CN-121981408-A - Intelligent decision method for river and lake health recovery scheme based on progressive ecological restoration theory

CN121981408ACN 121981408 ACN121981408 ACN 121981408ACN-121981408-A

Abstract

The invention discloses an intelligent decision method of a river and lake health recovery scheme based on a progressive ecological restoration theory, which belongs to the field of environmental management, and comprises the steps of deconstructing a target steady state in a river and lake ecological system into a plurality of ecological parameter equilibrium states, presetting corresponding response protocols for each ecological parameter equilibrium state, and forming an ecological response protocol cluster by the set of all the response protocols; according to the real-time state of the river and lake ecological system, a protocol execution sequence is activated and arranged from the ecological response protocol cluster, an elastic failure boundary model is built for each ecological parameter equilibrium state, the elastic failure boundary model is a dynamic state envelope surface, the accurate identification of toughness challenge events by building the dynamic elastic failure boundary model and combining a disturbance memory energy field can be realized, the limitation of a fixed repair scheme is solved, the complex scene of the river and lake ecological system, which is subjected to dynamic interference such as climate change and human activity, is effectively adapted, and the anti-interference capability of the repair scheme is improved.

Inventors

ZHONG QIJUN
XIE TINGTING
SU YUPING
ZHU JUNFAN
ZHU CAIHUA
ZHOU YUANYUAN
HUANG SHIYU

Assignees

福建省工大规划设计院有限公司
远勤建设集团有限公司
福建师范大学

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (10)

1. An intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory is characterized by comprising the following steps: step 1, constructing a target steady state solution in a river and lake ecosystem into a plurality of ecological parameter equilibrium states, presetting corresponding response protocols for each ecological parameter equilibrium state, and forming an ecological response protocol cluster by the set of all the response protocols; step 2, an elastic failure boundary model is built for each ecological parameter equilibrium state, wherein the elastic failure boundary model is a dynamic state envelope surface, the position and the motion track of the ecological parameter in the state envelope surface are monitored, when the motion track touches at a preset angle and acceleration and attempts to cross the current elastic failure boundary, the motion track is judged to be a toughness challenge event, and the activation process of a corresponding protocol sub-cluster in an ecological response protocol cluster is triggered; And 3, after a plurality of response protocols are triggered, synthesizing a simulated disturbance time sequence of a plurality of ecological parameter equilibrium states in a future preset period based on disturbance modes expressed by disturbance memory energy places, enabling all triggered candidate protocols and combinations thereof to be in parallel virtual river and lake models, synchronously executing pressure test of the disturbance time sequence, evaluating offset distances between ecological parameter tracks and failure boundaries of all candidate protocols or combinations under all disturbance time sequences, and selecting execution rights based on evaluation results.
2. The intelligent decision method for a river and lake health recovery scheme based on the progressive ecological restoration theory according to claim 1, further comprising: step 4, tracking the actual execution process and result of the agreement combination for obtaining the execution right, and accounting the elastic capital consumed by the response and the ecological technical liabilities generated, so as to generate an accounting result comprising instant benefits, elastic consumption and technical liabilities; step 5, dynamically correcting the elastic failure boundary model according to the accounting result; and 6, periodically integrating the execution records of the steps 1 to 5 to generate a toughness evolution map, generating a new response protocol according to the toughness evolution map, and adding the new response protocol to the ecological response protocol cluster.
3. The intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory according to claim 1, wherein the method for constructing the target steady state solution in the river and lake ecosystem into a plurality of ecological parameter equilibrium states comprises the following steps: Step 11, defining a complex of a target steady state consisting of a structural steady state, a process steady state and a functional steady state; step 12, based on the structure steady state, the process steady state and the function steady state, performing control factor mapping on each dimension, and identifying the ecological parameters corresponding to each dimension; And step 13, defining the condition state domain of the ecological parameter under a plurality of different reference scenes.
4. The intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory according to claim 3, wherein the constructing an elastic failure boundary model for each ecological parameter equilibrium state comprises: Step 21, extracting disturbance marks from the time sequence of each physiological parameter, identifying deviation events exceeding the condition state domain, and quantifying the deviation amplitude, duration and recovery rate of each deviation event; step 22, based on the disturbance memory energy field, the seasonal phase signal and the real-time state of the related ecological parameter, performing multi-factor coupling operation to calculate a dynamic boundary base value; And step 23, carrying out elastic bandwidth modulation according to the states of the disturbance memory energy field and the associated ecological parameters by taking the dynamic boundary base value as a reference, and generating an elastic failure boundary defined by the dynamic boundary base value and the dynamic elastic bandwidth.
5. The intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory of claim 4, wherein the decision of the toughness challenge event comprises the following steps: step 24, tracking the motion of the ecological parameter in the state envelope surface defined by the elastic failure boundary in real time, and continuously calculating a track dynamics vector, wherein the track dynamics vector comprises a pointing component, an approach component and an inertia component; step 25, carrying out joint analysis on the dynamic characteristics of the track dynamics vector and the current elastic failure boundary, executing crossing risk prediction, and calculating an instantaneous crossing risk index; And step 26, performing pattern recognition on the time sequence of the instantaneous crossing risk index, judging as a toughness challenge event when the instantaneous crossing risk index accords with a predefined pulse impact pattern or a continuous compression pattern, and activating a corresponding protocol sub-cluster from the ecological response protocol cluster to start a competition flow.
6. The intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory according to claim 1 or 5, wherein the synthesizing of the simulated disturbance time sequence of the equilibrium state of a plurality of ecological parameters in a preset future period comprises the following steps: Step 31, analyzing historical disturbance data, executing disturbance event slicing and pattern primitive extraction, and constructing a disturbance pattern primitive library containing three dimensions of intensity envelope curves, frequency spectrum characteristics and covariant relations; Step 32, generating primitive selection and combination rules according to the current disturbance memory energy field, the elastic failure boundary and the toughness challenge event mode; and step 33, synthesizing a plurality of future disturbance time sequences by adopting a path-dependent Monte Carlo method according to the combination rule.
7. The intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory according to claim 6, wherein the performing of the pressure test of the disturbance sequence and evaluating the offset distance between the ecological parameter trace and the failure boundary under each disturbance sequence of each candidate protocol or combination comprises: Step 34, creating independent virtual river and lake model copies for each triggered candidate protocol, taking the state containing the ecological parameter real-time value, the elastic failure boundary and the disturbance memory energy field as a reference snapshot, and inputting a protocol preloading influence field according to the action mechanism of the candidate protocol; step 35, applying future disturbance time sequences in parallel in each virtual river and lake model copy, enabling the protocol to dynamically respond according to conditions and action modes, and recording the evolution track of ecological parameters; Step 36, calculating an offset distance spectrum according to the evolution track and the dynamic elastic failure boundary at the corresponding moment, wherein the offset distance spectrum comprises a minimum offset distance, an offset danger duration and an offset trend integral.
8. The method for intelligent decision-making of a river and lake health recovery scheme based on progressive ecological restoration theory according to claim 2, wherein tracking actual execution and results of the combination of agreements to obtain execution rights, accounting for elastic capital consumed in response and ecological technical liabilities generated, comprises: step 41, synchronously recording actual observation tracks of ecological parameters during and after protocol combination execution, generating synchronous natural fluctuation baseline prediction tracks, and obtaining an intervention net effect through comparison; step 42, calculating state maintenance energy and process acceleration energy according to the intervention net effect, and summarizing to obtain elastic capital consumption; Step 43, after the agreement execution is finished, comparing the actual recovery track and the expected natural recovery track of the parameters, identifying hysteresis distortion and calculating an ecological technology liability value; Step 44, generating a three-dimensional accounting table recording the calculated immediate benefit, elastic capital consumption, and ecological liability values based on the amount of improvement in the elastic failure boundary of the challenged ecological parameter trajectory in the toughness challenge event after the agreement is executed.
9. The intelligent decision method for the river and lake health recovery scheme based on the progressive ecological restoration theory according to claim 8, wherein the dynamic correction of the elastic failure boundary model according to the accounting result comprises the following steps: step 51, analyzing a three-dimensional accounting table, establishing an association mapping of protocol types and weak points, and determining association of the weak points and elastic failure boundaries; step 52, extracting cost efficiency ratio, liability hysteresis index and associated parameter sensitivity change from the three-dimensional accounting table as quantitative influence factors based on the associated mapping; And step 53, correcting the modulation coefficient used for generating the dynamic elastic bandwidth, the decay time constant of the disturbance memory energy field and the weight of the multi-factor coupling rule in the elastic failure boundary model according to the quantized influence factor.
10. The method for intelligently deciding a river and lake health recovery scheme based on a progressive ecological restoration theory according to claim 2 or 8, wherein generating a toughness evolution map, generating a new response protocol according to the toughness evolution map and adding the new response protocol to an ecological response protocol cluster comprises: Step 61, periodically extracting the execution records of the steps 1 to 5, carrying out time axis alignment and state space reconstruction, identifying turning points, and carrying out mode extraction on track segments among the turning points to generate a structural toughness evolution knowledge network; Step 62, based on the structured toughness evolution knowledge network, performing pattern diagnosis and root cause association analysis to obtain a deteriorated track pattern and root cause assumption; step 63, generating a strategy level protocol for the deteriorated track mode and root cause hypothesis through reverse design and inputting an ecological response protocol cluster.

Description

Intelligent decision method for river and lake health recovery scheme based on progressive ecological restoration theory Technical Field The invention relates to the field of environmental management, in particular to an intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory. Background In the actual ecological restoration work of rivers and lakes, the restoration process is often full of uncertainty due to external interference such as climate change and human activity, and the current mainstream restoration method is mostly implemented by pushing according to a preset fixed scheme, but the period of the river and lake restoration engineering is usually long, short and long for decades, in the process, once the environmental disturbance such as sudden change of climate pattern and sudden pollution is encountered, the originally designed scheme is often difficult to effectively cope, and finally the preset restoration path gradually deviates from an expected target in actual implementation and even completely fails, so that the obvious defect of the fixed scheme in coping with the dynamically-changed environment is fully exposed. If the method is advanced strictly according to a preset progressive treatment and repair plan, the final treatment and repair effect can be influenced due to the occurrence of any unexpected situation in the middle, so that the scheme is gradually invalidated in the actual advancement, the conventional decision method excessively pursues the optimal solution of the single-stage repair scheme, the one-sided optimization guidance can weaken the overall toughness of the repair system for dealing with continuous uncertainty interference, the requirements of progressive repair work on multi-stage advancement and dynamic adjustment cannot be adapted, and currently, the intelligent decision method of the ecological repair scheme of the river and lake has obvious defects in the aspects of uncertainty management and control, long-term effect evaluation feedback, dynamic adaptation capability and the like, and the problems influence the effect of the health recovery of the river and lake and also influence sustainable advancement of the repair work. Disclosure of Invention Aiming at the problems existing in the prior art, the invention aims to provide an intelligent decision method of a river and lake health recovery scheme based on a progressive ecological restoration theory, which can accurately identify toughness challenge events by constructing a dynamic elastic failure boundary model and combining a disturbance memory energy field, solve the limitation of a fixed restoration scheme, effectively adapt to complex scenes of dynamic disturbance of a river and lake ecological system due to climate change, human activities and the like, and improve the anti-interference capability of the restoration scheme. In order to solve the problems, the invention adopts the following technical scheme: an intelligent decision method for a river and lake health recovery scheme based on a progressive ecological restoration theory comprises the following steps: step 1, constructing a target steady state solution in a river and lake ecosystem into a plurality of ecological parameter equilibrium states, presetting corresponding response protocols for each ecological parameter equilibrium state, and forming an ecological response protocol cluster by the set of all the response protocols; step 2, an elastic failure boundary model is built for each ecological parameter equilibrium state, wherein the elastic failure boundary model is a dynamic state envelope surface, the position and the motion track of the ecological parameter in the state envelope surface are monitored, when the motion track touches at a preset angle and acceleration and attempts to cross the current elastic failure boundary, the motion track is judged to be a toughness challenge event, and the activation process of a corresponding protocol sub-cluster in an ecological response protocol cluster is triggered; And 3, after a plurality of response protocols are triggered, synthesizing a simulated disturbance time sequence of a plurality of ecological parameter equilibrium states in a future preset period based on disturbance modes expressed by disturbance memory energy places, enabling all triggered candidate protocols and combinations thereof to be in parallel virtual river and lake models, synchronously executing pressure test of the disturbance time sequence, evaluating offset distances between ecological parameter tracks and failure boundaries of all candidate protocols or combinations under all disturbance time sequences, and selecting execution rights based on evaluation results. Further, the method further comprises the following steps: step 4, tracking the actual execution process and result of the agreement combination for obtaining the execution right, and accounting