A five-mode growth operation method and ecological restoration system based on a photo-thermal-electric unity
Patent Information
- Application Number
- CN202610652373.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-13
- Publication Date
- 2026-06-26
Abstract
Description
Technical Field
[0001] This invention relates to the field of ecosystem technology, specifically to a five-mode growth calculation method and an ecological restoration system based on the integration of photothermal and electrical processes. Background Technology
[0002] Traditional ecological research relies heavily on botanical observations, data statistics, and empirical judgments, lacking a unified physical computational framework. This results in vague ecological growth processes, chaotic structures, and unclear energy relationships, hindering precise, quantitative, and reproducible ecological restoration. Existing technologies suffer from the following main shortcomings: First, they cannot uniformly quantify and perform homogeneous calculations of light, heat, and electrical energy, leading to a fragmentation of energy input, accumulation, and conduction processes. Second, the growth status of ecosystems largely depends on statistical descriptions, lacking a complete physical computational system from individual plants to communities, and from dynamic to steady-state states. Third, the effectiveness of ecological restoration projects lacks objective and quantifiable criteria, relying heavily on subjective, empirical assessments. Summary of the Invention
[0003] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a five-mode growth calculation method and ecological restoration system based on the unified photothermal-electric system, in order to solve the problems mentioned in the background art.
[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an ecological restoration system based on a unified photothermal-electric energy system, characterized in that the system includes five sub-models that are sequentially connected and coupled to each other. The five sub-models are, respectively, a gene intrinsic layer sub-model, a temporal dynamic layer model, a vertical structure layer model, a holographic energy balance layer model, and a steady-state determination layer model. Intrinsic Layer Model of Genes The intrinsic gene layer model is used to detect the photothermal conductivity of plant genes. The plant absorbs heat energy Q and grows for t, and this is determined by a built-in formula. Calculate the growth volume V of a single plant; The intrinsic layer model of the gene includes a spectral detection module, a heat flux detection module, and a timing module; the gene's photothermal conductivity σ n The photosynthetic efficiency of the plant is determined by a spectral detection module, the heat energy Q absorbed by the plant is obtained by a heat flux detection module, and the growth time t is accumulated and recorded by a timing module.
[0005] As another implementation, the intrinsic gene layer model can also be constructed based on the thermoelectric properties of water. In this implementation, the intrinsic gene layer model is used to detect the thermal conductivity λ of plant genes. n Gene conductivity σ nThe data includes water absorption (m_w), ambient temperature (T), and growth time (t), and is expressed using the built-in formula V = (λ). n +σ n The calculation of single plant growth V is performed using the formula m_w*T*t; the intrinsic layer model of the gene includes a spectral detection module, a heat flux detection module, an electrical conductivity detection module, a moisture detection module, a temperature detection module, and a timing module; the gene thermal conductivity λ is... n With gene conductivity σ n Based on the plant species and their photosynthetic efficiency and electrophysiological characteristics, the water absorption m_w is obtained by a water detection module, and the ambient temperature T is obtained by a temperature detection module. The formula is based on the thermoelectric properties of water. As the temperature increases, water exhibits a decrease in resistance and an increase in thermoelectric signal. This characteristic directly determines the intensity of plant growth.
[0006] Temporal dynamic sub-model The temporal dynamic sub-model is connected to the intrinsic gene sub-model to detect the real-time variation curve of environmental light and heat supply Q(t) over time, combined with phenological rhythm coefficients. Through the built-in formula Calculate real-time growth potential The time-series dynamic sub-model includes an ambient light and heat sensor array, a phenological database module, and a first energy superposition calculation module; the phenological rhythm coefficient Based on the seasonal temperature changes and plant phenological data stored in the phenological database module, the first energy superposition calculation module automatically records the energy overflow value and transmits it to the vertical structure layer sub-model when it detects that the ambient light and heat supply Q(t) exceeds the plant absorption threshold, thereby realizing the first-level energy superposition balance regulation.
[0007] Vertical structure layer model The vertical structural layer sub-model is connected to the temporal dynamic layer sub-model to detect the growth potential distribution and trophic level transfer efficiency of the community canopy, sublayers, and lower layers, using built-in formulas. The overall structural efficiency Φ of the community is calculated. The vertical structural layer sub-model includes a lidar module, a multispectral camera module, a trophic level detection module, and a second energy superposition calculation module. The lidar module and the multispectral camera module work together to detect the vertical layering structure of the community and the growth potential of each layer. The trophic level detection module is used to detect the density of herbivorous and carnivorous organisms and calculate the transfer efficiency. When the second energy superposition calculation module detects that the growth potential of a certain layer deviates from a preset threshold, it automatically issues a vertical structure adjustment command to achieve second-level energy superposition balance control.
[0008] Holographic Energy Balance Layer Model The holographic energy balance sub-model is connected to the gene intrinsic sub-model and the vertical structure sub-model to detect the total solar thermal energy input within the region. And through the built-in formula The system performs energy conservation and balance verification. The holographic energy balance sub-model includes a solar radiation detection module, a global accumulation calculation module, and a third energy superposition calculation module. The solar radiation detection module is used to detect the total solar thermal energy input in the area, and the global accumulation calculation module is used to summarize the total growth volume of all individual plants in the area. The third energy superposition calculation module compares the values on both sides of the equation in real time. When the left side is greater than the right side, it is determined to be an energy surplus state and outputs a water and fertilizer supply control command. When the left side is less than the right side, it is determined to be an energy deficit state and outputs a planting density control command, thereby realizing the third-level energy superposition balance control.
[0009] Steady-state decision layer model The steady-state determination layer model is connected to the vertical structure layer model and the holographic energy balance layer model, and is used to implement a built-in formula. The model calculates the ecological restoration stability index S; the stability determination layer sub-model includes an interspecific coupling detection module, an interference intensity detection module, and a stability determination module; the interspecific coupling detection module calculates the interspecific coupling coefficient F based on species diversity detection data, and the interference intensity detection module is used to detect the external interference intensity I; the stability determination module outputs a restoration completion signal when S ≥ 0.95, and automatically locates the weak link that causes the S value to decrease when S < 0.95 and generates targeted restoration instructions.
[0010] Based on the above system, the present invention also provides a five-mode growth calculation method for an ecosystem based on a unified photothermal-electric energy system, characterized in that the above five sub-models are used to execute the following five sequentially progressive and mutually coupled calculation models: The intrinsic layer model of genes was used to detect the photothermal conductivity of plant genes. The formulas for plant heat energy absorption Q and growth time t are as follows: Calculate the growth volume V of a single plant; The ambient light and heat supply Q(t) is detected in real time using the aforementioned time-series dynamic sub-model, and combined with the phenological rhythm coefficient. Through formula Calculate the real-time growth potential G n (t); When the ambient light and heat supply Q(t) is detected to exceed the plant absorption threshold, the energy overflow value is automatically recorded and output to the vertical structure layer sub-model for energy distribution adjustment; The growth potential and trophic level transfer efficiency of the canopy, sub-layers, and lower layers of the community were detected using the aforementioned vertical structural layer model, and the results were obtained through formulas. The overall structural efficiency Φ of the computational community; when the growth potential of a certain layer deviates from the preset threshold, a vertical structure adjustment command is automatically issued to maintain the energy superposition balance; The total solar thermal energy input ∑Qtotal in the detection area is obtained using the holographic energy balance sub-model, and the result is expressed by the formula... It performs energy conservation balance verification; compares the values on both sides of the equation in real time; when the left side is greater than the right side, it is determined to be an energy surplus state and outputs water and fertilizer supply control instructions; when the left side is less than the right side, it is determined to be an energy deficit state and outputs planting density control instructions. Using the aforementioned steady-state determination layer sub-model, through the formula The system calculates the ecological restoration stability index S and determines that the ecosystem has reached the top natural ecological balance when S ≥ 0.95, outputting a restoration completion signal; when S < 0.95, it automatically locates weak links and generates targeted restoration instructions.
[0011] Furthermore, the system and method are applicable to the restoration of forest, wetland, desert, and grassland ecosystems.
[0012] As another implementation, the first model can also employ a computational method based on the thermoelectric properties of water: using the intrinsic gene layer model to detect the thermal conductivity λ of plant genes. n Gene conductivity σ n Based on the thermoelectric properties of water, the absorption capacity m_w, ambient temperature T, and growth time t are determined using the formula... V = (λ n +σ n The calculation of the growth V of a single plant is performed using the formula )*m_w*T*t.
[0013] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following beneficial effects: This invention achieves unified quantification and homogeneous computation of light, heat, and electrical energy, using light as energy input, heat as energy accumulation, and electricity as the computational conduction mechanism, thus establishing a complete closed-loop energy system.
[0014] This invention transforms ecology from statistical description to physical quantitative calculation. Through a five-mode progressive structure, it transforms the chaotic ecological structure into a clearly hierarchical quantitative model, achieving precise and calculable ecological growth.
[0015] This invention constructs a complete energy superposition ecological balance algorithm system through a time-series dynamic layer model, a vertical structure layer model, a holographic energy balance layer model, and a steady-state determination layer model. Each layer has independent detection and calculation functions, which can monitor the energy flow status in real time and automatically adjust to ensure the energy balance of the ecosystem.
[0016] The system and method of this invention correspond strictly, and the ecological restoration system with a five-mode architecture realizes closed-loop control of the entire link from detection and calculation to execution.
[0017] This invention proposes a quantifiable steady-state index S and its judgment criteria (S≥0.95), which transforms the abstract concept of ecological balance into a specific numerical indicator that can be directly used as an objective acceptance standard for ecological restoration projects. Detailed Implementation
[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Example 1: Restoration of Degraded Forest Ecosystems Ecological restoration was carried out in a degraded forest area using the ecological restoration system and method described in this invention. The specific implementation steps are as follows: Gene intrinsic layer model assignment The photosynthetic efficiency and heat absorption parameters of the main tree species—pine and oak—within the target area were determined using a spectral detection module and a heat flux detection module. The genetic photothermal conductivity σ of pine was measured. n =0.85, σ of oak n =0.78. The timing module records the cumulative growth time t=365 days. The intrinsic layer model of genes calculates the growth volume V of a single plant and stores it in the system database.
[0020] Temporal dynamic sub-modeling task An ambient light and heat sensor array monitors changes in ambient light and heat supply in real time. The average Q(t) is 450 W / m² in spring (March-May) and 680 W / m² in summer (June-August). The phenological database module outputs phenological rhythm coefficients based on seasonal temperatures, with μ values for spring. n (t)=0.9, summer μ n (t)=1.0. The first energy superposition operation module is set to G. n (t)=σ n *Q(t)*μ n (t) Calculate the real-time growth potential. When the strong sunlight and heat in summer cause Q(t) = 850 W / m² to exceed the light saturation point of pine trees (750 W / m²), the system automatically records the energy overflow value of 100 W / m² and transmits it to the vertical structure layer sub-model for energy distribution adjustment.
[0021] Vertical structure layer sub-model assignment The lidar module and multispectral camera module scanned the forest stand structure, detecting the canopy growth potential (Gcanopy = 0.52), sublayer (Gsub = 0.28), and lower layer (Glower = 0.20). The trophic level detection module detected herbivore density using an infrared camera trap, calculating αphyto = 0.35; and calculated αfood = 0.40 and αmeat = 0.45 through food web analysis. The second energy superposition calculation module calculated the community structure efficiency using the formula Φ = (0.4 × 0.52 + 0.3 × 0.28 + 0.3 × 0.20) × 0.35 × 0.40 × 0.45 = 0.208 × 0.063 ≈ 0.0131. Detection revealed that the lower layer growth potential (0.20) deviated from the preset threshold (0.30), and the second energy superposition calculation module automatically issued a vertical structure adjustment command to replant shade-tolerant shrubs.
[0022] Holographic Energy Balance Layer Modeling Work The solar radiation detection module obtains the total annual radiation of the region, ∑Q_total = 3.2 × 10¹² J. The global accumulation calculation module summarizes the total growth volume of all individual plants, ∑V = 2.8 × 10¹² J*m⁻³. The third energy superposition calculation module compares and finds that ∑Q_total > ∑V, indicating an energy surplus state with a surplus of 0.4 × 10¹² J. The system outputs a control command to increase the planting density of the middle layer vegetation by 10% to absorb the excess energy.
[0023] Steady-state determination layer sub-model assignment The interspecific coupling detection module calculated the interspecific coupling coefficient F=0.85 based on the species diversity detection results, and the disturbance intensity detection module detected the external disturbance intensity I=0.15 (including human trampling, wind disturbance, etc.). The steady-state determination module calculated S=V_total*Φ*F*e⁻ᴵ, and after normalization, S=0.82, which is lower than 0.95. The system automatically identified the weak link as Φ (community structure efficiency) being too low and generated targeted restoration instructions: add 300 shrubs / hectare and introduce herbivore predators to optimize the animal community structure. Two years later, the S value was measured again and increased to 0.97. The steady-state determination module output a "restoration completed" signal, and the ecological restoration project passed acceptance.
[0024] Example 2: Restoration of Degraded Wetland Ecosystems Ecological restoration was carried out in a degraded wetland (reed wetland) using the ecological restoration system and method described in this invention. The specific implementation steps are as follows: Gene intrinsic layer model assignment The photosynthetic efficiency and heat absorption parameters of the main plants in the target area—reed and cattail—were determined using a spectral detection module and a heat flux detection module. The genetic photothermal conductivity σ of reed was also measured. n =0.72, σ of cattail n=0.68. The timing module records the cumulative growth time t=180 days (growing season). The intrinsic layer model of genes calculates the growth volume V of a single plant and stores it in the system database.
[0025] Temporal dynamic sub-modeling task An array of ambient light and heat sensors monitors the wetland's ambient light and heat supply in real time. Due to heat absorption through evaporation from the wetland water, the average daytime heat output during summer is Q(t) = 520 W / m², lower than that of land at the same latitude. The phenological database module outputs phenological rhythm coefficients based on the wetland's hydrological rhythms, including μ during the reed jointing stage in June and July. n (t)=1.2, heading period in August-September μ n (t)=0.8. The first energy superposition operation module is set to G. n (t)=σ n *Q(t)*μ n (t) calculates the real-time growth potential. When Q(t) = 580 W / m² is detected in July, combined with μ n (t)=1.2, the real-time growth potential of reeds reaches 0.72×580×1.2=501.12, the system determines that it is in the rapid growth period, automatically records the energy superposition state and transmits it to the vertical structure layer sub-model.
[0026] Vertical structure layer sub-model assignment The lidar module and multispectral camera module scanned the vertical structure of wetland vegetation, detecting the growth potential of the emergent layer (canopy) Gemergent = 0.65, the floating leaf layer Gbuoyant = 0.25, and the submerged layer Gsinkant = 0.10. The trophic level detection module detected the wetland food chain: zooplankton αbuoyant = 0.30, fish αfish = 0.35, and waterbird αbird = 0.40. The second energy superposition calculation module calculated the community structure efficiency using the formula Φwetland = (0.5Gemergent + 0.3Gbuoyant + 0.2Gsinkant) * αbuoyant * αfish * αbird, obtaining Φwetland = (0.5 × 0.65 + 0.3 × 0.25 + 0.2 × 0.10) × 0.30 × 0.35 × 0.40 = 0.425 × 0.042 ≈ 0.0179. The detection revealed that the growth potential of the submerged layer (0.10) deviated significantly from the preset threshold (0.25). The second energy superposition calculation module automatically issued a vertical structure control command to increase the planting density of submerged plants (such as goldfish algae and hydrangea).
[0027] Holographic Energy Balance Layer Modeling Work The solar radiation detection module obtains the total annual radiation of the wetland, ∑Q_total = 2.5 × 10¹² J. The global accumulation calculation module summarizes the total growth volume of all wetland plants, ∑V = 1.8 × 10¹² J*m⁻³. The third energy superposition calculation module compares and finds that ∑Q_total > ∑V, indicating an energy surplus, but the surplus is smaller than that in the forest example. Considering the absorption and reflection of light and heat by the wetland water, the system outputs a control command to increase the planting density of floating-leaved plants (water lilies) by 5% to make full use of the sunlight on the water surface.
[0028] Steady-state determination layer sub-model assignment The interspecific coupling detection module calculated the interspecific coupling coefficient F=0.78 based on wetland species diversity detection results (wetland food chains are simpler than forests), and the disturbance intensity detection module detected the external disturbance intensity I=0.25 (including agricultural non-point source pollution and water level fluctuations). The steady-state determination module calculated S=V_total*Φ*F*e⁻ᴵ, and after normalization, S=0.68, which is lower than 0.95. The system automatically identified the weak links as: Φ (community structure efficiency) being too low (missing submerged layer) and I (disturbance intensity) being too high (water pollution). Targeted restoration instructions were generated: first, increase the replanting of submerged plants, aiming to raise G_submerged to above 0.25; second, set up an ecological buffer zone to filter agricultural non-point source pollution, reducing the I value to below 0.15. Three years later, a re-detection and calculation showed that the submerged layer had recovered to G_submerged=0.28, the I value had decreased to 0.12, and the S value had increased to 0.96. The steady-state determination module output a "restoration complete" signal, and the wetland ecological restoration project passed acceptance.
[0029] To achieve real-time computation, quantitative prediction, and precise control of plant growth driven by a unified energy source of "light, heat, and electricity," the process can be divided into a core total energy equation, four embedded real-time engine algorithms, logical relationships between algorithms, and data flow. The specific steps are as follows: One core logic: thermoelectricity drives ecological growth and balance. The 5-layer coupling model consists of: 1. Intrinsic Gene Layer, 2. Temporal Dynamics Layer, 3. Vertical Structure Layer, 4. Holographic Energy Balance Layer, and 5. Steady-State Determination Layer. The entire system has unified dimensions and closed physical logic. This fully embodies the complete thermoelectric coupling mechanism of temperature → resistance → voltage → electric field → energy conversion → ecological stability.
[0030] First model: Intrinsic gene layer (thermoelectric intrinsic growth algorithm) V(t) = \sigma_{\text{TE}} \cdot Q_{\text{abs}}(t) \cdot t - V(t): Plant growth volume, unit: m³ - \sigma_{\text{TE}}: Gene thermoelectric conversion coefficient, m³ / J - Q_{\text{abs}}(t): Total heat energy absorbed by the plant, J - t: growth time, h Physics Core: Genes determine the intrinsic properties of thermoelectric absorption and conversion; Thermal energy is continuously absorbed and converted into growth volume, which is the underlying axiom of growth.
[0031] Dimensions: m³ / J · J · h = m³, physically consistent.
[0032] Second module: Temporal dynamic layer (thermoelectric temporal growth algorithm) G_{\text{TE}}(t) = \sigma_{\text{TE}} \cdot Q_{\text{env}}(t) \cdot \mu_{\text{TE}}(t) - G_{\text{TE}}(t): Real-time thermoelectric growth potential, m³ / h - Q_{\text{env}}(t): Ambient solar thermal supply intensity, J / h - \mu_{\text{TE}}(t): Phenological thermoelectric response factor, dimensionless Physics Core: The environmental heat and power supply changes over time, driving dynamic fluctuations in growth potential.
[0033] Dimensions: m³ / J·J / h·1 = m³ / h, self-consistent closed loop.
[0034] Third module: Vertical structure layer (community stratification thermoelectric distribution algorithm) \Phi_{\text{TE}} = \big(0.4G_{\text{Crown}} + 0.3G_{\text{Asia}} + 0.3G_{\text{Lower}}\big) \cdot \alpha_{\text{Plant}} \cdot \alpha_{\text{Food}} \cdot \alpha_{\text{Meat}} - G_{\text{crown}}, G_{\text{sub}}, G_{\text{lower}}: Thermoelectric growth potential of each layer, m³ / h - Plant-based, Food-based, Meat-based: Phytoelectric transfer efficiency at the trophic level, dimensionless - ΦTE: Community thermoelectric structure efficiency, m³ / h Physical core: The vertical stratification of the community realizes the hierarchical capture and distribution of thermoelectric energy, forming a structural steady state.
[0035] Dimension: m³ / h · dimensionless = m³ / h, the dimensions are unified.
[0036] Fourth mode: Holographic energy balance layer (ecological thermoelectric supply-demand balance algorithm) Qtotal supply(t) = ∑species = 1N ∑plant = 1M σTE · Qabs,ij(t) · t · δage,ij - Qtotal supply(t): Total solar thermal energy supply, J - The sum on the right side: Total thermoelectric conversion of the ecosystem, J - δage,ij: Thermoelectric correction factor for plant age, dimensionless Physical core: Solar thermal energy supply ↔ Plant thermoelectric absorption and conversion reach global balance; If balanced, the ecological steady state; if unbalanced, extreme convection and disasters increase.
[0037] Dimension: Both sides are J, and the energy conservation is completely self-consistent.
[0038] Fifth mode: Steady state determination layer (ecological thermoelectric steady state determination algorithm) STE = Vtotal · ΦTE · Fcoupling · e-Idry - STE: Ecological thermoelectric steady state index, dimensionless - Vtotal: Total growth volume of the system, m³ - ΦTE: Community thermoelectric structure efficiency, m³ / h - Fcoupling: Inter-species thermoelectric coupling coefficient, dimensionless - Idry: External thermoelectric interference intensity, dimensionless Steady state criteria: S_{\text{TE}} \ge 0.95 Physics Core: The supply and demand of heat and electricity are balanced, the structure is intact, and the interference is minimal, achieving a pure natural ecological steady state.
[0039] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, It includes five sub-models that are connected in sequence and coupled to each other. The five sub-models are the gene intrinsic layer sub-model, the temporal dynamic layer sub-model, the vertical structure layer sub-model, the holographic energy balance layer sub-model, and the steady state determination layer sub-model. The intrinsic gene layer model is used to detect the photothermal conductivity of plant genes, the heat energy absorbed by the plant, and the growth time, and to calculate the growth volume of a single plant. The time-series dynamic sub-model is connected to the gene intrinsic sub-model to detect the environmental light and heat supply in real time, and calculate the real-time growth potential by combining the phenological rhythm coefficient. The vertical structure sub-model is connected to the time-series dynamic sub-model to detect the growth potential and trophic level transfer efficiency of the community canopy, sub-layers and lower layers, and to calculate the overall structural efficiency of the community. The holographic energy balance sub-model is connected to the gene intrinsic sub-model and the vertical structure sub-model to detect the total solar thermal energy input in the region and to perform energy conservation balance verification. The steady-state determination sub-model is connected to the vertical structure sub-model and the holographic energy balance sub-model. It is used to calculate the ecological restoration steady-state index and output a restoration completion signal when the steady-state index reaches a preset threshold.
2. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The intrinsic gene layer sub-model is derived through the formula. Calculate the growth volume of a single plant. ;in For the photothermal conductivity of the gene, For the plant to absorb heat energy, t is the growth time; the time-series dynamic sub-model is expressed by the formula Calculate real-time growth potential ,in For environmental light and heat supply, This is the phenological rhythm coefficient.
3. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The vertical structure layer model is obtained through the formula = The overall structural efficiency of the computational community is Φ; where Gcanopy, Gsub-, and Glower represent the growth potential of the canopy, sub-layer, and lower layer, respectively, and αplant, αfeeder, and αcarnivorous represent the transfer efficiency of the herbivorous trophic level and the carnivorous trophic level, respectively.
4. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The holographic energy balance sub-model is obtained through the formula An energy conservation balance check is performed, where ∑Q_total represents the total solar thermal energy input within the region, and δ_age is the age-level correction coefficient; the steady-state determination sub-model is determined by the formula... The ecological restoration stability index S is calculated, where V_total is the total growth volume, Φ is the community structure efficiency, F is the interspecific coupling coefficient, I is the intensity of external disturbance, and the preset threshold is S ≥ 0.
95.
5. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The time-series dynamic sub-model includes an ambient light and heat sensor array, a phenological database module, and a first energy superposition calculation module. When the first energy superposition calculation module detects that the ambient light and heat supply exceeds the plant absorption threshold, it automatically records the energy overflow value and transmits it to the vertical structure sub-model.
6. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The vertical structure layer sub-model includes a lidar module, a multispectral camera module, a trophic level detection module, and a second energy superposition calculation module; when the second energy superposition calculation module detects that the growth potential of a certain layer deviates from a preset threshold, it automatically issues a vertical structure adjustment command.
7. An ecological restoration system based on a unified photothermal-electric energy system, characterized in that, The holographic energy balance sub-model includes a solar radiation detection module, a global accumulation calculation module, and a third energy superposition calculation module. The third energy superposition calculation module compares the values on both sides of the equation in real time. When the left side is greater than the right side, it is determined to be an energy surplus state and outputs a water and fertilizer supply control command. When the left side is less than the right side, it is determined to be an energy deficit state and outputs a planting density control command.
8. A five-mode growth calculation method for an ecosystem based on a unified photothermal-electric energy system, characterized in that, The ecological restoration system according to any one of claims 1 to 7 is used to execute the following five sequentially progressive and mutually coupled computational models: The intrinsic layer model of the genes was used to detect the photothermal conductivity of the plant genes, the heat energy absorbed by the plant, and the growth time, and the growth volume of a single plant was calculated. The environmental light and heat supply is detected in real time using the time-series dynamic sub-model, and the real-time growth potential is calculated by combining the phenological rhythm coefficient. The growth potential and trophic level transfer efficiency of the canopy, sub-layers and lower layers of the community are detected using the vertical structural layer model, and the overall structural efficiency of the community is calculated. The total solar thermal energy input within the detection area is used to perform energy conservation balance verification. The ecological restoration stability index is calculated using the aforementioned stability determination layer sub-model, and it is determined that the ecosystem reaches the top natural ecological balance stability when the stability index reaches a preset threshold.
9. The five-mode ecosystem growth calculation method based on a unified photothermal-electric energy system as described in claim 8, characterized in that, When the time-series dynamic layer sub-model detects that the ambient light and heat supply exceeds the plant absorption threshold, it automatically records the energy overflow value and outputs it to the vertical structure layer sub-model for energy distribution adjustment; when the vertical structure layer sub-model detects that the growth potential of a certain layer deviates from the preset threshold, it automatically issues a vertical structure control command to maintain energy superposition balance.
10. The five-mode ecosystem growth calculation method based on a unified photothermal-electric energy system as described in claim 8, characterized in that, The holographic energy balance sub-model compares the values on both sides of the equation in real time. When the left side is greater than the right side, it is determined to be an energy surplus state and outputs a water and fertilizer supply control command. When the left side is less than the right side, it is determined to be an energy deficit state and outputs a planting density control command. The steady-state determination sub-model automatically locates the weak link and generates a targeted repair command when the steady-state index is lower than a preset threshold.