Method, device and equipment for judging health of chicken and storage medium

Abstract

The application discloses a chicken health discrimination method, device, equipment and storage medium, which comprises the following steps: acquiring the crop physical state characteristic data and excrement chemical component characteristic data of a target chicken, and associating the two based on the individual identity of the target chicken; and performing differential discrimination according to the crop physical state characteristic data. The application realizes the detection of various health problems by detecting the crop physical state characteristics and the chemical component characteristics in excrement, and the differential discrimination mechanism can accurately distinguish the health problem types of the chicken, thereby providing more targeted decision basis for breeders. In addition, the application can discover abnormalities in the digestion stage after the chicken eats by monitoring the physical state (fullness, hardness, etc.) of the crop and the chemical components of the excrement in real time, and the time point of health intervention is advanced, and the time delay is low.

Description

Technical Field

[0001] This invention relates to the field of chicken farming technology, and in particular to a method, apparatus, equipment and storage medium for judging the health of chickens. Background Technology

[0002] As a crucial component of modern agricultural economy, the development level of the chicken farming industry directly impacts food safety and agricultural economic efficiency. Statistics show that global chicken consumption is increasing year by year, and chicken farming has become a pillar industry of agricultural economies in many countries and regions. However, the health of chickens is a key factor affecting farming efficiency. Disease outbreaks not only lead to stunted growth and decreased egg production but can also trigger large-scale epidemics, causing severe economic losses.

[0003] Currently, most assessments of chicken health are conducted after a decline in production performance (such as an increased feed conversion ratio) has been observed, requiring manual intervention. Some chicken farms may periodically send samples of chicken excrement for testing of chemical components. However, assessments based on production performance are a reactive, "hindsight-based" approach. By the time production indicators show significant abnormalities, disease has often already spread for some time, missing the optimal window for early intervention. Furthermore, sampling and testing typically require offline operations, with cumbersome sample collection, transportation, and analysis processes, resulting in long data feedback cycles and hindering continuous real-time monitoring. Therefore, both methods suffer from time-consuming and delayed results. Additionally, methods that only test the chemical components of chicken excrement cannot accurately determine whether a chicken's problem is digestive or pathological, resulting in low accuracy. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, device, and storage medium for judging the health of chickens, aiming to solve the problems of long time consumption, high latency, and low accuracy in the prior art.

[0005] In a first aspect, to achieve the above objective, the present invention provides a method for judging the health of chickens, characterized in that it includes:

[0006] The physical state characteristics of the crop and the chemical composition characteristics of the excrement of the target chickens are obtained, and the two are associated based on the individual identification of the target chickens.

[0007] Perform differential discrimination based on the crop physical state characteristic data:

[0008] Only when the crop physical state characteristic data is determined to be within the full range, it is further determined whether the content of key nutrients in the associated excrement chemical composition characteristic data exceeds the undigested food threshold; if so, an ingestion digestion disorder identifier is generated.

[0009] When the crop physical state feature data exceeds the upper limit of the full range, an oversized crop obstacle is generated.

[0010] When it is determined that the crop physical state characteristic data is within the full range and the rate of change of the crop physical state characteristic data per unit time is lower than the preset expulsion rate threshold, an expulsion obstacle identifier is generated.

[0011] When the hardness coefficient in the crop physical state characteristic data exceeds a preset hardness threshold, a crop hardness obstacle identifier is generated.

[0012] Optionally, the crop physical state characteristic data includes the crop fullness, and the crop fullness is considered to be within the full range when it is greater than or equal to the normal feeding threshold and less than or equal to the maximum threshold.

[0013] When the crop fullness exceeds the maximum threshold, a crop oversized obstacle marker is generated.

[0014] Optionally, the formula for calculating the fullness of the crop is:

[0015] P=H / L

[0016] Wherein, P is the fullness of the crop, H is the height of the crop protrusion relative to the neck plane, and L is the characteristic length of the chicken's neck.

[0017] Optionally, the normal eating threshold is 0.15~0.25, and the maximum threshold is 0.45~0.6;

[0018] The normal feeding threshold and the maximum threshold are selected within a range based on the breed and age of the target chickens.

[0019] Optionally, the preset discharge rate threshold is 0.06 / h to 0.11 / h;

[0020] The discharge rate threshold is selected within a range based on the breed and age of the target chickens.

[0021] Optionally, the hardness coefficient is the echo intensity of the ultrasonic echo signal;

[0022] The preset hard threshold is set to 150% to 200% of the reference echo intensity;

[0023] The reference echo intensity is the average echo intensity of healthy chickens within 2 hours after they start eating.

[0024] Optionally, after performing differential discrimination based on the crop physical state characteristic data, the method further includes:

[0025] Different control commands are output based on different output identifiers:

[0026] When an indicator of indigestion is present, a feed adjustment instruction is output, which is to reduce the proportion of the corresponding key nutrient element in the feed.

[0027] When the crop oversized obstacle is detected, the first alarm command is output;

[0028] When an obstacle clearance indicator is present, a second alarm command is output;

[0029] When the crop hardness indicator is present, a third alarm command is output;

[0030] When multiple obstacle markers are detected on a target chicken, the procedure is to simultaneously output multiple corresponding control commands or output a comprehensive alarm command.

[0031] Secondly, this application also provides a health assessment device for chickens, comprising:

[0032] The data acquisition module is used to acquire physical state characteristic data of the crop and chemical composition characteristic data of the excrement of the target chickens, and associate the two based on the individual identification of the target chickens;

[0033] The discrimination module is used to perform differential discrimination based on the crop physical state characteristic data and the chemical composition characteristic data.

[0034] Thirdly, this application also provides a chicken health assessment device, comprising: a processor, a memory, and a chicken health assessment program stored in the memory, wherein the chicken health assessment program is executed by the processor to implement the steps of the chicken health assessment method as described in any one of claims 1 to 7.

[0035] Fourthly, this application also provides a computer-readable storage medium storing a chicken health assessment program, which, when executed by a processor, implements the chicken health assessment method as described in any one of claims 1 to 7.

[0036] This embodiment detects various health problems by examining the physical state of the chicken's crop and the chemical composition of its excrement. This differentiated discrimination mechanism can accurately distinguish the types of health problems in chickens, providing farmers with more targeted decision-making basis (such as adjusting feed or administering medication). Furthermore, this embodiment detects crop physical state data within a full range to ensure normal feeding status in chickens, and then detects chemical composition in excrement. If the chemical composition exceeds the undigested threshold, it can determine if there is a digestive disorder. This distinguishes between pathological cases where chickens don't eat or eat less, but the chickens are essentially not digesting food, and the chemical composition in their excrement remains high. This solves the technical problem of not being able to accurately attribute the cause based solely on chemical composition, resulting in high accuracy. In addition, by monitoring the physical state of the crop (fullness, hardness, etc.) and the chemical composition of excrement in real time, this invention can detect abnormalities during the digestive stage after the chickens have eaten, without waiting for weight loss or an increase in feed conversion ratio. This advances the timing of health intervention from "after production performance is impaired" to "when digestive abnormalities occur," greatly reducing the time required before intervention and minimizing delays. Attached Figure Description

[0037] 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.

[0038] Figure 1 This is a schematic diagram of the structure of a chicken health assessment device according to an embodiment of the present invention;

[0039] Figure 2 This is a flowchart illustrating a method for determining the health of chickens according to an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of a chicken health assessment device according to an embodiment of the present invention.

[0041] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0043] In this invention, the use of terms such as "first," "second," etc., is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0044] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0045] The following embodiments of this application will illustrate the chicken health assessment device used in the technical implementation of this application:

[0046] like Figure 1 As shown, the chicken health assessment device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0047] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the health assessment device for chickens and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0048] The memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a chicken health assessment program.

[0049] exist Figure 1In the chicken health assessment device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the chicken health assessment device of this application can be set in the chicken health assessment device. The chicken health assessment device calls the chicken health assessment program stored in the memory 1005 through the processor 1001 and executes the chicken health assessment method provided in the embodiment of this application.

[0050] Based on, but not limited to, the hardware structure of the chicken health assessment device described above, this application provides a first embodiment of a chicken health assessment method. (Refer to...) Figure 2 , Figure 2 A flowchart illustrating the method for assessing the health of chickens is shown.

[0051] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0052] The health assessment model in this invention adopts a conventional rule engine + threshold assessment architecture, eliminating the need for complex machine learning models. The execution logic of the rule engine is as follows:

[0053] Using the physical state characteristics of the crop as the first criterion, we prioritize determining whether it is in the full range.

[0054] All preset discrimination rules are executed in parallel, including fullness range judgment, fullness change rate judgment, and hardness coefficient judgment.

[0055] When any rule meets the triggering condition, a corresponding obstacle identifier is generated;

[0056] When multiple rules are met simultaneously, all corresponding obstacle icons are generated at the same time.

[0057] All discrimination thresholds are determined based on large sample statistical data of healthy chickens of the same breed and age, and can be dynamically calibrated according to the breeding environment.

[0058] In this embodiment, the chicken health assessment method of the present invention, such as Figure 2 As shown, it includes the following steps:

[0059] Step S100: Obtain crop physical state characteristic data and excrement chemical composition characteristic data of the target chicken, and associate the two based on the individual identification of the target chicken;

[0060] It is understandable that linking the physical characteristics of the crop and the chemical composition of excrement of the same chicken through individual identification can ensure the accuracy of detection, avoid misjudgments caused by mismatch between the two, and improve the practicality of this application. Specifically, individual identification can be achieved by wearing RFID leg bands or using facial recognition tracking, etc., and this application does not impose specific limitations on this.

[0061] Step S201: Perform differential discrimination based on crop physical state characteristic data: determine whether the crop physical state characteristic data is within the full range;

[0062] Step S301: If yes, then further determine whether the content of key nutrients in the associated excrement chemical composition characteristic data exceeds the undigested food threshold; if yes, then proceed to step S401.

[0063] Understandably, when the physical state of a chicken's crop is within the full range, it indicates that the chicken is eating normally and the crop is well-filled. However, if the content of key nutrients in the chicken's excrement is higher than the undigested food threshold, it indicates that the chicken's appetite is normal, but the digestive efficiency is reduced due to changes in the environment, temperature, or its own digestive capacity. This suggests that the chicken is experiencing a digestive disorder, thus enabling precise monitoring of such disorders. When chickens are sick, they typically experience a decreased appetite, refusing or consuming less feed, and their crops usually do not reach a full state. Specifically, the key nutrient content in the excrement chemical composition data can include at least one of the following: undigested protein residue, nitrogen content, and phosphorus content; this application does not impose specific limitations on this.

[0064] In step S302, it is simultaneously determined whether the rate of change of the crop physical state characteristic data per unit time is lower than the preset excretion rate threshold; if so, step S402 is executed.

[0065] Understandably, by calculating the rate of change of crop physical state characteristics data per unit time, the excretion rate of chickens can be determined. This allows for the analysis of whether there are any abnormalities in crop excretion, ensuring normal growth, reducing unnecessary losses, and ultimately improving economic returns. Specifically, the excretion rate varies among different chickens and even among the same chicken at different ages; therefore, the excretion rate threshold can be set according to specific needs. For example, when using the ratio of the crop's protrusion height relative to the neck reference plane to the characteristic length of the chicken's neck to represent the fullness of the crop physical state characteristics data, the normal excretion rate for adult broilers should typically be greater than 0.07 / h.

[0066] Step S303: If the crop physical state characteristic data exceeds the upper limit of the fullness interval, then proceed to step S403.

[0067] It should be noted that our technicians have also observed in actual chicken farming that chickens may experience acid reflux, gas regurgitation, and vomiting during the summer. These phenomena can lead to excessively large crops, requiring timely intervention to prevent hindering normal growth. Therefore, when the physical state characteristics of a chicken's crop exceed the upper limit of the fullness range, an excessively large crop obstacle marker is generated. This allows staff to promptly identify and intervene in cases of excessive crop size, ensuring normal chicken growth, reducing unnecessary losses, and improving economic returns.

[0068] In step S202, it is further determined whether the hardness coefficient in the crop physical state characteristic data exceeds a preset hardness threshold; if so, step S304 is executed.

[0069] Understandably, an excessively high crop hardness coefficient in chickens suggests a pathological feed digestion problem, rather than just a single nutrient digestion disorder, or even crop blockage. Monitoring crop hardness allows for timely disease detection. It's important to note that in actual farming, an excessively high crop hardness coefficient is more indicative of a pathological issue. Abnormal crop excretion is more likely related to individual behavioral abnormalities, such as excessive feeding after starvation. In such cases, feed remains in the crop for a longer period, potentially up to ten hours or more.

[0070] Step S401: Generate an indigestion marker.

[0071] Step S402: Generate an obstacle removal marker;

[0072] Step S403: Generate an obstacle marker for an oversized crop;

[0073] Step S304: Generate crop hardness obstacle marker.

[0074] This embodiment detects various health problems by examining the physical state of the chicken's crop and the chemical composition of its excrement. This differentiated discrimination mechanism can accurately distinguish the types of health problems in chickens, providing farmers with more targeted decision-making basis (such as adjusting feed or administering medication). Furthermore, this embodiment detects crop physical state data within a full range to ensure normal feeding status in chickens, and then detects chemical composition in excrement. If the chemical composition exceeds the undigested threshold, it can determine if there is a digestive disorder. This distinguishes between pathological cases where chickens don't eat or eat less, but the chickens are essentially not digesting food, and the chemical composition in their excrement remains high. This solves the technical problem of not being able to accurately attribute the cause based solely on chemical composition, resulting in high accuracy. In addition, by monitoring the physical state of the crop (fullness, hardness, etc.) and the chemical composition of excrement in real time, this invention can detect abnormalities during the digestive stage after the chickens have eaten, without waiting for weight loss or an increase in feed conversion ratio. This advances the timing of health intervention from "after production performance is impaired" to "when digestive abnormalities occur," greatly reducing the time required before intervention and minimizing delays.

[0075] Understandably, chickens need time to digest their food. Therefore, in the data acquisition process, the acquisition of physical characteristic data of the chicken's crop must be at least a preset time interval from the acquisition of chemical characteristic data of the chicken's excrement. The preset interval can be set as needed, and is usually set to 2-4 hours. To address the problem of wasted computing power caused by full-scale analysis in existing sampling and testing methods, this invention introduces an energy-saving mechanism of "physical characteristic-triggered chemical analysis." The system only activates the complex chemical composition calculation module when the crop's physical state is in the "full range" (i.e., after the chicken has eaten normally and has digestible analytical value). For a large number of samples with empty or abnormal crops, the system directly skips the energy-intensive chemical analysis. This "on-demand calculation" mode effectively reduces the number of invalid calculations by more than 50%, significantly reducing the system's processing latency and energy consumption.

[0076] Specifically, in this embodiment, the chemical composition characteristics of excrement are obtained using an online Fourier transform near-infrared diffuse reflectance spectrometer, with parameters conforming to the "DB12T 1402—2024 Technical Specification for Near-Infrared Spectroscopic Acquisition of Livestock and Poultry Manure":

[0077] Wavenumber range: 4000 cm⁻¹~12000 cm⁻¹, resolution 8 cm⁻¹;

[0078] Scanning parameters: 32 scans, scanning speed ≤ 0.5 cm⁻¹ / s;

[0079] Sample processing: Excrement is a dry basis sample (to remove free moisture and avoid detection errors), and non-target substances (grass, feathers) are screened out according to NY / T 3298—2018;

[0080] Data output: Outputted in absorbance form, automatically converted to percentage of undigested protein / nitrogen / phosphorus content.

[0081] Furthermore, the crop physical state characteristic data includes the crop fullness. When the crop fullness is greater than or equal to the normal feeding threshold and less than or equal to the maximum threshold, it is considered to be in the full range.

[0082] When the crop fullness exceeds the maximum threshold, a crop oversized obstacle marker is generated.

[0083] Understandably, crop fullness is not a single absolute value. Instead, it is expressed differently depending on the detection method. Furthermore, the absolute size of the crop varies greatly between different stages of a chicken's life, such as between chicks and adults.

[0084] Furthermore, the formula for calculating crop fullness is:

[0085] P=H / L

[0086] Where P is the crop fullness, H is the crop protrusion height relative to the neck plane, and L is the characteristic length of the chicken's neck.

[0087] The calculation formula in this embodiment realizes the digital representation of crop fullness, making the determination of crop fullness in chickens more accurate and improving the reliability of this application.

[0088] Specifically, the empirical reference ranges for the normal feeding threshold and the maximum threshold are as follows: When crop fullness is expressed as the ratio of the height of the crop protrusion relative to the neck reference plane to the characteristic length of the chicken's neck: the normal feeding threshold is set at 0.15~0.25. When the crop fullness is <0.15, it is considered that the crop has been basically expelled or that there has been no effective feeding; when the crop fullness enters the range of 0.15~0.25, it indicates that the chicken has basically completed its basic feed intake, and the crop begins to show obvious physiological bulging. The maximum threshold is set at 0.45~0.6. The normal feeding threshold and the maximum threshold are selected within the range according to the breed and age of the target chicken.

[0089] This embodiment provides a data reference range for the degree of fullness, which can be digitally distinguished for different breeds and different ages of chickens, thus improving the practicality of this application.

[0090] More specifically, when some chickens have thick and stiff feathers, the height of the crop protruding from the neck reference plane can be measured optically (when the crop is full, it will push up the feathers; the height of the crop protruding from the feather surface is obtained with the original feather surface as a reference, and then calculated by adding the average thickness of the feather surface of the same breed of chickens at the same age, thus measuring the height of the crop protruding from the neck reference plane), for example, by three-dimensional depth camera measurement or mechanical measurement, such as using a feather separating device or manually parting the feathers at the crop to expose the crop for measurement.

[0091] In another embodiment, the preset excretion rate threshold is 0.06 / h to 0.11 / h; the excretion rate threshold is selected within the range according to the breed and age of the target chickens.

[0092] This embodiment provides a data reference range for the excretion rate threshold, which can be digitally distinguished for different breeds and ages of chickens, further improving the practicality of this application.

[0093] Preferably, the hardness coefficient is the echo intensity of the ultrasonic echo signal; the preset overhardness threshold is set to 150% to 200% of the reference echo intensity; wherein, the reference echo intensity is the average echo intensity of healthy chickens within 2 hours after they have eaten.

[0094] Specifically, the echo intensity can be measured by an ultrasonic probe. The probe emits a short-pulse ultrasonic signal of 5-10MHz into the crop region, receives and amplifies the echo signal, and then digitizes the echo signal using a signal processing module (such as a high-speed ADC sampling circuit). The sampling rate is ≥50MHz to ensure accurate capture of the echo peak value, which is the echo intensity.

[0095] This embodiment defines the hardness coefficient digitally through echo intensity, achieving digital discrimination of the hardness coefficient and improving the reliability of this application. Furthermore, the base echo intensity of this solution can be determined by the average echo intensity of healthy chickens of the same breed and age over a 2-hour period, making the data more reliable and further enhancing the reliability of this application.

[0096] Preferably, when the condition is determined to be a crop hardness disorder, the method further includes:

[0097] Obtain the location information of the breeding unit where the chicken is located;

[0098] If a predetermined number of chickens appear consecutively in the same breeding unit, they are judged to have crop hardness disorder.

[0099] Generate group epidemic prevention instructions; among which, group epidemic prevention instructions include: closing the feeding passage of the breeding unit and / or activating environmental disinfection equipment and / or sending an epidemic warning signal to the management personnel.

[0100] Understandably, while an excessively high crop hardness coefficient in chickens usually indicates a pathological cause, it's impossible to determine the specific disease or its nature without first identifying it. Therefore, isolation measures cannot be implemented every time this problem occurs to prevent transmission. A better approach is to set a scenario where five or more chickens in the same flock exhibit the same excessively high crop hardness coefficient within 48 hours. This would suggest a possible pathological cause and contagiousness, allowing for preventative measures such as closing the feeding passage in that unit, activating environmental disinfection equipment, and sending an epidemic warning signal to management. This would prevent the pathological cause from spreading further and avoid greater losses.

[0101] In one embodiment, after performing differential discrimination based on crop physical state characteristic data, the method further includes: outputting different control commands based on different output identifiers: when there is a feeding and digestion disorder identifier, outputting a feed adjustment command, wherein the feed adjustment command is to reduce the ratio of the corresponding key nutrient element content in the feed; when there is a crop oversized disorder identifier, outputting a first alarm command; when there is an excretion disorder identifier, outputting a second alarm command; when there is a crop overhard disorder identifier, outputting a third alarm command; when multiple disorder identifiers are detected in the target chicken at the same time, executing the step of simultaneously outputting multiple corresponding control commands or outputting a comprehensive alarm command.

[0102] Understandably, a chicken may have multiple health problems at the same time. This embodiment recognizes the identification marks and outputs different control commands or alarm commands to achieve the function of taking measures for multiple problems at the same time for chickens with multiple health problems. This further reduces the time spent from when a problem occurs to when health intervention is carried out, ensures the normal growth of chickens, and improves economic efficiency.

[0103] Preferably, the undigested food threshold can be refined into a set of step-like data, and different feed ratios can be adjusted for different degrees of digestive disorders to achieve more refined feed ratio adjustments.

[0104] For example, in the case of mild digestive disorders, the chemical composition of excrement is characterized by: undigested protein residue: 5.8%, which is higher than the undigested protein index of 5.0% for mild disorders. The model determines it as mild protein digestive disorders, and the feed adjustment instructions are: the original crude protein level of the feed is 20%, and the adjusted crude protein level is 18% (a decrease of 2 percentage points). The amount of exogenous enzyme preparation added is: 100 g of protease per ton of feed.

[0105] In cases of moderate digestive disorders, the chemical composition of excrement is characterized as follows: undigested protein residue: 6.5%, which is higher than the undigested protein index of 6.0% in moderate disorders. The model determines that the condition is moderate protein digestive disorder. Feed adjustment instructions: the original crude protein level of the feed is 20%, and the adjusted crude protein level is 17.5% (a decrease of 2.5 percentage points). The amount of exogenous enzyme preparation added is 120 g of protease per ton of feed.

[0106] In cases of severe digestive disorders, the chemical composition of excrement is characterized as follows: undigested protein residue: 7.5%, which is higher than the undigested protein index of 7.0% in moderate disorders. The model determines that the condition is severe protein digestive disorder. Feed adjustment instructions: the original crude protein level of the feed is 20%, and the adjusted crude protein level is 17% (a decrease of 3 percentage points). The amount of exogenous enzyme preparation added is 150 g of protease per ton of feed.

[0107] Furthermore, different alarm methods can be used to address different alarm signals. For example, different alarm light colors can be used: a blue alarm light for a second alarm requiring more attention, a yellow alarm light for a third alarm requiring immediate attention, and a red alarm light for a first alarm requiring immediate intervention. By using different alarm methods, managers can be informed of the situation in a timely manner, enabling them to make timely plans and improve work efficiency.

[0108] Based on the same concept, this application also provides a health assessment device for chickens, such as... Figure 3 As shown, it includes: a data acquisition module, used to acquire crop physical state characteristic data and excrement chemical composition characteristic data of target chickens, and associate the two based on the individual identification of the target chickens; and a discrimination module, used to perform differential discrimination based on crop physical state characteristic data and chemical composition characteristic data.

[0109] The technical solution of this embodiment, through the cooperation of various functional modules, ultimately achieves the detection of multiple health problems. This differentiated discrimination mechanism can accurately distinguish the types of health problems in chickens, providing farmers with more targeted decision-making basis (such as adjusting feed or administering medication). Furthermore, this embodiment detects the physical state characteristics of the crop to ensure the chickens are in a full range, and then detects the chemical components in the excrement. If the chemical components exceed the undigested threshold, it can determine if there is a digestive disorder. This distinguishes between pathological cases where chickens don't eat or eat less, but the chickens are essentially not digesting food, and the chemical components in the excrement are still high. This solves the technical problem that relying solely on chemical components cannot accurately attribute the cause, resulting in high accuracy. In addition, by monitoring the physical state of the crop (fullness, hardness, etc.) and the chemical components of the excrement in real time, this invention can detect abnormalities during the digestive stage after the chickens have eaten, without waiting for weight loss or an increase in the feed conversion ratio. This advances the timing of health intervention from "after production performance is impaired" to "when digestive abnormalities occur," greatly reducing the time required before intervention and minimizing delays.

[0110] Furthermore, this application also proposes a computer-readable storage medium, characterized in that a chicken health assessment program is stored on the computer-readable storage medium, and when the chicken health assessment program is executed by a processor, it implements the chicken health assessment method as described above. Therefore, it will not be described again here. In addition, the beneficial effects of using the same method will not be described again. For technical details not disclosed in the embodiments of the computer-readable storage medium involved in this application, please refer to the description of the method embodiments of this application. The program instructions can be deployed to be executed on a computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed in multiple locations and interconnected through a communication network. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The above-mentioned program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. The above-mentioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0111] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort. Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, and of course, it can also be implemented by dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for this application, software program implementation is more often a preferred implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, portable hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0112] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for judging the health of chickens, characterized in that, include: The physical state characteristics of the crop and the chemical composition characteristics of the excrement of the target chickens are obtained, and the two are associated based on the individual identification of the target chickens. Perform differential discrimination based on the crop physical state characteristic data: Only when the crop physical state characteristic data is determined to be within the full range, it is further determined whether the content of key nutrients in the associated excrement chemical composition characteristic data exceeds the undigested food threshold; if so, an ingestion digestion disorder identifier is generated. When the crop physical state feature data exceeds the upper limit of the full range, an oversized crop obstacle is generated. When it is determined that the crop physical state characteristic data is within the full range and the rate of change of the crop physical state characteristic data per unit time is lower than the preset expulsion rate threshold, an expulsion obstacle identifier is generated. When the hardness coefficient in the crop physical state characteristic data exceeds a preset hardness threshold, a crop hardness obstacle identifier is generated.

2. The method for judging the health of chickens as described in claim 1, characterized in that, The crop physical state characteristic data includes the crop fullness. When the crop fullness is greater than or equal to the normal eating threshold and less than or equal to the maximum threshold, it is considered to be in the full range. When the crop fullness exceeds the maximum threshold, a crop oversized obstacle marker is generated.

3. The method for judging the health of chickens as described in claim 2, characterized in that, The formula for calculating the fullness of the crop is: P=H / L Wherein, P is the fullness of the crop, H is the height of the crop protrusion relative to the neck plane, and L is the characteristic length of the chicken's neck.

4. The method for judging the health of chickens as described in claim 3, characterized in that, The normal eating threshold is 0.15~0.25, and the maximum threshold is 0.45~0.6; The normal feeding threshold and the maximum threshold are selected within a range based on the breed and age of the target chickens.

5. The method for judging the health of chickens as described in claim 3, characterized in that, The preset discharge rate threshold is 0.06 / h to 0.11 / h; The discharge rate threshold is selected within a range based on the breed and age of the target chickens.

6. The method for determining the health of chickens as described in any one of claims 1 to 5, characterized in that, The hardness coefficient is the echo intensity of the ultrasonic echo signal; The preset hard threshold is set to 150% to 200% of the reference echo intensity; The reference echo intensity is the average echo intensity of healthy chickens within 2 hours after they start eating.

7. The method for determining the health of chickens as described in any one of claims 1 to 5, characterized in that, After performing differential discrimination based on the crop physical state characteristic data, the method further includes: Different control commands are output based on different output identifiers: When an indicator of indigestion is present, a feed adjustment instruction is output, which is to reduce the proportion of the corresponding key nutrient element in the feed. When the crop oversized obstacle is detected, the first alarm command is output; When an obstacle clearance indicator is present, a second alarm command is output; When the crop hardness indicator is present, a third alarm command is output; When multiple obstacle markers are detected on a target chicken, the procedure is to simultaneously output multiple corresponding control commands or output a comprehensive alarm command.

8. A health assessment device for chickens, characterized in that, include: The data acquisition module is used to acquire physical state characteristic data of the crop and chemical composition characteristic data of the excrement of the target chickens, and associate the two based on the individual identification of the target chickens; The discrimination module is used to perform differential discrimination based on the crop physical state characteristic data and the chemical composition characteristic data.

9. A health assessment device for chickens, characterized in that, include: A processor, a memory, and a chicken health assessment program stored in the memory, wherein the chicken health assessment program is executed by the processor to implement the steps of the chicken health assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a chicken health assessment program, which, when executed by a processor, implements the chicken health assessment method as described in any one of claims 1 to 7.

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