System and method for measuring growth performance of multi-channel step-in flat-raised poultry

Abstract

The invention discloses a multi-channel step-in growth performance measuring system and method for flat-raised poultry, and belongs to the field of poultry farming, the method adopts a step-in multi-channel growth performance measuring device structure based on behavior characteristics so as to adapt to poultry farming groups of different scales, flexible expansion and deployment are realized, and the measuring accuracy is improved. Through a signal calibration matching strategy, RFID signals of different channels are accurately matched with data of a strain sensor to ensure that the signals of the different channels do not interfere with each other, error-free individual information identification is realized, the RFID technology is utilized to record the activity frequency and the residence time of poultry in different areas, the activity distribution rule of the poultry is analyzed, and the identification accuracy is improved. Data support is provided for further behavior research and health status, a large amount of effective data support is provided for screening of excellent poultry, unmanned automatic monitoring of the poultry farm is realized, the production benefit of the poultry farm is improved, poultry group stress caused by manual operation is reduced, and normalization and reliability of data acquisition are ensured.

Description

Technical Field

[0001] The invention relates to the field of poultry breeding, and in particular to a multi-channel walk-in flat-breeding poultry growth performance measurement system and method. Background Art

[0002] At present, poultry farming is mainly divided into two modes: caged and free-range. Compared with caged, free-range animals will not have too much stress and are more in line with animal welfare standards. The individual growth performance of free-range poultry is an important physiological parameter to monitor during the farming process. Real-time monitoring of the individual growth performance of large groups of free-range poultry can reflect the growth status and welfare of poultry, and timely detect and eliminate poultry individuals with abnormal weight.

[0003] At present, the weighing method used by farms to measure the live weight of poultry is mainly traditional manual static weighing. Although the live weight measured by manual weighing is more accurate, it is difficult to operate when weighing a large group of poultry, and it takes a lot of manpower and time to measure the weight of each poultry. In addition, the time span of this method is relatively large, making it difficult to achieve real-time monitoring of the poultry group. Moreover, manual weighing can easily cause stress to the poultry, reducing the production efficiency of poultry.

[0004] In order to solve the above problems, researchers have used different technologies to obtain the live weight of animals without exciting them. The existing technology uses 3D vision technology to estimate the weight of pigs. After using an RGBD camera to collect images of pigs, the 3D point cloud model is used to obtain the three-dimensional model, and then the weight of the pigs is obtained by combining the weight estimation model. Although this method saves time and effort and does not cause stress, it is difficult to perform three-dimensional modeling on small individual poultry, and it is difficult to accurately estimate the weight of poultry due to feathers and other conditions. The perch-type device invented by utilizing the perching habits of poultry automatically measures the weight of poultry when they perch on the pole, but the poultry groups willing to perch are still some individuals, and it is difficult to measure other poultry in a large group. In addition, it is difficult to measure the live weight of waterfowl such as geese that do not have this habit. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A multi-channel walk-in flat-raised poultry growth performance measurement system includes a control processing module, a position information module, a behavior analysis module, a weight calculation module and a result storage module, wherein: The position information module transmits the poultry position information to the control processing module in a unidirectional manner, and the control processing module obtains the poultry position information after data processing; the control processing module then sends an instruction to activate the weight calculation module, collects the dynamic weight of the poultry passing through the channel, and calculates the actual weight of the poultry and returns it to the control processing module; the control processing module obtains the behavior type of the poultry through the collected information through the behavior analysis module, and finally analyzes the predicted weight of the poultry based on the obtained dynamic weight and the behavioral characteristics of the poultry, and transmits the data to the result storage module for storage.

[0007] A method for a multi-channel walk-in flat-raising poultry growth performance measurement system comprises the following steps: S1. Build a multi-channel walk-in flat-raising poultry growth performance measurement device, including a ramp, an RFID reader, a channel platform, a bottom resistive strain sensor, a channel railing, an RFID reader, a system host, a multi-functional weighing and force measuring instrument, a system display, a piano-type operating shell platform, and a channel baffle; S2. When the poultry steps onto the channel platform, the weight calculation module is activated. At this time, the resistive strain sensor at the bottom of the channel platform starts to work and continuously reads the weight information of the poultry. When the weight returns to below the threshold, the weight reading work is completed. S3, calculating the actual weight of the poultry from the collected weight information; S4. Calculate the distribution location of the poultry based on the read poultry identity information.

[0008] Furthermore, the S3 specifically includes the following steps: S31, calculating the expected value of the overall dynamic weight data according to the weight information of the poultry continuously read and obtained in S2; S32, judging whether the dynamic body weight is balanced according to the expected value calculated in S31, and if it is unbalanced, filtering the abnormally fluctuating body weight value through a median filtering algorithm; S33, performing mean filtering on the weight value after median filtering to reduce overall dynamic weight fluctuation; S34, based on the processing result of S33, input the processed data into the weight calculation module for weight calculation, and output the weight calculation result; S35, dividing the weight calculation result outputted by S34 into multiple groups of data slices, and calculating the slope, intercept and root mean square error of each group of data; S36, repeating steps S31-S35 until there is no next set of data, completing the weight calculation, and outputting the optimal predicted weight as the actual weight of the poultry.

[0009] Furthermore, the expected value in S32 is calculated as follows:

[0010] Where: E represents the expected value of the dynamic weight curve; n represents the number of weights collected; w i Represents the actual weight value.

[0011] Furthermore, the specific method of the median filtering in S32 is:

[0012] Among them: i represents the time, Represents the predicted weight value, w i Represents the actual weight value; m represents the window size of the median filter, and median() means calculating the median of a set of data.

[0013] Furthermore, the specific calculation formula of the mean filter in S33 is:

[0014] Among them: i represents the time, represents the predicted weight value; k represents the position of the weight data in the filter window; w k Represents the weight value before mean filtering; m is the window size of mean filtering.

[0015] Furthermore, the specific calculation formula of the root mean square error in S35 is:

[0016] Among them, i represents the time, Represents the predicted weight value w i represents the true weight value; M represents the number of samples for calculating the root mean square error.

[0017] Furthermore, the S4 specifically includes the following steps: S41, after the poultry passes through the channel, the information collected by the RFID reader is transmitted to the location information module; and the information is sorted according to the time sequence of collection; S42, after pre-processing the data, comparing the RFID antenna numbers of the signals read before and after the poultry passes through the channel, to determine whether the poultry has passed through the channel; S43, judging the entry and exit direction of the poultry according to the front and rear RFID antenna numbers of the read identity information, and judging the current location information of the poultry according to the RFID antenna position and the entry and exit direction of the poultry currently read; S44. By comparing the time, weight, reading position and other related data collected in different time periods, the distribution pattern of the poultry is obtained.

[0018] The present invention has the following beneficial effects: (1) Design of a detachable modular walk-in multi-channel design that can adapt to poultry groups of different sizes and has good flexibility and scalability; (2) Realize unmanned, automated, 24-hour real-time monitoring, reduce manual intervention, save a lot of labor and time costs, and reduce the stress of manual operation on poultry; (3) By combining low-cost chips with RFID antennas, the identity and location information of poultry can be accurately read while greatly saving costs; (4) Big data statistics of poultry individuals can be automatically collected, including multi-dimensional data such as individual growth performance, behavioral characteristics, and location distribution, to achieve comprehensive information collection and provide accurate and reliable data support; (5) The data system platform can analyze various types of collected data in real time and quickly provide feedback on abnormal situations, which helps to make management decisions in a timely manner and improve breeding management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a three-dimensional schematic diagram of the complete structure of the present invention.

[0020] Figure 2 It is a three-dimensional schematic diagram of a complete channel of the present invention.

[0021] Figure 3 It is the device control logic diagram of the present invention.

[0022] Figure 4 It is a flow chart of the device implementation of the present invention.

[0023] Figure 5 It is a flow chart of weight calculation of the present invention.

[0024] Figure 6 It is a flow chart of poultry position analysis of the present invention. DETAILED DESCRIPTION

[0025] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0026] like Figure 1-Figure 2As shown in FIG. 1 , it is a schematic diagram of the specific structure of the device of the present invention. In the figure, the device is composed of a ramp 1, an RFID reader 2, a channel platform 3, a bottom resistive strain sensor 4 (the installation position is shown in the figure, located on the bottom back of the channel platform), a channel railing 5, an RFID reader 6, a system host 7, a multi-functional weighing & force measuring instrument 8, a system display 9, a piano-type operating shell platform 10, a channel baffle 11, etc.

[0027] like Figure 3 The figure shows the control logic diagram of the device of the present invention. The device can be divided into five modules, with the host control processing module as the center. The position information module transmits the poultry position information to the host control processing module in one direction, and the control processing module obtains the poultry position information after data processing; the control processing module then sends instructions to activate the weight calculation module, collects the dynamic weight of the poultry passing through the channel, and calculates the real weight of the poultry and returns it to the host control processing module; the host control processing module obtains the behavior type of the poultry through the collected information through the behavior analysis module, and finally analyzes the predicted weight of the poultry based on the obtained dynamic weight and the behavior characteristics of the poultry, and transmits the data to the result storage module for storage.

[0028] like Figure 4-Figure 6 Shown is a specific implementation flow chart of the present invention.

[0029] like Figure 4 The figure shows all the states of poultry passing through the platform. The specific steps are: When S1 poultry steps onto the channel platform, the RFID antenna installed at the bottom of the slope will continuously read the poultry's identity information and transmit the information and time to the host control processing module. S2 When the poultry steps onto the channel platform, the weight calculation module is activated. At this time, the resistive strain sensor at the bottom of the channel platform starts working and continuously reads the weight information of the poultry. When the weight returns to below the threshold, the weight reading work is completed.

[0030] S3. Calculate the actual weight of the poultry from the collected weight information, such as Figure 5 As shown in the figure, the specific steps for calculating the real weight are: S31, calculating the expected value of the overall dynamic weight data according to the weight information of the poultry continuously read and obtained in S2; S32: Calculate the expected value of the entire dynamic weight data using an expected calculation formula, where the expected calculation formula is as follows:

[0031] Where: E represents the expected value of the dynamic weight curve; n represents the number of weights collected; w i Represents the actual weight value.

[0032] S33, performing mean filtering on the weight value after median filtering to reduce overall dynamic weight fluctuation; The calculated expected value is used to determine whether the dynamic weight is stable, and the dynamic weight is filtered. First, the median filter algorithm is used to filter the weight values ​​with abnormal fluctuations and reduce the maximum value interval of the curve. The median filter calculation formula is as follows:

[0033] Where: E represents the expected value of the dynamic weight curve; n represents the number of weights collected; w i+1 and w i Represent the actual weight values ​​at time i+1 and i respectively.

[0034] After performing the median filter algorithm, the mean filter algorithm is performed again to reduce the fluctuation of the overall dynamic weight and make the next step of weight calculation more accurate. The mean filter calculation formula is as follows:

[0035] Where: i represents the position of the current weight data to be processed; ŵ i represents the weight value after filtering; k represents the position of the weight data in the filtering window; w k represents the weight value before filtering; m is the window size of mean filtering.

[0036] S34: After pre-processing the data, the processed data is input into a weight calculation algorithm for weight calculation.

[0037] S35, slice the data into five groups, and calculate the slope k, intercept b and root mean square error of the group. The root mean square error calculation formula is as follows:

[0038] Among them, i represents the moment; Represents the predicted weight value, w i represents the true weight value, and M represents the number of samples for calculating the root mean square error.

[0039] S36: This step is continuously repeated until there is no next set of data, and the weight calculation is completed, and the optimal predicted weight is output as the actual weight of the poultry.

[0040] S4. Calculate the distribution location of poultry based on the poultry identity information read like Figure 6 The figure shows the process of determining the distribution location of poultry. The process of determining the distribution location of poultry: S41: After the poultry passes through the channel, the information collected by the RFID reader is transmitted to the location information module; and the information is sorted according to the time sequence of collection.

[0041] S42: After pre-processing the data, the RFID antenna numbers of the signals read before and after the poultry passes through the channel are compared to determine whether the poultry has passed through the channel.

[0042] S43: Determine the entry and exit direction of the poultry according to the front and rear RFID antenna numbers of the read identity information. Determine the current location information of the poultry according to the RFID antenna position and the entry and exit direction of the poultry currently read.

[0043] S44: By comparing the time, weight, reading position and other related data collected in different time periods, the distribution pattern of the poultry is obtained. This method performs data cleaning, preprocessing, analysis and other operations on the behavioral data collected in the above steps, which can reduce the impact of data interference items and greatly improve the accuracy of the analysis of the growth performance of individual poultry; at the same time, the distribution pattern and growth performance pattern of poultry are analyzed by big data through the collected information such as the position and weight of poultry. The following are the specific steps for the pattern analysis.

[0044] In order to explore the individual growth patterns of poultry, we first obtain the daily live weight data of poultry collected in step S3. Taking weeks and months as time units, the most accurate live weight of each poultry is calculated based on the behavioral characteristics of poultry and different location information. The live weight data change curve of each poultry is drawn, and the average live weight change of the group is sorted out in units of weeks and months, and visualized using line graphs. Through the line graph of the average live weight change of the group, the weight change trend of the poultry in the entire group is analyzed to determine the growth period and market period of the poultry group. In addition, by drawing the live weight data change curve of each poultry and comparing it with the average weight change of its poultry group, individuals with abnormal weight are screened out and eliminated as soon as possible. By controlling the feeding of poultry at different periods and eliminating poultry with abnormal weight, the economic benefits of the farm can be improved while saving feeding costs.

[0045] In order to explore the distribution patterns of poultry, we sorted the poultry location information collected in step S4 by time. Then, we displayed the length of time the poultry stayed in different areas using a stacked graph to understand the preferences and activity patterns of poultry in different areas.

[0046] Next, we draw a time-region distribution map to analyze the location distribution patterns of poultry at different times, and conduct in-depth analysis based on the weight changes of poultry. By comparing the relationship between the activity frequency of poultry in different areas and their weight gain, we can predict the weight change trend of poultry under different environmental conditions and improve the accuracy of weight management. At the same time, the system will continuously monitor the location information of each poultry, and use big data analysis methods based on the location information of poultry to conduct in-depth mining of their long-term activities, and further study the activity patterns and weight changes of poultry under different environmental factors, thereby providing data support for predicting poultry weight trends and optimizing scientific management.

[0047] In order to allow users to see the individual growth performance of poultry more intuitively, the system platform provides data dashboard module, growth performance module, behavior analysis module, data warning module and operation management module. The technology stack uses Vue3 framework and Alibaba open source components to implement the front-end style, and uses Java's SpringBoot framework, middleware redis and MyBatisPlus framework to implement the back-end structure; The data dashboard module provides users with a page for viewing data information of poultry groups, which displays a summary of information such as on-site installation diagrams of equipment, site camera videos, number of equipment, behavioral characteristics of poultry groups, growth performance data information, and equipment abnormality alarms.

[0048] The growth performance module provides a page for users to view the growth performance of individual poultry, which displays the poultry's weight on the day, yesterday, last month and initial weight, and displays the growth of poultry from different time spans, giving users a reference criterion for judging the future growth trend of poultry.

[0049] The behavior analysis module provides users with a page for viewing the behavioral patterns of poultry in different time periods. The page displays the different behaviors of poultry in different time periods, helping users to accurately locate the distribution of poultry and the living habits of poultry groups. Relying on these data, a more scientific poultry management strategy can be implemented.

[0050] The alarm information module provides a user with an alarm information page, prompting the user of the current problems of the equipment type, poultry, etc., so that the user can promptly discover the problems of the equipment and poultry and check and repair them.

[0051] The operation management module provides users with a threshold management and equipment management page. Users can control the normal weight range and other parameters of poultry by adjusting different parameter types in threshold management, which supports users to adjust the behavior parameters of poultry according to actual production scenarios. The equipment management page displays all equipment types deployed by the farm users, and each device can be clearly viewed, managed, and operated.

[0052] The management system and user interaction platform provided by this measuring device enable users to more clearly view the growth performance, behavioral characteristics and other parameters of poultry in the farm, realize high-precision real-time monitoring of poultry, and have strong practical value.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0057] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A multi-channel walk-in flat-raising poultry growth performance measurement system, characterized in that: It includes a control processing module, a position information module, a behavior analysis module, a weight calculation module and a result storage module, wherein: The position information module transmits the poultry position information to the control processing module in a unidirectional manner, and the control processing module obtains the poultry position information after data processing; the control processing module then sends an instruction to activate the weight calculation module, collects the dynamic weight of the poultry passing through the channel, and calculates the actual weight of the poultry and returns it to the control processing module; the control processing module obtains the behavior type of the poultry through the collected information through the behavior analysis module, and finally analyzes the predicted weight of the poultry based on the obtained dynamic weight and the behavioral characteristics of the poultry, and transmits the data to the result storage module for storage.

2. The measuring method based on the multi-channel walk-in flat-raising poultry growth performance measuring system according to claim 1 is characterized in that: The steps include: S1. Build a multi-channel walk-in flat-raising poultry growth performance measurement device, including a ramp, an RFID reader, a channel platform, a bottom resistive strain sensor, a channel railing, an RFID reader, a system host, a multi-functional weighing and force measuring instrument, a system display screen, a piano-type operating shell platform, and a channel baffle; S2. When the poultry steps onto the channel platform, the weight calculation module is activated. At this time, the resistive strain sensor at the bottom of the channel platform starts to work and continuously reads the weight information of the poultry. When the weight returns to below the threshold, the weight reading work is completed. S3, calculating the actual weight of the poultry from the collected weight information; S4. Calculate the distribution location of the poultry based on the read poultry identity information.

3. The method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner according to claim 2, characterized in that: The S3 specifically includes the following steps: S31, calculating the expected value of the overall dynamic weight data according to the weight information of the poultry continuously read and obtained in S2; S32, judging whether the dynamic body weight is balanced according to the expected value calculated in S31, and if it is unbalanced, filtering the abnormally fluctuating body weight value through a median filtering algorithm; S33, performing mean filtering on the weight value after median filtering to reduce overall dynamic weight fluctuation; S34, based on the processing result of S33, input the processed data into the weight calculation module for weight calculation, and output the weight calculation result; S35, dividing the weight calculation result outputted by S34 into multiple groups of data slices, and calculating the slope, intercept and root mean square error of each group of data; S36, repeating steps S31-S35 until there is no next set of data, completing the weight calculation, and outputting the optimal predicted weight as the actual weight of the poultry.

4. The method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner according to claim 3, characterized in that: The calculation method of the expected value in S32 is: Where: E represents the expected value of the dynamic weight curve; n represents the number of weights collected; w i+1 and w i Represent the actual weight values ​​at time i+1 and time i respectively.

5. The method of the multi-channel walk-in flat-raising poultry growth performance measurement system according to claim 3, characterized in that: The specific method of median filtering in S32 is: Among them: i represents the time, Represents the predicted weight value, w i Represents the actual weight value; m represents the window size of the median filter, and median() means calculating the median of a set of data.

6. The method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner according to claim 3, characterized in that: The specific calculation formula of the mean filtering in S33 is: Among them: i represents the moment; represents the predicted weight value; k represents the position of the weight data in the filter window; w k Represents the weight value before mean filtering; m is the window size of mean filtering.

7. The method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner according to claim 3, characterized in that: The specific calculation formula of the root mean square error in S35 is: Among them, i represents the moment; Represents the predicted weight value, w i represents the true weight value, and M represents the number of samples for calculating the root mean square error.

8. The method for measuring the growth performance of poultry in a multi-channel walk-in flat-raising manner according to claim 2, characterized in that: The S4 specifically includes the following steps: S41, after the poultry passes through the channel, the information collected by the RFID reader is transmitted to the location information module; and the information is sorted according to the time sequence of collection; S42, after pre-processing the data, comparing the RFID antenna numbers of the signals read before and after the poultry passes through the channel, to determine whether the poultry has passed through the channel; S43, judging the entry and exit direction of the poultry according to the front and rear RFID antenna numbers of the read identity information, and judging the current location information of the poultry according to the RFID antenna position and the entry and exit direction of the poultry currently read; S44. By comparing the time, weight, reading position and other related data collected in different time periods, the distribution pattern of the poultry is obtained.

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