KR20230027759A - Smart beekeeping management system based on big data analysis - Google Patents

Abstract

A smart beekeeping management system based on big data analysis is provided. According to an embodiment of the present invention, the smart beekeeping management system based on big data analysis comprises: a smart beehive including a beekeeping frame, a sensor unit detecting the inside of the beekeeping frame, a thermal camera for photographing the inside of the beekeeping frame, an entrance and exit camera for photographing the entrance and exit of the beekeeping frame, and a communication unit; and a management server that analyzes and predicts bee activities and beekeeping-related environments through AI analysis of big data transmitted from the smart beehive. Here, the management server visualizes an analyzed and predicted result by images and transmits them to a manager terminal.

Description

Smart beekeeping management system based on big data analysis}

The present invention relates to a smart beekeeping management system based on big data analysis.

Generally, beekeepers install artificial hives to harvest beekeeping honey. Such beekeeping honey has a higher yield than natural honey obtained by natural beehives. In beekeeping, bees are very sensitive to climatic conditions and conditions inside the hive, so beekeepers need to closely observe the inside and surroundings of the hive.

Recently, due to the development of information and communication technology, a technology for collecting various information about artificial beehives has emerged. Most of these technologies are at the level of remotely collecting simple information sensed by sensors through communication.

However, for effective beekeeping, it is important to analyze and predict environmental conditions such as the activities of bees, such as spawning activities and harvesting activities, and the corresponding flowering time. Therefore, there is a need for a system that predicts and analyzes bee activities or beekeeping-related environments using big data detected from the beehive.

KR 2020-0064475 A

In order to solve the problems of the prior art as described above, an embodiment of the present invention collects various information from the beehive and analyzes big data that can predict and analyze the activities of bees and the environment related to beekeeping using the collected big data. It is intended to provide a smart beekeeping management system based on

According to one aspect of the present invention for solving the above problems, a smart camera including consumption, a sensor unit for detecting the inside of the consumption, a thermal camera for photographing the inside of the consumption, an entrance and exit camera for photographing the entrance of the consumption, and a communication unit. wooden beehive; and a management server that analyzes and predicts the bee's activities and beekeeping-related environments through AI analysis of the big data transmitted from the smart beehive, wherein the management server visualizes the analyzed and predicted results as images to a manager terminal. A smart beekeeping management system based on big data analysis is provided.

In one embodiment, the management server includes an access counting unit recognizing the image of the access camera to calculate the number of access objects and counting the number of access objects for the consumption; a state monitoring unit that monitors the state of the smart beehive according to the state information obtained by the sensor unit to determine the amount of bee activity, weather, environmental adequacy of the smart beehive, the number of populations inside the hive, and whether or not it is harvested; and a colony state analyzer for determining the possibility of swarming by determining whether there is a queen bee in the bee according to whether or not a frequency corresponding to the queen bee is present in the sound in the bee sensed by the sensor unit.

In one embodiment, the management server determines an external intrusion of a heterogeneous bee into the consumption according to whether a frequency corresponding to a wasp or a wild bee in the sound in the consumption sensed by the sensor unit is detected, and the data of the sensor unit is determined. an anomaly symptom determining unit for determining an abnormal state when the rate of change is greater than or equal to a predetermined value; and an alarm unit for notifying the manager terminal of the abnormal symptom and corresponding measures according to the determination of the abnormal symptom.

In one embodiment, the management server includes a pollen analyzer for analyzing the type of pollen according to the color of the pollen of the bee entering the consumption; and a flowering time predictor for predicting the flowering time of a corresponding flower according to the analyzed pollen type.

In one embodiment, the sensor unit temperature sensor for sensing the temperature inside the consumption; a humidity sensor for sensing humidity inside the consumer; a weight sensor for detecting the weight of the consumption; a CO2 sensor for sensing the CO2 concentration inside the consumption; and a sound sensor for detecting a sound inside the consumption.

In one embodiment, the access camera may be provided on one side of the entrance, and a blocking plate may be provided on the other side of the entrance.

In one embodiment, the smart beehive may further include a temperature control unit for adjusting the internal temperature of the consumption.

In one embodiment, the manager terminal is a portable terminal including a smart phone, and may display results analyzed and predicted in the management server as an image by a dedicated app for beekeeping management.

A smart beekeeping management system based on big data analysis according to an embodiment of the present invention collects big data related to bee activities by sensors and analyzes the collected big data by AI to predict and analyze bee activities and beekeeping-related environments. Therefore, the efficiency and reliability of beekeeping management can be improved.

In addition, the smart beekeeping management system based on big data analysis according to an embodiment of the present invention can collect various information such as the number of entering and exiting bees and the color of pollen by photographing with a camera at the entrance of the beehive. activities as well as the status of the environment related to beekeeping.

In addition, the big data analysis-based smart beekeeping management system according to an embodiment of the present invention monitors the presence or absence of queen bees through a sound sensor, thereby predicting the possibility of swarming for the corresponding consumption, thereby preventing swarming through prompt measures to prevent crowding. can keep

In addition, the big data analysis-based smart beekeeping management system according to an embodiment of the present invention is equipped with a CO2 sensor and a sound sensor as well as a temperature and humidity sensor and a weight sensor, so that more diverse information about the inside of the hive can be obtained and analyzed. growth status can be monitored more effectively.

In addition, the big data analysis-based smart beekeeping management system according to an embodiment of the present invention detects the internal heat distribution through a thermal camera, thereby solving the locational restriction by the temperature sensor, so that the internal state can be monitored more accurately. can

1 is a block diagram of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
2a is a block diagram of a smart beehive of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
2B is a front view showing an installation example of a door of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
2C is a perspective view illustrating an example of a door driving unit of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
3 is a block diagram of a management server of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
4 is an exemplary diagram of a manager terminal of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
5 is a cross-sectional view showing an installation example of an entry and exit camera of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
6 is an acquired picture of an entry and exit camera of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.
7 is a graph of a manager terminal showing abnormal symptoms of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily carry out the present invention. This invention may be embodied in many different forms and is not limited to the embodiments set forth herein. In order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and the same reference numerals are assigned to the same or similar components throughout the specification.

Hereinafter, a smart beekeeping management system based on big data analysis according to an embodiment of the present invention will be described in more detail with reference to the drawings. 1 is a block diagram of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

Referring to FIG. 1, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention collects big data from the smart beehive 100 where consumption is formed to predict and predict bee activities and beekeeping-related environments. for analysis. Here, consumption is a house where bees lay eggs, store food and honey, and are made of beeswax secreted by worker bees, and several hexagonal rooms are gathered to form layers.

The big data analysis-based smart beekeeping management system 10 may include a smart beehive 100, a management server 200, and a manager terminal 300.

The smart beehive 100 has a consumption built inside the case 101, and an antenna 102 for communication may be provided outside the case 101. This smart beehive 100 may be provided with an entrance 103 on one side for the bees to enter and exit for consumption. Here, one side of the smart beehive 100 may be open to the outside. Thereby, the operator can collect honey from consumption inside the smart hive 100.

In addition, the smart beehive 100 may communicate with the management server 200 through a wired or wireless communication network. At this time, the smart beehive 100 may detect the state of consumption therein and transmit the detected data to the management server 200.

The management server 200 may analyze the big data transmitted from the smart beehive 100 by AI. At this time, the management server 200 may analyze and predict the environment related to bee activities and beekeeping through AI analysis.

In addition, the management server 200 may transmit analysis and predicted results to the manager terminal 300 . Here, the management server 200 may visualize the analyzed and predicted results as an image such as a graph. These images may be displayed on the manager terminal 300 through an app dedicated to beekeeping management.

Accordingly, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention can predict and analyze bee activities and beekeeping-related environments, thereby improving beekeeping management efficiency and reliability.

The manager terminal 300 is a terminal of the manager of the smart beehive 100, and can receive AI analysis results for big data from the management server 200. For example, the manager terminal 300 may be a portable terminal. Here, the portable terminal may include a smart phone or a notepad.

In addition, the manager terminal 300 may have an app dedicated to beekeeping management for displaying AI analysis results provided from the management server 200 . Here, the beekeeping management dedicated app can visualize and display the AI analysis result provided by the management server 200 as an image such as a graph.

2a is a block diagram of a smart beehive of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

Referring to FIG. 2A, the smart beehive 100 includes a sensor unit 110, a thermal camera 120, an access camera 130, a temperature control unit 140, a communication unit 150, a storage unit 160, and a door drive unit. 170 and a control unit 180 may be included.

The sensor unit 110 may be provided within consumption to detect various states of consumption. The sensor unit 110 may include a temperature sensor 112 , a humidity sensor 114 , a weight sensor 116 , a CO2 sensor 118 , and a sound sensor 119 .

The temperature sensor 112 can sense the temperature inside the consumer. This temperature sensor 112 can be placed inside the consumer. Here, the temperature sensor 112 can detect the sum of the temperature corresponding to the heat generated from the bees present inside the smart beehive 100 and the temperature caused by the external environment.

Humidity sensor 114 can sense the humidity inside the consumer. This humidity sensor 114 may be placed inside the consumer. Here, the humidity sensor 114 may detect the humidity of the external environment.

The weight sensor 116 may detect the weight of consumption. This weight sensor 116 may be placed on the lower side of the consumption or smart hive 100. Here, the weight sensor 116 may detect the sum of the weight corresponding to the number of bees present in the smart hive 100, the weight corresponding to the amount of collected honey, and the weight of consumption itself.

The CO2 sensor 118 can sense the CO2 concentration inside the consumer. This CO2 sensor 118 may be placed inside the consumer. Here, the CO2 sensor 118 can detect the CO2 emitted from the bees present within the consumption.

The sound sensor 119 can detect sound inside the consumer. This sound sensor 119 can be placed inside the consumer. Here, the sound sensor 119 may detect internal sound for each frequency.

As a result, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention includes a general temperature sensor 112, humidity sensor 114, and weight sensor 116 as well as the inside of the smart beehive 100 Since a variety of information can be obtained, the growth status of bees can be monitored more effectively.

The thermal camera 120 can take pictures of the interior of the consumer. This thermal camera 120 can photograph the heat distribution inside the consumer. As an example, the thermal camera 120 may capture the heat distribution inside a hexagonal chamber made of beeswax inside the consumption.

As a result, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention can eliminate locational restrictions caused by temperature detection by location, such as a temperature sensor, so that the internal state can be monitored more accurately. .

The access camera 130 may be provided above the entrance 103 . The access camera 130 may capture the vicinity of the entrance 103 . That is, the entry and exit camera 130 may capture a state in which bees enter and exit through the entrance 103 .

The temperature control unit 140 may adjust the internal temperature of consumption. For example, when the temperature of consumption is low, such as in winter, the temperature control unit 140 may increase the internal temperature of consumption through a heating element. As another example, the temperature control unit 140 may lower the internal temperature of the consumer through the cooling body when the temperature of the consumer is high, such as in summer.

The communication unit 150 may communicate with the management server 200 by wire or wirelessly. As an example, the communication unit 150 may communicate with WiFi or BLE in a short-range communication method. In this case, the communication unit 150 may be connected to the Internet network through an access point (AP). As another example, the communication unit 150 may communicate using a long-distance communication method such as 5G.

The storage unit 160 may store consumption state information detected by the sensor unit 110 . The storage unit 160 may be deleted when a predetermined time elapses after the stored state information is transmitted to the management server 200 .

The door driver 170 may drive the door provided in the doorway 103 . For example, the door driver 170 may be a motor. At this time, the door driver 170 may slide the door horizontally to adjust the open area of the door 103 according to rotation.

The control unit 180 communicates with the sensor unit 110, the thermal camera 120, the access camera 130, the temperature control unit 140, the communication unit 150, the storage unit 160, and the door driving unit 170. It is connected and can control the overall operation of the smart beehive (100).

The control unit 180 may control the state information detected by the sensor unit 110 and stored in the storage unit 160 to be transmitted to the remote management server 200 through the communication unit 150 .

In addition, the controller 180 may control the thermal camera 120 to capture a state of consumption. In addition, the control unit 180 may control the access camera 130 to photograph the vicinity of the entrance 103 . The controller 180 may control the temperature controller 140 to increase or decrease the temperature of consumption according to the detection result of the temperature sensor 112 . In addition, the controller 180 may control the door driver 170 to adjust the open area of the door 103 according to the detection result of the temperature sensor 112 .

2B is a front view showing an installation example of a door of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention, and FIG. 2C is a front view of a door driving unit of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention. It is a perspective view showing an example.

Referring to FIG. 2B , an entrance door 171 may be provided in the entrance 103 . The door 171 may be slid by the door driver 170 . That is, as the door 171 slides, the open area of the door 103 can be adjusted. For example, the entrance door 171 may be provided with a flexible plate-shaped steel strip. In this way, the entrance door 171 has flexibility in the longitudinal direction but rigidity in the thickness direction, so it may be suitable for adjusting the open area of the entrance 103.

Referring to FIG. 2C , the door driver 170 and the door 171 may be integrally provided. For example, the door driving unit 170 may include a motor and a door 171 embedded therein. That is, since the door 171 has flexibility, the door 171 is wound inward or unwound outward of the door driver 170 according to the rotation of the motor, so that the door 171 can slide in a horizontal direction, thereby opening the door 103. area can be adjusted.

3 is a block diagram of a management server of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

Referring to FIG. 3 , the management server 200 may include a communication unit 210 , a control unit 220 and a database 230 .

The communication unit 210 may communicate with the smart beehive 100 and the manager terminal 300 by wire or wirelessly. As an example, the communication unit 210 may communicate with WiFi or BLE in a short-range communication method. In this case, the communication unit 210 may be connected to the Internet network through an AP. As another example, the communication unit 210 may communicate using a long-distance communication method such as 5G.

The control unit 220 is communicatively connected to the communication unit 210 and the database 230 and can control the overall operation of the management server 200 . In addition, the control unit 220 can analyze and predict bee activities and beekeeping-related environments by AI-analyzing the big data transmitted from the smart beehive 100.

In addition, the controller 220 includes an access counting unit 221, a state monitoring unit 222, a cluster state analysis unit 223, an anomaly symptom determination unit 224, an alarm unit 225, a pollen analysis unit 226 and A flowering time prediction unit 227 may be included.

The access counting unit 221 may recognize an image captured by the access camera 130 of the smart beehive 100. At this time, the access counting unit 221 may calculate the number of access objects for consumption from the recognized image. As a result, the entry and exit counting unit 221 may count the number of people entering and exiting per hour for consumption, as will be described later with reference to FIGS. 5 and 6 .

The state monitoring unit 222 may monitor the state of the smart hive 100 according to state information acquired from the sensor unit 110 and the thermal camera 120 of the smart hive 100 . At this time, the state monitoring unit 222 can determine the amount of activity of bees, the weather, the adequacy of the internal environment of the smart hive 100, the number of individuals inside, and whether or not they are harvested according to the state information of the smart hive 100.

More specifically, the state monitoring unit 222 may predict and analyze the amount of activity and weather of bees according to the number of individuals entering the consumption counted by the entry and exit counter 221 . For example, when the number of entrances and exits is large, it can be predicted or analyzed that the activity of bees has increased because the weather is clear and good.

In addition, the condition monitoring unit 222 can predict and analyze changes in the external environment, such as seasonal changes, according to the detection result of the temperature sensor 112 of the smart beehive 100. In addition, the state monitoring unit 222 may analyze that the amount of activity of bees increases when the temperature inside the consumption increases. In this case, the state monitoring unit 222 may refer to the detection result of the weight sensor 116 . That is, the state monitoring unit 222 may determine that an increase in the amount of activity of the bees when the weight of the smart beehive 100 increases.

In addition, the state monitoring unit 222 may determine the appropriateness of the growth process of the bees according to the heat distribution inside the consumption according to the detection result of the thermal camera 120 of the smart beehive 100.

In addition, the condition monitoring unit 222 can analyze and predict changes in the external environment, such as the rainy season, according to the detection result of the humidity sensor 114 of the smart beehive 100. For example, the condition monitoring unit 222 may analyze and predict that it will rain if the humidity in consumption is high. In this case, the state monitoring unit 222 may refer to the counting result of the entry/exit counting unit 221 . That is, the state monitoring unit 222 may determine that it is raining when the number of entering and exiting objects decreases.

In addition, the state monitoring unit 222 may analyze and predict a change in the amount of honey in consumption or a change in the number of bees according to the detection result of the weight sensor 116 of the smart hive 100. For example, the state monitoring unit 222 may determine the time to harvest wheat when the weight of consumption increases. In addition, the state monitoring unit 222 may determine an increase in the number of objects when the weight of consumption increases.

In addition, the state monitoring unit 222 can analyze and predict changes in the number of bees in consumption according to the detection result of the CO2 sensor 118 of the smart beehive 100. For example, the state monitoring unit 222 may determine that the number of objects within the consumption increases when the concentration of CO2 within the consumption increases. In this case, the state monitoring unit 222 may refer to the detection result of the weight sensor 116 . That is, the state monitoring unit 222 may determine that the number of objects increases when the weight of consumption increases.

The colony state analysis unit 223 may determine the presence or absence of a queen bee in consumption according to the detection result of the sound sensor 119 of the smart beehive 100. Accordingly, the colony state analysis unit 223 can determine the possibility of swarming according to the presence or absence of queen bees. Therefore, the colony state analysis unit 223 can predict whether or not the colony will collapse according to the presence or absence of queen bees. At this time, the colony state analysis unit 223 may determine whether a queen bee exists or not according to whether or not a frequency corresponding to the queen bee is present in the sound sensed within consumption. Here, workers, queens and drones produce sounds of different frequencies.

As a result, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention can predict the possibility of swarming for sound consumption, thereby preventing swarming through prompt measures to replace the queen bee to prevent clustering. can keep

The anomaly symptom determination unit 224 may determine external intrusion of heterogeneous bees according to the detection result of the sound sensor 119 of the smart beehive 100. At this time, the anomaly symptom determination unit 224 may determine whether a heterogeneous bee invades the consumption according to whether a frequency corresponding to a wasp or a wild bee is present in the sound sensed within the consumption. Here, heterogeneous bees, such as wasps or wild bees, generate sounds of a different frequency band from bees for beekeeping.

As a result, the big data analysis-based smart beekeeping management system 10 according to an embodiment of the present invention can determine whether or not an external intrusion such as heterogeneous bees can quickly identify and take action in a dangerous state.

In addition, the abnormal symptom determination unit 224 may determine an abnormal state when the rate of change of each data detected by the sensor unit 110 of the smart beehive 100 is equal to or greater than a predetermined value. For example, the abnormal symptom determination unit 224 may determine an abnormal state when any one of the temperature, humidity, weight, and CO2 concentration within consumption exceeds a predetermined value.

The alarm unit 225 may alarm the corresponding abnormal symptom to the manager terminal 300 according to the abnormal symptom determined by the abnormal symptom determination unit 224 . At this time, the alarm unit 225 may also provide action items for abnormal symptoms. For example, the alarm unit 225 may alarm an abnormal symptom to the manager terminal 300 through a text message. As another example, the alarm unit 225 may alarm an abnormal symptom so as to activate a dedicated beekeeping management app in the manager terminal 300 .

The pollen analyzer 226 may recognize an image taken by the entry and exit camera 130 of the smart beehive 100. At this time, the pollen analysis unit 226 may identify the color of pollen (pollen) collected by the bee entering the consumption from the recognized image. Accordingly, the pollen analyzer 226 may analyze the type of flower corresponding to the color of the pollen.

The flowering time predictor 227 may predict the flowering time of a corresponding flower according to the type of pollen analyzed by the pollen analyzer 226 . Here, the flowering time prediction unit 227 can predict the type of flowers inhabiting the vicinity of the smart beehive 100 and the flowering time thereof.

As a result, the smart beekeeping management system 10 based on big data analysis according to an embodiment of the present invention can collect various information such as the number of bees entering and exiting through the entrance 103 and the color of pollen collected by the bees. there is. Therefore, the big data analysis-based smart beekeeping management system 10 may analyze the beekeeping-related environment conditions as well as bee activities.

The database 230 may store big data transmitted from the smart beehive 100. The database 230 may store state information for each sensor included in the sensor unit 110 . In addition, the database 230 may store results predicted and analyzed by the control unit 220 . In addition, the database 230 may store information provided from the management server 200 to the manager terminal 300 .

4 is an exemplary diagram of a manager terminal of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

Referring to FIG. 4 , the manager terminal 300 may display results analyzed and predicted in the management server 200 as an image by a beekeeping management dedicated app. For example, the manager terminal 300 may display a first graph 310 and a second graph 320 representing abnormal symptoms.

The first graph 310 may temporarily show abnormal symptoms compared to before or after. For example, the first graph 310 may indicate an abnormal symptom in which the frequency of a sound instantaneously increases due to an invasion of wasps or wild bees.

The second graph 320 may indicate abnormal symptoms that rapidly decrease compared to the previous period. For example, the second graph 320 may indicate an abnormal symptom in which the population rapidly decreases due to swarming.

5 is a cross-sectional view showing an installation example of an access camera of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention, and FIG. 6 is a cross-sectional view of an access camera of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention. It is an acquired picture of

Referring to FIG. 5 , the access camera 130 may be provided above the entrance 103 . Here, the access camera 130 may be coupled to the case 101 by the support 132 . At this time, the blocking plate 104 may be provided on the lower side of the entrance 103 to face the entrance camera 130 . The blocking plate 104 may be formed in a plate shape having an area corresponding to a photographing range of the entrance/exit camera 130 .

At this time, the access camera 130 may capture an image of the blocking plate 104 at the front side of the entrance 103 . Here, the case 101 side of the blocking plate 104 may be understood as a position close to the entrance 103 and that the bees enter the entrance 103 (In). In addition, the opposite side of the case 101 of the blocking plate 104 is a position far from the entrance 103 and can be understood as an out of the entrance 103.

Referring to FIG. 6 , in a picture taken by the access camera 130, a red line may be displayed as In as the entrance 103 side, and a light green line may be displayed as Out as the opposite side of the entrance 103. Here, the red line and the light green line are predetermined distances from the entrance 103 and may be reference lines for determining the number of individuals entering (In) and going out (Out) for consumption. That is, the number of individuals entering consumption is the number of individuals near the red line, and the number of individuals exiting consumption may be calculated as the number of individuals near the light green line.

7 is a graph of a manager terminal showing abnormal symptoms of a smart beekeeping management system based on big data analysis according to an embodiment of the present invention.

In (a) of FIG. 7 , the manager terminal 300 may provide a graph capable of representing rapidly decreasing abnormal symptoms at an arbitrary point in time compared to the previous time. For example, an abnormal symptom that rapidly decreases at any point in time may appear when the population rapidly decreases due to swarming. In this case, the management server 200 may provide the manager terminal 300 with abnormal symptoms occurring at that time and measures for them.

In (b) of FIG. 7 , the administrator terminal 300 may exhibit abnormal symptoms that temporarily increase compared to before or after at any point in time. For example, an abnormal symptom that temporarily increases at a certain point in time may appear when the frequency of a sound instantaneously increases due to an infestation of wasps or wild bees. At this time, the management server 200 may provide the manager terminal 300 with abnormal symptoms occurring at that time and measures for them.

Although one embodiment of the present invention has been described above, the spirit of the present invention is not limited to the embodiments presented herein, and those skilled in the art who understand the spirit of the present invention may add elements within the scope of the same spirit. However, it will be possible to easily suggest other embodiments by means of changes, deletions, additions, etc., but this will also be said to fall within the scope of the present invention.

10: Smart beekeeping management system based on big data analysis
100: smart hive 101: case
102: antenna 103: entrance
104: blocking plate 110: sensor unit
112: temperature sensor 114: humidity sensor
116: weight sensor 118: CO2 sensor
119: sound sensor 120: thermal camera
130: entrance camera 140: temperature controller
150: communication unit 160: storage unit
170: door drive unit 171: door
180: control unit 200: management server
210: communication unit 220: control unit
221: access counting unit 222: condition monitoring unit
223: cluster state analysis unit 224: anomaly symptom determination unit
225: alarm unit 226: pollen analysis unit
227: flowering time prediction unit 230: database
300: manager terminal 310: first graph
320: second graph

Claims (8)

A smart beehive including a consumer, a sensor unit for detecting the inside of the consumer, a thermal camera for photographing the inside of the consumer, an entrance and exit camera for photographing the entrance and exit of the consumer, and a communication unit; and
A management server that analyzes and predicts the bee's activity and beekeeping-related environment through AI analysis of the big data transmitted from the smart beehive;
The management server is a big data analysis-based smart beekeeping management system that visualizes the analyzed and predicted results as images and transmits them to the manager terminal. According to claim 1,
The management server,
an access counting unit recognizing the image of the access camera to calculate the number of access objects and counting the number of access objects for the consumption;
a state monitoring unit that monitors the state of the smart beehive according to the state information obtained by the sensor unit to determine the amount of bee activity, weather, environmental adequacy of the smart beehive, the number of populations inside the hive, and whether or not it is harvested; and
A smart beekeeping management system based on big data analysis including a colony state analyzer for determining the possibility of swarming by determining whether there is a queen bee in the consumption according to whether a frequency corresponding to the queen bee is present in the sound in the consumption detected by the sensor unit. According to claim 1,
The management server,
An external intrusion of a heterogeneous bee into the consumption is judged according to whether a frequency corresponding to a wasp or a wild bee in the sound in the consumption sensed by the sensor unit is detected, and an abnormal state is determined when the change rate of the data of the sensor unit is equal to or greater than a predetermined value. Abnormal symptom determination unit to determine; and
A big data analysis-based smart beekeeping management system comprising an alarm unit for notifying the manager terminal of the abnormal symptom and corresponding action according to the determination of the abnormal symptom. According to claim 1,
The management server,
a pollen analyzer for analyzing the type of pollen according to the color of the pollen of the bee entering the consumption; and
Big data analysis-based smart beekeeping management system including a flowering time predictor for predicting the flowering time of the flower according to the analyzed pollen type. According to claim 1,
The sensor unit,
a temperature sensor for sensing a temperature inside the consumer;
a humidity sensor for sensing humidity inside the consumer;
a weight sensor for detecting the weight of the consumption;
a CO2 sensor for sensing the CO2 concentration inside the consumption; and
Big data analysis-based smart beekeeping management system including a sound sensor for detecting the sound inside the consumption. According to claim 1,
The access camera is provided on one side of the entrance,
Big data analysis-based smart beekeeping management system provided with a blocking plate on the other side of the entrance. According to claim 1,
The smart beehive is a big data analysis-based smart beekeeping management system further comprising a temperature controller for adjusting the internal temperature of the consumption. According to claim 1,
The manager terminal is a portable terminal including a smartphone,
A smart beekeeping management system based on big data analysis that displays the results analyzed and predicted in the management server by the beekeeping management dedicated app as an image.

Images