CN111273550A - Pig house environment intelligent control system - Google Patents
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- 230000007613 environmental effect Effects 0.000 claims abstract description 52
- 238000012549 training Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims description 31
- 230000005540 biological transmission Effects 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 6
- 238000000034 method Methods 0.000 claims description 4
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 3
- 229910021529 ammonia Inorganic materials 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 3
- 210000003608 fece Anatomy 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 description 10
- 238000009395 breeding Methods 0.000 description 5
- 230000001488 breeding effect Effects 0.000 description 5
- 238000001816 cooling Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 241000282887 Suidae Species 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000007790 scraping Methods 0.000 description 2
- 238000004332 deodorization Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 244000144972 livestock Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The invention relates to an intelligent pigsty environment control system which comprises a control host, controlled equipment and environmental factor acquisition equipment, wherein the controlled equipment and the environmental factor acquisition equipment are connected with the control host; the control server is used for receiving the pigsty environment data and control uploaded by the control host and the user instruction uploaded by the user terminal, training a control algorithm model according to the pigsty environment data and the control data, controlling and sending the trained control algorithm model to the control host and sending the user instruction to the control host; the control host generates a control instruction and a data acquisition instruction through the control algorithm model, or generates the control instruction and the data acquisition instruction according to a user instruction, sends the control instruction to the corresponding controlled equipment, and sends the data acquisition instruction to the corresponding environmental factor acquisition equipment; the system makes up the problem that the control of the pigsty environment cannot be optimized in an autonomous learning mode in the prior art, and improves the intelligence and accuracy of pigsty control.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to an intelligent control system for a pigsty environment.
Background
With the rapid development of livestock breeding industry in China, the breeding farm gradually develops from small-sized pig breeding to large-sized pig breeding. The scale of the farm is continuously enlarged, and the intensive degree of pig breeding is higher and higher. The environmental change and the quality degree of the pigsty directly influence the growth of the live pigs and the economic benefit of pig-raising enterprises. In the traditional pigsty environment control, a plurality of systems are independently controlled, if the measured value of a temperature sensor is increased, a ventilating fan and a water curtain are started, but the opening size and the opening time of the ventilating fan cannot be accurately controlled, so that the fine control degree is not high; meanwhile, the traditional control mode still has the problem of control lag, for example, when the temperature reaches the cooling, after the ventilation fan is started, the cooling needs a certain time for the pigsty to cool, so that the pigsty is not cooled timely. Similar problems exist in the temperature rise, deodorization control and the like of the pigsty; moreover, the remote detection and control of the control equipment cannot be realized through the traditional control mode.
Disclosure of Invention
The invention provides an intelligent pigsty environment control system, which solves the problem that the control of the pigsty environment cannot be optimized in an autonomous learning mode according to pigsty environment factors.
The invention is realized by the following technical scheme:
an intelligent pigsty environment control system comprises a control host, controlled equipment and environmental factor acquisition equipment, wherein the controlled equipment and the environmental factor acquisition equipment are connected with the control host,
the environment factor acquisition equipment is used for receiving a data acquisition command sent by the control host, acquiring pigsty environment data and sending the pigsty environment data to the control host;
the controlled equipment is used for receiving the control instruction sent by the control host, adjusting the environmental parameters of the pigsty according to the control instruction and sending control data to the control host;
the control host is used for sending a data acquisition instruction to the environmental factor acquisition equipment and a control instruction to the controlled equipment, and receiving the piggery environmental data sent by the environmental factor acquisition equipment and receiving the control data sent by the controlled equipment; the system also comprises a control server and a user terminal;
the control server is used for receiving the pigsty environment data and control uploaded by the control host and the user instruction uploaded by the user terminal, training a control algorithm model according to the pigsty environment data and the control data, controlling and sending the trained control algorithm model to the control host and sending the user instruction to the control host;
the control host also sends the received pigsty environment data and control data to the control server, generates a control instruction and a data acquisition instruction through a control algorithm model, or generates the control instruction and the data acquisition instruction according to a user instruction, sends the control instruction to corresponding controlled equipment, and sends the data acquisition instruction to corresponding environment factor acquisition equipment;
in the technical scheme, the control server is used for effectively training the existing control algorithm model according to the pigsty environment data and the control data, wherein the pigsty environment data and the control data comprise historical data and latest data, so that a large enough training set is formed by the two data, the control server has enough data to train the control algorithm model, the adjustment of control parameters in the control algorithm model is completed, the trained control algorithm model is sent to the control host, and the control host generates control instructions corresponding to various control devices according to the new control algorithm model, so that the intelligent control of various controlled devices is realized; meanwhile, the control host can also generate data acquisition instructions corresponding to various environmental factor acquisition equipment, so that the intelligent control on the various environmental factor acquisition equipment is realized; by adding the user terminal, the controlled equipment and the environmental factor acquisition equipment can be manually controlled under special conditions, so that a manual intervention function is provided for environmental control of the pigsty, and the safety production of the pigsty is further ensured; because the control host has more functions, the control algorithm model is trained through the control server, so that the workload of the control host can be effectively reduced, the training efficiency is improved, and the control algorithm model is more effective; according to the technical scheme, the system continuously optimizes the control parameters through the pigsty environment data and the control data to complete the optimization of the control algorithm model, better controls and controls the controlled equipment and the environmental factor acquisition equipment through the control algorithm model, optimizes the control of the pigsty environment, improves the pigsty environment index and provides a more favorable environment for the growth of live pigs.
As a further improvement of the invention, the control host comprises a control processing module, a data transmission module, a control module and a data acquisition module; the data transmission module, the control module and the data acquisition module are all connected with the control processing module, the data transmission module is also connected with the control server, the control module and the data acquisition module, the control module is also connected with controlled equipment, and the data acquisition module is also connected with environmental factor acquisition equipment;
the data transmission module is used for receiving the trained control algorithm model and the forwarded user instruction sent by the control server, sending the trained control algorithm model and the trained user instruction to the control processing module, receiving the control data sent by the control module and the pigsty environment data sent by the data acquisition module, and sending the control data and the environment acquisition data to the control server and the control processing module;
the control processing module is used for storing the control algorithm model, optimizing the existing control algorithm model according to the received trained control algorithm model sent by the data transmission module, generating a control instruction and a data acquisition instruction by using the optimized control algorithm model or generating the control instruction and the data acquisition instruction according to a user instruction according to the pigsty environment data and the control data, sending the control instruction to the control module and sending the data acquisition instruction to the data acquisition module;
the control module is used for receiving the control instruction sent by the control processing module and sending the control instruction to the controlled equipment, and meanwhile, the control module is also used for receiving the control data sent by the controlled equipment and sending the control data to the data transmission module;
the data acquisition module is used for receiving the data acquisition instruction sent by the control processing module and sending the data acquisition instruction to the environmental factor acquisition equipment, and meanwhile, the data acquisition module is also used for receiving the environmental acquisition data sent by the environmental factor acquisition equipment and sending the environmental acquisition data to the data transmission module;
in the technical scheme, the control processing module is used for storing a control algorithm model, taking pigsty environment data and control data as model input, generating a control instruction after the operation of the control algorithm model, sending the control instruction to controlled equipment through the control module, and simultaneously generating a data acquisition instruction and sending the data acquisition instruction to environmental factor acquisition equipment through the data acquisition module; when the control processing module receives a user instruction, the control processing module suspends the use of the control algorithm model and only carries out manual regulation and control according to the user instruction; when the control processing module receives the trained control algorithm model, the control processing module updates the existing control algorithm model, and the trained control algorithm model is used to complete the optimization of the control algorithm model and improve the control efficiency of the pigsty.
Further, the controlled equipment comprises a window water curtain, air circulation equipment, a ceiling control system, a floor heating system, a dung scraping machine, a dehumidifier and a humidifier.
Further, the environmental factor collection equipment comprises a light intensity sensor, a dust sensor, a hydrogen sulfide sensor, a temperature sensor, a humidity sensor, an ammonia sensor and an oxygen sensor.
Further, the user terminal comprises a mobile terminal and a PC terminal.
The control algorithm model building process comprises the following steps:
s1, first, defining variables for the data (here, window water curtain, air flow device, ceiling control system data are selected as input variables), where Δ t: indoor and outdoor temperature difference; f, the running frequency of the fan; f (x): the air quantity; t: the running time of the fan is long; x: indoor temperature variation; s: cross-sectional area of the pigsty;
s2, the following display outputs exist between variables: f (a), (b)ΔT, s, f, x) corresponds to the output T, f;
s3, collecting m groups of sorted historical data
S4, fitting and training the historical data;
in this stage, we assume that the input and the output are in a linear relationship, and find a linear relationship function as follows:
f(x)＝βTx+b
f (x) satisfies βTβ, this will translate into a convex optimization problem:
obedience:
relaxation variables ξ for data pointsnAnd ξnThe presence of regression errors will be allowed as long as they are smaller than the values of ξ n and ξ n, while still satisfying the required conditions;
the constraint constant C is a positive value that controls the penalty imposed on observations that lie outside the epsilon margin and helps prevent overfitting (regularization);
the loss is measured based on the distance between the observed value y and the epsilon boundary, and errors within the epsilon boundary are all considered to be zero. The loss function is therefore:
the goal of this step is to minimize the loss function β;
s5, incrementUpdating output, namely updating newly collected data to a historical database in real time after the latest sample data is acquired, so that the aim of updating the newly collected data can be fulfilledΔthe relationships between T, s, f, x and T are continuously updated.
In conclusion, the pigsty control method has the advantages that a control algorithm model of the pigsty is trained according to the pigsty environment data and the control data, control parameters are optimized, and accordingly the control of the pigsty environment is optimized; the invention solves the problems that the control of the pigsty environment cannot be optimized in an autonomous learning mode according to the pigsty environment factors, and manual remote regulation and control can be carried out.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a system block diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1:
as shown in fig. 1, an intelligent pigsty environment control system comprises a control host, controlled equipment and environment factor acquisition equipment, wherein the controlled equipment and the environment factor acquisition equipment are both connected with the control host, and the environment factor acquisition equipment is used for receiving a data acquisition instruction sent by the control host, acquiring pigsty environment data and sending the pigsty environment data to the control host; and the controlled equipment is used for receiving the control instruction sent by the control host, adjusting the environmental parameters of the pigsty according to the control instruction and sending the control data to the control host.
The control host is used for sending a data acquisition instruction to the environmental factor acquisition equipment and a control instruction to the controlled equipment, and receiving the piggery environmental data sent by the environmental factor acquisition equipment and receiving the control data sent by the controlled equipment.
The control host comprises a control processing module, a data transmission module, a control module and a data acquisition module; the data transmission module, the control module and the data acquisition module are all connected with the control processing module, the data transmission module is further connected with the control server, the control module and the data acquisition module, the control module is further connected with controlled equipment, and the data acquisition module is further connected with environmental factor acquisition equipment.
The data transmission module is used for receiving the trained control algorithm model and the forwarded user instruction sent by the control server, sending the trained control algorithm model and the trained user instruction to the control processing module, and meanwhile, receiving the control data sent by the control module and the pigsty environment data sent by the data acquisition module, and sending the control data and the environment acquisition data to the control server and the control processing module.
The control processing module is used for storing the control algorithm model, optimizing the existing control algorithm model according to the received trained control algorithm model sent by the data transmission module, generating a control instruction and a data acquisition instruction by using the optimized control algorithm model or generating the control instruction and the data acquisition instruction according to a user instruction according to the pigsty environment data and the control data, sending the control instruction to the control module, and sending the data acquisition instruction to the data acquisition module.
The control module is used for receiving the control instruction sent by the control processing module and sending the control instruction to the controlled equipment, and meanwhile, the control module is also used for receiving the control data sent by the controlled equipment and sending the control data to the data transmission module;
the data acquisition module is used for receiving the data acquisition instruction sent by the control processing module and sending the data acquisition instruction to the environmental factor acquisition equipment, and meanwhile, the data acquisition module is also used for receiving the environmental acquisition data sent by the environmental factor acquisition equipment and sending the environmental acquisition data to the data transmission module.
The system also comprises a control server and a user terminal; the control server is used for receiving the piggery environment data and control uploaded by the control host and the user instruction uploaded by the user terminal, training the control algorithm model according to the piggery environment data and the control data, controlling and sending the trained control algorithm model to the control host, and sending the user instruction to the control host.
The control host also sends the received pigsty environment data and the control data to the control server, generates a control instruction and a data acquisition instruction through the control algorithm model, or generates the control instruction and the data acquisition instruction according to the user instruction, sends the control instruction to the corresponding controlled equipment, and sends the data acquisition instruction to the corresponding environment factor acquisition equipment.
In the embodiment, the controlled equipment comprises a window water curtain, air circulation equipment, a ceiling control system, a floor heating system, a dung scraping machine, a dehumidifier and a humidifier, and the controlled equipment is used for adjusting the participation of the pigsty environment, so that each environmental parameter keeps a proper size, and the effective production of the pigsty is ensured; the environment factor acquisition equipment comprises a light intensity sensor, a dust sensor, a hydrogen sulfide sensor, a temperature sensor, a humidity sensor, an ammonia sensor and an oxygen sensor, and completes the acquisition of environment parameters of each area of the pigsty; the environmental factor acquisition equipment can increase and reduce the configuration quantity according to the actual situation; the user terminal comprises a mobile terminal and a PC terminal, and can be connected with the control service in a wired or wireless mode.
By using the control server, the existing control algorithm model can be effectively trained according to the pigsty environment data and the control data, wherein the pigsty environment data and the control data comprise historical data and latest data, so that the two data form a large enough training set, the control server has enough data to train the control algorithm model, the adjustment of control parameters in the control algorithm model is completed, the trained control algorithm model is sent to the control host, and the control host generates control instructions corresponding to various control devices according to the new control algorithm model, so that the intelligent control of various controlled devices is realized; for example, according to historical temperature data acquired by a temperature sensor, a pigsty temperature change rule and the time required by equipment for cooling the pigsty, namely temperature control parameters, can be obtained by training a control algorithm model, so that the pigsty can be subjected to advanced cooling operation and accurate control of temperature by correcting the temperature control parameters through the predictability of the control algorithm model; meanwhile, the control host can also generate data acquisition instructions corresponding to various environmental factor acquisition equipment, so that the intelligent control on the various environmental factor acquisition equipment is realized; by adding the user terminal, the controlled equipment and the environmental factor acquisition equipment can be manually controlled under special conditions, so that a manual intervention function is provided for environmental control of the pigsty, and the safety production of the pigsty is further ensured; because the control host has more functions, the control algorithm model is trained through the control server, so that the workload of the control host can be effectively reduced, the training efficiency is improved, and the control algorithm model is more effective; the system continuously optimizes the control parameters through the pigsty environment data and the control data to complete the optimization of the control algorithm model, better controls and controls the controlled equipment and the environmental factor acquisition equipment through the control algorithm model, optimizes the control of the pigsty environment, improves the pigsty environment index and provides a more favorable environment for the growth of live pigs.
The control algorithm model building process comprises the following steps:
s1, first, a variable definition is made of the data (here, window water curtain, air flow device, ceiling control system data are selected as input variables), whereinΔt: indoor and outdoor temperature difference; f, the running frequency of the fan; f (x): the air quantity; t: the running time of the fan is long; x: indoor temperature variation; s: cross-sectional area of the pigsty;
s2, the following display outputs exist between variables: f (a), (b)ΔT, s, f, x) corresponds to the output T, f;
s3, collecting m groups of sorted historical data
S4, fitting and training the historical data;
in this stage, we assume that the input and the output are in a linear relationship, and find a linear relationship function as follows:
f(x)＝βTx+b
f (x) satisfies βTβ, this will translate into a convex optimization problem:
obedience:
relaxation variables ξ for data pointsnAnd ξnThe presence of regression errors will be allowed as long as they are smaller than the values of ξ n and ξ n, while still satisfying the required conditions;
the constraint constant C is a positive value that controls the penalty imposed on observations that lie outside the epsilon margin and helps prevent overfitting (regularization);
the loss is measured based on the distance between the observed value y and the epsilon boundary, and errors within the epsilon boundary are all considered to be zero. The loss function is therefore:
the goal of this step is to minimize the loss function β;
and S5, incremental updating output, namely updating newly collected data to a historical database in real time after the latest sample data is acquired, so that the relation between delta T, S, f, x and T can be continuously updated.
The control processing module is used for storing the control algorithm model, inputting the environmental data and the control data of the pigsty as the model, generating a control instruction after the operation of the control algorithm model, sending the control instruction to the controlled equipment through the control module, and simultaneously generating a data acquisition instruction and sending the data acquisition instruction to the environmental factor acquisition equipment through the data acquisition module; when the control processing module receives a user instruction, the control processing module suspends the use of the control algorithm model and only carries out manual regulation and control according to the user instruction; when the control processing module receives the trained control algorithm model, the control processing module updates the existing control algorithm model, and the trained control algorithm model is used to complete the optimization of the control algorithm model and improve the control efficiency of the pigsty.
According to the pigsty environment data and the control data, a control algorithm model of the pigsty is trained, and control parameters are optimized, so that the control of the pigsty environment is optimized and optimized, meanwhile, manual remote control is realized through a user terminal, manual intervention on the pigsty under special conditions is achieved, and the safety production of the pigsty is ensured; the invention solves the problems that the control of the pigsty environment cannot be optimized in an autonomous learning mode according to the pigsty environment factors, and manual remote regulation and control can be carried out.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (6)
1. An intelligent pigsty environment control system comprises a control host, controlled equipment and environmental factor acquisition equipment, wherein the controlled equipment and the environmental factor acquisition equipment are connected with the control host,
the environment factor acquisition equipment is used for receiving a data acquisition command sent by the control host, acquiring pigsty environment data and sending the pigsty environment data to the control host;
the controlled equipment is used for receiving the control instruction sent by the control host, adjusting the environmental parameters of the pigsty according to the control instruction and sending control data to the control host;
the control host is used for sending a data acquisition instruction to the environmental factor acquisition equipment and a control instruction to the controlled equipment, and receiving the piggery environmental data sent by the environmental factor acquisition equipment and receiving the control data sent by the controlled equipment;
the system is characterized by also comprising a control server and a user terminal;
the control server is used for receiving the pigsty environment data and control uploaded by the control host and the user instruction uploaded by the user terminal, training a control algorithm model according to the pigsty environment data and the control data, controlling and sending the trained control algorithm model to the control host and sending the user instruction to the control host;
the control host also sends the received pigsty environment data and the control data to the control server, generates a control instruction and a data acquisition instruction through the control algorithm model, or generates the control instruction and the data acquisition instruction according to the user instruction, sends the control instruction to the corresponding controlled equipment, and sends the data acquisition instruction to the corresponding environment factor acquisition equipment.
2. The pigsty environment intelligent control system of claim 1, wherein the control host comprises a control processing module, a data transmission module, a control module and a data acquisition module; the data transmission module, the control module and the data acquisition module are all connected with the control processing module, the data transmission module is also connected with the control server, the control module and the data acquisition module, the control module is also connected with controlled equipment, and the data acquisition module is also connected with environmental factor acquisition equipment;
the data transmission module is used for receiving the trained control algorithm model and the forwarded user instruction sent by the control server, sending the trained control algorithm model and the trained user instruction to the control processing module, receiving the control data sent by the control module and the pigsty environment data sent by the data acquisition module, and sending the control data and the environment acquisition data to the control server and the control processing module;
the control processing module is used for storing the control algorithm model, optimizing the existing control algorithm model according to the received trained control algorithm model sent by the data transmission module, generating a control instruction and a data acquisition instruction by using the optimized control algorithm model or generating the control instruction and the data acquisition instruction according to a user instruction according to the pigsty environment data and the control data, sending the control instruction to the control module and sending the data acquisition instruction to the data acquisition module;
the control module is used for receiving the control instruction sent by the control processing module and sending the control instruction to the controlled equipment, and meanwhile, the control module is also used for receiving the control data sent by the controlled equipment and sending the control data to the data transmission module;
the data acquisition module is used for receiving the data acquisition instruction sent by the control processing module and sending the data acquisition instruction to the environmental factor acquisition equipment, and meanwhile, the data acquisition module is also used for receiving the environmental acquisition data sent by the environmental factor acquisition equipment and sending the environmental acquisition data to the data transmission module.
3. A pigsty environment intelligent control system according to any one of claims 1 to 2, wherein the controlled equipment comprises window water curtains, air circulation equipment, a ceiling control system, a floor heating system, a dung scraper, a dehumidifier and a humidifier.
4. The intelligent pigsty environment control system according to any one of claims 1 to 2, wherein the environmental factor collecting device comprises a light intensity sensor, a dust sensor, a hydrogen sulfide sensor, a temperature sensor, a humidity sensor, an ammonia sensor and an oxygen sensor.
5. The intelligent pigsty environment control system according to claim 1, wherein the user terminal comprises a mobile terminal and a PC terminal.
6. The pigsty environment intelligent control system of claim 1, wherein the control algorithm model building process is as follows:
s1, firstly, carrying out variable definition on the controlled equipment data (data of a window water curtain, air circulation equipment and a ceiling control system are selected as input variables), wherein △ T is the indoor and outdoor temperature difference, f is the fan operation frequency, f (X) is the air volume, T is the fan operation time length, X is the indoor temperature variation, and S is the piggery cross-sectional area;
s2, displaying output among variables, wherein f (△ T, S, f, x) corresponds to output T, f;
s3, collecting m groups of sorted historical data
S4, fitting and training the historical data;
in this stage, we assume that the input and the output are in a linear relationship, and find a linear relationship function as follows:
f(x)＝βTx+b
f (x) satisfies βTβ, this will translate into a convex optimization problem:
obedience:
relaxation variables ξ for data pointsnAnd ξnThe presence of regression errors will be allowed as long as they are smaller than the values of ξ n and ξ n, while still satisfying the required conditions;
the constraint constant C is a positive value that controls the penalty imposed on observations that lie outside the epsilon margin and helps prevent overfitting (regularization);
the loss is measured based on the distance between the observed value y and the epsilon boundary, and errors within the epsilon boundary are all considered to be zero. The loss function is therefore:
the goal of this step is to minimize the loss function β;
and S5, incremental updating output, namely updating newly collected data to a historical database in real time after the latest sample data is acquired, so that the relation between △ T, S, f, x and T can be continuously updated.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112596392A (en) * | 2020-12-24 | 2021-04-02 | 青岛科创信达科技有限公司 | Big data based pigsty environment controller parameter automatic configuration and optimization method |
| CN113566864A (en) * | 2021-09-03 | 2021-10-29 | 合肥米克光电技术有限公司 | Distributed machine vision system based on 5G and edge calculation |
| CN114296348A (en) * | 2021-12-27 | 2022-04-08 | 山东钧龙新能源科技有限公司 | Internet of things remote monitoring method for flameless heat energy generator |
| CN116795153A (en) * | 2023-05-11 | 2023-09-22 | 重庆市畜牧技术推广总站 | Pig farm environment control system and method |
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| CN113566864A (en) * | 2021-09-03 | 2021-10-29 | 合肥米克光电技术有限公司 | Distributed machine vision system based on 5G and edge calculation |
| CN114296348A (en) * | 2021-12-27 | 2022-04-08 | 山东钧龙新能源科技有限公司 | Internet of things remote monitoring method for flameless heat energy generator |
| CN116795153A (en) * | 2023-05-11 | 2023-09-22 | 重庆市畜牧技术推广总站 | Pig farm environment control system and method |
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