Plant factory energy consumption optimization method, system, equipment and medium considering crop dynamic growth model

Abstract

The invention relates to the technical field of crop energy consumption optimization, and discloses a plant factory energy consumption optimization method, system, equipment and medium considering a crop dynamic growth model, and the method comprises the steps: obtaining crop data, and constructing a relation between a crop net photosynthesis rate and illumination intensity; based on the net photosynthesis rate, the yield of the crops is obtained, and the selling income of the crops is obtained; obtaining the relation between the illumination intensity and the power of the crops to obtain the illumination energy consumption cost of the crops; and constructing a crop income optimization model, and optimizing the energy consumption of the plant factory. By establishing the plant factory economic benefit optimization model, the economic benefit of the plant factory is improved, and the effect of reducing the energy consumption cost per unit yield of the plant factory is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of crop energy consumption optimization, and in particular to a plant factory energy consumption optimization method and system considering a crop dynamic growth model, a plant factory energy consumption optimization device and a plant factory energy consumption optimization medium. BACKGROUND

[0002] At present, the population's demand for food is increasing, and with the worsening of environmental and climate problems, the continuous expansion of the population and the continuous reduction of cultivated land in the process of urbanization, how to use limited cultivated land to meet the growing demand for food has become a global problem. Plant factory is a highly integrated agricultural production system, which overturns the traditional agricultural mode of relying on the weather, does not depend on sunlight and soil, and uses advanced engineering technology to produce plants in a completely closed or controlled environment. This production method breaks the dependence of traditional agriculture on natural environment, realizes precise control of environmental factors such as light, temperature, humidity and carbon dioxide concentration, reduces the impact of extreme climate on agricultural production, and greatly improves the growth rate and yield of plants. The yield per unit area can reach several dozen times that of open field production, and the plant factory can vertically utilize space to cope with the problem of food production reduction caused by the reduction of cultivated land in the process of urbanization. At present, plant factory has become an important means to solve the problem of food supply.

[0003] However, the energy consumption of plant factory which completely relies on artificial environment control equipment is too high, and the cost of crop production is large, which has become the main bottleneck of the current development of plant factory. Therefore, it is of great significance to study how to reduce the operating cost of plant factory by reducing energy consumption to increase the economic benefit of plant factory. SUMMARY

[0004] In view of the above existing problems, the present application is proposed. Therefore, the present application provides a plant factory energy consumption optimization method considering a crop dynamic growth model to solve the problem of how to reduce the operating cost of plant factory by reducing energy consumption to increase the economic benefit of plant factory.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a plant factory energy consumption optimization method considering a crop dynamic growth model, comprising: obtaining crop data, and constructing a relationship between net photosynthetic rate of crops and light intensity;

[0007] Based on the net photosynthetic rate, the yield of crops is obtained to obtain the selling benefit of crops;

[0008] Based on the crop data, a relationship between light intensity and power of crops is obtained to obtain the lighting energy consumption cost of crops;

[0009] Based on the selling income of the crops and the lighting energy consumption cost of the crops, a yield optimization model of the crops is constructed to optimize the energy consumption of the plant factory.

[0010] As a preferred scheme of the plant factory energy consumption optimization method considering the dynamic growth model of the crops, wherein: constructing the relationship between the net photosynthetic rate of the crops and the light intensity comprises:

[0011] A plurality of groups of light intensity, the respiration consumption rate of the crops in darkness and the maximum rate of photosynthesis of the crops are obtained, and a plurality of groups of net photosynthetic rates of the crops are calculated;

[0012] Based on the plurality of groups of net photosynthetic rates of the crops and the light intensity, a relationship curve between the net photosynthetic rate and the light intensity is obtained to calculate the corresponding net photosynthetic rate according to the light intensity.

[0013] As a preferred scheme of the plant factory energy consumption optimization method considering the dynamic growth model of the crops, wherein: obtaining the selling income of the crops comprises:

[0014] According to the net photosynthetic rate, the photoperiod of the crops and the photosynthetic active area, the daily net carbon accumulation amount of the crops is calculated;

[0015] Based on the daily net carbon accumulation amount, a relationship between the daily net carbon accumulation amount and the dry matter accumulation amount is constructed to calculate the dry matter quality corresponding to the carbon assimilation amount.

[0016] Based on the dry matter quality, the dry matter content is obtained to obtain the proportion of the edible part of the crops in the total fresh matter, and a relationship between the selling income of the crops and the net photosynthetic rate is constructed through the edible part of the crops to obtain the selling income of the crops;

[0017] The beneficial effect of the preferred scheme is to establish the correlation between the net photosynthetic rate and the yield, to predict the yield of the crops, and to realize the maximization of the income according to the yield and the income prediction.

[0018] As a preferred scheme of the plant factory energy consumption optimization method considering the dynamic growth model of the crops, wherein: obtaining the lighting energy consumption cost of the crops comprises:

[0019] According to the light intensity of the crops, the corresponding lighting power is calculated;

[0020] Based on the lighting power, a relationship between the lighting energy consumption cost and the lighting power is constructed to calculate the lighting energy consumption cost of the crops;

[0021] The beneficial effect of the preferred embodiment is that the relationship between the light intensity of the crops and the power is obtained, the power consumption under different light intensity requirements can be clearly understood, the lighting energy consumption cost can be accurately calculated, and the relationship provides a direction for optimizing the lighting system and reducing the energy consumption cost of the plant factory.

[0022] As a preferred embodiment of the plant factory energy optimization method considering the dynamic growth model of crops, wherein:

[0023] The lighting power is set as a decision variable, the difference between the economic benefits of the crops under the optimal growth light intensity and after reducing the energy consumption is maximized as the target, and a target function is established;

[0024] The constraint conditions of the revenue optimization model are set, and the constraint conditions include economic benefit constraints, photoperiod and light intensity constraints, and lighting power constraints;

[0025] The beneficial effect of the preferred embodiment is that the optimal energy consumption scheme is found by comprehensively considering the revenue and cost, the energy consumption cost is reduced while ensuring the yield and quality of the crops, the overall economic benefit of the plant factory is improved, and energy waste is reduced.

[0026] As a preferred embodiment of the plant factory energy optimization method considering the dynamic growth model of crops, wherein:

[0027] maxF=F1-F0

[0028] F1=W1-w1

[0029] F0=W0-w0

[0030] Wherein, F1 represents the economic benefit after reducing the energy consumption, F0 represents the economic benefit of the crops under the optimal growth light intensity, W1 represents the economic benefit of the crops after reducing the energy consumption, w1 represents the lighting energy consumption cost after reducing the energy consumption, W0 represents the economic benefit of the crops under the optimal growth light intensity, and w0 represents the lighting energy consumption cost under the optimal growth light intensity of the crops.

[0031] As a preferred embodiment of the plant factory energy optimization method considering the dynamic growth model of crops, wherein:

[0032] The economic benefit constraints are set by the difference between the economic benefits after reducing the energy consumption and before reducing the energy consumption, and are represented as:

[0033] F1-F0>0

[0034] The photoperiod and light intensity constraints are represented as:

[0035] L≤16

[0036] I≤1000

[0037] Wherein, L represents photoperiod, I represents light intensity;

[0038] The lighting power constraint is expressed as:

[0039]

[0040] Wherein, P l represents the power of the lighting tool, and k is a conversion coefficient.

[0041] In a second aspect, the present application provides a plant factory energy optimization system considering a dynamic growth model of crops, comprising:

[0042] A first calculation module is configured to obtain crop data and build a relationship between a net photosynthetic rate of crops and light intensity;

[0043] A second calculation module is configured to obtain a yield of crops based on the net photosynthetic rate, so as to obtain a selling revenue of crops;

[0044] A third calculation module is configured to obtain a relationship between light intensity and power of crops based on the crop data, so as to obtain a lighting energy cost of crops;

[0045] An optimization module is configured to build a revenue optimization model of crops based on the selling revenue of crops and the lighting energy cost of crops, and to optimize energy consumption of a plant factory.

[0046] In a third aspect, the present application provides a computer device, comprising:

[0047] A memory and a processor;

[0048] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to realize steps of the plant factory energy optimization method considering the dynamic growth model of crops.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor, so as to realize steps of the plant factory energy optimization method considering the dynamic growth model of crops.

[0050] Compared with the prior art, the present application has the beneficial effects that: the present application describes the relationship between the net photosynthetic rate of crops and the light intensity in the plant factory environment according to the mathematical model in plant physiological ecology, analyzes the relationship between the selling income of crops and the net photosynthetic rate of crops, establishes an economic income optimization model of the plant factory, realizes the maximization of the operating profit of the plant factory, and improves the economic benefit of the plant factory; the present application considers the crop production in the plant factory under the condition of deviating from the optimal growth environment of crops, reduces the energy consumption cost of the plant factory under the premise of as little as possible reducing the crop yield, so as to realize the effect of reducing the energy consumption cost per unit yield of the plant factory. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0052] Figure 1 The overall flowchart of the plant factory energy optimization method considering the dynamic growth model of crops according to an embodiment of the present application is shown in the figure.

[0053] Figure 2 The relationship diagram between the economic income of crop sales and the electric energy consumption cost and the LED lamp power of the plant factory energy optimization method considering the dynamic growth model of crops according to an embodiment of the present application is shown in the figure.

[0054] Figure 3 The relationship diagram between the economic income of the plant factory and the LED lamp power of the plant factory energy optimization method considering the dynamic growth model of crops according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0056] REFERENCE Figure 1 For an embodiment of the present application, a plant factory energy optimization method considering a dynamic growth model of crops is provided, which comprises:

[0057] S101, obtaining crop data to construct the relationship between the net photosynthesis rate of crops and the light intensity;

[0058] S102, obtaining the yield of the crop based on the net photosynthetic rate to obtain the selling revenue of the crop;

[0059] S103, obtaining the relationship between the light intensity and the power of the crop based on the crop data to obtain the lighting energy consumption cost of the crop;

[0060] S104, constructing a revenue optimization model of the crop based on the selling revenue of the crop and the lighting energy consumption cost of the crop to optimize the energy consumption of the plant factory.

[0061] It should be noted that photosynthesis is the core means for most crops to accumulate organic matter, and the photosynthetic rate is deeply affected by the light intensity, so the present application first constructs the relationship between the net photosynthetic rate of the crop and the light intensity, then obtains the yield of the lettuce from the net photosynthetic rate and further obtains the economic revenue of the lettuce plant factory, then obtains the lighting energy consumption cost of the plant factory by depicting the relationship between the light intensity and the power of the LED lamp, and finally obtains the economic revenue optimization model of the lettuce plant factory.

[0062] It should be noted that the photosynthetic rate of most crops is significantly affected by the light intensity, and the influencing mechanism follows the basic law of plant physiology, i.e. the photosynthetic rate increases with the increase of the light intensity and tends to be saturated at high light intensity.

[0063] In a preferred embodiment, obtaining the crop data and constructing the relationship between the net photosynthetic rate of the crop and the light intensity comprises:

[0064] Obtaining multiple sets of light intensity, dark respiration consumption rate of the crop and maximum photosynthetic rate of the crop, and calculating multiple sets of net photosynthetic rate of the crop;

[0065] Based on the multiple sets of net photosynthetic rate of the crop and the light intensity, a relationship curve between the net photosynthetic rate and the light intensity is obtained to calculate the corresponding net photosynthetic rate according to the light intensity.

[0066] In this embodiment, the Norman-Hartnett model (NH model) is used to depict the relationship between the net photosynthetic rate of the crop and the light intensity of the plant factory, which specifically comprises:

[0067] The NH model represents the relationship between the net photosynthetic rate of the plant and the light intensity as:

[0068]

[0069] wherein, P N represents the net photosynthetic rate, P Nmaxrepresents the maximum rate of photosynthesis, I represents the light intensity, a represents the initial slope of the curve (P N -I curve) between the net photosynthetic rate and the light intensity, and θ is a curvature factor that can adjust the concave-convex degree of the shape curve of the photosynthetic rate curve. D R

[0070] The first-order derivative of the above formula is represented as:

[0071]

[0072] When , I = 0; when , I > 0, and thus, P N The P N -I curve obtained by the expression is an asymptote.

[0073] It should be noted that the relationship between the net photosynthetic rate of crops and the light intensity is constructed, the change of the net photosynthetic rate of crops under different light intensities is determined, the photosynthetic characteristics of crops are deeply understood, a basis is provided for subsequent analysis, and the light energy utilization rate is improved.

[0074] In an alternative embodiment, the relationship between the net photosynthetic rate of plants and the light intensity can also be determined by experiments or machine learning models, such as regression models, neural networks or random forests.

[0075] In a preferred embodiment, obtaining the selling income of crops comprises:

[0076] According to the net photosynthetic rate, the photoperiod of the crops, and the photosynthetic effective area, the daily net carbon accumulation amount of the crops is calculated;

[0077] Based on the daily net carbon accumulation amount, the relationship between the daily net carbon accumulation amount and the dry matter accumulation amount is constructed, and the dry matter quality corresponding to the carbon assimilation amount is calculated.

[0078] Based on the dry matter quality, the dry matter content is obtained to obtain the proportion of the edible part of crops in the total fresh matter, and the relationship between the selling income of crops and the net photosynthetic rate is constructed through the edible part of crops to obtain the selling income of crops.

[0079] It should be noted that the meaning of the photosynthetic rate is the amount of carbon dioxide fixed by plants per unit time, and the unit is μmol·m -2 ·s -1 .

[0080] In this embodiment, the daily net carbon accumulation amount is first calculated and represented as:

[0081]

[0082] wherein, is the daily net carbon accumulation, P N is the net photosynthetic rate, L is the light period, and A is the photosynthetic active area.

[0083] The carbon fixed by photosynthesis is converted into dry matter, and the carbohydrate content accounts for a large proportion of the dry matter of the plant. By known physiological parameters, the amount of dry matter corresponding to the carbon assimilation can be calculated, and thus the relationship between the daily net carbon accumulation and the dry matter accumulation is as follows:

[0084]

[0085] wherein, ΔW is the accumulation of dry matter, is the daily net carbon accumulation, a is the mass constant corresponding to the carbon fixed in glucose, and is taken as 3*10 -5 .

[0086] The dry matter of the plant refers to the solid part after removing the water in the plant body, and the dry matter content s represents the percentage of the plant dry matter in the total fresh weight. The total yield of the plant includes the edible part and the inedible part (roots, stems, leaves, etc.). Generally, the yield is represented by the economic coefficient (Harvest Index, HI), i.e., the proportion of the edible part of the plant in the total fresh matter, which is represented as:

[0087]

[0088] wherein, Y is the yield of the edible part, HI is the economic coefficient, which is usually taken as 0.5-0.8 (depending on the crop type), and ΔW is the accumulation of dry matter.

[0089] The edible part of the plant can be converted into the economic benefit of the plant factory by being sold in the market, and thus the relationship between the selling revenue of the plant factory crop and the net photosynthetic rate is represented as:

[0090] W=c3600P N LAaHI

[0091] wherein, W is the selling revenue of the crop of the plant factory, and c is the latest market price of high-quality crops.

[0092] It should be noted that the correlation between the net photosynthetic rate and the yield is established to predict the crop yield, and the yield and the revenue are predicted to achieve the maximization of the revenue.

[0093] In an alternative embodiment, based on the net photosynthetic rate, the yield of the crop is obtained to obtain the selling income of the crop, which can also be obtained by experimental determination or data-driven method. For the crop variety, a direct correlation between net photosynthetic rate and biomass accumulation is established by controlling the light intensity of the potting experiment or the plant factory experiment. The crops are planted under different light intensities, and the net photosynthetic rate (such as using a photosynthetic instrument) and the biomass (such as harvesting and weighing) are measured regularly. The mathematical model (such as a linear model, an exponential model) of the net photosynthetic rate and the biomass accumulation is fitted; or a prediction model (such as a regression tree, a neural network) is trained using historical light-intensity-yield data. Multi-dimensional data such as light intensity, net photosynthetic rate, growth cycle, yield, etc. in the plant factory are collected to build a model for obtaining.

[0094] In a preferred embodiment, obtaining the lighting energy consumption cost of the crop includes:

[0095] According to the light intensity of the crop, the corresponding lighting power is calculated;

[0096] Based on the lighting power, the relationship between the lighting energy consumption cost and the lighting power is constructed, and the lighting energy consumption cost of the crop is calculated.

[0097] It should be noted that the relationship between the light intensity and the power of the crop is obtained, which can clearly understand the power consumption under different light intensity requirements, accurately calculate the lighting energy consumption cost, and provide a direction for optimizing the lighting system and reducing the energy consumption cost of the plant factory through the relationship.

[0098] In this embodiment, in the low-power range, the light intensity and the power of the plant factory LED lamp follow a linear change relationship, which is expressed as follows:

[0099] I=kP l

[0100] Wherein, k is the conversion coefficient, that is, the ability of each watt of electric power to convert into light intensity, P l is the power of the LED lamp.

[0101] The plant factory will generate a large amount of electric energy consumption during operation, and the lighting is one of the main energy consumptions. The relationship between the lighting energy consumption cost and the power of the LED lamp is as follows:

[0102] w=P l Le

[0103] Wherein, w is the electricity bill, and e is the local electricity price.

[0104] In a preferred embodiment, constructing a yield optimization model of the crop includes:

[0105] The illumination power is set as a decision variable, and a target function is established with the maximum difference in economic benefits of crops under optimal growth light intensity and after reducing energy consumption as the target;

[0106] The constraint conditions of the benefit optimization model are set, including economic benefit constraints, photoperiod and light intensity constraints, and illumination power constraints.

[0107] It should be noted that this step comprehensively considers the benefits and costs to find the optimal energy use scheme, which can reduce energy consumption costs and improve the overall economic benefits of the plant factory while ensuring crop yield and quality. Through energy optimization, energy waste can be reduced, and the green and sustainable development of the plant factory can be promoted.

[0108] In a preferred embodiment, the target function is represented as:

[0109] maxF = F1 - F0

[0110] F1 = W1 - w1

[0111] F0 = W0 - w0

[0112] Wherein, F1 represents the economic benefit after reducing energy consumption, F0 represents the economic benefit of crops under optimal growth light intensity, W1 represents the economic benefit of crops after reducing energy consumption, w1 represents the illumination energy consumption cost after reducing energy consumption, W0 represents the economic benefit of crops under optimal growth light intensity, and w0 represents the illumination energy consumption cost under optimal growth light intensity.

[0113] Specifically, based on the relationship analysis of the previous steps, the economic benefit formula after reducing energy consumption is represented as:

[0114]

[0115] In a preferred embodiment, the constraint conditions include:

[0116] In order to ensure the economic benefit of the plant factory, the economic benefit constraints are set by the difference between the economic benefit after reducing energy consumption and the economic benefit before reducing energy consumption, which is represented as:

[0117] F1 - F0 > 0

[0118] Considering that lettuce is a typical continuous light sensitive crop, too long photoperiod may cause physiological damage, in order to ensure that the production environment inside the plant factory meets the actual situation, the photoperiod and light intensity constraints are represented as:

[0119] L ≤ 16

[0120] I ≤ 1000

[0121] Wherein, L represents photoperiod, I represents light intensity;

[0122] Based on the relationship analysis between the light intensity and the power of the LED light and the above light intensity constraint, the lighting power constraint is expressed as:

[0123]

[0124] Wherein, P l represents the power of the lighting tool, and k is a conversion coefficient.

[0125] It should be explained that the present application describes the relationship between the net photosynthetic rate of crops and the light intensity in the plant factory environment according to the mathematical model in plant physiological ecology, analyzes the relationship between the selling income of crops and the net photosynthetic rate of crops, establishes the economic income optimization model of the plant factory, realizes the maximum profit of the plant factory operation, and improves the economic benefit of the plant factory; the present application considers the crop production in the plant factory under the deviation of the optimal growth environment of crops, reduces the energy consumption cost of the plant factory as much as possible under the premise of reducing the crop yield as little as possible, so as to realize the effect of reducing the energy consumption cost per unit yield of the plant factory.

[0126] The above is a schematic scheme of the plant factory energy optimization method considering the dynamic growth model of crops of the present embodiment. It should be noted that the technical scheme of the plant factory energy optimization system considering the dynamic growth model of crops belongs to the same concept as the technical scheme of the plant factory energy optimization method considering the dynamic growth model of crops described above, and the technical scheme of the plant factory energy optimization system considering the dynamic growth model of crops in the present embodiment is not described in detail. The details can be referred to the description of the technical scheme of the plant factory energy optimization method considering the dynamic growth model of crops.

[0127] Embodiment 2

[0128] The present embodiment provides a plant factory energy optimization system considering the dynamic growth model of crops, comprising:

[0129] The first calculation module is configured to obtain crop data and construct the relationship between the net photosynthesis rate of crops and the light intensity;

[0130] The second calculation module is configured to obtain the yield of crops based on the net photosynthesis rate to obtain the selling income of crops;

[0131] The third calculation module is configured to obtain the relationship between the light intensity and the power of crops based on the crop data to obtain the lighting energy consumption cost of crops;

[0132] The optimization module is configured to construct the income optimization model of crops based on the selling income of crops and the lighting energy consumption cost of crops, and optimize the energy consumption of the plant factory.

[0133] The embodiment also provides a computer device suitable for plant factory energy optimization considering a crop dynamic growth model, comprising:

[0134] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the plant factory energy optimization method considering the crop dynamic growth model.

[0135] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the plant factory energy optimization method considering the crop dynamic growth model.

[0136] The storage medium proposed in the embodiment belongs to the same inventive concept as the plant factory energy optimization method considering the crop dynamic growth model proposed in the above embodiment, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk, or an optical disc, etc., including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0138] Embodiment 3

[0139] Referring to Tables 1-2 and Figures 2-3 For an embodiment of the present application, a plant factory energy optimization method considering a crop dynamic growth model is provided, and in order to verify its beneficial effects, economic benefit calculation and simulation experiments are carried out for scientific demonstration.

[0140] Lettuce has a short growth cycle, easy-to-control environmental requirements, and a relatively short plant height suitable for three-dimensional cultivation, and thus is widely planted in plant factories, so lettuce is selected as the research object in the present patent. Taking a leafy vegetable plant factory in southern China as an example, the parameters in the lettuce plant factory economic benefit optimization model established in the embodiment are set as shown below:

[0141] Based on the experimental data, the maximum photosynthetic rate P Nmax Take 20 μmol·m-2·s-1, the initial slope α takes 0.05, the curvature factor θ takes 0.8, the dark respiration rate R D Take 2 μmol·m-2·s-1; the plant factory covers an area of 2600 m2, the photosynthetic effective area A is 2000 m2, the total installed capacity of LED lamp is 200 kw, and the maximum unit area light intensity can reach 1000 μmol·m-2·s-1, so the light intensity and power conversion coefficient k of LED lamp takes 5 μmol·m-2·s-1·kw-1; According to the latest market price c of 45 yuan / kg of organic lettuce planted in the plant factory found on a domestic online shopping platform; According to the operation of the plant factory, the light period L is 16 h, the mass constant a corresponding to the fixed carbon in glucose is 3·10-5 g / μmol, the dry matter ratio s takes 0.75, and the economic coefficient HI takes 0.6; According to the "Notice of the National Development and Reform Commission on Adjusting the Classification Structure of Sales Price", the agricultural production and agricultural product processing in the plant factory implement the agricultural electricity price, so the electricity price e in this paper takes 0.484 yuan / kwh, and the parameter value setting is shown in Table 1.

[0142] Table 1: Model parameter setting

[0143]

[0144]

[0145] Based on the above analysis, the parameter values are substituted into the economic benefit optimization model of lettuce plant factory, a function of LED lamp power as the independent variable and the economic benefit of plant factory as the dependent variable is obtained, and the function image between the economic benefit of product sales and the power consumption cost of LED lamp power is drawn, as shown in Figure 2 .

[0146] When considering the optimal yield of plant factory crops and the maximum W, the light intensity should take the maximum value, that is, 1000 μmol·m-2·s-1, at this time the total power of plant factory LED lamp is 200 kw, but it can be seen from the two images that the economic benefit brought by sacrificing part of the yield of lettuce may be greater than the economic loss brought by the reduction of lettuce yield, based on this analysis, the function image of the economic benefit function F of the plant factory is drawn, as shown in Figure 3 . Figure 3 It can be seen that when the power of LED lamp is 200 kw, the economic benefit of plant factory is not the maximum value, and the economic benefit of plant factory under different power is shown in Table 2:

[0147] Table 2: Correspondence table of economic benefit of plant factory and power of LED lamp

[0148]

[0149]

[0150] As can be seen from Table 2, the economic benefit of the plant factory is not optimal when only the lettuce yield is optimal, and the economic benefit of the plant factory can be higher under non-optimal light conditions for lettuce.

[0151] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing energy use in plant factories considering crop dynamic growth models, characterized in that, include: Acquire crop data and construct the relationship between crop net photosynthetic rate and light intensity; Based on the net photosynthetic rate, the crop yield is obtained to obtain the revenue from the sale of the crop; Based on the crop data, the relationship between crop light intensity and power is obtained to determine the lighting energy consumption cost of the crop. Based on the sales revenue of the crops and the energy consumption cost of lighting for the crops, a crop revenue optimization model is constructed to optimize the energy use of the plant factory.

2. The plant factory energy optimization method considering crop dynamic growth model as described in claim 1, characterized in that, Constructing the relationship between crop net photosynthetic rate and light intensity includes: Obtain multiple sets of light intensity, crop respiration consumption rate in darkness, and maximum photosynthetic rate for crops, and calculate the net photosynthetic rate for multiple sets of crops; Based on multiple sets of net photosynthetic rates of the crops and light intensity, a relationship curve between net photosynthetic rate and light intensity is obtained, so as to calculate the corresponding net photosynthetic rate according to the light intensity.

3. The energy optimization method for plant factories considering crop dynamic growth models as described in claim 2, characterized in that, Revenue from the sale of crops includes: Based on the net photosynthetic rate, combined with the crop's photoperiod and photosynthetically active area, the daily net carbon accumulation of the crop is calculated. Based on the daily net carbon accumulation, the relationship between the daily net carbon accumulation and the dry matter accumulation is constructed, and the dry matter mass corresponding to the carbon assimilation is calculated. Based on the dry matter content, the proportion of edible portion of the crop to the total fresh matter is obtained. Through the edible portion of the crop, the relationship between the crop sales revenue and the net photosynthetic rate is constructed to obtain the crop sales revenue.

4. The energy optimization method for plant factories considering crop dynamic growth models as described in claim 3, characterized in that, The energy cost of lighting for crops includes: The corresponding lighting power is calculated based on the light intensity of the crop; Based on the lighting power, a relationship between lighting energy consumption cost and lighting power is established, and the lighting energy consumption cost of crops is calculated.

5. The energy optimization method for plant factories considering crop dynamic growth models as described in claim 1, characterized in that, Constructing a crop yield optimization model includes: Let lighting power be the decision variable, and establish an objective function with the goal of maximizing the difference between the economic benefits of crops under optimal light intensity and after reducing energy consumption. The constraints of the revenue optimization model are set, including economic revenue constraints, photoperiod and light intensity constraints, and lighting power constraints.

6. The plant factory energy optimization method considering a crop dynamic growth model as described in claim 5, characterized in that, The objective function is expressed as: maxF = F1 - F0 F1 = W1 - w1 F0 = W0 - w0 Where F1 represents the economic benefit after considering energy reduction, F0 represents the economic benefit of crops under optimal light intensity for growth, W1 represents the economic benefit of selling crops after reducing energy consumption, w1 represents the lighting energy cost after reducing energy consumption, W0 represents the economic benefit of selling crops under optimal light intensity for growth, and w0 represents the lighting energy cost under optimal light intensity for growth.

7. The plant factory energy optimization method considering a crop dynamic growth model as described in claim 6, characterized in that, The constraints include: The economic benefit constraint is set by the difference between the economic benefits before and after reducing energy consumption, and is expressed as follows: F1-F0>0 The constraints on optical period and illumination intensity are expressed as follows: L≤16 I≤1000 Where L represents the photoperiod and I represents the light intensity; The lighting power constraint is expressed as follows: Among them, P l The power of the lighting tool is represented by k, which is the conversion factor.

8. A plant factory energy optimization system considering a crop dynamic growth model, employing the plant factory energy optimization method considering a crop dynamic growth model as described in any one of claims 1 to 7, characterized in that, include, The first calculation module is used to acquire crop data and construct the relationship between crop net photosynthetic rate and light intensity. The second calculation module is used to obtain the crop yield based on the net photosynthetic rate in order to obtain the sales revenue of the crop. The third calculation module is used to obtain the relationship between the light intensity and power of the crop based on the crop data, so as to obtain the lighting energy consumption cost of the crop. The optimization module is used to construct a crop revenue optimization model based on the crop's sales revenue and the crop's lighting energy consumption cost, thereby optimizing the energy consumption of the plant factory.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the plant factory energy optimization method considering a crop dynamic growth model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the plant factory energy optimization method considering a crop dynamic growth model as described in any one of claims 1 to 7.

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