Ecological system stability evaluation method based on multi-time scale coupling and application thereof
By employing a multi-timescale coupled ecosystem stability assessment method, and utilizing historical data of dynamic and steady-state parameters combined with a multi-factor weighted algorithm, a deep coupling and adaptive adjustment of ecosystem stability is achieved. This addresses the shortcomings of existing assessment methods and improves the accuracy of assessment results and monitoring efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-03
AI Technical Summary
Existing ecosystem assessment methods cannot effectively integrate information across multiple time scales and lack adaptive adjustment capabilities, resulting in biased assessment results or unreasonable resource allocation, making it difficult to accurately reflect the complex stability of ecosystems.
By acquiring historical data of dynamic and steady-state parameters, a multi-timescale coupled ecosystem stability assessment method is established. A multi-factor weighted algorithm is used to evaluate the stability index, and the sampling frequency of dynamic parameters is adjusted according to the newly added sampling data of steady-state parameters, thereby achieving deep coupling and adaptive adjustment of dynamic and steady-state parameters.
It enables sensitive responses to the immediate state of the ecosystem and reflects its long-term evolution, improves the accuracy and adaptability of assessment results, reduces monitoring costs, and enhances monitoring efficiency and effectiveness.
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Figure CN121787741A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ecosystem assessment, and more specifically to an ecosystem stability assessment method based on multi-timescale coupling and its application. Background Technology
[0002] Existing ecosystem assessment methods for evaluating ecosystem stability suffer from the following shortcomings: First, most methods rely on monitoring data at a single time scale or focus only on high-frequency, immediate parameters, leading to biased assessment results that fail to simultaneously capture the system's rapid disturbance response and long-term evolution trends. Second, sampling strategies are often fixed and cannot be adaptively adjusted according to the real-time state of the ecosystem, resulting in unreasonable allocation of monitoring resources or insufficient monitoring density at critical moments, thus missing risk warnings. Third, there is a lack of effective mechanisms to dynamically correlate and couple parameters across different time scales, making it difficult for assessment models to accurately reflect the complex stability state of ecosystems driven by multiple factors.
[0003] Therefore, there is an urgent need for an ecosystem stability assessment method that can integrate information from multiple time scales and has adaptive adjustment capabilities. Summary of the Invention
[0004] The purpose of this invention is to provide an ecosystem stability assessment method based on multi-timescale coupling and its application, which solves the problem that existing assessment methods are difficult to effectively utilize multi-timescale coupling data to assess ecosystem stability.
[0005] The present invention achieves the above objectives through the following technical solutions: Ecosystem stability assessment methods based on multi-timescale coupling and their applications include the following steps: S1. Obtain historical data of dynamic parameters and steady-state parameters associated with the dynamic parameters, wherein the relative rate of change of the dynamic parameters increases as the stability of the ecosystem weakens, and the relative rate of change of the steady-state parameters is lower than a set ratio. S2. Based on historical data with dynamic parameters and a multi-factor weighted algorithm, assess the ecosystem stability index for historical periods; S3. Establish a two-dimensional rectangular coordinate system based on time and ecosystem stability index. Divide the risk interval with the preset stability risk value as the boundary. Take the starting point of the risk interval as the reference time node, select key data from the historical data of steady-state parameters, and obtain the critical value based on the key data. S4. Obtain newly sampled data of steady-state parameters, and adjust the sampling frequency of dynamic parameters by comparing the newly sampled data of steady-state parameters with the critical value, and perform multi-time-scale coupled sampling of dynamic parameters. S5. Based on dynamic parameter data obtained from multi-timescale coupled sampling and multi-factor weighting algorithm, evaluate the real-time ecosystem stability index.
[0006] As a further optimization of the invention, when selecting key data, historical data of steady-state parameters outside the risk range and adjacent to the reference time node are taken as key data to be determined, and the key data to be determined are preprocessed to obtain key data.
[0007] As a further optimization of the invention, during the preprocessing process, outlier data in the undetermined key data is removed based on the standard deviation method to obtain the key data, and the mean of the key data is used as the critical value.
[0008] As a further optimization of the invention, during the multi-timescale coupled sampling process, when the newly added sampling data of the steady-state parameter reaches a critical value, the sampling frequency of the dynamic parameter is increased.
[0009] As a further optimization of the invention, the formula for the multi-factor weighting algorithm is as follows: in, It is the first One dynamic parameter in Sampling data at any given time, It is the first A health threshold for a dynamic parameter It is the first Risk threshold for each dynamic parameter It is the first The maximum value setting for each dynamic parameter. It is an ecosystem stability index. It is a dynamic factor corresponding to the dynamic parameter. For the first When a dynamic parameter is between a health threshold and a risk threshold Numerical value for The maximum value, It is the first The weights of each dynamic factor.
[0010] As a further optimization of the invention, in the process of evaluating the real-time ecosystem stability index, when the real-time ecosystem stability index rises to the stability risk value, the key data of the newly added steady-state parameters are obtained, the critical value is updated based on the key data of the newly added steady-state parameters, and steps S4 and S5 are repeated after the real-time ecosystem stability index falls below the stability risk value.
[0011] As a further optimization of the invention, the newly added steady-state parameter key data is divided into supplementary sampling data and near-risk sampling data. When the real-time ecosystem stability index rises to the stability risk value, the steady-state parameter is supplemented by sampling to obtain the supplementary sampling data. The steady-state parameter newly added sampling data before the critical value is updated is the near-risk sampling data.
[0012] As a further optimization of the invention, the formula for calculating the critical value is as follows: in, For the first The first steady-state parameter A key data point, For the first The number of key data points for each steady-state parameter This represents the number of times the critical value has been updated. For the first The steady-state parameter is the first The critical value for the next update. For the first Historical critical values of a steady-state parameter For the first The steady-state parameter is the first The next update of supplementary sampling data, For the first The steady-state parameter is the first The latest update of near-risk sampling data , and They are respectively , and The weight.
[0013] An application of the aforementioned assessment method in eutrophic lakes or reservoirs.
[0014] As a further optimization of the invention, the dynamic parameters include the change in dissolved oxygen, Changes in cyanobacteria concentration; steady-state parameters include sediment oxygen consumption rate, total alkalinity of water, and available phosphorus in sediments.
[0015] The beneficial effects of this invention are as follows: 1) This invention defines dynamic parameters and steady-state parameters, and establishes a stability index assessment model based on historical data of dynamic parameters. It performs correlation analysis on dynamic parameters that reflect short-term rapid changes and steady-state parameters that reflect long-term cumulative characteristics within a unified framework. By using the critical value of steady-state parameters to adjust the sampling frequency of dynamic parameters, it achieves deep coupling between high-frequency sampling signals and low-frequency sampling signals in the assessment process. This enables the assessment results to not only sensitively respond to the immediate state of the ecosystem, but also reflect its long-term evolution background. 2) This invention dynamically adjusts the sampling frequency of dynamic parameters by comparing the newly added sampling data of steady-state parameters with their critical values. This trigger-based sampling mechanism changes the traditional fixed-frequency monitoring mode, enabling monitoring resources to focus on critical periods when the stability of the ecosystem may change significantly. While ensuring the timeliness of early warning, it reduces the monitoring cost under normal circumstances and maximizes the monitoring efficiency and benefits. 3) When the stability index reaches a risk value in real-time assessment, this invention supplements the steady-state parameters by sampling and updates the original critical value using the supplementary data and nearby risk data. This allows the assessment model to self-correct and optimize as the ecosystem evolves, avoiding assessment bias caused by using fixed thresholds and improving the adaptability of the method to different environments and periods and the long-term reliability of the assessment results. Attached Figure Description
[0016] Figure 1 This is a flowchart of the ecosystem stability assessment method of the present invention. Detailed Implementation
[0017] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.
[0018] Example like Figure 1 As shown, this embodiment relates to an ecosystem stability assessment method based on multi-timescale coupling, which includes the following steps: Step S1: Obtain historical data of dynamic parameters and associated steady-state parameters. The relative rate of change of dynamic parameters increases as ecosystem stability weakens, while the relative rate of change of steady-state parameters is lower than a set ratio. In this embodiment, the relative rate of change is the monthly relative rate of change, and the set ratio is preferably... In other embodiments, the relative rate of change may also be a quarterly relative rate of change or an annual relative rate of change, and the set ratio may be adjusted accordingly based on the actual application scenario.
[0019] Step S2: Based on historical data of dynamic parameters and a multi-factor weighted algorithm, assess the ecosystem stability index for historical periods. The formula for the multi-factor weighted algorithm is as follows: in, It is the first One dynamic parameter in Sampling data at any given time, It is the first A health threshold for a dynamic parameter It is the first Risk threshold for each dynamic parameter It is the first The maximum value setting for each dynamic parameter. It is an ecosystem stability index. It is a dynamic factor corresponding to the dynamic parameter. For the first When a dynamic parameter is between the health threshold and the risk threshold of that dynamic parameter Numerical value for The maximum value, It is the first The weights of each dynamic factor. In this embodiment... , .
[0020] Step S3, based on time and ecosystem stability index Establish a two-dimensional rectangular coordinate system with a preset stability risk value. The risk zone is defined by a boundary, and the ecosystem stability index within that risk zone is determined accordingly. The value should not be less than the stability risk value. Historical data of each steady-state parameter are mapped to this two-dimensional Cartesian coordinate system based on their corresponding sampling time. Using the start point of the risk interval as a reference time node, key data is selected from the historical data of the steady-state parameters, and critical values are obtained based on this key data. When selecting key data, undetermined key data is obtained first. Historical data of steady-state parameters outside the risk interval and immediately adjacent to the reference time node on the time axis are used as undetermined key data. Then, the undetermined key data is preprocessed to obtain the key data. Alternatively, in some other embodiments, undetermined key data can be obtained without mapping the historical data of the steady-state parameters; instead, it can be obtained solely through the sampling time of the steady-state parameters, i.e., historical data of steady-state parameters whose sampling time is outside the risk interval and immediately adjacent to the reference time node are used as undetermined key data.
[0021] During the preprocessing of the key data to be determined, the standard deviation method is used to remove outlier data outside the set range, and the mean of the preprocessed key data is used as the critical value. The upper and lower thresholds of the set range are as follows: in, The upper limit threshold, The lower threshold is... For setting value, It is a constant. The standard deviation is the set value, which is the mean of the key data points to be determined, and is a constant. and standard deviation The specific value can be adjusted according to the type of dynamic parameter. Taking the change in dissolved oxygen as an example, its constant is... and standard deviation The possible values are as follows: , mg / L.
[0022] Step S4: Obtain newly sampled data for steady-state parameters. By comparing the newly sampled data with the critical value, adjust the sampling frequency of dynamic parameters and perform multi-timescale coupled sampling of dynamic parameters. During multi-timescale coupled sampling, when the newly sampled data for steady-state parameters reaches the critical value, increase the sampling frequency of dynamic parameters. When ignoring the supplementary sampling in step S5, the newly sampled data for steady-state parameters is periodically sampled at a fixed sampling frequency.
[0023] Step S5: Based on the dynamic parameter data obtained from multi-timescale coupled sampling and the multi-factor weighted algorithm, evaluate the real-time ecosystem stability index.
[0024] Furthermore, in step S5, the critical value can be updated. The update process is as follows: During the evaluation of the real-time ecosystem stability index, when the real-time ecosystem stability index rises to the stability risk value, the key data of the newly added steady-state parameter is obtained, the critical value is updated based on the key data of the newly added steady-state parameter, and steps S4 and S5 are repeated after the real-time ecosystem stability index falls below the stability risk value.
[0025] Specifically, the key data for newly added steady-state parameters are divided into supplementary sampling data and near-risk sampling data. Supplementary sampling of steady-state parameters is conducted when the real-time ecosystem stability index rises to a stability risk value, obtaining supplementary sampling data. The latest steady-state parameter sampling data before updating the critical value is considered near-risk sampling data. Specifically, near-risk sampling data is the most recent steady-state parameter sampling data before updating the critical value. Additionally, during the critical value update process, an alarm is triggered to remind sampling personnel to conduct supplementary sampling; or a warning message is sent to the mobile terminal carried by the sampling personnel via wireless communication equipment to remind them to conduct supplementary sampling.
[0026] The formula for calculating the critical value is as follows: in, For the first The first steady-state parameter A key data point, For the first The number of key data points for each steady-state parameter may be the same or different for different steady-state parameters. This represents the number of times the critical value has been updated. For the first The steady-state parameter is the first The critical value for the next update. For the first The historical critical value of the nth steady-state parameter, i.e. the nth The steady-state parameter is the first The critical value for the next update. For the first The steady-state parameter is the first The next update of supplementary sampling data, For the first The steady-state parameter is the first The latest update of near-risk sampling data , and They are respectively , and The weights. The number of times the critical value is updated before the first critical value update in multi-timescale coupled sampling. The value is zero. Furthermore, in some other embodiments, the critical value may not be updated; instead, it may be calculated using only historical data.
[0027] Next, taking the application of the above assessment method in eutrophic shallow lakes as an example, the specific assessment process will be described in detail. In this application scenario, in step S1, the selected dynamic parameters include the change in dissolved oxygen, Changes in dissolved oxygen and concentration of cyanobacteria. Steady-state parameters include the sediment oxygen consumption rate corresponding to changes in dissolved oxygen, and... The change corresponds to the total alkalinity of the water body, and the effective phosphorus in the sediment corresponds to the concentration of blue-green algae.
[0028] In step S2, the specific formula for the multi-factor weighting algorithm is as follows: in, It is dissolved oxygen factor. yes factor, It is a concentration factor for blue-green algae. , and They are , and The weights. In this embodiment... , , .
[0029] in, It is the change in dissolved oxygen. It is the health threshold for changes in dissolved oxygen. It is the danger threshold for changes in dissolved oxygen. This is the maximum set value for the change in dissolved oxygen. In this embodiment, The value represents the maximum change in dissolved oxygen in historical data. mg / L, mg / L.
[0030] in, yes Change yes Health threshold for changes in quantity yes The danger threshold of the change yes The maximum value is set for the amount of change. In this embodiment, The value is from historical data The maximum value of the change , .
[0031] in, It is the concentration of blue-green algae. It is the healthy threshold for the concentration of cyanobacteria. This is the dangerous threshold for cyanobacteria concentration. This is the set maximum value for the concentration of cyanobacteria. In this embodiment, The value represents the maximum concentration of cyanobacteria in historical data. μg / L, μg / L.
[0032] In step S3, time The horizontal axis represents the ecosystem stability index. Establish a two-dimensional rectangular coordinate system with the vertical axis as the ordinate. Assign stability risk values... Set as This divides the coordinate system into multiple risk intervals, each of which satisfies... Then, following step S3 above, key data are selected, and the critical values of each steady-state parameter are obtained. This is the time to proceed. for The steady-state parameters set in this embodiment include sediment oxygen consumption rate, total alkalinity of the water body, and available phosphorus in the sediment. These steady-state parameters correspond to changes in dissolved oxygen, etc. The three dynamic parameters are the change in concentration and the concentration of cyanobacteria. In other embodiments, a single dynamic parameter can also correspond to multiple steady-state parameters. For example, the steady-state parameters corresponding to the concentration of cyanobacteria could be the available phosphorus in the sediment and the proportion of large cladocerans. When the available phosphorus in the sediment exceeds a critical value or the proportion of large cladocerans exceeds a critical value, the sampling frequency of the cyanobacteria concentration is increased.
[0033] In step S4, sampling continues for both steady-state and dynamic parameters, acquiring newly added sampling data for both steady-state and dynamic parameters. When the newly added sampling data for one of the steady-state parameters reaches a critical value for that parameter, the sampling frequency of each dynamic parameter is increased. For example, the sampling frequency is adjusted from once per day to once every 30 minutes. Alternatively, in other embodiments, the sampling frequency may be increased only for the dynamic parameter corresponding to that steady-state parameter. Then, step S5 is followed to evaluate the real-time ecosystem stability index and update the critical values for each steady-state parameter.
[0034] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.
Claims
1. An ecosystem stability assessment method based on multi-timescale coupling, characterized in that, Includes the following steps: S1. Obtain historical data of dynamic parameters and steady-state parameters associated with the dynamic parameters, wherein the relative rate of change of the dynamic parameters increases as the stability of the ecosystem weakens, and the relative rate of change of the steady-state parameters is lower than a set ratio. S2. Based on historical data with dynamic parameters and a multi-factor weighted algorithm, assess the ecosystem stability index for historical periods; S3. Establish a two-dimensional rectangular coordinate system based on time and ecosystem stability index. Divide the risk interval with the preset stability risk value as the boundary. Take the starting point of the risk interval as the reference time node, select key data from the historical data of steady-state parameters, and obtain the critical value based on the key data. S4. Obtain newly sampled data of steady-state parameters, and adjust the sampling frequency of dynamic parameters by comparing the newly sampled data of steady-state parameters with the critical value, and perform multi-time-scale coupled sampling of dynamic parameters. S5. Based on dynamic parameter data obtained from multi-timescale coupled sampling and multi-factor weighting algorithm, evaluate the real-time ecosystem stability index.
2. The ecosystem stability assessment method based on multi-timescale coupling according to claim 1, characterized in that: When selecting key data, historical data of steady-state parameters outside the risk range and immediately adjacent to the reference time node are taken as undetermined key data. The undetermined key data are preprocessed to obtain the key data.
3. The ecosystem stability assessment method based on multi-timescale coupling according to claim 1, characterized in that: During the preprocessing process, outlier data in the undetermined key data is removed based on the standard deviation method to obtain the key data, and the mean of the key data is used as the critical value.
4. The ecosystem stability assessment method based on multi-timescale coupling according to claim 1, characterized in that: In the multi-timescale coupled sampling process, when the newly added sampling data of the steady-state parameter reaches the critical value, the sampling frequency of the dynamic parameter is increased.
5. The ecosystem stability assessment method based on multi-timescale coupling according to claim 1, characterized in that, The formula for the multi-factor weighting algorithm is as follows: in, It is the first One dynamic parameter in Sampling data at time, It is the first A health threshold for a dynamic parameter It is the first Risk threshold for each dynamic parameter It is the first The maximum value setting for each dynamic parameter. It is an ecosystem stability index. It is the dynamic factor corresponding to the dynamic parameter. For the first When a dynamic parameter is between a health threshold and a risk threshold Numerical value for The maximum value, It is the first The weights of each dynamic factor.
6. The ecosystem stability assessment method based on multi-timescale coupling according to claim 1, characterized in that: During the evaluation of the real-time ecosystem stability index, when the real-time ecosystem stability index rises to the stability risk value, the key data of the newly added steady-state parameters are obtained, the critical value is updated based on the key data of the newly added steady-state parameters, and steps S4 and S5 are repeated after the real-time ecosystem stability index falls below the stability risk value.
7. The ecosystem stability assessment method based on multi-timescale coupling according to claim 6, characterized in that: The newly added steady-state parameter key data is divided into supplementary sampling data and near-risk sampling data. When the real-time ecosystem stability index rises to the stability risk value, the steady-state parameter is supplemented by sampling to obtain the supplementary sampling data. The steady-state parameter newly added sampling data before the critical value is updated is the near-risk sampling data.
8. The ecosystem stability assessment method based on multi-timescale coupling according to claim 7, characterized in that: The formula for calculating the critical value is as follows: in, For the first The first steady-state parameter A key data point, For the first The number of key data points for each steady-state parameter This represents the number of times the critical value has been updated. For the first The steady-state parameter is the first The critical value for the next update. For the first Historical critical values of a steady-state parameter For the first The steady-state parameter is the first The next update of supplementary sampling data, For the first The steady-state parameter is the first The latest update of near-risk sampling data , and They are respectively , and The weight.
9. The application of an assessment method as described in any one of claims 1-8 in eutrophic lakes or reservoirs.
10. The application according to claim 9, characterized in that: Dynamic parameters include changes in dissolved oxygen, Variations and concentrations of blue-green algae; steady-state parameters include sediment oxygen consumption rate, total alkalinity of the water body, and available phosphorus in the sediment.