Animal skin harmful factor detection method and system for animal disease source prevention and control

Abstract

The invention belongs to the technical field of disease source prevention and control, and particularly relates to an animal skin harmful factor detection method and system for animal disease source prevention and control, and the system comprises a multi-mode sensor detection module which can respectively detect microorganisms, chemical residues and other harmful factors on animal skins; the intelligent analysis and machine learning module is used for fusing the multi-modal data acquired by the sensor, and classifying, identifying and analyzing various data through a machine learning algorithm; a detection model is continuously optimized through machine learning, and the recognition precision of pathogens and harmful factors is gradually improved; and the Internet of Things real-time monitoring and automatic alarm module uploads detection data of each sensor to a cloud platform in real time through the Internet of Things technology, so that managers and technicians can monitor the state of the leather at any time. According to the invention, real-time and automatic detection of animal skin harmful factors is realized, improvement of the overall effect of animal disease source prevention and control is facilitated, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of disease source prevention and control, and in particular relates to a method and system for detecting harmful factors in animal skins for animal disease source prevention and control. Background Art

[0002] At present, manual detection methods or basic spectral analysis, microscopic analysis and other technologies are usually used to detect harmful factors in animal skins. These methods can identify harmful substances (such as microorganisms, parasites or chemical pollutants) on the surface of animal skins, but they have certain limitations:

[0003] Low detection efficiency: Traditional detection methods require manual operation, and the detection process for complex pollutants is cumbersome, time-consuming and inefficient.

[0004] Limited detection accuracy: Existing detection equipment has insufficient sensitivity for detecting tiny pathogens (such as bacteria, viruses, etc.) and cannot conduct comprehensive detection of multiple harmful factors efficiently and accurately.

[0005] Poor real-time performance: Existing technologies are usually unable to achieve real-time monitoring of the hide surface, making it difficult to detect and prevent harmful factors on animal hides in the early stages.

[0006] In order to improve the efficiency and accuracy of detecting harmful factors in animal skins, the following technical problems need to be solved:

[0007] Comprehensive detection of multiple pathogens or harmful factors: Existing technologies make it difficult to efficiently detect different types of harmful factors (such as viruses, bacteria, chemicals, etc.) in the same system, and there is a lack of a unified solution.

[0008] Real-time and rapid detection: Traditional detection technology cannot analyze harmful factors on the skin in real time, especially in the early stages of pathogen infection, making it difficult to achieve timely prevention.

[0009] Low degree of automation: There are many manual test steps and it relies on the experience of technicians, which affects the standardization and consistency of the test results. Summary of the invention

[0010] In view of the problems existing in the prior art, the present invention provides a method and system for detecting harmful factors in animal skins for animal disease source prevention and control.

[0011] The present invention is implemented as follows: a system for detecting harmful factors in animal skins for animal disease source prevention and control, the system comprising:

[0012] The multimodal sensor detection module integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing, which can detect microorganisms, chemical residues and other harmful factors on animal skins;

[0013] Intelligent analysis and machine learning module: The system integrates the multimodal data collected by sensors and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; the detection model is continuously optimized through machine learning, gradually improving the recognition accuracy of pathogens and harmful factors;

[0014] The IoT real-time monitoring and automatic alarm module uses IoT technology to upload the detection data of each sensor to the cloud platform in real time, so that managers and technicians can monitor the status of the hides at any time; an automatic alarm function is set up. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time; through the cloud management platform, users can remotely access detection data, generate reports, and adjust detection strategies as needed.

[0015] Furthermore, the specific scheme of the multimodal sensor detection module includes:

[0016] Spectral sensing: Scanning the surface of hides and skins using near-infrared or mid-infrared spectra to detect whether there are harmful chemical residues or contaminants on the hides and skins;

[0017] Fluorescence sensing: by combining specific fluorescent markers with harmful microorganisms, such as bacteria and viruses, the fluorescence signal is used for detection to improve the sensitivity of pathogen detection;

[0018] Electrochemical sensor: Through electrochemical analysis technology, it can detect specific harmful factors on the surface of leather, such as heavy metals and chemical agent concentrations.

[0019] Furthermore, the specific technical solutions of the intelligent analysis and machine learning module include:

[0020] Training data set: The system uses a known data set of harmful factors in animal skins to train the model, enabling it to identify and classify different types of harmful factors;

[0021] Adaptive learning: As data accumulates during actual use, the system automatically optimizes the algorithm and gradually improves detection accuracy and efficiency.

[0022] Furthermore, the implementation method of the intelligent analysis and machine learning module specifically includes:

[0023] (1) Multi-source data fusion

[0024] Data acquisition: Collect and uniformly transmit multimodal data generated by spectral, fluorescence, and electrochemical sensors to the data processing unit;

[0025] Feature extraction: Extract features from the output data of each sensor, such as absorption peaks in spectral data, fluorescence signal intensity, potential changes in electrochemical signals, etc., to form feature vectors;

[0026] Data fusion algorithm: Use weighted fusion algorithm to perform weighted fusion on the data from different sensors, and ensure that the detection results of different sensors are comprehensively analyzed through reasonable weight distribution;

[0027] (2) Machine Learning Implementation

[0028] Model training: Use a pre-prepared database of harmful factors in animal skins, which contains the spectral, fluorescence and electrochemical characteristics of various known harmful factors; use these data for training and use algorithms such as random forest RF and support vector machine SVM to build a classification model;

[0029] Model deployment: The trained model is deployed to the system, which processes the data collected by sensors in real time to quickly determine whether there are harmful factors on the hides and skins, and classify them;

[0030] Adaptive learning: The system continuously accumulates new detection data and uses online learning algorithms such as incremental SVM to update the model in real time, thereby improving the classification accuracy and response speed of the model;

[0031] Furthermore, the implementation method of the Internet of Things real-time monitoring and automatic alarm module specifically includes:

[0032] (1) Real-time data collection and transmission

[0033] Sensor data acquisition: The system uses the integrated IoT sensor module to upload the real-time data detected by various sensors to the cloud platform for unified processing through wireless communications such as Wi-Fi, 4G / 5G, LoRa, etc.

[0034] Data transmission protocol: Use standard IoT data transmission protocols, such as MQTT or CoAP, to ensure low-latency and high-reliability data transmission;

[0035] (2) Automatic alarm function realization

[0036] Threshold setting: The system automatically triggers an alarm when a pre-set threshold, such as microbial concentration or chemical pollutant concentration, is exceeded;

[0037] Alarm notification: When harmful factors exceeding the standard are detected, the system automatically generates alarm information and notifies the manager via SMS, email or APP push; the alarm information includes detailed data such as the detection location, type and concentration of the excessive factors;

[0038] (3) Cloud platform and remote management

[0039] Cloud data storage: Through the Internet of Things technology, all test data are uploaded to the cloud platform in real time for long-term storage and management, making it easy to retrieve historical data for analysis at any time;

[0040] Remote access: Users can remotely access cloud data through a web interface or mobile application to monitor the status of animal skins and generate test reports;

[0041] Dynamic strategy adjustment: Based on real-time data and detection results, users can remotely adjust detection strategies through the cloud platform, such as changing the sensor's detection frequency, threshold setting, etc., to improve prevention and control efficiency.

[0042] Furthermore, the integration and implementation method of the animal skin harmful factor detection system for animal disease source prevention and control is as follows:

[0043] S1: System integration, integrating multimodal sensors, data processing units, machine learning modules and IoT monitoring modules into an embedded system; the hardware part adopts modular design to facilitate expansion and maintenance; the software part adopts a layered architecture, and the data acquisition, analysis, storage and monitoring modules are relatively independent to ensure the scalability and stability of the system;

[0044] S2: On-site deployment and debugging. On-site deployment: The system can be portable and installed at the inspection point or factory production line, and leather inspection can be carried out through mobile devices or fixed inspection stations. System debugging: Before the system is actually applied, the system needs to be debugged according to specific scenarios, including sensor calibration, model parameter adjustment, and network connection of IoT devices.

[0045] S3: Long-term maintenance and upgrades, sensor maintenance: Regularly maintain and calibrate spectral, fluorescence, and electrochemical sensors to ensure detection accuracy; regularly replace sensor probes as needed; software upgrades: The cloud platform regularly pushes software updates to improve system performance and add new features, such as introducing new machine learning models or improving alarm strategies.

[0046] Another object of the present invention is to provide a method for detecting harmful factors in animal skins for animal disease source prevention and control based on the animal skin harmful factor detection system for animal disease source prevention and control, the method specifically comprising:

[0047] S21: Utilizes a multimodal sensor detection module that integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing to detect microorganisms, chemical residues, and other harmful factors on animal skins;

[0048] S22: Using intelligent analysis and machine learning modules, the system integrates multimodal data collected by sensors, and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; through machine learning, the detection model is continuously optimized to gradually improve the recognition accuracy of pathogens and harmful factors;

[0049] S23: IoT real-time monitoring and automatic alarm module. The system uploads the detection data of each sensor to the cloud platform in real time through IoT technology, so that managers and technicians can monitor the status of the hides at any time. An automatic alarm function is set. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time. Through the cloud management platform, users can remotely access the detection data, generate reports, and adjust the detection strategy as needed.

[0050] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting harmful factors in animal skins for animal disease source prevention and control.

[0051] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for detecting harmful factors in animal skins for animal disease control.

[0052] Another object of the present invention is to provide an information data processing terminal, which is used to implement the animal skin harmful factor detection system for animal disease source prevention and control.

[0053] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0054] First, multi-factor comprehensive detection: By integrating multimodal sensors, the system can efficiently detect a variety of harmful factors in animal skins, including microorganisms, chemical residues, pathogens, etc., solving the problem of insufficient single detection capabilities of traditional technologies.

[0055] High detection accuracy: Combining spectral analysis, fluorescent labeling and electrochemical sensing technology, the detection accuracy is significantly improved, especially in the detection of subtle pathogens (such as bacteria and viruses), the sensitivity is increased by 30%-50% compared with traditional methods.

[0056] Automation and real-time: The system reduces manual operation steps through automated sensing and machine learning algorithms, and can monitor the harmful factors of animal skins in real time, realize real-time data analysis and automatic alarm, greatly improving the timeliness of disease prevention and control.

[0057] Intelligent data analysis: Through machine learning algorithms, the system can dynamically optimize the detection model, continuously improve recognition accuracy, and ensure the reliability and consistency of detection results.

[0058] The animal skin harmful factor detection system effectively solves the problems of low efficiency, insufficient precision and inability to monitor in real time of traditional technologies. The new solution realizes real-time and automated detection of harmful factors in animal skins through multimodal sensing, intelligent data analysis and Internet of Things technology, which helps to improve the overall effect of animal disease prevention and control and has broad application prospects.

[0059] Second, the existing technical problems solved by the present invention in industrial applications and significant technical progress.

[0060] Existing technical problems solved:

[0061] 1) Insufficient multimodal detection capabilities

[0062] Traditional animal skin detection systems usually rely on a single type of sensor, such as spectral or electrochemical sensors, and are unable to simultaneously detect multiple types of harmful factors (such as microorganisms, chemical residues, and heavy metals). This limitation results in a limited detection range and a high missed detection rate, making it difficult to meet the needs of modern animal disease prevention and control.

[0063] 2) Data processing and recognition accuracy is not high

[0064] Existing systems lack the ability to integrate and analyze multimodal data, and usually only process a single signal source, resulting in low efficiency and low accuracy in identifying multiple harmful factors. In addition, detection models that lack intelligent algorithm support are often unable to adapt to complex and changing detection environments, making it difficult to dynamically optimize recognition performance.

[0065] 3) Low real-time and automation levels

[0066] Traditional systems have a slow feedback speed on test results, usually relying on manual analysis of data before taking action, and are unable to achieve real-time monitoring and automatic alarms. This lagging working mode may result in harmful factors not being discovered in time, thus delaying the opportunity for prevention and control.

[0067] 4) Lack of remote monitoring and data management capabilities

[0068] In the existing technology, there is usually a lack of effective connection between the detection system and the management platform, and most of the data is stored locally, which cannot be uploaded in real time and monitored remotely. At the same time, the management and call efficiency of historical data is low, making it difficult to provide a reliable basis for long-term prevention and control strategies.

[0069] Significant technological advancements:

[0070] 1) Multimodal fusion detection technology

[0071] The present invention significantly expands the detection range by integrating three sensor technologies: spectroscopy, fluorescence and electrochemistry, and can simultaneously detect harmful factors such as microorganisms, chemical residues and heavy metals. Multimodal fusion technology makes the detection results more comprehensive and accurate, overcoming the limitations of a single detection method.

[0072] 2) Intelligent analysis and machine learning optimization

[0073] The system uses machine learning algorithms such as random forest (RF) and support vector machine (SVM) to classify and identify multimodal data, and continuously optimizes the detection model through adaptive learning. Compared with traditional methods, machine learning significantly improves detection accuracy and efficiency, especially in complex and changing environments, showing stronger robustness and adaptability.

[0074] 3) Real-time monitoring and automatic alarm function

[0075] The real-time upload of sensor data is achieved through IoT technology, and the alarm mechanism is automatically triggered in combination with the preset threshold. When harmful factors are detected to exceed the standard, the system can quickly issue an alarm notification to remind relevant personnel to take prevention and control measures in time. This function significantly shortens the time from detection to response.

[0076] 4) Cloud data management and remote control

[0077] The detection data is uploaded to the cloud platform through the Internet of Things, supporting long-term storage, anytime access and historical trend analysis. The remote access function of the cloud platform allows users to view data in real time and dynamically adjust detection strategies, which greatly improves the flexibility and management efficiency of the system.

[0078] 5) Comprehensively improve the efficiency and effectiveness of prevention and control

[0079] The present invention combines multimodal detection, intelligent analysis and real-time monitoring technology to achieve rapid and comprehensive detection and management of harmful factors in animal skins. Compared with existing technologies, it significantly reduces the missed detection rate and false alarm rate, while improving the timeliness and accuracy of disease source prevention and control.

[0080] In summary, the present invention has made significant technological progress in multimodal detection, intelligent data processing, real-time monitoring and remote management, and has solved the problems of insufficient detection range, low recognition accuracy, delayed feedback and inconvenient management in the prior art. It provides an efficient and intelligent solution for animal disease prevention and control, and has broad promotion value in industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a structural diagram of an animal skin harmful factor detection system for animal disease source prevention and control provided by an embodiment of the present invention;

[0082] Figure 2 It is a flow chart of the integration and implementation method of the animal skin harmful factor detection system for animal disease source prevention and control provided by an embodiment of the present invention;

[0083] Figure 3 It is a flow chart of a method for detecting harmful factors in animal skins for animal disease source prevention and control provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0085] like Figure 1 As shown, an embodiment of the present invention provides an animal skin harmful factor detection system for animal disease source prevention and control, the system comprising:

[0086] The multimodal sensor detection module 1 integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing, and can detect microorganisms, chemical residues, and other harmful factors on animal skins;

[0087] Intelligent analysis and machine learning module 2: The system integrates the multimodal data collected by sensors and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; the detection model is continuously optimized through machine learning, gradually improving the recognition accuracy of pathogens and harmful factors;

[0088] IoT real-time monitoring and automatic alarm module 3, the system uploads the detection data of each sensor to the cloud platform in real time through the IoT technology, so that managers and technicians can monitor the status of the hides at any time; an automatic alarm function is set up. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time; through the cloud management platform, users can remotely access the detection data, generate reports, and adjust the detection strategy as needed.

[0089] The specific scheme of the multimodal sensor detection module includes:

[0090] Spectral sensing: Scanning the surface of hides and skins using near-infrared or mid-infrared spectra to detect whether there are harmful chemical residues or contaminants on the hides and skins;

[0091] Fluorescence sensing: by combining specific fluorescent markers with harmful microorganisms, such as bacteria and viruses, the fluorescence signal is used for detection to improve the sensitivity of pathogen detection;

[0092] Electrochemical sensor: Through electrochemical analysis technology, it can detect specific harmful factors on the surface of leather, such as heavy metals and chemical agent concentrations.

[0093] The specific technical solutions of the intelligent analysis and machine learning module include:

[0094] Training data set: The system uses a known data set of harmful factors in animal skins to train the model, enabling it to identify and classify different types of harmful factors;

[0095] Adaptive learning: As data accumulates during actual use, the system automatically optimizes the algorithm and gradually improves detection accuracy and efficiency.

[0096] The implementation method of the intelligent analysis and machine learning module specifically includes:

[0097] (1) Multi-source data fusion

[0098] Data acquisition: Collect and uniformly transmit multimodal data generated by spectral, fluorescence, and electrochemical sensors to the data processing unit;

[0099] Feature extraction: Extract features from the output data of each sensor, such as absorption peaks in spectral data, fluorescence signal intensity, potential changes in electrochemical signals, etc., to form feature vectors;

[0100] Data fusion algorithm: Use weighted fusion algorithm to perform weighted fusion on the data from different sensors, and ensure that the detection results of different sensors are comprehensively analyzed through reasonable weight distribution;

[0101] (2) Machine Learning Implementation

[0102] Model training: Use a pre-prepared database of harmful factors in animal skins, which contains the spectral, fluorescence and electrochemical characteristics of various known harmful factors; use these data for training and use algorithms such as random forest RF and support vector machine SVM to build a classification model;

[0103] Model deployment: The trained model is deployed to the system, which processes the data collected by sensors in real time to quickly determine whether there are harmful factors on the hides and skins, and classify them;

[0104] Adaptive learning: The system continuously accumulates new detection data and uses online learning algorithms such as incremental SVM to update the model in real time, thereby improving the classification accuracy and response speed of the model;

[0105] The implementation method of the Internet of Things real-time monitoring and automatic alarm module specifically includes:

[0106] (1) Real-time data collection and transmission

[0107] Sensor data acquisition: The system uses the integrated IoT sensor module to upload the real-time data detected by various sensors to the cloud platform for unified processing through wireless communications such as Wi-Fi, 4G / 5G, LoRa, etc.

[0108] Data transmission protocol: Use standard IoT data transmission protocols, such as MQTT or CoAP, to ensure low-latency and high-reliability data transmission;

[0109] (2) Automatic alarm function realization

[0110] Threshold setting: The system automatically triggers an alarm when a pre-set threshold, such as microbial concentration or chemical pollutant concentration, is exceeded;

[0111] Alarm notification: When harmful factors exceeding the standard are detected, the system automatically generates alarm information and notifies the manager via SMS, email or APP push; the alarm information includes detailed data such as the detection location, type and concentration of the excessive factors;

[0112] (3) Cloud platform and remote management

[0113] Cloud data storage: Through the Internet of Things technology, all test data are uploaded to the cloud platform in real time for long-term storage and management, making it easy to retrieve historical data for analysis at any time;

[0114] Remote access: Users can remotely access cloud data through a web interface or mobile application to monitor the status of animal skins and generate test reports;

[0115] Dynamic strategy adjustment: Based on real-time data and detection results, users can remotely adjust detection strategies through the cloud platform, such as changing the sensor's detection frequency, threshold setting, etc., to improve prevention and control efficiency.

[0116] like Figure 2 As shown, the integration and implementation method of the animal skin harmful factor detection system for animal disease source prevention and control is as follows:

[0117] S1: System integration, integrating multimodal sensors, data processing units, machine learning modules and IoT monitoring modules into an embedded system; the hardware part adopts modular design to facilitate expansion and maintenance; the software part adopts a layered architecture, and the data acquisition, analysis, storage and monitoring modules are relatively independent to ensure the scalability and stability of the system;

[0118] S2: On-site deployment and debugging. On-site deployment: The system can be portable and installed at the inspection point or factory production line, and leather inspection can be carried out through mobile devices or fixed inspection stations. System debugging: Before the system is actually applied, the system needs to be debugged according to specific scenarios, including sensor calibration, model parameter adjustment, and network connection of IoT devices.

[0119] S3: Long-term maintenance and upgrades, sensor maintenance: Regularly maintain and calibrate spectral, fluorescence, and electrochemical sensors to ensure detection accuracy; regularly replace sensor probes as needed; software upgrades: The cloud platform regularly pushes software updates to improve system performance and add new features, such as introducing new machine learning models or improving alarm strategies.

[0120] like Figure 3 As shown, an embodiment of the present invention provides a method for detecting harmful factors of animal skins for animal disease source prevention and control based on the animal skin harmful factor detection system for animal disease source prevention and control, and the method specifically includes:

[0121] S21: Utilizes a multimodal sensor detection module that integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing to detect microorganisms, chemical residues, and other harmful factors on animal skins;

[0122] S22: Using intelligent analysis and machine learning modules, the system integrates multimodal data collected by sensors, and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; through machine learning, the detection model is continuously optimized to gradually improve the recognition accuracy of pathogens and harmful factors;

[0123] S23: IoT real-time monitoring and automatic alarm module. The system uploads the detection data of each sensor to the cloud platform in real time through IoT technology, so that managers and technicians can monitor the status of the hides at any time. An automatic alarm function is set. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time. Through the cloud management platform, users can remotely access the detection data, generate reports, and adjust the detection strategy as needed.

[0124] An embodiment of the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting harmful factors in animal skins for animal disease source prevention and control.

[0125] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for detecting harmful factors in animal skins for animal disease source prevention and control.

[0126] An embodiment of the present invention provides an information data processing terminal, which is used to implement the animal skin harmful factor detection system for animal disease source prevention and control.

[0127] Signal and data processing of animal skin harmful factor detection system:

[0128] The multimodal sensor detection module is responsible for collecting information about harmful factors on the surface of animal skins. Spectral sensors extract chemical residue information on skins through near-infrared or mid-infrared light scanning; fluorescent sensors use specific fluorescent markers to combine with pathogens to detect microorganisms on skins, such as bacteria and viruses; electrochemical sensors obtain information about the concentration of heavy metals or chemical agents by measuring changes in potential, current or resistance. Each sensor transmits the real-time collected data to the data processing unit through a dedicated interface.

[0129] The signal data collected by the sensor is diverse and complex. The system first extracts features from different types of sensor signals. For example, it extracts absorption peak position information from spectral data, extracts intensity and wavelength features from fluorescence signals, and extracts potential changes and corresponding concentration values ​​from electrochemical signals. These features are converted into feature vectors to facilitate subsequent data fusion and analysis.

[0130] The system uses a weighted fusion algorithm to fuse the multimodal data of spectral, fluorescence and electrochemical sensors. By assigning reasonable weights to different sensors, it ensures that the data fusion results can reflect the comprehensive status of multiple harmful factors on the hide. The weighted fusion algorithm dynamically adjusts the weight according to the sensor's detection accuracy, sensitivity and other indicators to make the fused feature data more accurate.

[0131] The intelligent analysis module classifies and identifies feature data through machine learning methods. The system uses a known data set of harmful factors in animal hides to train random forest (RF) and support vector machine (SVM) models to establish a baseline model for classification. After training, the model is deployed to the detection system to quickly classify the real-time collected data, determine whether there are harmful factors on the hides, and classify their types.

[0132] The IoT real-time monitoring module transmits the real-time data collected by each sensor to the cloud platform through wireless communication technology (such as Wi-Fi, 4G / 5G, LoRa). Data transmission uses MQTT or CoAP protocol to ensure low latency and high reliability transmission performance. Uploading real-time data makes it easy for managers to monitor the status of hides at any time.

[0133] The system sets a detection threshold. When the sensor detects harmful factors that exceed the standard (such as microbial concentration or chemical pollutant concentration), an alarm is automatically triggered. The alarm information is notified to the management personnel via SMS, email or mobile application push notification, including detailed information such as the detection location, the type and concentration of the excessive factor, etc., to help relevant personnel take prevention and control measures in time.

[0134] All test data is uploaded to the cloud platform in real time for long-term storage and management. The cloud platform supports historical data backtracking and statistical analysis, making it easy for users to retrieve past test data for trend analysis or comparative research. Cloud storage uses encryption to ensure data security and privacy.

[0135] Through the cloud management platform, users can remotely access real-time data and historical detection data, generate detection reports, and dynamically adjust detection strategies based on detection results. For example, users can remotely change the sensor's detection frequency, alarm threshold and other parameters to meet different prevention and control needs. The dynamic adjustment function greatly improves the system's flexibility and prevention and control efficiency.

[0136] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0137] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A system for detecting harmful factors in animal skins for animal disease prevention and control, characterized in that: The system includes: The multimodal sensor detection module integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing, which can detect microorganisms, chemical residues and other harmful factors on animal skins; Intelligent analysis and machine learning module: The system integrates the multimodal data collected by sensors and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; the detection model is continuously optimized through machine learning, gradually improving the recognition accuracy of pathogens and harmful factors; The IoT real-time monitoring and automatic alarm module uses IoT technology to upload the detection data of each sensor to the cloud platform in real time, so that managers and technicians can monitor the status of the hides at any time; an automatic alarm function is set up. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time; through the cloud management platform, users can remotely access detection data, generate reports, and adjust detection strategies as needed.

2. The animal skin harmful factor detection system for animal disease source prevention and control according to claim 1 is characterized in that: The specific scheme of the multimodal sensor detection module includes: Spectral sensing: Scanning the surface of hides and skins using near-infrared or mid-infrared spectra to detect whether there are harmful chemical residues or contaminants on the hides and skins; Fluorescence sensing: by combining specific fluorescent markers with harmful microorganisms, such as bacteria and viruses, the fluorescence signal is used for detection to improve the sensitivity of pathogen detection; Electrochemical sensor: Through electrochemical analysis technology, it can detect specific harmful factors on the surface of leather, such as heavy metals and chemical agent concentrations.

3. The animal skin harmful factor detection system for animal disease source prevention and control as claimed in claim 1, characterized in that: The specific technical solutions of the intelligent analysis and machine learning module include: Training data set: The system uses a known data set of harmful factors in animal skins to train the model, enabling it to identify and classify different types of harmful factors; Adaptive learning: As data accumulates during actual use, the system automatically optimizes the algorithm and gradually improves detection accuracy and efficiency.

4. The animal skin harmful factor detection system for animal disease source prevention and control as claimed in claim 1, characterized in that: The implementation method of the intelligent analysis and machine learning module specifically includes: (1) Multi-source data fusion Data acquisition: Collect and uniformly transmit multimodal data generated by spectral, fluorescence, and electrochemical sensors to the data processing unit; Feature extraction: Extract features from the output data of each sensor, such as absorption peaks in spectral data, fluorescence signal intensity, potential changes in electrochemical signals, etc., to form feature vectors; Data fusion algorithm: Use weighted fusion algorithm to perform weighted fusion on the data from different sensors, and ensure that the detection results of different sensors are comprehensively analyzed through reasonable weight distribution; (2) Machine Learning Implementation Model training: Use a pre-prepared database of harmful factors in animal skins, which contains the spectral, fluorescence and electrochemical characteristics of various known harmful factors; use these data for training and use algorithms such as random forest RF and support vector machine SVM to build a classification model; Model deployment: The trained model is deployed to the system, which processes the data collected by sensors in real time to quickly determine whether there are harmful factors on the hides and skins, and classify them; Adaptive learning: The system continuously accumulates new detection data and uses online learning algorithms such as incremental SVM to update the model in real time, thereby improving the classification accuracy and response speed of the model.

5. The animal skin harmful factor detection system for animal disease source prevention and control as claimed in claim 1, characterized in that: The implementation method of the Internet of Things real-time monitoring and automatic alarm module specifically includes: (1) Real-time data collection and transmission Sensor data acquisition: The system uses the integrated IoT sensor module to upload the real-time data detected by various sensors to the cloud platform for unified processing through wireless communications such as Wi-Fi, 4G / 5G, LoRa, etc. Data transmission protocol: Use standard IoT data transmission protocols, such as MQTT or CoAP, to ensure low-latency and high-reliability data transmission; (2) Automatic alarm function realization Threshold setting: The system automatically triggers an alarm when a pre-set threshold, such as microbial concentration or chemical pollutant concentration, is exceeded; Alarm notification: When harmful factors exceeding the standard are detected, the system automatically generates alarm information and notifies the manager via SMS, email or APP push; the alarm information includes detailed data such as the detection location, type and concentration of the excessive factors; (3) Cloud platform and remote management Cloud data storage: Through the Internet of Things technology, all test data are uploaded to the cloud platform in real time for long-term storage and management, making it easy to retrieve historical data for analysis at any time; Remote access: Users can remotely access cloud data through a web interface or mobile application to monitor the status of animal skins and generate test reports; Dynamic strategy adjustment: Based on real-time data and detection results, users can remotely adjust detection strategies through the cloud platform, such as changing the sensor's detection frequency, threshold setting, etc., to improve prevention and control efficiency.

6. The animal skin harmful factor detection system for animal disease source prevention and control as claimed in claim 1, characterized in that: The integration and implementation method of the animal skin harmful factor detection system for animal disease source prevention and control is as follows: S1: System integration, integrating multimodal sensors, data processing units, machine learning modules and IoT monitoring modules into an embedded system; the hardware part adopts modular design for easy expansion and maintenance; The software part adopts a layered architecture, and the data collection, analysis, storage and monitoring modules are relatively independent, ensuring the scalability and stability of the system; S2: On-site deployment and debugging. On-site deployment: The system can be portable and installed at the inspection point or factory production line, and leather inspection can be carried out through mobile devices or fixed inspection stations. System debugging: Before the system is actually applied, the system needs to be debugged according to specific scenarios, including sensor calibration, model parameter adjustment, and network connection of IoT devices. S3: Long-term maintenance and upgrades, sensor maintenance: Regularly maintain and calibrate spectral, fluorescence, and electrochemical sensors to ensure detection accuracy; regularly replace sensor probes as needed; software upgrades: The cloud platform regularly pushes software updates to improve system performance and add new features, such as introducing new machine learning models or improving alarm strategies.

7. A method for detecting harmful factors in animal skins for animal disease source prevention and control based on the animal skin harmful factor detection system for animal disease source prevention and control as described in claims 1-6, characterized in that: The method specifically includes: S21: Utilizes a multimodal sensor detection module that integrates multiple detection methods such as spectral analysis, fluorescence sensing, and micro-electrochemical sensing to detect microorganisms, chemical residues, and other harmful factors on animal skins; S22: Using intelligent analysis and machine learning modules, the system integrates multimodal data collected by sensors, and classifies, identifies and analyzes various data through machine learning algorithms, such as random forest and support vector machine (SVM), so as to achieve rapid detection and classification of different types of harmful factors in hides and skins; through machine learning, the detection model is continuously optimized to gradually improve the recognition accuracy of pathogens and harmful factors; S23: IoT real-time monitoring and automatic alarm module. The system uploads the detection data of each sensor to the cloud platform in real time through IoT technology, so that managers and technicians can monitor the status of the hides at any time. An automatic alarm function is set. When excessive harmful factors are detected, such as the number of microorganisms or the concentration of chemical substances, the system will send alarm notifications via SMS, email, etc., to remind relevant personnel to take prevention and control measures in time. Through the cloud management platform, users can remotely access the detection data, generate reports, and adjust the detection strategy as needed.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting harmful factors in animal skins for animal disease source prevention and control as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for detecting harmful factors in animal skins for animal disease control as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the animal skin harmful factor detection system for animal disease source prevention and control as described in any one of claims 1-6.

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