Rapid quarantine method suitable for cross-border animal transportation

Abstract

The invention provides a rapid quarantine method suitable for cross-border animal transportation, and the method combines intelligent body temperature monitoring equipment, a portable immunodiagnosis kit, an environment monitoring sensor, wearable equipment and a data analysis platform, and collects animal health data, environment data and behavior data in real time through an Internet of Things technology. And performing multi-dimensional data analysis by using artificial intelligence and a machine learning algorithm to generate a comprehensive health assessment index. By monitoring the health condition and the transportation environment of animals in real time, the system can detect and identify potential health risks in time and automatically feed back and adjust environmental parameters, so that the animals are in proper transportation conditions, the disease transmission risk is reduced, the quarantine efficiency can be remarkably improved, manual operation errors are reduced, and the safety of quarantine is improved. The quarantine process in the cross-border animal transportation process is optimized, and animal health and transportation safety are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of animal transportation quarantine, and in particular to a rapid quarantine method suitable for cross-border animal transportation. Background Art

[0002] With the advancement of globalization, cross-border animal transportation plays an increasingly important role in global trade. Especially in international trade and agricultural development, animals flow around the world as important goods. Countries and regions involved in animal transportation have different quarantine standards and regulations. Animal health protection and quarantine work are extremely important. However, in actual operation, there are some problems that cannot be ignored in traditional animal quarantine methods. These problems not only reduce quarantine efficiency, but also may cause health risks and affect the safety of cross-border animal transportation. The following are the shortcomings of current technology:

[0003] At present, quarantine work for cross-border animal transport mostly relies on manual inspection and laboratory testing. For example, quarantine personnel make a preliminary judgment on the health status of animals through visual inspection and temperature testing, and then take further sample collection and pathogen testing as needed. This traditional quarantine method usually takes a long time, and there are many manual operations in the process, which is prone to omissions and misjudgments. Especially when dealing with large quantities of animals, manual inspection is extremely cumbersome and inefficient.

[0004] Disadvantages: Quarantine personnel need to spend a lot of time inspecting each animal, causing delays in the transportation process.

[0005] Failure to monitor animal health changes in real time may result in missing early health warnings.

[0006] Relying on manual operation, the probability of error and missed detection is high, making it difficult to achieve 100% accuracy.

[0007] At present, pathogen detection in cross-border animal transportation mostly adopts traditional laboratory detection methods, such as PCR detection, culture detection, etc. Although these detection methods are accurate, they usually require a long laboratory culture and detection process, and are highly dependent on equipment and professionals. This not only increases the time cost for large-scale cross-border animal transportation, but may also result in the failure to timely detect potential infectious diseases during transportation, increasing the uncertainty of animal quarantine.

[0008] Disadvantages: Traditional pathogen detection methods (such as PCR testing) are time-consuming and may take several hours or even days to get results, which is obviously not applicable for fast-moving animal transportation.

[0009] Specialized laboratories and equipment are required, as well as highly professional quarantine personnel for operation and analysis, which increases labor and material costs.

[0010] Pathogen testing has poor flexibility in field applications and cannot respond to emergencies in real time. Its high cost also makes it difficult to popularize in low-cost cross-border animal transportation.

[0011] During animal transportation, environmental factors (such as temperature, humidity, air quality, etc.) have an important impact on animal health. Although environmental factors directly affect the physiological state and immune system of animals, the current animal quarantine process often ignores the monitoring and regulation of the transportation environment. Abnormalities in the transportation environment (such as too high or too low temperature, too high or too low humidity) may cause the health of animals to deteriorate rapidly and even cause disease outbreaks. However, traditional quarantine methods are usually unable to monitor environmental changes in real time and cannot adjust environmental conditions in a timely manner.

[0012] Disadvantages: Traditional quarantine methods rarely involve real-time monitoring and adjustment of the transportation environment, and ignore the impact of the environment on animal health.

[0013] During animal transportation, changes in temperature and humidity and fluctuations in air quality may affect the animals' immune capabilities and lead to health problems, but these changes often cannot be responded to in a timely manner.

[0014] Environmental equipment needs to be adjusted manually, which has the limitations of slow response and inconvenient operation.

[0015] Traditional animal quarantine usually relies on paper records and manual information transmission, which is slow and prone to data loss, errors or delays. This not only affects the quarantine personnel's judgment on the health status of animals, but also makes it impossible to achieve real-time monitoring and data updates during the transportation of animals, thereby increasing health risks and transportation time.

[0016] Disadvantages: Information transmission is delayed, especially during cross-border transportation, and key health data cannot be updated and transmitted in a timely manner.

[0017] Quarantine information often relies on manual recording and transmission, which is prone to errors and omissions and cannot be effectively traced.

[0018] The collation and transmission of quarantine data are not efficient enough, which affects the transparency and overall efficiency of the quarantine process.

[0019] Traditional quarantine methods mostly rely on a single health detection method or data analysis, lacking a comprehensive and intelligent evaluation system. The health status of animals is often simply determined by basic indicators such as body temperature and heart rate, without using big data and artificial intelligence for comprehensive evaluation. This approach often fails to detect potential health problems and is prone to missing early warning signs of animals.

[0020] Disadvantages: Traditional methods fail to fully utilize big data and artificial intelligence technologies to conduct intelligent and comprehensive assessments of the health status of animals.

[0021] It lacks comprehensive analytical capabilities and is unable to combine multiple data (such as body temperature, behavioral monitoring, environmental factors, etc.) to conduct multi-dimensional health assessments.

[0022] There is a lack of intelligent decision-making support systems during the quarantine process, and it is impossible to make dynamic responses based on real-time monitoring data.

[0023] To this end, we urgently need to design a rapid quarantine method suitable for cross-border animal transportation to solve the above problems. Summary of the invention

[0024] In one aspect, the present invention provides a rapid quarantine method applicable to cross-border animal transportation, comprising the following steps:

[0025] Step A: Health data collection: Use smart temperature monitoring equipment to measure the animal's body temperature T, and use machine learning algorithms to detect potential health problems through temperature fluctuations; use smart wearable devices to monitor the animal's activity level and calculate the animal's activity assessment index A eval and compare it with the normal behavior pattern to identify abnormal behavior; obtain the animal's heart rate data P through a portable heart rate monitoring device and calculate the heart rate assessment index;

[0026] Step B: Pathogen detection: Use a portable immunodiagnostic kit to detect pathogens in animal body fluid samples (such as blood, saliva, urine, etc.) and calculate the pathogen detection index D test , and combined with image recognition technology to automatically analyze the reaction results of the kit;

[0027] Step C: Environmental monitoring: Use environmental sensors to monitor the temperature T during animal transportation in real time e 、Humidity e 、Air Quality A e , oxygen concentration O 2 The parameters are transmitted to the cloud platform in real time through the Internet of Things technology, and the transportation environment assessment index E is calculated based on the environmental monitoring data. eval ;

[0028] Step D: Data analysis and health assessment: Based on the data collected in steps A, B, and C, an intelligent algorithm is used to comprehensively assess the health status of the animals and calculate the comprehensive health assessment index S. status , generate health assessment reports;

[0029] Step E: Automatic feedback and decision-making: Based on the comprehensive health assessment index S status, automatically triggering the feedback mechanism and dynamically adjusting the transportation environment or performing other emergency measures (such as isolation or re-testing) based on the feedback algorithm.

[0030] As a preferred technical solution of the present invention, in step A, the animal's body temperature T, respiratory rate R, heart rate P and other health data are used for health assessment based on artificial intelligence algorithm combined with historical data analysis. The health assessment formula is:

[0031] H eval =α 1 T+α 2 (RR 0 )+α 3 P+α 4 A eval

[0032] Among them, H eval is the health assessment index, T is the animal's body temperature, R is the respiratory rate, and R 0 is the baseline respiratory rate, P is the heart rate, A eval is the activity evaluation index, α 1 ,α 2 ,α 3 ,α 4 is the weighting factor, which is adjusted according to the health standard of the animal.

[0033] As a preferred technical solution of the present invention, in step B, the pathogen detection result D test Calculated by the following formula:

[0034]

[0035] Among them, D test is the pathogen detection result, C i is the detection coefficient of the ith pathogen, B i is the biomarker concentration of the ith pathogen, and n is the total number of pathogens detected. test 〉threshold, it is judged as unqualified for quarantine.

[0036] As a preferred technical solution of the present invention, in step C, the environmental assessment index E eval Calculated according to the following formula:

[0037] E eval =γ 1 (T e -T target )+γ 2 (H e -H target )+γ 3 (A e -A target

[0038] +γ 4 (O 2 -O target )

[0039] Among them, E eval is the environmental assessment index, T e is the transport temperature, H e is humidity, A e is the air quality, O 2 is the oxygen concentration,

[0040] T target ,H target ,A target ,O target is the target environment parameter, γ 1 ,γ 2 ,γ 3 ,γ 4 is the weight coefficient, which is used to adjust the impact of each parameter on health.

[0041] As a preferred technical solution of the present invention, in step D, the comprehensive health assessment index S status Calculated by the following formula:

[0042] S status =δ 1 H eval +δ 2 D test +δ 3 E eval +δ 4 A eval

[0043] Among them, S status is the comprehensive health assessment index, H eval is the health assessment index, D test is the pathogen detection result, E eval is the environmental assessment index, A eval is the activity evaluation index, δ 1 ,δ 2 ,δ 3 ,δ 4 is the weighting coefficient. If S status >threshold, the animal's health status is normal; if it is below the threshold, it is abnormal.

[0044] As a preferred technical solution of the present invention, in step E, the automatic feedback mechanism is calculated by the following formula:

[0045] ΔT e =∈ 1 9T target -T e )

[0046] ΔH e =∈ 2 (H target -H e )

[0047] ΔA e =∈ 3 (A target -A e )

[0048] ΔO 2 =∈ 4 (O target -O 2 )

[0049] Where, ΔT e ,ΔH e ,ΔA e ,ΔO 2 are the adjustments for temperature, humidity, air quality and oxygen concentration, respectively.

[0050] T target ,H target ,A target ,O target is the target value, ∈ 1 ,∈ 2 ,∈ 3 ,∈ 4 is the adjustment factor. The required adjustment is calculated based on real-time data

[0051] quantity, automatically adjust the environment.

[0052] As a preferred technical solution of the present invention, the data collection and transmission in steps A to E are carried out through Internet of Things technology, and all data are wirelessly transmitted to a remote quarantine center or cloud platform for quarantine personnel to view in real time and make decisions.

[0053] As a preferred technical solution of the present invention, the health assessment report in step D is generated through an intelligent platform and includes animal health assessment results, pathogen detection results, transportation environment monitoring data, etc., for relevant departments to conduct quarantine and decision-making.

[0054] As a preferred technical solution of the present invention, the system continuously collects new health data, environmental data and behavioral data, optimizes the health assessment model through machine learning algorithms, and improves quarantine efficiency and accuracy.

[0055] As a preferred technical solution of the present invention, the system has self-learning capabilities, optimizes parameters according to new data during each transportation process, and continuously improves the accuracy of comprehensive health assessment through a reinforcement learning algorithm.

[0056] Beneficial Effects

[0057] Through intelligent health data collection and real-time environmental monitoring, the present invention can quickly collect and analyze health indicators such as animal temperature, heart rate, respiratory rate, activity level, etc., and combine sensor technology to monitor the transportation environment in real time. This data-driven intelligent quarantine method avoids manual errors and time delays in traditional quarantine, and significantly improves the efficiency and accuracy of the quarantine process. By acquiring and processing data in real time, quarantine personnel can make timely judgments, so that potential health problems and disease risks can be discovered as early as possible during animal transportation.

[0058] This method can not only accurately assess the health status of animals, but also automatically adjust the transportation environment based on real-time monitoring data, so that environmental parameters such as temperature, humidity, and oxygen concentration are always kept within an appropriate range. This feedback mechanism can respond to environmental changes in real time, avoid environmental stress reactions of animals during transportation, and further improve animal health protection. Compared with traditional artificial environmental adjustment, the present invention provides an automated and intelligent way to make the transportation process safer and smoother.

[0059] By introducing machine learning algorithms and intelligent decision support systems, the present invention can integrate and analyze the collected multidimensional data to generate a comprehensive health assessment index. This makes animal quarantine no longer rely on a single health indicator, but can conduct a comprehensive assessment from multiple dimensions to draw a more comprehensive and accurate conclusion on the health status. By combining big data analysis, the system can make intelligent predictions and early warnings on animal health, helping quarantine personnel make more scientific and accurate decisions, thereby improving the level of health and safety protection during cross-border animal transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of a rapid quarantine method applicable to cross-border animal transport. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] The following combines multiple embodiments and Figure 1 , the specific implementation methods of the present invention are described in detail.

[0064] The present invention provides a rapid quarantine method suitable for cross-border animal transportation, which mainly collects animal health data, environmental data and behavioral data in real time through the integrated application of intelligent body temperature monitoring equipment, portable immune diagnosis kits, environmental sensors, wearable devices and data analysis platforms, and combines artificial intelligence and machine learning algorithms to perform data analysis and health assessment. By generating a comprehensive health assessment index, judging the health status of animals and performing automated feedback responses, the health and safety of animals during transportation is ensured.

[0065] Health data collection: During animal transportation, the animal's body temperature T is first measured through intelligent temperature monitoring equipment (such as an infrared thermometer), and the embedded artificial intelligence algorithm is used to analyze the temperature fluctuation to determine whether there is an abnormality. For example, if the body temperature is higher than the preset threshold (such as over 39°C), the system automatically issues an alarm and records the data. Next, use smart wearable devices to monitor the animal's activities, including gait, exercise, appetite, etc., and analyze the animal's activity data through machine learning algorithms.

[0066] Calculate the activity evaluation index A eval If you find that the animal's activity level is significantly reduced or its behavior is abnormal, you may judge that it may have health problems. eval <0.3 (less than 30% of the normal activity index), the system will prompt an abnormality. At the same time, the animal's heart rate data P is obtained through a portable heart rate monitoring device, and the heart rate assessment index is calculated. Assuming that the normal heart rate range is 60-100 beats / minute, if the measured heart rate is higher or lower than this range, the system will automatically detect and issue a health alarm.

[0067] Health assessment formula: Based on body temperature, respiratory rate, heart rate, activity assessment and other data, we can use the following formula to calculate the health assessment index H eval :

[0068] H eval =α 1 T+α 2 (RR 0 )+α 3P+α 4 A eval

[0069] Where: H eval is the health assessment index; T is body temperature; R is respiratory rate; R 0 is the baseline respiratory rate; P is the heart rate; A eval is the activity evaluation index; α 1 ,α 2 ,α 3 ,α 4 is the weight coefficient of each indicator, which depends on the animal species and health standards.

[0070] If H eval 〉threshold, the animal is considered to be in normal health; if H eval Below the threshold, it indicates that the animal may have health problems.

[0071] Pathogen detection: Pathogen detection is a crucial part of the present invention. Pathogens in animal body fluid samples are detected by portable immunodiagnostic kits, and the test results are automatically analyzed in combination with image recognition technology. For common animal diseases (such as foot-and-mouth disease, avian influenza, etc.), the use of immunodiagnostic kits can produce results within 15 to 30 minutes.

[0072] Specifically, the pathogen detection index D test Calculated by the following formula:

[0073]

[0074] Where: D test is the pathogen detection index; C i is the detection coefficient of the i-th pathogen; B i is the marker concentration of the i-th pathogen; n is the number of pathogen types. test >threshold, the animal is considered to be likely to carry pathogens, the system will sound an alarm and initiate further isolation or re-examination procedures.

[0075] Environmental monitoring and adjustment: The transport environment is crucial to animal health. The system is equipped with temperature sensors, humidity sensors, air quality sensors and oxygen concentration sensors to monitor the temperature, humidity, air quality and other parameters in the transport environment in real time. All monitoring data is transmitted to the cloud platform in real time through the Internet of Things technology for quarantine personnel to view. Environmental Assessment Index E eval Calculated by the following formula: E eval =γ 1 (T e -T target )+γ 2 (H e -H target )+γ3 (A e -A target )+γ 4 (O 2 -O target ) Where: E eval is the environmental assessment index; T e ,H e ,A e ,O 2 They are the temperature, humidity, air quality and oxygen concentration in the actual environment;

[0076] T target ,H target ,A target ,O target is the target environment parameter; γ 1 ,γ 2 ,γ 3 ,γ 4 is the weight coefficient. If E eval If the preset tolerance threshold is exceeded, the system will automatically adjust the transportation environment (such as air conditioning, humidifier, etc.) to keep the animals in healthy environmental conditions.

[0077] Comprehensive health assessment: Comprehensive health assessment index S status Combining animal health data, pathogen detection results and environmental monitoring data, the following formula is used to calculate:

[0078] S status =δ 1 H eval +δ 2 D test +δ 3 E eval +δ 4 A eval

[0079] Where: S status is the comprehensive health assessment index; H eval D is the health assessment index; test E is the pathogen detection result; eval is the environmental assessment index; A eval is the activity evaluation index; δ 1 ,δ 2 ,δ 3 ,δ 4 is the weight coefficient. status > the threshold, it indicates that the animal is in normal health; when it is below the threshold, it indicates that the animal may be in an unhealthy state and the system will issue an alarm.

[0080] Automatic feedback and adjustment: If an anomaly is detected, the system will automatically adjust the transportation environment or initiate emergency measures based on the feedback algorithm.

[0081] The feedback formula is as follows:

[0082] ΔT e =∈ 1 (T target -T e )

[0083] ΔH e =∈ 2 (H target -H e )

[0084] ΔA e =∈3(A target -A e )

[0085] ΔO 2 =∈ 4 (O target -O 2 )

[0086] Using the above formula, the system will automatically adjust the ambient temperature, humidity, air quality and oxygen concentration to keep the animals in the most suitable transportation conditions.

[0087] The following is a specific description with reference to several embodiments:

[0088] Example 1: Intelligent quarantine of cross-border transportation of chickens;

[0089] Step 1.1: Body temperature monitoring: Before the animals leave the country, use a portable infrared thermometer to measure the body temperature of 100 chickens. The normal range of body temperature is set at 38.0℃-39℃. The measurement results show that the body temperature of all chickens is 38.5℃. This data is transmitted to the cloud platform for analysis by the quarantine system. The system calculates the health assessment index H according to the formula eval :

[0090] H eval =α 1 T+α 2 (RR 0 )+α 3 P+α 4 A eval

[0091] Assume the weight coefficient is: α 1 =0.5,α 2 =0.2,α 3 =0.1,α 4 =0.2, body temperature data T = 38.5°C, respiratory rate R = 30 times / minute, reference value R 0 =25 beats / minute, heart rate P = 90 beats / minute, activity index A eval=0.85.

[0092] Health Assessment Index H eval The calculation results are:

[0093] H eval =0.5×38.5+0.2×(30-25 ) +0.1×90+0.2×0.85

[0094] =19.25+1+9+0.17

[0095] Due to H eval The result is less than the preset threshold (40), and the system considers the flock to be healthy and normal. Step 1.2: Activity monitoring Use smart wearable devices to monitor the activity of the flock.

[0096] The device monitors the gait and movement of the chickens and analyzes whether the animals have abnormal behavior. Based on the device data, the activity assessment index A is calculated. eval .

[0097] Assuming that all chickens are active for 5 hours, inactive for 2 hours, and the normal activity index range is 0.7-1.0, the calculation result is:

[0098]

[0099] A eval It is within the normal range and the system believes that the chickens have no obvious behavioral abnormalities.

[0100] Step 2.1: Pathogen detection;

[0101] Blood samples from 100 chickens were tested using an immunodiagnostic kit, mainly detecting foot-and-mouth disease virus, avian influenza virus, etc.

[0102] The results in the test kit are automatically analyzed through image recognition technology and AI algorithms. If the reaction color changes, the AI ​​system will analyze the change and give a result whether the virus is detected. After processing, the pathogen detection result D test The value is 0.15 (less than the set threshold of 0.2), indicating that no pathogens were found. The system records the result and continues monitoring.

[0103] Step 3.1: Real-time environmental monitoring: During the transportation process, the system continuously monitors the temperature, humidity, oxygen concentration and other parameters of the transportation environment through IoT sensors.

[0104] Assume that the current transportation environment is as follows: Temperature T e =28℃; humidity H e =75%; oxygen concentration O 2 =18%.

[0105] The environmental assessment index E is calculated according to the following formula eval :

[0106] E eval =γ 1 (T e -T target )+γ 2 (H e -H target )+γ 3 (O 2 -O target )

[0107] Assume that the target environmental parameters are: target temperature T target =25℃; target humidity H target =70%; target oxygen concentration O target =21%.

[0108] Use weight factor: γ 1 =0.5,γ 2 =0.3,γ 3 =0.2.

[0109] Calculated: E eval =0.5×(28-25)+0.3×(75-70)+0.2×(18-21)=1.5+1.5-0.6=2.4 Due to E eval =2.4 is higher than the set threshold of 2.0, and the system automatically adjusts the transportation environment, adjusting the temperature to 25°C and the humidity to 70% to keep the animals healthy.

[0110] Step 4.1: Comprehensive health assessment;

[0111] Based on various data, the health assessment results are calculated comprehensively.

[0112] Calculate the comprehensive health assessment index S status :

[0113] S status =δ 1 H eval +δ 2 D test +δ 3 E eval +δ 4 A eval

[0114] Assume that the weight coefficients are: 1 =0.4,δ 2 =0.2,δ 3 =0.3,δ 4 =0.1.

[0115] Substitute the data into the formula: S status =0.4×29.42+0.2×0.15+0.3×2.4+0.1×0.71=11.768+0.03+0.72+0.071=12.589;

[0116] Because S status >10, indicating that the flock is in good health.

[0117] Step 5.1: Automatically adjust the environment: The system automatically issues instructions to adjust the transportation environment based on the comprehensive evaluation results. Temperature adjustment formula:

[0118] ΔT e =∈1(T target -T e )

[0119] Substitute the target temperature T target =25℃ and current temperature T e =28℃, the adjustment amount is:

[0120] ΔT e =0.3×(25-28)=-0.9°C;

[0121] The system instructs to adjust the temperature to 25°C to stabilize the transportation environment.

[0122] Example 2: Quarantine of cross-border transportation of sheep;

[0123] Step 1.1: Body temperature monitoring: Measure the body temperature of the sheep flock using an infrared thermometer, with the body temperature range being 37.5℃-39.0℃.

[0124] Step 1.2: Activity monitoring: The body temperature of the 20 sheep was monitored to be 38.2℃, and the system calculated the health assessment index H eval , and the flock is considered healthy. Use smart wearable devices to monitor the activity data of the flock and calculate A eval =0.80. This index indicates that the herd activity is normal and the system does not issue an alarm.

[0125] Step 2.1: Pathogen detection: Use an immunoassay kit to detect common pathogens (such as tuberculosis, Brucella, etc.) in the blood of the flock. Pathogen detection index D test The result was 0.08, and no abnormality was found.

[0126] Step 3.1: Real-time environmental monitoring: temperature is 26°C, humidity is 70%, and oxygen concentration is 20.8%. E is calculated eval =0.3, the result is within the normal range and no adjustment is required.

[0127] Step 4.1: Comprehensive health assessment: Comprehensive health assessment index S status It is 12.2, indicating that the health of the flock is good and the transportation process will continue to be monitored.

[0128] Step 5.1: Feedback and automatic response: Since no abnormality was detected, the system did not trigger automatic feedback adjustments and only monitored the health status of the flock and environmental conditions.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid quarantine method suitable for cross-border animal transportation, characterized in that: The following steps are involved: Step A: Health data collection: Use smart temperature monitoring equipment to measure the animal's body temperature T, and use machine learning algorithms to detect potential health problems through temperature fluctuations; use smart wearable devices to monitor the animal's activity level and calculate the animal's activity assessment index A eval and compare it with normal behavior patterns to identify abnormal behavior; Obtain the animal's heart rate data P through a portable heart rate monitoring device and calculate the heart rate assessment index; Step B: Pathogen detection: Use a portable immunodiagnostic kit to detect pathogens in the animal's body fluid samples and calculate the pathogen detection index D test , and combined with image recognition technology to automatically analyze the reaction results of the kit; Step C: Environmental monitoring: Use environmental sensors to monitor the temperature T during animal transportation in real time e 、Humidity e 、Air Quality A e , oxygen concentration O2 parameters are transmitted to the cloud platform in real time through the Internet of Things technology, and the transportation environment assessment index E is calculated based on environmental monitoring data eval , Step D: Data analysis and health assessment: Based on the data collected in steps A, B, and C, an intelligent algorithm is used to comprehensively assess the health status of the animals and calculate the comprehensive health assessment index S. status , generate health assessment reports; Step E: Automatic feedback and decision-making: Based on the comprehensive health assessment index S status , automatically trigger the feedback mechanism, and dynamically adjust the transportation environment or perform other emergency measures based on the feedback algorithm.

2. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: In step A, the animal's body temperature T, respiratory rate R, and heart rate P health data are evaluated based on an artificial intelligence algorithm combined with historical data analysis. The health evaluation formula is: H eval =α1T+α2(R-R0)+α3P+α4A eval Among them, H e val is the health assessment index, T is the animal's body temperature, R is the respiratory rate, R0 is the baseline respiratory rate, P is the heart rate, A eval is the activity assessment index, α1, α2, α3, and α4 are weight coefficients, which are adjusted according to the health standards of the animals.

3. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: In step B, the pathogen detection result D test Calculated by the following formula: Among them, D test is the pathogen detection result, C i is the detection coefficient of the ith pathogen, B i is the biomarker concentration of the ith pathogen, n is the total number of pathogens detected, if D test >threshold, it is judged as failing quarantine.

4. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: In step C, the environmental assessment index E eval Calculated according to the following formula: E eval =γ1(T e -T target )+γ2(H e -H target )+γ3(A e -A target )+γ4(O2-O target ) Among them, E eval is the environmental assessment index, T e is the transport temperature, H e is humidity, A e is the air quality, O2 is the oxygen concentration, T target , H target , A target , O target is the target environmental parameter, γ1, γ2, γ3, γ4 are weight coefficients used to adjust the impact of each parameter on health.

5. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: In the step D, the comprehensive health assessment index S status Calculated by the following formula: S status =δ1H eval +δ2D test +δ3E eval +δ4A eval Among them, S status is the comprehensive health assessment index, H eval is the health assessment index, D test is the pathogen detection result, E eval is the environmental assessment index, A eval is the activity evaluation index, δ1, δ2, δ3, δ4 are weighted coefficients, if S status >threshold, the animal's health status is normal; if it is below the threshold, it is abnormal.

6. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: In step E, the automatic feedback mechanism is calculated by the following formula: ΔT e =∈1(T target -T e ) ΔH e =∈2(H target -H e ) ΔA e =∈3(A target -IN e ) ΔO2=∈4(O target -O2) Where, ΔT e , ΔH e , ΔA e , ΔO2 are the adjustments for temperature, humidity, air quality and oxygen concentration, T target , H target , A target , O target is the target value, ∈1, ∈2, ∈3, ∈4 are adjustment coefficients, and the required adjustment amount is calculated according to the real-time data to automatically adjust the environment.

7. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: The data collection and transmission in steps A to E are carried out through the Internet of Things technology, and all data are wirelessly transmitted to a remote quarantine center or cloud platform for quarantine personnel to view in real time and make decisions.

8. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1 is characterized in that: The health assessment report in step D is generated through the intelligent platform and includes the animal's health assessment results, pathogen detection results, and transportation environment monitoring data for relevant departments to conduct quarantine and decision-making.

9. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1, characterized in that: The system continuously collects new health data, environmental data, and behavioral data, and optimizes the health assessment model through machine learning algorithms to improve quarantine efficiency and accuracy.

10. The intelligent rapid quarantine method for cross-border animal transportation according to claim 1, characterized in that: The system has self-learning capabilities, optimizing parameters based on new data during each transport process and continuously improving the accuracy of comprehensive health assessment through reinforcement learning algorithms.

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