Livestock epidemic prevention medicine intelligent putting and tracing system

Abstract

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent livestock anti-epidemic drug delivery and tracing system which comprises a physical sign acquisition module, a dose derivation module, a drug delivery sequencing module, an inoculation execution module and a delivery tracing module. According to the method, through fusion calculation of the temperature difference rate, the motion frequency and the breathing amplitude difference, multi-dimensional physiological indexes are constructed, dynamic recognition of the livestock state is achieved, the body weight and breathing difference value is superposed to the physiological stress value and converted, the individualized dose intensity is generated, the difference and accuracy of dose setting are improved, and the accuracy of the livestock body state is improved. The injection sequence is adjusted in combination with the muscle contraction frequency difference and the amplitude decreasing trend, injection in a muscle stress area is avoided, injection compliance is judged through thrust and displacement error comparison, it is ensured that the process is stable and controllable, injection abnormity is analyzed through the dose intensity and execution sequence deviation relation, and corresponding mapping of ear tags and behavior data is established. And the dosing traceability and the abnormity identification capability are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent delivery and tracing system for animal husbandry epidemic prevention drugs. Background Art

[0002] The field of intelligent monitoring technology includes intelligent management systems based on information collection and processing. The core content is real-time perception, data collection and control execution of the environment, personnel, equipment and operation processes through the Internet of Things, embedded systems and automatic control. This technical field is widely used in industrial production, agricultural breeding, public safety, medical health and other scenarios. The technical implementation relies on sensor deployment, data communication protocols, information fusion strategies and execution mechanism configuration to form a closed-loop structure of perception, decision-making and control to support automatic monitoring and management of key behavior nodes and improve management efficiency and safety level.

[0003] Among them, the intelligent delivery and traceability system for animal husbandry epidemic prevention drugs refers to the epidemic prevention management needs in the livestock and poultry breeding process. By setting up a fixed track conveying structure, a drug storage warehouse control unit, a positioning drive device, and an identification information reading device, the quantitative distribution of different epidemic prevention drugs is automatically completed according to the livestock and poultry unit identity information and the preset program, and the drug delivery data and the corresponding label information of the target livestock and poultry are simultaneously recorded to form a drug circulation record data set. The system also uploads the drug circulation records to the central database through the local area network communication node, and generates a queryable drug use traceability chain in combination with historical records to realize the information recording and management of the whole process of epidemic prevention behavior.

[0004] In the process of drug delivery, the existing technology mainly relies on static identity tags and preset programs to complete the distribution action. It does not embed the collection and identification mechanism of the actual physiological state of the livestock, resulting in individuals with different physical conditions facing the same drug dosage configuration, which is difficult to meet personalized needs. In terms of drug injection node selection, the existing technology mostly sets the vaccination order through fixed logic, ignoring the impact of muscle state on injection conditions, which can easily cause local pain caused by abnormal muscle tightness, injection failure, or uneven distribution of drug solution. The data recording link is mainly based on timestamps and tags, and fails to integrate process variables such as execution errors and dosage differences. It lacks the ability to cross-validate behavioral abnormalities and drug delivery deviations, which limits the accuracy of problem location. For example, in the continuous injection of a large group, if a certain livestock has abnormal behavior and causes drug delivery to be delayed, the system cannot establish a corresponding relationship between the physiological state and the offset position, and identify it as valid abnormal data, which can easily cause data loss, analysis distortion, and subsequent traceability difficulties in the drug delivery process, affecting management accuracy and epidemic prevention response efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent delivery and tracing system for livestock epidemic prevention drugs.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A system for intelligent delivery and tracing of livestock and poultry epidemic prevention drugs comprises: The vital sign collection module obtains the temperature data of the body surface temperature sensor tag, calculates the rate of change of the temperature difference between adjacent points, collects the periodic motion frequency of the action foot ring, detects the difference between the real-time frequency of the respiratory belt and the resting value and normalizes it, and jointly calculates the three signals to generate a joint value of physiological stress; The dose derivation module calls the ear tag weight information based on the physiological stress combined value, collects the breathing rate at the injection site and compares it with the resting value, determines whether the temperature change rate and the movement frequency exceed the cycle average value, and if so, multiplies the weight and the breathing difference and converts them to generate an individualized dose intensity value; The medication sorting module calls the individualized dose intensity value, obtains the difference between the contraction frequency and the resting frequency of the muscle probe, analyzes whether the amplitude change and the resting interval are decreasing, and if so, marks it as a delayed object, arranges the vaccination points in descending order according to the intensity value, and generates a serialized injection instruction number; The vaccination execution module calls the serialized injection instruction number, and the injection head locates the target point and sets the dosage, reads the intensity value as the current setting, records the thrust and displacement error after the injection is completed, and determines whether it is within the device threshold range. If so, it is marked as compliant, and a compliant result for the vaccination action is generated.

[0007] As a further scheme of the present invention, the physiological stress combined value includes the temperature change rate difference, the movement frequency fluctuation amplitude, and the normalized respiratory frequency value; the individualized dose intensity value includes the body weight parameter, the respiratory frequency difference, and the physiological stress combined value weighting coefficient; the serialized injection instruction number includes the dose intensity ranking value, the muscle contraction frequency difference, and the contraction amplitude decreasing trend; the vaccination action compliance result includes the thrust deviation value, the needle displacement error value, and the stability compliance mark.

[0008] As a further solution of the present invention, the vital sign collection module includes: The temperature difference rate calculation submodule collects the temperature values ​​uploaded by the temperature sensing tags worn by the animals in the fence, distinguishes the temperature data of adjacent detection points according to the timestamp, calculates the ratio of the temperature change per unit time to the time interval, obtains the temperature difference change rate between adjacent detection points, and obtains the value of the temperature difference change rate on the surface of the animals; The motion frequency extraction submodule obtains the three-cycle motion data sequence of the animal wearing the motion recognition foot ring, divides the cycle boundary segments according to the change of the motion amplitude, accumulates the number of motion points in each cycle and performs ratio processing on the cycle length, obtains the average motion frequency value in three cycles, and obtains the three-cycle motion frequency value; The joint value generation submodule calls the surface temperature difference change rate value and the three-cycle action frequency value, detects the real-time respiratory frequency and resting respiratory frequency recorded by the respiratory belt sensor, calculates the difference between the two and normalizes them, using the formula: ; The combined value of physiological stress is obtained by operation, and the combined value of physiological stress is obtained according to the synchronization of the signal-like data and the value fluctuation characteristics; in, represents the rate of change of temperature difference per unit time of the i-th pair of detection points, Represents the average of the temperature difference change rate of all detection points, represents the respiratory frequency at the jth moment, represents the resting respiratory rate, represents the kth segment action frequency value, is the combined value of physiological stress at the jth moment, n is the total number of respiratory frequency collection points, m is the index number in the respiratory data, Represents the respiratory frequency value corresponding to the mth sampling point.

[0009] As a further solution of the present invention, the dose derivation module includes: The weight parameter extraction submodule collects the weight parameters recorded by the ear tag identifier, and selects the weight data at the time of collecting the physiological stress combined value according to the recognition time point to obtain the weight value of the same period; The respiratory difference discrimination submodule collects the respiratory rate data uploaded by the respiratory synchronization probe at the injection site, extracts the resting frequency record, judges the fluctuation amplitude of the respiratory state based on the difference between the respiratory rate and the resting frequency, calculates the respiratory difference, and judges whether the current value is higher than the corresponding mean value at the same time based on the temperature change rate and the action frequency mean value during the collection period, and obtains the respiratory difference and state judgment value; The dose intensity calculation submodule calls the physiological stress combined value, the weight value and the respiratory difference value and the state judgment value in the same period, filters the records with the judgment result being established, calculates the product based on the weight value and the respiratory difference, and adds it to the physiological stress combined value for proportional conversion, using the formula: ; Obtaining individualized dose intensity values ​​through calculation, and establishing individualized dose intensity values ​​based on the joint calculation relationship between participating items; in, represents the individualized dose intensity value, represents the combined physiological stress value, Represents the respiratory difference, Represents the weight value of the same period, Represents the temperature change rate and action frequency judgment coefficient, Represents the average stress value during the cycle, Represents the conversion factor of the respiratory rate-amplitude ratio.

[0010] As a further solution of the present invention, the medication sequencing module comprises: The muscle frequency extraction submodule collects the continuous contraction frequency data of the muscle sensing probe at the injection site, extracts the average frequency value in the static state, calculates the difference between the two based on the corresponding time segment, and obtains the contraction frequency difference; The decreasing state identification submodule determines whether the period sequence presents a unidirectional decreasing trend according to the contraction amplitude data and the static interval duration in the continuous period. If both the amplitude change trend and the interval duration trend meet the decreasing condition, the current inoculation site state is marked as delayed, and the delayed inoculation mark value is obtained; The injection instruction generation submodule calls the individualized dose intensity value, the contraction frequency difference value and the delayed vaccination mark value, sorts the vaccination sites in descending order according to the dose intensity value, and adjusts the sorting structure based on the delayed mark, using the formula: ; The injection sequence determination value is obtained by operation, and a sequence number is assigned accordingly to generate a serialized injection instruction number; in, Represents the injection sorting judgment value, represents the dose intensity value of the νth vaccination point, represents the νth contraction frequency difference, represents the value of the delayed vaccination marker at position ν, Represents the total number of vaccination sites, Represents the cumulative value of all deferred markers, Represents the average value of the top positions in the ranking. Represents the average value after sorting.

[0011] As a further solution of the present invention, the vaccination execution module includes: The injection parameter setting submodule calls the current number information in the serialized injection instruction number, and the automatic injection head actuator completes the spatial positioning of the corresponding vaccination point, sets the output upper limit value of the injection head flow regulator, and reads the dosage intensity value corresponding to the number as the injection dosage setting input to obtain the injection parameter setting value; The motion error acquisition submodule records the thrust value generated after the injection is completed and the displacement error value generated by the needle during the injection process according to the injection parameter setting value, and uniformly aggregates them into injection error index parameters to obtain the injection error index value; The compliance result determination submodule calls the injection parameter setting value and the injection error index value, compares the thrust value and the displacement error value according to the stability threshold standard set by the equipment, and performs normalized evaluation based on multiple injection parameters, using the formula: ; The injection action deviation judgment value is obtained by calculation, and the judgment is completed by comparing the stability threshold. If the conditions are met, the compliance status is marked and the vaccination action compliance result is generated; in, Represents the injection action deviation judgment value, Represents the set injection dose value, Represents the upper limit of device flow. Represents the injection thrust value, Represents the needle displacement error value, Representative The thrust value recorded, Representative The displacement error value recorded is The total number of records.

[0012] As a further solution of the present invention, the system further includes a delivery tracing module: The delivery traceability module compares the injection number and time of the trajectory calibration point according to the compliance result of the vaccination action, extracts the unfinished or abnormal points, determines whether the dosage and sequence are offset, and if the offset is established, records the ear tag number as the abnormal source, and generates a carcass delivery traceability record set; The livestock release traceability record set includes an abnormal ear tag number identifier, an abnormal injection sequence number, and a dosage and position offset association item.

[0013] As a further solution of the present invention, the delivery tracing module includes: The abnormal number extraction submodule extracts the sequence numbers marked as incomplete or abnormal status according to the compliance result of the vaccination action and compares the injection sequence numbers and injection times recorded at the path trajectory calibration points to obtain the abnormal number set value; The dose deviation discrimination submodule calls the abnormal number set value, compares the dose intensity value associated with each number with the execution sequence number position, and performs difference analysis on the position difference and dose difference, using the formula: ; Obtaining a position vector offset judgment value by operation, and establishing an offset associated judgment value according to a comparison result between the judgment value and a judgment threshold; in, Represents the position dose deviation judgment value, Representative Injection sequence number, Representative The dose intensity value, and Respectively represent Injection time and route site number, is the total number of numbers, is the number comparison index bit; The ear tag data recording submodule screens and determines the abnormal numbers with associated offset relationship between position and dosage according to the offset association judgment value, extracts the ear tag number information corresponding to the number, marks it as the source of abnormal data, and generates a carcass delivery traceability record set.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, multi-dimensional physiological indicators are constructed through the fusion calculation of temperature difference rate, movement frequency and breathing amplitude difference, and dynamic recognition of animal body state is realized. The weight and breathing difference are superimposed on the physiological stress value and converted to generate individualized dosage intensity, thereby improving the differentiation and accuracy of dosage setting. The injection sequence is adjusted in combination with the muscle contraction frequency difference and the amplitude decreasing trend to avoid injection in the muscle stress area. The injection compliance is judged by comparing the thrust and displacement errors to ensure the process is stable and controllable. The relationship between dosage intensity and execution sequence offset is used to analyze injection abnormalities, and a corresponding mapping between ear tags and behavioral data is established to enhance the drug administration traceability and abnormality recognition capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the vital signs collection module of the present invention; Figure 3 is a flow chart of the dosage derivation module of the present invention; Figure 4 This is a flow chart of the medication sequencing module of the present invention; Figure 5 It is a flow chart of the vaccination execution module of the present invention; Figure 6 This is a flow chart of the delivery tracing module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 , an intelligent delivery and traceability system for livestock epidemic prevention drugs includes: The vital signs collection module obtains the multi-point temperature values ​​uploaded by the temperature sensor tags worn by the animals in the fence, calculates the rate of change of the temperature difference between adjacent detection points per unit time, collects the three-cycle movement frequency of the motion recognition foot ring, detects the amplitude difference between the real-time breathing frequency and the resting frequency of the breathing belt sensor and performs normalization conversion, performs joint operation on the three types of signal data of temperature, motion and breathing, and generates a joint value of physiological stress; The dose derivation module uses the weight parameter recorded by the ear tag identifier based on the combined value of physiological stress, collects the respiratory rate uploaded by the respiratory synchronization probe at the injection site, and calculates the amplitude difference with the resting frequency to determine whether the temperature change rate and movement frequency in the previous period are both higher than the average change value of the corresponding acquisition period. If the judgment is established, the weight value is multiplied by the respiratory difference, and the product is added to the combined value of physiological stress for proportional conversion to generate an individualized dose intensity value. The medication sorting module calls the individualized dose intensity value, obtains the continuous contraction frequency of the muscle sensing probe at the injection site, and performs differential analysis with the resting frequency. It collects the contraction amplitude change and the resting interval duration in the continuous cycle and determines whether they are both decreasing. If the two conditions are met, the current vaccination site is marked as a delayed object, and then all vaccination points are sorted in descending order according to the dose intensity value to generate a serialized injection instruction number. The vaccination execution module calls the current number in the serialized injection instruction number, and the automatic injection head actuator completes the target point positioning, sets the output upper limit of the injection head flow regulator, and reads the corresponding dose intensity value as the current injection dose setting value. The thrust value and needle displacement error after the injection action are recorded and compared with the device stability threshold. If the judgment condition is met, the record is marked as compliant, and the vaccination action compliance result is generated; The delivery traceability module compares the injection sequence number and the corresponding injection time recorded at the path trajectory calibration point according to the compliance results of the vaccination action, extracts the numbers that are incomplete or marked as abnormal, and analyzes whether there is a corresponding relationship between the position and dose offset between the dose intensity value and the execution sequence number. If the judgment is established, the ear tag number is recorded and marked as the source of abnormal data, and a livestock delivery traceability record set is generated.

[0019] The combined physiological stress values ​​include the temperature change rate difference, movement frequency fluctuation amplitude, and respiratory rate normalization value. The individualized dose intensity values ​​include body weight parameters, respiratory rate difference, and physiological stress combined value weighting coefficient. The serialized injection instruction number includes the dose intensity ranking value, muscle contraction frequency difference, and contraction amplitude decreasing trend. The vaccination action compliance results include thrust deviation value, needle displacement error value, and stability compliance mark. The livestock release traceability record set includes ear tag number abnormality mark, abnormal injection sequence number, and dose and position offset association items.

[0020] See also Figure 2 , the vital signs collection module includes: The temperature difference rate calculation submodule collects the temperature values ​​uploaded by the temperature sensing tags worn by the animals in the fence, distinguishes the temperature data of adjacent detection points according to the timestamp, calculates the ratio of the temperature change per unit time to the time interval, obtains the temperature difference change rate between adjacent detection points, and obtains the value of the temperature difference change rate on the surface of the animals; First, it is necessary to clarify the layout of the temperature sensing tags. Assume that a total of 6 temperature sensing tags are installed on the front and back, left and right shoulders, back, abdomen and other locations on the body of a cow. Each sensor records temperature data every 5 minutes. For example, in a certain collection, the temperatures of the front shoulder, back shoulder, back, abdomen, left side and right side are 38.5℃, 39.0℃, 38.8℃, 38.6℃, 38.7℃, and 39.2℃ respectively. After sorting the data according to the sensor data timestamp, it is necessary to pair the tags in the order of adjacent layout (such as front shoulder-back shoulder, back-abdomen) in pairs, and process the temperature changes of each pair of sensors at two consecutive time nodes. The calculation method is to subtract the temperature at the next time point from the temperature at the previous time point, and then divide it by the interval between the two collections to obtain the temperature difference change rate per unit time. If the temperature of the front shoulder tag at time t1 and time t2 is 38.2℃ and 38.5℃ respectively, and the interval time is 5 minutes, then the temperature difference change rate per unit time is (38.5-38.2) / 5= 0.06℃ / min, perform the same operation for other sensors and list the rates of all adjacent sensor pairs respectively; when performing this step, please note that if there is an abnormal data mutation exceeding ±1.5℃, mark the data and exclude or replace it with the average of the values ​​before and after the point as a correction. For example, the abnormal temperature record of the rear shoulder is 41.5℃, the previous value is 39.0℃, and the latter value is 39.2℃, then the correction value is (39.0+39.2) / 2=39.1℃, and then all valid rate values ​​are calculated. Normalization processing is performed to unify the scale. The calculation formula is: (x-min) / (max-min), where x is the original value, min and max are the minimum and maximum values ​​in the sample. For example, if the minimum rate is 0.03 and the maximum is 0.12, then when a certain rate is 0.06, its normalized value is (0.06-0.03) / (0.12-0.03)=0.33. The final normalized temperature difference change rate is used for subsequent processing, that is, to obtain the surface temperature difference change rate value.

[0021] The motion frequency extraction submodule obtains the three-cycle motion data sequence of the animal wearing the motion recognition foot ring, divides the cycle boundary segments according to the change of the motion amplitude, accumulates the number of motion points in each cycle and performs ratio processing on the cycle length, obtains the average motion frequency value in three cycles, and obtains the three-cycle motion frequency value; First, it is necessary to make it clear that the foot ring collects acceleration data once per second, in a total of three axes, with about 60 data points in each cycle. With one minute as a cycle, a total of 540 sets of acceleration vectors can be collected in three consecutive cycles, i.e. 180 seconds. The instantaneous exercise intensity is calculated based on the composite value of the three-axis acceleration. The composite formula is a= , if the acceleration at a certain moment is (0.2, 0.1, 0.3), then a= =0.374, and so on to generate the motion intensity sequence of the entire time period. After that, the continuous fluctuation band of intensity needs to be judged to divide the cycle boundary. The intensity change threshold is set to 0.15. If the intensity change of several consecutive data exceeds the threshold, it is marked as a new cycle boundary. For example, the intensity fluctuation in the first 30s is less than 0.1, which is determined to be the same cycle. After dividing 3 complete cycles, the points of motion amplitude change in each cycle are counted. If there are 28 peak points in the first cycle, 32 in the second cycle, and 30 in the third cycle, the average cycle frequency is (28+32+30) / 3=30 times / cycle, and then divided by the cycle length, that is, 60 seconds, the frequency is 0.5Hz, which is used as the average motion frequency value in the current three cycles. In this process, abnormal cycles caused by static state should be eliminated. For example, if the motion intensity of a cycle is lower than 0.05 for a long time, it is determined to be static and excluded from the frequency calculation. The final frequency value obtained is the motion frequency value of the three cycles.

[0022] The joint value generation submodule calls the value of the body surface temperature difference change rate and the three-cycle action frequency value, detects the real-time respiratory rate and resting respiratory rate recorded by the respiratory belt sensor, calculates the difference between the two and normalizes them, using the formula: ; The combined value of physiological stress is obtained by operation, and the combined value of physiological stress is obtained according to the synchronization of the signal-like data and the value fluctuation characteristics; in, represents the rate of change of temperature difference per unit time of the i-th pair of detection points, Represents the average of the temperature difference change rate of all detection points, represents the respiratory frequency at the jth moment, represents the resting respiratory rate, represents the kth segment action frequency value, is the combined value of physiological stress at the jth moment, n is the total number of respiratory frequency collection points, m is the index number in the respiratory data, Represents the respiratory frequency value corresponding to the mth sampling point; Combine the real-time respiratory rate and resting rate recorded by the respiratory belt sensor. The resting rate needs to be collected for 10 consecutive minutes when the animal is stationary to obtain the average value, which is set as =20 times / min, real-time respiratory rate The data is collected at 5-second intervals, and at the jth moment, it is 24 times / minute. =0.06℃ / min, =0.07℃ / min, =0.5Hz, call the above data and substitute it into the innovative formula: ; Among them, ∑ represents the sum of the deviations between the respiratory frequency and the resting frequency at the past n moments. For example, the respiratory frequencies of the last five moments are 21, 22, 20, 23, and 25, respectively; Then∑| |=|21-20|+|22-20|+|20-20|+|23-20|+|25-20|=1+2+0+3+5=11, substitute into the formula and we get: ; The final calculation results in a combined physiological stress value of 2.0025, which is a relatively high value. The judgment interval needs to be set in combination with historical records. If the reference baseline value is 1.2, it is determined that the current state of stress is relatively strong. The formula is beneficial in that it integrates the abnormal degree of temperature fluctuation, the fluctuation of movement activity and breathing changes into a score indicator, forming a unified measurement method for cross-dimensional signals, avoiding misjudgment of single abnormalities.

[0023] See also Figure 3 , the dose derivation module includes: The weight parameter extraction submodule collects the weight parameters recorded by the ear tag identifier, and filters the weight data at the time of collection of the physiological stress combined value according to the recognition time point to obtain the weight value of the same period; After obtaining the original weight value recorded by the ear tag collection device, first filter the time tag and remove the data that is not within the required time range to match the data points synchronized with the physiological stress joint value collection time. For example, when the physiological stress joint value collection time is from 10:00 to 10:10 on September 10, 2024, only the weight records uploaded during this time period are retained. If the animal number is E-202, and the system collects a weight value of 472.8 kg at 10:03, then this value is used as a valid data item, and the number of digits retained for the obtained weight value is adjusted to only retain one decimal place. At the same time, determine whether there are multiple matching records. If so, take the median value within this period as the representative value. After the screening is completed, the data in the valid records are normalized for the following The calculation provides the basic data source. In practical applications, for example, in a batch of beef cattle weight data, the numbers E-205, E-206, and E-207 were recorded as 471.5 kg, 475.0 kg, and 473.6 kg respectively in the same period. After median determination and timestamp screening, the extracted weight value is 473.6 kg. When normalizing the weight, the normalization interval is set to 460 kg to 490 kg, and 473.6 kg is standardized. The calculation formula is: (473.6-460) / (490-460)=0.453, and the standardized weight value is 0.453. The standardized value interval is set to [0, 1] with reference to the normalized benchmark value. It is judged whether there is an out-of-bounds value. If so, the boundary value is used for correction to finally generate the weight value of the same period.

[0024] The respiratory difference discrimination submodule collects the respiratory rate data uploaded by the respiratory synchronization probe at the injection site, extracts the resting frequency record, judges the fluctuation amplitude of the respiratory state based on the difference between the respiratory rate and the resting frequency, calculates the respiratory difference, and judges whether the current value is higher than the corresponding mean value at the same time based on the temperature change rate and the action frequency mean value during the collection period, and obtains the respiratory difference and state judgment value; And extract the resting frequency record, obtain the real-time respiratory data uploaded by the respiratory synchronization probe within the time window set by the system, for example, with 5 seconds as a data cycle, a total of 12 sets of frequency values ​​are recorded every minute. If the respiratory frequency records of individual No. B-112 in a certain minute are 33, 32, 34, 31, 33, 35, 36, 34, 35, 32, 31, 30 times / minute, then the average value within one minute is calculated to be 32.9 times / minute. The resting frequency is estimated by the average value of the static state for 20 minutes. The resting stage is recorded in the non-exercise state of the animal in the early morning without eating. Assuming that the resting frequency is 28.2 times / minute, the respiratory difference The value is 32.9-28.2=4.7 times / min, and then the difference is normalized, and the upper and lower limits are set to 0 to 10 times / min. The normalized difference is 4.7 / 10=0.47. Then, it is determined whether the temperature change rate and movement frequency in the previous section are greater than the average value in the acquisition period. The temperature difference change rate is set to 0.6℃ per minute, and the average value is 0.52℃ / min, which is judged to meet the conditions; the movement frequency value is 3.2Hz, and the acquisition period average is 2.7Hz, which also meets the conditions. The Boolean judgment method is used for the two judgment results. If both are 1, the judgment result is 1, that is, the judgment is established, and finally the breathing difference and state judgment value are obtained.

[0025] The dose intensity calculation submodule calls the physiological stress combined value, the weight value and the respiratory difference value in the same period, and the state judgment value, and selects the records with the judgment result being established, calculates the product based on the weight value and the respiratory difference, and adds it to the physiological stress combined value for proportional conversion, using the formula: ; Obtaining individualized dose intensity values ​​through calculation, and establishing individualized dose intensity values ​​based on the joint calculation relationship between participating items; in, represents the individualized dose intensity value, represents the combined physiological stress value, Represents the respiratory difference, Represents the weight value of the same period, Represents the temperature change rate and action frequency judgment coefficient, Represents the average stress value during the cycle, The conversion factor representing the respiratory rate-amplitude ratio; Call the physiological stress joint value, the weight value and the breathing difference value at the same time, and the state judgment value to build a composite calculation logic. After the judgment condition is met, perform multi-parameter joint calculations. The physiological stress joint value is set to =0.63, the breathing difference is set to =0.47, the weight value in the same period is set to =473.6kg, temperature change and action frequency judgment coefficient The average stress value of the cycle is set to 0.28. Set to 0.52, respiratory rate amplitude conversion factor Set it to 1.35 and substitute it into the formula: ; After substituting specific values, the calculation is as follows: ; ; ; ; ; The final individualized dose intensity value was 575.26. The result showed that this value was used to quantify the current individual's comprehensive stress response and drug dosage requirements under body weight load. The higher the value, the greater the dose intensity level. The benefit of the formula is that, by introducing multi-source monitoring data such as the difference between body weight and respiratory linkage, the dose intensity not only takes into account physiological changes, but also integrates physical sign loads in structural fluctuations, thereby enhancing the ability of the dose value to reflect the true physiological load.

[0026] See also Figure 4 , the dosing sequencing module includes: The muscle frequency extraction submodule collects the continuous contraction frequency data of the muscle sensing probe at the injection site, extracts the average frequency value in the static state, calculates the difference between the two based on the corresponding time segment, and obtains the contraction frequency difference; The continuous contraction frequency data of the muscle sensing probe at the injection site is obtained, and its acquisition cycle is fixed to once every 10 seconds. The continuous contraction frequency values ​​are extracted within this cycle and numbered in time series. Assume that the frequency data obtained within one acquisition cycle is [3.8Hz, 4.1Hz, 4.0Hz, 3.7Hz, 3.5Hz], which represents the number of contractions per second. Then the muscle contraction frequency in the resting state is collected. Usually, the resting state frequency is maintained at [2.0Hz]. The frequency difference sequence is obtained by subtracting the resting frequency from each current frequency value, that is, [1.8Hz, 2.1Hz, 2.0Hz, 1.7Hz, 1.5Hz]. The frequency difference is used to reflect the dynamic activity of the muscle within the continuous acquisition cycle. Then the sequence is normalized once so that the maximum value is scaled to 1 and the remaining values ​​are scaled proportionally. The normalized frequency difference sequence [0.857, 1.000, 0.952, 0.810, 0.714] is obtained, where the normalization operation is performed using the formula F1′=F2 / max(F), where F1′ represents the original frequency difference, and max(F) is the maximum frequency difference of 1.8Hz. After normalization, trend analysis can be performed for subsequent decreasing judgment. In order to illustrate the division of high or low frequency differences, 0.8 is set as the decreasing trend judgment baseline. If three consecutive groups of values ​​are lower than this value, it is judged that the decreasing trend is initially emerging. According to the analysis of the first five groups of data, each group of normalized difference values ​​decreases by no more than 10%, but the fourth and fifth groups of normalized values ​​are continuously lower than 0.8, showing a preliminary decreasing feature. Therefore, the subsequent module needs to call this trend for logical judgment in the decreasing state recognition module, and finally obtain the contraction frequency difference.

[0027] The decreasing state identification submodule determines whether the period sequence presents a unidirectional decreasing trend according to the contraction amplitude data and the static interval duration in the continuous period. If both the amplitude change trend and the interval duration trend meet the decreasing condition, the current inoculation site state is marked as delayed, and the delayed inoculation mark value is obtained; According to the contraction amplitude data and static interval duration data in continuous cycles, a time series set is established for the two types of data at each acquisition point. Assuming that the contraction amplitude values ​​in the current 5 acquisition cycles are [12mm, 10.5mm, 9.3mm, 8.1mm, 6.8mm], and the static interval duration values ​​are [3.2s, 3.5s, 3.8s, 4.1s, 4.4s], the trend is judged by calculating the difference between the two adjacent items in sequence. If all the differences satisfy the negative amplitude and positive interval, it can be preliminarily determined that the double-item decreasing trend is established. In this example, the amplitude difference is [-1.5mm, -1.2mm, -1.2mm, -1.3mm], and the interval difference is [+ 0.3s, +0.3s, +0.3s, +0.3s], all changes are in line with the trend, and the decreasing thresholds are further set to -1.0mm and +0.2s as the trend benchmark. If any item is lower than this standard, it will not be judged as a decreasing trend. This group of data all meet the standard, so the mark value of the current vaccination site is assigned "1", representing a delayed state, and is marked as "0" when the conditions are not met for the sorting module to call. This process does not require the use of external methods or judgment rules. The judgment can be completed only by comparing the numerical trend with the preset threshold. In the actual system, the vaccination site status is stored by number, and the mark value of the corresponding number will interact with the sorting rule to finally obtain the delayed vaccination mark value.

[0028] The injection instruction generation submodule calls the individualized dose intensity value, contraction frequency difference value and delayed vaccination mark value, sorts the vaccination sites in descending order according to the dose intensity value, and adjusts the sorting structure based on the delayed mark, using the formula: ; The injection sequence determination value is obtained by operation, and a sequence number is assigned accordingly to generate a serialized injection instruction number; in, Represents the injection sorting judgment value, represents the dose intensity value of the νth vaccination point, represents the νth contraction frequency difference, represents the value of the delayed vaccination marker at position ν, Represents the total number of vaccination sites, Represents the cumulative value of all deferred markers, Represents the average value of the top positions in the ranking. Represents the average value after sorting; The individualized dose intensity value is set to 26.4 (unit: mg / kg), the corresponding contraction frequency difference is set to 1.7Hz, the delayed vaccination mark value is 1, and the total number of vaccination points in the system is set to 5, each point is numbered from 1 to 5, assuming that the dose intensity value array is [26.4, 24.2, 27.8, 25.0, 23.9], the contraction frequency difference array is [1.7, 1.6, 1.9, 1.5, 1.4], and the delayed vaccination mark array is [1, 0, 0, 0, 1]. First, substitute each vaccination point into the innovative formula in turn: ; in , , Respectively The dose intensity value, contraction frequency difference and delayed vaccination mark value of the vaccination point, =5 is the total number of vaccination sites, =1+1=2 is the cumulative value of all delayed vaccination marks, and are the mean of the first three doses and the mean of the last three doses, that is, =(26.4+24.2+27.8) / 3=26.13, =(25.0+23.9) / 2=24.45, substitute the data into the formula to get: ; The denominator is +1= +1≈1.92+1=2.92; final ≈47.2÷2.92≈16.16, the result is the ranking judgment value of injection object No. 5, which means that due to the large delayed mark value, the sorting priority will be lowered. After combining all the points for calculation, the sorting result will generate the corresponding serialized injection instruction number in descending order based on this value. The benefit of the formula is that the product of the delayed mark and the dose intensity difference is used to participate in the sorting, and the difference between the current and target sorting intervals is introduced in the denominator to moderately balance the sorting structure, so that the sorting has the ability to adjust the periodic trend based on the response to the immediate state.

[0029] See also Figure 5 , the vaccination execution module includes: The injection parameter setting submodule calls the current number information in the serialized injection instruction number, and the automatic injection head actuator completes the spatial positioning of the corresponding vaccination point, sets the output upper limit value of the injection head flow regulator, and reads the dosage intensity value corresponding to the number as the injection dosage setting input to obtain the injection parameter setting value; First, it is necessary to retrieve the generated instruction queue from the storage area. Each number in the queue corresponds to an individual data associated with an inoculation target point. For example, the individual with ID 005 has a corresponding position number L12 and an assigned dose intensity of 17.4 units. This data is calculated by the sorting module in the previous stage and recorded in the queue. After receiving the current instruction, the automatic injection head actuator completes the three-dimensional spatial positioning according to the L12 position corresponding to the number. During the positioning process, the distance between the target point and the injection head is measured by a laser rangefinder. When the positioning error is controlled within 0.5 mm, the locking operation is triggered, and then the maximum flow rate is set through the flow regulator in the injection system. The maximum output value, for example, sets the upper limit to 2.0 ml per second, which is derived from the upper limit of the 95% confidence interval in the historical sampling statistical interval of the individual tolerance limit value. Then, the dose intensity value 17.4 units corresponding to the number is read and written into the execution parameter area as the dose setting value for this injection. The setting value will be converted into the corresponding pulse number through the controller to control the pump body push injection process. If the set dose is 17.4 units and the number of pulses required per unit is 25, the pulse value required for this push injection is 435, and the injection controller will send an execution signal accordingly. Finally, the injection head enters the predetermined posture and stays for 0.8 seconds to start the injection action. After the process is completed, it constitutes the injection parameter setting value.

[0030] The motion error acquisition submodule records the thrust value generated after the injection is completed and the displacement error value generated by the needle during the injection process according to the injection parameter setting value, and uniformly aggregates them into injection error index parameters to obtain the injection error index value; According to the injection execution status, two key feedback data are recorded after the injection action is completed, namely the thrust value and the needle displacement error value during the injection process. The thrust value is measured in real time by the internal pressure sensing element, and the measurement frequency is 10 times per second. The thrust peak value takes the maximum value in the measurement cycle. If the thrust of a certain injection is 38.6 Newtons, the recorded value is 38.6N; the needle displacement error is calculated in real time by the position tracking module. The ideal needle displacement is set to 10.00 mm, and the actual measured displacement is 9.46 mm. The displacement error is |10.00-9.46|=0.54 mm. This value is recorded as the current injection error item. In a complete injection process, this type of record will be collected after the injection head completes the push and pull actions. The thrust values ​​and displacement error values ​​of multiple injection points are uniformly combined into an injection error index set. The system uses this set for subsequent judgment preparation and finally forms the injection error index value corresponding to this injection.

[0031] The compliance result determination submodule calls the injection parameter setting value and the injection error index value, compares the thrust value and the displacement error value according to the stability threshold standard set by the equipment, and performs normalized evaluation based on multiple injection parameters, using the formula: ; The injection action deviation judgment value is obtained by calculation, and the judgment is completed by comparing the stability threshold. If the conditions are met, the compliance status is marked and the vaccination action compliance result is generated; in, Represents the injection action deviation judgment value, Represents the set injection dose value, Represents the upper limit of device flow. Represents the injection thrust value, Represents the needle displacement error value, Representative The thrust value recorded, Representative The displacement error value recorded is is the total number of records; In actual operation, setting the dose value 17.4 units, the upper limit of device flow The injection force is 20.0 units. is 38.6N, displacement error is 0.54 mm, the historical thrust value sequence is [38.1, 38.2, 38.5] N, and the corresponding error value sequence is [0.48, 0.50, 0.52] mm. Substitute it into the formula ; Calculate in sequence: ①| |=|17.4-20.0|=2.6; ② = ≈38.63; ③ =|38.1-0.48|+|38.2-0.50|+|38.5-0.52|=37.62+37.70+37.98=113.30; ④ The overall calculation formula is: ; When the compliance threshold is 0.40, the current result =0.361 is less than the threshold value of 0.40, so the system determines that the current injection is compliant, and the result generates a compliant vaccination action result.

[0032] The results show that the vaccination action did not exceed the threshold in terms of dose setting and execution deviation, and can be judged as a compliant action. The formula is beneficial in that it comprehensively reflects the execution stability and injection accuracy by introducing the combined influencing factors of dose difference, thrust and error and comparing historical samples, and effectively enhances the ability to respond to actual equipment operation deviations.

[0033] See also Figure 6 , the delivery traceability module includes: The abnormal number extraction submodule extracts the sequence numbers marked as incomplete or abnormal status according to the compliance results of the vaccination action and compares them with the injection sequence numbers and injection times recorded at the path trajectory calibration points to obtain the abnormal number set value; First, the compliance result of the vaccination action is decomposed into fields, including injection number, vaccination timestamp, action thrust value, needle displacement error and compliance mark field. The compliance field is represented in binary form, 1 for compliance and 0 for abnormality. Combined with the vaccination timestamp and number index, all the injected records in the trajectory calibration file are retrieved to build a complete injection list. The set difference operation is performed to obtain the incomplete or abnormal state number set. For example, when the injection number recorded in the trajectory is [101, 102, 103, 104, 105], the record number in the compliance result is [101, 103, 105], where the compliance mark of 103 is 0, then the abnormal number set is [102, 10 3, 104], further bind the abnormal set number with the corresponding injection time, check whether there is a record with an injection time shorter than the set benchmark, and for the injection time threshold, this embodiment uses 3.5 seconds as the judgment benchmark. When the injection time corresponding to the number is less than 3.5 seconds, its short-term abnormal state is additionally recorded. Through this process, a complete abnormal number set value can be formed. The acquisition process reflects the combination of compliance judgment and trajectory information, and the extraction of non-injected items and non-compliant items is realized through the cross operation of the number sequence and the timestamp. There is no need to introduce an external model here, and identification and extraction can be realized only by relying on data field differences and basic operation logic, and finally the abnormal number set value is obtained.

[0034] The dose deviation discrimination submodule calls the abnormal number set value, compares the dose intensity value associated with each number with the execution sequence number position, and performs difference analysis on the position difference and dose difference, using the formula: ; Obtaining a position vector offset judgment value by operation, and establishing an offset associated judgment value according to a comparison result between the judgment value and a judgment threshold; in, Represents the position dose deviation judgment value, Representative Injection sequence number, Representative The dose intensity value, and Respectively represent Injection time and route site number, is the total number of numbers, is the number comparison index bit; Get the injection sequence and dose intensity value of the corresponding numbered item, and compare the difference between the position number and the intensity parameter in turn. The detailed operation process is as follows: first map the abnormal number set to the execution sequence in the injection list, and extract its relative position in all injection points. For example, the number [102, 103, 104] corresponds to the sequence position [2, 3, 4]. Get the dose intensity value corresponding to each item, for example, [1.25, 1.55, 1.20]. Dose intensity value As paired data, construct the difference term, calculate its product and then add it to the injection time and the path site number For example, the first number 102 corresponds to =2, =1.25, =25.4s, =Point 8, Take the value as 8, set the serial number ρ=3, and substitute into the following formulas in sequence: ; Enter the value: =1 item is |2×1.25-(25.4+8) / 2|=|2.5-16.7|=14.2; =2 term is |3×1.55-(26.1+9) / 2|=|4.65-17.55|=12.9; =3 items are |4×1.20-(24.7+10) / 2|=|4.8-17.35|=12.55; The total numerator is 14.2+12.9+12.55=39.65; The denominator is =2.45; Finally, it is concluded =39.65÷2.45≈16.18; The judgment threshold is set according to the complexity of the vaccination sequence. >15, it is determined that there is an offset relationship. The value of 16.18 has exceeded the set benchmark and is determined to be associated with a position-dose offset. The benefit of the formula is that, through the combination of the product of the sequence number and the dose value with the injection time and the path point, the calculation result can sensitively reflect the aggregation phenomenon of operational logic confusion or abnormal data offset, and finally establish an offset association judgment value.

[0035] The ear tag data recording submodule screens out abnormal numbers that are determined to have an associated offset relationship between position and dosage according to the offset association judgment value, extracts the ear tag number information corresponding to the number, marks it as the source of abnormal data, and generates a carcass delivery traceability record set; A binary judgment is performed based on the offset association judgment value. When the judgment value is greater than the offset judgment threshold set by the system, the corresponding abnormal number is mapped to the ear tag identification record list, and the corresponding ear tag number is extracted for recording. The detailed process is as follows: first, the injection number and ear tag number binding list in the system is read. For example, the numbers [102, 103, 104] correspond to ear tag numbers [E1002, E1003, E1004] respectively. When number 103 is marked as abnormal because its offset judgment value 16.18 exceeds the threshold 15, the corresponding ear tag number E1003 is extracted into the abnormal data source list. When executing this step, the system maps the injection number and ear tag number in the batch record one by one. The rule is used to compare whether the injection number is within the offset judgment mark range item by item. At the same time, the basic parameters such as the vaccination time and point location corresponding to the ear tag number can also be recorded to build a complete traceability record data structure. For example, for record E1003, the injection time is 10:21:34 on May 12, 2024, the corresponding point number is path 9, the dosage strength is 1.55mL, and the offset value is 16.18. This record item will be written into the traceability record set as livestock traceability information. This process does not require complex reasoning or model judgment. It only needs to perform the number and ear tag mapping operation under the judgment result, and append the record field according to the rule to complete the traceability record generation, and finally generate the livestock release traceability record set.

[0036] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent delivery and tracing system for livestock epidemic prevention drugs, characterized in that: The system comprises: The vital sign collection module obtains the temperature data of the body surface temperature sensor tag, calculates the rate of change of the temperature difference between adjacent points, collects the periodic motion frequency of the action foot ring, detects the difference between the real-time frequency of the respiratory belt and the resting value and normalizes it, and jointly calculates the three signals to generate a joint value of physiological stress; The dose derivation module calls the ear tag weight information based on the physiological stress combined value, collects the breathing rate at the injection site and compares it with the resting value, determines whether the temperature change rate and the movement frequency exceed the cycle average value, and if so, multiplies the weight and the breathing difference and converts them to generate an individualized dose intensity value; The medication sorting module calls the individualized dose intensity value, obtains the difference between the contraction frequency and the resting frequency of the muscle probe, analyzes whether the amplitude change and the resting interval are decreasing, and if so, marks it as a delayed object, arranges the vaccination points in descending order according to the intensity value, and generates a serialized injection instruction number; The vaccination execution module calls the serialized injection instruction number, and the injection head locates the target point and sets the dosage, reads the intensity value as the current setting, records the thrust and displacement error after the injection is completed, and determines whether it is within the device threshold range. If so, it is marked as compliant, and a compliant result for the vaccination action is generated.

2. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The physiological stress combined value includes the temperature change rate difference, the movement frequency fluctuation amplitude, and the normalized respiratory frequency value; the individualized dose intensity value includes the body weight parameter, the respiratory frequency difference, and the physiological stress combined value weighting coefficient; the serialized injection instruction number includes the dose intensity ranking value, the muscle contraction frequency difference, and the contraction amplitude decreasing trend; the vaccination action compliance result includes the thrust deviation value, the needle displacement error value, and the stability compliance mark.

3. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The vital signs collection module comprises: The temperature difference rate calculation submodule collects the temperature values ​​uploaded by the temperature sensing tags worn by the animals in the fence, distinguishes the temperature data of adjacent detection points according to the timestamp, calculates the ratio of the temperature change per unit time to the time interval, obtains the temperature difference change rate between adjacent detection points, and obtains the value of the temperature difference change rate on the surface of the animals; The motion frequency extraction submodule obtains the three-cycle motion data sequence of the animal wearing the motion recognition foot ring, divides the cycle boundary segments according to the change of the motion amplitude, accumulates the number of motion points in each cycle and performs ratio processing on the cycle length, obtains the average motion frequency value in three cycles, and obtains the three-cycle motion frequency value; The joint value generation submodule calls the surface temperature difference change rate value and the three-cycle action frequency value, detects the real-time respiratory frequency and resting respiratory frequency recorded by the respiratory belt sensor, calculates the difference between the two and normalizes them, using the formula: ； The combined value of physiological stress is obtained by operation, and the combined value of physiological stress is obtained according to the synchronization of the signal-like data and the value fluctuation characteristics; in, represents the rate of change of temperature difference per unit time of the i-th pair of detection points, Represents the average of the temperature difference change rate of all detection points, represents the respiratory frequency at the jth moment, represents the resting respiratory rate, represents the kth segment action frequency value, is the combined value of physiological stress at the jth moment, n is the total number of respiratory frequency collection points, m is the index number in the respiratory data, Represents the respiratory frequency value corresponding to the mth sampling point.

4. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The dose derivation module comprises: The weight parameter extraction submodule collects the weight parameters recorded by the ear tag identifier, and selects the weight data at the time of collecting the physiological stress combined value according to the recognition time point to obtain the weight value of the same period; The respiratory difference discrimination submodule collects the respiratory rate data uploaded by the respiratory synchronization probe at the injection site, extracts the resting frequency record, judges the fluctuation amplitude of the respiratory state based on the difference between the respiratory rate and the resting frequency, calculates the respiratory difference, and judges whether the current value is higher than the corresponding mean value at the same time based on the temperature change rate and the action frequency mean value during the collection period, and obtains the respiratory difference and state judgment value; The dose intensity calculation submodule calls the physiological stress combined value, the weight value and the respiratory difference value and the state judgment value in the same period, filters the records with the judgment result being established, calculates the product based on the weight value and the respiratory difference, and adds it to the physiological stress combined value for proportional conversion, using the formula: ； Obtaining individualized dose intensity values ​​through calculation, and establishing individualized dose intensity values ​​based on the joint calculation relationship between participating items; in, represents the individualized dose intensity value, represents the combined physiological stress value, Represents the respiratory difference, Represents the weight value of the same period, Represents the temperature change rate and action frequency judgment coefficient, Represents the average stress value during the cycle, Represents the conversion factor of the respiratory rate-amplitude ratio.

5. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The medication sequencing module comprises: The muscle frequency extraction submodule collects the continuous contraction frequency data of the muscle sensing probe at the injection site, extracts the average frequency value in the static state, calculates the difference between the two based on the corresponding time segment, and obtains the contraction frequency difference; The decreasing state identification submodule determines whether the period sequence presents a unidirectional decreasing trend according to the contraction amplitude data and the static interval duration in the continuous period. If both the amplitude change trend and the interval duration trend meet the decreasing condition, the current inoculation site state is marked as delayed, and the delayed inoculation mark value is obtained; The injection instruction generation submodule calls the individualized dose intensity value, the contraction frequency difference value and the delayed vaccination mark value, sorts the vaccination sites in descending order according to the dose intensity value, and adjusts the sorting structure based on the delayed mark, using the formula: ； The injection sequence determination value is obtained by operation, and a sequence number is assigned accordingly to generate a serialized injection instruction number; in, Represents the injection sorting judgment value, represents the dose intensity value of the νth vaccination point, represents the νth contraction frequency difference, represents the value of the delayed vaccination marker at position ν, Represents the total number of vaccination sites, Represents the cumulative value of all deferred markers, Represents the average value of the top positions in the ranking. Represents the average value after sorting.

6. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The vaccination execution module includes: The injection parameter setting submodule calls the current number information in the serialized injection instruction number, and the automatic injection head actuator completes the spatial positioning of the corresponding vaccination point, sets the output upper limit value of the injection head flow regulator, and reads the dosage intensity value corresponding to the number as the injection dosage setting input to obtain the injection parameter setting value; The motion error acquisition submodule records the thrust value generated after the injection is completed and the displacement error value generated by the needle during the injection process according to the injection parameter setting value, and uniformly aggregates them into injection error index parameters to obtain the injection error index value; The compliance result determination submodule calls the injection parameter setting value and the injection error index value, compares the thrust value and the displacement error value according to the stability threshold standard set by the equipment, and performs normalized evaluation based on multiple injection parameters, using the formula: ； The injection action deviation judgment value is obtained by calculation, and the judgment is completed by comparing the stability threshold. If the conditions are met, the compliance status is marked and the vaccination action compliance result is generated; in, Represents the injection action deviation judgment value, Represents the set injection dose value, Represents the upper limit of device flow. Represents the injection thrust value, Represents the needle displacement error value, Representative The thrust value recorded, Representative The displacement error value recorded is The total number of records.

7. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 1 is characterized in that: The system also includes a delivery tracing module: The delivery traceability module compares the injection number and time of the trajectory calibration point according to the compliance result of the vaccination action, extracts the unfinished or abnormal points, determines whether the dosage and sequence are offset, and if the offset is established, records the ear tag number as the abnormal source, and generates a carcass delivery traceability record set; The livestock release traceability record set includes an abnormal ear tag number identifier, an abnormal injection sequence number, and a dosage and position offset association item.

8. The intelligent delivery and tracing system for animal husbandry epidemic prevention drugs according to claim 7 is characterized in that: The delivery tracing module includes: The abnormal number extraction submodule extracts the sequence numbers marked as incomplete or abnormal status according to the compliance result of the vaccination action and compares the injection sequence numbers and injection times recorded at the path trajectory calibration points to obtain the abnormal number set value; The dose deviation discrimination submodule calls the abnormal number set value, compares the dose intensity value associated with each number with the execution sequence number position, and performs difference analysis on the position difference and dose difference, using the formula: ； Obtaining a position vector offset judgment value by operation, and establishing an offset associated judgment value according to a comparison result between the judgment value and a judgment threshold; in, Represents the position dose deviation judgment value, Representative Injection sequence number, Representative The dose intensity value, and Respectively represent Injection time and route site number, is the total number of numbers, is the number comparison index bit; The ear tag data recording submodule screens and determines the abnormal numbers with associated offset relationship between position and dosage according to the offset association judgment value, extracts the ear tag number information corresponding to the number, marks it as the source of abnormal data, and generates a carcass delivery traceability record set.

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