An incubation status recognition system and method

Abstract

This invention provides an incubation status identification system and method. The system includes: multiple wireless temperature acquisition units, an ambient temperature acquisition unit, and a data processing unit. The ambient temperature acquisition units are installed inside the incubator to collect ambient temperature data in real time. Each wireless temperature acquisition unit is distributed among preset nodes inside the incubator. The temperature acquisition end of each wireless temperature acquisition unit contacts the target egg at the preset node to collect eggshell temperature data and wirelessly transmit the eggshell temperature data to the data processing unit. The data processing unit generates feature parameters for each target egg using the corresponding eggshell temperature data and ambient temperature data. Using a preset incubation status identification model and the feature parameters corresponding to each target egg, the incubation status of each target egg is identified. This not only avoids interference from wiring during incubation operations and increases costs but also improves identification efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to an incubation status identification system and method. Background Technology

[0002] Poultry hatchability is a core indicator for measuring poultry farming efficiency, and accurate monitoring of embryonic development is a crucial prerequisite for improving hatching quality. Studies have shown that changes in eggshell temperature during embryonic development are closely related to embryonic metabolic activity, developmental stage, and survival status. For example, eggs that successfully hatch at the end of incubation exhibit a significant temperature drop, with the lowest temperature point highly synchronized with the hatching time. This phenomenon stems from the heat dissipation effect caused by the disappearance of the embryonic heat source and the evaporation of liquid within the egg after hatching. This provides a biological basis for monitoring hatching status through eggshell temperature.

[0003] Existing incubation status identification technologies have significant limitations: First, they lack distributed deployment capabilities. Most systems use wired connections or single-node data collection, which are limited by transmission distance (usually ≤15 meters). This makes them unsuitable for the simultaneous monitoring of eggshell temperature of hundreds or thousands of eggs in large incubators. Furthermore, wired cabling leads to cumbersome and costly systems. Second, they lack the ability to identify the incubation status of eggs. Existing multi-site monitoring systems can only collect eggshell temperature data, requiring manual interpretation of the data to identify the incubation status, resulting in low efficiency and accuracy. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an incubation status identification system and method to avoid interference from wiring during incubation operations and increase costs, thereby improving identification efficiency and accuracy.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of this invention discloses an incubation status identification system, the system comprising: multiple wireless temperature acquisition units, an ambient temperature acquisition unit, and a data processing unit;

[0007] The ambient temperature acquisition unit is installed inside the incubator and is used to collect ambient temperature data in real time.

[0008] Each of the wireless temperature acquisition units is distributed and set in a preset node in the incubator. The temperature acquisition end of each wireless temperature acquisition unit is in contact with the target egg at the preset node to collect eggshell temperature data and wirelessly transmit the eggshell temperature data to the data processing unit.

[0009] The data processing unit is used to generate feature parameters for each target egg using the corresponding eggshell temperature data and ambient temperature data; and to identify the incubation status of each target egg using a preset incubation status recognition model and the feature parameters corresponding to each target egg.

[0010] Optionally, the wireless temperature acquisition unit includes: a first antenna, a microcontroller unit, a wireless transmission module, a data connection line, a chip temperature sensor, and a first power supply unit;

[0011] The microcontroller unit is connected to the wireless transmission module, the wireless transmission module is connected to the first antenna, the microcontroller unit is connected to the chip temperature sensor via the data connection line, and the chip temperature sensor is in contact with the target egg.

[0012] The first power supply unit is connected to the microcontroller unit and is used to supply power to the microcontroller unit.

[0013] The microcontroller unit is used to collect eggshell temperature data at a preset frequency using the chip temperature sensor, and to wirelessly transmit the pre-processed eggshell temperature data to the data processing unit through the wireless transmission module and the first antenna.

[0014] Optionally, the wireless temperature acquisition unit further includes: a first outer shell, a polyester insulation layer, and a silicone sheet;

[0015] The inner surface of the first housing is provided with the polyester insulation layer, the microcontroller unit, the wireless transmission module and the first antenna are disposed in the internal cavity of the first housing, the first antenna extends out of the first housing, and the chip temperature sensor contacts the target egg through the silicone sheet.

[0016] Optionally, the data processing unit includes: a main controller, a data storage module, a second antenna, and a second power supply unit;

[0017] The main controller is connected to the data storage module and the second antenna, and the main controller is embedded with a preset incubation status recognition model;

[0018] The second power supply unit is connected to the main controller and is used to supply power to the main controller;

[0019] The second antenna is used to receive the eggshell temperature data of each target egg and store it in the data storage module;

[0020] The main controller is configured to extract the eggshell temperature data at each time moment from the data storage module for each target egg, calculate the difference between the eggshell temperature data at each time moment and the ambient temperature data at the corresponding time moment to obtain a difference signal; for each target egg, extract feature parameters from the corresponding difference signal, and use the incubation status recognition model and the feature parameters corresponding to each target egg to identify the incubation status of each target egg.

[0021] Optionally, the data processing unit further includes: a second housing and an electronic display screen;

[0022] The main controller, data storage module, second antenna, and second power supply unit are disposed in the internal cavity of the second housing;

[0023] The electronic display screen is fixedly mounted on the second outer shell and is connected to the main controller. For each target egg, the electronic display screen displays at least one or more of the following: the node ID of the preset node, the current eggshell temperature data, and the incubation status of the target egg.

[0024] Optionally, the feature parameters include: a first duration, a second duration, and a maximum temperature difference; the first duration is the time from the moment when the ΔT_filtered data drops to its minimum value to the start of incubation; the second duration is the duration during which the ΔT_filtered data drops from its maximum value to its minimum value; the maximum temperature difference is the difference between the maximum and minimum values ​​of the ΔT_filtered data; and the ΔT_filtered data is the smoothed difference signal.

[0025] The identification logic of the incubation status identification model includes:

[0026] Determine whether the first incubation time is less than or equal to the first incubation time; the first incubation time is the normal incubation time of the target hatching egg;

[0027] If the first duration is less than or equal to the first incubation duration, then determine whether the first duration is less than or equal to 0h;

[0028] If the first duration is less than or equal to 0h, the target hatching egg is output as infertile or in an early death state; if the first duration is greater than 0h, it is determined whether the first duration is less than or equal to the preset second incubation duration.

[0029] If the first incubation duration is less than or equal to the second incubation duration, the target egg is output as being in a mid-stage death state; if the first incubation duration is greater than the second incubation duration, the target egg is output as being in a late-stage death state.

[0030] If the first duration is less than or equal to the first incubation duration, then it is determined whether the second duration is greater than or equal to a preset first threshold, and whether the maximum temperature difference is less than or equal to a preset second threshold; the first threshold and the second threshold are set by the user based on historical experience;

[0031] If the second duration is greater than or equal to the first threshold, and the maximum temperature difference is less than or equal to the second threshold, then the target egg is output as successfully hatched; if the second duration is less than the first threshold, or the maximum temperature difference is greater than the second threshold, then the target egg is output as late-stage dead.

[0032] Optionally, the system may further include: a cloud platform;

[0033] The main controller is further configured to: periodically generate hatching data for each target egg, and upload the hatching data of each target egg to the cloud platform via the second antenna; the hatching data includes one or more of the following: eggshell temperature data for the current period, difference signal for the current period, characteristic parameters for the current period, and hatching status for the current period;

[0034] The cloud platform is used to update one or more preset hatching data views for each target egg based on the corresponding hatching data when it receives the hatching data of each target egg, and display them through a client.

[0035] Optionally, the cloud platform is also used for:

[0036] For each target hatching egg, if the incubation status indicator for the current period is abnormal, an abnormal warning message is generated and pushed to the client.

[0037] Optionally, the cloud platform is also used for:

[0038] When the preset incubation end time is reached, an incubation cycle report is generated based on the incubation data of each target egg during each cycle of the incubation process, and the incubation cycle report is displayed through the client.

[0039] A second aspect of this invention discloses an incubation status identification method, applied to any of the incubation status identification systems described in the first aspect of this invention, the method comprising:

[0040] The ambient temperature acquisition unit collects ambient temperature data in real time.

[0041] Each wireless temperature acquisition unit collects eggshell temperature data and wirelessly transmits the eggshell temperature data to the data processing unit.

[0042] For each target hatching egg, the data processing unit generates characteristic parameters using the corresponding eggshell temperature data and ambient temperature data.

[0043] The incubation status of each target egg is identified by using a preset incubation status recognition model and the characteristic parameters corresponding to each target egg.

[0044] An incubation status identification system and method based on the above embodiments of the present invention includes: multiple wireless temperature acquisition units, an ambient temperature acquisition unit, and a data processing unit; the ambient temperature acquisition units are installed inside the incubator to collect ambient temperature data in real time; each wireless temperature acquisition unit is distributed at preset nodes inside the incubator, and the temperature acquisition end of each wireless temperature acquisition unit contacts the target egg at the preset node to collect eggshell temperature data and wirelessly transmit the eggshell temperature data to the data processing unit; the data processing unit generates feature parameters for each target egg using the corresponding eggshell temperature data and ambient temperature data; and identifies the incubation status of each target egg using a preset incubation status identification model and the feature parameters corresponding to each target egg. In this solution, the wireless temperature acquisition units are deployed evenly at various distributed nodes inside the incubator, and wireless data transmission avoids interference with incubation operations and avoids increased costs. In addition, based on eggshell temperature data and a preset incubation status identification model, the incubation status of the target egg at each node is identified, improving identification efficiency and accuracy. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 This is a curve showing the relationship between eggshell temperature and incubation time, as disclosed in an embodiment of the present invention.

[0047] Figure 2 This is a structural block diagram of an incubation status recognition system disclosed in an embodiment of the present invention;

[0048] Figure 3 This is an application scenario diagram of an incubation status recognition system disclosed in an embodiment of the present invention;

[0049] Figure 4 This is a structural diagram of a wireless temperature acquisition unit disclosed in an embodiment of the present invention;

[0050] Figure 5 This is a structural diagram of a data processing unit disclosed in an embodiment of the present invention;

[0051] Figure 6 This is a flowchart of an incubation status recognition method disclosed in an embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of a display list of an incubation cycle report disclosed in an embodiment of the present invention;

[0053] Figure 8 This is a statistical chart of incubation status distribution disclosed in an embodiment of the present invention;

[0054] Figure 9 This is a flowchart of an incubation status identification method disclosed in an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0057] As the background technology indicates, research shows that changes in eggshell temperature during embryonic development are closely related to embryonic metabolic activity, developmental stage, and survival status. For example, eggs that successfully hatch at the end of incubation exhibit a significant temperature drop, with the lowest temperature point highly synchronized with the hatching time. This phenomenon stems from the heat dissipation effect caused by the disappearance of the embryonic heat source and the evaporation of liquid within the egg after hatching. This provides a biological basis for monitoring incubation status through eggshell temperature.

[0058] like Figure 1 The figure shown is a curve of eggshell temperature versus incubation time disclosed in an embodiment of the present invention.

[0059] As described above, the point of lowest temperature is the hatching time. After hatching, the eggshell temperature rises again due to the influence of the ambient temperature.

[0060] On the other hand, it can be seen that due to various factors, the hatching time of each hatching egg is slightly different. For example, egg1 hatched after 485 hours of incubation (from the start of incubation to the hatching time), while egg2, which is of the same species, hatched after 488 hours of incubation.

[0061] Existing incubation status identification technologies have significant limitations: First, they lack distributed deployment capabilities. Most systems use wired connections or single-node data collection, which are limited by transmission distance (usually ≤15 meters). This makes them unsuitable for the simultaneous monitoring of eggshell temperature of hundreds or thousands of eggs in large incubators. Furthermore, wired cabling leads to cumbersome and costly systems. Second, they lack the ability to identify the incubation status of eggs. Existing multi-site monitoring systems can only collect eggshell temperature data, requiring manual interpretation of the data to identify the incubation status, resulting in low efficiency and accuracy.

[0062] Therefore, this invention discloses an incubation status identification system and method. In this solution, wireless temperature acquisition units are deployed evenly in various distributed nodes within the incubator. Data is transmitted wirelessly, avoiding interference from wiring during incubation operations and preventing increased costs. Furthermore, based on eggshell temperature data and a preset incubation status identification model, the incubation status of the target eggs at each node is identified, improving identification efficiency and accuracy.

[0063] like Figure 2 The diagram shown is a structural block diagram of an incubation status identification system disclosed in an embodiment of the present invention. The system includes: multiple wireless temperature acquisition units 101, an ambient temperature acquisition unit 102, and a data processing unit 103.

[0064] An ambient temperature acquisition unit 102 is installed inside the incubator to collect ambient temperature data in real time.

[0065] Each wireless temperature acquisition unit 101 is distributed in a preset node in the incubator. The temperature acquisition end of each wireless temperature acquisition unit 101 is in contact with the target egg at the preset node to collect eggshell temperature data and wirelessly transmit the eggshell temperature data to the data processing unit 103.

[0066] The data processing unit 103 is used to generate characteristic parameters for each target egg using the corresponding eggshell temperature data and ambient temperature data; and to identify the incubation status of each target egg using a preset incubation status recognition model and the characteristic parameters corresponding to each target egg.

[0067] like Figure 3The diagram shown is an application scenario diagram of an incubation status recognition system disclosed in an embodiment of the present invention.

[0068] It should be noted that there is a corresponding relationship between the preset node, the node ID, and the target hatching egg. The selection of the preset node needs to be representative. For example, if the incubator is designed with a layered incubation rack, a preset node can be set up on each layer to deploy the wireless temperature acquisition unit 101. The temperature acquisition end of the wireless temperature acquisition unit 101 on each layer is in close contact with the target hatching egg on that layer (on the same layer as the wireless temperature acquisition unit 101 and near the wireless temperature acquisition unit 101).

[0069] It is easy to understand that in actual production, the hatching eggs in the incubator usually start incubating at the same time. However, due to the unavoidable unevenness of the temperature in the incubator, such as the inconsistent ambient temperature of each incubation rack, the incubation process of each rack may be inconsistent. Therefore, it is necessary to deploy the ambient temperature acquisition unit 101 in a distributed manner to accurately identify the incubation status under slight temperature differences.

[0070] Optionally, the eggshell temperature data includes: node ID, timestamp, and eggshell temperature value; the ambient temperature data includes: timestamp and ambient temperature value.

[0071] Optionally, wireless temperature acquisition units 101 are evenly deployed along each layer of the incubator rack inside the incubator (ensuring that the preset nodes are representative), with a node spacing of ≥15cm to avoid wireless signal interference.

[0072] Optionally, the ambient temperature acquisition unit 102 includes two ambient temperature sensors, distributed within the incubator, with a sampling frequency of 5 minutes per sampling.

[0073] Optionally, the data processing unit 103 is installed on the outer center of the incubator, 1.5m above the ground.

[0074] like Figure 4 The diagram shown is a structural diagram of a wireless temperature acquisition unit disclosed in an embodiment of the present invention. The wireless temperature acquisition unit 101 includes: a first antenna 1, a first outer shell 2, a polyester insulation layer 3, a microcontroller unit 4, a wireless transmission module 5, a data connection line 6, a silicone sheet 7, a chip temperature sensor 8, and a first power supply unit 9.

[0075] The microcontroller unit 4 is connected to the wireless transmission module 5, the wireless transmission module 5 is connected to the first antenna 1, and the microcontroller unit 4 is connected to the chip temperature sensor 8 through the data connection cable 6. The chip temperature sensor 8 is in contact with the target egg.

[0076] The first power supply unit 9 is connected to the microcontroller unit 4 and is used to supply power to the microcontroller unit 4;

[0077] The microcontroller unit 4 is used to collect eggshell temperature data at a preset frequency using a chip temperature sensor 8, and to wirelessly transmit the pre-processed eggshell temperature data to the data processing unit 103 via a wireless transmission module 5 and a first antenna 1.

[0078] Furthermore, a polyester insulation layer 3 is provided on the inner surface of the first outer shell 2, and the microcontroller unit 4, the wireless transmission module 5 and the first antenna 1 are disposed in the internal cavity of the first outer shell 2. The first antenna 1 extends out of the first outer shell 2, and the chip temperature sensor 8 contacts the target egg through the silicone sheet 7.

[0079] The following sections describe the selection and parameters of each component of the wireless temperature acquisition unit 101:

[0080] The first antenna 1 is a detachable antenna.

[0081] The first outer shell 2 is made of ABS (acrylonitrile-butadiene-styrene) plastic, which is lightweight, waterproof, durable and has certain thermal insulation properties, making it easy to distribute in the incubator without putting extra burden on the egg trays.

[0082] The polyester insulation layer 3 is made of polyester fiber or polyester foam. Polyester material has a low thermal conductivity, effectively reducing heat transfer and ensuring that eggshell temperature measurement is not affected by heating fluctuations of the internal components of the wireless temperature acquisition unit 101. This is crucial for accurate monitoring of embryonic development, as temperature changes are highly correlated with incubation status. Polyester material also has some moisture resistance, resisting the high humidity environment inside the incubator; and it is non-toxic and harmless, meeting the hygiene requirements for poultry incubation.

[0083] The microcontroller unit 4 is equipped with a lightweight data processing algorithm to perform preliminary filtering and noise reduction on the raw eggshell temperature data from the chip temperature sensor 8, preserving the core characteristics of eggshell temperature changes and reducing the amount of data transmitted subsequently. It also integrates a low-power management mechanism, supporting multiple power consumption modes such as deep sleep and light sleep. Combined with dynamic clock adjustment technology, it minimizes the overall power consumption of the unit while ensuring the real-time performance of data acquisition and processing, so as to stably support uninterrupted monitoring throughout the incubation cycle.

[0084] Specifically, the microcontroller unit 4 uses the ESP32 development board, which is equipped with a dual-core 240MHz processor, supports deep sleep mode (power consumption as low as 5μA), and is equipped with a lightweight moving average filtering algorithm to perform noise reduction preprocessing on the eggshell temperature data.

[0085] The wireless transmission module 5 has built-in WiFi and Bluetooth dual-mode wireless communication capabilities. Through software configuration and protocol optimization, this invention can flexibly adapt to the collaborative work of long-distance wireless communication modules such as LoRa. It not only meets the high-speed data interaction at close range (such as between preset nodes inside the incubator), but also reserves interfaces for future system function expansion, providing flexible communication solutions for the system to be applied in different scales and scenarios.

[0086] Specifically, the wireless communication module 5 is externally connected to a LoRa module and a first antenna 1. The wireless communication module 5 is connected to and communicates with the ESP32 development board through the SPI interface, and has the ability to penetrate the metal shell of the incubator over a long distance, meeting the communication needs of distributed nodes in a large incubator.

[0087] The chip temperature sensor 8 uses the TSIC 716 chip temperature sensor, which is a low-power, high-sensitivity contact chip sensor. It is tightly attached to the equator of the eggshell by a silicone sheet 7 and connected to the ESP32 development board through a data connection cable 6 to collect eggshell temperature data in real time. The sampling frequency is, for example, once per minute, to accurately capture dynamic temperature changes.

[0088] Specifically, a TSIC 716 chip temperature sensor is used to measure temperatures with an accuracy of ±0.07℃. A 0.5mm thick medical-grade silicone sheet is tightly attached to the equator of the hatching egg to ensure good thermal contact with the eggshell and avoid damage to the embryo (non-invasive design).

[0089] The first power supply unit consists of 9 button batteries. Combined with the ESP32 development board's timed wake-up, sampling, and then sleep strategy (i.e., sampling frequency once per minute, wake-up time ≤ 10ms), the button batteries can last up to 23 days, covering the incubation period. It also supports low power warnings (when the voltage is ≤ 2.7V, a warning signal is sent to the cloud platform via the LoRa module).

[0090] like Figure 5 The diagram shown is a structural diagram of a data processing unit disclosed in an embodiment of the present invention. The data processing unit 103 includes: a second housing 10, an electronic display screen 11, a second antenna 12, a main controller 13, a data storage module 14, and a second power supply unit.

[0091] The second power supply unit includes a lithium battery 15 and a power adapter 16.

[0092] The main controller 13 is connected to the data storage module 14 and the second antenna 12. The main controller 13 is embedded with a preset incubation status recognition model.

[0093] The second power supply unit is connected to the main controller 13 and is used to supply power to the main controller 13.

[0094] The second antenna 12 is used to receive the eggshell temperature data of each target egg and store it in the data storage module 14.

[0095] The main controller 13 is used to extract the eggshell temperature data at each time moment from the data storage module 14 for each target egg, calculate the difference between the eggshell temperature data at each time moment and the ambient temperature data at the corresponding time moment to obtain the difference signal; for each target egg, extract the feature parameters from the corresponding difference signal, and use the incubation status recognition model and the feature parameters corresponding to each target egg to identify the incubation status of each target egg.

[0096] Furthermore, the main controller 13, data storage module 14, second antenna 12, and second power supply unit are disposed in the internal cavity of the second housing 10;

[0097] The electronic display screen 11 is fixedly installed on the second outer shell 10. The electronic display screen 11 is connected to the main controller 13 and is used to display at least one or more of the following for each target egg: the node ID of the preset node, the current eggshell temperature data and the incubation status of the target egg.

[0098] In the prior art, eggshell temperature data needs to be manually interpreted to identify the incubation status, which cannot distinguish key states such as infertile eggs, early death, mid-term death, and late death. Therefore, in this embodiment of the invention, a preset incubation status identification model is pre-embedded in the main controller 13. The preset incubation status identification model can be a trained decision tree model, so as to efficiently and accurately identify the incubation status and distinguish various key states.

[0099] Please see Figure 6 This is a flowchart of an incubation status recognition method disclosed in an embodiment of the present invention.

[0100] In this embodiment of the invention, the feature parameters include: a first duration, a second duration, and a maximum temperature difference; the first duration is the time from the moment when the ΔT_filtered data drops to its minimum value to the moment when incubation begins; the second duration is the duration during which the ΔT_filtered data drops from its maximum value to its minimum value; the maximum temperature difference is the difference between the maximum and minimum values ​​of the ΔT_filtered data; and the ΔT_filtered data is a smoothed difference signal.

[0101] The incubation status identification process (i.e., the identification logic of the incubation status identification model) is as follows:

[0102] Determine whether the first incubation time is less than or equal to the first hatching time; the first incubation time is the normal incubation time for the target eggs;

[0103] If the first duration is less than or equal to the first incubation duration, then determine whether the first duration is less than or equal to 0h;

[0104] If the first incubation period is less than or equal to 0 hours, the target hatching egg is output as either infertile or in an early death state; if the first incubation period is greater than 0 hours, it is determined whether the first incubation period is less than or equal to the preset second incubation period.

[0105] If the first incubation period is less than or equal to the second incubation period, the target egg will be output as being in a mid-stage death state; if the first incubation period is greater than the second incubation period, the target egg will be output as being in a late-stage death state.

[0106] If the first duration is less than or equal to the first incubation duration, then it is determined whether the second duration is greater than or equal to the preset first threshold, and whether the maximum temperature difference is less than or equal to the preset second threshold; the first threshold and the second threshold are set by the user based on historical experience.

[0107] If the second duration is greater than or equal to the first threshold and the maximum temperature difference is less than or equal to the second threshold, the target egg is output as successfully hatched; if the second duration is less than the first threshold or the maximum temperature difference is greater than the second threshold, the target egg is output as late-stage dead.

[0108] The following sections describe the preferred selection and parameters of each component in the data processing unit 103:

[0109] The main controller 13 uses the ESP32 development board. Relying on the ESP32 dual-core 32-bit processor, it can simultaneously receive and process real-time data from ≥1000 wireless temperature acquisition units 101 (adapting to the large-scale monitoring needs of large incubators). The data reception delay is ≤1 second, ensuring the real-time nature of data acquisition throughout the incubation cycle. The built-in verification algorithm can perform integrity verification on the raw eggshell temperature data transmitted wirelessly, eliminating erroneous data caused by signal interference. At the same time, it converts the raw eggshell temperature data and ambient temperature data into a standardized format (such as timestamp + node ID + eggshell temperature value + ambient temperature value), providing a unified data foundation for calculating the difference signal.

[0110] Specifically, the main controller 13 uses an ESP32 development board and connects to two LoRa receiver modules (dual-module redundancy design to avoid single point of failure) and a second antenna 12 (using a detachable antenna) via an SPI interface. It supports concurrent reception of data from ≥1000 wireless temperature acquisition units 101, with a data reception delay of ≤1s per node.

[0111] The data storage module 14 uses a 4MB Flash memory built into the ESP32 development board to cache high-frequency data from the most recent 24 hours (raw eggshell temperature data and difference signal values ​​for each target egg), avoiding short-term data loss due to SD card insertion / removal or network fluctuations. Additionally, an external 32GB MicroSD card stores complete hatching data (including eggshell temperature data for each cycle, difference signal for each cycle, characteristic parameters for each cycle, and the hatching status for each cycle) in the format "Date: YYYY-MM-DD / NodeID.csv". The data volume per node is approximately 30KB / day (total ≤1MB for 21 days), and the 32GB SD card can support storage for 3000+ nodes.

[0112] It should be noted that the decision tree model is embedded in the main controller 13 specifically in the Flash memory built into the ESP32 development board.

[0113] The electronic display screen 11 uses a 2.9-inch e-ink screen (static display does not consume power), with a resolution of 296×128 and a refresh cycle of 30 minutes. For each preset node, the real-time display content includes: the node ID of the preset node, the current eggshell temperature data, and the incubation status of the target egg.

[0114] Furthermore, it can also display the current incubation time (h), the average / maximum / minimum eggshell temperature of each incubation rack (±0.1℃), the number of preset online nodes, the distribution of incubation status (such as "infertile / early death: 28 eggs, mid-term death: 15 eggs, late-term death: 12 eggs, successful hatching: 1145 eggs"), SD card storage remaining and battery power.

[0115] The second power supply unit includes a lithium battery 15 and a power adapter 16. The lithium battery 15 is a 5000mAh rechargeable lithium battery (3.7V), supporting the coordinated power supply of the ESP32 development board, LoRa wireless receiver module, and e-ink screen, with a battery life of ≥72 hours without external power. An integrated charging management module is also included. The device supports external power adapter 16, which uses a 12V / 2A power adapter. When the battery level is ≤20%, a low battery warning is automatically triggered. The ESP32 development board controls the e-ink screen to flash a warning, and simultaneously, a low-power mode command (temporarily reducing the sampling frequency to 5 minutes / time) is sent to the wireless temperature acquisition unit 101 via the LoRa module, extending the overall battery life.

[0116] In one embodiment, the incubation status identification system further includes a cloud platform.

[0117] Correspondingly, the main controller 13 is also used to: periodically generate hatching data for each target egg, and upload the hatching data of each target egg to the cloud platform via the second antenna; the hatching data includes one or more of the following: eggshell temperature data for the current period, difference signal for the current period, characteristic parameters for the current period, and hatching status for the current period.

[0118] The frequency of periodically uploading incubation data can be 15 minutes per time (configurable).

[0119] The cloud platform is used to update one or more preset hatching data views for each target egg based on the corresponding hatching data and display them through the client when receiving hatching data for each target egg; if the hatching status of each target egg in the current cycle indicates an abnormal status, an abnormal warning message is generated and pushed through the client; when the preset hatching end time is reached, an hatching cycle report is generated based on the hatching data of each target egg in each cycle during the hatching process, and the hatching cycle report is displayed through the client.

[0120] like Figure 7 The image shown is a schematic diagram of an incubation cycle report display list disclosed in an embodiment of the present invention. Users can enter the corresponding incubation cycle report display page by clicking on a record in the list.

[0121] In this embodiment of the invention, the main controller 13 uploads the hatching data to the cloud platform via 4G / Ethernet, enabling cloud storage and backup of the data (storage time ≥ 365 days) and supporting data interruption resumption (caching to the SD card when the network is interrupted, and re-uploading after recovery). Simultaneously, the cloud platform pushes hatching data views (such as real-time temperature curves and hatching status distribution statistics), abnormal warning information, and other information to mobile apps and computer clients. Staff can remotely view the monitoring data of individual eggs or entire boxes of eggs, and historical data can be queried and exported, facilitating the retrospective analysis of the hatching process.

[0122] like Figure 8 As shown, this is a statistical chart of the incubation status distribution disclosed in an embodiment of the present invention. As an incubation data view, it shows the incubation status of eggs on different incubation racks in the incubator. In this way, hatchery staff can intuitively understand the incubation progress and results of eggs on each incubation rack in the incubator.

[0123] The cloud platform supports access from various clients, such as mobile app clients and computer clients.

[0124] Mobile APP client: Supports Android / iOS systems, with functions including: real-time viewing of temperature curves for each preset node (last 24 hours), incubation status distribution statistics (layered by incubation rack), and push notifications of abnormal warning information (such as "Node 128: LD status, descent time 420h").

[0125] PC Client: Supports web access, provides historical data export (CSV format), incubation status trend analysis charts (such as time distribution histograms of MD / LD / H), and multi-incubator cluster management functions, adapting to the unified monitoring needs of multiple devices in incubators.

[0126] Based on the above-described incubation status identification system disclosed in this invention, in this solution, wireless temperature acquisition units are deployed evenly at various distributed nodes within the incubator. Data is transmitted wirelessly, avoiding interference from wiring during incubation operations and preventing increased costs. Furthermore, based on eggshell temperature data and a preset incubation status identification model, the incubation status of the target eggs at each node is identified, improving identification efficiency and accuracy.

[0127] The following will use egg incubation as an example to illustrate the full-cycle workflow of an incubation status identification system disclosed in the above embodiments of the present invention. The workflow is divided into four stages according to the incubation cycle, and the collaborative logic of the modules in each stage is as follows:

[0128] 1. Hardware deployment operation

[0129] The staff attached the wireless temperature acquisition unit to the equator of the hatching egg using a 0.5mm medical silicone sheet (ensuring no embryonic damage and non-invasiveness). The preset nodes must be representative and fixed on the incubation rack tray. The data processing unit 103 was installed on the outside of the incubator, connected to a 12V / 2A power adapter, and a 32GB SD card was inserted. After turning on the device, a self-test was completed (LoRa module, screen, and 4G communication were checked one by one).

[0130] 2. Parameter Configuration and Calibration

[0131] Log in to the cloud platform using the computer client, enter the incubator number (e.g., "HQ-2024-001"), and associate the node IDs of multiple preset nodes (multiple wireless temperature acquisition units 101 are automatically paired via LoRa broadcast; the node ID corresponds to the wireless temperature acquisition unit 101, and this process is completed within 5 minutes); set the baseline parameters: ① Tair (ambient temperature) target range; ② Sampling frequency 1 time / minute (acquisition unit), data upload frequency 15 minutes / time (receiving unit); ③ Abnormal warning threshold (Tegg > 39℃ or < 36℃, node offline for more than 30 minutes).

[0132] Phase 2: Data Collection and Processing during the Incubation Period (Day 1 - Day 21 of Incubation)

[0133] Multiple wireless temperature acquisition units 101 operate in a cycle of timed wake-up, sampling, and sleep, waking up once per minute (wake-up time ≤ 10ms). The TSIC716 collects Tegg data, and the ESP32 removes noise through a 5-times moving average filter (lightweight algorithm to reduce power consumption). Then, the eggshell temperature data, including node ID, timestamp, and Tegg data, is sent to the data processing unit 103 via the LoRa module.

[0134] The dual LoRa modules of the data processing unit 103 concurrently receive eggshell temperature data from each node (received delay ≤1s per node), and the ESP32 development board synchronously collects Tair data from two distributed environmental sensors in the incubator (5 minutes / time), and calculates the difference signal ΔT1=Tegg-Tair.

[0135] Preprocessing optimization: A 10-hour moving average filter was used to smooth ΔT1 to obtain ΔT_filtered data (eliminating short-term fluctuations caused by damper opening and closing and egg turning). At the same time, three types of feature parameters were extracted: First duration (temperature drop time, T_trop, in h): the moment when ΔT_filtered reaches a local minimum (relative to the number of hours since incubation); Second duration (temperature drop duration, L, in h): the duration of ΔT_filtered from a local maximum to a local minimum; Maximum temperature difference (temperature drop amplitude, ΔT2, in °C), where ΔT2 represents the difference between the local maximum and local minimum of ΔT_filtered.

[0136] The ESP32 development board of the data processing unit 103 caches the original eggshell temperature data, ΔT_filtered, characteristic parameters and incubation status as incubation data in real time to the built-in 4MB Flash (retaining the data of the most recent 24 hours), and writes it to the SD card in the format of "Date: YYYY-MM-DD / Node ID.csv" (the amount of data for a single node in 21 days is approximately 1MB, and a 32GB SD card can store 3000+ nodes).

[0137] When the network is normal, the incubation data will be uploaded to the cloud platform in batches every 15 minutes (supports interrupted transmission, cached to SD card when the network is interrupted, and re-transmitted after the network is restored).

[0138] Phase 3: Incubation Status Identification and Anomaly Response (Day 1 - Day 21 of Incubation)

[0139] 1. Batch recognition using decision tree models

[0140] The data processing unit 103ESP32 development board calls the built-in decision tree model every 5 minutes to perform batch recognition of three types of feature parameters. The recognition logic is as follows:

[0141] ① If T_trop (first duration) ≤ 0h (no decreasing characteristic) → output "INF / ED" (infertility / early death);

[0142] ②If 383h < T_trop (first duration) ≤ 455h → output "LD" (late-stage death);

[0143] ③ If T_trop (first duration) ≤ 383h → output "MD" (mid-game death);

[0144] ④ If T_trop (first duration) > 455h and satisfies a large ΔT2 (maximum temperature difference) and a short L (second duration) → output "H" (successful hatching); otherwise output "LD" (late-stage death). Typical statistical comparisons are as follows: H: L≈6.78h, ΔT2≈0.73℃ (faster and larger drop); LD: L≈15.8–18.8h, ΔT2≈0.19–0.30℃ (slower and smaller drop). Based on this, an "empirical threshold" can be set, such as L≤10h and ΔT2≥0.5℃ to judge H, otherwise judge LD. The threshold can be fine-tuned with batch data.

[0145] The recognition results are updated to the e-ink screen in real time and simultaneously uploaded to the cloud platform.

[0146] 2. Anomaly warning and manual intervention

[0147] Communication anomaly: If a node has no data for 3 consecutive times (LoRa packet loss), the corresponding wireless temperature acquisition unit 101 will be marked as offline. The cloud platform will push an alert to the staff's mobile APP client (e.g., "Node 862: offline for more than 30 minutes, location: 2nd floor, 15th tray"). Staff can check the node deployment on-site (e.g., silicone sheet falling off, battery dead).

[0148] Abnormal Status: If the “MD / LD” status (embryo death) is detected, the cloud platform will push a death warning, along with the eggshell temperature curve of that node (last 24 hours). Staff can use the curve to determine the cause of death (such as whether the sudden drop in eggshell temperature coincides with the egg-turning failure).

[0149] Low battery warning: When the battery voltage of the wireless temperature acquisition unit 101 is ≤2.7V, a low battery signal is sent. The data processing unit 103 forwards the signal to the cloud platform. Staff members replace the batteries in batches according to the warning prompt (prioritizing replacement of late-stage incubation nodes to avoid affecting H status identification).

[0150] Phase 4: End-of-Incubation Management and Data Review (Day 18-21 of Incubation)

[0151] 1. Hatching preparation and process monitoring

[0152] Day 18 of incubation: The system counts the number of "H" status nodes at each level, and the cloud platform pushes a "hatching preparation reminder" to the computer client. Staff prepare hatching baskets according to the number of times, reducing the number of times the hatching baskets need to be opened (only 1 deployment of hatching baskets is required, while the traditional method requires 3-4 times).

[0153] Days 19-21 of incubation: The system updates the percentage of "H" status every 30 minutes (e.g., "Day 20: H percentage 85%). The mobile APP client displays the hatching progress in real time, allowing staff to monitor the incubation dynamics without opening the box, avoiding temperature and humidity fluctuations (traditional box opening causes Tair fluctuations of ±0.8℃, while this system is ≤±0.3℃).

[0154] 2. Data review and report generation

[0155] Day 21 of incubation (512h): The system automatically generates an incubation cycle report, which may include:

[0156] ① Core metrics: Total hatching rate (number of Hs / total number of nodes, e.g., "92.5%)", percentage of each state (INF / ED: 3.2%, MD: 2.1%, LD: 2.2%).

[0157] ②Temperature analysis: Average Tegg values ​​for each layer (e.g., "Layer 1: 37.7±0.2℃"), statistics of abnormal temperature nodes (e.g., "12 nodes with Tegg values ​​exceeding 39℃");

[0158] ③Time distribution: Temperature decrease time distribution in state H (concentrated in 460-480h);

[0159] The report supports export in PDF / CSV format, which staff can use to review incubation environment issues (such as low temperature in a certain layer of eggshells, requiring repair of the heating element) and optimize subsequent incubation parameters.

[0160] like Figure 9 The diagram shows a flowchart of an incubation status identification method disclosed in an embodiment of the present invention. This method is applied to any of the incubation status identification systems disclosed in the above embodiments of the present invention and includes the following steps:

[0161] Step S101: The ambient temperature acquisition unit acquires ambient temperature data in real time.

[0162] Step S102: Each wireless temperature acquisition unit collects eggshell temperature data and wirelessly transmits the eggshell temperature data to the data processing unit.

[0163] Step S103: For each target egg, the data processing unit generates characteristic parameters using the corresponding eggshell temperature data and ambient temperature data.

[0164] Step S104: Using the preset incubation status recognition model and the feature parameters corresponding to each target egg, the incubation status of each target egg is identified.

[0165] It should be noted that the explanations and specific implementation processes of each step have been described in the incubation status identification system disclosed in the above embodiments of the present invention, and can be referred to accordingly, and will not be repeated here.

[0166] Based on the above-described incubation status identification method disclosed in this invention, in this solution, wireless temperature acquisition units are deployed evenly in various distributed nodes within the incubator. Data is transmitted wirelessly, avoiding interference from wiring during incubation operations and preventing increased costs. Furthermore, based on eggshell temperature data and a preset incubation status identification model, the incubation status of the target eggs at each node is identified, improving identification efficiency and accuracy.

[0167] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0168] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0169] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An incubation status recognition system, characterized in that, The system includes: multiple wireless temperature acquisition units, an ambient temperature acquisition unit, and a data processing unit; The ambient temperature acquisition unit is installed inside the incubator and is used to collect ambient temperature data in real time. Each of the wireless temperature acquisition units is distributed and set in a preset node in the incubator. The temperature acquisition end of each wireless temperature acquisition unit is in contact with the target egg at the preset node to collect eggshell temperature data and wirelessly transmit the eggshell temperature data to the data processing unit. The data processing unit is used to generate feature parameters for each target egg using the corresponding eggshell temperature data and ambient temperature data; and to identify the incubation status of each target egg using a preset incubation status recognition model and the feature parameters corresponding to each target egg.

2. The system according to claim 1, characterized in that, The wireless temperature acquisition unit includes: a first antenna, a microcontroller unit, a wireless transmission module, a data connection cable, a chip temperature sensor, and a first power supply unit; The microcontroller unit is connected to the wireless transmission module, the wireless transmission module is connected to the first antenna, the microcontroller unit is connected to the chip temperature sensor via the data connection line, and the chip temperature sensor is in contact with the target egg. The first power supply unit is connected to the microcontroller unit and is used to supply power to the microcontroller unit. The microcontroller unit is used to collect eggshell temperature data at a preset frequency using the chip temperature sensor, and to wirelessly transmit the pre-processed eggshell temperature data to the data processing unit through the wireless transmission module and the first antenna.

3. The system according to claim 2, characterized in that, The wireless temperature acquisition unit further includes: a first outer shell, a polyester insulation layer, and a silicone sheet; The inner surface of the first housing is provided with the polyester insulation layer, the microcontroller unit, the wireless transmission module and the first antenna are disposed in the internal cavity of the first housing, the first antenna extends out of the first housing, and the chip temperature sensor contacts the target egg through the silicone sheet.

4. The system according to claim 1, characterized in that, The data processing unit includes: a main controller, a data storage module, a second antenna, and a second power supply unit; The main controller is connected to the data storage module and the second antenna, and the main controller is embedded with a preset incubation status recognition model; The second power supply unit is connected to the main controller and is used to supply power to the main controller; The second antenna is used to receive the eggshell temperature data of each target egg and store it in the data storage module; The main controller is configured to extract the eggshell temperature data at each time moment from the data storage module for each target egg, calculate the difference between the eggshell temperature data at each time moment and the ambient temperature data at the corresponding time moment to obtain a difference signal; for each target egg, extract feature parameters from the corresponding difference signal, and use the incubation status recognition model and the feature parameters corresponding to each target egg to identify the incubation status of each target egg.

5. The system according to claim 4, characterized in that, The data processing unit further includes: a second housing and an electronic display screen; The main controller, data storage module, second antenna, and second power supply unit are disposed in the internal cavity of the second housing; The electronic display screen is fixedly mounted on the second outer shell and is connected to the main controller. For each target egg, the electronic display screen displays at least one or more of the following: the node ID of the preset node, the current eggshell temperature data, and the incubation status of the target egg.

6. The system according to claim 4, characterized in that, The characteristic parameters include: a first duration, a second duration, and a maximum temperature difference; the first duration is the time elapsed between the moment when the ΔT_filtered data drops to its minimum value and the start of incubation; the second duration is the duration during which the ΔT_filtered data drops from its maximum value to its minimum value; the maximum temperature difference is the difference between the maximum and minimum values ​​of the ΔT_filtered data; and the ΔT_filtered data is the smoothed difference signal. The identification logic of the incubation status identification model includes: Determine whether the first incubation time is less than or equal to the first incubation time; the first incubation time is the normal incubation time of the target hatching egg; If the first duration is less than or equal to the first incubation duration, then determine whether the first duration is less than or equal to 0h; If the first duration is less than or equal to 0h, the target hatching egg is output as infertile or in an early death state; if the first duration is greater than 0h, it is determined whether the first duration is less than or equal to the preset second incubation duration. If the first incubation duration is less than or equal to the second incubation duration, the target egg is output as being in a mid-stage death state; if the first incubation duration is greater than the second incubation duration, the target egg is output as being in a late-stage death state. If the first duration is less than or equal to the first incubation duration, then it is determined whether the second duration is greater than or equal to a preset first threshold, and whether the maximum temperature difference is less than or equal to a preset second threshold; the first threshold and the second threshold are set by the user based on historical experience; If the second duration is greater than or equal to the first threshold, and the maximum temperature difference is less than or equal to the second threshold, then the target egg is output as successfully hatched; if the second duration is less than the first threshold, or the maximum temperature difference is greater than the second threshold, then the target egg is output as late-stage dead.

7. The system according to claim 4, characterized in that, The system also includes: a cloud platform; The main controller is further configured to: periodically generate hatching data for each target egg, and upload the hatching data of each target egg to the cloud platform via the second antenna; the hatching data includes one or more of the following: eggshell temperature data for the current period, difference signal for the current period, characteristic parameters for the current period, and hatching status for the current period; The cloud platform is used to update one or more preset hatching data views for each target egg based on the corresponding hatching data when it receives the hatching data of each target egg, and display them through a client.

8. The system according to claim 7, characterized in that, The cloud platform is also used for: For each target hatching egg, if the incubation status indicator for the current period is abnormal, an abnormal warning message is generated and pushed to the client.

9. The system according to claim 7, characterized in that, The cloud platform is also used for: When the preset incubation end time is reached, an incubation cycle report is generated based on the incubation data of each target egg during each cycle of the incubation process, and the incubation cycle report is displayed through the client.

10. A method for identifying incubation status, characterized in that, The method, applied to the incubation status identification system according to any one of claims 1 to 9, comprises: The ambient temperature acquisition unit collects ambient temperature data in real time. Each wireless temperature acquisition unit collects eggshell temperature data and wirelessly transmits the eggshell temperature data to the data processing unit. For each target hatching egg, the data processing unit generates characteristic parameters using the corresponding eggshell temperature data and ambient temperature data. The incubation status of each target egg is identified by using a preset incubation status recognition model and the characteristic parameters corresponding to each target egg.

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