer to ecological products that exist in the K sub-areas, and the monitoring data of each ecological product is greater than or equal to the preset monitoring threshold at any time within the confidence period;

[0076] Vectorize the amplitude data of the shared ecological products in the sub-region corresponding to the topological node that establishes a topological edge with the topological node corresponding to the k-th sub-region in the second topological network at the i-th moment to obtain the second initial sample data; vectorize the amplitude data of the shared ecological products in the k-th sub-region at the i-th moment to obtain the second initial sample label;

[0077] After normalizing the second initial sample data and the second initial sample label, the second sample data and the second sample label are obtained; and a second verification model is obtained by training based on the second sample data and the second sample label; wherein the second verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

[0078] Specifically, in the second verification model, since the input data comes from the sub-regions after spatial division, its amplitude data is also extracted through a one-dimensional convolutional neural network for time series feature extraction, and the Euclidean distance between the final generated prediction vector and the actual amplitude vector is calculated as the discrimination criterion. In this process, the number of layers and channels of the deep neural network is adjusted according to the complexity of the watershed and the dimension of the data to ensure that the model has sufficient expressive power. Through the above-mentioned one-dimensional convolutional neural network construction process based on deep learning, the present invention effectively improves the model's ability to understand the internal structure and spatial propagation laws of ecological monitoring data, thereby enhancing the robustness and reliability of ecological interference discrimination while ensuring that the system structure remains unchanged.

[0079] It should be noted that, based on the top-down division of the watershed into several subregions at fixed distances, the second topological network is used to reflect the amplitude data trends of shared ecological products within each subregion. A shared ecological product is defined as one whose monitoring data is greater than or equal to a threshold at every moment within the confidence period. For example, if the monitoring data for algae A in K subregions is greater than a threshold of 100 at every moment within the confidence period, then algae A is considered a shared ecological product.

[0080] Specifically, the system first identifies the shared ecological products present in all K subregions. It then calculates the amplitude data for the shared ecological product monitoring data collected within the confidence interval for each subregion and vectorizes these amplitude data to form the second initial sample data. Simultaneously, the amplitude data for the shared ecological products in each subregion at the corresponding moment are vectorized and used as the second initial sample labels. After normalization, the second initial sample data and labels are trained using a one-dimensional convolutional neural network and a mean squared error loss function to obtain the second validation model. This model can effectively reflect the spatial diffusion and changing trends of shared ecological product data from upstream to downstream within the basin, thereby determining whether the data changes conform to the laws of natural propagation.

[0081] In one embodiment of the present invention, the second verification model includes K hidden units, wherein the kth hidden unit is used to input a normalized vector constructed by the amplitude data of the common ecological products of the sub-region corresponding to the topological node that establishes an edge relationship with the topological node corresponding to the kth sub-region, and output the predicted amplitude data of the common ecological products of the kth sub-region.

[0082] In one embodiment of the present invention, the first verification includes:

[0083] In the target time period Within, the amplitude data of the mth ecological product at the jth moment is normalized and used as the first data to be matched; , and j is a positive integer;

[0084] In the first topological network, the amplitude data of the ecological product corresponding to the topological node with which the topological node corresponding to the mth ecological product has a topological edge is used as the first input data;

[0085] The first input data is normalized and input into the first verification model to obtain the output data of the first verification model; if the difference between the output data of the first verification model and the first data to be matched is less than or equal to the preset first matching threshold, the match is successful and the first verification is obtained, otherwise the match fails.

[0086] It should be noted that if the difference between the predicted value output by the first verification model and the actual monitored value exceeds the preset threshold, it can be determined that there is potential human interference or abnormal change; on the contrary, if the difference is within a reasonable range, it means that the change of the ecological product at this moment is consistent with the system's prediction of natural ecological relationships, thereby completing the first verification process.

[0087] In one embodiment of the present invention, the second verification includes:

[0088] In the target time period, the amplitude data of the common ecological products in the kth sub-region are normalized and used as the second data to be matched;

[0089] In the second topological network, the amplitude data of the sub-region corresponding to the topological node having the topological edge established with the topological node corresponding to the k-th sub-region is obtained as the second input data;

[0090] The second input data is normalized and input into the second verification model to obtain the output data of the second verification model; if the Euclidean distance between the output data of the second verification model and the second data to be matched is less than or equal to the preset second matching threshold, the match is successful and the second verification is obtained, otherwise the match fails.

[0091] It should be noted that if the difference (Euclidean distance) between the model output result and the actual monitoring data of the kth sub-region does not exceed the set threshold, it indicates that the change trend of the monitoring data of the sub-region is consistent with the natural diffusion law of the basin, and passes the second verification; if the difference exceeds the threshold, it may mean that there are abnormal fluctuations or human interference in the area.

[0092] In one embodiment of the present invention, the results of human intervention in the target watershed include:

[0093] During the target time period, if the amplitude data of any ecological product in the target basin at any time passes the first verification, and the amplitude data of the common ecological product in any sub-area in the target basin at any time passes the second verification, then there is no human interference in the target basin; otherwise, there is human interference in the target basin.

[0094] Specifically, the logic for determining human interference in the target watershed can be summarized as follows: if the first verification is passed, it means that within the target time period, the amplitude data of any ecological product at any time are consistent with the reasonable variation range predicted based on the first topological network (internal relationship of ecological products); if the second verification is passed, it means that within the target time period, the amplitude data of the shared ecological product in any sub-region at any time are consistent with the reasonable variation range predicted based on the second topological network (spatial diffusion relationship from upstream to downstream); if and only if both verifications are passed, the system determines that there is no human interference in the target watershed; if any of the verifications fails, it is inferred that there is human interference in the target watershed.

[0095] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A data collection and analysis system for watershed ecological product monitoring, characterized in that: include: Data acquisition module, used to collect data in the confidence period At fixed time intervals Collect monitoring data of M types of ecological products in the target watershed, as well as the corresponding amplitude data of the monitoring data; wherein the monitoring data represents the dimension value of the ecological product in unit dimension; A first processing module is used to construct a first topological network based on the M types of ecological products in the target watershed, and to construct a first verification model based on the first topological network; The second processing module is used to divide the target watershed into K sub-areas at fixed distance intervals from upstream to downstream to construct a second topological network, and to construct a second verification model based on the second topological network; An interference analysis module is used to obtain monitoring data of M ecological products in a target watershed within a target time period, and perform first and second verifications on the monitoring data using a first verification model and a second verification model, respectively, to obtain the human intervention results of the target watershed; Constructing a first verification model based on the first topology network includes: Obtain the amplitude data of M types of ecological products at each adjacent moment within the confidence period, and build a first verification model based on the first topological network, specifically including: The amplitude data of the ecological product corresponding to the topological node with a topological edge established with the topological node corresponding to the mth ecological product in the first topological network at the i-th moment is used as the first initial sample data; the amplitude data of the mth ecological product at the i-th moment is used as the first initial sample label, where, ,and is a positive integer; Normalizing the first initial sample data and the first initial sample label to obtain first sample data and a first sample label; and training a first verification model based on the first sample data and the first sample label; wherein the first verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function; Construct the second validation model, including: Determine the common ecological products in the K sub-areas; where the common ecological products refer to ecological products that exist in the K sub-areas, and the monitoring data of each ecological product is greater than or equal to the preset monitoring threshold at any time within the confidence period; The amplitude data of the shared ecological products in the sub-region corresponding to the topological node that establishes a topological edge with the topological node corresponding to the k-th sub-region in the second topological network at the i-th moment are vectorized to obtain the second initial sample data; the amplitude data of the shared ecological products in the k-th sub-region at the i-th moment are vectorized to obtain the second initial sample label. , is a positive integer; After normalizing the second initial sample data and the second initial sample label, the second sample data and the second sample label are obtained; and a second verification model is obtained by training based on the second sample data and the second sample label; wherein the second verification model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

2. A data acquisition and analysis system for watershed ecological product monitoring according to claim 1, characterized in that: The confidence period indicates that there is no human interference in the monitoring data collected on several ecological products in the target watershed at fixed time intervals within a predetermined period of time.

3. A data acquisition and analysis system for watershed ecological product monitoring according to claim 2, characterized in that: Amplitude data, including: obtaining the monitoring data of the mth ecological product at adjacent moments in the target watershed within the confidence period, calculating the difference between the corresponding monitoring data to obtain the amplitude data of the corresponding monitoring data; wherein, , Is a positive integer.

4. A data acquisition and analysis system for watershed ecological product monitoring according to claim 3, characterized in that: Constructing a topological network based on the M kinds of ecological products in the target watershed, including: classifying the M kinds of ecological products in the target watershed; The characteristic types include: if the mth ecological product belongs to the biological category, it is classified as the first type; if the mth ecological product belongs to the environmental category, it is classified as the second type; if the mth ecological product belongs to the GEP system indicator category, it is classified as the third type; Map the first type of ecological products to first topological nodes respectively. If any two ecological products corresponding to the first topological nodes have a predator-prey relationship or a symbiotic relationship, a topological edge is constructed between the corresponding first topological nodes. Mapping the second type of ecological products into second topological nodes respectively, and building a topological edge between each second topological node and each first topological node; The third type of ecological products are mapped to third topological nodes respectively, and topological edges are constructed between each third topological node and each first topological node and second topological node to obtain a first topological network.

5. A data acquisition and analysis system for watershed ecological product monitoring according to claim 4, characterized in that: Construct the second topology network, including: Each sub-region is mapped to a fourth topological node, and topological edges are constructed between the fourth topological nodes corresponding to adjacent sub-regions to obtain a second topological network.

6. A data acquisition and analysis system for watershed ecological product monitoring according to claim 5, characterized in that: The first verification includes: In the target time period Within, the amplitude data of the mth ecological product at the jth moment is normalized and used as the first data to be matched; , and j is a positive integer; In the first topological network, the amplitude data of the ecological product corresponding to the topological node with which the topological node corresponding to the mth ecological product has a topological edge is used as the first input data; The first input data is normalized and input into the first verification model to obtain the output data of the first verification model; if the difference between the output data of the first verification model and the first data to be matched is less than or equal to the preset first matching threshold, the match is successful and the first verification is obtained, otherwise the match fails.

7. A data acquisition and analysis system for watershed ecological product monitoring according to claim 6, characterized in that: The second verification includes: In the target time period, the amplitude data of the common ecological products in the kth sub-region are normalized and used as the second data to be matched; In the second topological network, the amplitude data of the sub-region corresponding to the topological node having the topological edge established with the topological node corresponding to the k-th sub-region is obtained as the second input data; The second input data is normalized and input into the second verification model to obtain the output data of the second verification model; if the Euclidean distance between the output data of the second verification model and the second data to be matched is less than or equal to the preset second matching threshold, the match is successful and the second verification is obtained, otherwise the match fails.

8. The data collection and analysis system for watershed ecological product monitoring according to claim 7 is characterized in that: Results of human intervention in the target watershed, including: During the target time period, if the amplitude data of any ecological product in the target basin at any time passes the first verification, and the amplitude data of the common ecological product in any sub-area in the target basin at any time passes the second verification, then there is no human interference in the target basin; otherwise, there is human interference in the target basin.

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