Pig Transfer Pen Path Planning and Anti-Error Warning Device

Abstract

The present invention discloses a pig transfer path planning and error prevention warning device, which relates to the field of intelligent breeding technology; the device includes: a laser driving module, an AI path planning engine, a digital twin pig farm sand table, a compliance judgment module and an error prevention warning module, wherein the laser driving module emits a light beam of a specific wavelength through a laser pen operated by the user, and the irradiation position of the laser pen is associated with the virtual path in the digital twin pig farm sand table in real time. The shortest compliant path is generated by the AI path planning engine and combined with the laser pen to guide the pigs to transfer in real time. The average time consumed is shorter than that of the traditional manual driving mode. The real-time verification function of the digital twin pig farm sand table ensures that the transfer path strictly follows biosafety rules to avoid repeated driving or path reversal due to route confusion. The linkage mechanism of the error prevention warning module and the AI path planning engine realizes automatic processing of abnormal events, guides the pigs to detour, avoids transfer interruption caused by manual decision-making delay, and improves production efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent breeding technology, and in particular to a pig house transfer path planning and error prevention early warning device. Background Art

[0002] Modern large-scale pig farms have placed higher demands on the efficiency and biosafety of pig transfer management. Pig transfer, such as from nursery houses to fattening houses and sick pigs to isolation houses, is a key link in the breeding process.

[0003] However, some pig farms have tried to track the location of pigs through RFID ear tags or visual cameras. However, the existing systems are mainly used for static inventory management and lack dynamic path planning functions. For example, cameras can identify the area where the pigs are located, but cannot generate real-time navigation routes based on biosecurity rules. A few smart pig farms have linked environmental sensors with management software, but have not deeply integrated them into the transfer process.

[0004] However, the traditional transfer house management model can no longer meet the needs of modern farming. Among them, path planning relies on the experience of breeders to formulate transfer routes, and lacks a quantitative model based on the behavioral characteristics of pig herds. This can easily lead to long routes or repeated driving, increasing the probability of pig stress. In addition, there is a lack of differentiated path planning mechanisms based on the differences in biosafety levels of pig herds of different ages. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a pig transfer path planning and error prevention warning device, which solves the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a pig transfer path planning and error prevention warning device, the device comprising: a laser drive module, an AI path planning engine, a digital twin pig farm sandbox, a compliance determination module and an error prevention warning module, wherein:

[0007] The laser driving module emits a beam of light of a specific wavelength through a laser pen operated by the user to generate a pregnant pig movement path signal. The irradiation position of the laser pen is associated with the virtual path in the digital twin pig farm sandbox in real time;

[0008] The AI path planning engine generates differentiated transfer paths based on pig type, physiological status, and environmental parameters. The paths include topological isolation rules for pregnant pig paths and piglet paths.

[0009] The digital twin pig farm sandbox synchronizes data with the physical sandbox through the Internet of Things protocol, and sets key detection points in the virtual environment, including the entrance to the farrowing area, the disinfection area, and the prohibited area;

[0010] The compliance determination module dynamically authorizes or terminates subsequent transfer operations based on the detection point trigger status, path deviation and time efficiency parameters, and generates multi-level alarm instructions;

[0011] The error prevention warning module integrates lidar point cloud data, voiceprint recognition unit and gas sensor to detect path deviation, group stress risk and environmental exceeding standard events in real time, triggering a graded response strategy.

[0012] The further improvement of the technical solution of the present invention is that the emission wavelength of the laser pen is in the range of 850-1050nm and the light intensity is , and has an automatic intermittent emission mode to stimulate pigs to move and mark their movement paths.

[0013] A further improvement of the technical solution of the present invention is that the AI path planning engine can distinguish pregnant pigs from piglets based on RFID ear tags or visual recognition technology, extract individual attributes, and integrate environmental parameters and the physiological status of pigs to construct a multi-dimensional constraint space.

[0014] A further improvement of the technical solution of the present invention is that the path includes topological isolation rules for pregnant pig paths and piglet paths, and based on the behavioral differences and biosafety rules of pregnant pigs and piglets, a physically or temporally isolated transfer path is generated, which satisfies:

[0015] Physical isolation: There is no spatial intersection between the pregnant pig path and the piglet path, and the minimum distance is controlled 2m;

[0016] The pregnant pig path is based on the physiological characteristics of pregnancy, limiting the slope and turning radius to assist in guiding the pregnant pigs;

[0017] The piglet path is guided by the straight line ratio and width limit to guide the piglet transfer path;

[0018] Time isolation: When there is spatial overlap between the paths of pregnant pigs and piglets in the digital twin model, the time interval between the transfer of pregnant pigs and piglets is marked. 30 minutes, and trigger the disinfection channel.

[0019] A further improvement of the technical solution of the present invention is that the laser drive module uses lidar point cloud data to generate channel three-dimensional point cloud data to detect the position offset of pigs in real time, the voiceprint recognition unit classifies the voiceprint characteristics of the pig group through a convolutional neural network, and the gas sensor detects that the environmental concentration exceeds a specific threshold and lasts for 5 minutes, triggering an alarm.

[0020] A further improvement of the technical solution of the present invention is that the voiceprint recognition unit introduces the pig voiceprint characteristics into the path planning algorithm, and the voiceprint characteristic parameters are calculated as follows:

[0021]

[0022] in:

[0023] is the stress coefficient of channel noise level on pigs, which is fitted by historical behavioral data;

[0024] It is the surge in stress caused by sudden change in light intensity;

[0025] is the nonlinear influencing factor of the pig population density in the path;

[0026] Dynamic weighting parameters based on pig breed and age group.

[0027] A further improvement of the technical solution of the present invention is that the digital twin pig farm sandbox synchronizes data with the physical sandbox through the Internet of Things protocol to include a path scoring algorithm. The scoring algorithm formula is:

[0028]

[0029] in:

[0030] is the weight coefficient, and Dynamic adjustment based on detection point type;

[0031] To plan the path length, is the actual path length;

[0032] The detection point passes the status;

[0033] To plan the transfer time, The actual transfer time.

[0034] A further improvement of the technical solution of the present invention is that: the weight coefficient Dynamic optimization through regression analysis of historical operation data and stress coefficient indicators.

[0035] A further improvement of the technical solution of the present invention is that the compliance determination module generates channel three-dimensional point cloud data based on the laser radar point cloud data, detects the position deviation of the pigs in real time, and compares the actual transfer time. and planning time , calculate the time loss rate:

[0036]

[0037] The compliance determination module uses a logic controller to determine the deviation , time loss rate and the number of missed detection points Mapped to a compliance score.

[0038] A further improvement of the technical solution of the present invention is that the multi-level warning instructions are generated based on the compliance score:

[0039] (1) Level 1 alarm: Push path correction suggestions to the administrator terminal;

[0040] (2) Level 2 alarm: Forcefully close the current channel, activate the backup path, and trigger the sound and light alarm;

[0041] (3) Level 3 alarm: lock the system and trigger the biosafety protocol.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The AI path planning engine generates the shortest compliant path, combined with real-time guidance from a laser pointer. The average time spent on pig transfers is shorter than with traditional manual driving. The real-time verification function of the digital twin pig farm sandbox ensures that the transfer path strictly follows biosafety regulations, avoiding repeated driving or retracing due to route confusion. The linkage mechanism between the error-proofing warning module and the AI path planning engine enables automatic handling of abnormal events, guiding pigs to detour, avoiding transfer interruptions caused by manual decision-making delays, and improving production efficiency.

[0044] 2. Through the compliance judgment module, based on the virtual detection point matrix, the transfer path is verified at the millisecond level for compliance. The multi-level alarm mechanism ensures risk classification and disposal. The topological isolation rules realize the physical or time isolation of the paths of pregnant pigs and piglets. The gas sensor and voiceprint recognition unit capture the environmental deterioration and pig herd stress signals in real time. The AI path planning engine dynamically adjusts the route to avoid high-risk areas, thereby reducing the incidence of heat stress in pregnant pigs and improving biosafety prevention and control capabilities.

[0045] 3. The annual report generated by the path scoring algorithm can intuitively display the operation quality of each breeder, provide objective data support for performance appraisal, and avoid evaluation bias. The system accumulates and stores the transfer operation data to form a breeding database, which supports managers to analyze the efficiency bottleneck of transfer through historical data retrospectively, provide a quantitative basis for the infrastructure renovation of the pig farm, and upgrade the intelligence of management decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the workflow of each module;

[0047] Figure 2 A logical flow chart for judging path compliance;

[0048] Figure 3 Schematic diagram of voiceprint spectrum. DETAILED DESCRIPTION

[0049] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0050] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0051] In addition, numerous specific details are provided in the following specific examples to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, methods, means, and components well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0052] The present invention provides a pig transfer path planning and error prevention warning device.

[0053] The laser driving module emits a light beam of a specific wavelength through a laser pen operated by the user to generate a pregnant pig movement path signal. The irradiation position of the laser pen is associated with the virtual path in the digital twin pig farm sandbox in real time.

[0054] The module emits a wavelength beam through a laser pen held and operated by the user. At this time, the beam can generate a pregnant pig movement path signal. When the breeder uses the laser pen to guide the pregnant pig to move, its irradiation trajectory in the physical space will be synchronously reflected in the virtual scene of the digital twin pig farm sandbox, providing intuitive and accurate data support for subsequent path analysis and adjustment.

[0055] The laser pen has an emission wavelength range of 850-1050nm and a light intensity of This wavelength range can effectively attract the pigs' attention and guide their movement without causing damage to the pigs' eyes and bodies. At the same time, the laser pen has an automatic intermittent emission mode, which can continuously stimulate the pigs to move by intermittently emitting light beams, while avoiding the pigs' adaptive fatigue caused by long-term continuous exposure, thereby more effectively marking their movement path.

[0056] The AI path planning engine generates differentiated transfer paths based on pig type (pregnant pigs / piglet), physiological status and environmental parameters. The paths include topological isolation rules for pregnant pig paths and piglet paths.

[0057] It can clearly distinguish pregnant pigs and piglets according to the type of pigs, the gestation stage of pregnant pigs in physiological state, the weight and health status of piglets, and environmental parameters to produce differentiated transfer paths. The generated paths strictly follow the topological isolation rules of pregnant pig paths and piglet paths, ensuring the safety of the transfer of different types of pigs from spatial and temporal dimensions.

[0058] In terms of physical isolation, there are no spatial intersection nodes between the pregnant pig path and the piglet path, and the minimum interval distance is controlled at 2m; the pregnant pig path limits the slope and turning radius based on the physiological characteristics of pregnancy, providing pregnant pigs with appropriate guidance directions; the piglet path guides the efficient transfer of piglets through straight line proportion and width restrictions. In terms of time isolation, when there is spatial overlap between the pregnant pig and piglet paths in the digital twin model, the system will automatically mark that the time interval for the transfer of pregnant pigs and piglets must reach 30 minutes, and trigger the disinfection channel to prevent cross-infection.

[0059] The digital twin pig farm sandbox synchronizes data with the physical sandbox through the Internet of Things protocol, and sets key detection points in the virtual environment, including the entrance to the delivery area, the disinfection area and the prohibited area.

[0060] Data synchronization is achieved through the use of IoT collaboration on physical pig farms, and includes a path scoring algorithm:

[0061]

[0062] in, is the weight coefficient, and Dynamic adjustment based on detection point type;

[0063] To plan the path length, is the actual path length;

[0064] The pass status of the detection point (1 for pass, 0 for fail);

[0065] To plan the transfer time, In order to calculate the actual transfer time, the path scoring algorithm can be used to quantitatively evaluate the path planning quality of each transfer operation, providing data support for subsequent path optimization and management decisions.

[0066] The compliance determination module dynamically authorizes or terminates subsequent transfer operations based on the detection point trigger status, path deviation and time efficiency parameters, and generates multi-level alarm instructions.

[0067] The compliance determination module conducts a comprehensive and dynamic assessment of the transfer operation based on the detection point trigger status, path deviation and time efficiency parameters. Once it is found that the transfer path deviates from the preset plan, passes through a prohibited area, or the actual transfer time exceeds a reasonable range, the compliance determination module will respond quickly, dynamically authorize or terminate subsequent transfer operations, and generate multi-level alarm instructions.

[0068] When a slight path deviation occurs, a path correction suggestion is pushed to the administrator terminal. If the deviation is serious, the current channel is forcibly closed, the backup path is activated and the sound and light alarm is triggered. In the case of serious violation of biosafety rules or extremely low transfer efficiency, the system is locked and the biosafety protocol is triggered to minimize the risk.

[0069] The compliance determination module generates channel 3D point cloud data based on the lidar point cloud data, detects pig position deviation in real time, and compares the actual transfer time. and planning time , calculate the time loss rate:

[0070]

[0071] The compliance determination module uses a logic controller to determine the deviation , time loss rate and the number of missed detection points Mapped into a compliance score. Specifically, the logic controller comprehensively calculates the three parameters according to preset rules and weights to obtain a score value that can reflect the compliance level of the transfer operation. The greater the deviation, the higher the time loss rate, and the more missed detection points, the lower the compliance score. In this way, the compliance judgment module can more comprehensively and objectively evaluate the compliance of the transfer operation, providing a basis for subsequent alarm instruction generation and decision-making.

[0072] The error prevention warning module integrates lidar point cloud data, voiceprint recognition unit and gas sensor to detect path deviation, group stress risk and environmental exceeding standard events in real time, triggering a hierarchical response strategy (sound and light alarm → path re-planning → emergency channel opening → administrator intervention).

[0073] The error prevention warning module integrates lidar point cloud data, voiceprint recognition unit and gas sensor to achieve real-time detection of path deviation, group stress risk and environmental exceedance events. Lidar point cloud data can accurately generate channel three-dimensional point cloud data and monitor pig position deviation in real time; the voiceprint recognition unit uses convolutional neural network (CNN) to classify the voiceprint characteristics of pig groups, such as abnormal sounds such as screaming, trampling, collision, etc., to determine whether the pig group is in a state of stress; the gas sensor constantly detects ammonia in the environment , hydrogen sulfide ,carbon dioxide When the concentration exceeds a specific threshold and lasts for 5 minutes, an alarm is triggered. Once an abnormal situation is detected, the error prevention warning module immediately triggers a hierarchical response strategy, from simple sound and light alarms, to route re-planning, to the opening of emergency channels, and notifying the administrator to intervene when necessary, forming a complete and efficient risk prevention and control system.

[0074] The laser radar point cloud data generates channel three-dimensional point cloud data. Through real-time analysis of this point cloud data, it is possible to accurately detect pig position deviation and promptly discover whether the pigs have deviated from the preset transfer path. The voiceprint recognition unit classifies the pig group voiceprint characteristics through a convolutional neural network (CNN) and introduces the pig group voiceprint characteristics into the path planning algorithm. Specifically, the voiceprint feature parameters are calculated using a specific formula:

[0075]

[0076] in:

[0077] is the stress coefficient of channel noise level on pigs, which is fitted by historical behavioral data;

[0078] It is the surge in stress caused by sudden change in light intensity;

[0079] is the nonlinear influencing factor of the pig population density in the path;

[0080] is the stress coefficient of channel noise level on pigs, which is obtained by fitting historical behavioral data. It reflects the impact of different noise levels on the stress level of pigs. The sudden change in light intensity may cause a surge in stress, taking into account the stimulation that sudden changes in light intensity may cause to pigs. It is a nonlinear influencing factor of the pig population density in the path, reflecting the complex relationship between pig population density and stress response. Pigs of different breeds and age groups have different sensitivities to stress factors. The voiceprint feature parameters are comprehensively calculated through this formula to provide the path planning algorithm with more comprehensive and accurate pig population status information, so that path planning can better adapt to the actual situation of the pig population and reduce stress response.

[0081] The gas sensor detects an event in which the ambient concentration exceeds the standard When the concentration of these harmful gases exceeds a specific threshold and lasts for 5 minutes, an alarm is triggered, which can promptly detect the deterioration of the pig house environment quality, prevent pigs from being in a bad environment for a long time, and ensure the health of pigs. At the same time, it provides an environmental risk reference for transfer route planning. If the environmental gas concentration in a certain area exceeds the standard, the AI path planning engine can adjust the transfer route accordingly to avoid the area.

[0082] Furthermore, the multi-level warning instructions are generated based on the compliance score:

[0083] Level 1 Alarm: When the compliance score is within a certain range, indicating that there are some minor deviations in the transfer operation, but have not yet caused serious impact on biosafety and transfer efficiency, the system will push path correction suggestions to the administrator terminal, reminding the administrator to adjust the transfer path in time to ensure that the operation returns to normal.

[0084] Level 2 alarm: If the compliance score drops further, it indicates that there is a problem with the transfer operation. The system will forcibly close the current channel, activate the backup path and trigger the sound and light alarm, attracting the attention of relevant personnel and guiding the pigs to be transferred to a safe and compliant path as soon as possible.

[0085] Level 3 Alarm: When the compliance score is extremely low, it means that the transfer operation cannot be carried out normally, posing a threat to the biosafety and production order of the pig farm. The system will immediately lock the system and trigger the biosafety protocol, initiating a series of emergency response measures, such as comprehensive disinfection, restricting the movement of people and pigs, etc. At the same time, the administrator will be notified to intervene quickly to minimize risks and losses.

[0086] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0087] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0088] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program that, when executed by the data processing unit, executes the invention of the pig transfer path planning and error prevention warning device provided by the present invention, as well as some or all of the steps in each embodiment. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0089] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a computer program, i.e., a software product. This computer program software product can be stored in a storage medium and includes instructions for enabling a device including a data processing unit (such as a personal computer, server, single-chip microcomputer, MCU, or network device) to execute the methods described in various embodiments of the present invention or certain portions of these embodiments.

[0090] The present invention provides a pig roosting path planning and error prevention warning device. There are many methods and approaches to implement this technical solution. The above is only a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also considered to be within the scope of protection of the present invention. Any components not specified in this embodiment may be implemented using existing technologies.

Claims

1. The pig transfer path planning and error prevention warning device is characterized by: The device includes: a laser drive module, an AI path planning engine, a digital twin pig farm sandbox, a compliance determination module, and an error prevention warning module, wherein: The laser driving module emits a beam of light of a specific wavelength through a laser pen operated by the user to generate a pregnant pig movement path signal. The irradiation position of the laser pen is associated with the virtual path in the digital twin pig farm sandbox in real time; The AI path planning engine generates differentiated transfer paths based on pig type, physiological status, and environmental parameters. The paths include topological isolation rules for pregnant pig paths and piglet paths. The digital twin pig farm sandbox synchronizes data with the physical sandbox through the Internet of Things protocol, and sets key detection points in the virtual environment, including the entrance to the farrowing area, the disinfection area, and the prohibited area; The compliance determination module dynamically authorizes or terminates subsequent transfer operations based on the detection point trigger status, path deviation and time efficiency parameters, and generates multi-level alarm instructions; The error prevention warning module integrates lidar point cloud data, voiceprint recognition unit and gas sensor to detect path deviation, group stress risk and environmental exceeding standard events in real time, triggering a graded response strategy; The laser driving module uses the laser radar point cloud data to generate channel three-dimensional point cloud data to detect the position deviation of pigs in real time. The voiceprint recognition unit classifies the voiceprint characteristics of pigs through a convolutional neural network. The gas sensor detects that the environmental concentration exceeds a specific threshold and lasts for 5 minutes, triggering an alarm. The voiceprint recognition unit introduces the pig herd voiceprint features into the path planning algorithm, and the voiceprint feature parameters are calculated: in: is the stress coefficient of channel noise level on pigs, which is fitted by historical behavioral data; It is the surge in stress caused by sudden change in light intensity; is the nonlinear influencing factor of the pig population density in the path; Dynamic weighting parameters based on pig breed and age group.

2. The pig transfer path planning and error prevention warning device according to claim 1 is characterized in that: The laser pen has an emission wavelength range of 850-1050nm and a light intensity of , and has an automatic intermittent emission mode to stimulate pigs to move and mark their movement paths.

3. The pig transfer path planning and error prevention warning device according to claim 1 is characterized in that: The AI path planning engine can distinguish pregnant pigs from piglets based on RFID ear tags or visual recognition technology, extract individual attributes, and integrate environmental parameters and the physiological status of pigs to construct a multi-dimensional constraint space.

4. The pig transfer path planning and error prevention warning device according to claim 3 is characterized in that: The AI path planning engine includes topological isolation rules for pregnant pig paths and piglet paths. Based on the behavioral differences between pregnant pigs and piglets and biosafety rules, it generates physically or temporally isolated transfer paths that meet the following requirements: Physical isolation: There is no spatial intersection between the pregnant pig path and the piglet path, and the minimum distance is controlled 2m; The pregnant pig path is based on the physiological characteristics of pregnancy, limiting the slope and turning radius to assist in guiding the pregnant pigs; The piglet path is guided by the straight line ratio and width limit to guide the piglet transfer path; Time isolation: When there is spatial overlap between the paths of pregnant pigs and piglets in the digital twin model, the time interval between the transfer of pregnant pigs and piglets is marked. 30 minutes, and trigger the disinfection channel.

5. The pig transfer path planning and error prevention warning device according to claim 1 is characterized in that: The digital twin pig farm sandbox synchronizes data with the physical sandbox through the Internet of Things protocol and includes a path scoring algorithm. The scoring algorithm formula is: in: is the weight coefficient, and Dynamic adjustment based on detection point type; To plan the path length, is the actual path length; The detection point passes the status; To plan the transfer time, Actual transfer time 。 6. The pig transfer path planning and error prevention warning device according to claim 5 is characterized in that: The weight coefficient in the digital twin pig farm sandbox Dynamic optimization through regression analysis of historical operation data and stress coefficient indicators.

7. The pig transfer path planning and error prevention warning device according to claim 6 is characterized in that: The compliance determination module generates channel three-dimensional point cloud data based on the laser radar point cloud data, detects the pig position deviation in real time, and compares the actual transfer time. and planning time , calculate the time loss rate: The compliance determination module uses a logic controller to determine the deviation , time loss rate and the number of missed detection points Mapped to a compliance score.

8. The pig transfer path planning and error prevention warning device according to claim 1 is characterized by: The multi-level warning instructions are generated based on the compliance score: (1) Level 1 alarm: Push path correction suggestions to the administrator terminal; (2) Level 2 alarm: Forcefully close the current channel, activate the backup path, and trigger the sound and light alarm; (3) Level 3 alarm: lock the system and trigger the biosafety protocol.

Images