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CN-121998405-A - Digital-twinning-fused river and lake healthy phase space diagnosis early warning regulation method

CN121998405ACN 121998405 ACN121998405 ACN 121998405ACN-121998405-A

Abstract

The invention relates to the technical field of intelligent management of river and lake ecosystems and discloses a digital twinning-fused river and lake healthy phase space diagnosis and early warning regulation method, which comprises the steps of constructing a five-dimensional dynamic healthy phase space of river and lake health, and generating an elastic healthy reference track based on a trans-scale coupling mechanism model; the method comprises the steps of obtaining a real state track based on multi-source data fusion, carrying out multi-source tracing diagnosis by calculating a dynamic weight deviation vector between the real state track and an elastic health reference track, constructing a multi-scale trend evolution matrix based on high-order dynamic characteristics of the dynamic weight deviation vector and carrying out stability analysis to realize multi-level risk early warning, calculating a restoring force vector based on the dynamic weight deviation vector through self-adaptive PID control, generating an accurate regulation and control instruction through robust mapping, and issuing and executing. The invention breaks through the limitations of staticizing, fracturing and lagging in the prior art.

Inventors

  • LI ZHIYONG
  • Feng Taoli
  • YE WEN

Assignees

  • 杭州禹川信息科技有限公司

Dates

Publication Date
20260508
Application Date
20251217

Claims (10)

  1. 1. A digital-twinned river and lake healthy phase space diagnosis and early warning regulation method is characterized by comprising the following steps: constructing a five-dimensional dynamic health phase space of river and lake health, and generating an elastic health reference track based on a cross-scale coupling mechanism model; Acquiring a real state track based on multi-source data fusion, and performing multi-source tracing diagnosis by calculating a dynamic weight deviation vector between the real state track and an elastic health reference track; based on the high-order dynamic characteristics of the dynamic weight deviation vector, constructing a multi-scale trend evolution matrix and carrying out stability analysis to realize multi-level risk early warning; and calculating a restoring force vector through self-adaptive PID control based on the dynamic weight deviation vector, generating a precise regulation and control instruction through robust mapping, and issuing and executing.
  2. 2. The digital twinning-fused river and lake healthy phase space diagnosis and early warning regulation method according to claim 1, wherein when constructing a five-dimensional dynamic healthy phase space of river and lake health and generating an elastic healthy reference track based on a trans-scale coupling mechanism model, the method comprises the following steps: Defining a state vector of a five-dimensional healthy phase space; constructing a trans-scale physical-ecological-socioeconomic coupling mechanism model as a digital twin; inputting climate adaptation type ideal driving conditions into the trans-scale physical-ecological-socioeconomic coupling mechanism model, simulating and generating an elastic health reference track, and defining a dynamic elastic track pipeline around the elastic health reference track based on a system restoring force vector.
  3. 3. The digital-twinned river and lake healthy phase space diagnosis and early warning regulation method is characterized in that, The dimension of the five-dimensional dynamic healthy phase space comprises hydrologic variable X 1 , water level, flow rate, hydraulic retention time and flood season frequency, water quality variable X 2 , water temperature, pH, dissolved oxygen, permanganate index, total phosphorus, total nitrogen, ammonia nitrogen and heavy metal concentration, aquatic ecological variable X 3 , chlorophyll a, zooplankton density, submerged plant coverage, fish biomass index and benthonic biological diversity index, socioeconomic variable X 4 , water taking strength, sewage treatment rate, ecological buffer zone coverage r and chemical fertilizer application strength, climate change variable X 5 , extreme rainfall frequency, annual average temperature change rate and evaporation amount; The state vector is: ; Wherein X (t, τ) represents a variable with time t and hydrologic cycle factor τ, t represents time, τ represents hydrologic cycle factor, τ=1 represents flood season, and τ=0.5 represents dry season; Normalization of X (t, τ) yields X i ' (t, τ).
  4. 4. The digital twinning-fused river and lake healthy phase space diagnosis and early warning regulation method according to claim 2, wherein the trans-scale physical-ecological-socioeconomic coupling mechanism model comprises an integrated hydrodynamic sub-model, a sediment-water interface substance exchange sub-model, an aquatic ecological dynamics sub-model, a socioeconomic driving sub-model and a climate change response sub-model.
  5. 5. The digital-twinned river and lake health phase space diagnosis and early warning regulation method is characterized in that the method is based on multi-source data fusion to obtain a real state track, and the method comprises the following steps of: Fusing in-situ monitoring, remote sensing monitoring and socioeconomic statistical data, and carrying out data assimilation and quality control through robust Kalman filtering to construct a real state vector and an evolving real state track thereof; Calculating a health deviation vector of the real state track and the elastic health reference track in a phase space, and introducing a dynamic weight matrix to calculate a dynamic weight deviation degree; And carrying out projection and fuzzy membership calculation on the health deviation vector and a predefined typical pathology model base vector library, quantitatively identifying the contribution degree of a single or composite etiology, and analyzing the etiology propagation path through time sequence clustering.
  6. 6. The digital-twinned river and lake health phase space diagnosis and early warning regulation method of claim 5, wherein the health deviation vector is: ; In the above formula, D (T) represents a health deviation vector at time T, X R (T) represents a real state vector at time T, X H (T) represents an elastic health reference trajectory state vector at time T, D i (T) represents a deviation amount of an ith variable (i.e. a difference value between an actual value of the ith variable and a health reference value) at time T, n represents a total number of variables, and T represents a transpose of vectors (converting a row vector into a column vector).
  7. 7. The digital-twinned river and lake healthy phase space diagnosis and early warning regulation method according to claim 5, wherein the dynamic weight deviation degree is as follows: ; In the above formula, S D (t) represents the degree of deviation of the dynamic weights, w i (t, τ) represents the i-th dynamic weight matrix, and d i (t) represents the amount of deviation of the i-th variable; If S D (t) <0.1, the state of health is indicated, if 0.1 is less than or equal to S D (t) <0.3, the sub-health state is indicated, and if S D (t) <0.3, the pathological state is indicated.
  8. 8. The method for diagnosing and controlling the river and lake healthy phase space by fusion digital twin according to claim 5, wherein the method for constructing a multi-scale trend evolution matrix and analyzing stability based on the high-order dynamic characteristics of the dynamic weight deviation vector comprises the following steps: extracting a first derivative, a second derivative and a third derivative of the dynamic weight deviation vector to respectively serve as a deviation speed vector, a deviation acceleration vector and a deviation Jerk vector; constructing a multi-scale trend evolution matrix containing the dynamic weight deviation vector and derivatives thereof aiming at different spatial scales and time scales; Calculating the characteristic value of the multi-scale trend evolution matrix, judging the stability of the system under each scale according to the difference value of the characteristic value modular length and 1, dividing the early warning level according to the number and the degree of the unstability scale, and simultaneously evaluating the early warning confidence degree by combining a Bayesian method.
  9. 9. The method for diagnosing and controlling the river and lake healthy phase space by fusion digital twin according to claim 1, wherein the calculating the restoring force vector by the self-adaptive PID control based on the dynamic weight deviation vector and generating the accurate control instruction by the robust mapping comprises the following steps of: Calculating a restoring force vector by a self-adaptive PID control algorithm based on the dynamic weight deviation vector and the integral and derivative thereof; Establishing a robust mapping matrix between the restoring force vector and the regulating actuator vector, solving the robust mapping matrix by adopting a minimum and maximum criterion, generating a regulating instruction which is effective in an uncertainty range, and prioritizing regulating measures according to the component size of the restoring force vector.
  10. 10. The digital-twinned river and lake healthy phase space diagnosis and early warning regulation method of claim 9, wherein the regulation actuator vector comprises a plurality of reservoir ecological drainage flow, aerator power, medicament addition amount, ecological buffer zone irrigation amount, sewage treatment plant standard lifting and transformation strength, sediment dredging strength, submerged plant planting area and water taking limiting coefficient.

Description

Digital-twinning-fused river and lake healthy phase space diagnosis early warning regulation method Technical Field The invention relates to the technical field of intelligent management of river and lake ecosystems, in particular to a digital-twinning-fused river and lake healthy phase space diagnosis early warning regulation method. Background At present, river and lake health management mainly depends on three types of technical paradigms with fundamental defects, namely a monitoring and early warning system based on static indexes and threshold alarming, wherein the key problems are that the indexes are cut and cracked and response are lagged, nonlinear synergistic effects among multiple elements cannot be captured, the set of the rigidified threshold is easy to cause false alarm or omission, the alarm is usually sent out after the system is subjected to irreversible damage, and secondly, the system is based on a data-driven machine learning and probability model, the limitation of the static threshold is partially broken through, the 'black box' characteristic causes the decision process to lack of physical significance, the diagnosis result is difficult to guide accurate regulation and control, meanwhile, the coverage and quality of historical data are severely limited, extrapolation capability is poor when the system faces to the extreme situations which are not experienced, the probabilistic output is easy to delay decision time, and thirdly, the ecological optimal solution result is difficult to be 'mathematical optimal' but not 'optimal' and the ecological response is not sensitive to the initial parameter is ignored based on the regulation and control decision scheme of multi-objective optimization, and the ecological optimal solution is difficult to meet the requirements of emergency response management. In summary, the prior art generally has structural defects of "statics, cutting cracking and hysteresis", and cannot effectively cope with compound disturbance. Therefore, it is necessary to provide a digital-twinned river and lake healthy phase space diagnosis and early warning regulation method for solving the problems that the prior art does not break through the limitations of statics, fracture and hysteresis and cannot cope with the composite disturbance of climate change and human activity. Disclosure of Invention In view of the above, the invention provides a river and lake healthy phase space diagnosis and early warning regulation method integrating digital twinning, which aims to solve the problems that the prior art does not break through the limitations of statics, fracture and hysteresis and cannot cope with the composite disturbance of climate change and human activity. The invention provides a digital-twinned river and lake healthy phase space diagnosis and early warning regulation method, which comprises the following steps: constructing a five-dimensional dynamic health phase space of river and lake health, and generating an elastic health reference track based on a cross-scale coupling mechanism model; Acquiring a real state track based on multi-source data fusion, and performing multi-source tracing diagnosis by calculating a dynamic weight deviation vector between the real state track and an elastic health reference track; based on the high-order dynamic characteristics of the dynamic weight deviation vector, constructing a multi-scale trend evolution matrix and carrying out stability analysis to realize multi-level risk early warning; and calculating a restoring force vector through self-adaptive PID control based on the dynamic weight deviation vector, generating a precise regulation and control instruction through robust mapping, and issuing and executing. Further, when constructing a five-dimensional dynamic healthy phase space of river and lake health and generating an elastic healthy reference track based on a cross-scale coupling mechanism model, the method comprises the following steps: Defining a state vector of a five-dimensional healthy phase space; constructing a trans-scale physical-ecological-socioeconomic coupling mechanism model as a digital twin; inputting climate adaptation type ideal driving conditions into the trans-scale physical-ecological-socioeconomic coupling mechanism model, simulating and generating an elastic health reference track, and defining a dynamic elastic track pipeline around the elastic health reference track based on a system restoring force vector. Further, the dimensions of the five-dimensional dynamic healthy phase space comprise hydrologic variables X 1 including water level, flow rate, hydraulic retention time and flood season frequency, water quality variables X 2 including water temperature, pH, dissolved oxygen, permanganate index, total phosphorus, total nitrogen, ammonia nitrogen and heavy metal concentration, aquatic ecological variables X 3 including chlorophyll a, zooplankton density, submerged plant coverage, fish biomass in