Intelligent inspection method and system based on AI

Abstract

The invention provides an AI-based intelligent inspection method and system, and the method comprises the steps: collecting environment data, poultry behavior data and equipment operation data, forming a multi-dimensional fusion data set containing time-space coordinates, and constructing a digital twinborn model of a farm; updating the digital twinborn model and establishing a virtual-real mapping relation; inputting real-time data output by the digital twin model into a pre-trained multi-task learning AI model to obtain a comprehensive risk assessment report containing confidence and a time window; according to the comprehensive risk assessment report, an ant colony algorithm is combined with a real-time risk thermodynamic diagram to dynamically generate an optimal inspection path, and the inspection frequency and key areas are adjusted in real time; new data and processing results obtained in the inspection process are fed back to update parameters of the digital twinborn model and the multi-task learning AI model, and an inspection strategy is continuously optimized through a reinforcement learning algorithm. According to the method, the anomaly detection accuracy can be improved, the loss is greatly reduced, and the labor cost and poultry stress response are reduced.

Description

Technical Field

[0001] This invention relates to the field of security technology, specifically to an AI-based intelligent inspection method and system. Background Technology

[0002] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, digitalization and intelligentization have become an inevitable trend in the development of the livestock industry. In modern farms, efficient inspections are crucial for ensuring the health of poultry and livestock and improving management levels. However, traditional manual inspection methods suffer from problems such as low efficiency, low accuracy, high cost, and missed inspections. Summary of the Invention

[0003] Based on the aforementioned problems, this invention proposes an AI-based intelligent inspection method and system. By combining multimodal data fusion and digital twin technology, it improves the accuracy of anomaly detection; through an AI multi-task learning model, it enables early warning of diseases and equipment malfunctions, significantly reducing losses; an adaptive path optimization algorithm enhances inspection efficiency, reducing labor costs and poultry stress; a closed-loop feedback mechanism allows the system to adapt to changes in different seasons and breeding stages, possessing autonomous learning capabilities; and through precise monitoring and prediction, it reduces drug use and maintains the ecological balance of the farm.

[0004] In view of this, one aspect of the present invention proposes an AI-based intelligent inspection method, comprising: By simultaneously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm, a multidimensional fusion dataset containing spatiotemporal coordinates is formed. A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system.

[0005] Optionally, the step of constructing a digital twin model of the farm based on the multi-dimensional fused dataset, and updating the environmental state, individual poultry health status, and group behavior patterns in the digital twin model in real time through a deep neural network to establish a virtual-real mapping relationship includes: Based on the poultry identification information in the multi-dimensional fusion dataset, a unique identifier is assigned to each poultry individual, and a four-dimensional association matrix framework including individual identification dimension, spatial location dimension, physiological parameter dimension and behavioral feature dimension is established. Based on the actual physical layout of the farm, a three-dimensional virtual scene model is constructed, including building structure, equipment and facilities, and environmental areas. In the virtual scene, a corresponding digital avatar is created for each individual poultry. The digital avatar carries the individual information in the four-dimensional correlation matrix, realizing a one-to-one mapping relationship between physical poultry and virtual objects. A multi-layer deep neural network is constructed specifically for updating digital twin models. The multi-layer deep neural network receives real-time input from a multi-dimensional fusion dataset. Through three core modules—feature extraction layer, state prediction layer, and parameter update layer—it automatically identifies and analyzes changes in the environment, individual health status, and group behavior patterns, providing intelligent support for the real-time updating of the four-dimensional correlation matrix. A dual update mechanism based on event triggering and timed polling is established. When a significant change in the state of poultry is detected in the physical world, the corresponding update of the digital twin model is immediately triggered. At the same time, the four-dimensional correlation matrix is ​​fully refreshed at preset time intervals to ensure a high degree of synchronization and consistency between the virtual model and physical reality. By comparing measured data from physical sensors with predicted data from digital twin models, the accuracy and deviation of the virtual-real mapping are calculated. A feedback adjustment mechanism is used to continuously optimize the model parameters of the deep neural network and the data structure of the four-dimensional correlation matrix, thereby improving the digital twin model's ability to represent the real world and its prediction accuracy.

[0006] Optionally, the step of inputting the real-time data output by the digital twin model into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources, includes: Receive real-time data output by the digital twin model, including poultry individual health index, abnormal group behavior measurement value, environmental comfort index and equipment operation status parameters in the four-dimensional correlation matrix, perform standardization processing and feature vectorization transformation on various types of data, and generate a multi-dimensional feature tensor in a unified format as the standardized input of the multi-task learning AI model; The pre-trained attention weight allocation module automatically calculates the importance weight of each data source in the current prediction task based on the real-time status and historical patterns of the current farm. The weight allocation covers four main data sources: individual health data, group behavior data, environmental monitoring data, and equipment status data, realizing dynamic adjustment and intelligent allocation of the importance of data sources.

[0007] Optionally, the multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window, including: Real-time data that has undergone attention weight modulation by the attention weight allocation module is feature-separated according to task relevance, and three independent data processing channels are constructed: a dedicated channel for disease early warning, a dedicated channel for environmental risk assessment, and a dedicated channel for equipment failure prediction. Three data processing channels simultaneously initiate parallel computing processing. The disease early warning channel uses a deep learning network to identify signs of poultry diseases and determine the probability of disease onset. The environmental risk assessment channel analyzes the degree and trend of environmental parameters deviating from the normal range. The equipment failure prediction channel detects equipment performance degradation and early signs of failure. Each channel independently outputs the preliminary risk assessment results and anomaly severity rating for its corresponding field. Based on the historical prediction accuracy, current data quality, and model convergence status of each data processing channel, a corresponding confidence score is calculated for each preliminary risk assessment result. The confidence score reflects the reliability of the prediction result. At the same time, the stability and fluctuation range of the prediction result are evaluated through an uncertainty quantification algorithm to provide a credibility reference for subsequent decision-making. Based on the risk types and severity identified by each data processing channel, and combined with the development patterns of similar situations in historical data, the time window and development trend of potential risk events are predicted. The time window includes three key time nodes: the earliest possible time, the latest time that must be dealt with, and the best time for intervention, providing time-dimensional guidance information for inspection decisions. By integrating the prediction results, confidence assessment, and time window analysis from three data processing channels, a structured comprehensive risk assessment report is generated, which includes risk event type, risk level, confidence score, prediction time window, impact range assessment, and recommended response measures. The report organizes information in a standardized format to ensure that the subsequent inspection path optimization algorithm can accurately parse and use the assessment results.

[0008] Optionally, the step of dynamically generating the optimal inspection route based on the comprehensive risk assessment report using an ant colony algorithm combined with a real-time risk heatmap, and adjusting the inspection frequency and key areas in real time, wherein the optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response, including: Based on the risk event types, risk levels, and impact range information in the comprehensive risk assessment report, a real-time risk heat map is constructed on the farm's plan map. Different risk levels are assigned different heat values ​​and color depths. At the same time, combined with the distribution density of poultry individuals and the division of activity areas, a spatial mapping relationship between farm areas and risk levels is established, providing a visual risk distribution basis for subsequent route planning. Based on the current operational status and management priorities of the farm, corresponding weight coefficients and constraints are set for four optimization objectives: risk level, geographical distance, inspection cost, and minimization of poultry stress response. Among them, the risk level objective requires priority access to high-risk areas, the geographical distance objective aims to minimize the total path length, the inspection cost objective controls manpower and time consumption, and the stress response minimization objective reduces interference with the normal behavior of poultry. A parameter configuration framework for multi-objective optimization is established. Based on the real-time risk heat map, the ant colony algorithm is initialized. Multiple virtual ants are deployed at the entrance of the farm as path search agents. Each virtual ant carries four target optimization parameters and current risk heat map information. Through the pheromone initialization mechanism, a higher pheromone concentration is preset in high-risk areas to guide ants to prioritize the exploration of dangerous areas that need to be inspected. The virtual ant's path search process is initiated. Each ant calculates the optimal direction for its next move based on the risk heat value of its current location, the distance cost to reach each candidate area, the estimated inspection time, and the stress intensity of poultry. Through iterative search and pheromone update mechanisms, the process gradually converges to generate a set of candidate inspection paths that take into account the four optimization objectives. The path with the highest comprehensive score is then selected as the current optimal inspection path. Based on the changes in the optimal inspection path and the real-time risk heat map, the inspection frequency and dwell time of each area are dynamically adjusted. For high-risk areas, the inspection frequency is increased and the inspection time is extended, while for low-risk areas, the inspection frequency is appropriately reduced. At the same time, a real-time path update mechanism is established. When a new high-risk event is detected or the original risk status changes significantly, the inspection path is immediately recalculated and adjusted to ensure that the inspection strategy is dynamically matched with the actual risk distribution.

[0009] Optionally, the step of updating the parameters of the digital twin model and the multi-task learning AI model by feeding back new data and processing results obtained during the inspection process, and continuously optimizing the inspection strategy through reinforcement learning algorithms to form a self-learning and self-optimizing closed-loop system includes: During the inspection route execution, the behavior trajectory data of the inspectors, the actual findings at each detection point, the implementation status of the disposal measures and the feedback on the disposal effect are collected in real time. At the same time, the stress response of poultry, changes in environmental conditions and equipment response are recorded during the inspection. The predicted results before the inspection are compared and analyzed with the actual problems found, and a comprehensive execution effect evaluation report including prediction accuracy, response timeliness and disposal effectiveness is generated. The feedback data in the comprehensive performance evaluation report are classified and organized. Data on virtual-real mapping deviation, poultry status change, and environmental parameter correction related to the digital twin model are classified into the twin model update dataset. Data on prediction error cases, newly discovered abnormal patterns, and risk assessment deviation related to the multi-task learning AI model are classified into the AI ​​model training dataset, providing standardized data input for subsequent model parameter updates and optimization. Establish a reinforcement learning reward mechanism based on inspection results, setting positive rewards for successful prediction and timely detection of risk events, and negative rewards for missed detections, false alarms, and resource waste. Quantitatively evaluate the merits of the current inspection strategy through a reward function. At the same time, construct a strategy value evaluation system to comprehensively score the rationality of path selection decisions, frequency adjustment decisions, and resource allocation decisions, providing clear optimization directions and objective functions for reinforcement learning algorithms. Based on the categorized feedback data, an incremental learning method is used to update the parameters of the digital twin model and the multi-task learning AI model. The digital twin model focuses on updating the mapping relationship of the four-dimensional correlation matrix and the accuracy of the virtual-real synchronization mechanism. The multi-task learning AI model focuses on adjusting the weight parameters of the three data processing channels and the allocation strategy of the attention mechanism. Through knowledge fusion technology, the newly learned experience is organically combined with historical knowledge to avoid catastrophic forgetting and improve the generalization ability of the model. By employing reinforcement learning algorithms based on reward mechanisms and policy evaluation results, the system continuously optimizes inspection path generation strategies, risk prediction strategies, and resource scheduling strategies. Through policy gradient updates and experience replay mechanisms, the system can learn and improve from each inspection experience, gradually enhancing prediction accuracy, path optimization effectiveness, and emergency response capabilities. This forms a self-learning, self-optimizing, and self-evolving intelligent closed-loop system, achieving continuous improvement in inspection performance and enhanced system adaptability.

[0010] Optionally, the step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fusion dataset containing spatiotemporal coordinates includes: Multimodal sensor nodes are deployed in the breeding farm according to the grid layout principle. Each sensor node is configured with a unique spatial coordinate identifier and device ID. The time reference of all sensor nodes is unified through a wireless clock synchronization protocol to ensure the consistency of data collection time. Each sensor node synchronously acquires three types of basic data according to a preset acquisition frequency: the environmental data includes temperature, humidity, light intensity, and air quality parameters; the poultry behavior data includes individual location, movement trajectory, voiceprint characteristics, and body temperature distribution; and the equipment operation data includes feeding system status, ventilation system parameters, and lighting system operating conditions. Each type of data carries an acquisition timestamp and sensor location coordinates. The collected raw data undergoes noise filtering, outlier detection, and missing value compensation. Invalid data is removed through data integrity verification and sensor fault diagnosis algorithms to ensure that the data entering the fusion process meets the requirements of subsequent analysis. Add three-dimensional spatiotemporal coordinate markers to each valid data record, including two-dimensional spatial coordinates and one-dimensional time coordinates, to establish a precise mapping relationship between the data and physical spatial location and time node, forming structured data with spatial positioning capabilities; Environmental data, poultry behavior data, and equipment operation data that have been linked and tagged with spatiotemporal coordinates are encapsulated and integrated in a unified data format to generate a standardized multi-dimensional fusion dataset containing data type identifiers, spatiotemporal coordinates, numerical content, and quality levels for subsequent digital twin modeling.

[0011] Optionally, in the step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fused dataset containing spatiotemporal coordinates, the multidimensional data fusion employs an adaptive weight fusion algorithm, specifically calculating the dynamic fusion weights of each sensor data using the following formula:

[0012] in, The dynamic fusion weights for the m-th sensor; Let be the reliability impact factor of the m-th sensor, with a value range of [0.1, 2.0]. Let be the real-time reliability index of the m-th sensor, calculated based on historical accuracy and current signal-to-noise ratio; Let be the coverage influence factor of the m-th sensor, with a value range of [0.1, 1.5]. Let m be the spatial coverage efficiency index of the m-th sensor; This represents the total number of sensors; This is the type characteristic correction coefficient for the m-th sensor.

[0013] Optionally, based on the comprehensive risk assessment report, the multi-target ant colony algorithm, which dynamically generates the optimal inspection path using an ant colony algorithm combined with a real-time risk heatmap, updates the state transition probability of ants between nodes using the following formula:

[0014] in, Let be the probability that an ant moves from node u to node v; Let be the pheromone concentration on edge (u,v); Let the visibility heuristic factor from node u to v be defined as follows: ; Let be the Euclidean distance from node u to v; The risk attraction factor for node v increases with higher risk. , where is the avian stress response intensity factor at node v; Let u be the set of allowed nodes that can be reached from node u. , , , These are the relative importance parameters for pheromones, visibility, risk attraction, and stress avoidance, respectively.

[0015] Another aspect of the present invention provides an AI-based intelligent inspection system for performing an AI-based intelligent inspection method, comprising: a multimodal sensor network and a server deployed in different areas of a farm; The server is configured as follows: The system acquires environmental data, poultry behavior data, and equipment operation data synchronously collected by the multimodal sensor network, and forms a multi-dimensional fusion dataset containing spatiotemporal coordinates. A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system.

[0016] The AI-based intelligent inspection method of this invention includes: synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm, forming a multi-dimensional fusion dataset containing spatiotemporal coordinates; constructing a digital twin model of the farm based on the multi-dimensional fusion dataset, and updating the environmental state, individual poultry health status, and group behavior patterns in the digital twin model in real time through a deep neural network, establishing a virtual-real mapping relationship; wherein, the digital twin model includes a four-dimensional correlation matrix of individual poultry identifiers, spatial locations, physiological parameters, and behavioral characteristics; and inputting the real-time data output by the digital twin model into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model employs attention... The mechanism assigns weights to different data sources to emphasize their importance. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on real-time data, outputting a comprehensive risk assessment report including confidence level and time window. Based on this report, an ant colony algorithm combined with a real-time risk heatmap dynamically generates the optimal inspection path, adjusting inspection frequency and key areas in real time. This optimal path considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. New data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system. This invention combines multimodal data fusion and digital twin technology to improve the accuracy of anomaly detection; it uses an AI multi-task learning model to provide early warnings of diseases and equipment failures, significantly reducing losses; an adaptive path optimization algorithm improves inspection efficiency, reducing labor costs and poultry stress; a closed-loop feedback mechanism enables the system to adapt to changes in different seasons and different stages of breeding, and it has autonomous learning capabilities; through precise monitoring and prediction, it reduces drug use and maintains the ecological balance of the farm. Attached Figure Description

[0017] Figure 1 This is a flowchart of an AI-based intelligent inspection method provided in one embodiment of the present invention; Figure 2This is a schematic block diagram of an AI-based intelligent inspection system provided in one embodiment of the present invention. Detailed Implementation

[0018] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] The following reference Figures 1 to 2 This invention describes an AI-based intelligent inspection method and system provided by some embodiments of the present invention.

[0023] like Figure 1 As shown, one embodiment of the present invention provides an AI-based intelligent inspection method, including: By simultaneously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm, a multidimensional fusion dataset containing spatiotemporal coordinates is formed. It is understood that the multimodal sensor network includes infrared thermal imaging sensors, ultrasonic sensors, gas concentration sensors, voiceprint recognition sensors, and vibration sensors.

[0024] A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. It is understood that the real-time data includes: real-time monitoring values ​​of each multimodal sensor obtained from the multimodal sensor network, and individual poultry health index, abnormal group behavior measurement value, environmental comfort index and equipment operating status parameters calculated from the digital twin model.

[0025] Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system, which improves the accuracy and efficiency of subsequent inspections.

[0026] In this embodiment of the invention, multimodal data fusion and digital twin technology are combined to improve the accuracy of anomaly detection; an AI multi-task learning model enables early warning of diseases and equipment failures, significantly reducing losses; an adaptive path optimization algorithm improves inspection efficiency, reducing labor costs and poultry stress; a closed-loop feedback mechanism enables the system to adapt to changes in different seasons and different breeding stages, and has autonomous learning capabilities; through precise monitoring and prediction, drug use is reduced, and the ecological balance of the farm is maintained.

[0027] In some possible embodiments of the present invention, the step of constructing a digital twin model of the farm based on the multi-dimensional fused dataset, updating the environmental state, individual poultry health status, and group behavior patterns in the digital twin model in real time through a deep neural network, and establishing a virtual-real mapping relationship includes: Based on the poultry identification information in the multi-dimensional fusion dataset, a unique identifier is assigned to each poultry individual, and a four-dimensional association matrix framework including individual identification dimension, spatial location dimension, physiological parameter dimension and behavioral feature dimension is established. Understandably, the individual identification dimension records poultry identity information, the spatial location dimension stores real-time coordinate data, the physiological parameter dimension includes health indicators such as body temperature, heart rate, and weight, and the behavioral characteristic dimension covers behavioral data such as activity patterns, feeding behavior, and interactions between individuals.

[0028] Based on the actual physical layout of the farm, a three-dimensional virtual scene model is constructed, including building structure, equipment and facilities, and environmental areas. In the virtual scene, a corresponding digital avatar is created for each individual poultry. The digital avatar carries the individual information in the four-dimensional correlation matrix, realizing a one-to-one mapping relationship between physical poultry and virtual objects. A multi-layer deep neural network is constructed specifically for updating digital twin models. The multi-layer deep neural network receives real-time input from a multi-dimensional fusion dataset. Through three core modules—feature extraction layer, state prediction layer, and parameter update layer—it automatically identifies and analyzes changes in the environment, individual health status, and group behavior patterns, providing intelligent support for the real-time updating of the four-dimensional correlation matrix. A dual update mechanism based on event triggering and timed polling is established. When a significant change in the state of poultry is detected in the physical world, the corresponding update of the digital twin model is immediately triggered. At the same time, the four-dimensional correlation matrix is ​​fully refreshed at preset time intervals to ensure a high degree of synchronization and consistency between the virtual model and physical reality. By comparing measured data from physical sensors with predicted data from digital twin models, the accuracy and deviation of the virtual-real mapping are calculated. A feedback adjustment mechanism is used to continuously optimize the model parameters of the deep neural network and the data structure of the four-dimensional correlation matrix, thereby improving the digital twin model's ability to represent the real world and its prediction accuracy.

[0029] In this embodiment, the combination of a four-dimensional correlation matrix and a deep neural network achieves consistency between the physical poultry and their digital states, significantly improving the fidelity of the digital twin. A dual update mechanism ensures that the latency between the virtual model and the real world is controlled within seconds, achieving near real-time state synchronization. The four-dimensional correlation matrix organically unifies individual identity, spatial location, physiological state, and behavioral characteristics, forming a complete digital poultry file. A feedback-based model optimization mechanism enables the digital twin system to possess autonomous learning and continuous improvement capabilities, adapting to different breeding environments and management models. The high-quality digital twin model provides a reliable data foundation and simulation platform for subsequent intelligent prediction and decision analysis, supporting precision breeding management.

[0030] In some possible embodiments of the present invention, the step of inputting the real-time data output by the digital twin model into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model employs an attention mechanism to weight the importance of different data sources, includes: Receive real-time data output by the digital twin model, including poultry individual health index, abnormal group behavior measurement value, environmental comfort index and equipment operation status parameters in the four-dimensional correlation matrix, perform standardization processing and feature vectorization transformation on various types of data, and generate a multi-dimensional feature tensor in a unified format as the standardized input of the multi-task learning AI model; The pre-trained attention weight allocation module automatically calculates the importance weight of each data source in the current prediction task based on the real-time status and historical patterns of the current farm. The weight allocation covers four main data sources: individual health data, group behavior data, environmental monitoring data, and equipment status data, realizing dynamic adjustment and intelligent allocation of the importance of data sources.

[0031] In this embodiment, the attention mechanism automatically adjusts the data source weights according to the real-time situation, enabling the model to adapt to environmental changes in different seasons and different breeding stages, thereby improving the robustness of predictions.

[0032] In some possible embodiments of the present invention, the step of the multi-task learning AI model simultaneously performing three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputting a comprehensive risk assessment report including confidence level and time window, includes: Real-time data that has undergone attention weight modulation by the attention weight allocation module is feature-separated according to task relevance, and three independent data processing channels are constructed: a dedicated channel for disease early warning, a dedicated channel for environmental risk assessment, and a dedicated channel for equipment failure prediction. Understandably, the disease early warning channel mainly processes individual poultry health indicators and abnormal group behavior data, the environmental risk assessment channel focuses on analyzing environmental parameters such as temperature, humidity, and gas concentration, and the equipment failure prediction channel specifically monitors the operating status data of equipment such as feeding, ventilation, and lighting. Three data processing channels simultaneously initiate parallel computing processing. The disease early warning channel uses a deep learning network to identify signs of poultry diseases and determine the probability of disease onset. The environmental risk assessment channel analyzes the degree and trend of environmental parameters deviating from the normal range. The equipment failure prediction channel detects equipment performance degradation and early signs of failure. Each channel independently outputs the preliminary risk assessment results and anomaly severity rating for its corresponding field. Based on the historical prediction accuracy, current data quality, and model convergence status of each data processing channel, a corresponding confidence score is calculated for each preliminary risk assessment result. The confidence score reflects the reliability of the prediction result. At the same time, the stability and fluctuation range of the prediction result are evaluated through an uncertainty quantification algorithm to provide a credibility reference for subsequent decision-making. Based on the risk types and severity identified by each data processing channel, and combined with the development patterns of similar situations in historical data, the time window and development trend of potential risk events are predicted. The time window includes three key time nodes: the earliest possible time, the latest time that must be dealt with, and the best time for intervention, providing time-dimensional guidance information for inspection decisions. By integrating the prediction results, confidence assessment, and time window analysis from three data processing channels, a structured comprehensive risk assessment report is generated, which includes risk event type, risk level, confidence score, prediction time window, impact range assessment, and recommended response measures. The report organizes information in a standardized format to ensure that the subsequent inspection path optimization algorithm can accurately parse and use the assessment results.

[0033] In this embodiment, the three-channel parallel processing architecture shortens task completion time compared to serial execution, achieving true real-time multi-task analysis capabilities. Through confidence calculation and uncertainty quantification, it provides reliability assessments for each prediction result, improving prediction confidence accuracy and enhancing the scientific rigor of decision-making. The time window prediction function accurately estimates the timing of risk events, significantly improving the timeliness of emergency response. The comprehensive risk assessment report covers multi-dimensional information such as risk identification, severity, credibility, time prediction, and impact assessment, providing comprehensive support for management decisions. The structured report format ensures seamless integration with subsequent system modules, improving the overall system's integration and automation level.

[0034] In some possible embodiments of the present invention, the step of dynamically generating the optimal inspection path based on the comprehensive risk assessment report using an ant colony algorithm combined with a real-time risk heat map, and adjusting the inspection frequency and key areas in real time, wherein the optimal inspection path comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response, including: Based on the risk event types, risk levels, and impact range information in the comprehensive risk assessment report, a real-time risk heat map is constructed on the farm's plan map. Different risk levels are assigned different heat values ​​and color depths. At the same time, combined with the distribution density of poultry individuals and the division of activity areas, a spatial mapping relationship between farm areas and risk levels is established, providing a visual risk distribution basis for subsequent route planning. Based on the current operational status and management priorities of the farm, corresponding weight coefficients and constraints are set for four optimization objectives: risk level, geographical distance, inspection cost, and minimization of poultry stress response. Among them, the risk level objective requires priority access to high-risk areas, the geographical distance objective aims to minimize the total path length, the inspection cost objective controls manpower and time consumption, and the stress response minimization objective reduces interference with the normal behavior of poultry. A parameter configuration framework for multi-objective optimization is established. Based on the real-time risk heat map, the ant colony algorithm is initialized. Multiple virtual ants are deployed at the entrance of the farm as path search agents. Each virtual ant carries four target optimization parameters and current risk heat map information. Through the pheromone initialization mechanism, a higher pheromone concentration is preset in high-risk areas to guide ants to prioritize the exploration of dangerous areas that need to be inspected. The virtual ant's path search process is initiated. Each ant calculates the optimal direction for its next move based on the risk heat value of its current location, the distance cost to reach each candidate area, the estimated inspection time, and the stress intensity of poultry. Through iterative search and pheromone update mechanisms, the process gradually converges to generate a set of candidate inspection paths that take into account the four optimization objectives. The path with the highest comprehensive score is then selected as the current optimal inspection path. Based on the changes in the optimal inspection path and the real-time risk heat map, the inspection frequency and dwell time of each area are dynamically adjusted. For high-risk areas, the inspection frequency is increased and the inspection time is extended, while for low-risk areas, the inspection frequency is appropriately reduced. At the same time, a real-time path update mechanism is established. When a new high-risk event is detected or the original risk status changes significantly, the inspection path is immediately recalculated and adjusted to ensure that the inspection strategy is dynamically matched with the actual risk distribution.

[0035] In this embodiment, the ant colony algorithm with four-objective comprehensive optimization improves overall inspection efficiency compared to traditional single-objective path planning, while also taking into account risk coverage and cost control. The combination of real-time risk heat map and dynamic path adjustment mechanism shortens the response time to sudden high-risk events, significantly improving emergency response capabilities. By introducing the stress response minimization objective, the interference of inspection activities on the normal behavior of poultry is effectively reduced, the intensity of stress response is lowered, and the stability of the breeding environment is maintained. The dynamic inspection frequency adjustment mechanism realizes the rational allocation of human resources, with high-risk areas receiving key attention and low-risk areas avoiding over-inspection, thus improving the overall resource utilization efficiency. The real-time update mechanism enables the inspection path to quickly adapt to the dynamic changes in the risk status of the farm, with a path adjustment response speed of minutes, maintaining a high degree of matching between the inspection strategy and actual needs.

[0036] In some possible embodiments of the present invention, the step of feeding back new data and processing results obtained during the inspection process to update the parameters of the digital twin model and the multi-task learning AI model, continuously optimizing the inspection strategy through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system, and improving the accuracy and efficiency of subsequent inspections includes: During the inspection route execution, the behavior trajectory data of the inspectors, the actual findings at each detection point, the implementation status of the disposal measures and the feedback on the disposal effect are collected in real time. At the same time, the stress response of poultry, changes in environmental conditions and equipment response are recorded during the inspection. The predicted results before the inspection are compared and analyzed with the actual problems found, and a comprehensive execution effect evaluation report including prediction accuracy, response timeliness and disposal effectiveness is generated. The feedback data in the comprehensive performance evaluation report are classified and organized. Data on virtual-real mapping deviation, poultry status change, and environmental parameter correction related to the digital twin model are classified into the twin model update dataset. Data on prediction error cases, newly discovered abnormal patterns, and risk assessment deviation related to the multi-task learning AI model are classified into the AI ​​model training dataset, providing standardized data input for subsequent model parameter updates and optimization. Establish a reinforcement learning reward mechanism based on inspection results, setting positive rewards for successful prediction and timely detection of risk events, and negative rewards for missed detections, false alarms, and resource waste. Quantitatively evaluate the merits of the current inspection strategy through a reward function. At the same time, construct a strategy value evaluation system to comprehensively score the rationality of path selection decisions, frequency adjustment decisions, and resource allocation decisions, providing clear optimization directions and objective functions for reinforcement learning algorithms. Based on the categorized feedback data, an incremental learning method is used to update the parameters of the digital twin model and the multi-task learning AI model. The digital twin model focuses on updating the mapping relationship of the four-dimensional correlation matrix and the accuracy of the virtual-real synchronization mechanism. The multi-task learning AI model focuses on adjusting the weight parameters of the three data processing channels (task branches) and the allocation strategy of the attention mechanism. Through knowledge fusion technology, the newly learned experience is organically combined with historical knowledge to avoid catastrophic forgetting and improve the generalization ability of the model. By employing reinforcement learning algorithms based on reward mechanisms and policy evaluation results, the system continuously optimizes inspection path generation strategies, risk prediction strategies, and resource scheduling strategies. Through policy gradient updates and experience replay mechanisms, the system can learn and improve from each inspection experience, gradually enhancing prediction accuracy, path optimization effectiveness, and emergency response capabilities. This forms a self-learning, self-optimizing, and self-evolving intelligent closed-loop system, achieving continuous improvement in inspection performance and enhanced system adaptability.

[0037] In this embodiment, through continuous learning from feedback data, the accuracy of the virtual-to-real mapping of the digital twin model and the accuracy of the AI ​​prediction model are improved; the reinforcement learning mechanism enables the system to automatically adapt to changes in different seasons, different breeding stages, and different management modes, improving environmental adaptability and reducing the need for manual parameter adjustment; through deep learning of historical misjudgment cases, the system's ability to identify new abnormal patterns is continuously enhanced, reducing the prediction miss rate; the strategy optimization iteration mechanism realizes intelligent scheduling and precise allocation of inspection resources, reducing ineffective inspections and improving the coverage of key risks; the closed-loop learning mechanism enables the system to have fault self-healing capabilities. When some sensors fail or environmental conditions change, the system can quickly adjust its strategy and maintain stable operation, improving system availability.

[0038] In some possible embodiments of the present invention, the step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fusion dataset containing spatiotemporal coordinates includes: Multimodal sensor nodes are deployed in the breeding farm according to the grid layout principle. Each sensor node is configured with a unique spatial coordinate identifier and device ID. The time reference of all sensor nodes is unified through a wireless clock synchronization protocol to ensure the consistency of data collection time. Each sensor node synchronously acquires three types of basic data according to a preset acquisition frequency: the environmental data includes temperature, humidity, light intensity, and air quality parameters; the poultry behavior data includes individual location, movement trajectory, voiceprint characteristics, and body temperature distribution; and the equipment operation data includes feeding system status, ventilation system parameters, and lighting system operating conditions. Each type of data carries an acquisition timestamp and sensor location coordinates. The collected raw data undergoes noise filtering, outlier detection, and missing value compensation. Invalid data is removed through data integrity verification and sensor fault diagnosis algorithms to ensure that the data entering the fusion process meets the requirements of subsequent analysis. Add three-dimensional spatiotemporal coordinate markers to each valid data record, including two-dimensional spatial coordinates (X coordinates, Y coordinates) and one-dimensional time coordinates (time stamps), to establish a precise mapping relationship between data and physical spatial location and time node, forming structured data with spatial positioning capabilities; Environmental data, poultry behavior data, and equipment operation data that have been linked and tagged with spatiotemporal coordinates are encapsulated and integrated in a unified data format to generate a standardized multi-dimensional fusion dataset containing data type identifiers, spatiotemporal coordinates, numerical content, and quality levels for subsequent digital twin modeling.

[0039] In this embodiment, data acquisition achieves millisecond-level time accuracy and centimeter-level spatial accuracy through gridded deployment and clock synchronization, thus improving accuracy. Multi-level data preprocessing and quality inspection mechanisms ensure data validity, providing a reliable foundation for subsequent AI analysis. Three-dimensional spatiotemporal coordinate correlation enables precise recording and traceability of the status at any location and time within the farm. A unified data format and encapsulation standard are established to solve the integration challenges of multi-source heterogeneous data, improving system compatibility and scalability. Optimized data acquisition and preprocessing processes achieve second-level data updates, meeting the needs of real-time monitoring and rapid response.

[0040] In some possible embodiments of the present invention, in the step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fused dataset containing spatiotemporal coordinates, the multidimensional data fusion adopts an adaptive weight fusion algorithm, specifically calculating the dynamic fusion weight of each sensor data using the following formula:

[0041] in, The dynamic fusion weights for the m-th sensor; Let be the reliability impact factor of the m-th sensor, with a value range of [0.1, 2.0]. Let be the real-time reliability index of the m-th sensor, calculated based on historical accuracy and current signal-to-noise ratio; Let be the coverage influence factor of the m-th sensor, with a value range of [0.1, 1.5]. Let m be the spatial coverage efficiency index of the m-th sensor; This represents the total number of sensors; This is the type characteristic correction coefficient for the m-th sensor.

[0042] This embodiment realizes intelligent weighted fusion of sensor data, automatically adjusts the contribution of each sensor under different environmental conditions, and improves the accuracy and robustness of data fusion.

[0043] In some possible embodiments of the present invention, the multi-target ant colony algorithm, which dynamically generates the optimal inspection path based on the comprehensive risk assessment report using an ant colony algorithm combined with a real-time risk heatmap, updates the state transition probability of ants between nodes using the following formula:

[0044] in, Let be the probability that an ant moves from node u to node v; Let be the pheromone concentration on edge (u,v); Let the visibility heuristic factor from node u to v be defined as follows: ; Let be the Euclidean distance from node u to v; The risk attraction factor for node v increases with higher risk. , where is the avian stress response intensity factor at node v; Let u be the set of allowed nodes that can be reached from node u. , , , These are the relative importance parameters for pheromones, visibility, risk attraction, and stress avoidance, respectively.

[0045] This embodiment innovatively introduces risk attraction and stress avoidance factors to achieve a balance between efficiency and animal welfare in the inspection path, which is more adaptable than the traditional ant colony algorithm.

[0046] In some possible embodiments of the present invention, the digital twin model employs a poultry health status assessment algorithm based on spatiotemporal correlation, specifically calculating the comprehensive health assessment index of individual poultry using the following formula:

[0047] in, The comprehensive health assessment index of the p-th bird, with a value range of [-1, 1]; The total number of health indicators (including body temperature, activity level, food intake, vocalization frequency, etc.); Let the importance weight of the q-th health indicator satisfy... ; Let q be the current measurement value of the q-th health indicator; Let q be the normal mean of the q-th health indicator; Let q be the normal standard deviation of the q-th health indicator; This is a time decay function used to handle the timeliness of indicators; This is the current timestamp; Let q be the last update time of the q-th indicator; Let q be the time decay constant of the q-th index.

[0048] This embodiment uses the hyperbolic tangent function to achieve a nonlinear mapping of the degree of abnormality, and combines the time decay function to consider the timeliness of the data, thereby achieving a precise quantitative assessment of the health status of poultry.

[0049] Please refer to Figure 2 Another embodiment of the present invention provides an AI-based intelligent inspection system for performing an AI-based intelligent inspection method, comprising: a multimodal sensor network and a server deployed in different areas of the farm; The server is configured as follows: The system acquires environmental data, poultry behavior data, and equipment operation data synchronously collected by the multimodal sensor network, and forms a multi-dimensional fusion dataset containing spatiotemporal coordinates. A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system, which improves the accuracy and efficiency of subsequent inspections.

[0050] It should be known that, Figure 2 The block diagram of the AI-based intelligent inspection system shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of this invention. The AI-based intelligent inspection system provided in this embodiment can be used to execute various embodiments of the corresponding AI-based intelligent inspection methods. For specific implementation details, please refer to the descriptions of the respective method embodiments, which will not be repeated here.

[0051] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0052] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0054] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0056] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0057] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0058] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0059] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. An AI-based intelligent inspection method, characterized in that, include: By simultaneously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm, a multidimensional fusion dataset containing spatiotemporal coordinates is formed. A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system.

2. The AI-based intelligent inspection method according to claim 1, characterized in that, The steps of constructing a digital twin model of the farm based on the multi-dimensional fused dataset, and updating the environmental state, individual poultry health status, and group behavior patterns in the digital twin model in real time through a deep neural network to establish a virtual-real mapping relationship include: Based on the poultry identification information in the multi-dimensional fusion dataset, a unique identifier is assigned to each poultry individual, and a four-dimensional association matrix framework including individual identification dimension, spatial location dimension, physiological parameter dimension and behavioral feature dimension is established. Based on the actual physical layout of the farm, a three-dimensional virtual scene model is constructed, including building structure, equipment and facilities, and environmental areas. In the virtual scene, a corresponding digital avatar is created for each individual poultry. The digital avatar carries the individual information in the four-dimensional correlation matrix, realizing a one-to-one mapping relationship between physical poultry and virtual objects. A multi-layer deep neural network is constructed specifically for updating digital twin models. The multi-layer deep neural network receives real-time input from a multi-dimensional fusion dataset. Through three core modules—feature extraction layer, state prediction layer, and parameter update layer—it automatically identifies and analyzes changes in the environment, individual health status, and group behavior patterns, providing intelligent support for the real-time updating of the four-dimensional correlation matrix. A dual update mechanism based on event triggering and timed polling is established. When a significant change in the state of poultry is detected in the physical world, the corresponding update of the digital twin model is immediately triggered. At the same time, the four-dimensional correlation matrix is ​​fully refreshed at preset time intervals to ensure a high degree of synchronization and consistency between the virtual model and physical reality. By comparing measured data from physical sensors with predicted data from digital twin models, the accuracy and deviation of the virtual-real mapping are calculated. A feedback adjustment mechanism is used to continuously optimize the model parameters of the deep neural network and the data structure of the four-dimensional correlation matrix, thereby improving the digital twin model's ability to represent the real world and its prediction accuracy.

3. The AI-based intelligent inspection method according to claim 2, characterized in that, The step of inputting the real-time data output by the digital twin model into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to different data sources, includes: Receive real-time data output by the digital twin model, including poultry individual health index, abnormal group behavior measurement value, environmental comfort index and equipment operation status parameters in the four-dimensional correlation matrix, perform standardization processing and feature vectorization transformation on various types of data, and generate a multi-dimensional feature tensor in a unified format as the standardized input of the multi-task learning AI model; The pre-trained attention weight allocation module automatically calculates the importance weight of each data source in the current prediction task based on the real-time status and historical patterns of the current farm. The weight allocation covers four main data sources: individual health data, group behavior data, environmental monitoring data, and equipment status data, realizing dynamic adjustment and intelligent allocation of the importance of data sources.

4. The AI-based intelligent inspection method according to claim 3, characterized in that, The steps of the multi-task learning AI model, which simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window, include: Real-time data that has undergone attention weight modulation by the attention weight allocation module is feature-separated according to task relevance, and three independent data processing channels are constructed: a dedicated channel for disease early warning, a dedicated channel for environmental risk assessment, and a dedicated channel for equipment failure prediction. Three data processing channels simultaneously initiate parallel computing processing. The disease early warning channel uses a deep learning network to identify signs of poultry diseases and determine the probability of disease onset. The environmental risk assessment channel analyzes the degree and trend of environmental parameters deviating from the normal range. The equipment failure prediction channel detects equipment performance degradation and early signs of failure. Each channel independently outputs the preliminary risk assessment results and anomaly severity rating for its corresponding field. Based on the historical prediction accuracy, current data quality, and model convergence status of each data processing channel, a corresponding confidence score is calculated for each preliminary risk assessment result. The confidence score reflects the reliability of the prediction result. At the same time, the stability and fluctuation range of the prediction result are evaluated through an uncertainty quantification algorithm to provide a credibility reference for subsequent decision-making. Based on the risk types and severity identified by each data processing channel, and combined with the development patterns of similar situations in historical data, the time window and development trend of potential risk events are predicted. The time window includes three key time nodes: the earliest possible time, the latest time that must be dealt with, and the best time for intervention, providing time-dimensional guidance information for inspection decisions. By integrating the prediction results, confidence assessment, and time window analysis from three data processing channels, a structured comprehensive risk assessment report is generated, which includes risk event type, risk level, confidence score, prediction time window, impact range assessment, and recommended response measures. The report organizes information in a standardized format to ensure that the subsequent inspection path optimization algorithm can accurately parse and use the assessment results.

5. The AI-based intelligent inspection method according to claim 4, characterized in that, The steps described above involve dynamically generating the optimal inspection route using an ant colony algorithm combined with a real-time risk heatmap based on the comprehensive risk assessment report, and adjusting the inspection frequency and key areas in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. Based on the risk event types, risk levels, and impact range information in the comprehensive risk assessment report, a real-time risk heat map is constructed on the farm's plan map. Different risk levels are assigned different heat values ​​and color depths. At the same time, combined with the distribution density of poultry individuals and the division of activity areas, a spatial mapping relationship between farm areas and risk levels is established, providing a visual risk distribution basis for subsequent route planning. Based on the current operational status and management priorities of the farm, corresponding weight coefficients and constraints are set for four optimization objectives: risk level, geographical distance, inspection cost, and minimization of poultry stress response. Among them, the risk level objective requires priority access to high-risk areas, the geographical distance objective aims to minimize the total path length, the inspection cost objective controls manpower and time consumption, and the stress response minimization objective reduces interference with the normal behavior of poultry. A parameter configuration framework for multi-objective optimization is established. Based on the real-time risk heat map, the ant colony algorithm is initialized. Multiple virtual ants are deployed at the entrance of the farm as path search agents. Each virtual ant carries four target optimization parameters and current risk heat map information. Through the pheromone initialization mechanism, a higher pheromone concentration is preset in high-risk areas to guide ants to prioritize the exploration of dangerous areas that need to be inspected. The virtual ant's path search process is initiated. Each ant calculates the optimal direction for its next move based on the risk heat value of its current location, the distance cost to reach each candidate area, the estimated inspection time, and the stress intensity of poultry. Through iterative search and pheromone update mechanisms, the process gradually converges to generate a set of candidate inspection paths that take into account the four optimization objectives. The path with the highest comprehensive score is then selected as the current optimal inspection path. Based on the changes in the optimal inspection path and the real-time risk heat map, the inspection frequency and dwell time of each area are dynamically adjusted. For high-risk areas, the inspection frequency is increased and the inspection time is extended, while for low-risk areas, the inspection frequency is appropriately reduced. At the same time, a real-time path update mechanism is established. When a new high-risk event is detected or the original risk status changes significantly, the inspection path is immediately recalculated and adjusted to ensure that the inspection strategy is dynamically matched with the actual risk distribution.

6. The AI-based intelligent inspection method according to claim 5, characterized in that, The steps of updating the parameters of the digital twin model and the multi-task learning AI model by feeding back new data and processing results obtained during the inspection process, and continuously optimizing the inspection strategy through reinforcement learning algorithms to form a self-learning and self-optimizing closed-loop system include: During the inspection route execution, the behavior trajectory data of the inspectors, the actual findings at each detection point, the implementation status of the disposal measures and the feedback on the disposal effect are collected in real time. At the same time, the stress response of poultry, changes in environmental conditions and equipment response are recorded during the inspection. The predicted results before the inspection are compared and analyzed with the actual problems found, and a comprehensive execution effect evaluation report including prediction accuracy, response timeliness and disposal effectiveness is generated. The feedback data in the comprehensive performance evaluation report are classified and organized. Data on virtual-real mapping deviation, poultry status change, and environmental parameter correction related to the digital twin model are classified into the twin model update dataset. Data on prediction error cases, newly discovered abnormal patterns, and risk assessment deviation related to the multi-task learning AI model are classified into the AI ​​model training dataset, providing standardized data input for subsequent model parameter updates and optimization. Establish a reinforcement learning reward mechanism based on inspection results, setting positive rewards for successful prediction and timely detection of risk events, and negative rewards for missed detections, false alarms, and resource waste. Quantitatively evaluate the merits of the current inspection strategy through a reward function. At the same time, construct a strategy value evaluation system to comprehensively score the rationality of path selection decisions, frequency adjustment decisions, and resource allocation decisions, providing clear optimization directions and objective functions for reinforcement learning algorithms. Based on the categorized feedback data, an incremental learning method is used to update the parameters of the digital twin model and the multi-task learning AI model. The digital twin model focuses on updating the mapping relationship of the four-dimensional correlation matrix and the accuracy of the virtual-real synchronization mechanism. The multi-task learning AI model focuses on adjusting the weight parameters of the three data processing channels and the allocation strategy of the attention mechanism. Through knowledge fusion technology, the newly learned experience is organically combined with historical knowledge to avoid catastrophic forgetting and improve the generalization ability of the model. By employing reinforcement learning algorithms based on reward mechanisms and policy evaluation results, the system continuously optimizes inspection path generation strategies, risk prediction strategies, and resource scheduling strategies. Through policy gradient updates and experience replay mechanisms, the system can learn and improve from each inspection experience, gradually enhancing prediction accuracy, path optimization effectiveness, and emergency response capabilities. This forms a self-learning, self-optimizing, and self-evolving intelligent closed-loop system, achieving continuous improvement in inspection performance and enhanced system adaptability.

7. The AI-based intelligent inspection method according to claim 6, characterized in that, The step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fusion dataset containing spatiotemporal coordinates includes: Multimodal sensor nodes are deployed in the breeding farm according to the grid layout principle. Each sensor node is configured with a unique spatial coordinate identifier and device ID. The time reference of all sensor nodes is unified through a wireless clock synchronization protocol to ensure the consistency of data collection time. Each sensor node synchronously acquires three types of basic data according to a preset acquisition frequency: the environmental data includes temperature, humidity, light intensity, and air quality parameters; the poultry behavior data includes individual location, movement trajectory, voiceprint characteristics, and body temperature distribution; and the equipment operation data includes feeding system status, ventilation system parameters, and lighting system operating conditions. Each type of data carries an acquisition timestamp and sensor location coordinates. The collected raw data undergoes noise filtering, outlier detection, and missing value compensation. Invalid data is removed through data integrity verification and sensor fault diagnosis algorithms to ensure that the data entering the fusion process meets the requirements of subsequent analysis. Add three-dimensional spatiotemporal coordinate markers to each valid data record, including two-dimensional spatial coordinates and one-dimensional time coordinates, to establish a precise mapping relationship between the data and physical spatial location and time node, forming structured data with spatial positioning capabilities; Environmental data, poultry behavior data, and equipment operation data that have been linked and tagged with spatiotemporal coordinates are encapsulated and integrated in a unified data format to generate a standardized multi-dimensional fusion dataset containing data type identifiers, spatiotemporal coordinates, numerical content, and quality levels for subsequent digital twin modeling.

8. The AI-based intelligent inspection method according to claim 7, characterized in that, In the step of synchronously collecting environmental data, poultry behavior data, and equipment operation data through a multimodal sensor network deployed in different areas of the farm to form a multidimensional fused dataset containing spatiotemporal coordinates, the multidimensional data fusion adopts an adaptive weight fusion algorithm, specifically calculating the dynamic fusion weight of each sensor data using the following formula: in, The dynamic fusion weights for the m-th sensor; Let be the reliability impact factor of the m-th sensor, with a value range of [0.1, 2.0]. Let be the real-time reliability index of the m-th sensor, calculated based on historical accuracy and current signal-to-noise ratio; Let be the coverage influence factor of the m-th sensor, with a value range of [0.1, 1.5]. Let m be the spatial coverage efficiency index of the m-th sensor; This represents the total number of sensors; This is the type characteristic correction coefficient for the m-th sensor.

9. The AI-based intelligent inspection method according to claim 8, characterized in that, Based on the comprehensive risk assessment report, the multi-target ant colony algorithm, which dynamically generates the optimal inspection path using an ant colony algorithm combined with a real-time risk heatmap, updates the state transition probability of ants between nodes using the following formula: in, Let be the probability that an ant moves from node u to node v; Let be the pheromone concentration on edge (u,v); Let the visibility heuristic factor from node u to v be defined as follows: ; Let be the Euclidean distance from node u to v; The risk attraction factor for node v increases with higher risk. , where is the avian stress response intensity factor at node v; Let u be the set of allowed nodes that can be reached from node u. , , , These are the relative importance parameters for pheromones, visibility, risk attraction, and stress avoidance, respectively.

10. An AI-based intelligent inspection system for executing the AI-based intelligent inspection method as described in any one of claims 1 to 9, characterized in that, include: A multimodal sensor network and servers deployed in different areas of the farm; The server is configured as follows: The system acquires environmental data, poultry behavior data, and equipment operation data synchronously collected by the multimodal sensor network, and forms a multi-dimensional fusion dataset containing spatiotemporal coordinates. A digital twin model of the farm is constructed based on the multi-dimensional fusion dataset. The environmental state, individual poultry health status, and group behavior patterns in the digital twin model are updated in real time through a deep neural network to establish a virtual-real mapping relationship. The digital twin model includes a four-dimensional correlation matrix of individual poultry identification, spatial location, physiological parameters, and behavioral characteristics. The real-time data output by the digital twin model is input into a pre-trained multi-task learning AI model, wherein the multi-task learning AI model uses an attention mechanism to assign weights to the importance of different data sources. The multi-task learning AI model simultaneously performs three tasks—disease early warning, environmental risk assessment, and equipment failure prediction—based on the real-time data, and outputs a comprehensive risk assessment report including confidence level and time window. Based on the comprehensive risk assessment report, the optimal inspection route is dynamically generated using the ant colony algorithm combined with the real-time risk heat map, and the inspection frequency and key areas are adjusted in real time. The optimal inspection route comprehensively considers four optimization objectives: risk level, geographical distance, inspection cost, and minimizing poultry stress response. The new data and processing results obtained during the inspection process are fed back to update the parameters of the digital twin model and the multi-task learning AI model. The inspection strategy is continuously optimized through reinforcement learning algorithms, forming a self-learning and self-optimizing closed-loop system.

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