A method and system for detecting pigsty defects

Abstract

The present application provides a method and system for detecting pigsty defects. By determining the monitoring areas of each sensor node in the pigsty frame structure, using intelligent ultrasonic sensors to monitor the ultrasonic signals in each monitoring area, screening out the damaged areas in the pigsty frame structure from all the monitoring areas, and determining the structural abnormal state of the pigsty frame in the damaged areas through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged areas. Extract all local damaged features of the pigsty frame structure, map and associate the structural abnormal state and each local damaged feature to obtain the defect significance of the pigsty frame structure. When the defect significance is greater than a preset defect threshold, it is determined that the pigsty frame structure has defects, and the damaged area is used as the defect position in the pigsty frame structure. Based on the above solution, dynamic abnormal detection of ultrasonic signal echoes in the pigsty can be realized, thereby improving the detection accuracy of pigsty structure defects.

Description

Technical Field

[0001] This application relates to the technical field of defect detection. More specifically, this application relates to a method and system for detecting pigsty defects. Background Art

[0002] Defect detection is a technology used to identify and evaluate defects in objects. A pigsty is a basic facility that restricts pigs to move within a specific range. With the development of intelligent sensor technology, intelligent sensors can be used to detect problems such as cracks, water leakage, temperature and humidity changes in pigsties, so as to achieve early detection and prevention of potential risks and provide scientific management and maintenance plans for pig farms.

[0003] Traditional manual inspection methods or simple surface scans can only detect visible defects or significant damages, and there are limitations in detecting internal structures, connection parts or hidden areas. Since the structure of a pigsty contains various materials and complex components, it is impossible to fully evaluate the dynamic performance of the pigsty during use, and it is difficult to monitor the overall condition of the pigsty in real time and comprehensively. Therefore, the detection technology is required to have strong penetration and multi-angle detection capabilities. The dynamic anomaly detection technology using ultrasonic signal echoes can deeply detect the internal structure of the pigsty, capture tiny cracks, cavities or material aging problems, provide more comprehensive detection information, and help to timely discover potential safety hazards. Therefore, how to achieve the dynamic anomaly detection of ultrasonic signal echoes in the pigsty so as to improve the detection accuracy of pigsty structure defects is a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a method and system for detecting pigsty defects, which can realize the dynamic anomaly detection of ultrasonic signal echoes in the pigsty, thereby improving the detection accuracy of pigsty structure defects.

[0005] In the first aspect, this application provides a method for detecting pigsty defects, including:

[0006] Collect the frame structure information of the pigsty, determine all sensor nodes in the pigsty based on the frame structure information, and then determine the monitoring areas of each sensor node in the pigsty frame structure;

[0007] Set intelligent ultrasonic sensors in each monitoring area, and then use the intelligent ultrasonic sensors to monitor the ultrasonic signals in each monitoring area, screen out the damaged areas in the pigsty frame structure from all monitoring areas, and determine the structural abnormal state of the pigsty frame in the damaged areas through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged areas;

[0008] Extract all local damaged features of the pigsty frame structure, map and associate the structural abnormal state with each local damaged feature, and then obtain the defect significance of the pigsty frame structure;

[0009] When the defect significance is greater than the preset defect threshold, it is determined that the pigsty frame structure has a defect, and the damaged area is used as the defect position in the pigsty frame structure.

[0010] In some embodiments, determining all sensor nodes in the pigsty according to the frame structure information specifically includes:

[0011] Obtain all key connection positions in the frame structure information;

[0012] Perform clustering assignment on all key connection positions to obtain all sensor nodes in the pigsty.

[0013] In some embodiments, determining the monitoring area of each sensor node in the pigsty frame structure specifically includes:

[0014] For each sensor node, obtain all key connection positions corresponding to the sensor node;

[0015] Determine the monitoring area of the sensor node in the pigsty frame structure through all key connection positions, and then obtain the monitoring area of each sensor node in the pigsty frame structure.

[0016] In some embodiments, screening out the damaged area in the pigsty frame structure from all monitoring areas specifically includes:

[0017] For each monitoring area, filter and denoise the ultrasonic signal of the monitoring area to obtain the denoised signal;

[0018] Extract features from the denoised signal to obtain the fluctuation features of the ultrasonic signal in the monitoring area;

[0019] Perform feature matching between the fluctuation features and the standard echo characteristics to obtain the feature matching value of the monitoring area, and then obtain the feature matching value of each monitoring area;

[0020] The set of all monitoring areas corresponding to the feature matching values lower than the preset matching threshold is the damaged area in the pigsty frame structure.

[0021] In some embodiments, determining the structural abnormal state of the pigsty frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area specifically includes:

[0022] Obtain the position information of each sensor node in the pigsty, and then construct the topological structure relationship of the intelligent ultrasonic sensors in the damaged area through the position information;

[0023] Determine the abnormal echo state in the damaged area according to the echo characteristics of each ultrasonic signal;

[0024] Perform state comparison on the abnormal echo state through the topological structure relationship to obtain all state differences in the damaged area;

[0025] Determine the structural abnormal state of the pigsty frame in the damaged area according to all state differences.

[0026] In some embodiments, extracting all local damaged features of the pigsty frame structure specifically includes:

[0027] For each monitoring area, extract the historical defect records of the monitoring area from the historical defect data of the pigsty frame structure;

[0028] Extract features from the historical defect records to obtain the local damaged features of the monitoring area, and then obtain the local damaged features of each monitoring area.

[0029] In some embodiments, mapping and associating the structural abnormal state and each local damaged feature to obtain the defect significance of the pigsty frame structure specifically includes:

[0030] For each monitoring area, obtain the local damaged features of the monitoring area;

[0031] Perform associative matching on the local damaged feature and the structural abnormal state to obtain the state deviation degree of the defect in the monitoring area, and then obtain the state deviation degree of the defect in each monitoring area;

[0032] Determine the defect significance of the pigsty frame structure through all state deviation degrees.

[0033] In a second aspect, the present application provides a pigsty defect detection system, including:

[0034] An acquisition module, configured to acquire the frame structure information of the pigsty, determine all sensor nodes in the pigsty according to the frame structure information, and then determine the monitoring area of each sensor node in the pigsty frame structure;

[0035] A processing module, configured to set intelligent ultrasonic sensors in each monitoring area, and then use the intelligent ultrasonic sensors to monitor the ultrasonic signals in each monitoring area, screen out the damaged areas in the pigsty frame structure from all monitoring areas, and determine the structural abnormal state of the pigsty frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area;

[0036] The processing module is further configured to extract all local damage features of the pigsty frame structure, map and associate the structural abnormal state and each local damage feature, and then obtain the defect significance of the pigsty frame structure.

[0037] An execution module, configured to determine that the pigsty frame structure has a defect when the defect significance is greater than a preset defect threshold, and use the damaged area as the defect position in the pigsty frame structure.

[0038] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned pigsty defect detection method.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes run on a computer, the computer is enabled to execute the above-mentioned pigsty defect detection method.

[0040] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0041] In a pigsty defect detection method and system provided by the present application, the frame structure information of the pigsty is collected, all sensor nodes in the pigsty are determined according to the frame structure information, and then the monitoring areas of each sensor node in the pigsty frame structure are determined; intelligent ultrasonic sensors are set in each monitoring area, and then the intelligent ultrasonic sensors are used to monitor the ultrasonic signals in each monitoring area, the damaged areas in the pigsty frame structure are screened out from all monitoring areas, and the structural abnormal state of the pigsty frame in the damaged areas is determined through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged areas; all local damage features of the pigsty frame structure are extracted, the structural abnormal state and each local damage feature are mapped and associated, and then the defect significance of the pigsty frame structure is obtained; when the defect significance is greater than a preset defect threshold, it is determined that the pigsty frame structure has a defect, and the damaged area is used as the defect position in the pigsty frame structure.

[0042] It can be seen that in this application, when the defect significance is greater than the preset defect threshold, it is determined that there is a defect in the pigsty frame structure, and the damaged area is used as the defect position in the pigsty frame structure; First, determining the abnormal structure state can obtain the behavior index that measures the difference between the pigsty frame structure and the expected state, and can associate the abnormal signal with the actual structure state to achieve precise detection of the pigsty structure, so that it can be discovered in time when the abnormal structure state occurs, thus giving an early warning and reducing the potential risk of structural damage, ensuring the long-term use safety of the pigsty and the growth environment of the pig herd; Then, determining the defect significance can obtain the index that measures the degree of influence of the defect in the pigsty frame structure on the overall structural stability. When the defect significance exceeds the preset threshold, it can be judged whether there are safety problems in the pigsty structure and maintenance and repair are required. Among them, mapping association helps to quantitatively evaluate the overall defect degree of the pigsty frame structure, can identify and screen out the areas that seriously affect structural safety through data-driven methods, avoid over-detection and maintenance of the entire pigsty, optimize resource allocation, improve detection efficiency, and at the same time helps managers determine the priority of maintenance, ensure key inspections and repairs in the places where they are most needed, so as to effectively extend the service life of the pigsty and reduce maintenance costs; In summary, based on the above solution, dynamic abnormal detection of ultrasonic signal echoes in the pigsty can be realized, thereby improving the detection accuracy of pigsty structure defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 is an exemplary flowchart of a pigsty defect detection method shown in some embodiments of the present application;

[0045] Figure 2 is a logic schematic diagram of an ultrasonic sensor in pigsty defect detection shown in some embodiments of the present application;

[0046] Figure 3 is a schematic flowchart of determining defect significance shown in some embodiments of the present application;

[0047] Figure 4 is a schematic structural diagram of a pigsty defect detection system shown in some embodiments of the present application;

[0048] Figure 5 is a schematic structural diagram of a computer device for implementing the pigsty defect detection method shown in some embodiments of the present application. Detailed implementation manners

[0049] To better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0050] Reference Figure 1 , which is an exemplary flowchart of a pigsty defect detection method shown according to some embodiments of the present application. The pigsty defect detection method mainly includes the following steps:

[0051] In step 101, the frame structure information of the pigsty is collected, all sensor nodes in the pigsty are determined based on the frame structure information, and then the monitoring areas of each sensor node in the pigsty frame structure are determined.

[0052] It should be noted that in the present application, the frame structure information represents a set of structural data in the pigsty frame. The frame structure information includes frame geometric features, the main frame and its key connection positions. Among them, the geometric features reflect the basic composition and boundary range of the structure, the main frame refers to the spatial distribution of the main components in the frame, and the key connection positions refer to the positions where key structural connections are made between each component and node in the frame; specifically, the frame structure information of the pigsty can be collected from the design specification of the pigsty.

[0053] In some embodiments, reference Figure 2 is made to the figure, which is a logic schematic diagram of an ultrasonic sensor in pigsty defect detection shown according to some embodiments of the present application. In pigsty defect detection, the application of the ultrasonic sensor involves the coordinated operation of multiple key components. First, the controller precisely controls the emission frequency and intensity of the ultrasonic wave through a regulator to adapt to different detection requirements. Then, the oscillator generates an ultrasonic signal, and the ultrasonic signal is converted into a sound wave by a transducer and then transmitted into the pigsty structure. When the ultrasonic wave encounters a defect in the pigsty, such as a crack or a cavity, the ultrasonic wave will be reflected back. The receiving detector captures these reflected waves and measures the time difference between the emission and the reception through a timer. This time difference is crucial for calculating the depth and position of the defect.

[0054] In addition, in the transmitting and receiving mechanism of the ultrasonic sensor, the transmitter usually uses piezoelectric ceramic materials, which can generate mechanical vibrations when the voltage changes, thereby emitting ultrasonic waves; the receiver is responsible for converting the reflected ultrasonic waves into electrical signals; the control circuit, as the core part of the sensor, is responsible for controlling the transmitting timing of the transmitter, the receiving state of the receiver, and processing and calculating the received signals; signal processing algorithms, such as noise suppression, echo recognition, and distance calculation, can improve the measurement accuracy. Through this high-precision detection, potential problems in the pigsty can be discovered and repaired in time to ensure the safety and stability of the pigsty. The significant advantages of the ultrasonic sensor, such as non-contact, high precision, and fast response, enable it to work stably in various complex environments without being affected by light, color, or transparency. Therefore, it can be widely used in the detection of objects of various materials and shapes, including the defect detection of pigsties.

[0055] In some embodiments, determining all sensor nodes in the pigsty according to the frame structure information can be implemented by the following steps:

[0056] Obtain all key connection positions in the frame structure information;

[0057] Cluster and allocate all key connection positions to obtain all sensor nodes in the pigsty.

[0058] It should be noted that in this application, a sensor node is a node in the pigsty frame structure for collecting, transmitting, and processing monitoring signals; specifically, first, all key connection positions are obtained from the frame structure information; then, a clustering algorithm is used to group the key connection positions according to spatial distribution and structural similarity, ensuring that the position concentration in the same group can meet the monitoring coverage range of a sensor node. Thus, the center point of the monitoring coverage range of each group can be used as a sensor node, and all sensor nodes can be obtained. Among them, the position concentration represents the density of all key connection positions in the same group, and the mean or standard deviation of the Euclidean distances between all positions in the same group can be used as a quantization index of the concentration, thereby obtaining the position concentration in the same group.

[0059] In some embodiments, determining the monitoring area of each sensor node in the pigsty frame structure can be implemented by the following steps:

[0060] For each sensor node, obtain all key connection positions corresponding to the sensor node;

[0061] Determine the monitoring area of the sensor node in the pigsty frame structure through all key connection positions, and then obtain the monitoring area of each sensor node in the pigsty frame structure.

[0062] It should be noted that in this application, the monitoring area refers to the spatial range monitored by the sensor nodes in the pigsty frame structure; specifically, in implementation, first, for each sensor node, all key connection positions corresponding to the sensor node are obtained; then, the set of areas composed of all key connection positions can be used as the monitoring area of the sensor node in the pigsty frame structure, and the monitoring areas of each sensor node in the pigsty frame structure can be obtained through the above method.

[0063] In step 102, intelligent ultrasonic sensors are set in each monitoring area, and then the ultrasonic signals in each monitoring area are monitored using the intelligent ultrasonic sensors. The damaged areas in the pigsty frame structure are screened out from all the monitoring areas, and the structural abnormal state of the pigsty frame in the damaged area is determined through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area.

[0064] It should be noted that in this application, the intelligent ultrasonic sensor is an ultrasonic sensor with automatic detection, processing, and analysis capabilities; the ultrasonic signal is an acoustic signal transmitted and received by the ultrasonic sensor, and the information contained in the ultrasonic signal in this application can be used to detect the physical state, structural integrity, and potential defects of the monitoring area.

[0065] Specifically, in implementation, for each monitoring area, an intelligent ultrasonic sensor is set at the sensor node in the monitoring area of the pigsty, and the intelligent ultrasonic sensor is used to monitor the ultrasonic signal in the monitoring area of the pigsty at the current moment. The ultrasonic signals of each monitoring area can be obtained through the above method.

[0066] In some embodiments, screening out the damaged areas in the pigsty frame structure from all the monitoring areas can be implemented by the following steps:

[0067] For each monitoring area, the ultrasonic signal in the monitoring area is filtered and denoised to obtain a denoised signal;

[0068] Feature extraction is performed on the denoised signal to obtain the fluctuation characteristics of the ultrasonic signal in the monitoring area;

[0069] The fluctuation characteristics are subjected to feature matching with the standard echo characteristics to obtain the feature matching value of the monitoring area, and then the feature matching values of each monitoring area are obtained;

[0070] The set of all monitoring areas corresponding to the feature matching values lower than the preset matching threshold is the damaged area in the pigsty frame structure.

[0071] It should be noted that in this application, the damaged area represents the range of abnormal positions in the pigsty frame structure; the denoised signal is a clear signal used to improve the signal accuracy; the fluctuation feature is a characteristic parameter that measures the characteristics of the ultrasonic signal when it is reflected back to the sensor during propagation; the feature matching value is an index that quantifies the matching degree between the current fluctuation feature and the standard echo characteristics.

[0072] In specific implementation, first, for each monitoring area, a filtering algorithm (such as Kalman filtering) can be used to filter the ultrasonic signals in the monitoring area to remove environmental noise and system interference, ensuring the purity of the signals, so as to obtain the denoised signals. Secondly, a frequency-domain analysis method (such as Fourier transform) can be used to extract the spatial fluctuations of the denoised signals, and the extracted features are used as the fluctuation features of the ultrasonic signals in the monitoring area. Then, the Euclidean distance between the fluctuation features and the standard echo characteristics can be used as the matching result between the echo in the monitoring area and the standard echo characteristics, and the matching result is used as the feature matching value of the monitoring area. Through the above method, the feature matching values of each monitoring area can be obtained. Finally, the set of all monitoring areas corresponding to the feature matching values lower than the preset matching threshold is the damaged area in the pigsty frame structure.

[0073] In some embodiments, determining the structural abnormal state of the pigsty frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area can be implemented by the following steps:

[0074] Obtain the position information of each sensor node in the pigsty, and then construct the topological structure relationship of the intelligent ultrasonic sensors in the damaged area through the position information;

[0075] Determine the abnormal echo state in the damaged area according to the echo characteristics of each ultrasonic signal;

[0076] Perform state comparison on the abnormal echo state through the topological structure relationship to obtain all state differences in the damaged area;

[0077] Determine the structural abnormal state of the pigsty frame in the damaged area according to all state differences.

[0078] It should be noted that in this application, the structural abnormal state is a behavioral index that measures the difference between the pigsty frame structure and the expected state; the position information represents the specific position of the sensor in the pigsty frame structure; the topological structure relationship is a structural description that measures the spatial and functional connections between each intelligent ultrasonic sensor in the damaged area; the state difference is an index that quantifies the degree of difference between the current monitoring state and the expected state in the pigsty frame structure.

[0079] In specific implementation, first, the three-dimensional coordinate information of each sensor node in the damaged area can be obtained from the central control console of the pigsty as the position information, so as to obtain the position information of each intelligent ultrasonic sensor in the damaged area. Each intelligent ultrasonic sensor serves as a vertex of the graph, and the framework connections serve as edges. A topology graph is generated using the position information of the intelligent ultrasonic sensors, so that the connection relationship between the intelligent ultrasonic sensors in the topology graph can be used as the topological structure relationship of the intelligent ultrasonic sensors in the damaged area. Secondly, the signal amplitude change, propagation delay, and intensity attenuation of each ultrasonic signal can be used as the echo characteristics of each ultrasonic signal, so that the set of echo characteristics corresponding to each ultrasonic signal in the damaged area can be used as the abnormal echo state in the damaged area. Then, for each echo characteristic in the abnormal echo state, a simulation environment for the signal propagation of the intelligent ultrasonic sensors is built using the topological structure relationship, and the echo characteristics in the damaged area are simulated using this simulation environment. The mean value of all state variables of the echo characteristics after simulation is used as the standard state variable of the echo characteristics, and the absolute value of the difference between the state variable of the echo characteristic in the abnormal echo state and the standard state variable is used as the state difference of the echo characteristic. In the above manner, the state differences of each echo characteristic can be obtained, and all the state differences in the damaged area can be obtained. Finally, the set of all state differences can be used as the structural abnormal state of the damaged area.

[0080] In step 103, all local damaged characteristics of the pigsty framework structure are extracted, and the structural abnormal state and each local damaged characteristic are mapped and associated, so as to obtain the defect significance of the pigsty framework structure.

[0081] In some embodiments, all local damaged characteristics of the pigsty framework structure can be extracted by the following steps:

[0082] For each monitoring area, historical defect records of the monitoring area are extracted from the historical defect data of the pigsty framework structure;

[0083] Feature extraction is performed on the historical defect records to obtain the local damaged characteristics of the monitoring area, and thus the local damaged characteristics of each monitoring area are obtained.

[0084] It should be noted that in this application, the local damaged characteristic represents the local attribute characteristic of the defect in the pigsty framework structure; the historical defect data refers to the set of defect records that occurred in the pigsty framework structure within a specified time, where the defect records include defect location, defect time, defect type, defect severity, and defect signals.

[0085] In specific implementation, first, a set of all defect records of the pigsty frame structure within a specified time period (default is the most recent 3 months) can be obtained in the intelligent central console of the pigsty as historical defect data. For each monitoring area, all defect records with defect locations in the monitoring area are extracted from the historical defect data, and the set of all defect records can be used as the historical defect records of the monitoring area. Then, all defect records in the historical defect records are divided into multiple defect groups according to the defect severity in each defect record. For each defect group, a method combining time-domain analysis (e.g., envelope extraction) and frequency-domain analysis (e.g., Fourier transform) can be used to perform feature fusion extraction on the defect signals in the defect records of the defect group. The features obtained by the fusion extraction can be used as the signal sub-features of the defect group. In this way, the signal sub-features of each defect group can be obtained, and the set of all signal features can be used as the local damage features of the monitoring area. In this way, the local damage features of each monitoring area can be obtained. Among them, the signal sub-features are a set of features that measure the signal regularity in the defect group.

[0086] In some embodiments, the structural abnormal state and each local damage feature are mapped and associated to obtain the defect significance of the pigsty frame structure. Refer to Figure 3 As described, this figure is a schematic flowchart of determining the defect significance in some embodiments of the present application. In this embodiment, the defect significance can be determined by the following steps:

[0087] In step 1031, for each monitoring area, the local damage features of the monitoring area are obtained;

[0088] In step 1032, the local damage features and the structural abnormal state are associated and matched to obtain the state deviation degree of the defect in the monitoring area, and then the state deviation degree of the defect in each monitoring area is obtained;

[0089] In step 1033, the defect significance of the pigsty frame structure is determined through all the state deviation degrees.

[0090] It should be noted that in this application, the defect significance is an index to measure the degree of influence of defects in the pigsty frame structure on the overall structural stability. Specifically, in implementation, first, for each monitoring area, local damage characteristics of the monitoring area are obtained. Then, the cosine similarity between the local damage characteristics and the structural abnormal state can be used to quantify the matching relationship between the current structural abnormal state and the historical local damage characteristics, so as to use the quantified value of this matching relationship as the state deviation degree of the defect in the monitoring area. Through the above method, the state deviation degrees of the defects in each monitoring area can be obtained. This state deviation degree is a parameter to quantify the deviation degree between the detection state and the historical state of the monitoring area. Finally, the influence weights of each monitoring area on the stability of the pigsty frame are obtained from the pigsty frame design manual, so as to use each influence weight as the weight of the corresponding state deviation degree, and calculate the weighted average of all state deviation degrees as the defect significance of the pigsty frame structure.

[0091] In step 104, when the defect significance is greater than the preset defect threshold, it is determined that there are defects in the pigsty frame structure, and the damaged area is used as the defect position in the pigsty frame structure.

[0092] It should be noted that in this application, the defect information is data used to judge the health state of the pigsty frame structure; the defect threshold is a quantification standard to measure when the defect significance in the pigsty frame structure reaches the preset standard. When the defect significance is greater than the preset defect threshold, it indicates that the stability of the pigsty frame is insufficient at this time.

[0093] Specifically, when the defect significance is greater than the preset defect threshold, it is determined that there are defects in the pigsty frame structure. The defect significance is used as the defect severity in the defect information, and the damaged area is used as the defect position in the defect information. When the defect significance is less than or equal to the preset defect threshold, it is determined that there are no defects in the pigsty frame structure.

[0094] In this application, when the defect significance is greater than a preset defect threshold, it is determined that there is a defect in the pigsty frame structure, and the damaged area is used as the defect location in the pigsty frame structure. First, determining the abnormal structural state can obtain a behavior index that measures the difference between the pigsty frame structure and the expected state, which can associate the abnormal signal with the actual structural state to achieve precise detection of the pigsty structure, so that it can be detected in time when the abnormal structural state occurs, thus giving an early warning and reducing the potential risk of structural damage, ensuring the long-term use safety of the pigsty and the growth environment of the pig herd. Then, determining the defect significance can obtain an index that measures the degree of influence of the defect on the overall structural stability of the pigsty frame structure. When the defect significance exceeds the preset threshold, it can be judged whether there are safety problems in the pigsty structure and maintenance and repair are required. Among them, mapping association helps to quantitatively evaluate the overall defect degree of the pigsty frame structure, can identify and screen out areas that seriously affect structural safety through data-driven methods, avoid over-detection and maintenance of the entire pigsty, optimize resource allocation, improve detection efficiency, and at the same time helps managers determine the priority of maintenance, ensure key inspections and repairs in the places where they are most needed, so as to effectively extend the service life of the pigsty and reduce maintenance costs. In summary, based on the above solution, dynamic abnormal detection of ultrasonic signal echoes in the pigsty can be realized, thereby improving the detection accuracy of pigsty structure defects.

[0095] In addition, on the other hand of this application, in some embodiments, this application provides a pigsty defect detection system. Refer to Figure 4 , which is a schematic structural diagram of the pigsty defect detection system shown according to some embodiments of this application. The pigsty defect detection system includes: a collection module 201, a processing module 202, and an execution module 203, which are described as follows:

[0096] The collection module 201. In this application, the collection module 201 is mainly used to collect the frame structure information of the pigsty, determine all sensor nodes in the pigsty based on the frame structure information, and then determine the monitoring areas of each sensor node in the pigsty frame structure.

[0097] The processing module 202. In this application, the processing module 202 is used to set intelligent ultrasonic sensors in each monitoring area, and then use the intelligent ultrasonic sensors to monitor the ultrasonic signals in each monitoring area, screen out the damaged areas in the pigsty frame structure from all monitoring areas, and determine the abnormal structural state of the pigsty frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area.

[0098] It should be noted that the processing module 202 is also used to extract all local damaged features of the pigsty frame structure, map and associate the abnormal structural state with each local damaged feature, and then obtain the defect significance of the pigsty frame structure.

[0099] Execution module 203. In this application, the execution module 203 is mainly used to determine that there is a defect in the pigsty frame structure when the defect significance is greater than a preset defect threshold, and use the damaged area as the defect position in the pigsty frame structure.

[0100] The above has introduced in detail the examples of the pigsty defect detection method and system provided by the embodiments of this application. It can be understood that, in order to implement the above functions, the corresponding device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed in this article, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0101] In some embodiments, this application also provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned pigsty defect detection method.

[0102] In some embodiments, refer to Figure 5 , the dotted line in this figure indicates that the unit or the module is optional. This figure is a schematic structural diagram of a computer device for implementing the pigsty defect detection method provided by the embodiments of this application. The pigsty defect detection method described in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device includes at least one processor 301, a memory 302, and at least one communication unit 305. The computer device can be a terminal device, a server, or a chip.

[0103] The processor 301 can be a general-purpose processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU). The CPU can be used to control the computer device, execute software programs, and process the data of software programs. The computer device can also include a communication unit 305 for implementing signal input (reception) and output (transmission).

[0104] For example, the computer device can be a chip, and the communication unit 305 can be the input and / or output circuit of the chip, or the communication unit 305 can be the communication interface of the chip. The chip can be used as a component of a terminal device, a network device, or other devices.

[0105] For another example, the computer device may be a terminal device or a server, the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0106] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be run by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiments according to the instructions 303. Optionally, data (such as a target audit model) may also be stored in the memory 302. Optionally, the processor 301 may also read the data stored in the memory 302. This data may be stored at the same storage address as the program 304, or this data may be stored at a different storage address from the program 304.

[0107] The processor 301 and the memory 302 may be provided separately or integrated together. For example, they may be integrated on a system on chip (SOC) of the terminal device.

[0108] It should be understood that the steps of the above method embodiments can be completed by a logic circuit in hardware form or instructions in software form in the processor 301. The processor 301 may be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices. For example, discrete gates, transistor logic devices, or discrete hardware components.

[0109] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code.

[0110] For example, in some embodiments, the present application further provides a computer-readable storage medium, in which instructions or code are stored. When the instructions or code run on a computer, the computer is caused to execute the above-mentioned pigsty defect detection method.

[0111] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting defects in a pig pen, characterized in that: The steps include: Collect the frame structure information of the pigsty, determine all the sensor nodes in the pigsty according to the frame structure information, and then determine the monitoring area of ​​each sensor node in the pigsty frame structure; Intelligent ultrasonic sensors are set in each monitoring area, and then the intelligent ultrasonic sensors are used to monitor the ultrasonic signals in each monitoring area, and the damaged areas in the pig pen frame structure are screened out from all the monitoring areas, and the structural abnormality of the pig pen frame in the damaged area is determined through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area; Extract all local damaged features of the pig pen frame structure, map and associate the structural abnormal state with each local damaged feature, and then obtain the defect significance of the pig pen frame structure; When the defect significance is greater than a preset defect threshold, it is determined that there is a defect in the pig pen frame structure, and the damaged area is used as the defect position in the pig pen frame structure; The abnormal state of the structure and each local damage feature are mapped and associated to obtain the defect significance of the pig pen frame structure, which specifically includes: For each monitoring area, obtain the local damage characteristics of the monitoring area; Correlate and match the local damage feature with the structural abnormal state to obtain the state deviation of the defect in the monitoring area, and then obtain the state deviation of the defect in each monitoring area; The significance of defects in the pig pen frame structure is determined by all state deviations.

2. The method according to claim 1, characterized in that Determining all sensor nodes in the pig pen according to the framework structure information specifically includes: Obtain all key connection positions in the framework structure information; All key connection locations are clustered and assigned to obtain all sensor nodes in the pig pen.

3. The method according to claim 1, characterized in that Determine the monitoring area of ​​each sensor node in the pig pen frame structure, including: For each sensor node, obtain all key connection locations corresponding to the sensor node; The monitoring area of ​​the sensor node in the pigpen frame structure is determined through all key connection positions, and then the monitoring area of ​​each sensor node in the pigpen frame structure is obtained.

4. The method according to claim 1, characterized in that The damaged areas in the pig pen frame structure were screened out from all monitoring areas, including: For each monitoring area, filtering and denoising the ultrasonic signal of the monitoring area to obtain a denoised signal; Extracting features from the denoised signal to obtain fluctuation features of the ultrasonic signal in the monitoring area; Perform feature matching of the fluctuation feature with the standard echo feature to obtain a feature matching value of the monitoring area, and then obtain a feature matching value of each monitoring area; The feature matching values ​​below the preset matching threshold are corresponded to the damaged areas in the collective pig pen frame structure of all monitoring areas.

5. The method according to claim 1, characterized in that Determining the structural abnormality of the pig pen frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensor in the damaged area specifically includes: Obtaining the location information of each sensor node in the pig pen, and then constructing the topological structure relationship of the intelligent ultrasonic sensors in the damaged area through the location information; determining an abnormal echo state in the damaged area according to the echo characteristics of each ultrasonic signal; Comparing the abnormal echo states through the topological structure relationship to obtain all state differences in the damaged area; The structural abnormality of the pig pen frame in the damaged area is determined based on all status differences.

6. The method according to claim 1, characterized in that Extraction of all local damage features of the pig pen frame structure specifically includes: For each monitoring area, the historical defect records of the monitoring area are extracted from the historical defect data of the pig pen frame structure; Feature extraction is performed on the historical defect records to obtain local damage features of the monitoring area, and then local damage features of each monitoring area are obtained.

7. A pigsty defect detection system, which uses the method according to any one of claims 1 to 6 to perform pigsty defect detection, characterized in that: The system includes: A collection module is used to collect the frame structure information of the pigsty, determine all the sensor nodes in the pigsty according to the frame structure information, and then determine the monitoring area of ​​each sensor node in the pigsty frame structure; A processing module is used to set up intelligent ultrasonic sensors in each monitoring area, and then use the intelligent ultrasonic sensors to monitor the ultrasonic signals in each monitoring area, screen out the damaged areas in the pig pen frame structure from all the monitoring areas, and determine the structural abnormality of the pig pen frame in the damaged area through the echo characteristics of each ultrasonic signal and the topological structure relationship of the intelligent ultrasonic sensors in the damaged area; The processing module is also used to extract all local damaged features of the pigsty frame structure, map and associate the abnormal state of the structure with each local damaged feature, and then obtain the defect significance of the pigsty frame structure; The execution module is used to determine that there are defects in the pigpen frame structure when the defect significance is greater than a preset defect threshold value, and to use the damaged area as the defect position in the pigpen frame structure.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the pig pen defect detection method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the pig pen defect detection method as described in any one of claims 1 to 6.

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