Constant-temperature livestock bio-safety transportation intelligent management and control system based on internet of things

Abstract

The present application relates to the technical field of data security, and discloses a constant-temperature livestock bio-safety transportation intelligent management and control system based on an Internet of Things, which comprises: an Internet of Things sensing module, which is used for collecting the position, physiological state, activity state, carriage environment parameters and vehicle running state of livestock, and forming multi-source state data of livestock transportation; and a dynamic heat source analysis module, which is used for determining the dynamic heat contribution of each livestock according to the multi-source state data of livestock transportation. The dynamic heat source analysis module is arranged, each livestock is regarded as a mobile heat source changing with position and state, the dynamic heat contribution is determined according to the body weight, activity state, physiological state and local aggregation degree of the livestock, and the heat source migration trend is predicted in combination with the vehicle running state, so that the effect of early identification of local heat load change of the carriage is achieved. Compared with the prior art, which regards the heat load of livestock as fixed or uniformly distributed, the problem of inaccurate identification of local high-temperature areas and temperature regulation lag is solved.

Description

Technical Field

[0001] This invention relates to the field of agricultural product transportation technology, specifically to an intelligent management and control system for temperature-controlled livestock biosafety transportation based on the Internet of Things. Background Technology

[0002] With the continuous improvement of the scale and intensification of animal husbandry, long-distance transportation of livestock between farms, trading markets, slaughterhouses, and quarantine sites has become a routine part of the industry chain. During such transportation, environmental parameters such as temperature, humidity, and air quality inside the vehicle directly affect the physiological homeostasis and transportation safety of livestock. Especially in scenarios of high-density loading and long-term transportation, livestock continuously release metabolic heat, and the combined effects of respiration, excretion, and limb movement cause heat to accumulate rapidly inside the vehicle, resulting in significant local temperature rises. To alleviate heat stress caused by high temperature or stuffy environments, existing transport vehicles are generally equipped with temperature sensors, ventilation equipment, and cooling or heating devices. By collecting temperature signals inside the vehicle and controlling the operation of environmental regulation equipment, the aim is to maintain the temperature within a preset comfort range.

[0003] However, current mainstream control strategies still rely on the overall temperature of the vehicle compartment or the results of temperature measurements at fixed points, adjusting cooling and ventilation capacities accordingly and treating livestock heat load as a relatively fixed or homogeneously distributed static parameter. But in actual transportation, vibrations and accelerations / decelerations caused by vehicle movement, along with the herd instincts and stress responses of livestock, lead to frequent movement and localized aggregation within the compartment. Simultaneously, differences in weight, activity level, and physiological state among individuals further exacerbate the non-uniformity and time-varying nature of heat source distribution. This dynamic change makes control methods based on fixed measurement points and average temperatures unable to accurately capture localized heat load fluctuations caused by the evolution of livestock spatial distribution. This results in recognition delays in the initial stages of localized high temperatures, leading to untimely adjustment responses and ultimately impacting overall transportation safety and animal welfare.

[0004] However, it must be acknowledged that the aforementioned control methods are essentially passive feedback regulation, with particularly prominent technical limitations. On the one hand, existing systems rely excessively on temperature values ​​from a limited number of measuring points, lacking the ability to perceive the spatial distribution density of livestock and individual differences in heat production within the transport compartment in real time. This effectively creates a large-scale blind spot in thermal field monitoring, resulting in localized overheating areas remaining undetected for extended periods. On the other hand, because the control logic is based on a unified scheduling of overall average temperature, it cannot implement differentiated supply according to the actual heat load of different areas. This not only leads to a wasteful use of cooling and ventilation capacity but also creates a regulatory paradox where overall temperature meets standards while localized areas continue to deteriorate when local heat sources rise sharply. More critically, existing strategies lack forward-looking judgment on the spatiotemporal evolution of heat load, and regulatory actions always lag behind changes in the thermal field. By the time sensors detect temperature anomalies, localized thermal stress has often already formed, resulting in a persistently high risk of heatstroke, agitation, and even death in livestock during transport. This makes it difficult to meet the dual demands of modern animal husbandry for precise transportation safety and efficient energy management. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent management and control system for the constant-temperature livestock biosafety transportation based on the Internet of Things, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management and control system for constant-temperature livestock biosafety transportation based on the Internet of Things, comprising: The IoT sensing module is used to collect the location, physiological state, activity state, environmental parameters of the vehicle compartment, and vehicle operation status of livestock, forming multi-source status data for livestock transportation. The dynamic heat source analysis module determines the dynamic heat contribution of each animal based on the multi-source status data of livestock transportation, and predicts the heat load changes of each carriage area by combining the animal's location and movement trend, thereby obtaining the predicted heat risk. The heat-disease composite risk analysis module determines biosafety risks based on the predicted heat risk, livestock physiological status, air quality, and livestock distribution, and performs a comprehensive analysis of the predicted heat risk and biosafety risks to generate dynamic risk zoning results. The collaborative control module adjusts the cooling capacity, air supply volume, return air status and exhaust air status of different carriage areas according to the dynamic risk zoning results, and implements local circulating heat exchange and directional exhaust air collaborative control for areas with both thermal risk and biosafety risk. The feedback correction module corrects the relevant parameters of the dynamic heat source analysis module and the heat-epidemic composite risk analysis module based on the actual temperature, air quality and changes in livestock status after coordinated control, and uses them for the next control cycle.

[0007] This invention provides an IoT-based intelligent control system for constant-temperature livestock biosafety transportation. By incorporating an IoT sensing module, a dynamic heat source analysis module, a heat-epidemic composite risk analysis module, a collaborative control module, and a feedback correction module, it constructs a closed-loop intelligent control architecture encompassing sensing, analysis, decision-making, execution, and correction. Compared to existing technologies that rely solely on fixed temperature measurement points for passive temperature regulation, this invention, for the first time, integrates the dynamic heat contribution of individual livestock, spatial migration trends, and biosafety risks into a unified analysis framework. This enables proactive prediction and differentiated control of the thermal environment and biosafety status within the transport compartment. In particular, the feedback correction module endows the system with self-learning and adaptive capabilities, continuously optimizing the analysis model parameters based on actual control effects. This overcomes the shortcomings of traditional open-loop or fixed-parameter control in adapting to complex dynamic changes during transportation, improving the system's robustness and control accuracy under different transportation conditions, livestock species, and loading densities, thus providing comprehensive environmental safety assurance for long-distance livestock transportation.

[0008] Preferably, the IoT sensing module includes: The livestock status sensing unit is used to collect information on the livestock’s spatial location, body surface temperature, respiratory status, and activity status. The carriage environment sensing unit is used to collect temperature, humidity, air flow rate, carbon dioxide concentration, ammonia concentration and aerosol concentration in different areas of the carriage. The vehicle status sensing unit is used to collect data on the vehicle's speed, longitudinal acceleration, lateral acceleration, steering status, and slope status. The data time-series correlation unit is used to correlate the livestock status, the carriage environment status, and the vehicle operation status according to the collection time to form the multi-source status data of the livestock transportation.

[0009] The IoT sensing module in the above solution achieves synchronous collection and temporal correlation of individual livestock status, multi-area environmental parameters of the carriage, and vehicle driving status by setting up livestock status sensing units, carriage environment sensing units, vehicle status sensing units, and data temporal correlation units. Compared with existing technologies that only collect the overall temperature of the carriage or environmental parameters at limited points, this invention fully considers the driving effect of vehicle driving dynamics, such as acceleration, deceleration, steering, and gradient, on the spatial distribution of livestock and heat source migration. Furthermore, the data temporal correlation unit ensures strict alignment of multi-source heterogeneous data in the time dimension, providing a comprehensive, accurate, and traceable data foundation for subsequent dynamic heat source analysis and heat source migration prediction. This multi-dimensional sensing architecture effectively eliminates the control blind spots caused by insufficient sensing information in traditional solutions, enabling the system to keenly capture the redistribution of livestock and dynamic evolution of heat sources caused by changes in vehicle driving status.

[0010] Preferably, the dynamic heat source analysis module includes: The individual heat contribution calculation unit determines the dynamic heat contribution of each animal based on its basic heat production status, activity status, physiological status, and local aggregation degree. The spatial heat contribution mapping unit maps the dynamic heat contribution to the corresponding carriage area according to the spatial location of each animal, forming a dynamic heat contribution distribution. The heat source migration prediction unit predicts the migration trend of each animal between different carriage areas based on the animal's movement status and the vehicle's operating status, and predicts the heat load change of each carriage area based on the migration trend.

[0011] The dynamic heat source analysis module of this invention achieves a leapfrog improvement from the static fixed heat source assumption to dynamic real-time tracking of individual heat sources through the coordinated operation of an individual heat contribution calculation unit, a spatial heat contribution mapping unit, and a heat source migration prediction unit. The individual heat contribution calculation unit can calculate the dynamic heat contribution of each animal in real time based on its activity intensity, physiological state, and local aggregation degree, avoiding the crude approach of treating all animals as uniform heat sources in traditional schemes. The spatial heat contribution mapping unit accurately locates the individual heat contribution to the corresponding vehicle compartment area, forming a dynamic heat contribution distribution that updates with the animal's position. The heat source migration prediction unit further combines the animal's movement status and the vehicle's operating status to predict the migration trend of each animal between different areas, thereby achieving advanced prediction of heat load changes. This technical approach solves the problems of delayed heat field monitoring and missed detection of local high temperatures caused by frequent animal movement and local aggregation in existing systems, enabling the system to take pre-adjustment measures before the heat load increases, improving the timeliness and proactivity of temperature control.

[0012] Preferably, the method for determining dynamic thermal contribution by the individual thermal contribution calculation unit includes: Step 1.1, determine the number of animals based on their species and weight. Basic heat contribution of head livestock The baseline heat contribution is then corrected based on the livestock's activity intensity, physiological deviation, local aggregation level, and local heat dissipation capacity to obtain the first... The first animal at any time The dynamic thermal contribution is calculated using the following formula: , Among them, among them, Indicates the first The first animal at any time Dynamic thermal contribution, Indicates the first The basic heat contribution of the head of livestock Indicates the first Normalized activity intensity of livestock Indicates the first The normalized clustering degree of the head of livestock within the preset neighborhood. Indicates the first The degree of deviation of the body surface temperature of the first animal, Indicates the first The degree of deviation in the breathing state of the lead animal. Indicates the first Normalized local heat dissipation capacity of the location of the lead animal. , , , , All are dynamic heat contribution adjustment coefficients; Step 1.2: Based on the spatial location of each animal, map the dynamic heat contribution of each animal to the corresponding carriage area according to the spatial influence range, and superimpose the dynamic heat contributions mapped to the same carriage area to form a dynamic heat contribution distribution that is updated as the animal's location and state change.

[0013] The individual heat contribution calculation unit in the above scheme constructs a dynamic heat contribution calculation formula that includes multi-dimensional correction factors. This nonlinearly couples the basic heat contribution with multiple dynamic variables such as activity intensity, aggregation degree, body surface temperature deviation, respiratory state deviation, and local heat dissipation capacity, achieving precise quantification of the real-time heat production of each animal. Compared to existing technologies that treat livestock heat load as a fixed constant, the above formula fully considers the changes in heat production caused by stress, crowding, activity, and physiological state fluctuations during transportation. This makes the heat contribution calculation closer to the actual physical process. In particular, the introduction of aggregation degree correction terms and local heat dissipation capacity correction terms accurately reflects the cumulative heat production effect in high-density aggregation areas and the constraint of poor local ventilation on heat dissipation capacity. This provides scientific and accurate data support for subsequent thermal risk identification, improving the accuracy and reliability of heat source analysis.

[0014] Preferably, the method by which the heat source migration prediction unit predicts heat source migration includes: Step 2.1, according to the first The direction of movement of the first animal, the direction of vehicle inertia, the spatial distance to the target carriage area, and the degree of animal concentration in the target carriage area are used to determine the first... First livestock to the first The heat source transfer potential corresponding to the migration of the carriage area: , in, Indicates the first The dynamic thermal contribution of the first animal to the first The heat source transfer potential of the carriage area migration Indicates the first The direction and direction of the first animal's movement The degree of consistency between the directions of the carriage areas Indicates the direction and orientation of the vehicle's inertia. The degree of consistency between the directions of the carriage areas Indicates the first First livestock and the first Normalized spatial distances between carriage areas Indicates the first The degree of normalized livestock concentration in the carriage area Indicates the first Normalized movement speed of the first animal, Indicates the prediction time interval. , , , , All are heat source migration adjustment coefficients; Step 2.2: Normalize the allocation of the dynamic heat contribution of each livestock based on the heat transfer potential of each heat source to obtain the future time step. Predicted thermal contribution of the carriage area: , in, Indicates the future time. Predicted heat contribution of the carriage area Indicates the total number of livestock. Indicates the total number of carriage areas. Indicates the carriage area index. This represents the migration normalization constant to prevent the denominator from being zero.

[0015] The heat source migration prediction unit of this invention achieves accurate prediction of the heat contribution distribution of each carriage area at future times by constructing a heat source transfer potential calculation formula that incorporates multiple factors such as livestock movement direction, vehicle inertia, spatial distance, and target area aggregation degree. Compared with the static analysis method in the prior art that completely ignores the impact of livestock spatial migration on heat distribution, this invention, for the first time, incorporates vehicle driving status as an important driving factor affecting livestock migration into the prediction model, fully considering the forced influence of acceleration, deceleration, and turning during transportation on the distribution of livestock positions. At the same time, by using the product of livestock movement speed and prediction time interval as a distance attenuation factor, the prediction model can dynamically adjust the prediction sensitivity according to the actual movement speed of livestock. The prediction results are more in line with the physical laws of actual transportation scenarios, providing a reliable quantitative basis for early warning of heat risks and early determination of pre-temperature adjustment areas.

[0016] Preferably, the dynamic heat source analysis module determines the predicted heat risk based on the predicted heat contribution of each carriage area, and the calculation formula is as follows: , in, Indicates the first Predicted thermal risk in the carriage area Indicates the first Predicted heat contribution of the carriage area Indicates the first Effective heat exchange capacity of the carriage area Indicates the first The degree of livestock concentration in the carriage area , , , All are thermal risk adjustment coefficients; When the Predicted thermal risk in carriage area Greater than the preset thermal risk threshold, and the first When the current actual temperature in the carriage area does not reach the preset temperature adjustment threshold, the first... The carriage area was designated as a pre-temperature controlled zone.

[0017] The dynamic heat source analysis module of the above-mentioned scheme constructs a formula for predicting thermal risk, comprehensively considering the predicted heat contribution with the effective heat exchange capacity of the carriage area and the degree of livestock aggregation, thus achieving a scientific quantitative assessment of the severity of thermal risk. In particular, this invention introduces a pre-temperature-adjustment zone determination mechanism. When the predicted thermal risk exceeds a threshold but the current actual temperature has not yet reached the adjustment threshold, the system determines that the area is a pre-temperature-adjustment zone and initiates adjustment measures in advance. This mechanism changes the passive response mode in the prior art, giving the system the ability to intervene in advance for impending high-temperature risks, avoiding the time lag between heat load accumulation and temperature overshoot, and transforming temperature control from post-event remediation to pre-event proactive prevention, reducing the probability of livestock suffering sudden heat stress during transportation.

[0018] Preferably, the method for determining biosafety risks by the heat-epidemic composite risk analysis module includes: Step 3.1: Identify abnormal livestock based on the degree of deviation of their body surface temperature, respiratory status, and activity status, and determine the abnormal livestock as dynamic biological risk sources. Step 3.2: Based on the physiological abnormality risk intensity of the dynamic biological risk source, the spatial distance between the dynamic biological risk source and the target carriage area, the airflow direction, and the air pollution level of the target carriage area, calculate the first... Biosafety risks in the carriage area: , Among them, among them, Indicates the first Biosafety risks in the carriage area Indicates an abnormal collection of livestock. Indicates the first The risk intensity of physiological abnormalities in livestock with abnormal head conditions Indicates the first The first abnormal livestock and the first Normalized spatial distances between carriage areas Indicates the first The spatial decay scale of risk corresponding to abnormal livestock head Indicates the first Normalized air pollution levels in the carriage area This indicates the direction from the location of the i-th abnormal animal to the i-th abnormal animal. The degree of consistency between the orientation of the carriage area and the actual direction of airflow. , , All are biosafety risk adjustment coefficients, representing the biosafety risk coefficient of the j-th carriage area.

[0019] The heat-disease composite risk analysis module in the above-mentioned scheme, by identifying dynamic biological risk sources and constructing a biosafety risk calculation formula, is the first to incorporate the risk of disease transmission caused by individual livestock physiological abnormalities into the transportation environment risk assessment system. Compared with the single-dimensional assessment methods of existing technologies that only focus on the thermal environment and ignore biosafety risks, this invention fully considers the transmission mechanism of pathogens spread to other areas by abnormal livestock as biological risk sources through airflow and spatial distance, realizing spatial quantitative assessment of biosafety risks. This technical solution effectively fills the technical gap in real-time assessment of biosafety risks during livestock transportation, making early warning and transmission blocking of diseases during transportation possible, and providing dual protection of thermal environment and biosafety to ensure the safety of livestock transportation.

[0020] Preferably, the heat-epidemic composite risk analysis module couples the prediction of heat risk and biosafety risk to obtain the first... The combined risks of heat and epidemic in the carriage area: , in, Indicates the first The combined risks of heat and COVID-19 in the carriage area This represents the normalized predicted heat risk. This represents the normalized biosafety risk, where mHm_HmH represents the thermal risk sensitivity coefficient. Indicates the biosafety risk sensitivity coefficient. This represents the coupling enhancement coefficient when both predicted thermal risk and biosafety risk increase simultaneously. Based on the predicted thermal risk, biosafety risk, and combined thermal-epidemic risk, each carriage area is divided into a general control area, a thermal load growth area, a biosafety risk area, and a combined thermal-epidemic high-risk area, forming the dynamic risk zoning result.

[0021] The thermal-biosafety composite risk analysis module of this invention constructs a coupled calculation model for thermal-biosafety composite risks, nonlinearly fusing predicted thermal risks and biosafety risks. It also introduces a coupling enhancement coefficient when both risks increase simultaneously, achieving accurate capture of the interaction and superposition effects of the two risks. Compared to the existing approach of separating and independently handling thermal management and biosafety management, this invention, by setting thermal risk sensitivity coefficients and biosafety risk sensitivity coefficients, allows the system to flexibly adjust its sensitivity to both types of risks according to different transportation scenarios. The introduction of the coupling enhancement coefficient ensures that when thermal risk and biosafety risk increase simultaneously in the same carriage area, the composite risk is reasonably amplified and enhanced, thereby attracting high attention and priority in control strategies. The resulting dynamic risk zoning provides a scientific, precise, and executable basis for the collaborative control module, achieving risk-oriented differentiated and precise management.

[0022] Preferably, the collaborative control module determines the heat-epidemic conflict coefficient for high-risk areas with combined heat and epidemic risks. , in, Indicates the first The coefficient of thermal-epidemic conflict in the carriage area This represents the stability constant used in calculating the conflict coefficient. Parameters indicating enhanced risk of combined heat and epidemic; When the heat-epidemic conflict coefficient is greater than the preset conflict threshold, the proportion of common return air between the heat-epidemic composite high-risk area and other carriage areas is reduced, the proportion of internal circulation heat exchange in the heat-epidemic composite high-risk area is increased, and the directional exhaust volume is increased according to the biosafety risk, so that the air inside the area completes heat transfer through the circulation heat exchange path, and the air with biosafety risk is discharged through the directional exhaust path. The proportion of internal heat exchange in the region is determined according to the following formula: , in, Indicates the first The internal circulation heat exchange ratio in the carriage area This indicates the preset minimum internal circulation heat exchange ratio. This indicates the preset maximum internal circulation heat exchange ratio. This represents the stability constant calculated based on the cyclic heat exchange ratio.

[0023] The collaborative control module of this invention solves the conflict between the control objectives of heat dissipation through exhaust ventilation and the need to reduce cross-contamination through exhaust ventilation in high-risk areas with both heat and disease by constructing a heat-disease conflict coefficient and establishing an adaptive calculation formula for the internal circulation heat exchange ratio. When the heat-disease conflict coefficient exceeds a preset threshold, the system automatically reduces the proportion of common return air between high-risk areas and other areas, while increasing the proportion of internal circulation heat exchange and increasing the directional exhaust volume. This allows heat to be transferred through the internal circulation path, while air posing a biosafety risk is directly discharged through the directional exhaust path, achieving a multi-path collaborative treatment of heat removal and pollutant isolation. This technical solution overcomes the limitation of single ventilation and cooling control objectives in existing systems, achieving compatibility and balance between thermal environment regulation and biosafety protection for the first time under the same control framework. It avoids the dilemma of exacerbating disease transmission in order to reduce heat risk or worsening the thermal environment in order to control the epidemic, and has important practical value for ensuring the health of livestock during high-density long-distance transportation.

[0024] Preferably, the feedback correction module dynamically corrects the relevant parameters for the next control cycle based on the deviation between the actual environmental response and the predicted environmental response after coordinated control. , in, This indicates the parameter to be corrected in the next control cycle. This indicates the parameter to be corrected in the current control cycle. This represents the actual environmental response value after coordinated control. This represents the predicted environmental response value. Indicates the parameter correction rate. The stability coefficient represents the stability of the correction process. These represent the lower and upper limits of the parameter to be corrected, respectively. This indicates that the correction result will be limited between the lower and upper limits; The parameters to be corrected include the dynamic heat contribution adjustment coefficient, the heat source migration adjustment coefficient, the heat risk adjustment coefficient, and the biosafety risk adjustment coefficient. The corrected corresponding parameters are fed back to the dynamic heat source analysis module and the heat-epidemic composite risk analysis module for the calculation of dynamic heat contribution, prediction of heat source migration, calculation of predicted heat risk, and calculation of biosafety risk in the next control cycle.

[0025] The feedback correction module of this invention achieves online adaptive updates of key model parameters such as the dynamic heat contribution adjustment coefficient, heat source migration adjustment coefficient, thermal risk adjustment coefficient, and biosafety risk adjustment coefficient by constructing an iterative correction formula that includes the deviation between the actual response and the predicted response. The corrected parameters are then fed back to the front-end analysis module for calculation in the next control cycle. Compared with the open-loop control method in the prior art where model parameters are fixed once set, this invention enables the entire intelligent control system to possess complete closed-loop self-learning capabilities. It can automatically adjust model parameters according to the deviation between the actual control effect and the predicted effect, ensuring that the system maintains optimal control performance under different transportation conditions, loading states, and external environmental conditions. This online adaptive parameter correction mechanism overcomes the drawback of fixed-parameter models where control performance declines due to changes in the scenario in practical applications, ensuring the long-term stability and high-precision control capability of the system in complex and ever-changing actual transportation environments.

[0026] This invention provides an intelligent management and control system for the biosafety transportation of livestock based on the Internet of Things. It has the following beneficial effects: 1. This invention sets up a dynamic heat source analysis module, treating each animal as a mobile heat source that changes with its location and state. The dynamic heat contribution is determined based on the animal's weight, activity state, physiological state, and local aggregation degree. Combined with the vehicle's operating status, the heat source migration trend is predicted, which can identify changes in local heat load in the vehicle compartment in advance. Compared with the existing technology that treats the animal's heat load as fixed or uniformly distributed, this invention solves the problems of inaccurate identification of local high temperature areas and lagging temperature regulation.

[0027] 2. This invention sets up a heat-disease composite risk analysis module to couple the analysis of predicted heat risk with the biosafety risks corresponding to abnormal livestock conditions, air pollution levels, and airflow propagation directions. This achieves the effect of dynamically identifying areas of increasing heat load, biosafety risk areas, and high-risk areas of heat-disease composite risk. Compared with existing technologies that implement constant temperature control and biosafety management separately, this invention solves the problem of difficulty in comprehensively judging and classifying the risks of heat stress and pathogen transmission in the same area.

[0028] 3. By setting up a collaborative control module, this invention adopts a control method that combines local circulating heat exchange, restricting cross-regional return air, and directional exhaust for high-risk areas with combined heat and epidemic risks. This effectively separates the regional cooling path from the risky air emission path. Compared with existing technologies that cool down by increasing the overall ventilation volume, this invention solves the problems of polluted air easily spreading to other areas during the cooling process and the conflict between the constant temperature requirement and the biosafety isolation requirement. Attached Figure Description

[0029] Figure 1This is a block diagram of the overall architecture of the IoT-based intelligent management and control system for constant-temperature livestock biosafety transportation of the present invention. Figure 2 This is a block diagram of the IoT sensing module in this invention; Figure 3 This is a block diagram of the dynamic heat source analysis module in this invention; Figure 4 This is a flowchart of the dynamic thermal contribution calculation process in this invention; Figure 5 This is a flowchart of the heat source migration prediction process in this invention; Figure 6 This is a flowchart of the process for predicting thermal risks and determining the pre-temperature control zone in this invention. Figure 7 This is a flowchart of the biosafety risk calculation process in this invention; Figure 8 This is a flowchart of the dynamic risk partitioning generation process in this invention; Figure 9 This is a flowchart illustrating the execution process of the collaborative control strategy in this invention. Figure 10 This is a block diagram of the feedback correction closed-loop control in this invention. Detailed Implementation

[0030] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0031] like Figures 1-10 As shown in the accompanying drawings, the present invention will now be described in detail: This invention provides an intelligent management and control system for the biosafety transportation of temperature-controlled livestock based on the Internet of Things, comprising: The IoT sensing module is used to collect the location, physiological state, activity state, environmental parameters of the vehicle compartment, and vehicle operation status of livestock, forming multi-source status data for livestock transportation. The dynamic heat source analysis module determines the dynamic heat contribution of each animal based on the multi-source status data of livestock transportation, and predicts the heat load changes of each carriage area by combining the animal's location and movement trend, thereby obtaining the predicted heat risk. The heat-disease composite risk analysis module determines biosafety risks based on the predicted heat risk, livestock physiological status, air quality, and livestock distribution, and performs a comprehensive analysis of the predicted heat risk and biosafety risks to generate dynamic risk zoning results. The collaborative control module adjusts the cooling capacity, air supply volume, return air status and exhaust air status of different carriage areas according to the dynamic risk zoning results, and implements local circulating heat exchange and directional exhaust air collaborative control for areas with both thermal risk and biosafety risk. The feedback correction module corrects the relevant parameters of the dynamic heat source analysis module and the heat-epidemic composite risk analysis module based on the actual temperature, air quality and changes in livestock status after coordinated control, and uses them for the next control cycle.

[0032] The IoT sensing module includes: The livestock status sensing unit is used to collect information on the livestock’s spatial location, body surface temperature, respiratory status, and activity status. The carriage environment sensing unit is used to collect temperature, humidity, air flow rate, carbon dioxide concentration, ammonia concentration and aerosol concentration in different areas of the carriage. The vehicle status sensing unit is used to collect data on the vehicle's speed, longitudinal acceleration, lateral acceleration, steering status, and slope status. The data time-series correlation unit is used to correlate the livestock status, the carriage environment status, and the vehicle operation status according to the collection time, to form the multi-source status data of the livestock transportation.

[0033] Specifically, an IoT sensing module is installed to synchronously collect data on livestock location, body surface temperature, respiratory status, activity level, vehicle compartment environmental parameters, and vehicle operating status. Correlation relationships are established between different types of data based on the time of collection, characterizing the transportation process from multiple dimensions: individual livestock status, vehicle compartment environmental status, and vehicle operating conditions. This provides a complete and continuous data foundation for dynamic heat source calculation and biosafety risk analysis. Compared to existing technologies that primarily rely on the overall temperature of the vehicle compartment or a few fixed temperature measurement points for environmental assessment, this invention can more promptly reflect the dynamic changes in livestock distribution and local environments, solving the problems of limited sensing dimensions and difficulty in timely identification of local anomalies. The dynamic heat source analysis module includes: The individual heat contribution calculation unit determines the dynamic heat contribution of each animal based on its basic heat production status, activity status, physiological status, and local aggregation degree. The spatial heat contribution mapping unit maps the dynamic heat contribution to the corresponding carriage area according to the spatial location of each animal, forming a dynamic heat contribution distribution. The heat source migration prediction unit predicts the migration trend of each animal between different carriage areas based on the animal's movement status and the vehicle's operating status, and predicts the heat load change of each carriage area based on the migration trend.

[0034] The method for determining dynamic thermal contribution by the individual thermal contribution calculation unit includes: Step 1.1, determine the number of animals based on their species and weight. Basic heat contribution of head livestock The baseline heat contribution is then corrected based on the livestock's activity intensity, physiological deviation, local aggregation level, and local heat dissipation capacity to obtain the first... The first animal at any time The dynamic thermal contribution is calculated using the following formula: , Among them, among them, Indicates the first The first animal at any time Dynamic thermal contribution, Indicates the first The basic heat contribution of the head of livestock Indicates the first Normalized activity intensity of livestock Indicates the first The normalized clustering degree of the head of livestock within the preset neighborhood. Indicates the first The degree of deviation of the body surface temperature of the first animal, Indicates the first The degree of deviation in the breathing state of the lead animal. Indicates the first Normalized local heat dissipation capacity of the location of the lead animal. , , , , All are dynamic heat contribution adjustment coefficients; Step 1.2: Based on the spatial location of each animal, map the dynamic heat contribution of each animal to the corresponding carriage area according to the spatial influence range, and superimpose the dynamic heat contributions mapped to the same carriage area to form a dynamic heat contribution distribution that is updated as the animal's location and state change.

[0035] The method for predicting heat source migration by the heat source migration prediction unit includes: Step 2.1, according to the first The direction of movement of the first animal, the direction of vehicle inertia, the spatial distance to the target carriage area, and the degree of animal concentration in the target carriage area are used to determine the first... First livestock to the first The heat source transfer potential corresponding to the migration of the carriage area: , in, Indicates the first The dynamic thermal contribution of the first animal to the first The heat source transfer potential of the carriage area migration Indicates the first The direction and direction of the first animal's movement The degree of consistency between the directions of the carriage areas Indicates the direction and orientation of the vehicle's inertia. The degree of consistency between the directions of the carriage areas Indicates the first First livestock and the first Normalized spatial distances between carriage areas Indicates the first The degree of normalized livestock concentration in the carriage area Indicates the first Normalized movement speed of the first animal, Indicates the prediction time interval. , , , , All are heat source migration adjustment coefficients; Step 2.2: Normalize the allocation of the dynamic heat contribution of each livestock based on the heat transfer potential of each heat source to obtain the future time step. Predicted thermal contribution of the carriage area: , in, Indicates the future time. Predicted heat contribution of the carriage area Indicates the total number of livestock. Indicates the total number of carriage areas. Indicates the carriage area index. This represents the migration normalization constant to prevent the denominator from being zero.

[0036] Preferably, the dynamic heat source analysis module determines the predicted heat risk based on the predicted heat contribution of each carriage area, and the calculation formula is as follows: , in, Indicates the first Predicted thermal risk in the carriage area Indicates the first Predicted heat contribution of the carriage area Indicates the first Effective heat exchange capacity of the carriage area Indicates the first The degree of livestock concentration in the carriage area , , , All are thermal risk adjustment coefficients; When the Predicted thermal risk in carriage area Greater than the preset thermal risk threshold, and the first When the current actual temperature in the carriage area does not reach the preset temperature adjustment threshold, the first... The carriage area was designated as a pre-temperature controlled zone.

[0037] Specifically, by setting up a dynamic heat source analysis module, the dynamic heat contribution of each animal is determined based on its weight, activity level, physiological state, local aggregation degree, and the heat dissipation conditions of its location. Combined with the animal's movement direction and speed, as well as the vehicle's acceleration, turning state, and slope, the migration trend of the dynamic heat source between different compartment areas is predicted. This allows for the identification of potentially high heat load areas before the actual temperature exceeds a set range. Compared to existing technologies that treat animal heat load as a fixed or uniformly distributed heat source, this invention accurately characterizes the local heat load changes caused by animal movement, aggregation, and physiological state changes, solving the problems of delayed identification of local high-heat areas and insufficient targeted temperature regulation. The methods used by the heat-epidemic composite risk analysis module to determine biosafety risks include: Step 3.1: Identify abnormal livestock based on the degree of deviation of their body surface temperature, respiratory status, and activity status, and determine the abnormal livestock as dynamic biological risk sources. Step 3.2: Based on the physiological abnormality risk intensity of the dynamic biological risk source, the spatial distance between the dynamic biological risk source and the target carriage area, the airflow direction, and the air pollution level of the target carriage area, calculate the first... Biosafety risks in the carriage area: , Among them, among them, Indicates the first Biosafety risks in the carriage area Indicates an abnormal collection of livestock. Indicates the first The risk intensity of physiological abnormalities in livestock with abnormal head conditions Indicates the first The first abnormal livestock and the first Normalized spatial distances between carriage areas Indicates the first The spatial decay scale of risk corresponding to abnormal livestock head Indicates the first Normalized air pollution levels in the carriage area This indicates the direction from the location of the i-th abnormal animal to the i-th abnormal animal. The degree of consistency between the orientation of the carriage area and the actual direction of airflow. , , All are biosafety risk adjustment coefficients, representing the biosafety risk coefficient of the j-th carriage area.

[0038] The aforementioned heat-epidemic composite risk analysis module couples the prediction of heat risk and biosafety risk to obtain the first... The combined risks of heat and epidemic in the carriage area: , in, Indicates the first The combined risks of heat and COVID-19 in the carriage area This represents the normalized predicted heat risk. This represents the normalized biosafety risk, where mHm_HmH represents the thermal risk sensitivity coefficient. Indicates the biosafety risk sensitivity coefficient. This represents the coupling enhancement coefficient when both predicted thermal risk and biosafety risk increase simultaneously. Based on the predicted thermal risk, biosafety risk, and combined thermal-epidemic risk, each carriage area is divided into a general control area, a thermal load growth area, a biosafety risk area, and a combined thermal-epidemic high-risk area, forming the dynamic risk zoning result.

[0039] Specifically, by setting up a heat-epidemic composite risk analysis module, the predicted heat risk obtained by the dynamic heat source analysis module is coupled with the biosafety risk determined based on abnormal livestock physiological state, air pollution level, livestock spatial distribution, and air flow direction. This results in dynamic risk zoning results for ordinary control areas, heat load growth areas, biosafety risk areas, and heat-epidemic composite high-risk areas, enabling different types and levels of risks to be treated separately. Compared with existing technologies that implement constant temperature regulation and biosafety management separately, this invention can identify the superposition and conflict between heat stress risk and biocontamination transmission risk in the same area, solving the problems of difficulty in comprehensively judging composite risks and difficulty in classifying and managing different risk areas.

[0040] The collaborative control module determines the heat-epidemic conflict coefficient for high-risk areas of heat-epidemic complexes. , in, Indicates the first The coefficient of thermal-epidemic conflict in the carriage area This represents the stability constant used in calculating the conflict coefficient. Parameters indicating enhanced risk of combined heat and epidemic; When the heat-epidemic conflict coefficient is greater than the preset conflict threshold, the proportion of common return air between the heat-epidemic composite high-risk area and other carriage areas is reduced, the proportion of internal circulation heat exchange in the heat-epidemic composite high-risk area is increased, and the directional exhaust volume is increased according to the biosafety risk, so that the air inside the area completes heat transfer through the circulation heat exchange path, and the air with biosafety risk is discharged through the directional exhaust path. The proportion of internal heat exchange in the region is determined according to the following formula: , in, Indicates the first The internal circulation heat exchange ratio in the carriage area This indicates the preset minimum internal circulation heat exchange ratio. This indicates the preset maximum internal circulation heat exchange ratio. This represents the stability constant calculated based on the cyclic heat exchange ratio.

[0041] Specifically, by setting up a collaborative control module, the cooling capacity, air supply volume, return air status, and exhaust air status of each compartment area are adjusted according to the dynamic risk zoning results. For high-risk areas with combined heat and disease risks, a control method combining local circulating heat exchange, restricting cross-regional return air, and directional exhaust air is adopted. This allows the air inside the area to complete heat transfer through the circulating heat exchange path, while air with biosafety risks is discharged through an independent exhaust path, thereby achieving separation of the cooling path and the risk air emission path. Compared with existing technologies that mainly cool down by increasing the overall ventilation volume, this invention can take into account both local constant temperature and biosafety isolation, solving the problems of risky air easily spreading with cross-regional airflow during the cooling process, excessive cooling of non-risk areas, and high overall energy consumption.

[0042] The feedback correction module dynamically corrects the relevant parameters for the next control cycle based on the deviation between the actual environmental response and the predicted environmental response after coordinated control. , in, This indicates the parameter to be corrected in the next control cycle. This indicates the parameter to be corrected in the current control cycle. This represents the actual environmental response value after coordinated control. This represents the predicted environmental response value. Indicates the parameter correction rate. The stability coefficient represents the stability of the correction process. These represent the lower and upper limits of the parameter to be corrected, respectively. This indicates that the correction result will be limited between the lower and upper limits; The parameters to be corrected include the dynamic heat contribution adjustment coefficient, the heat source migration adjustment coefficient, the heat risk adjustment coefficient, and the biosafety risk adjustment coefficient. The corrected corresponding parameters are fed back to the dynamic heat source analysis module and the heat-epidemic composite risk analysis module for the calculation of dynamic heat contribution, prediction of heat source migration, calculation of predicted heat risk, and calculation of biosafety risk in the next control cycle.

[0043] Specifically, based on the deviations between the actual temperature changes, air quality changes, and livestock status changes after coordinated control and the predicted results, the dynamic heat contribution parameters, heat source migration parameters, thermal risk parameters, and biosafety risk parameters are adaptively corrected. The corrected parameters are then used in the next control cycle, enabling the risk prediction model and control strategy to be continuously updated according to changes in livestock species, loading density, carriage structure, and transportation conditions. Compared to existing technologies that use fixed control parameters and static thresholds, this invention can reduce the impact of model deviations on the continuous control process and improve the accuracy of risk prediction, environmental regulation stability, and system adaptability under complex transportation conditions.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent management and control system for constant-temperature livestock biosafety transportation based on the Internet of Things, characterized in that, include: The IoT sensing module is used to collect the location, physiological state, activity state, environmental parameters of the vehicle compartment, and vehicle operation status of livestock, forming multi-source status data for livestock transportation. The dynamic heat source analysis module determines the dynamic heat contribution of each animal based on the multi-source status data of livestock transportation, and predicts the heat load changes of each carriage area by combining the animal's location and movement trend, thereby obtaining the predicted heat risk. The heat-disease composite risk analysis module determines biosafety risks based on the predicted heat risk, livestock physiological status, air quality, and livestock distribution, and performs a comprehensive analysis of the predicted heat risk and biosafety risks to generate dynamic risk zoning results. The collaborative control module adjusts the cooling capacity, air supply volume, return air status and exhaust air status of different carriage areas according to the dynamic risk zoning results, and implements local circulating heat exchange and directional exhaust air collaborative control for areas with both thermal risk and biosafety risk. The feedback correction module corrects the relevant parameters of the dynamic heat source analysis module and the heat-epidemic composite risk analysis module based on the actual temperature, air quality and changes in livestock status after coordinated control, and uses them for the next control cycle.

2. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 1, characterized in that, The IoT sensing module includes: The livestock status sensing unit is used to collect information on the livestock’s spatial location, body surface temperature, respiratory status, and activity status. The carriage environment sensing unit is used to collect temperature, humidity, air flow rate, carbon dioxide concentration, ammonia concentration and aerosol concentration in different areas of the carriage. The vehicle status sensing unit is used to collect data on the vehicle's speed, longitudinal acceleration, lateral acceleration, steering status, and slope status. The data time-series correlation unit is used to correlate the livestock status, the carriage environment status, and the vehicle operation status according to the collection time to form the multi-source status data of the livestock transportation.

3. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 2, characterized in that, The dynamic heat source analysis module includes: The individual heat contribution calculation unit determines the dynamic heat contribution of each animal based on its basic heat production status, activity status, physiological status, and local aggregation degree. The spatial heat contribution mapping unit maps the dynamic heat contribution to the corresponding carriage area according to the spatial location of each animal, forming a dynamic heat contribution distribution. The heat source migration prediction unit predicts the migration trend of each animal between different carriage areas based on the animal's movement status and the vehicle's operating status, and predicts the heat load change of each carriage area based on the migration trend.

4. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 3, characterized in that, The method for determining dynamic thermal contribution by the individual thermal contribution calculation unit includes: Step 1.1, determine the number of animals based on their species and weight. Basic heat contribution of head livestock The baseline heat contribution is then corrected based on the livestock's activity intensity, physiological deviation, local aggregation level, and local heat dissipation capacity to obtain the first... The first animal at any time The dynamic thermal contribution is calculated using the following formula: ， Among them, among them, Indicates the first The first animal at any time Dynamic thermal contribution, Indicates the first The basic heat contribution of the head of livestock Indicates the first Normalized activity intensity of livestock Indicates the first The normalized clustering degree of the head of livestock within the preset neighborhood. Indicates the first The degree of deviation of the body surface temperature of the first animal, Indicates the first The degree of deviation in the breathing state of the lead animal. Indicates the first Normalized local heat dissipation capacity of the location of the lead animal. , , , , All are dynamic heat contribution adjustment coefficients; Step 1.2: Based on the spatial location of each animal, map the dynamic heat contribution of each animal to the corresponding carriage area according to the spatial influence range, and superimpose the dynamic heat contributions mapped to the same carriage area to form a dynamic heat contribution distribution that is updated as the animal's location and state change.

5. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 4, characterized in that, The method for predicting heat source migration by the heat source migration prediction unit includes: Step 2.1, according to the first The direction of movement of the first animal, the direction of vehicle inertia, the spatial distance to the target carriage area, and the degree of animal concentration in the target carriage area are used to determine the first... First livestock to the first The heat source transfer potential corresponding to the migration of the carriage area: , in, Indicates the first The dynamic thermal contribution of the first animal to the first The heat source transfer potential of the carriage area migration Indicates the first The direction and direction of the first animal's movement The degree of consistency between the directions of the carriage areas Indicates the direction and orientation of the vehicle's inertia. The degree of consistency between the directions of the carriage areas Indicates the first First livestock and the first Normalized spatial distances between carriage areas Indicates the first The degree of normalized livestock concentration in the carriage area Indicates the first Normalized movement speed of the first animal, Indicates the prediction time interval. , , , , All are heat source migration adjustment coefficients; Step 2.2: Normalize the allocation of the dynamic heat contribution of each livestock based on the heat transfer potential of each heat source to obtain the future time step. Predicted thermal contribution of the carriage area: ， in, Indicates the future time. Predicted heat contribution of the carriage area Indicates the total number of livestock. Indicates the total number of carriage areas. Indicates the carriage area index. This represents the migration normalization constant to prevent the denominator from being zero.

6. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 5, characterized in that, The dynamic heat source analysis module determines the predicted heat risk based on the predicted heat contribution of each carriage area, and the calculation formula is as follows: ， in, Indicates the first Predicted thermal risk in the carriage area Indicates the first Predicted heat contribution of the carriage area Indicates the first Effective heat exchange capacity of the carriage area Indicates the first The degree of livestock concentration in the carriage area , , , All are thermal risk adjustment coefficients; When the Predicted thermal risk in carriage area Greater than the preset thermal risk threshold, and the first When the current actual temperature in the carriage area does not reach the preset temperature adjustment threshold, the first... The carriage area was designated as a pre-temperature controlled zone.

7. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 6, characterized in that, The methods used by the heat-epidemic composite risk analysis module to determine biosafety risks include: Step 3.1: Identify abnormal livestock based on the degree of deviation of their body surface temperature, respiratory status, and activity status, and determine the abnormal livestock as dynamic biological risk sources. Step 3.2: Based on the physiological abnormality risk intensity of the dynamic biological risk source, the spatial distance between the dynamic biological risk source and the target carriage area, the airflow direction, and the air pollution level of the target carriage area, calculate the first... Biosafety risks in the carriage area: ， Among them, among them, Indicates the first Biosafety risks in the carriage area Indicates an abnormal collection of livestock. Indicates the first The risk intensity of physiological abnormalities in livestock with abnormal head conditions Indicates the first The first abnormal livestock and the first Normalized spatial distances between carriage areas Indicates the first The spatial decay scale of risk corresponding to abnormal livestock. Indicates the first Normalized air pollution levels in the carriage area This indicates the direction from the location of the i-th abnormal animal to the i-th abnormal animal. The degree of consistency between the orientation of the carriage area and the actual direction of airflow. , , All are biosafety risk adjustment coefficients, representing the biosafety risk coefficient of the j-th carriage area.

8. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 7, characterized in that, The aforementioned heat-epidemic composite risk analysis module couples the prediction of heat risk and biosafety risk to obtain the first... The combined risks of heat and epidemic in the carriage area: ， in, Indicates the first The combined risks of heat and COVID-19 in the carriage area This represents the normalized predicted heat risk. This represents the normalized biosafety risk, where mHm_HmH represents the thermal risk sensitivity coefficient. Indicates the biosafety risk sensitivity coefficient. This represents the coupling enhancement coefficient when both predicted thermal risk and biosafety risk increase simultaneously. Based on the predicted thermal risk, biosafety risk, and combined thermal-epidemic risk, each carriage area is divided into a general control area, a thermal load growth area, a biosafety risk area, and a combined thermal-epidemic high-risk area, forming the dynamic risk zoning result.

9. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 8, characterized in that, ， in, Indicates the first The coefficient of thermal-epidemic conflict in the carriage area This represents the stability constant used in calculating the conflict coefficient. Parameters indicating enhanced risk of combined heat and epidemic; When the heat-epidemic conflict coefficient is greater than the preset conflict threshold, the proportion of common return air between the heat-epidemic composite high-risk area and other carriage areas is reduced, the proportion of internal circulation heat exchange in the heat-epidemic composite high-risk area is increased, and the directional exhaust volume is increased according to the biosafety risk, so that the air inside the area completes heat transfer through the circulation heat exchange path, and the air with biosafety risk is discharged through the directional exhaust path. The proportion of internal heat exchange in the region is determined according to the following formula: ， in, Indicates the first The internal circulation heat exchange ratio in the carriage area This indicates the preset minimum internal circulation heat exchange ratio. This indicates the preset maximum internal circulation heat exchange ratio. This represents the stability constant calculated based on the cyclic heat exchange ratio.

10. The intelligent control system for constant-temperature livestock biosafety transportation based on the Internet of Things as described in claim 9, characterized in that, The feedback correction module dynamically corrects the relevant parameters for the next control cycle based on the deviation between the actual environmental response and the predicted environmental response after coordinated control. ， in, This indicates the parameter to be corrected in the next control cycle. This indicates the parameter to be corrected in the current control cycle. This represents the actual environmental response value after coordinated control. This represents the predicted environmental response value. Indicates the parameter correction rate. The stability coefficient represents the stability of the correction process. These represent the lower and upper limits of the parameter to be corrected, respectively. This indicates that the correction result will be limited between the lower and upper limits; The parameters to be corrected include the dynamic heat contribution adjustment coefficient, the heat source migration adjustment coefficient, the heat risk adjustment coefficient, and the biosafety risk adjustment coefficient. The corrected corresponding parameters are fed back to the dynamic heat source analysis module and the heat-epidemic composite risk analysis module for the calculation of dynamic heat contribution, prediction of heat source migration, calculation of predicted heat risk, and calculation of biosafety risk in the next control cycle.

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