An optimized planning method and system for an agro-pastoral combined ecological recycling base

Abstract

The embodiment of the specification provides an optimization planning method, system and storage medium of an agricultural and pastoral ecological circulation base, the optimization planning method of the agricultural and pastoral ecological circulation base comprises the following steps: obtaining first input information based on an acquisition device, predicting waste output information based on a first prediction module; obtaining at least one group of first preset resource allocation values, predicting at least one of biogas output information and marsh liquid output information based on the waste output information and the at least one group of first preset resource allocation values based on the first prediction module; obtaining at least one group of second preset resource allocation values, predicting the income through the second prediction module based on the waste output information, the biogas output information, the marsh liquid output information and the at least one group of second preset resource allocation values, so as to determine a resource planning scheme.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of computer, and in particular, to an optimized planning method and system of an agricultural and animal husbandry combined ecological recycling base and a storage medium. BACKGROUND

[0002] Traditional agricultural planting and breeding often face the problem of environmental pollution, such as the use of chemical fertilizers, straw burning, and water eutrophication, which threaten the ecological environment. The current solution to this problem is to combine traditional planting and breeding to form an agricultural ecological system and recycle agricultural waste to achieve zero discharge of "three wastes", but while considering environmental protection, the economic value of the agricultural ecological system also needs to be considered.

[0003] Therefore, a method for reasonably planning the disposal of agricultural waste is needed to achieve the most economical treatment of agricultural waste under the premise of protecting the environment. SUMMARY

[0004] One aspect of the present specification provides an optimized planning method of an agricultural and animal husbandry combined ecological recycling base. The method comprises: obtaining first input information based on a collection device, predicting waste output information based on a first prediction module; obtaining at least one group of first preset resource allocation values, based on the waste output information and the at least one group of first preset resource allocation values, predicting at least one of biogas output information and biogas slurry output information through the first prediction module, the at least one group of first preset resource allocation values including the percentage of the waste output allocated to the biogas output and the biogas slurry output; obtaining at least one group of second preset resource allocation values, based on the waste output information, the biogas output information, the biogas slurry output information, and the at least one group of second preset resource allocation values, predicting revenue through a second prediction module to determine a resource planning scheme, the at least one group of second preset resource allocation values including a self-use percentage and a sale percentage.

[0005] Another aspect of the present specification provides an optimized planning system of an agro-pastoral ecological recycling base. The system comprises: a prediction module configured to acquire first input information based on a collection device, and predict waste output information based on a first prediction module; acquire at least one set of first preset resource allocation values, and predict at least one of biogas output information and biogas slurry output information based on the waste output information and the at least one set of first preset resource allocation values through the first prediction module, wherein the at least one set of first preset resource allocation values comprises a percentage of the waste output allocated to the biogas output and the biogas slurry output; and a second prediction module configured to acquire at least one set of second preset resource allocation values, and predict a revenue based on the waste output information, the biogas output information, the biogas slurry output information, and the at least one set of second preset resource allocation values through a second prediction module to determine a resource planning scheme, wherein the at least one set of second preset resource allocation values comprises a self-use percentage and a sale percentage.

[0006] Another aspect of the present specification provides an optimized planning device of an agro-pastoral ecological recycling base, comprising a processor configured to execute an optimized planning method of an agro-pastoral ecological recycling base.

[0007] Another aspect of the present specification provides a computer readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the optimized planning method of an agro-pastoral ecological recycling base. BRIEF DESCRIPTION OF DRAWINGS

[0008] The present specification will be further illustrated in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same reference numbers represent the same structures, wherein:

[0009] Figure 1 is an application scenario diagram of the optimized planning of an agro-pastoral ecological recycling base according to some embodiments of the present specification;

[0010] Figure 2 is a functional module block diagram of the optimized planning of an agro-pastoral ecological recycling base according to some embodiments of the present specification;

[0011] Figure 3 is an exemplary flowchart of the optimized planning method of an agro-pastoral ecological recycling base according to some embodiments of the present specification;

[0012] Figure 4 is an exemplary flowchart of the first prediction module predicting biogas output information and biogas slurry output information according to some embodiments of the present specification;

[0013] Figure 5is a model structure diagram generated according to a resource planning scheme shown in some embodiments of the present specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor on the basis of these drawings. Unless it is clear from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structures or operations.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0016] As shown in the present specification and claims, unless the context clearly indicates otherwise, the words "one", "a", "an", and / or "the" do not refer to the singular, but can also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0017] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to these processes, or one or more steps of the operation can be removed from these processes.

[0018] The embodiments of the present specification relate to an optimization planning method, system and storage medium of an agricultural and pastoral ecological recycling base. The optimization planning method, system and storage medium of the agricultural and pastoral ecological recycling base can be applied to the fields of agricultural production, agricultural research, ecological management, environmental protection, etc. In some embodiments, the optimization planning method, system and storage medium of the agricultural and pastoral ecological recycling base can be applied to the agricultural and pastoral ecological recycling base management end, such as agricultural research room, resource planning department, etc. In some embodiments, the optimization planning method, system and storage medium of the agricultural and pastoral ecological recycling base can be applied to the agricultural production end, such as the prediction terminal, the fermentation chamber terminal, etc. In some embodiments, the optimization planning method, system and storage medium of the agricultural and pastoral ecological recycling base can be applied to other fields, such as data analysis, machine learning, etc.

[0019] Figure 1 is a schematic diagram of an application scenario of a system for optimized planning of an agro-pastoral ecological recycling base according to some embodiments of the present specification.

[0020] The optimized planning system 100 for the agro-pastoral ecological recycling base can obtain the breeding information and the planting information of a certain ecological recycling base based on the acquisition device, predict the waste output information of the ecological recycling base based on the above information, predict the biogas output information and the biogas slurry output information of the ecological recycling base based on the waste output information and the first preset resource allocation value, and predict the income and determine the resource planning scheme based on the waste output information, the biogas output information, the biogas slurry output information and the second preset resource allocation value.

[0021] As shown in Figure 1 , the optimized planning system 100 for the agro-pastoral ecological recycling base can include a server 110, a processing device 120, a storage device 130, a first user terminal 140, a network 150, and a second user terminal 160.

[0022] In some embodiments, the server 110 can be used to process information and / or data related to the optimized planning system 100 for the agro-pastoral ecological recycling base, for example, to predict waste output information, to predict biogas output information and biogas slurry output information, to predict income and to determine resource planning scheme. In some embodiments, the server 110 can be a single server, or a group of servers. The group of servers can be centralized or distributed, for example, the server 110 can be a distributed system. In some embodiments, the server 110 can be local or remote. For example, the server 110 can access information and / or data stored in the storage device 130, the first user terminal 140 and the second user terminal 160 via the network 150. For another example, the server 110 can be directly connected to the storage device 130, the first user terminal 140 and / or the second user terminal 160 to access the stored information and / or data.

[0023] In some embodiments, the server 110 can include the processing device 120. The processing device 120 can process information and / or data related to the optimized planning system 100 for the agro-pastoral ecological recycling base to perform one or more functions described in the present specification. For example, the processing device 120 can be used to predict waste output information, to predict biogas output information and biogas slurry output information, to predict income and to determine resource planning scheme. In some embodiments, the processing device 120 can include one or more processing engines (e.g., single-chip processing engines or multi-chip processing engines).

[0024] The storage device 130 can be used to store data and / or instructions related to the optimization planning of the agro-pastoral ecological circulation base. In some embodiments, the storage device 130 can store data obtained / acquired from the first user terminal 140 and / or the second user terminal 160. In some embodiments, the storage device 130 can store data and / or instructions used by the server 110 to perform or use to complete the exemplary methods described in this specification.

[0025] In some embodiments, the storage device 130 can be implemented on a cloud platform. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an on-premises cloud, a multi-tier cloud, or the like, or any combination thereof. In some embodiments, the storage device 130 can be connected to the network 150 to communicate with one or more components of the optimization planning system 100 of the agro-pastoral ecological circulation base (e.g., the server 110, the first user terminal 140, the second user terminal 160). One or more components of the optimization planning system 100 of the agro-pastoral ecological circulation base can access data or instructions stored in the storage device 130 via the network 150. In some embodiments, the storage device 130 can be directly connected to or in communication with one or more components of the optimization planning system 100 of the agro-pastoral ecological circulation base (e.g., the server 110, the first user terminal 140, the second user terminal 160). In some embodiments, the storage device 130 can be part of the server 110. In some embodiments, the storage device 130 can be a separate memory.

[0026] The first user terminal 140 can be a requester of the first input information. In some embodiments, the first user terminal 140 can be a person, a tool, or other entity directly related to obtaining the first input information. In this specification, “user” and “user terminal” can be used interchangeably. In embodiments of the present application, the first user terminal 140 can be a collection device for obtaining the first input information. In some embodiments, the first user terminal 140 can include a mobile device 140-1, a tablet 140-2, a laptop 140-3, a notebook 140-4, a camera 140-5, or any combination thereof. In some embodiments, the camera 140-5 can include one or more of a 2D camera, a 3D camera, an infrared camera, or the like. The camera 140-5 can be used to collect first images related to farming and second images related to planting. In some embodiments, the camera 140-5 can be a standalone camera or a part of another device, such as a mobile phone camera, a computer camera, a vehicle camera, a drone camera, or the like. In some embodiments, the camera 140-5 can be fixed or movable. In some embodiments, the first user terminal 140 can include various sensors, such as a temperature sensor, a humidity sensor, a gas detection sensor, an image sensor, or the like, for obtaining the first input information.

[0027] The network 150 can facilitate exchange of information and / or data. In some embodiments, one or more components of the optimized planning system 100 for agro-pastoral ecological circulation bases, such as the server 110, the first user terminal 140, and the second user terminal 160, can send information and / or data to other components of the optimized planning system 100 for agro-pastoral ecological circulation bases via the network 150. For example, the server 110 can obtain the first input information from the first user terminal 140 via the network 150. In some embodiments, the network 150 can be a wired network, a wireless network, or the like, or any combination thereof. In some embodiments, the optimized planning system 100 for agro-pastoral ecological circulation bases can include one or more network access points. For example, base stations and / or wireless access points 150-1, 150-2, …, one or more components of the optimized planning system 100 for agro-pastoral ecological circulation bases can be connected to the network 150 to exchange data and / or information.

[0028] The second user terminal 160 can be configured to output and / or display information related to the resource planning scheme, such as a resource allocation scheme corresponding to a certain planning scheme, a cost and income corresponding to a certain planning scheme, etc. In some embodiments, the second user terminal 160 can output the resource planning scheme generated by the server 110, or directly display the resource planning scheme. In some embodiments, the second user terminal 160 can obtain the information to be output or displayed from the storage device 130. In some embodiments, the second user terminal 160 can be a device similar to or the same as the first user terminal 140. In some embodiments, the first user terminal 140 can be the same device as the second user terminal 160. In some embodiments, the second user terminal 160 can include a mobile device 160-1, a tablet computer 160-2, a laptop computer 160-3, a notebook computer 160-4, etc., or any combination thereof.

[0029] It should be noted that the optimized planning system 100 of the farming and animal husbandry combined ecological recycling base is provided for illustrative purposes only and is not intended to limit the scope of the present application. Those of ordinary skill in the art can make various modifications or changes based on the description of the present application. For example, the optimized planning system 100 of the farming and animal husbandry combined ecological recycling base can also include an information source. For another example, the optimized planning system 100 of the farming and animal husbandry combined ecological recycling base can implement similar or different functions on other devices. However, these changes and modifications will not depart from the scope of the present application.

[0030] Figure 2 is a functional module block diagram of the optimized planning system of the farming and animal husbandry combined ecological recycling base according to some embodiments of the present application. The system 200 can be executed by the processing device 120.

[0031] As shown in Figure 2 , the processing device 120 can include a first prediction module 210 and a second prediction module 220.

[0032] In some embodiments, the first prediction module 210 can obtain the first input information based on the acquisition device, and predict the waste output information based on the first prediction module.

[0033] In some embodiments, the first prediction module 210 can obtain at least one set of first preset resource allocation values, and predict at least one of the biogas output information and the biogas slurry output information based on the waste output information and the at least one set of first preset resource allocation values based on the first prediction module. In some embodiments, the first prediction module 210 can include a waste prediction unit 211 and a biogas and biogas slurry prediction unit 212. For specific description of the first prediction module, see Figure 3 and related description thereof, which will not be repeated here.

[0034] In some embodiments, the waste prediction unit 211 can be configured to predict the waste output information. In some embodiments, the waste prediction unit 211 can predict other information of the waste, such as the composition information of the waste, etc.

[0035] In some embodiments, the biogas-biogas slurry prediction unit 212 can be configured to predict at least one of the biogas output information, the biogas slurry output information. In some embodiments, the biogas-biogas slurry prediction unit 212 can predict other information of the biogas-biogas slurry, such as the composition information of the biogas-biogas slurry, etc.

[0036] In some embodiments, the first prediction module 210 can predict the waste output information based on at least one of the farming information, the planting information, by a first prediction model in the first prediction module. In some embodiments, the farming information comprises a first image related to farming, the planting information comprises a second image related to planting, and the first prediction model comprises a sub-model for image recognition.

[0037] In some embodiments, the first prediction module 210 can determine the amount of waste for fermentation based on the waste output information and at least one set of first preset resource allocation values. For specific description of the first preset resource allocation values, see Figure 3 and related description thereof, which will not be repeated here.

[0038] In some embodiments, the first prediction module 210 can predict at least one of the biogas output information, the biogas slurry output information based on the amount of waste for fermentation, by a fermentation prediction model in the first prediction module. For specific description of the first prediction module, see Figure 3 and related description thereof, which will not be repeated here.

[0039] In some embodiments, the second prediction module 220 can obtain at least one set of second preset resource allocation values, and predict the revenue based on the waste output information, the biogas output information, the biogas slurry output information, and at least one set of second preset resource allocation values, by the second prediction module, to determine the resource planning scheme. For specific description of the second prediction module and the second preset resource allocation values, see Figure 3 and related description thereof, which will not be repeated here.

[0040] Figure 3 is an exemplary flowchart of an optimization planning method of a farm-animal husbandry combined ecological recycling base according to some embodiments of the present specification. The flow 300 can be executed by the optimization planning system 100 of the farm-animal husbandry combined ecological recycling base. For example, the flow 300 can be executed by Figure 1 the processing device 120 described above.

[0041] At step S302, the first input information is acquired based on the acquisition device, and the waste output information is predicted based on the first prediction module. In some embodiments, the step S302 can be performed by the first prediction module 210.

[0042] The acquisition device can be a device used to acquire information related to the base in the agricultural and pastoral ecological recycling base. In some embodiments, the acquisition device can be one or a combination of a camera, a recording device, a network information acquisition terminal, etc., and can also be one or a combination of a user terminal such as a smart phone, a tablet computer, a large computer, etc.

[0043] The first input information can be information that can reflect the operation of the agricultural and pastoral ecological recycling base.

[0044] In some embodiments, the first input information can be breeding information and planting information. In some embodiments, the first input information can be information in the form of sound, image, text, etc.

[0045] The breeding information can be information related to the feeding of animals in the agricultural and pastoral ecological recycling base, such as animal breed, animal quantity, animal growth condition, animal health condition, etc. In some embodiments, the breeding information also includes information about waste generated during the breeding process, such as waste composition and waste quantity. For example, in the agricultural and pastoral ecological recycling base of “pig-biogas-vegetable”, the breeding information can be information about pig breed, pig quantity, and pig feeding time.

[0046] In some embodiments, the breeding information includes first images related to breeding. The first images can be images used to reflect the breeding information in the agricultural and pastoral ecological recycling base. For example, the first images can be pictures reflecting the growth condition of animals. Also for example, the first images can be pictures reflecting the size of the breeding field and the scale of breeding.

[0047] The planting information can be information related to the planting of plants in the agricultural and pastoral ecological recycling base. For example, plant breed, plant quantity, plant growth condition, plant health condition, etc. In some embodiments, the planting information also includes information about waste generated during the planting process, such as waste composition and waste quantity. For example, in the agricultural and pastoral ecological recycling base of “pig-biogas-vegetable”, the planting information can be information about corn breed, corn yield, corn planting time, corn health condition, and straw yield.

[0048] In some embodiments, the planting information includes second images related to planting. The second images can be images used to reflect the planting information in the agricultural and pastoral ecological recycling base. For example, the second images can be pictures reflecting the growth condition and health condition of plants. Also for example, the second images can be pictures reflecting the size of the farmland, the scale of planting, and the quality of the farmland.

[0049] In some embodiments, the collection device can periodically obtain the breeding information and the planting information in the farm-pasture combined ecological recycling base during the collection process. For example, the camera installed on the feed feeding machine can take pictures to obtain the breeding information of the animals every certain period of time (e.g., 1 day, 7 days, 1 month, etc.), or the unmanned aerial vehicle can take pictures of the farmland to obtain the planting information of the plants.

[0050] In some embodiments, the collection device can obtain the breeding information and the planting information in the farm-pasture combined ecological recycling base based on the input sound, text, image, etc. of the user (e.g., a farmer, a base operator, a researcher, etc.). In some embodiments, the collection device can obtain the breeding information and the planting information in the farm-pasture combined ecological recycling base based on the Internet.

[0051] The first prediction module can be a processing module for predicting the waste output information. In some embodiments, the first prediction module can be implemented by a machine learning model. For example, the first prediction module can include a model (referred to as a “waste output model”) for determining the waste output information. The waste output model can output the waste output information based on the input first input information.

[0052] In some embodiments, the waste output model includes an image recognition sub-model and a prediction sub-model. In some embodiments, the image recognition sub-model can be a CNN model, and the prediction sub-model can be a DNN model. In some embodiments, the first input information can be an image, for example, a first image and a second image. The input of the image recognition sub-model is the first input information, and the output is the image features. For example, the image features can include breeding information, planting information, etc. The input of the prediction sub-model is the image features output by the image recognition sub-model, and the output is the predicted waste output information. The input of the prediction sub-model also includes other information in the first input information except the image, for example, the text information input by the user.

[0053] In some embodiments, the waste output model can be trained by a plurality of labeled first training samples. For example, a plurality of labeled first training samples can be input into the waste output model, a loss function can be constructed based on the labels and the results of the initial first prediction module, and the parameters of the waste output model can be iteratively updated based on the loss function. When the loss function of the initial waste output model meets a preset condition, the model training is completed, and a trained waste output model is obtained. The preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0054] In some embodiments, the first training sample can include a plurality of pictures, which at least include farming information and planting information of the farming-pastoral ecologically-circulating base. The label can be the corresponding waste output information. The label can be obtained based on historical waste output information. For specific description of model training, see Figure 5 and related descriptions thereof, which are not repeated here.

[0055] The waste output information can be information such as yield of various wastes. In some embodiments, the waste output information can include farming waste output information and planting waste output information. For example, the waste output information can include yield of farming waste and yield of planting waste.

[0056] In some embodiments, the first prediction module can also determine the output information of the waste in other ways. For example, the first prediction module can obtain the corresponding relationship between the farming information, the planting information and the yield of the waste based on the historical farming information and the historical planting information, and the yield of the waste corresponding to the above information, and accordingly, based on the first input information and the corresponding relationship, the yield of the waste can be predicted. For another example, the waste output information can also be obtained by direct input of the user.

[0057] Through the above steps, the yield of the waste is predicted based on the image and other input information. On the one hand, the image is used as a new source of information, so that the waste generated by farming and planting can be obtained through the trained prediction model. On the other hand, the image and other input information can be automatically obtained by the system on a regular basis, which improves the degree of automation of the system and reduces the labor cost.

[0058] In step S304, at least one set of first preset resource allocation values is obtained, and based on the waste output information and the at least one set of first preset resource allocation values, at least one of the biogas output information and the biogas slurry output information is predicted by the first prediction module. The at least one set of first preset resource allocation values includes the percentage of the waste output allocated to the biogas output and the biogas slurry output. In some embodiments, this step S304 can be performed by the first prediction module 210.

[0059] The first preset resource allocation value can be an allocation value obtained by allocating the waste output according to different use modes, which can be embodied in the form of percentage. In some embodiments, the first preset resource allocation value includes the percentage of the waste output allocated to the biogas output and the biogas slurry output.

[0060] The biogas output percentage and the biogas slurry output percentage can correspond to the percentage of the total waste output that is used to produce biogas and biogas slurry, respectively. For example, the biogas output percentage can be 30%, and the biogas slurry output percentage can be 70%. In some embodiments, in addition to being used for biogas output and biogas slurry output, the waste output can also be used for other functions such as feed output. For example, the biogas output percentage can be 10%, the biogas slurry output percentage can be 40%, and the feed output percentage can be 50%. For a detailed description of the first preset resource allocation value, see Figure 4 and the related description, which will not be repeated here.

[0061] The biogas output information can be information such as the yield of biogas. In some embodiments, the yield of biogas is positively correlated with the biogas output percentage of the waste.

[0062] The biogas slurry output information can be information such as the yield of biogas slurry. In some embodiments, the yield of biogas slurry is positively correlated with the biogas slurry output percentage of the waste.

[0063] In some embodiments, based on the percentage of the waste output allocated to biogas output and biogas slurry output, the yield of biogas and biogas slurry can be predicted by the biogas slurry prediction unit of the first prediction module. For a detailed description of this prediction step, see Figure 4 and the related description, which will not be repeated here.

[0064] In some embodiments, the biogas output information and the biogas slurry output information can be obtained in other ways, such as by direct user input.

[0065] In step S306, at least one set of second preset resource allocation values is obtained, and based on the waste output information, the biogas output information, the biogas slurry output information, and the at least one set of second preset resource allocation values, the yield is predicted by the second prediction module to determine the resource planning scheme, wherein the at least one set of second preset resource allocation values includes a self-use percentage and a sale percentage. In some embodiments, this step S306 can be performed by the second prediction module 220.

[0066] The second preset resource allocation value can be an allocation value for output allocation according to different uses of waste, biogas slurry, and biogas, which can be expressed in the form of a percentage. In some embodiments, the second preset resource allocation value can include a self-use percentage and a sale percentage.

[0067] The self-use percentage can be the percentage of resources in the waste, biogas slurry, and biogas output that are needed to maintain the operation of the base. The sale percentage can be the percentage of resources in the waste, biogas slurry, and biogas output that are sold. In some embodiments, the self-use percentages of the waste, biogas slurry, and biogas can be 80%, 60%, and 60%, respectively. The sale percentages of the waste, biogas slurry, and biogas can be 20%, 40%, and 40%, respectively. In some embodiments, the self-use percentage and the sale percentage can be determined according to the market price of the resources and the self-use demand. For example, when the market price of fuel, feed, fertilizer, and the like is high, the cost of planting crops and breeding livestock increases, and at this time, the self-use percentage can be appropriately reduced, i.e., the breeding and planting scale is reduced, while the sale percentage is appropriately increased, i.e., the proportion of waste, biogas slurry, and biogas converted into fuel, feed, and fertilizer is increased to obtain more economic benefits.

[0068] In some embodiments, the second preset resource allocation value can be based on user input or obtained through the Internet. In some embodiments, the second preset resource allocation value can be determined based on historical second preset resource allocation values.

[0069] The second prediction module can be a processing module for predicting a resource planning scheme. In some embodiments, the second prediction module can be implemented by a machine learning model, for example, the second prediction module can include a model (referred to as a "resource planning scheme model") for determining a resource planning scheme. The resource planning scheme model can output a revenue based on input waste output information, biogas output information, biogas slurry output information, and at least one set of second preset resource allocation values. Further, the second prediction module can determine a resource planning scheme based on the revenue.

[0070] The resource planning scheme model can input a set of second preset resource allocation values each time, and the output is the input corresponding to the set of second preset resource allocation values. It can be understood that the resource planning scheme model can input multiple sets of second preset resource allocation values each time. The output revenue is also multiple, and corresponds to the multiple sets of second preset resource allocation values.

[0071] The biogas output information and the biogas slurry output information input by the resource planning scheme model correspond to the first preset resource allocation value. For details, see Figure 4and the related description. A plurality of benefits corresponding to combinations of at least one set of first preset resource allocation values and at least one set of second preset resource allocation values can be obtained by the resource planning scheme model. For example, at least one set of first preset resource allocation values includes a, b and c, and at least one set of second preset resource allocation values includes A, B and C, which can form 9 combinations in total. Correspondingly, the model can obtain 9 benefits corresponding to these combinations. In some embodiments, the resource planning scheme model can be a DNN model. In some embodiments, the input of the resource planning scheme model is waste output information, biogas output information, biogas slurry output information and at least one set of second preset resource allocation values, and the output is the corresponding benefit. The input of the resource planning scheme model also includes current resource market prices, user demand and other information. In some embodiments, the current resource market prices include biogas fuel prices, fertilizer prices, feed prices, crop prices, livestock prices, etc. In some embodiments, the user demand can include heating fuel consumption, breeding feed consumption, planting fertilizer consumption, etc.

[0072] In some embodiments, the resource planning scheme model can be trained by a plurality of labeled second training samples. For specific description of model training, see Figure 5 and the related description, which will not be repeated here.

[0073] The resource planning scheme can be a comprehensive scheme for waste, biogas, biogas slurry self-use and sale, and the corresponding economic benefits. In some embodiments, the resource planning scheme includes first preset resource allocation values and second preset resource allocation values and the corresponding economic benefits. In some embodiments, different first preset resource allocation values and second preset resource allocation values correspond to different economic benefits, the maximum value can be selected from different economic benefits, and the above information can be used as the final resource planning scheme. In some embodiments, the final resource planning scheme can be the scheme with the maximum benefit among all resource planning schemes. For example, by determining the allocation of waste, the self-use and sale of waste, biogas, biogas slurry and other resources, the sale of crops and livestock, and the maximum benefit generated by the sale, the resource planning scheme is integrated.

[0074] Figure 4 is an exemplary flowchart of the first prediction module according to some embodiments of the present specification for predicting biogas output information and biogas slurry output information. The flowchart 400 can be executed by the optimization planning system 100 of the farm-animal combination ecological recycling base. For example, the flowchart 400 can be executed by Figure 1 the processing device 120 described.

[0075] At step S402, the amount of waste for fermentation is determined based on the waste output information and at least one set of first preset resource allocation values. In some embodiments, the step S402 can be performed by the waste prediction unit 211.

[0076] In some embodiments, biogas and biogas slurry are obtained by fermenting part of the waste. The amount of waste for fermentation can be calculated based on the waste output information and at least one set of first preset resource allocation values. For example, the amount of waste for fermentation can satisfy the following calculation relationship:

[0077] Y = waste output x (biogas output percentage + biogas slurry output percentage)

[0078] wherein Y is the amount of waste for fermentation. In some embodiments, the amount of waste for fermentation has a nonlinear relationship with the waste output. For example, the temperature of the fermentation chamber can affect the progress of the fermentation process, and part of the biogas produced can be returned for combustion to keep the fermentation chamber warm; the microbial flora in the biogas slurry can promote the progress of fermentation, and part of the biogas slurry produced can be returned to the fermentation chamber to promote fermentation.

[0079] In some embodiments, the amount of waste for fermentation can be determined by other factors such as the historical amount of waste for fermentation, the size of the fermentation chamber, etc.

[0080] At step S404, at least one of the biogas output information and the biogas slurry output information is predicted based on the amount of waste for fermentation by a fermentation prediction model in the first prediction module. In some embodiments, the step S404 can be performed by the biogas and biogas slurry prediction unit 212.

[0081] The fermentation prediction model can be a model in the first prediction module for predicting the biogas output information and the biogas slurry output information. The input of the fermentation prediction model can include the amount of waste for fermentation, and the output can include at least one of the biogas output information and the biogas slurry output information.

[0082] The fermentation prediction model can be a DNN model. In some embodiments, the input of the fermentation prediction model can further include information such as the temperature of the fermentation chamber, which is related to the allocation amount of warming biogas. For example, in order to keep the fermentation chamber at a reasonable temperature condition (i.e. under mesophilic fermentation conditions), part of the generated biogas is used as fuel to keep the fermentation chamber warm, and the allocation amount of warming biogas can be determined according to the calorific value of the biogas, the current temperature of the fermentation chamber, and the heat utilization efficiency.

[0083] The amount of waste for fermentation input to the fermentation prediction model each time can be determined based on a set of first preset allocation values, and correspondingly, the output biogas output information and biogas slurry output information correspond to the set of first preset allocation values.

[0084] In some embodiments, the waste output information and at least one set of first preset allocation values can be directly input into the fermentation prediction model to obtain biogas output information and digestate output information. Each time of input can be a set of the at least one set of first preset allocation values, and the output biogas output information and digestate output information correspond to the set of first preset allocation values.

[0085] It can be understood that the fermentation prediction model can input multiple sets of first preset allocation values or the amounts of waste for fermentation determined by the multiple sets of first preset allocation values respectively, and output multiple sets of biogas output information and digestate output information, which correspond to the multiple sets of first preset allocation values.

[0086] In some embodiments, the biogas output information and the digestate output information can be obtained based on other manners, such as being directly input by a user.

[0087] Figure 5 FIG. 5 is a model structure diagram generated according to a resource planning scheme according to some embodiments of the present specification. The flow 500 can be executed by the optimized planning system 100 of the agro-pastoral ecological circular base. For example, the flow 500 can be executed by the processing device 120 described above. Figure 1 The processing device 120 described above.

[0088] In some embodiments, the first prediction module can include a waste output model 510 and a fermentation prediction model 520. The input of the waste output model 510 includes breeding information 511, planting information 512, or an image containing at least one of the breeding information 511 and the planting information 512, etc., and the output includes waste output information 513. The input of the fermentation prediction model 520 includes first preset resource allocation values 521, waste output information 513, etc., and the output includes biogas output information 522 and digestate output information 523. In some embodiments, the first preset resource allocation values 521 can include a biogas output percentage and a digestate output percentage.

[0089] In some embodiments, the second prediction module can include a resource planning scheme model 530. The input of the resource planning scheme model 530 includes waste output information 513, biogas output information 522, digestate output information 523, and at least one set of second preset resource allocation values 531, etc., and the output includes a benefit 532. The at least one set of second preset resource allocation values 531 and the corresponding benefits can be used to determine a resource planning scheme 533, i.e., the second preset resource allocation value with the maximum benefit in the resource planning scheme.

[0090] In some embodiments, the waste output model, the fermentation prediction model, and the planning scheme model can be obtained by separate training.

[0091] For example, the breeding information, the planting information, or the plurality of pictures containing the breeding information and the planting information are input into the waste output model as training sample data, waste output information output by the waste output model is obtained, and the corresponding waste output information is used as a label to train the trained waste output model. In some embodiments, the training input sample data and the label sample data of the waste output model can be obtained based on historical waste output information.

[0092] For example, the first preset resource allocation value, the waste output information are input into the fermentation prediction model as training sample data, the biogas output information and the biogas slurry output information output by the fermentation prediction model are obtained, the corresponding biogas output information and biogas slurry output information are used as labels, and the trained fermentation prediction model is obtained by training. In some embodiments, the training input sample data and the label sample data of the fermentation prediction model can be obtained based on historical biogas output information and historical biogas slurry output information.

[0093] For example, the waste output information, the biogas output information, the biogas slurry output information, and at least one set of second preset resource allocation values are input into the resource planning scheme model as training sample data, a resource planning scheme output by the resource planning scheme model is obtained, the corresponding resource planning scheme is used as a label, and the trained resource planning scheme model is obtained by training. In some embodiments, the training input sample data and the label sample data of the resource planning scheme model can be obtained based on historical resource planning schemes.

[0094] In some embodiments, the output of the waste output model can be the input of the resource planning scheme model, and the waste output model, the fermentation prediction model, and the resource planning scheme model can be jointly trained. For example, the sample for joint training includes breeding information, planting information, or a plurality of pictures containing breeding information and planting information, a first preset resource allocation value, a second prediction resource allocation value, and a label corresponding to the yield of each combination of each first prediction resource allocation value and each second prediction resource allocation value. The breeding information, the planting information, or the plurality of pictures containing the breeding information and the planting information are input into the waste output model, the first preset resource allocation value is input into the fermentation prediction model, the second prediction allocation value and the waste output information are input into the resource planning scheme model, a loss function is constructed based on the predicted value output by the resource planning scheme model and the label, and the parameters of the waste output model, the fermentation prediction model, and the resource planning scheme model are updated based on the loss function to obtain the trained waste output model, the fermentation prediction model, and the resource planning scheme model.

[0095] Some embodiments of the present specification can use joint training to obtain the waste output model, the fermentation prediction model and the resource planning scheme model. In this way, the training of the model is simplified, the training time of the model is saved, the workload of the model training is reduced, the total steps of multiple model training are reduced, and the training of the model can focus on the final result, i.e. the resource planning scheme, without needing to focus on the intermediate result of the single model output.

[0096] In the embodiments of the present specification, various waste outputs are predicted based on the machine learning model, and the resource planning scheme is determined according to the various waste outputs, so that the automatic waste recycling of the whole farming-pastoral ecocirculation base can be realized, the human cost waste is reduced, and the unreasonable situation caused by the subjective determination of the waste distribution scheme by the human is reduced. In addition, based on the prices of various resources in the market, on the premise of meeting the self-use demand of the farming-pastoral ecocirculation base, the resources with lower purchase cost are purchased, and the resources with higher market price are sold, so that the economic management of the farming-pastoral ecocirculation base is realized.

[0097] The above has described the basic concepts, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0098] At the same time, the present specification uses specific words to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0099] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements and sequences, however, the description herein of a process should be understood to include any and all combinations of one or more elements, and sequences that can be perceived as either open-ended or specific.

[0100] Similarly, it is to be noticed that the term "comprising", used in the description, should not be interpreted as being restricted only to the elements or steps listed thereafter; it does not exclude other elements or steps. It is thus to be interpreted as specifying the presence of the stated elements or steps as well as the presence of yet unrecited elements or steps. Furthermore, the word "a" or "an" preceding an element or step of the description does not exclude the presence of a plurality of such elements or steps, that is, "a" or "an" means one or more.

[0101] Some embodiments use numerical designations to describe components, quantities of attributes. It is to be understood that such numerical designations used in the description of embodiments are, in some examples, modified by the adjectives "about," "approximately," or "generally." Unless otherwise stated, "about," "approximately," or "generally" indicates that the stated numerical value is permitted to vary by ±20%. Accordingly, numerical values used in the description and claims are approximations that can vary depending upon the desired properties sought to be obtained by the individual embodiment. In some embodiments, numerical values should be considered in the context of the number of significant digits used for measurement and the acceptable rounding off errors. Although the numerical ranges and parameters setting forth the broad scope of the embodiments of the specification are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to be as precise as reasonably possible. However, some variations may occur depending on the choice of input used to develop or derive the numerical values used.

[0102] Every patent, patent application, publication, document, article, book, specification, and other material cited in this specification is hereby incorporated by reference in its entirety for all purposes. Documents, articles, books, specifications, and other materials cited in this specification are hereby expressly incorporated by reference for all purposes to the same extent as if each were individually, specifically and individually indicated to be incorporated by reference for all purposes. For any statement herein that recites an incoφoration by reference of a patent, patent application, publication, document, article, book, specification, or other material, other than statements expressly incorporating prior application history into the disclosure of this specification, it is stated herein that: (i) nothing herein should be construed as a disclaimer of all rights to this patent application publication, and (ii) to the extent that any meaning or definition of the term "application" in 35 U.S.C. § 101 or 35 U.S.C. § 112, is contrary to or

[0103] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. An optimized planning method for an agro-pastoral ecological cycle base, characterized in that, The method comprises: obtaining first input information based on the acquisition device, the first input information comprising at least one of breeding information and planting information, predicting waste output information based on at least one of the breeding information and the planting information through a first prediction module, the first prediction module comprising a sub-model for image recognition; the breeding information comprises animal breed, animal quantity, animal growth condition, animal health condition, waste condition generated in the breeding process, and a first image related to breeding; the planting information comprises plant breed, plant quantity, plant growth condition, plant health condition, waste condition generated in the planting process, and a second image related to planting; obtaining at least one set of first preset resource allocation values, predicting at least one of biogas output information and biogas slurry output information based on the waste output information and the at least one set of first preset resource allocation values through the first prediction module, the at least one set of first preset resource allocation values comprising a percentage of the waste output allocated to the biogas output and the biogas slurry output; obtaining at least one set of second preset resource allocation values, predicting a profit based on the waste output information, the biogas output information, the biogas slurry output information, and the at least one set of second preset resource allocation values through a second prediction module to determine a resource planning scheme, the at least one set of second preset resource allocation values comprising a self-use percentage and a sale percentage.

2. The method of claim 1, wherein, The method comprises: determining the amount of waste for fermentation based on the waste output information and the at least one set of first preset resource allocation values; predicting at least one of the biogas output information and the biogas slurry output information through a fermentation prediction model in the first prediction module based on the amount of waste for fermentation.

3. An optimized planning system for an agro-pastoral ecological cycle base, characterized by, The system comprises: a first prediction module configured to obtain first input information based on an acquisition device, the first input information comprising at least one of breeding information and planting information, predict waste output information based on at least one of the breeding information and the planting information through the first prediction module, the first prediction module comprising a sub-model for image recognition; the breeding information comprises animal breed, animal quantity, animal growth condition, animal health condition, waste condition generated in the breeding process, and a first image related to breeding; the planting information comprises plant breed, plant quantity, plant growth condition, plant health condition, and waste condition generated in the planting process; obtain at least one set of first preset resource allocation values, predict at least one of biogas output information and biogas slurry output information based on the waste output information and the at least one set of first preset resource allocation values through the first prediction module, the at least one set of first preset resource allocation values comprising a percentage of the waste output allocated to the biogas output, the biogas slurry output, and a second image related to planting. A second prediction module is configured to obtain at least one set of second preset resource allocation values, and predict a benefit based on the waste output information, the biogas output information, the biogas slurry output information and the at least one set of second preset resource allocation values, so as to determine a resource planning scheme, wherein the at least one set of second preset resource allocation values comprises a self-use percentage and a sale percentage.

4. The system of claim 3, wherein, The first prediction module is further configured to: determine an amount of waste for fermentation based on the waste output information and the at least one set of first preset resource allocation values; and predict at least one of the biogas output information and the biogas slurry output information based on the amount of waste for fermentation by using a fermentation prediction model in the first prediction module. 5.An optimization planning device for an agro-pastoral ecological recycling base, comprising a processor configured to execute the optimization planning method for the agro-pastoral ecological recycling base according to any one of claims 1 to 2. 6.A computer readable storage medium, wherein the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the optimization planning method for the agro-pastoral ecological recycling base according to any one of claims 1 to 2.

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