Intelligent animal epidemic disease monitoring management and early warning workstation

Abstract

The invention discloses an intelligent animal epidemic disease monitoring management and early warning workstation, which comprises a multi-modal data acquisition module, an epidemic disease risk assessment module, a dynamic early warning threshold generation and judgment module, a visual human-computer interaction interface and a block chain storage module, the multi-modal data acquisition module is used for acquiring animal data of each monitoring point in a preset monitoring area and environment data of animals; the epidemic disease risk assessment module is used for comprehensively analyzing the animal data and the data of the environment where the animals are located to obtain epidemic disease risk indexes of all monitoring points in the current monitoring area; the dynamic early warning threshold generation module is used for dynamically generating an early warning threshold and judging an epidemic disease risk index to obtain an epidemic disease risk level and an epidemic disease risk area; the visual human-computer interaction interface is used for displaying the epidemic disease risk condition in the monitoring area; and the block chain storage module is used for data storage. According to the invention, the accuracy and timeliness of early warning of epidemic diseases are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of animal disease monitoring and management, and more specifically, to an intelligent animal disease monitoring, management and early warning workstation. Background Art

[0002] Traditional animal disease monitoring systems mostly rely on single biosensors (such as thermometers) and fixed threshold judgments. They are unable to adapt to the high false alarm rate caused by dynamic environmental changes (such as day and night temperature differences and seasonal migration), and it is difficult to capture sporadic cases in the early stages of an epidemic in a timely manner. In addition, detection data are mostly stored in centralized servers, which poses a risk of tampering and difficulties in tracing the source. Summary of the Invention

[0003] In order to solve at least one of the above technical problems, the present invention provides an intelligent animal disease monitoring management and early warning workstation, which improves the accuracy of disease warnings through multimodal data fusion and dynamic generation of early warning thresholds; uses a visual human-computer interaction interface to reflect the disease situation in the monitoring area in real time, and uses blockchain technology to ensure the integrity of disease monitoring data.

[0004] The present invention provides an intelligent animal disease monitoring, management and early warning workstation, comprising:

[0005] Multimodal data acquisition module, disease risk assessment module, dynamic warning threshold generation and judgment module, visual human-computer interaction interface, and blockchain storage module;

[0006] The multimodal data acquisition module is used to collect animal data and animal environment data at each monitoring point in a preset monitoring area;

[0007] The disease risk assessment module is used to comprehensively analyze animal data and animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area;

[0008] The dynamic warning threshold generation module is used to dynamically generate warning thresholds and determine the disease risk index to obtain the disease risk level and disease risk area;

[0009] The visual human-computer interaction interface is used to display the disease risk situation in the monitoring area;

[0010] The blockchain storage module is used for data storage.

[0011] In this solution, the steps of comprehensively analyzing the animal data and the animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area specifically include:

[0012] Extracting animal physical sign data from the animal data, and scoring the animal physical sign data to obtain corresponding animal physical sign abnormality score values;

[0013] Obtain data and information on historical epidemics and the historical environments in which they occurred;

[0014] Compare and analyze the animal's environmental data with historical environmental data to obtain the environmental similarity value, and set the environmental similarity value as the epidemic matching degree;

[0015] The disease risk index of the monitoring point is set as R m , the formula is: Where m represents the monitoring point, α1, α2 and α3 are the corresponding weight coefficients, and 0<α1, α2, α3<1 and α1+α2+α3=1, w m―i represents the dynamic weight coefficient of the i-th animal sign in monitoring point m, m-n represents the set of animal signs in monitoring point m, S m―i DT represents the abnormal score value of the i-th animal sign in the monitoring point m; m represents the time attenuation factor of monitoring point m, H m Represents the epidemic matching degree of monitoring point m.

[0016] In this solution, the calculation formula of the dynamic weight coefficient of the i-th animal sign is specifically: Where k is the normalization constant, p m represents the data distribution probability of monitoring point m, x represents the total number of monitoring points, and i∈n.

[0017] In this solution, the dynamic warning threshold generation module includes a warning threshold generation unit, a disease risk index determination and level classification unit, and a disease risk area generation unit;

[0018] The dynamic warning threshold generation unit is used to dynamically generate a warning threshold;

[0019] The disease risk index determination and grading unit is used to determine the disease risk index of the monitoring point according to the early warning threshold and to grade the disease risk level;

[0020] The disease risk area generating unit generates a disease risk area for the purpose of disease prevention and control when there is disease risk warning information.

[0021] In this solution, the step of dynamically generating the warning threshold specifically includes:

[0022] Obtain historical disease risk index within a preset time period;

[0023] Calculate the average of the historical epidemic risk index within a preset time period to obtain the average value of the historical epidemic risk index;

[0024] Arrange the historical epidemic risk indexes within the preset time period in the order of their generation time to obtain the time node t0 corresponding to the last historical epidemic risk index;

[0025] The warning threshold is set to R0, and its formula is: in represents the average value of historical epidemic risk index, λ represents the sensitivity coefficient, σ R represents the standard deviation of the historical epidemic risk index, θ represents the time decay parameter, and t represents the current time node.

[0026] In this solution, after obtaining the historical disease risk index within a preset time period, the following steps are also included:

[0027] The historical epidemic risk index within the preset time period is judged against the corresponding warning threshold in turn. If the historical epidemic risk index is greater than the corresponding warning threshold, the corresponding historical epidemic risk index will be deleted.

[0028] In this solution, the steps of determining the disease risk index of the monitoring point according to the early warning threshold and classifying the disease risk level specifically include:

[0029] When the disease risk index of a monitoring point is greater than the warning threshold, a disease risk warning message is generated;

[0030] Subtract the early warning threshold from the disease risk index of the monitoring point to obtain the disease risk index difference;

[0031] The corresponding disease risk level is determined based on the preset range within which the difference in disease risk index falls.

[0032] In this plan, the steps for generating disease risk areas include:

[0033] When the disease risk index of a monitoring point is greater than the warning threshold, the animal breeding area where the monitoring point is located will be set as a source risk area;

[0034] Multiply the disease risk level by the preset diffusion distance weight coefficient to obtain the diffusion distance;

[0035] Based on the outer edge of the source risk area, the diffusion distance is extended outward to obtain the disease spread risk area;

[0036] The source risk area and the disease spread risk area are accumulated to obtain the disease risk area.

[0037] This plan also includes:

[0038] Extract the cross-species transmission risk index of the source risk area, and the formula is: Where A represents the cross-species transmission risk index, R represents the disease risk index of the source risk area, and C j N represents the transmission coefficient of the jth type of vector in the source risk area, j represents the density of the jth type of vector in the source risk area, k t represents the temperature impact factor, T represents the ambient temperature in the source risk area, T0 represents the reference temperature, and j∈M;

[0039] When the cross-species transmission risk index of the source risk area is greater than the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be merged into the disease spread risk area;

[0040] When the cross-species transmission risk index of the source risk area is less than or equal to the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be deleted from the spread risk area.

[0041] The present invention provides an intelligent animal disease monitoring, management and early warning workstation, which improves the accuracy of disease warnings through multimodal data fusion and dynamic generation of early warning thresholds; uses a visual human-computer interaction interface to reflect the disease situation in the monitoring area in real time, and uses blockchain technology to ensure the integrity of disease monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A block diagram of an intelligent animal disease monitoring, management and early warning workstation according to the present invention is shown. DETAILED DESCRIPTION

[0043] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0045] Figure 1 A block diagram of an intelligent animal disease monitoring, management and early warning workstation according to the present invention is shown.

[0046] like Figure 1 As shown, the present invention discloses a block diagram of an intelligent animal disease monitoring, management and early warning workstation, including:

[0047] Multimodal data acquisition module, disease risk assessment module, dynamic warning threshold generation and judgment module, visual human-computer interaction interface, and blockchain storage module;

[0048] The multimodal data acquisition module is used to collect animal data and animal environment data at each monitoring point in a preset monitoring area;

[0049] The disease risk assessment module is used to comprehensively analyze animal data and animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area;

[0050] The dynamic warning threshold generation module is used to dynamically generate warning thresholds and determine the disease risk index to obtain the disease risk level and disease risk area;

[0051] The visual human-computer interaction interface is used to display the disease risk situation in the monitoring area;

[0052] The blockchain storage module is used for data storage.

[0053] According to an embodiment of the present invention, the multimodal data acquisition module includes at least data acquisition equipment such as biosensors, environmental sensors, and cameras. The monitoring area includes at least one animal breeding area, and at least one monitoring point is set in each animal breeding area. After the multimodal data acquisition module collects data, the data is sent to the disease risk assessment module for comprehensive analysis to obtain a disease risk index corresponding to the monitoring data. The disease risk index is then sent to the dynamic warning threshold generation module to determine the disease risk level. When there is a disease risk, a disease risk area is generated; the disease risk warning information and the disease risk area are then displayed through a visual human-computer interaction interface, and the visual human-computer interaction interface can also access the historical data of the blockchain storage module; the animal data and animal environment data, disease risk warning information and disease risk area data collected from each monitoring point in the monitoring area are stored through the blockchain technology in the blockchain storage module to ensure that the data cannot be tampered with and irreversibly stored, providing data support for tracing the source of the disease.

[0054] According to an embodiment of the present invention, the step of comprehensively analyzing the animal data and the animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area specifically includes:

[0055] Extracting animal physical sign data from the animal data, and scoring the animal physical sign data to obtain corresponding animal physical sign abnormality score values;

[0056] Obtain data and information on historical epidemics and the historical environments in which they occurred;

[0057] Compare and analyze the animal's environmental data with historical environmental data to obtain the environmental similarity value, and set the environmental similarity value as the epidemic matching degree;

[0058] The disease risk index of the monitoring point is set as R m , the formula is: Where m represents the monitoring point, α1, α2 and α3 are the corresponding weight coefficients, and 0<α1, α2, α3<1 and α1+α2+α3=1, w m―i represents the dynamic weight coefficient of the i-th animal sign in monitoring point m, m-n represents the set of animal signs in monitoring point m, S m―i DT represents the abnormal score value of the i-th animal sign in the monitoring point m; m represents the time attenuation factor of monitoring point m, H m Represents the epidemic matching degree of monitoring point m.

[0059] It should be noted that the animal vital signs data include animal body temperature, respiratory rate and other data. By comparing the animal vital signs data with the preset standard vital signs data range of the corresponding animal, if the animal vital signs data is within the preset standard vital signs data range of the corresponding animal, the abnormal score value of the corresponding animal vital signs is zero. If the animal vital signs data is not within the preset standard vital signs data range of the corresponding animal, and when the animal vital signs data is greater than the preset standard vital signs data range of the corresponding animal, the animal vital signs data is subtracted from the maximum value in the preset standard vital signs data range of the corresponding animal to obtain a first difference value multiplied by the corresponding preset weight coefficient to obtain the abnormal score value of the corresponding animal vital signs; if the animal vital signs data is not within the preset standard vital signs data range of the corresponding animal, and when the animal vital signs data is less than the preset standard vital signs data range of the corresponding animal, the animal vital signs data is subtracted from the minimum value in the preset standard vital signs data range of the corresponding animal to obtain a second difference value multiplied by the corresponding preset weight coefficient to obtain the abnormal score value of the corresponding animal vital signs; DT m =e ―θΔt1 , where θ represents the time decay parameter, for example, θ = 0.1 / day, and Δt1 is the time difference between the current time and the time when the corresponding vital sign data is obtained.

[0060] According to an embodiment of the present invention, the calculation formula of the dynamic weight coefficient of the i-th animal vital sign is specifically: Where k is the normalization constant, p m represents the data distribution probability of monitoring point m, x represents the total number of monitoring points, and i∈n.

[0061] It should be noted that the weight is determined by the degree of chaos of the data distribution. The smaller the entropy value, the more orderly the current data distribution is, and the higher the corresponding dynamic weight coefficient is. For example, the normalization constant is set to k = 1 / lnx to eliminate the influence of the number of monitoring points x on the entropy value range.

[0062] According to an embodiment of the present invention, the dynamic warning threshold generation module includes a warning threshold generation unit, a disease risk index determination and level classification unit, and a disease risk area generation unit;

[0063] The dynamic warning threshold generation unit is used to dynamically generate a warning threshold;

[0064] The disease risk index determination and grading unit is used to determine the disease risk index of the monitoring point according to the early warning threshold and to grade the disease risk level;

[0065] The disease risk area generating unit generates a disease risk area for the purpose of disease prevention and control when there is disease risk warning information.

[0066] According to an embodiment of the present invention, the step of dynamically generating a warning threshold specifically includes:

[0067] Obtain historical disease risk index within a preset time period;

[0068] Calculate the average of the historical epidemic risk index within a preset time period to obtain the average value of the historical epidemic risk index;

[0069] Arrange the historical epidemic risk indexes within the preset time period in the order of their generation time to obtain the time node t0 corresponding to the last historical epidemic risk index;

[0070] The warning threshold is set to R0, and its formula is: in represents the average value of historical epidemic risk index, λ represents the sensitivity coefficient, σ R represents the standard deviation of the historical epidemic risk index, θ represents the time decay parameter, and t represents the current time node.

[0071] It should be noted that the historical disease risk index is periodically screened through a preset time period to improve the accuracy and timeliness of the warning threshold and reduce the impact of external factors such as changes in animal signs caused by seasonal changes; taking the current time node as the benchmark, if the preset time period is 7 days, the historical disease risk index 7 days ago will be extracted.

[0072] According to an embodiment of the present invention, after obtaining the historical disease risk index within a preset time period, the method further includes:

[0073] The historical epidemic risk index within the preset time period is judged against the corresponding warning threshold in turn. If the historical epidemic risk index is greater than the corresponding warning threshold, the corresponding historical epidemic risk index will be deleted.

[0074] It should be noted that by deleting the historical epidemic risk index that is greater than the corresponding warning threshold, the accuracy of the historical epidemic risk index within the preset time period is improved.

[0075] According to an embodiment of the present invention, the step of determining the disease risk index of the monitoring point according to the early warning threshold and classifying the disease risk level specifically includes:

[0076] When the disease risk index of a monitoring point is greater than the warning threshold, a disease risk warning message is generated;

[0077] Subtract the early warning threshold from the disease risk index of the monitoring point to obtain the disease risk index difference;

[0078] The corresponding disease risk level is determined based on the preset range within which the difference in disease risk index falls.

[0079] It should be noted that, for example, if the range of the difference in disease risk index is 0-10, and the difference between two disease risk indices is one level, then the preset range can be divided into (0, 2], (2, 4], ..., (8, 10], and each preset range corresponds to a disease risk level. The larger the value corresponding to the preset range, the higher the corresponding disease risk level. For example, if the preset range is (0, 2], the corresponding disease risk level is one, and if the preset range is (8, 10], the corresponding disease risk level is five. The higher the disease risk level, the greater the probability of the corresponding disease occurring.

[0080] According to an embodiment of the present invention, the step of generating a disease risk area specifically includes:

[0081] When the disease risk index of a monitoring point is greater than the warning threshold, the animal breeding area where the monitoring point is located will be set as a source risk area;

[0082] Multiply the disease risk level by the preset diffusion distance weight coefficient to obtain the diffusion distance;

[0083] Based on the outer edge of the source risk area, the diffusion distance is extended outward to obtain the disease spread risk area;

[0084] The source risk area and the disease spread risk area are accumulated to obtain the disease risk area.

[0085] Furthermore, after obtaining the epidemic risk area, preventive measures such as disinfection and isolation are taken for the epidemics that may occur according to the epidemic risk area.

[0086] Furthermore, the epidemic risk areas are marked with different colors according to the corresponding epidemic risk levels, where the higher the epidemic risk level, the darker and more obvious the corresponding marking color; the higher the epidemic risk level, the stricter the epidemic prevention measures, such as more frequent disinfection.

[0087] According to an embodiment of the present invention, the further embodiment includes:

[0088] Extract the cross-species transmission risk index of the source risk area, and the formula is: Where A represents the cross-species transmission risk index, R represents the disease risk index of the source risk area, and C j N represents the transmission coefficient of the jth type of vector in the source risk area, j represents the density of the jth type of vector in the source risk area, k t represents the temperature impact factor, T represents the ambient temperature in the source risk area, T0 represents the reference temperature, and j∈M;

[0089] When the cross-species transmission risk index of the source risk area is greater than the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be merged into the disease spread risk area;

[0090] When the cross-species transmission risk index of the source risk area is less than or equal to the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be deleted from the spread risk area.

[0091] It should be noted that the vector organisms are animals raised in the animal breeding area where the monitoring point is located, the monitoring data are the data for monitoring the corresponding vector organisms, and the animal vital signs data are the vital signs data of the vector organisms; the cross-species transmission risk index of the source risk area is used to optimize the diffusion risk area to provide a precise range for the prevention of animal diseases.

[0092] According to an embodiment of the present invention, the further embodiment includes:

[0093] Divide the disease risk area into a preset grid size to obtain a disease risk grid area;

[0094] Randomly select a disease risk grid area as the base point for disinfection, and then randomly select an adjacent disease risk grid area from the adjacent disease risk grid area corresponding to the disinfection starting point as the next disinfection grid area for disinfection, and so on, until all disease risk grid areas are disinfected, and the disinfection path is obtained;

[0095] After traversing all disinfection starting points, a disinfection path set is obtained, and the disinfection path corresponding to the minimum path length in the disinfection path set is extracted and set as the optimal disinfection path;

[0096] Set the current disinfection intensity to Q, and its formula is: Where Q0 represents the maximum disinfection intensity, C R Indicates the disease risk level of the current disease risk area, C max Indicates the highest level of disease risk;

[0097] The optimal disinfection path and disinfection intensity are sent to the preset management terminal to disinfect the disease risk area.

[0098] It should be noted that the disinfection path includes a return journey. For example, during the disinfection process, the disinfection path b passes through the disinfection grid areas b1, b2, b3, b2 and b4 in sequence. The corresponding disinfection path length is the sum of the path length from b1 to b2, the path length from b2 to b3, the path length from b3 to b2, and the path length from b2 to b4.

[0099] The present invention provides an intelligent animal disease monitoring, management and early warning workstation, which improves the accuracy of disease warnings through multimodal data fusion and dynamic generation of early warning thresholds; uses a visual human-computer interaction interface to reflect the disease situation in the monitoring area in real time, and uses blockchain technology to ensure the integrity of disease monitoring data.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0101] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0102] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0103] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0104] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An intelligent animal disease monitoring, management and early warning workstation, characterized by: include: Multimodal data acquisition module, disease risk assessment module, dynamic warning threshold generation and judgment module, visual human-computer interaction interface, and blockchain storage module; The multimodal data acquisition module is used to collect animal data and animal environment data at each monitoring point in a preset monitoring area; The disease risk assessment module is used to comprehensively analyze animal data and animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area; The dynamic warning threshold generation module is used to dynamically generate warning thresholds and determine the disease risk index to obtain the disease risk level and disease risk area; The visual human-computer interaction interface is used to display the disease risk situation in the monitoring area; The blockchain storage module is used for data storage.

2. The intelligent animal disease monitoring, management and early warning workstation according to claim 1, characterized in that: The step of comprehensively analyzing the animal data and the animal environment data to obtain the disease risk index of each monitoring point in the current monitoring area specifically includes: Extracting animal physical sign data from the animal data, and scoring the animal physical sign data to obtain corresponding animal physical sign abnormality score values; Obtain data and information on historical epidemics and the historical environments in which they occurred; Compare and analyze the animal's environmental data with historical environmental data to obtain the environmental similarity value, and set the environmental similarity value as the epidemic matching degree; The disease risk index of the monitoring point is set as R m , the formula is: Where m represents the monitoring point, α1, α2 and α3 are the corresponding weight coefficients, and 0<α1, α2, α3<1 and α1+α2+α3=1, w m―i represents the dynamic weight coefficient of the i-th animal sign in monitoring point m, m-n represents the set of animal signs in monitoring point m, S m―i DT represents the abnormal score value of the i-th animal sign in the monitoring point m; m represents the time attenuation factor of monitoring point m, H m Represents the epidemic matching degree of monitoring point m.

3. The intelligent animal disease monitoring, management and early warning workstation according to claim 2, characterized in that: The calculation formula of the dynamic weight coefficient of the i-th animal's physical sign is specifically: Where k is the normalization constant, p m represents the data distribution probability of monitoring point m, x represents the total number of monitoring points, and i∈n.

4. The intelligent animal disease monitoring, management and early warning workstation according to claim 1, characterized in that: The dynamic warning threshold generation module includes a warning threshold generation unit, a disease risk index determination and level classification unit, and a disease risk area generation unit; The dynamic warning threshold generation unit is used to dynamically generate a warning threshold; The disease risk index determination and grading unit is used to determine the disease risk index of the monitoring point according to the early warning threshold and to grade the disease risk level; The disease risk area generating unit generates a disease risk area for the purpose of disease prevention and control when there is disease risk warning information.

5. The intelligent animal disease monitoring, management and early warning workstation according to claim 4, characterized in that: The step of dynamically generating the warning threshold specifically includes: Obtain historical disease risk index within a preset time period; Calculate the average of the historical epidemic risk index within a preset time period to obtain the average value of the historical epidemic risk index; Arrange the historical epidemic risk indexes within the preset time period in the order of their generation time to obtain the time node t0 corresponding to the last historical epidemic risk index; The warning threshold is set to R0, and its formula is: in represents the average value of historical epidemic risk index, λ represents the sensitivity coefficient, σ R represents the standard deviation of the historical epidemic risk index, θ represents the time decay parameter, and t represents the current time node.

6. The intelligent animal disease monitoring, management and early warning workstation according to claim 5, characterized in that: After obtaining the historical disease risk index within a preset time period, the method further includes: The historical epidemic risk index within the preset time period is judged against the corresponding warning threshold in turn. If the historical epidemic risk index is greater than the corresponding warning threshold, the corresponding historical epidemic risk index will be deleted.

7. The intelligent animal disease monitoring, management and early warning workstation according to claim 4, characterized in that: The step of determining the disease risk index of the monitoring point according to the early warning threshold and classifying the disease risk level specifically includes: When the disease risk index of a monitoring point is greater than the warning threshold, a disease risk warning message is generated; Subtract the early warning threshold from the disease risk index of the monitoring point to obtain the disease risk index difference; The corresponding disease risk level is determined based on the preset range within which the difference in disease risk index falls.

8. The intelligent animal disease monitoring, management and early warning workstation according to claim 7, characterized in that: The steps to generate disease risk areas include: When the disease risk index of a monitoring point is greater than the warning threshold, the animal breeding area where the monitoring point is located will be set as a source risk area; Multiply the disease risk level by the preset diffusion distance weight coefficient to obtain the diffusion distance; Based on the outer edge of the source risk area, the diffusion distance is extended outward to obtain the disease spread risk area; The source risk area and the disease spread risk area are accumulated to obtain the disease risk area.

9. The intelligent animal disease monitoring, management and early warning workstation according to claim 8, characterized in that: Also includes: Extract the cross-species transmission risk index of the source risk area, and the formula is: Where A represents the cross-species transmission risk index, R represents the disease risk index of the source risk area, and C j N represents the transmission coefficient of the jth type of vector in the source risk area, j represents the density of the jth type of vector in the source risk area, k t represents the temperature impact factor, T represents the ambient temperature in the source risk area, T0 represents the reference temperature, j∈M; When the cross-species transmission risk index of the source risk area is greater than the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be merged into the disease spread risk area; When the cross-species transmission risk index of the source risk area is less than or equal to the preset cross-species transmission risk index threshold, if there are other types of animal breeding areas in the disease spread risk area, the entire breeding area corresponding to the other types of animal breeding areas in the disease spread risk area will be deleted from the spread risk area.

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