阅读说明：本技术 一种基于物联网的畜牧养殖用温湿度控制系统及方法 (Temperature and humidity control system and method for livestock breeding based on Internet of things ) 是由 江杨 于 2021-09-26 设计创作，主要内容包括：本申请公开一种基于物联网的畜牧养殖用温湿度控制系统及方法。所述方法包括采集牲畜养殖历史经验大数据和牲畜养殖历史实测大数据；构建牲畜养殖历史经验大数据和历史实测大数据的融合数据,并使用计算的修正系数修正融合数据,从融合数据中提取牲畜属性特征和所需温湿度特征,构建畜牧养殖用温湿度控制模型；定期采集待控畜牧养殖场的当前牲畜属性数据,输入畜牧养殖用温湿度控制模型中,得到当前牲畜所需的最佳温湿度,向待控畜牧养殖场内的温湿度传感器发送温湿度调整指示进行温湿度的实时调节。采用本申请的技术方案,可以更加准确地得到不同品种牲畜在不同生长阶段所需的最佳温湿度,满足各类牲畜的生长需求,提高养殖效率。(The application discloses humiture control system and method for livestock breeding based on Internet of things. The method comprises the steps of collecting big historical experience data and big actual measurement data of livestock breeding; constructing fusion data of historical experience big data and historical measured big data of livestock breeding, correcting the fusion data by using the calculated correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, and constructing a temperature and humidity control model for livestock breeding; the method comprises the steps of regularly collecting current livestock attribute data of a livestock farm to be controlled, inputting the data into a temperature and humidity control model for livestock breeding, obtaining the optimal temperature and humidity required by current livestock, and sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled to adjust the temperature and humidity in real time. By adopting the technical scheme, the optimal temperature and humidity required by different kinds of livestock in different growth stages can be obtained more accurately, the growth requirements of various kinds of livestock are met, and the breeding efficiency is improved.)

1. The temperature and humidity control method for livestock breeding based on the Internet of things is characterized by comprising the following steps:

collecting historical experience big data of livestock breeding from a livestock breeding platform, and collecting historical actual measurement big data of livestock breeding from a livestock farm to be controlled, wherein the collected livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

constructing fusion data of historical experience big data and historical measured big data of livestock breeding, calculating a correction coefficient between the historical experience big data and the historical measured big data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and constructing a temperature and humidity control model for livestock breeding;

the method comprises the steps of regularly collecting current livestock attribute data of a livestock farm to be controlled, inputting the current livestock attribute data into a temperature and humidity control model for livestock breeding, obtaining the optimal temperature and humidity required by current livestock, sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled, and carrying out real-time adjustment on the temperature and humidity.

2. The temperature and humidity control method for livestock breeding based on the internet of things according to claim 1, characterized in that an livestock breeding platform is constructed in advance, and each livestock farm can access and upload verified livestock breeding experience data, including livestock attributes and temperature and humidity required by each stage of good growth of livestock; the attributes of livestock include livestock species, livestock sex, livestock age per day, and livestock physical condition; the temperature and humidity required by the excellent growth stages of the livestock comprise the optimum temperature and humidity required by the excellent growth of the livestock at each stage in the whole process from the young livestock to the end of the cultivation.

3. The temperature and humidity control method for livestock breeding based on the internet of things according to claim 1, wherein calculating the correction coefficient between the historical empirical big data and the historical measured big data specifically comprises:

constructing an error set D { (| x'₁-x₁|,|y′₁-y₁|),(|x′₂-x₂|,|y′₂-y₂|)...(|x′_n-x_n|,|y′_n-y_n|)}；

Constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Namely the correction coefficient of the historical experience big data and the historical measured big data.

4. The temperature and humidity control method for livestock breeding based on the internet of things as claimed in claim 1, wherein the control center sends prompt information on whether the physical condition of livestock needs to be adjusted to the mobile terminal of the feeder for a plurality of times at regular time every day, if the adjustment instruction of the feeder is received, the current attribute data of the livestock is updated, and if the adjustment-free instruction of the feeder is received, the temperature and humidity calculation is performed according to the stored data of the system.

5. The utility model provides a humiture control center for livestock-raising based on thing networking which characterized in that includes: the system comprises a data acquisition module, a temperature and humidity control model construction module for livestock breeding, an optimal temperature and humidity calculation module and a temperature and humidity control module;

the data acquisition module is used for acquiring historical experience big data of livestock breeding from the livestock breeding platform and acquiring historical actual measurement big data of livestock breeding from a livestock farm to be controlled, and the acquired livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

the temperature and humidity control model building module for livestock breeding is used for building fusion data of historical empirical big data and historical measured big data of livestock breeding, calculating a correction coefficient between the historical empirical big data and the historical measured big data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and building the temperature and humidity control model for livestock breeding;

the optimal temperature and humidity calculation module is used for regularly acquiring current livestock attribute data of a livestock farm to be controlled and inputting the current livestock attribute data into a temperature and humidity control model for livestock breeding to obtain the optimal temperature and humidity required by current livestock;

and the temperature and humidity control module is used for sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled, and adjusting the temperature and humidity in real time.

6. The temperature and humidity control center for livestock breeding based on the internet of things as claimed in claim 5, wherein the temperature and humidity control model building module for livestock breeding comprises a correction coefficient calculating module for building an error set D { (| x } of the historical empirical data and the historical measured data'₁-x₁|,|y′₁-y₁|),(|x′₂-x₂|,|y′₂-y₂|)...(|x′_n-x_n|,|y′_n-y_n|) }; constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Namely the correction coefficient of the historical experience big data and the historical measured big data.

7. The temperature and humidity control center for livestock breeding based on the internet of things as claimed in claim 5, further comprising an adjustment instruction sending module, configured to send a prompt message indicating whether the physical condition of livestock needs to be adjusted to the mobile terminal of the feeder for a plurality of times at regular time every day, update the current livestock attribute data if the adjustment instruction of the feeder is received, and perform temperature and humidity calculation according to the stored data of the system if the adjustment-free instruction of the feeder is received.

8. An internet of things system, comprising: the livestock breeding platform comprises an animal breeding platform, a humiture control center, a humiture sensor and a mobile terminal of a feeder, wherein the livestock breeding platform can access and upload data in each farm, the humiture control center is arranged in each farm and is used for livestock breeding based on the internet of things according to any one of claims 5 to 7, the humiture sensor is arranged in each breeding area of each farm, and the mobile terminal of the feeder is arranged.

9. A computer storage medium, comprising: at least one memory and at least one processor;

the memory is used for storing one or more program instructions;

the processor is used for executing one or more program instructions to execute the temperature and humidity control method for livestock breeding based on the Internet of things as claimed in any one of claims 1-4.

Technical Field

The application relates to the technical field of communication, in particular to a humiture control system and method for livestock breeding based on the Internet of things.

Background

Animal husbandry is a production department for obtaining animal products such as meat, eggs, milk, wool, cashmere, skin, silk and medicinal materials by utilizing the physiological functions of animals such as livestock and poultry which are domesticated by human beings or wild animals such as deer, musk, fox, mink, otter and quail and converting pasture, feed and other plant energy into animal energy through artificial feeding and breeding. Is different from self-sufficient livestock breeding, and is mainly characterized by centralization, scale production and profit-making production purposes.

In the existing livestock breeding, the temperature and humidity in a breeding area are generally regulated by a feeder according to self experience, so the experience level of the feeder determines the breeding capacity of livestock, and the condition that the livestock cannot grow well due to the fact that the temperature and humidity are not regulated timely by the feeder or are not suitable according to the temperature and humidity regulated empirically can occur in artificial breeding, so that various problems caused by artificial breeding are solved by a method capable of intelligently regulating and controlling the breeding temperature and humidity urgently.

Disclosure of Invention

The application provides a temperature and humidity control method for livestock breeding based on the Internet of things, which comprises the following steps:

collecting historical experience big data of livestock breeding from a livestock breeding platform, and collecting historical actual measurement big data of livestock breeding from a livestock farm to be controlled, wherein the collected livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

constructing fusion data of historical experience big data and historical measured big data of livestock breeding, calculating a correction coefficient between the historical experience big data and the historical measured big data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and constructing a temperature and humidity control model for livestock breeding;

the method comprises the steps of regularly collecting current livestock attribute data of a livestock farm to be controlled, inputting the current livestock attribute data into a temperature and humidity control model for livestock breeding, obtaining the optimal temperature and humidity required by current livestock, sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled, and carrying out real-time adjustment on the temperature and humidity.

Preferably, a livestock breeding platform is constructed in advance, and each livestock farm can access and upload verified livestock breeding experience data including livestock attributes and temperature and humidity required by excellent growth stages of livestock; the attributes of livestock include livestock species, livestock sex, livestock age per day, and livestock physical condition; the temperature and humidity required by the excellent growth stages of the livestock comprise the optimum temperature and humidity required by the excellent growth of the livestock at each stage in the whole process from the young livestock to the end of the cultivation.

Preferably, the calculating of the correction coefficient between the historical empirical big data and the historical measured big data specifically includes:

constructing an error set D { (| x'₁-x₁|,|y′₁-y₁|),(′x′₂-x₂|,|y′₂-y₂|)...(|x′_n-x_n|,|y′_n-y_n|)}；

Constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Namely the correction coefficient of the historical experience big data and the historical measured big data.

Preferably, the control center sends prompt information whether the body condition of the livestock needs to be adjusted to the mobile terminal of the feeder for a plurality of times at regular time every day, if the adjustment instruction of the feeder is received, the current attribute data of the livestock is updated, and if the adjustment-free instruction of the feeder is received, the temperature and humidity calculation is carried out according to the stored data of the system.

The application still provides a humiture control center for livestock-raising based on thing networking, a serial communication port, include: the system comprises a data acquisition module, a temperature and humidity control model construction module for livestock breeding, an optimal temperature and humidity calculation module and a temperature and humidity control module;

the data acquisition module is used for acquiring historical experience big data of livestock breeding from the livestock breeding platform and acquiring historical actual measurement big data of livestock breeding from a livestock farm to be controlled, and the acquired livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

the temperature and humidity control model building module for livestock breeding is used for building fusion data of historical empirical big data and historical measured big data of livestock breeding, calculating a correction coefficient between the historical empirical big data and the historical measured big data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and building the temperature and humidity control model for livestock breeding;

the optimal temperature and humidity calculation module is used for regularly acquiring current livestock attribute data of a livestock farm to be controlled and inputting the current livestock attribute data into a temperature and humidity control model for livestock breeding to obtain the optimal temperature and humidity required by current livestock;

and the temperature and humidity control module is used for sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled, and adjusting the temperature and humidity in real time.

Preferably, a livestock breeding platform is constructed in advance, and each livestock farm can access and upload verified livestock breeding experience data including livestock attributes and temperature and humidity required by excellent growth stages of livestock; the attributes of livestock include livestock species, livestock sex, livestock age per day, and livestock physical condition; the temperature and humidity required by the excellent growth stages of the livestock comprise the optimum temperature and humidity required by the excellent growth of the livestock at each stage in the whole process from the young livestock to the end of the cultivation.

Preferably, the temperature and humidity control model building module for livestock breeding comprises a correction coefficient calculating module, and is used for building an error set D { (| x {) of historical empirical big data and historical actual measurement big data'₁-x₁|,|y′₁-y₁|),(|x′₂-x₂|,|y′₂-y₂|)…(′x′_n-x_n|,|y′_n-y_n|) }; constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Is the historical experienceAnd the correction coefficient of the big data and the historical measured big data.

Preferably, the control center further comprises an adjustment instruction sending module, which is used for sending prompt information about whether the physical condition of the livestock needs to be adjusted to the mobile terminal of the feeder for a plurality of times at regular time every day, updating the current attribute data of the livestock if the adjustment instruction of the feeder is received, and calculating the temperature and humidity according to the stored data of the system if the adjustment-free instruction of the feeder is received.

The present application further provides an internet of things system, comprising: the livestock-raising system comprises an animal-raising platform which can be accessed and uploaded by each farm, any one of the temperature and humidity control centers for livestock-raising based on the Internet of things, temperature and humidity sensors arranged in each breeding area of each farm, and a mobile terminal of a feeder, wherein the temperature and humidity control centers are arranged in each farm.

The present application further provides a computer storage medium, comprising: at least one memory and at least one processor;

the memory is used for storing one or more program instructions;

the processor is used for running one or more program instructions and executing any one of the temperature and humidity control methods for livestock breeding based on the internet of things.

The beneficial effect that this application realized is as follows: adopt the technical scheme of this application, combine together the good breed experience of the various livestock of each plant with local plant data and construct the humiture control model for livestock-raising, can obtain the required best humiture of different breed livestock in different growth stages more accurately from this, satisfy the growth demand of all kinds of livestock, improve cultivation efficiency.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.

Fig. 1 is a flowchart of a temperature and humidity control method for livestock breeding based on the internet of things according to an embodiment of the present application;

fig. 2 is a schematic view of an internet of things system for realizing temperature and humidity control for livestock breeding according to the second embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example one

As shown in fig. 1, an embodiment of the present application provides a temperature and humidity control method for livestock breeding based on the internet of things, including:

step 110, collecting historical experience big data of livestock breeding from a livestock breeding platform, and collecting historical actual measurement big data of livestock breeding from a livestock farm to be controlled, wherein the collected livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

because the livestock-raising probably has different growth differences under each area, each plant environment, also can be different along with the difference of natural environment and artificial environment to the requirement of humiture, consequently the historical experience angle that this application uploaded from each plant of livestock-raising and the historical actual measurement data of current plant carry out the atmospheric control of livestock-raising jointly.

Specifically, a livestock breeding platform is constructed in advance, each livestock farm can access and upload verified livestock breeding experience data, the livestock breeding experience data comprise livestock attributes and temperature and humidity required by each stage of good growth of livestock, the livestock attributes comprise livestock species, livestock gender, livestock age, livestock physical conditions (such as whether the livestock is young livestock, whether the livestock is pregnant, whether the livestock is in lactation and the like), and the like, and the temperature and humidity required by each stage of good growth of the livestock comprise the temperature and humidity required by each stage of good growth of the livestock from young livestockThe optimum temperature and humidity required for the excellent growth of livestock at each stage in the whole process from the completion of the cultivation of the livestock; in addition, each livestock farm independently stores respective breeding processes, including livestock attributes and temperature and humidity required by excellent growth stages of livestock; when temperature and humidity control is carried out on a certain livestock farm to be controlled, big historical experience data S of livestock breeding are collected₁＝{(x₁,y₁),(x₂,y₂)...(x_t,y_t) }, big data S measured in history of livestock breeding₂＝{(x′₁,y′₁),(x′₂,y′₂)...(x′_t,y′_t) In which x_i＝{a₁,a₂...a_n}，y_i＝{b₁,b₂...b_n}，a₁,a₂...a_nRepresenting animal attribute features in historical empirical data, b₁,b₂...b_nRepresents the temperature and humidity x 'required by each stage of good growth of livestock in historical empirical data'_i＝{a′₁,a′₂...a′_n}，y′_i＝{b′₁,b′₂...b′_n}，a′₁,a′₂...a′_nShowing livestock attribute feature, b 'in historical measured big data'₁,b′₂.., b' represents the temperature and humidity required by each stage of excellent growth of livestock in the historical measured big data, combines the breeding experience with the practical situation of breeding, and improves the reliability and accuracy of livestock breeding.

Step 120, constructing fusion data of the big historical experience data and the big historical actual measurement data of livestock breeding, calculating a correction coefficient between the big historical experience data and the big historical actual measurement data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and constructing a temperature and humidity control model for livestock breeding;

specifically, the fusion data of the big historical experience data and the big historical actual measurement data of livestock breeding are as follows:

wherein λ is₁And λ₂Correction coefficients of the historical experience big data and the historical measured big data are respectively;

specifically, the specific manner of determining the correction coefficient between the historical empirical big data and the historical measured big data is as follows:

constructing an error set of the historical experience big data and the historical measured big data: d { (| x'₁-x₁|,|y′₁-y₁|),(|x′₂-x₂|,|y′₂-y₂|)...(|x′_n-x_n|,|y′_n-y_n|) }; constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Namely the correction coefficient of the historical experience big data and the historical measured big data.

And then from the fused data S_rExtracting livestock attribute characteristics and required temperature and humidity characteristics to respectively form a livestock attribute characteristic set x_r＝{a₁,a₂...a_rAnd a set of required temperature and humidity characteristics y_r＝{b₁,b₂...b_rAnd constructing a temperature and humidity control model for livestock breeding by taking the livestock attribute feature set as input and the required temperature and humidity feature set as outputWherein, W₁As weights of the input layer to the hidden layer, W₂The weight from the hidden layer to the output layer; beta is a₁Is the threshold value from the input layer to the hidden layer; beta is a₂A threshold from the hidden layer to the output layer;as a function of the hidden layer to the output layer,e is a natural constant; μ (x) is a function of the input layer to the hidden layer.

Step 130, current livestock attribute data of the livestock farm to be controlled are collected regularly, the current livestock attribute data are input into a temperature and humidity control model for livestock breeding, the optimal temperature and humidity required by current livestock are obtained, a temperature and humidity adjustment instruction is sent to a temperature and humidity sensor in the livestock farm to be controlled, and real-time temperature and humidity adjustment is carried out;

the technical scheme of the application can be suitable for accurate control of the temperature and humidity of livestock of various types, when a certain to-be-controlled livestock farm needs to be configured, livestock varieties and livestock sexes of livestock in a breeding area, the day age of the livestock and the first body condition of the livestock are uploaded in advance by a breeder, the day age of the livestock is increased along with the system time after the configuration is completed, the body condition of the livestock is adjusted in real time according to the day age of the livestock, generally, in order to ensure accurate determination of the body condition of the livestock every day, a control center sends prompt information for judging whether the body condition of the livestock needs to be adjusted to a mobile terminal of the breeder for a plurality of times every day, and if the adjustment instruction of the breeder is received, the current livestock attribute data is updated, and if the adjustment-free instruction of the feeder is received, calculating the temperature and the humidity according to the stored data of the system.

Preferably, because the influence of the age of livestock on the temperature and humidity is also large, the livestock attribute data are generally selected to be collected once in the morning, at noon and evening every day, the data are input into a temperature and humidity control model for livestock breeding, the optimal temperature and humidity required by the current livestock are obtained, a temperature and humidity sensor capable of adjusting the temperature and humidity is installed in each breeding area, the temperature and humidity sensor and a control center are connected into an internet of things system through a network, the control center sends a temperature and humidity adjustment instruction to the temperature and humidity sensor after calculating the optimal temperature and humidity, and the temperature and humidity are adjusted in real time.

Example two

As shown in fig. 2, a second embodiment of the present application provides an internet of things system 20 for implementing humiture control for livestock breeding, including an livestock breeding platform 210 for each farm to access and upload data, a humiture control center 220 for livestock breeding disposed in each farm, and a humiture sensor 230 disposed in each breeding area of each farm; each farm comprises a control center 220 and a plurality of temperature and humidity sensors 230, the control center 220 and the temperature and humidity sensors 230 are connected through a network to realize data transmission, and the temperature and humidity sensors 230 are arranged in each breeding area of the farm and used for respectively adjusting the temperature and humidity of each breeding area; in addition, the physical network system also comprises a mobile terminal 240 of the feeder, which is used for receiving prompt information that whether the identity condition needs to be adjusted or not, which is sent by the control center for a plurality of times every day.

The control center 220 is used for temperature and humidity calculation and control, and for storing the big data of the historical actual measurement of livestock breeding, in addition, special equipment can be arranged in the farm for storing the big data of the historical actual measurement of livestock breeding, for example, when the farm is large, too much data stored by one control center can affect the data processing efficiency, a special storage device can be arranged for one or more breeding areas for uploading and storing the data of the historical actual measurement of livestock breeding, and the method is not limited herein;

specifically, the control center 220 includes a data acquisition module 221, a temperature and humidity control model building module 222 for livestock breeding, an optimal temperature and humidity calculation module 223 and a temperature and humidity control module 224;

the data acquisition module 221 is used for acquiring historical experience big data of livestock breeding from the livestock breeding platform 210 and acquiring historical actual measurement big data of livestock breeding from a livestock farm to be controlled, wherein the acquired livestock data comprise livestock attributes and temperature and humidity required by each stage of excellent growth of livestock;

the temperature and humidity control model building module 222 for livestock breeding is used for building fusion data of historical empirical big data and historical measured big data of livestock breeding, calculating a correction coefficient between the historical empirical big data and the historical measured big data, correcting the fusion data by using the correction coefficient, extracting livestock attribute characteristics and required temperature and humidity characteristics from the fusion data, taking the livestock attribute characteristics as input and the required temperature and humidity characteristics as output, and building a temperature and humidity control model for livestock breeding;

the optimal temperature and humidity calculation module 223 is used for regularly collecting current livestock attribute data of a livestock farm to be controlled, inputting the current livestock attribute data into a temperature and humidity control model for livestock breeding, and obtaining the optimal temperature and humidity required by current livestock;

and the temperature and humidity control module 224 is used for sending a temperature and humidity adjustment instruction to a temperature and humidity sensor in the livestock farm to be controlled, and adjusting the temperature and humidity in real time.

The control center 220 further includes an adjustment instruction sending module, configured to send a prompt message indicating whether the physical condition of the livestock needs to be adjusted to the mobile terminal 240 of the feeder for a plurality of times at regular time every day, update the current attribute data of the livestock if the adjustment instruction of the feeder is received, and perform temperature and humidity calculation according to stored data of the system if the adjustment-free instruction of the feeder is received.

The temperature and humidity control model building module 222 for livestock breeding includes a correction coefficient calculating module, and is configured to build an error set D { (| x {) of the historical empirical data and the historical actual measurement data'₁-x₁|,|y′₁-y₁|),(|x′₂-x₂|,|y′₂-y₂|)...(|x′_n-x_n|,|y′_n-y_n|) }; constructing an error function F (S, S') - λ₁S+λ₂S-ε(S,S′)，0<λ₁<1，0<λ₂<1, and λ₁+λ₂1 is ═ 1; ε (S, S') is the error term,let the error function be 0, and the calculated lambda₁And λ₂Namely the correction coefficient of the historical experience big data and the historical measured big data.

Corresponding to the above embodiments, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;

the memory is used for storing one or more program instructions;

the processor is used for running one or more program instructions and executing the temperature and humidity control method for livestock breeding based on the Internet of things.

Corresponding to the above embodiments, the present invention provides a computer-readable storage medium, where the computer storage medium contains one or more program instructions, and the one or more program instructions are used by a processor to execute a temperature and humidity control method for livestock breeding based on the internet of things.

The embodiment of the invention provides a computer-readable storage medium, wherein computer program instructions are stored in the computer-readable storage medium, and when the computer program instructions are run on a computer, the computer is enabled to execute the temperature and humidity control method for livestock breeding based on the internet of things.

In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.

The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.

The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.

The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.

The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.

Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.