Intelligent poultry feed-weight ratio measuring method and computer readable storage medium

Abstract

The invention discloses an intelligent feed-weight ratio measuring method for poultry and a computer readable storage medium, and the method comprises the steps: 1, carrying out the self-inspection through a data collection and control unit, and calibrating the tare weight of a first weighing sensor of a body weight monitoring unit and a second weighing sensor of a feed consumption monitoring unit; 2, binding a wearable electronic tag with a poultry individual, and when the poultry enters a weighing range, reading tag information to identify the identity of the poultry and synchronize with sensor acquisition; 3, collecting weight data of a single poultry in the rearing cage in real time through a first weighing sensor, and collecting weight data of feed in the feed box in real time through a second weighing sensor; step 4, data aggregation; step 5, wireless transmission; and step 6, according to the total feed consumption weight and the total poultry weight increase weight associated with the identity information in the specified time period, automatically calculating the individual feed-to-weight ratio, and generating a feed-to-weight ratio dynamic change curve. The invention aims to realize high-efficiency acquisition of the feed-weight ratio of individual poultry.

Description

Technical Field

[0001] This invention relates to the field of poultry farming technology, and in particular to a method for determining the intelligent feed conversion ratio of poultry and a computer-readable storage medium. Background Technology

[0002] Existing poultry farming equipment mainly focuses on feed production, feeding, and daily cleaning and maintenance, and lacks the function of recording poultry weight and feed intake data in real time during the farming process; the relevant data still relies on manual weighing, which not only easily causes stress to animals, but also makes it difficult to ensure the authenticity of the data.

[0003] Existing patent CN221037610U discloses a feed conversion ratio detection device. This device mainly includes a frame, multiple first weighing mechanisms, and multiple second weighing mechanisms. It calculates the feed conversion ratio by directly acquiring weight data through weighing sensors. Compared with traditional manual weighing, it achieves automatic monitoring of body weight and feed intake, saving labor costs. However, the device completely lacks individual identification capabilities and cannot distinguish data from multiple poultry in the same cage. When there are multiple poultry in the cage, the weight data reflects the total weight of the group, and cannot obtain accurate individual data. Summary of the Invention

[0004] The main objective of this invention is to provide a method for determining the feed conversion ratio of poultry and a computer-readable storage medium, aiming to solve the problem that existing poultry farming methods cannot achieve efficient determination of the feed conversion ratio of individual animals.

[0005] To achieve the above objectives, the present invention provides a method for determining the intelligent feed conversion ratio in poultry, the method comprising the following steps:

[0006] Step 1: Self-test and calibrate the tare weight of the first weighing sensor of the weight monitoring unit and the second weighing sensor of the feed consumption monitoring unit through the data acquisition and control unit.

[0007] Step 2: The wearable electronic tag is bound to the individual poultry. When the poultry enters the weighing range, the tag information is read to identify the poultry and synchronize with the sensor data. The weighing range includes a weight monitoring area and a feed consumption monitoring area.

[0008] Step 3: After identity recognition is triggered, the weight data of a single poultry in the feeding cage is collected in real time through the first weighing sensor. Each first weighing sensor corresponds to one feeding cage, and only the weight of one poultry is detected at a time to avoid interference from multiple poultry. The weight data of the feed in the feed box is collected in real time through the second weighing sensor. The data collection is carried out synchronously at a refresh rate of 55Hz, and the identity information is associated with the weight and feed intake data through timestamps.

[0009] Step 4: The data acquisition and control unit synchronously processes multiple sensor data at a preset refresh frequency, and binds the identity information, weight data and food intake data into a unified data packet, which is temporarily stored in the local storage chip.

[0010] Step 5: Upload the aggregated data packets to the remote management platform in real time by integrating a wireless WiFi module;

[0011] Step 6: In the remote management platform, based on the total feed consumption and total weight gain of poultry associated with the identity information within a specified time period, the feed conversion ratio of each individual is automatically calculated, and a dynamic curve of the feed conversion ratio is generated.

[0012] Optionally, the method further includes: when the feed intake of a single poultry bird fluctuates by more than 15% or the feed conversion ratio is abnormal, automatically triggering a directional remote early warning signal based on the bird's identity information.

[0013] Optionally, the tare calibration in step 1 includes: recording the empty cage weight of the first weighing sensor and the empty feed box weight of the second weighing sensor as reference values ​​under no-load conditions, and filtering the sensor data using a sliding window algorithm to ensure that the calibration error is less than 0.1g.

[0014] Optionally, in step 2, tag information is dynamically read using RFID identification chips, which are respectively set in the heavy monitoring area and the feed consumption monitoring area.

[0015] Optionally, in step 3, when the first weighing sensor detects that the weight exceeds the reasonable range for a single poultry, a data anomaly marker is automatically triggered.

[0016] Optionally, in step 6, the formula for calculating the material weight ratio is:

[0017] Feed conversion ratio (FCR) = Total weight of feed consumed within a specified time period / Total weight gain of poultry; where the total weight gain is obtained by linearly fitting the pre-feeding weight data, and the total weight of feed consumed is obtained by calculating the difference between the weight after feeding the previous day and the weight before feeding the current day.

[0018] Optionally, in step 3, the feed box has a single inlet structure, and the feed box is adjacent to and independently arranged with the feeding cage.

[0019] Optionally, the method further includes a data backup step:

[0020] The monitoring data for at least 7 days is backed up using the local storage chip of the data acquisition and control unit;

[0021] When the network is interrupted, local storage is enabled, and the data is synchronized to the remote platform once the network is restored.

[0022] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the poultry intelligent feed conversion ratio determination method as described in any of the preceding claims.

[0023] Beneficial effects:

[0024] (1) Using high-precision sensors and synchronously collecting data at a refresh rate of 55Hz, it can capture the instantaneous minute changes in poultry weight and feed intake, which is far superior to traditional manual measurement or low-frequency acquisition equipment. This ensures the accuracy and real-time nature of the data and provides technical support for solving the problems of errors easily generated by manual measurement and insufficient data accuracy of existing automated equipment.

[0025] (2) By attaching wearable electronic tags to each poultry, and deploying RFID readers in the weight monitoring area and feed consumption monitoring area respectively, the system can dynamically read the poultry's identity information when it enters any area, and associate the identity information with weight and feed intake data in real time through precise timestamps. This mechanism ensures that every piece of data can be accurately traced back to the individual poultry, effectively solving the problem of individual identification and data matching in floor-raising mode, and avoiding the phenomenon of data misattribution in group raising.

[0026] (3) The unique “single cage, single poultry” design and weight threshold judgment mechanism can effectively avoid data interference caused by multiple poultry eating or weighing at the same time.

[0027] (4) From identification, data collection, calculation and analysis to early warning, the entire measurement process requires no manual intervention and achieves 24-hour uninterrupted automated operation. This greatly reduces the workload of aquaculture personnel, saves a lot of labor and time costs, and allows them to focus on more important management decisions.

[0028] (5) The system can calculate the feed conversion ratio (FCR) of individuals in real time, and automatically trigger a targeted remote early warning signal based on the individual's identity information when the feed intake fluctuation exceeds 15% or the FCR is abnormal. This instant feedback capability enables farmers to detect problems such as abnormal growth, health risks, or feed waste as soon as possible, thereby taking rapid intervention measures, improving management efficiency, and reducing economic losses.

[0029] (6) By monitoring the growth performance data of individual poultry (such as daily weight gain, feed intake, feed conversion ratio, etc.) over a long period of time, the production efficiency and feed conversion capacity of each poultry can be accurately assessed. This provides an objective and quantitative scientific basis for the selection and breeding of core breeding groups, which helps to accelerate genetic progress and cultivate breeds with better performance.

[0030] (7) Accurate individual feed intake and feed conversion ratio data help farms to accurately assess the effectiveness of feed formulations, realize the configuration of daily rations on demand, avoid over- or under-nutrition, thereby effectively reducing feed costs and achieving the goal of "saving feed and reducing costs". Attached Figure Description

[0031] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating an embodiment of the intelligent feed conversion ratio determination method for poultry according to the present invention;

[0033] Figure 2 for Figure 1 A partial structural distribution diagram of poultry data collection in China.

[0034] Explanation of icon numbers:

[0035] 1-Feed box, 2-Feeding cage, 3-First weighing sensor, 4-Second weighing sensor, 5-Data acquisition and control unit.

[0036] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0037] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0038] See Figure 1-2 This invention provides a method for determining the intelligent feed conversion ratio in poultry, the method comprising the following steps:

[0039] Step 1, Initialization: Power on the poultry intelligent measuring device. The data acquisition and control unit 5 performs a self-test and calibrates the tare weight of the first weighing sensor 3 in the weight monitoring unit and the second weighing sensor 4 in the feed consumption monitoring unit. The weighing accuracy of the first weighing sensor 3 is ±1g, and the weighing accuracy of the second weighing sensor 4 is ±0.1g. Preferably, the first weighing sensor adopts a single-point parallel beam structure with an accuracy up to the Tg level, making it highly sensitive to minute changes in poultry weight. The second weighing sensor 4 uses a dedicated feed box sensor with an accuracy of 0.1g, accurately capturing every feeding behavior.

[0040] The specific operating steps are as follows:

[0041] Step 11: After the system is powered on, the data acquisition and control unit 5 performs a hardware self-test, including sensor channel connectivity, wireless communication module and storage chip status verification (testing the local storage chip SD card read and write function to ensure backup capacity ≥ 7 days of data).

[0042] Step 12: Under no-load conditions, continuously collect tare weight data from the first weighing sensor 3 and the second weighing sensor 4 using a sliding window algorithm, remove fluctuation values ​​(difference > 1g is considered abnormal), take the average value as the benchmark tare weight value and store it, with a calibration error of less than 0.1g;

[0043] Step 13: Use standard weights for dynamic verification to ensure that the accuracy of the first weighing sensor 3 is ±1g and the accuracy of the second weighing sensor 4 is ±0.1g.

[0044] Step 14. If calibration fails, automatically retry and trigger an alert, while recording the calibration log.

[0045] Furthermore, the control unit 5 has a built-in temperature sensor that performs linear compensation on the sensor readings based on the ambient temperature (-10℃~50℃). The formula is: calibration value = original value × (1+αΔT), where α is the temperature coefficient, such as 0.0005 / ℃.

[0046] Furthermore, LED indicator lights or voice prompts are added to the first weighing sensor 3 and the second weighing sensor 4 to clearly indicate the calibration status.

[0047] Step 2, Identification and Triggering: Wearable electronic tags are attached to individual poultry. When the poultry enters the weighing area, the tag information is read to identify the poultry and synchronize with sensor data collection. The weighing area includes a weight monitoring zone and a feed consumption monitoring zone. The electronic tags use passive HF 13.56MHz RFID animal tag chips, with a cylindrical size of 1.2mm × 12mm, conforming to ISO11784 / 5 standards. They are implanted subcutaneously in the poultry's neck and back using a sterile syringe, or secured to the legs using silicone straps. Each tag has a globally unique 64-bit ID code. The tag information is dynamically read via RFID identification chips, which are located in both the weight monitoring zone and the feed consumption monitoring zone. The RFID reader and data acquisition and control unit 5 are connected via an RS485 bus, with the sampling frequency synchronized with sensor data collection.

[0048] Furthermore, the detailed process of dynamically triggering the read includes:

[0049] Step 21: Low-power listening state. The RFID reader is normally in standby mode with a power consumption of ≤0.1W, and periodically transmits a 125kHz wake-up signal (every 100ms interval). When poultry wearing the electronic tag enters the reading range (within 5cm), the tag is activated and reflects a signal containing ID information.

[0050] Step 22: Identity information capture. The reader completes decoding within 50ms after detecting the signal, obtaining a 12-byte animal ID (e.g., "3D-47-1A-8C-00-01"). An anti-collision algorithm (based on the time-slotted ALOHA protocol) is used, supporting the simultaneous reading of multiple tags (up to 8), sorted by signal strength.

[0051] Step 23: Synchronously trigger sensor acquisition. The RFID read success signal is immediately sent to the data acquisition and control unit 5, triggering the following actions:

[0052] (1) Start the 55Hz high-speed acquisition of the first weighing sensor 3 or the second weighing sensor 4 in the corresponding area.

[0053] (2) Record timestamps accurate to milliseconds (format: YYYY-MM-DD HH:MM:SS.sss).

[0054] (3) Bind the animal ID, timestamp, and sensor channel number into the data packet header.

[0055] Step 24: Data verification and filtering. Verification rules: The read RSSI (Received Signal Strength Indicator) value must be within the preset range (-70dBm to -40dBm), and the reading distance must be within 5cm. Anomaly handling: If the ID is inconsistent for 3 consecutive reads, it is marked as "unreliable reading" and a reread is triggered.

[0056] Furthermore, for simultaneous reading of multiple tags: when multiple tags are detected to enter the reading range at the same time, a time-division multiple access mechanism is adopted, that is, sorted by signal strength, and the tag corresponding to the strongest signal is processed first (usually the nearest poultry). Then, a temporary time slot is allocated to each detected tag to complete the reading and data association in sequence.

[0057] Furthermore, the reader is set to operate at a bandwidth of 13.56MHz±7kHz, and frequency hopping technology is used to avoid co-channel interference.

[0058] This identification and triggering process ensures accurate identification of individual poultry and strict synchronization of data collection. The precise 5cm reading distance ensures that only poultry entering the effective monitoring area are identified, avoiding false triggers and data confusion.

[0059] Step 3, Continuous Monitoring: After identity recognition is triggered, the weight data of a single poultry in the feeding cage 2 is collected in real time through the first weighing sensor 3, wherein each first weighing sensor 3 corresponds to one feeding cage 2, and only the weight of one poultry is detected at a time to avoid interference from multiple poultry; and the weight data of the feed in the feed box 1 is collected in real time through the second weighing sensor 4. The data collection is carried out synchronously at a refresh rate of 55Hz, and the identity information, weight, and feed intake data are associated with timestamps; the feed box 1 has a single-entry structure, allowing only the head of a single poultry to enter at a time.

[0060] The weight monitoring data collection process is as follows: poultry enters the feeding cage → RFID identification is successful → the first weighing sensor 3 is triggered to start working → weight data is continuously collected at 55Hz → data filtering and processing → effective weight value extraction → timestamp binding.

[0061] Feed consumption monitoring data collection process: Poultry approaches the feed box → RFID identification is successful → the second weighing sensor 4 is triggered to start working → feed weight data is continuously collected at 55Hz → weight change rate is calculated → effective feed intake is extracted → timestamp binding.

[0062] Each cage 2 is designed to hold only one poultry, with mesh gaps ≤3cm to prevent multiple poultry from entering simultaneously. The cage door features a one-way opening design to prevent poultry from crossing cages. When the first weighing sensor 3 detects a weight exceeding the reasonable range for a single poultry (e.g., >5kg), it is automatically marked as "multiple poultry interference," and the collected data is ignored. By continuously sampling data at 55Hz, the weight change pattern is analyzed to identify whether the poultry is in a stable standing state. This ensures single-poultry detection and protection.

[0063] Meanwhile, regarding data association and timestamp synchronization, the timestamp generation mechanism uses a 32-bit timestamp counter with an accuracy of 1ms and a format of YYYY-MM-DD HH:MM:SS.sss, ensuring that all sensor data share a unified time reference.

[0064] Regarding data quality control and anomaly handling, (1) real-time data verification includes signal quality detection (monitoring the noise level of sensor signals, triggering re-acquisition when the signal-to-noise ratio is <20dB), numerical rationality check (weight data must be within a preset range (e.g., 100g-5000g), and those exceeding the range are marked as abnormal), and rate of change monitoring (when the weight change rate between adjacent sampling points is >10% / s, it is considered an abnormal fluctuation). (2) Abnormal data processing flow: upon detecting abnormal data, immediately mark the abnormal flag bit, trigger the backup sensor channel (e.g., configure redundant sensors), record the abnormal log, including the abnormal type, timestamp, and sensor ID, and send an abnormal alarm through the wireless module.

[0065] Step 4, Data Aggregation Step: The data acquisition and control unit 5 synchronously processes multi-channel sensor data at a frequency of 55Hz. The standard configuration includes multiple sensor channels, and the identity information, weight data, and food intake data are bound into a unified data packet and temporarily stored in the local storage chip. The sampling rate of the data acquisition and control unit 5 is fixed at 55Hz, supports parallel acquisition, and has a built-in core control board responsible for aggregating data from all sensors.

[0066] Step 5, Wireless Transmission Step: The aggregated data packets are uploaded to the remote management platform in real time by integrating a wireless WiFi module;

[0067] Step 6, Intelligent Calculation Step: In the remote management platform, based on the total feed consumption and total weight gain of poultry associated with the identity information within a specified time period, the feed conversion ratio of each individual is automatically calculated, and a dynamic change curve of the feed conversion ratio is generated.

[0068] The formula for calculating the material weight ratio is:

[0069] Feed conversion ratio (FCR) = total weight of feed consumed within a specified time period / total weight gain of poultry; where the total weight gain is obtained by linearly fitting the pre-feeding weight data, and the total weight of feed consumed is obtained by calculating the difference between the weight after feeding the previous day and the weight before feeding the current day.

[0070] Further, the workflow of the weight monitoring area is as follows: poultry approaches the feeding cage → enters the RFID reading range (within 5cm) → antenna activates the tag → reads the animal ID → triggers the first weighing sensor 3 to start 55Hz acquisition → records weight data + timestamp + animal ID → data is packaged and sent to the control unit 5.

[0071] The workflow for monitoring feed consumption is as follows: Poultry head is inserted into the feed box → enters the RFID reading range → antenna activates the tag → animal ID is read → trigger the second weighing sensor 4 to start 55Hz acquisition → record feed weight data + timestamp + animal ID → data is packaged and sent to the control unit 5.

[0072] Furthermore, the method also includes an early warning step: when a fluctuation in feed intake of a single poultry bird exceeds 15% or an abnormal feed conversion ratio is detected, a targeted remote early warning signal is automatically triggered based on the bird's identity information. The early warning is delivered to the administrator via SMS, App push notifications, etc. Simultaneously, the early warning utilizes long-term historical data comparison, based on the relative changes in time-series data and adaptive thresholds based on individual historical performance, thereby understanding the poultry's growth stage and health status changes. Furthermore, the method also includes a data backup step:

[0073] The monitoring data is backed up to 7 days through the local storage chip of the data acquisition and control unit 5; when the network is interrupted, the local storage is enabled and synchronized to the remote platform after the network is restored.

[0074] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for determining the intelligent feed conversion ratio in poultry, characterized in that, The method includes the following steps: Step 1: The tare weight of the first weighing sensor (3) of the weight monitoring unit and the second weighing sensor (4) of the feed consumption monitoring unit are self-checked and calibrated by the data acquisition and control unit (5); Step 2: The wearable electronic tag is bound to the individual poultry. When the poultry enters the weighing range, the tag information is read to identify the poultry and synchronize with the sensor data. The weighing range includes a weight monitoring area and a feed consumption monitoring area. Step 3: After the identity recognition is triggered, the weight data of a single poultry in the feeding cage (2) is collected in real time through the first weighing sensor (3), wherein each first weighing sensor (3) corresponds to a feeding cage (2), and only the weight of one poultry is detected at a time to avoid interference from multiple poultry. The weight data of the feed in the feed box (1) is collected in real time through the second weighing sensor (4). The data collection is carried out synchronously at a refresh rate of 55Hz, and the identity information is associated with the weight and feed intake data through timestamps. Step 4: The data acquisition and control unit (5) synchronously processes multiple sensor data at a preset refresh frequency, and binds the identity information, weight data and food intake data into a unified data packet, which is temporarily stored in the local storage chip. Step 5: Upload the collected data packets to the remote management platform in real time via the wireless WiFi module; Step 6: In the remote management platform, based on the total feed consumption and total weight gain of poultry associated with the identity information within a specified time period, the feed conversion ratio of each individual is automatically calculated, and a dynamic curve of the feed conversion ratio is generated.

2. The method according to claim 1, characterized in that, The method also includes: when the feed intake of a single poultry fluctuates by more than 15% or the feed conversion ratio is abnormal, a directional remote early warning signal is automatically triggered based on the poultry's identity information.

3. The method according to claim 1, characterized in that, The tare weight calibration in step 1 includes: recording the empty cage weight of the first weighing sensor (3) and the empty feed box weight of the second weighing sensor (4) as reference values ​​under no-load conditions, and filtering the sensor data through a sliding window algorithm to ensure that the calibration error is less than 0.1g.

4. The method according to claim 1, characterized in that, In step 2, the tag information is dynamically read through the RFID identification chip, which is set in the heavy monitoring area and the feed consumption monitoring area respectively.

5. The method according to claim 1, characterized in that, In step 3, when the first weighing sensor (3) detects that the weight exceeds the reasonable range for a single poultry, it automatically triggers a data anomaly marker.

6. The method according to claim 1, characterized in that, In step 6, the formula for calculating the material weight ratio is: Feed conversion ratio (FCR) = Total weight of feed consumed within a specified time period / Total weight gain of poultry; where the total weight gain is obtained by linearly fitting the pre-feeding weight data, and the total weight of feed consumed is obtained by calculating the difference between the weight after feeding the previous day and the weight before feeding the current day.

7. The method according to claim 1, characterized in that, In step 3, the feed box (1) is a single-entry structure, and the feed box (1) is adjacent to and independently arranged with the feeding cage (2).

8. The method according to claim 1, characterized in that, The method also includes a data backup step: The monitoring data for at least 7 days is backed up by the local storage chip of the data acquisition and control unit (5); When the network is interrupted, local storage is enabled, and the data is synchronized to the remote platform once the network is restored.

9. A computer-readable storage medium, characterized in that, The medium stores computer program instructions, which, when executed by a processor, implement the poultry intelligent feed conversion ratio determination method as described in any one of claims 1-8.

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