A forestry building operation system and method

Abstract

The present invention discloses a forestry building operation system and method, wherein the method comprises: an energy collection system, a power supply system, a heating system, a cooling system and an energy management system; the energy collection system is used to collect solar energy, coal and forest residual fellings; the energy management system comprises an improved vulture optimization algorithm, which optimizes the operation of forestry buildings with the goal of reducing environmental impact; the present invention utilizes renewable energy, reduces dependence on fossil fuels, and reduces energy costs to a greater extent under the constraints of the sustainable development index.

Description

Technical Field

[0001] The present invention relates to a forestry building operation system and method, and in particular to a forestry building operation system and method. Background Art

[0002] As global awareness of sustainable development and environmental protection continues to grow, traditional forestry building operating systems are facing tremendous pressure for change. Most traditional systems rely on a single energy supply, usually fossil fuels such as coal and oil, which is not only costly but also has a serious impact on the environment. The large amount of carbon dioxide and other greenhouse gas emissions generated by the combustion of fossil fuels has exacerbated the problem of climate change and caused irreversible damage to the ecosystem.

[0003] In forestry operations, how to efficiently, economically and environmentally manage the energy supply of forest buildings has become an urgent problem to be solved. However, the existing energy systems of forest buildings often lack flexibility and sustainability, and cannot make full use of local renewable resources, such as solar energy and forest residues. In addition, due to the lack of an effective energy management system, it is difficult to achieve optimal energy efficiency even when using renewable energy.

[0004] In terms of cooling and heating systems, many forestry buildings still use traditional, energy-intensive mechanical cooling and heating methods, which not only consume a lot of energy, but also have high operating costs. Therefore, a new forestry building operating system is needed that can integrate multiple energy sources, optimize energy efficiency, and reduce the impact on the environment. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a forestry building operation system and method that reduces unnecessary energy consumption.

[0006] Technical solution: A forestry building operation system described in the present invention includes: an energy collection system, a power supply system, a heating system, a cooling system and an energy management system;

[0007] The energy collection system is used to collect solar energy, coal and forest residues;

[0008] The power supply system includes: solar energy through photovoltaic generators to generate electricity, coal through coal-fired boilers to generate heat through turbines and generators to generate heat, forest residues through biomass boilers to generate heat through turbines and generators to generate electricity, and power grid power supply;

[0009] The heating system includes the following heating modes: heat energy generated by solar collectors is supplied through heat exchangers, heat energy generated by coal combustion in coal-fired boilers is supplied through heat exchangers, and heat energy generated by forest waste in biomass boilers is supplied through heat exchangers;

[0010] The energy management system includes an improved vulture optimization algorithm, which optimizes the operation of forest buildings with the goal of reducing environmental impact.

[0011] Furthermore, in the power supply system, coal is burned in a coal-fired boiler to generate heat energy which is then supplied to a turbine and a generator, and forest residues are burned in a biomass boiler to generate heat energy which is then supplied to a turbine and a generator. Specifically, the coal-fired boiler and the biomass boiler generate steam which is fed into a turbine which is connected to a generator. The steam heat is converted into mechanical energy in the turbine. The rotation of the turbine drives the generator to rotate, thereby converting the mechanical energy into electrical energy for supplying power.

[0012] Furthermore, in the heating system, the heat energy generated by the solar thermal collector is supplied through the heat exchanger. Specifically, the solar thermal collector captures solar radiation energy and converts it into heat energy;

[0013] The coal is burned in a coal-fired boiler to generate heat energy which is then supplied via a heat exchanger, and the forest residue is burned in a biomass boiler to generate heat energy which is then supplied via a heat exchanger. Specifically, the coal-fired boiler converts chemical energy into heat energy by burning coal, and the biomass boiler generates heat energy by burning forest residue.

[0014] Furthermore, the heating system also includes a heat storage device for storing heat.

[0015] Furthermore, the cooling system recovers waste heat through an absorption chiller to cool the building.

[0016] Furthermore, the objective function formula of the improved vulture optimization algorithm in the energy management system is:

[0017]

[0018] Where N is the total energy required for the operation system of the forest farm buildings, T is the energy obtained from solar energy, M is the energy obtained from coal, S is the energy obtained from forest felling, G is the energy obtained from the power grid, minX is the minimum cost of purchasing energy, and X is m represents the cost of purchasing coal, X GIt represents the cost of purchasing electricity from the power grid. The sustainable development index ESI is the ratio of the energy generation ratio EYR to the environmental load rate ELR. When the sustainable development index is higher than 1, the forest farm building operation system is sustainable. When the sustainable development index is higher than 10, it means that energy use is insufficient. The energy generation ratio EYR is the ratio of total energy input to purchased energy. The environmental load rate ELR is the ratio of total purchased energy to purchased non-renewable energy, which represents the impact of the forest farm building operation system on the environment.

[0019] Further, the improved vulture optimization algorithm comprises the following steps:

[0020] S1: Population initialization, specifically, the population is initialized using the Sobol sequence, the population size N and the maximum number of iterations T are set, the points are evenly distributed in the search space, and each population has a corresponding value;

[0021] S2: Bring the initial population into the objective function for evaluation, sort the population, and calculate the corresponding cost value of the population with the highest sorting degree;

[0022] S3: Soft siege, specifically when |E|≥0.5 and the dynamic coefficient r≥0.5, evaluate each cost value, and the mathematical model is described as follows:

[0023] X i (t+1)=ΔX(t)-E|JX p (t)-X i (t)|

[0024] ΔX(t)=X p (t)-X i (t)

[0025] Among them, E is the prey escape energy, ΔX(t) refers to the optimal individual position X at time t. p (t) and the position X of the i-th individual i The difference between (t), J refers to the jumping behavior of the prey during the escape process, and its value is 2(1–r5), where r5 is a random number between 0 and 1;

[0026] S4: Hard siege, when |E|<0.5, r≥0.5, evaluate the cost value using the following formula:

[0027] X i (t+1)=X p (t)-E|ΔX(t)|

[0028] The vulture takes two steps. Step 1:

[0029] X i (t+1)=X p (t)-E|ΔX(t)|

[0030] W=X p (t)-E|JX p (t)-X i (t)|

[0031] Among them, W represents jumping behavior;

[0032] Step 2: Determine whether the dive is effective. The formula is as follows:

[0033] Z=Y+L×SF(R)

[0034] Among them, Z represents the position after the dive, Y represents the current position, R is the dimension of the optimization problem, L is a 1×R-dimensional random vector, and SF is the Levy flight, which is expressed as follows:

[0035]

[0036] Among them, u, v are random numbers between (0, 1), σ is the scale parameter, Γ is the scale parameter of the control distribution, and β is a constant of 1.5;

[0037] S5: Dynamic reverse learning, specifically, the new population is mixed with the original population, and the energy cost value greedily selects the first N best individuals as the individuals of the new population. Assuming that the search space is D-dimensional, then individual X i The inverse solution in the jth dimension is defined as:

[0038]

[0039]

[0040] Among them, X ij ∈(LB j ,UB j ),j∈1,2,…,D,LB j and UB j is the lower and upper limits of the jth dimension. As the number of iterations C increases, the value of the dynamic coefficient r also increases nonlinearly;

[0041] S6: Calculate the updated individual position and update the current optimal individual;

[0042] S7: Determine whether the algorithm stop condition is reached. If so, the optimization algorithm ends; otherwise, return to step S2.

[0043] Based on the same inventive concept, the present invention also provides a forestry building operation method, comprising:

[0044] harvesting solar energy, coal and forest residues;

[0045] Power is generated by using solar energy through photovoltaic generators, by using coal through coal-fired boilers to generate heat through turbines and generators, by using forest residues through biomass boilers to generate heat through turbines and generators, and by obtaining electricity from the grid;

[0046] Using solar collectors to generate heat and heat through heat exchangers, using coal through coal-fired boilers to generate heat and heat through heat exchangers, and using forest waste to generate heat through biomass boilers and heat exchangers;

[0047] Through the improved vulture optimization algorithm, the operation of forest farm buildings is optimized with the goal of reducing environmental impact.

[0048] Furthermore, the objective function formula of the improved vulture optimization algorithm in the energy management system is:

[0049]

[0050] Where N is the total energy required for the operation system of the forest farm buildings, T is the energy obtained from solar energy, M is the energy obtained from coal, S is the energy obtained from forest felling, G is the energy obtained from the power grid, minX is the minimum cost of purchasing energy, and X is m represents the cost of purchasing coal, X G It represents the cost of purchasing electricity from the power grid. The sustainable development index ESI is the ratio of the energy generation ratio EYR to the environmental load rate ELR. When the sustainable development index is higher than 1, the forest farm building operation system is sustainable. When the sustainable development index is higher than 10, it means that energy use is insufficient. The energy generation ratio EYR is the ratio of total energy input to purchased energy. The environmental load rate ELR is the ratio of total purchased energy to purchased non-renewable energy, which represents the impact of the forest farm building operation system on the environment.

[0051] Further, the improved vulture optimization algorithm comprises the following steps:

[0052] S1: Population initialization, specifically, the population is initialized using the Sobol sequence, the population size N and the maximum number of iterations T are set, the points are evenly distributed in the search space, and each population has a corresponding value;

[0053] S2: Bring the initial population into the objective function for evaluation, sort the population, and calculate the corresponding cost value of the population with the highest sorting degree;

[0054] S3: Soft siege, specifically when |E|≥0.5 and the dynamic coefficient r≥0.5, evaluate each cost value, and the mathematical model is described as follows:

[0055] X i(t+1)=ΔX(t)-E|JX p (t)-X i (t)|

[0056] ΔX(t)=X p (t)-X i (t)

[0057] Among them, E is the prey escape energy, ΔX(t) refers to the optimal individual position X at time t. p (t) and the position X of the i-th individual i The difference between (t), J refers to the jumping behavior of the prey during the escape process, and its value is 2(1–r5), where r5 is a random number between 0 and 1;

[0058] S4: Hard siege, when |E|<0.5, r≥0.5, evaluate the cost value using the following formula:

[0059] X i (t+1)=X p (t)-E|ΔX(t)|

[0060] The vulture takes two steps. Step 1:

[0061] X i (t+1)=X p (t)-E|ΔX(t)|

[0062] W=X p (t)-E|JX p (t)-X i (t)|

[0063] Among them, W represents jumping behavior;

[0064] Step 2: Determine whether the dive is effective. The formula is as follows:

[0065] Z=Y+L×SF(R)

[0066] Among them, Z represents the position after the dive, Y represents the current position, R is the dimension of the optimization problem, L is a 1×R-dimensional random vector, and SF is the Levy flight, which is expressed as follows:

[0067]

[0068] Among them, u, v are random numbers between (0, 1), σ is the scale parameter, Γ is the scale parameter of the control distribution, and β is a constant of 1.5;

[0069] S5: Dynamic reverse learning, specifically, the new population is mixed with the original population, and the energy cost value greedily selects the first N best individuals as the individuals of the new population. Assuming that the search space is D-dimensional, then individual X i The inverse solution in the jth dimension is defined as:

[0070]

[0071]

[0072] Among them, X ij ∈(LB j ,UB j ),j∈1,2,…,D,LB j and UB j are the lower and upper limits of the jth dimension. As the number of iterations C increases, the value of the dynamic coefficient r also increases nonlinearly;

[0073] S6: Calculate the updated individual position and update the current optimal individual;

[0074] S7: Determine whether the algorithm stop condition is reached. If so, the optimization algorithm ends; otherwise, return to step S2.

[0075] Beneficial effects: Compared with the prior art, the present invention reduces the impact of undetectable fabric defects due to complex background on patterned fabrics, improves detection accuracy, and provides a basis for subsequent fabric defect detection; it improves the situation where the defects on the fabric account for a small proportion relative to the background pattern, and the added attention mechanism can quickly detect small defects and can detect multiple types of defects, and can be applied to relatively simple solid-color fabric defect detection, textile detection, headlight scratch detection, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a system schematic diagram of an embodiment of the present invention;

[0077] Figure 2 Schematic diagram of an optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0079] As attached Figure 1 As shown, a forestry building operation system of this embodiment includes: an energy collection system, a power supply system, a heating system, a cooling system and an energy management system;

[0080] The energy collection system is used to collect solar energy, coal and forest residues;

[0081] The power supply system includes: solar energy through photovoltaic generators to generate electricity, coal through coal-fired boilers to generate heat through turbines and generators to generate heat, forest residues through biomass boilers to generate heat through turbines and generators to generate electricity, and power grid power supply;

[0082] The heating system includes the following heating modes: heat energy generated by solar collectors is supplied through heat exchangers, heat energy generated by coal combustion in coal-fired boilers is supplied through heat exchangers, and heat energy generated by forest waste in biomass boilers is supplied through heat exchangers;

[0083] The energy management system includes an improved vulture optimization algorithm to optimize the operation of forest farm buildings with the goal of reducing environmental impact. The vulture optimization algorithm reduces energy costs, reduces dependence on fossil fuels, and maximizes the use of renewable resources under the constraints of the sustainable development index, while ensuring that the comfort and functional requirements in the building are met.

[0084] Furthermore, the energy collection system collects solar energy, coal, and forest waste.

[0085] Furthermore, in the power supply system, the photovoltaic generator collects solar energy to generate electricity; the steam generated by the coal-fired boiler and the biomass boiler can be fed into the turbine. The thermal energy of the steam is converted into mechanical energy (rotational kinetic energy of the turbine) in the turbine. The turbine is usually connected to a generator. When the turbine rotates, it drives the generator to rotate, thereby converting mechanical energy into electrical energy for power supply. The power grid can provide compensation power supply.

[0086] Furthermore, in the heating system, the solar collector is responsible for capturing solar radiation energy and converting it into heat energy; the coal-fired boiler is a device that heats water by burning coal to produce steam, and the chemical energy of the coal is converted into heat energy; the biomass boiler generates heat energy by burning forest residues. The heat energy generated by the above devices can be used for heating through heat exchangers and can also be stored in heat storage devices.

[0087] Furthermore, the absorption chiller in the cooling system cools the building by recovering waste heat.

[0088] Furthermore, the objective function formula of the improved vulture optimization algorithm is:

[0089]

[0090] Where N is the total energy required by the forest farm building system, T is the energy obtained from solar energy, M is the energy obtained from coal, S is the energy obtained from forest felling, G is the energy obtained from the power grid, minX is the minimum cost of purchasing energy, and X is m represents the cost of purchasing coal, X G It represents the cost of purchasing electricity from the grid. The sustainability index ESI is the ratio of the energy generation ratio EYR to the environmental load rate ELR. A system with a sustainability index higher than 1 is a good system, but a system with a sustainability index higher than 10 is a symbol of insufficient resource utilization. The energy generation ratio EYR is the ratio of total energy input to purchased energy. The environmental load rate ELR is the ratio of total purchased energy to purchased non-renewable energy. It refers to the impact of a system on the environment.

[0091] Further, the improved vulture optimization algorithm comprises the following steps:

[0092] S1 population initialization: Use the Sobol sequence to initialize the population, set the population size N and the maximum number of iterations T, and evenly distribute the points in the search space. Each population has a corresponding value;

[0093] S2 brings the initial population into the objective function for evaluation, sorts the population, and gives the corresponding cost value to the population with the highest sorting degree;

[0094] S3 soft siege, when |E|≥0.5 and r≥0.5, evaluate each cost value, and the mathematical model is described as follows:

[0095] X i (t+1)=ΔX(t)-E|JX p (t)-X i (t)|

[0096] ΔX(t)=X p (t)-X i (t);

[0097] Among them, ΔX(t) refers to the position difference between the optimal individual and the i-th individual at time t, J refers to the jumping behavior of the prey during the escape process, and its value is 2(1–r5), where r5 is a random number between 0 and 1;

[0098] S4 hard siege, when E<0.5, r≥0.5, evaluate the cost value, using the following formula:

[0099] X i (t+1)=X p (t)-E|ΔX(t)|

[0100] The vulture takes two steps. Step 1:

[0101] Xi (t+1)=X p (t)-E|ΔX(t)|

[0102] W=X p (t)-E|JX p (t)-X i (t)|

[0103] Step 2: Determine whether the dive is effective. The formula is as follows:

[0104] Z=Y+L×SF(R)

[0105] Where R refers to the dimension of the optimization problem, L is a 1×R dimensional random vector, and SF is the Levy flight, expressed as follows:

[0106]

[0107] Among them, u, v are random numbers between (0, 1), and β is a constant 1.5;

[0108] S5 dynamic reverse learning, the new population is mixed with the original population, the energy cost value greedily selects the first N best individuals as the individuals of the new population, assuming that the search space is D-dimensional, then individual X i The inverse solution in the jth dimension is defined as:

[0109]

[0110]

[0111] Among them, X ij ∈(LB j ,UB j ),j∈1,2,…,D,LB j and UB j are the upper and lower limits of the jth dimension. As the number of iterations C increases, the value of the dynamic coefficient r also increases nonlinearly;

[0112] S6 calculates the updated individual position and updates the current optimal individual;

[0113] S7 determines whether the algorithm stop condition is reached, if so, the algorithm ends, otherwise, returns to step S2.

[0114] The optimal value is obtained through algorithm optimization to ensure the normal operation of forest farm buildings, thereby achieving sustainable development of the forest farm, so that it can meet the needs of forest farm buildings while complying with the sustainable development index, increasing economic benefits and environmental protection.

[0115] Based on the same inventive concept, this embodiment also provides a forestry building operation method, including:

[0116] harvesting solar energy, coal and forest residues;

[0117] Power is generated by using solar energy through photovoltaic generators, by using coal through coal-fired boilers to generate heat through turbines and generators, by using forest residues through biomass boilers to generate heat through turbines and generators, and by obtaining electricity from the grid;

[0118] Using solar collectors to generate heat and heat through heat exchangers, using coal through coal-fired boilers to generate heat and heat through heat exchangers, and using forest waste to generate heat through biomass boilers and heat exchangers;

[0119] Through the improved vulture optimization algorithm, the operation of forest farm buildings is optimized with the goal of reducing environmental impact.

[0120] The present invention provides a forestry building operation system and method, which uses an advanced energy management system combined with a vulture optimization algorithm to optimize energy allocation and use, reduce costs, and reduce dependence on fossil energy. By integrating solar energy, biomass energy, and traditional energy, the system aims to achieve a balance between sustainability and economy while ensuring that the comfort and functional requirements of forestry buildings are met.

Claims

1. A forestry building operation system, characterized in that: include: Energy collection systems, power supply systems, heating systems, cooling systems and energy management systems; The energy collection system is used to collect solar energy, coal and forest residues; The power supply system includes: solar energy through photovoltaic generators to generate electricity, coal through coal-fired boilers to generate heat through turbines and generators to generate heat, forest residues through biomass boilers to generate heat through turbines and generators to generate electricity, and power grid power supply; The heating system includes the following heating modes: heat energy generated by solar collectors is supplied through heat exchangers, heat energy generated by coal combustion in coal-fired boilers is supplied through heat exchangers, and heat energy generated by forest waste in biomass boilers is supplied through heat exchangers; The energy management system includes an improved vulture optimization algorithm, which optimizes the operation of forest buildings with the goal of reducing environmental impact; the objective function formula of the improved vulture optimization algorithm is: Where N is the total energy required for the operation system of the forest farm buildings, T is the energy obtained from solar energy, M is the energy obtained from coal, S is the energy obtained from forest felling, G is the energy obtained from the power grid, minX is the minimum cost of purchasing energy, and X is m represents the cost of purchasing coal, X G It indicates the cost of purchasing electricity from the power grid. The sustainable development index ESI is the ratio of the energy generation ratio EYR to the environmental load rate ELR. When the sustainable development index is higher than 1, the forest farm building operation system is sustainable. When the sustainable development index is higher than 10, it indicates insufficient energy use. The energy generation ratio EYR is the ratio of total energy input to purchased energy. The environmental load rate ELR is the ratio of total purchased energy to purchased non-renewable energy. It indicates the impact of the forest farm building operation system on the environment. The improved vulture optimization algorithm comprises the following steps: S1: Population initialization, specifically, the population is initialized using the Sobol sequence, the population size N and the maximum number of iterations T are set, the points are evenly distributed in the search space, and each population has a corresponding value; S2: Bring the initial population into the objective function for evaluation, sort the population, and calculate the corresponding cost value of the population with the highest sorting degree; S3: Soft siege, specifically when |E|≥0.5 and the dynamic coefficient r≥0.5, evaluate each cost value, and the mathematical model is described as follows: X i (t+1)=ΔX(t)-E|JX p (t)-X i (t)| ΔX(t)=X p (t)-X i (t) Among them, E is the prey escape energy, ΔX(t) refers to the optimal individual position X at time t p (t) and the position X of the i-th individual i (t), J refers to the jumping behavior of the prey during the escape process, and its value is 2(1–r5), where r5 is a random number between 0 and 1; S4: Hard siege, when |E|<0.5, r≥0.5, evaluate the cost value using the following formula: X i (t+1)=X p (t)-E|ΔX(t)| The vulture takes two steps. Step 1: X i (t+1)=X p (t)-E|ΔX(t)| W=X p (t)-E|JX p (t)-X i (t)| Among them, W represents jumping behavior; Step 2: Determine whether the dive is effective. The formula is as follows: Z=Y+L×SF(R) Among them, Z represents the position after the dive, Y represents the current position, R is the dimension of the optimization problem, L is a 1×R-dimensional random vector, and SF is the Levy flight, which is expressed as follows: Among them, u, v are random numbers between (0, 1), δ is the scale parameter, Γ is the scale parameter of the control distribution, and β is a constant of 1.5; S5: Dynamic reverse learning, specifically, the new population is mixed with the original population, and the energy cost value greedily selects the top N best individuals as individuals in the new population. Assuming that the search space is D-dimensional, then individual X i The inverse solution in the jth dimension is defined as: Among them, X ij ∈(LB j ,UB j ),j∈1,2,L,D,LB j and UB j is the lower and upper limits of the jth dimension. As the number of iterations C increases, the value of the dynamic coefficient r also increases nonlinearly; S6: Calculate the updated individual position and update the current optimal individual; S7: Determine whether the algorithm stop condition is reached. If so, the optimization algorithm ends; otherwise, return to step S2.

2. The forestry building operation system according to claim 1, characterized in that: In the power supply system, coal is burned in a coal-fired boiler to generate heat energy which is then used to supply power via a turbine and a generator, and forest residues are burned in a biomass boiler to generate heat energy which is then used to supply power via a turbine and a generator. Specifically, the coal-fired boiler and the biomass boiler generate steam which is fed into a turbine which is connected to a generator. The steam heat energy is converted into mechanical energy in the turbine. The rotation of the turbine drives the generator to rotate, thereby converting the mechanical energy into electrical energy for supplying power.

3. The forestry building operation system according to claim 1, characterized in that: In the heating system, the solar thermal collector generates heat energy which is then used for heating via a heat exchanger. Specifically, the solar thermal collector captures solar radiation energy and converts it into heat energy. The coal is burned in a coal-fired boiler to generate heat energy which is then supplied via a heat exchanger, and the forest residue is burned in a biomass boiler to generate heat energy which is then supplied via a heat exchanger. Specifically, the coal-fired boiler converts chemical energy into heat energy by burning coal, and the biomass boiler generates heat energy by burning forest residue.

4. The forestry building operation system according to claim 1, characterized in that: The heating system also includes a heat storage device for storing heat.

5. The forestry building operation system according to claim 1, characterized in that: The cooling system recovers waste heat through an absorption refrigerator to cool the building.

6. A method for operating a forestry building, characterized in that: include: harvesting solar energy, coal and forest residues; Power is generated by using solar energy through photovoltaic generators, by using coal through coal-fired boilers to generate heat through turbines and generators, by using forest residues through biomass boilers to generate heat through turbines and generators, and by obtaining electricity from the grid; Using solar collectors to generate heat and heat through heat exchangers, using coal through coal-fired boilers to generate heat and heat through heat exchangers, and using forest waste to generate heat through biomass boilers and heat exchangers; Through the improved vulture optimization algorithm, the operation of forest farm buildings is optimized with the goal of reducing environmental impact; The objective function formula of the improved vulture optimization algorithm is: Where N is the total energy required for the operation system of the forest farm buildings, T is the energy obtained from solar energy, M is the energy obtained from coal, S is the energy obtained from forest felling, G is the energy obtained from the power grid, minX is the minimum cost of purchasing energy, and X is m represents the cost of purchasing coal, X G It indicates the cost of purchasing electricity from the power grid. The sustainable development index ESI is the ratio of the energy generation ratio EYR to the environmental load rate ELR. When the sustainable development index is higher than 1, the forest farm building operation system is sustainable. When the sustainable development index is higher than 10, it indicates insufficient energy use. The energy generation ratio EYR is the ratio of total energy input to purchased energy. The environmental load rate ELR is the ratio of total purchased energy to purchased non-renewable energy. It indicates the impact of the forest farm building operation system on the environment. The improved vulture optimization algorithm comprises the following steps: S1: Population initialization, specifically, the population is initialized using the Sobol sequence, the population size N and the maximum number of iterations T are set, the points are evenly distributed in the search space, and each population has a corresponding value; S2: Bring the initial population into the objective function for evaluation, sort the population, and calculate the corresponding cost value of the population with the highest sorting degree; S3: Soft siege, specifically when |E|≥0.5 and the dynamic coefficient r≥0.5, evaluate each cost value, and the mathematical model is described as follows: X i (t+1)=ΔX(t)-E|JX p (t)-X i (t)| ΔX(t)=X p (t)-X i (t) Among them, E is the prey escape energy, ΔX(t) refers to the optimal individual position X at time t p (t) and the position X of the i-th individual i (t), J refers to the jumping behavior of the prey during the escape process, and its value is 2(1–r5), where r5 is a random number between 0 and 1; S4: Hard siege, when |E|<0.5, r≥0.5, evaluate the cost value using the following formula: X i (t+1)=X p (t)-E|ΔX(t)| The vulture takes two steps. Step 1: X i (t+1)=X p (t)-E|ΔX(t)| W=X p (t)-E|JX p (t)-X i (t) Among them, W represents jumping behavior; Step 2: Determine whether the dive is effective. The formula is as follows: Z=Y+L×SF(R) Among them, Z represents the position after the dive, Y represents the current position, R is the dimension of the optimization problem, L is a 1×R-dimensional random vector, and SF is the Levy flight, which is expressed as follows: Among them, u, v are random numbers between (0, 1), σ is the scale parameter, Γ is the scale parameter of the control distribution, and β is a constant of 1.5; S5: Dynamic reverse learning, specifically, the new population is mixed with the original population, and the energy cost value greedily selects the top N best individuals as individuals in the new population. Assuming that the search space is D-dimensional, then individual X i The inverse solution in the jth dimension is defined as: Among them, X ij ∈(LB j ,UB j ),j∈1,2,L,D,LB j and UB j is the lower and upper limits of the jth dimension. As the number of iterations C increases, the value of the dynamic coefficient r also increases nonlinearly; S6: Calculate the updated individual position and update the current optimal individual; S7: Determine whether the algorithm stop condition is reached. If so, the optimization algorithm ends; otherwise, return to step S2.

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