CN116760719A - Method for constructing intelligent cultivation multi-mode internet of things information collection and early warning model - Google Patents
Method for constructing intelligent cultivation multi-mode internet of things information collection and early warning model Download PDFInfo
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Abstract
The application provides a method for constructing an intelligent culture multi-mode internet of things information collection and early warning model, which comprises the following steps: acquiring data to be transmitted; acquiring sensing data of the intelligent culture system detected by various sensors; determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data; acquiring reference environment data of livestock and poultry, and determining whether the intelligent breeding system accords with living conditions of the livestock and poultry based on the reference environment data and the target environment data; if the intelligent breeding system does not accord with the living conditions of the livestock, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information; and/or if the health state of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information. The application can perform early warning according to various sensing data so as to provide accurate management decision.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a method for constructing an intelligent cultivation multi-mode internet of things information collection and early warning model.
Background
At present, with the increase of population and the improvement of living standard, the demand for animals is continuously increasing, and large-scale agricultural production is developed in many countries. Along with the development of artificial intelligence and the Internet of things technology, the construction of the intelligent culture system has wide research and application prospects. The system can realize the functions of automatic control, data analysis, early warning and the like of animal growth environment based on technologies such as big data analysis, machine learning and the like, and the current intelligent breeding system has high cost, and can not ensure the healthy growth of livestock and poultry due to the fact that effective data can not be obtained in time. Therefore, how to reduce the cultivation cost and ensure the healthy growth of animals is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the application provides a method for constructing an intelligent breeding multi-mode internet of things information collection and early warning model, which is characterized in that target environment data of an environment where livestock and poultry are located and target physiological characteristic data of the livestock and poultry are determined by acquiring sensing data of an intelligent breeding system detected by various sensors, and further whether the intelligent breeding system meets living conditions of the livestock and poultry or not can be determined based on the reference environment data and the target environment data after the reference environment data is acquired, so that when the intelligent breeding system does not meet the living conditions of the livestock and poultry, the intelligent breeding system is controlled to adjust based on the reference environment data and generate first early warning information, or when the health state of the livestock and poultry is determined to be an unhealthy state according to the target physiological characteristic data, second early warning information is generated and the second early warning information is used for warning according to the second early warning information, the early warning can be performed according to various sensing data, and the situation that the health growth of the livestock and poultry cannot be guaranteed due to incapability of timely acquiring effective data is avoided, and accurate management decision is provided, and the health development of the breeding industry is promoted.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect, a method for constructing a information collection and early warning model of a smart culture multi-mode internet of things is provided, and the method is applied to a smart culture system and comprises the following steps: acquiring sensing data of the intelligent culture system detected by various sensors; determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data; acquiring reference environment data of the livestock and poultry, and determining whether the intelligent breeding system accords with living conditions of the livestock and poultry based on the reference environment data and target environment data; if the intelligent breeding system does not accord with the living conditions of the livestock, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information; and/or if the health state of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information.
According to the method of the first aspect, the target environmental data of the environment where the livestock and poultry are located and the target physiological characteristic data of the livestock and poultry are determined by acquiring the sensing data of the intelligent breeding system detected by the plurality of sensors, so that whether the intelligent breeding system meets the living conditions of the livestock and poultry or not can be determined based on the reference environmental data and the target environmental data after the reference environmental data is acquired, when the intelligent breeding system does not meet the living conditions of the livestock and poultry, the intelligent breeding system is controlled to adjust based on the reference environmental data and generate the first early warning information, or when the health state of the livestock and poultry is determined to be in an unhealthy state according to the target physiological characteristic data, the second early warning information is generated and the second early warning information is used for warning according to the plurality of sensing data, and therefore the situation that the health growth of the livestock and poultry cannot be guaranteed due to incapability of timely acquiring effective data is avoided, so that accurate management decisions are provided, and the health development of breeding industry is promoted.
With reference to the first aspect, in one possible design, the target environmental data at least includes target air relative humidity and target gas data of an environment where the livestock and poultry are located, the obtaining the reference environmental data of the livestock and poultry, and determining whether the intelligent breeding system meets the living condition of the livestock and poultry based on the reference environmental data and the target environmental data includes: determining the target type of the livestock and poultry; acquiring reference environmental data of the target species based on the target species, the reference environmental data including reference air relative humidity and reference gas data; determining a first difference between the reference air relative humidity and a target air relative humidity and a second difference between the reference gas data and the target gas data; if the first difference value is not in the first difference value range or the second difference value is not in the second difference value range, determining that the intelligent breeding system does not accord with the living conditions of the livestock and poultry; and if the first difference value is in the first difference value range and the second difference value is in the second difference value range, determining that the intelligent breeding system meets the living conditions of the livestock and poultry.
According to possible design schemes, the embodiment can determine the reference environment data of the livestock and poultry based on the target types of the livestock and poultry, and then can determine that the intelligent breeding system meets the living conditions of the livestock and poultry based on the reference environment data, the target air relative humidity of the environment of the livestock and poultry and the target gas data when determining the target air relative humidity and the target gas data of the environment of the livestock and poultry, thereby ensuring that the living environment of the livestock and poultry meets the requirements, and further ensuring the growth health of the livestock and poultry.
With reference to the first aspect, in one possible design, if the intelligent cultivation system does not meet the living condition of the livestock and poultry, controlling the intelligent cultivation system to adjust based on the reference environmental data, and generating first early warning information includes: if the first difference value is not in the first difference value range, determining and controlling a water pump and temperature control equipment of the intelligent culture system to adjust according to the target air relative humidity so that the target air relative humidity accords with the living condition of the target type; and/or if the second difference value is not in the second difference value range, adjusting ventilation equipment of the intelligent culture system according to the target gas data so that the target gas data accords with the living condition of the target type.
According to a possible design scheme, the intelligent breeding system is controlled to carry out adaptive adjustment through the stock information between the first difference value and the first teapot range and between the second difference value and the second difference value range, so that the current living conditions of the livestock and poultry accord with the living conditions of the livestock and poultry, and healthy growth of the livestock and poultry is ensured.
With reference to the first aspect, in one possible design, the determining target physiological characteristic data of the livestock and poultry according to the sensing data includes: determining feeding data and residual grain data of the intelligent culture system according to the sensing data; determining the feeding condition of the livestock and poultry according to the feeding grain data and the residual grain data; and acquiring body temperature data of the livestock and poultry, and determining the feeding condition and the body temperature data as the target physiological characteristic data.
According to a possible design scheme, the feeding data and the residual grain data of the intelligent breeding system are determined through the sensing data, so that the feeding condition of the livestock and poultry can be determined according to the feeding data and the residual grain data of the intelligent breeding system, the feeding condition and the body temperature data are conveniently determined to be target physiological characteristic data of the livestock and poultry, and the accuracy of the target physiological characteristic data is improved.
With reference to the first aspect, in one possible design, if the health status of the livestock and poultry is determined to be an unhealthy status according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information, including: if the feeding condition indicates that the difference value between the residual grain data and the feeding grain data is greater than or equal to a difference value threshold, determining target individual livestock and poultry with feeding time length smaller than a time length threshold in the livestock and poultry according to the sensing data; marking the target individual livestock and poultry, and obtaining the serial numbers of the target individual livestock and poultry; and generating the second early warning information according to the number and the feeding condition, and alarming according to the second early warning information.
According to possible design schemes, the target individual livestock and poultry with poor feeding is determined according to the running condition, so that the target individual livestock and poultry can be marked, the number of the target individual livestock and poultry can be obtained, the target individual livestock and poultry can be monitored according to the number and the mark, and further, the early warning is carried out on the target individual livestock and poultry with health problems, and the real-time performance of health monitoring on the livestock and poultry is improved.
With reference to the first aspect, in a possible design, before the determining that the health status of the livestock and poultry is an unhealthy status according to the target physiological characteristic data, the method further includes: acquiring monitoring video data of the livestock and poultry; inputting the monitoring video data into a disease diagnosis model, and outputting diagnosis results of the livestock and poultry; and determining the health state of the livestock and poultry according to the diagnosis result.
According to possible design schemes, whether the livestock and poultry are ill or not is diagnosed according to the monitoring video data of the livestock and poultry, so that the health state of the livestock and poultry can be determined, the accuracy of determining the health state of the livestock and poultry is improved, and the healthy growth of the livestock and poultry is ensured.
With reference to the first aspect, in one possible design, the method further includes: obtaining the serial numbers of the livestock and poultry and the livestock and poultry production information corresponding to the serial numbers; clustering livestock and poultry production information corresponding to the numbers according to the growth stages of the target species, so as to determine growth data of different growth stages of the target species; and establishing a traceability library according to the numbers and the growth data of different growth stages of the target types.
According to a possible design scheme, according to the embodiment, through clustering according to the production stages corresponding to the target types of the livestock and the livestock production information corresponding to the serial numbers of the livestock and the livestock, the growth data of the livestock and the livestock of the target types in different production stages are obtained, and then the serial numbers are associated with the growth data of the corresponding livestock and the livestock in different growth stages of the target types to establish a tracing library, so that tracing of the livestock and the livestock based on the serial numbers is realized, and the experience of consumers is improved.
In a second aspect, a device for constructing a information collection and early warning model of a smart culture multi-mode internet of things is provided, and the device is applied to a smart culture system and comprises: the sensing data acquisition module is used for acquiring sensing data of the intelligent culture system, which are detected by at least one plurality of sensors; the data determining module is used for determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data; the determining module is used for acquiring the reference environment data of the livestock and poultry and determining whether the intelligent breeding system accords with the living conditions of the livestock and poultry or not based on the reference environment data and the target environment data; the first processing module is used for controlling the intelligent breeding system to adjust based on the reference environment data and generating first early warning information if the intelligent breeding system does not accord with the living conditions of the livestock; and/or a second processing module, configured to generate second early warning information if the health status of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, and alarm according to the second early warning information.
In addition, the technical effects of the device for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things according to the second aspect can refer to the technical effects of the method for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things according to the first aspect, and are not described herein.
In a third aspect, a smart farming system is provided, the system comprising:
a processor;
and the memory is stored with computer readable instructions, and when the computer readable instructions are executed by the processor, any one of the methods for constructing the intelligent culture multi-mode internet of things information collection and early warning model is realized.
In addition, the technical effects of the intelligent cultivation system according to the third aspect may refer to the technical effects of the device for constructing the information collection and early warning model of the intelligent cultivation multi-mode internet of things according to the second aspect, which are not described herein.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where a program code is stored, where the program code may be invoked by a processor to perform any one of the methods for constructing a information collection and early warning model of a smart culture multi-modal internet of things according to the first aspect.
Drawings
FIG. 1 is an application scenario diagram of a method for constructing an intelligent culture multi-mode Internet of things information collection and early warning model according to an embodiment of the application;
FIG. 2 is a flowchart of a method for constructing an information collection and early warning model of a multi-mode Internet of things for intelligent cultivation according to an embodiment of the application;
FIG. 3 is a flowchart of a method for constructing an information collection and early warning model of a multi-modal Internet of things for intelligent culture according to another embodiment of the present application;
FIG. 4 is a block diagram of a device for constructing an information collection and early warning model of a multi-mode Internet of things for intelligent cultivation according to an embodiment of the application;
fig. 5 is a schematic structural diagram of a smart culture system according to an embodiment of the present application.
Detailed Description
The technical scheme of the application is described below with reference to the accompanying drawings.
In embodiments of the application, words such as "exemplary," "such as" and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term use of an example is intended to present concepts in a concrete fashion. Furthermore, in embodiments of the present application, the meaning of "and/or" may be that of both, or may be that of either, optionally one of both.
In the embodiments of the present application, "image" and "picture" may be sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized. "of", "corresponding" and "corresponding" are sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized.
In the embodiment of the application, sometimes the subscript is W 1 May possiblyThe meaning of the expression is consistent when the distinction is de-emphasized by the fact that the pen error is in a non-subscripted form, such as W1.
With the continuous development of information technology, intelligent agriculture is gradually becoming the direction of technical innovation in the agricultural field. The intelligent cultivation is taken as an important component of intelligent agriculture, and is a novel cultivation mode for comprehensively and intelligently managing the cultivation process by utilizing advanced technical means such as the Internet of things, cloud computing, big data, artificial intelligence and the like. The intelligent cultivation can realize comprehensive collection of information, accurate analysis and prediction of data and intelligent decision making so as to realize the purpose of intelligent cultivation, improve cultivation benefits, guarantee cultivation environment and eliminate cultivation risks.
In the current cultivation production, due to the lack of timely and accurate data information feedback, some problems are difficult to discover and solve in time, so that cultivation benefits are low, and even economic losses and social contradictions are caused. Therefore, how to realize timely monitoring, early warning and management of key information in the cultivation process becomes an important research direction for intelligent development in the current agricultural field.
Therefore, in order to overcome the above-mentioned drawbacks, in the embodiments of the present application, by determining a path set from the data to be transmitted to the next forwarding node according to a transmission path, a transmission weight corresponding to each path is input in the path set according to a path length, and since the transmission weight and the path length have a negative correlation, transmission can be performed on a short path at last during transmission. The method and the device avoid transmission congestion caused by directly forwarding the data to be transmitted on all paths, and improve the data transmission efficiency.
Fig. 1 is a schematic diagram of an application scenario, as shown in fig. 1, including a smart farming system 110 and an electronic device 120 communicatively coupled to the smart farming system 110, according to an embodiment of the present application. The electronic device 120 may be a mobile phone, a computer, a tablet computer, a smart watch, etc. that is communicatively connected to the smart farming system. Alternatively, the communication connection may be a wireless network connection, a virtual local area network connection, an API communication interface connection, or the like.
For some embodiments, after the intelligent cultivation system 110 generates the first early warning information and/or the second early warning information, the first early warning information and/or the second early warning information is sent to the electronic device 120 through the communication connection, so that the electronic device 120 can alarm according to the received first early warning information and/or the second early warning information, and further, the cultivation personnel can timely monitor the livestock and poultry in the intelligent cultivation system and timely respond.
Referring to fig. 2, fig. 2 shows a flowchart of a method for constructing an information collection and early warning model of a multi-modal internet of things for intelligent cultivation according to an embodiment of the application, and the method includes steps 210 to 250.
Step 210: and acquiring sensing data of the intelligent culture system detected by various sensors.
Step 220: and determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data.
Step 230: and acquiring reference environment data of the livestock and poultry, and determining whether the intelligent breeding system accords with living conditions of the livestock and poultry based on the reference environment data and the target environment data.
Step 240: and if the intelligent breeding system does not accord with the living conditions of the livestock, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information.
Step 250: and if the health state of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information.
For some embodiments, a plurality of different kinds of sensors may be provided in the smart culture system, so as to obtain a plurality of different sensing data. Alternatively, the sensor may include an image acquisition sensor, an audio acquisition sensor, an infrared sensor, a humidity sensor, a gas sensor, etc., and may be set according to actual needs.
For some embodiments, after the sensing data of the intelligent breeding system detected by the plurality of sensors are obtained, the collected sensing data are transmitted to the cloud end through the internet of things, and the sensing data collected by different sensors are combined according to the time stamps to form uniform multi-mode sensing data, so that the target environment data of the environment where the livestock and poultry are located and the target physiological characteristic data of the livestock and poultry are conveniently determined according to the multi-mode sensing data. For example. For poultry cultivation, equipment such as a temperature and humidity sensor, a carbon dioxide and ammonia concentration sensor, a video monitor and the like can be installed in a bird house, and data such as environment, bird group activities and the like can be acquired. Uploading the acquired sensing data to the cloud, preprocessing different types of data, such as removing interference signals by filtering the temperature and the humidity, removing the interference signals by using a noise reduction algorithm by using the gas concentration, and performing target detection, tracking and other processing by using video monitoring.
Optionally, the sensing data may be data obtained by fusing data collected by multiple sensors, and the sensing data may be analyzed to determine target environmental data of the environment where the livestock and poultry are located and target physiological characteristic data of the livestock and poultry. The target physiological characteristic data of the beasts and birds can be removed according to the life activity data.
For other embodiments, the information to be collected during the corresponding cultivation process of multiple different livestock and poultry needs to be preprocessed before the intelligent cultivation system is applied offline, for example, an information summary table of the cultivation process is built, the data collection frequency of the sensor is set, and the networking and transmission modes inside the intelligent cultivation system are set. The information summary table needs to include information such as equipment numbers, sensor types, sensor acquisition indexes, acquisition time, data types, data units and the like. The acquisition frequency of the sensor data is set according to different breeding environments and equipment characteristics so as to meet the integrity and the accuracy of the data to the greatest extent. The networking and transmission modes comprise wired (such as USB wire, RJ45 wire and the like) or wireless (such as WiFi, zigbee and the like) transmission of the sensor data.
Optionally, an internet of things architecture of the intelligent aquaculture system needs to be built before the intelligent aquaculture system is put into use, wherein the internet of things architecture comprises a sensor node, a communication module, a data processing center and a monitoring management platform. The sensor nodes are responsible for collecting various data information in the cultivation process and transmitting the data information to the communication module, the communication module processes and stores the sensor data, the data is uploaded to the data processing center for storage and analysis, and the monitoring management platform is responsible for monitoring and management of the whole Internet of things architecture. The specific implementation mode of the Internet of things architecture can adopt a conventional Internet of things application architecture, such as a central type, a distributed type, a mixed type and the like.
For some embodiments, the obtained sensing data needs to be preprocessed before determining the target environmental data of the environment where the livestock and poultry are located and the target physiological characteristic data of the livestock and poultry according to the sensing data. Optionally, the quality and accuracy of the sensing data can be ensured by preprocessing the sensing data and screening, extracting and filtering the sensing data according to the difference of different culture environments. The pretreatment measures can comprise data cleaning, duplication removal, repair, standardization, normalization and other operations. In addition, the method can also perform dimension reduction processing on the sensing data, reduce repeated data and invalid information and improve processing efficiency.
For some embodiments, the living conditions of the livestock and poultry can be humidity, gas concentration, temperature and the like of the living of the livestock and poultry, and different living conditions of different types of livestock and poultry can be set to corresponding living conditions according to actual needs.
For some embodiments, after the first early warning information or the second early warning information is generated, the intelligent breeding system can alarm according to the first early warning information or the second early warning information, so that breeding personnel can timely process based on early warning fineness, and the health of livestock and poultry is guaranteed. Optionally, the first early warning information or the second early warning information can be sent to an electronic device in communication connection with the intelligent breeding system, so that breeding personnel can monitor the health of livestock and poultry remotely.
Optionally, the target environmental data at least includes target air relative humidity and target gas data of the environment where the livestock and poultry are located, and the step 230 includes: determining the target type of the livestock and poultry; acquiring reference environmental data of the target species based on the target species, the reference environmental data including reference air relative humidity and reference gas data; determining a first difference between the reference air relative humidity and a target air relative humidity and a second difference between the reference gas data and the target gas data; if the first difference value is not in the first difference value range or the second difference value is not in the second difference value range, determining that the intelligent breeding system does not accord with the living conditions of the livestock and poultry; and if the first difference value is in the first difference value range and the second difference value is in the second difference value range, determining that the intelligent breeding system meets the living conditions of the livestock and poultry.
For some embodiments, the image data collected by the image collecting sensor can be input into the identification model, and the target type of the livestock and poultry can be determined through the identification model.
Specifically, if the intelligent breeding system does not meet the living conditions of the livestock and poultry, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information, including: if the first difference value is not in the first difference value range, determining and controlling a water pump and temperature control equipment of the intelligent culture system to adjust according to the target air relative humidity so that the target air relative humidity accords with the living condition of the target type; and/or if the second difference value is not in the second difference value range, adjusting ventilation equipment of the intelligent culture system according to the target gas data so that the target gas data accords with the living condition of the target type.
Specifically, the step 230 includes: determining feeding data and residual grain data of the intelligent culture system according to the sensing data; determining the feeding condition of the livestock and poultry according to the feeding grain data and the residual grain data; and acquiring body temperature data of the livestock and poultry, and determining the feeding condition and the body temperature data as the target physiological characteristic data.
Optionally, if the health status of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information, including: if the feeding condition indicates that the difference value between the residual grain data and the feeding grain data is greater than or equal to a difference value threshold, determining target individual livestock and poultry with feeding time length smaller than a time length threshold in the livestock and poultry according to the sensing data; marking the target individual livestock and poultry, and obtaining the serial numbers of the target individual livestock and poultry; and generating the second early warning information according to the number and the feeding condition, and alarming according to the second early warning information.
Optionally, before the step 250, the method further includes: acquiring monitoring video data of the livestock and poultry; inputting the monitoring video data into a disease diagnosis model, and outputting diagnosis results of the livestock and poultry; and determining the health state of the livestock and poultry according to the diagnosis result.
In this embodiment, the target environmental data of the environment where the livestock and poultry are located and the target physiological characteristic data of the livestock and poultry are determined by acquiring the sensing data of the intelligent breeding system detected by the plurality of sensors, so that whether the intelligent breeding system meets the living conditions of the livestock and poultry or not can be determined based on the reference environmental data and the target environmental data after the reference environmental data is acquired, when the intelligent breeding system does not meet the living conditions of the livestock and poultry, the intelligent breeding system is controlled to adjust based on the reference environmental data and generate the first early warning information, or when the health state of the livestock and poultry is determined to be in an unhealthy state according to the target physiological characteristic data, the second early warning information is generated and the second early warning information is used for warning, and early warning can be performed according to the plurality of sensing data, so that the healthy growth of the livestock and poultry cannot be guaranteed due to incapability of timely acquiring effective data is avoided, so that accurate management decision is provided, and the healthy development of the breeding industry is promoted.
Referring to fig. 3, fig. 3 shows a flowchart of a method for constructing an information collection and early warning model of a multi-modal internet of things for intelligent cultivation according to an embodiment of the application, and the method includes steps 310 to 330.
Step 310, obtaining the serial numbers of the livestock and poultry and the livestock and poultry production information corresponding to the serial numbers.
And 320, clustering the livestock and poultry production information corresponding to the numbers according to the growth stages of the target species, so as to determine the growth data of different growth stages of the target species.
And 330, establishing a traceability library according to the numbers and the growth data of different growth stages of the target species.
For some embodiments, in order to facilitate the construction of a trusted food traceability system for consumers and the creation of branded agricultural products, in the breeding link of livestock and poultry, the generated data are in butt joint with a blockchain platform by wearing electronic tags carrying numbers on the livestock and poultry, and the information such as height weight, immunization program, environmental factors, product processing and the like in the breeding process is recorded. In the consumption link, related production information can be rapidly inquired through a specific bar code or a two-dimensional code, and a consumer can realize 'knowledge root tracing' through code scanning and trace back the whole course of information such as raw material collection, logistics transportation, store sales and the like. The method meets the awareness of consumers and ensures the safe purchasing and consumption.
In the embodiment, through clustering according to the production stages corresponding to the target types of the livestock and poultry production information corresponding to the serial numbers of the livestock and poultry, the growth data of the livestock and poultry of the target types in different production stages are obtained, and then the serial numbers are associated with the growth data of the corresponding livestock and poultry of the target types in different growth stages to establish a traceable library, so that the livestock and poultry can be traced based on the serial numbers, and the experience of consumers is improved.
The method for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things provided by the embodiment of the application is described in detail based on fig. 2, and the virtual device corresponding to the method for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things provided by the embodiment of the application, namely the device for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things, is described in detail below.
Fig. 4 is a schematic structural diagram of an apparatus 400 for constructing an information collection and early warning model of a multi-modal internet of things for smart culture according to an embodiment of the present application. As shown in fig. 4, the apparatus 400 for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things comprises: a sensed data acquisition module 410, a data determination module 420, a determination module 430, a first processing module 440, and a second processing module 450.
For convenience of explanation, fig. 4 only shows main components of the apparatus 400 for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things.
A sensing data acquisition module 410, configured to acquire sensing data of the smart culture system detected by a plurality of sensors;
the data determining module 420 is configured to determine target environmental data of an environment where the livestock and poultry are located according to the sensing data and determine target physiological characteristic data of the livestock and poultry according to the sensing data;
Optionally, the data determining module 420 includes: determining feeding data and residual grain data of the intelligent culture system according to the sensing data; determining the feeding condition of the livestock and poultry according to the feeding grain data and the residual grain data; and acquiring body temperature data of the livestock and poultry, and determining the feeding condition and the body temperature data as the target physiological characteristic data.
Further, if the feeding condition indicates that the difference value between the residual grain data and the feeding grain data is greater than or equal to a difference value threshold, determining target individual livestock and poultry with feeding time length smaller than a time length threshold in the livestock and poultry according to the sensing data; marking the target individual livestock and poultry, and obtaining the serial numbers of the target individual livestock and poultry; and generating the second early warning information according to the number and the feeding condition, and alarming according to the second early warning information.
A determining module 430, configured to obtain reference environmental data of the livestock and poultry, and determine whether the intelligent cultivation system meets the living condition of the livestock and poultry based on the reference environmental data and the target environmental data;
optionally, the target environmental data at least includes target air relative humidity and target gas data of the environment where the livestock and poultry are located, and the determining module 430 includes: determining the target type of the livestock and poultry; acquiring reference environmental data of the target species based on the target species, the reference environmental data including reference air relative humidity and reference gas data; determining a first difference between the reference air relative humidity and a target air relative humidity and a second difference between the reference gas data and the target gas data; if the first difference value is not in the first difference value range or the second difference value is not in the second difference value range, determining that the intelligent breeding system does not accord with the living conditions of the livestock and poultry; and if the first difference value is in the first difference value range and the second difference value is in the second difference value range, determining that the intelligent breeding system meets the living conditions of the livestock and poultry.
Further, if the intelligent breeding system does not meet the living conditions of the livestock, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information, including: if the first difference value is not in the first difference value range, determining and controlling a water pump and temperature control equipment of the intelligent culture system to adjust according to the target air relative humidity so that the target air relative humidity accords with the living condition of the target type; and/or if the second difference value is not in the second difference value range, adjusting ventilation equipment of the intelligent culture system according to the target gas data so that the target gas data accords with the living condition of the target type.
The first processing module 440 is configured to control the intelligent cultivation system to adjust based on the reference environmental data and generate first early warning information if the intelligent cultivation system does not meet the living conditions of the livestock and poultry; and/or
And the second processing module 450 is configured to generate second early warning information if the health status of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, and alarm according to the second early warning information.
Optionally, the device 400 for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things further comprises: the monitoring video data acquisition module is used for acquiring the monitoring video data of the livestock and poultry; the diagnosis module is used for inputting the monitoring video data into a disease diagnosis model and outputting diagnosis results of the livestock and poultry; and the health state determining module is used for determining the health state of the livestock and poultry according to the diagnosis result.
Optionally, the device 400 for constructing the information collection and early warning model of the intelligent aquaculture multi-mode internet of things further comprises: the acquisition module is used for acquiring the serial numbers of the livestock and poultry and the livestock and poultry production information corresponding to the serial numbers; the clustering module is used for clustering the livestock and poultry production information corresponding to the numbers according to the growth stages of the target species so as to determine the growth data of different growth stages of the target species; and the traceability library construction module is used for constructing the traceability library according to the numbers and the growth data of different growth stages of the target types.
As shown in fig. 5, the smart culture system may include the apparatus for constructing the information collection and early warning model of the smart culture multi-modal internet of things shown in fig. 4. Optionally, the smart cut system 110 may include a processor 2001.
Optionally, the smart farming system 110 may also include a memory 2002 and a transceiver 2003.
The processor 2001 may be connected to the memory 2002 and the transceiver 2003 via a communication bus, for example.
The following describes the various components of the intelligent farming system 110 in detail with reference to fig. 5:
the processor 2001 is a control center of the intelligent cultivation system 110, and may be one processor or a collective term of a plurality of processing elements. For example, processor 2001 is one or more central processing units (central processing unit, CPU), but may also be an integrated circuit (application specific integrated circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present application, such as: one or more microprocessors (digital signalprocessor, DSPs), or one or more field programmable gate arrays (field programmable gate array, FPGAs).
Alternatively, processor 2001 may perform various functions of smart farming system 110 by running or executing software programs stored in memory 2002, and invoking data stored in memory 2002.
In a particular implementation, the processor 2001 may include one or more CPUs, such as CPU0 and CPU1 shown in FIG. 5, as an example.
In particular implementations, as one example, the smart farming system 110 may also include multiple processors, such as the processor 2001 and processor 2004 shown in fig. 5. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The memory 2002 is used for storing a software program for executing the solution of the present application, and is controlled by the processor 2001 to execute the solution, and the specific implementation may refer to the above method embodiment, which is not described herein again.
Alternatively, memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electricallyerasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation. Memory 2002 may be integrated with processor 2001 or may exist separately and be coupled to processor 2001 via interface circuitry (not shown in fig. 5) of smart culture system 110, as embodiments of the application are not limited in this regard.
A transceiver 2003 for communicating with a network device or with a terminal device.
Alternatively, transceiver 2003 may include a receiver and a transmitter (not separately shown in fig. 5). The receiver is used for realizing the receiving function, and the transmitter is used for realizing the transmitting function.
Alternatively, transceiver 2003 may be integrated with processor 2001 or may exist separately and be coupled to processor 2001 through interface circuitry (not shown in fig. 5) of router 110, as embodiments of the application are not specifically limited.
It should be noted that the configuration of the smart farming system 110 shown in fig. 5 is not limiting of the router, and that an actual smart farming system may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
In addition, the technical effects of the intelligent cultivation system 110 can be referred to the technical effects of the data transmission method described in the above method embodiments, and will not be described herein.
It is to be appreciated that the processor 2001 in embodiments of the application may be a central processing unit (central processing unit, CPU) which may also be other general purpose processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (applicationspecific integrated circuit, ASIC), off-the-shelf programmable gate arrays (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should also be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (programmableROM, PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (electricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be random access memory (random access memory, RAM) which acts as an external cache. By way of example but not limitation, many forms of random access memory (randomaccess memory, RAM) are available, such as Static RAM (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (enhancedSDRAM, ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM).
The above embodiments may be implemented in whole or in part by software, hardware (e.g., circuitry), firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: there are three cases, a alone, a and B together, and B alone, wherein a, B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The method for constructing the intelligent cultivation multi-mode internet of things information collection and early warning model is characterized by being applied to an intelligent cultivation system and comprising the following steps of:
acquiring sensing data of the intelligent culture system detected by various sensors;
determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data;
acquiring reference environment data of the livestock and poultry, and determining whether the intelligent breeding system accords with living conditions of the livestock and poultry based on the reference environment data and target environment data;
if the intelligent breeding system does not accord with the living conditions of the livestock, controlling the intelligent breeding system to adjust based on the reference environment data, and generating first early warning information; and/or
And if the health state of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information.
2. The method of claim 1, wherein the target environmental data at least includes target air relative humidity and target gas data of an environment in which the livestock and poultry are located, the obtaining the reference environmental data of the livestock and poultry, and determining whether the intelligent farming system meets the living condition of the livestock and poultry based on the reference environmental data and the target environmental data, comprises:
determining the target type of the livestock and poultry;
acquiring reference environmental data of the target species based on the target species, the reference environmental data including reference air relative humidity and reference gas data;
determining a first difference between the reference air relative humidity and a target air relative humidity and a second difference between the reference gas data and the target gas data;
if the first difference value is not in the first difference value range or the second difference value is not in the second difference value range, determining that the intelligent breeding system does not accord with the living conditions of the livestock and poultry;
And if the first difference value is in the first difference value range and the second difference value is in the second difference value range, determining that the intelligent breeding system meets the living conditions of the livestock and poultry.
3. The method of claim 2, wherein if the intelligent farming system does not meet the living conditions of the livestock, controlling the intelligent farming system to adjust based on the reference environmental data, and generating first warning information, comprises:
if the first difference value is not in the first difference value range, determining and controlling a water pump and temperature control equipment of the intelligent culture system to adjust according to the target air relative humidity so that the target air relative humidity accords with the living condition of the target type; and/or
And if the second difference value is not in the second difference value range, adjusting the ventilation equipment of the intelligent culture system according to the target gas data so that the target gas data accords with the living condition of the target type.
4. The method of claim 1, wherein said determining target physiological characteristic data of said livestock from said sensed data comprises:
Determining feeding data and residual grain data of the intelligent culture system according to the sensing data;
determining the feeding condition of the livestock and poultry according to the feeding grain data and the residual grain data;
and acquiring body temperature data of the livestock and poultry, and determining the feeding condition and the body temperature data as the target physiological characteristic data.
5. The method of claim 4, wherein if the health status of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data, generating second early warning information, and alarming according to the second early warning information, comprises:
if the feeding condition indicates that the difference value between the residual grain data and the feeding grain data is greater than or equal to a difference value threshold, determining target individual livestock and poultry with feeding time length smaller than a time length threshold in the livestock and poultry according to the sensing data;
marking the target individual livestock and poultry, and obtaining the serial numbers of the target individual livestock and poultry;
and generating the second early warning information according to the number and the feeding condition, and alarming according to the second early warning information.
6. The method of claim 1, wherein prior to the generating the second pre-warning information if the health status of the livestock is determined to be unhealthy based on the target physiological characteristic data, the method further comprises:
Acquiring monitoring video data of the livestock and poultry;
inputting the monitoring video data into a disease diagnosis model, and outputting diagnosis results of the livestock and poultry;
and determining the health state of the livestock and poultry according to the diagnosis result.
7. The method according to any one of claims 1-6, further comprising:
obtaining the serial numbers of the livestock and poultry and the livestock and poultry production information corresponding to the serial numbers;
clustering livestock and poultry production information corresponding to the numbers according to the growth stages of the target species, so as to determine growth data of different growth stages of the target species;
and establishing a traceability library according to the numbers and the growth data of different growth stages of the target types.
8. The device is applied to a smart culture system and comprises the following components:
the sensing data acquisition module is used for acquiring sensing data of the intelligent culture system, which are detected by various sensors;
the data determining module is used for determining target environment data of the environment where the livestock and poultry are located according to the sensing data and determining target physiological characteristic data of the livestock and poultry according to the sensing data;
The determining module is used for acquiring the reference environment data of the livestock and poultry and determining whether the intelligent breeding system accords with the living conditions of the livestock and poultry or not based on the reference environment data and the target environment data;
the first processing module is used for controlling the intelligent breeding system to adjust based on the reference environment data and generating first early warning information if the intelligent breeding system does not accord with the living conditions of the livestock; and/or
And the second processing module is used for generating second early warning information and alarming according to the second early warning information if the health state of the livestock and poultry is determined to be unhealthy according to the target physiological characteristic data.
9. A smart farming system, the system comprising:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 7
10. A computer readable storage medium having stored therein program code which is callable by a processor to perform the method of any one of claims 1 to 7.
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| CN117854012B (en) * | 2024-03-07 | 2024-05-14 | 成都智慧城市信息技术有限公司 | Crop environment monitoring method and system based on big data |
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