Livestock veterinary blood sampling data real-time management system

Abstract

The invention relates to the technical field of data management, in particular to a animal husbandry veterinary blood sampling data real-time management system which comprises a blood sampling data input module, a data classification and arrangement module, an anomaly detection module and a data synchronization and backup module. According to the method, the accuracy and the integrity of the data are improved by binding the animal image with the data, the position and the adjacency of the blood sampling point are evaluated through spatial analysis, the organization structure of the data is optimized, the data are more systematized and convenient to analyze, and then the accuracy and the integrity of the data are improved when animal diseases are detected subsequently. According to the invention, blood samples of adjacent blood sampling points can be detected in real time, animal diseases can be identified in time, abnormity detection can be carried out on standard deviation of the blood sampling amount, data quality control is enhanced, risks caused by data errors are reduced, data synchronization intervals are adjusted, effective data backup is implemented, data safety and recovery capability are improved, and the method is suitable for large-scale popularization and application. The security and continuous availability of the data are guaranteed, and long-term data analysis and research requirements are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a real-time management system for livestock and veterinary blood collection data. Background Art

[0002] The technical field of data management involves collecting, storing, protecting, validating, and processing data to ensure the accessibility, reliability, and timeliness of data. Effective data management helps enhance the decision-making process, optimize data usage, safeguard data security, and ensure compliance with relevant laws and policies. This field uses a variety of technologies and methods to process data, including database management systems, data mining, data integration, and data quality management, etc. These technologies enable organizations to extract valuable information from large and complex data sets to support business processes and strategic planning.

[0003] Among them, the real-time management system for livestock and veterinary blood collection data mainly focuses on the real-time collection, storage, and management of data generated during the animal blood collection process. The core use of the system is to efficiently manage blood collection data, timely track and record detailed information of each blood collection event, such as blood collection time, blood collection volume, blood collection personnel, and the health status of the blood collection animal, etc. Ensure that the data can be updated in real time and is easy to query, so as to support veterinarians and livestock experts to make quick and accurate decisions in animal health management and disease diagnosis.

[0004] Traditional data management systems lack effective data classification and sorting capabilities in the processing of animal blood collection data, are insufficient to support complex data analysis requirements, lack the annotation of the relationships between blood collection locations, resulting in difficulty in immediately identifying adjacent breeding areas when diseases are detected subsequently, and the lack of targeted anomaly detection will also cause incorrect data to fail to be corrected in time, increasing the difficulty of management and decision-making. The security and backup of data rely on fixed policies, which will result in high backup costs or insufficient data protection effects, and it is difficult to achieve good usage effects. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a real-time management system for livestock and veterinary blood collection data.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A real-time management system for livestock and veterinary blood collection data, the system includes:

[0007] The blood collection data entry module, based on the animal blood collection environment, according to the type and age of the animal, combines with the standard blood collection volume to evaluate and recommend the blood collection volume. The veterinarian conducts the blood collection work, and records the animal individual information, blood collection time, blood collection location, and blood collection personnel information, and captures pictures of the blood collection tube and the animal. Bind the images with the data to obtain a data-bound file;

[0008] The data classification and sorting module performs spatial analysis on the blood collection locations based on the data-bound archives, evaluates the adjacency between blood collection points according to the distance and terrain type between blood collection locations, marks the data of adjacent blood collection points, and classifies and sorts the blood collection data of a single location by analyzing the animal species and animal age at the single location to obtain the sorted data;

[0009] The anomaly detection module analyzes the deviation between the animal blood collection volume and the standard range based on the sorted data, evaluates the anomaly level according to the deviation magnitude, determines whether there are data entry errors, and notifies the veterinarian to immediately change the data to obtain the anomaly detection and processing result;

[0010] The data synchronization and backup module evaluates the input frequency of the blood collection data based on the anomaly detection and processing result and the sorted data, adjusts the data synchronization interval, transmits the data to the cloud server, and adjusts the number of redundant backup nodes for the data according to the importance of the data to back up the data and obtains the blood collection data backup management result.

[0011] The improvement of the present invention is that the method for evaluating the recommended blood collection volume is as follows:

[0012] Based on the animal blood collection environment, extract the animal type, age, and weight data, and obtain the blood collection volume correlation data by acquiring the basic blood collection volume corresponding to the animal type;

[0013] Based on the blood collection volume correlation data, through the formula:

[0014] V rec = B·(1 + k w ·log(c w ·W + 1) + k a ·log(c a ·A + 1))

[0015] Calculate the recommended blood collection volume V rec , where V rec is the recommended blood collection volume, B represents the basic blood collection volume of the animal, W is the weight of the animal, A is the age of the animal, k w and k a are weight coefficients, and c w and c a are logarithmic influence coefficients;

[0016] Based on the recommended blood collection volume V rec , compare it with the capacity of the blood collection tube to determine whether the blood collection tube can accommodate it. If it cannot accommodate, then use the maximum capacity of the blood collection tube as the target blood collection volume to obtain the blood collection volume adjustment result.

[0017] The improvement of the present invention is that the method for marking the data of adjacent blood collection points is as follows:

[0018] Based on the data - binding file, extract the coordinate information of the differentiated blood - collection points to obtain the extraction result of the blood - collection position information;

[0019] Based on the extraction result of the blood - collection position information, through the formula:

[0020]

[0021] Calculate the adjacency score S between blood - collection points, where (x1, y1) and (x2, y2) are the coordinates of two blood - collection points respectively, and C t is the terrain influence coefficient, and S is the adjacency score between blood - collection points;

[0022] Based on the adjacency score S between blood - collection points, compare it with a preset adjacency threshold, mark two blood - collection points with an adjacency score exceeding the adjacency threshold as adjacent, and mark the corresponding blood - collection data to obtain the marked result of adjacent blood - collection data.

[0023] An improvement of the present invention is that the obtaining step of the sorted data is as follows:

[0024] Based on the data - binding file and the marked result of adjacent blood - collection data, screen the blood - collection records of the same blood - collection point, archive the data of the same blood - collection point, and extract the animal species information, animal age data, and animal weight data to obtain the basic data of animals at the location;

[0025] Based on the basic data of animals at the location, group the blood - collection data after classifying the blood - collection points according to the animal species, compare with a preset classification standard according to the age and weight of the animals, perform intra - group division on the blood - collection data, and sort the data according to the animal age to obtain the sorted data.

[0026] An improvement of the present invention is that the method for analyzing the deviation of the animal blood - collection volume from the standard range is as follows:

[0027] Based on the sorted data, extract the actual blood - collection volume data of the animals, and obtain the blood - collection deviation - related data by acquiring the standard blood - collection range data;

[0028] Based on the blood - collection deviation - related data, through the formula:

[0029] δV = max(V actual - V max ,V min - V actual ,0)

[0030] Calculate the blood - collection volume deviation δV to obtain the evaluation result of the blood - collection volume deviation, where V actual is the actual blood - collection volume of the animal, V min and V maxare the minimum and maximum values of the standard blood collection volume range, and δV is the deviation of the blood collection volume.

[0031] The improvement of the present invention is that the step of obtaining the abnormal detection and processing result is as follows:

[0032] Based on the evaluation result of the blood collection volume deviation, through the formula:

[0033]

[0034] calculate the abnormal level value L, where δV is the deviation of the blood collection volume, V min and V max are the minimum and maximum values of the standard blood collection volume range respectively, and k L is the conversion coefficient. L min is the minimum abnormal level, and L is the abnormal level value;

[0035] Based on the abnormal level value L, compare it with the preset abnormal level table, implement the corresponding abnormal notification measure, notify the veterinarian to immediately change the data, and obtain the abnormal detection and processing result.

[0036] The improvement of the present invention is that the method for adjusting the synchronization interval of the data is as follows:

[0037] Based on the abnormal detection and processing result and the sorted data, extract the data entry time points within a period of time to obtain the data entry time information;

[0038] Based on the data entry time information, combined with the minimum synchronization time interval, through the formula:

[0039]

[0040] calculate the adjusted synchronization time interval T sync , and obtain the synchronization interval adjustment result, where t i is the time point of the i-th data entry, n is the total number of data entries, T min is the minimum synchronization time interval, C T is the adjustment coefficient, and T sync is the adjusted synchronization time interval.

[0041] The improvement of the present invention is that the step of obtaining the blood collection data backup management result is as follows:

[0042] Based on the synchronization interval adjustment result, transmit the data to the cloud server, and collect the animal value information corresponding to the blood collection data to obtain the data backup association information;

[0043] Based on the data backup association information, through the formula:

[0044]

[0045] Calculate the adjusted number of backup nodes N adjusted , where N base is the number of basic backup nodes, V economic is the economic value of the animal, C factor is the adjustment coefficient, P importance is the importance level of the data, N adjusted is the number of backup nodes after adjustment;

[0046] Based on the adjusted number of backup nodes N adjusted , back up the differentiated blood collection data and obtain the blood collection data backup management results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by binding animal images with data, the accuracy and completeness of the data are improved, the location and proximity of blood collection points are evaluated through spatial analysis, the organizational structure of the data is optimized, the data is made more systematic and easy to analyze, and then when animal diseases are detected subsequently, blood samples from adjacent blood collection points can be detected immediately, animal diseases can be identified in time, and abnormalities can be detected for the standard deviation of blood collection volume, thereby enhancing data quality control, reducing risks caused by data errors, adjusting data synchronization intervals and implementing effective data backup, improving data security and recovery capabilities, ensuring data security and continuous availability, and supporting long-term data analysis and research needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 A flow chart of the recommended blood collection volume for the present invention evaluation;

[0051] Figure 3 This is a flow chart of marking data of adjacent blood sampling points according to the present invention;

[0052] Figure 4 A flow chart for obtaining the collated data for the present invention;

[0053] Figure 5 A flow chart for analyzing the deviation of animal blood collection volume from the standard range according to the present invention;

[0054] Figure 6 A flowchart of obtaining anomaly detection processing results for the present invention;

[0055] Figure 7 A flowchart of obtaining a synchronization interval for adjusting data according to the present invention;

[0056] Figure 8 This is a flowchart for obtaining the backup management result of blood collection data in the present invention. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0058] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings. They 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. Therefore, they should not be construed as limitations to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0059] Please refer to Figure 1 , the present invention provides a technical solution: a real-time management system for blood collection data of animal husbandry and veterinary medicine. The system includes:

[0060] The blood collection data entry module evaluates and recommends the blood collection volume based on the animal blood collection environment, the type and age of the animal, and in combination with the standard blood collection volume. The veterinarian conducts the blood collection work, records the individual information of the animal, the blood collection time, the blood collection location, and the information of the blood collection personnel, and captures pictures of the blood collection tube and the animal, and binds the images and data to obtain a data binding file.

[0061] The data classification and sorting module conducts a spatial analysis of the blood collection locations based on the data binding file, evaluates the adjacency between the blood collection points according to the distance and terrain type between the blood collection locations, marks the data of adjacent blood collection points, and classifies and sorts the blood collection data of a single location by analyzing the animal species and animal age at a single location to obtain the sorted data.

[0062] The anomaly detection module analyzes the deviation between the animal blood collection volume and the standard range based on the sorted data, evaluates the anomaly level according to the deviation size, determines whether there is an input error in the data, and notifies the veterinarian to immediately change the data to obtain the anomaly detection and processing result.

[0063] The data synchronization and backup module evaluates the input frequency of the blood collection data based on the anomaly detection and processing result and the sorted data, adjusts the synchronization interval of the data, transmits the data to the cloud server, and adjusts the number of redundant backup nodes of the data according to the importance of the data to back up the data and obtain the backup management result of the blood collection data.

[0064] The data binding file includes animal ID, blood collection timestamp, geographical coordinates, blood collection personnel ID, blood collection tube image, and animal image. The sorted data includes the animal species classification result and the dataset of geographical neighbors. The abnormal detection processing result includes abnormal data records, abnormal types, and alarm levels. The blood collection data backup management result includes the blood collection data synchronization interval and the number of backup nodes.

[0065] Please refer to Figure 2 , and the method for evaluating the recommended blood collection volume is as follows:

[0066] Based on the animal blood collection environment, extract the type, age, and weight data of the animal, and obtain the basic blood collection volume corresponding to the animal type to get the blood collection volume correlation data;

[0067] Based on the blood collection volume correlation data, through the formula:

[0068] V rec = B·(1 + k w ·log(c w ·W + 1) + k a ·log(c a ·A + 1))

[0069] Calculate the recommended blood collection volume V rec , where V rec is the recommended blood collection volume, B represents the basic blood collection volume of the animal, W is the weight of the animal, A is the age of the animal, k w and k a are weight coefficients, c w and c a are logarithmic influence coefficients;

[0070] Based on the recommended blood collection volume V rec , compare it with the blood collection tube capacity to determine whether the blood collection tube can accommodate it. If it cannot accommodate, then use the maximum capacity of the blood collection tube as the target blood collection volume to obtain the blood collection volume adjustment result.

[0071] Formula:

[0072] V rec = B·(1 + k w ·log(c w ·W + 1) + k a ·log(c a ·A + 1))

[0073] Meaning and acquisition method of parameters:

[0074] B: The basic blood collection volume, which is obtained from a preset medical form according to the type of animal. The form is pre-set by veterinarians and researchers based on a large amount of animal medical data. Here, the blood collection volume that can be accepted by newborn individuals of this type of animal is adopted.

[0075] W: The weight of the animal, which is directly measured by a veterinarian using standard weight measurement equipment before blood collection.

[0076] A: The age of the animal, which is usually provided by the owner of the animal or determined according to the animal's medical records.

[0077] k w and k a : The coefficients are the weight and age weighting coefficients, which respectively adjust the influence of weight and age on the blood collection volume. These coefficients are obtained through historical data analysis. Through statistical methods, such as regression analysis, the appropriate ratio for adjusting the blood collection volume is determined.

[0078] c w and c a : The coefficients are used to adjust the actual input values of weight and age to ensure numerical stability and reflect the actual influence when used in the logarithmic function. These coefficients are usually set according to the physiological characteristics of the animal species to ensure the applicability and accuracy of the calculation.

[0079] Calculation example:

[0080] Set the following parameters

[0081] The animal is an adult chicken: B = 50 ml, weight W = 5 kg, age A = 3 years, weighting coefficient k w = 0.05 and k a = 0.02, adjustment coefficient c w = 0.1 and c a = 0.5.

[0082] The calculation process is as follows:

[0083] V rec = 50·(1 + 0.05·log(0.1·5 + 1) + 0.02·log(0.5·3 + 1))

[0084] = 50·(1 + 0.05·0.176 + 0.02·0.398)

[0085] = 50·(1 + 0.0088 + 0.00796)

[0086] ≈ 50·1.01676

[0087] = 50.838

[0088] Therefore, for this chicken, the recommended blood collection volume is approximately 50.838 ml. The calculation reflects that due to the small body weight and young age, there will be no significant change in the blood collection volume.

[0089] Please refer to Figure 3 , the method for marking the data of adjacent blood collection points is as follows:

[0090] Based on the data binding file, extract the coordinate information of the differential blood collection points to obtain the extraction result of the blood collection position information;

[0091] Based on the extraction result of the blood collection position information, through the formula:

[0092]

[0093] Calculate the adjacency score S between blood collection points. Among them, (x1, y1) and (x2, y2) are the coordinates of two blood collection points respectively, and C t is the terrain influence coefficient, and S is the adjacency score between blood collection points;

[0094] Based on the adjacency score S between blood collection points, compare it with the preset adjacency threshold, mark the two blood collection points whose adjacency score exceeds the adjacency threshold as adjacent, and mark the corresponding blood collection data to obtain the marked result of adjacent blood collection data.

[0095] Formula:

[0096]

[0097] The meaning and acquisition method of parameters

[0098] Coordinates (x1, y1 and x2, y2): The coordinates represent the positions of two blood collection locations on the map. They can be obtained through a GPS device or directly read from map data.

[0099] Terrain influence coefficient C t : The coefficient is preset according to the obstructive nature of the terrain. For example, obstacles such as rivers and mountains will be given a higher coefficient value. The coefficient can be determined in cooperation with terrain types and geographic information system data.

[0100] Calculation example:

[0101] Suppose there are the coordinates of two blood collection locations: the coordinates of location A (x1, y1) = (100, 200), and the coordinates of location B (x2, y2) = (110, 220). The terrain influence coefficient C t is flat grassland, and the coefficient is relatively low, set to 0.1.

[0102] Calculation process

[0103] Calculate the Euclidean distance between two points:

[0104]

[0105] Calculate the adjacency score:

[0106]

[0107] The calculated result of the adjacency score S is approximately 0.0406. This score is based on the distance between Location A and Location B and the type of terrain. The low score reflects that even though the distance between the two points is far, it reduces their adjacency. It can help decision-makers understand whether animals between different blood collection locations are likely to come into contact with each other. Thus, when an animal disease is detected subsequently, the blood samples of adjacent blood collection points can be immediately tested to timely identify animal diseases.

[0108] Please refer to Figure 4 , the steps for obtaining the sorted data are as follows:

[0109] Based on the data binding archives and the marked results of adjacent blood collection data, screen the blood collection records of the same blood collection point, archive the data of the same blood collection point, and extract the animal species information, animal age data, and animal weight data to obtain the basic data of animals at the location.

[0110] Based on the basic data of animals at the location, group the blood collection data after classifying the blood collection points according to the animal species, and compare it with the preset classification criteria according to the age and weight of the animals, divide the blood collection data within the group, and sort the data according to the animal age to obtain the sorted data.

[0111] Please refer to Figure 5 , the method for analyzing the deviation of the animal blood collection volume from the standard range is as follows:

[0112] Based on the sorted data, extract the actual blood collection volume data of the animals and obtain the standard blood collection range data to get the associated data of blood collection deviation;

[0113] Based on the associated data of blood collection deviation, through the formula:

[0114] δV = max(V actual - V max , V min - V actual , 0)

[0115] Calculate the blood collection volume deviation δV to obtain the evaluation result of the blood collection volume deviation, where V actual is the actual blood collection volume of the animal, V min and V max are respectively the minimum and maximum values of the standard blood collection range, and δV is the blood collection volume deviation.

[0116] Formula:

[0117] δV = max(V actual - V max , V min - V actual , 0)

[0118] Meaning and acquisition method of parameters

[0119] V actual : The actual blood collection volume, which is obtained directly from the data entered by veterinarians or medical technicians.

[0120] V min and V max : The minimum and maximum values of the standard range of blood collection volume. The values are determined according to the species, weight, and health status of the animal, usually guided by veterinary scientific research or industry standards, and recorded in medical guidelines or operation manuals.

[0121] Calculation example:

[0122] For a chicken, the standard blood collection volume range is set as V min = 40ml and V max = 50ml. The actually recorded blood collection volume V actual is 55ml.

[0123] Calculation of deviation:

[0124] δV = max(V actual - V max , V min - V actual , 0)

[0125] = max(55 - 50, 40 - 55, 0)

[0126] = max(5, -15, 0)

[0127] = 5ml

[0128] The calculated deviation δV = 5ml, indicating that the actual blood collection volume exceeds the standard upper limit by 5ml, suggesting possible operation errors or inaccurate records, and further investigation or correction is required.

[0129] Please refer to Figure 6 , the steps to obtain the abnormal detection processing results are as follows:

[0130] Based on the evaluation result of the blood collection volume deviation, through the formula:

[0131]

[0132] Calculate the abnormal level value L, where δV is the blood collection volume deviation, V min and V maxThey are the minimum and maximum values of the standard range of blood collection volume, respectively, and k L is the conversion coefficient. L min is the minimum abnormal level, and L is the abnormal level value;

[0133] Based on the abnormal level value L, compare it with the preset abnormal level table, implement the corresponding abnormal notification measure, notify the veterinarian to immediately change the data, and obtain the abnormal detection and processing result.

[0134] Formula:

[0135]

[0136] Meaning and acquisition method of parameters:

[0137] δV: Blood collection volume deviation, obtained by calculation in the previous step.

[0138] V min and V max : The minimum and maximum values of the standard range of blood collection volume. The values are determined according to the species, weight and health status of the animal, usually guided by veterinary scientific research or industry standards, and recorded in medical guidelines or operation manuals.

[0139] k L : It is the conversion coefficient, used to convert the deviation ratio into the abnormal level. The coefficient determines the conversion rate of the deviation ratio to the abnormal level.

[0140] L min : The minimum abnormal level, which is a preset value used to ensure that even a small deviation can maintain a basic abnormal level. This is usually set based on clinical requirements or data security standards.

[0141] Calculation example:

[0142] It is set that during the animal health examination, the actual blood collection volume V of an animal actual is 55 ml. According to the standard of this animal species, the set blood collection volume range V min = 40 ml and V max = 50 ml. The conversion coefficient k L is set to 10, indicating that for every 10% exceeding the standard range, the abnormal level increases by 1 level. The minimum abnormal level L min is set to 1, and the deviation δV = 5.

[0143] Calculate the abnormal level L:

[0144]

[0145] The calculated anomaly level L is 5, indicating that the actual blood collection volume far exceeds the standard range, and it is necessary to review and adjust the accuracy of the blood collection process or data entry. Anomaly at this level may prompt veterinarians or medical staff to take emergency measures to correct potential health risks or data errors.

[0146] Please refer to Figure 7 , and the method for adjusting the synchronization interval of data is as follows:

[0147] Based on the anomaly detection processing results and the sorted data, extract the data entry time points within a period of time to obtain the data entry time information;

[0148] Based on the data entry time information, combined with the minimum synchronization time interval, through the formula:

[0149]

[0150] [[ID=1...]]Calculate the adjusted synchronization time interval T sync , to obtain the synchronization interval adjustment result, where t i is the time point of the i-th data entry, n is the total number of data entries, T min is the minimum synchronization time interval, C T is the adjustment coefficient, and T sync is the adjusted synchronization time interval. <...]]

[0151] Formula:

[0152]

[0153] Meaning and acquisition method of parameters

[0154] t i : It is the specific time point of data entry, recording the time when the user or system inputs data each time. The time point is usually directly provided by the system's log file or database, ensuring the accuracy of time recording.

[0155] n: This represents the total number of data entries, that is, the number of time points t i . It can be obtained by counting the number of record entries in the database or the number of events in the log file.

[0156] T min : The minimum synchronization time interval, which is set by the administrator or developer based on network load and server processing capabilities. This value is designed to ensure that the synchronization operation does not affect the system performance due to frequent execution.

[0157] C T : The adjustment coefficient, which is a proportional factor used to adjust the synchronization time interval according to the data entry frequency. This coefficient needs to be set according to actual requirements and network conditions to optimize the synchronization efficiency.

[0158] Calculation example:

[0159] Set the time points for the following five data entries recorded within a certain period of time:

[0160] t1 = 08:00, t2 = 09:00, t3 = 11:00, t4 = 15:00, t5 = 18:00, n = 5 (total number of data entries). Set T min = 1 hour to ensure that data is synchronized at least once per hour, C T = 2, with the aim of increasing the synchronization time interval and reducing the server load.

[0161] Calculation process:

[0162] Calculate the total time interval:

[0163] From 08:00 to 09:00 = 1 hour

[0164] From 09:00 to 11:00 = 2 hours

[0165] From 11:00 to 15:00 = 4 hours

[0166] From 15:00 to 18:00 = 3 hours

[0167] Total time interval:

[0168] 1 + 2 + 4 + 3 = 10 hours

[0169] Calculate the average time interval:

[0170]

[0171] Calculate the synchronization time interval:

[0172]

[0173] The calculated synchronization time interval T sync is 1.25 hours. This means that the system will synchronize data to the cloud server every 1.25 hours. This calculation method dynamically adjusts the synchronization time interval by balancing the data entry frequency and system load requirements, aiming to improve data management efficiency while avoiding resource waste caused by overly frequent data synchronization.

[0174] Please refer to Figure 8 , and the steps to obtain the management result of blood collection data backup are as follows:

[0175] Based on the synchronization interval adjustment result, transfer the data to the cloud server and collect the animal value information corresponding to the blood collection data to obtain the data backup association information;

[0176] Based on the data backup correlation information, through the formula:

[0177]

[0178] Calculate the adjusted number of backup nodes N adjusted , where N base is the basic number of backup nodes, V economic is the economic value of the animal, C factor is the adjustment coefficient, P importance is the importance level of the data, N adjusted is the adjusted number of backup nodes;

[0179] Based on the adjusted number of backup nodes N adjusted , back up the differential blood collection data to obtain the management result of blood collection data backup.

[0180] Formula:

[0181]

[0182] Meanings and acquisition methods of parameters:

[0183] N base : The basic number of backup nodes. This is a fixed value set by the data center according to the standard operation process and the minimum data protection requirements. Usually determined by the basic data protection policy of the system.

[0184] V economic : The economic value of the animal. The value is evaluated through market analysis or the importance of the animal in the research and can be obtained from the financial database or relevant market research reports.

[0185] C factor : The adjustment coefficient. This is a preset coefficient used to convert the economic value into the increase ratio of the number of backup nodes. Usually set by the data management team based on past data protection experience.

[0186] P importance : The importance level of the data. This is rated according to the sensitivity and business impact of the data, ranging from 1 to 10. A higher level indicates greater importance of the data to the business, so a higher level of protection is required.

[0187] Calculation example:

[0188] Set the following parameters: N base = 3: The basic number of nodes, V economic = 120: The economic value of the animal is evaluated as 120 units, C factor = 0.05: Increase 5% of the backup nodes for each unit of economic value, P importance = 7: The importance level of the data is 7.

[0189] Calculate the number of backup nodes:

[0190]

[0191] The calculated adjusted number of backup nodes N adjusted is 6. This indicates that according to the economic value of the animals and the importance of the data, the number of backup nodes has increased from the basic 3 to 6. The calculation method ensures that high-value and highly important data can obtain more backup nodes, thereby improving the security and reliability of the data.

[0192] The above is only the preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A real-time management system for livestock and veterinary blood sampling data, characterized in that, The system includes: The blood collection data entry module evaluates and recommends the blood collection volume based on the animal blood collection environment, the type and age of the animal, and in combination with the standard blood collection volume. The veterinarian performs the blood collection work and records the individual information of the animal, the blood collection time, the blood collection location, and the information of the blood collection personnel. The pictures of the blood collection tube and the animal are captured, and the images are bound to the data to obtain a data-bound file; Based on the data-bound file, the data classification and sorting module performs spatial analysis on the blood collection locations, evaluates the adjacency between blood collection points according to the distance and terrain type between blood collection locations, marks the data of adjacent blood collection points, and classifies and sorts the blood collection data of a single location by analyzing the animal species and animal age at the single location to obtain the sorted data; Based on the sorted data, the anomaly detection module analyzes the deviation of the animal blood collection volume from the standard range, evaluates the anomaly level according to the size of the deviation, determines whether there is an input error in the data, and notifies the veterinarian to immediately correct the data to obtain the anomaly detection and processing result; Based on the anomaly detection and processing result and the sorted data, the data synchronization and backup module evaluates the input frequency of the blood collection data, adjusts the synchronization interval of the data, transmits the data to the cloud server, and adjusts the number of redundant backup nodes of the data according to the importance of the data to back up the data to obtain the blood collection data backup management result.

2. The real-time management system for livestock and veterinary blood sampling data according to claim 1, wherein The method for evaluating and recommending the blood collection volume is as follows: Based on the animal blood collection environment, the type, age, and weight data of the animal are extracted, and the basic blood collection volume corresponding to the animal type is obtained to obtain blood collection volume-related data; Based on the blood collection volume-related data, through the formula: V rec = B·(1 + k w · log(c w · W + 1) + k a · log(c a · A + 1)) Calculate the recommended blood collection volume V rec , where V rec is the recommended blood collection volume, B represents the animal's basic blood collection volume, W is the animal's weight, A is the animal's age, k w and k a are weight coefficients, c w and c a are logarithmic influence coefficients; Based on the recommended blood collection volume V rec , compare it with the volume of the blood collection tube to determine whether the blood collection tube can accommodate it. If it cannot accommodate, use the maximum volume of the blood collection tube as the target blood collection volume to obtain the blood collection volume adjustment result.

3. The real-time management system for livestock and veterinary blood sampling data according to claim 1, characterized in that, The method for marking the data of adjacent blood collection points is as follows: Based on the data-bound file, the coordinate information of the differential blood collection points is extracted to obtain the blood collection position information extraction result; Based on the blood collection position information extraction result, through the formula: Calculate the adjacency score S between blood collection points, where (x1, y1) and (x2, y2) are the coordinates of two blood collection points respectively, and C t is the terrain influence coefficient, and S is the adjacency score between blood collection points; Based on the adjacency score S between the blood collection points, it is compared with a preset adjacency threshold. Two blood collection points with an adjacency score exceeding the adjacency threshold are marked as adjacent, and the corresponding blood collection data is marked to obtain the adjacent blood collection data marking result.

4. The real-time management system for livestock and veterinary blood sampling data according to claim 3, characterized in that, The steps for obtaining the sorted data are as follows: Based on the data-bound file and the adjacent blood collection data marking result, the blood collection records of the same blood collection point are screened, the data of the same blood collection point are archived, and the animal species information, animal age data, and animal weight data are extracted to obtain the location animal basic data; Based on the location animal basic data, according to the animal species, the blood collection data classified by blood collection points is grouped, and compared with the preset classification standard according to the age and weight of the animal, the blood collection data is divided within the group, and the data is sorted according to the animal age to obtain the sorted data.

5. The real-time management system for livestock and veterinary blood sampling data according to claim 1, characterized in that The method for analyzing the deviation of the animal blood collection volume from the standard range is as follows: Based on the sorted data, the actual blood collection volume data of the animal is extracted, and the standard blood collection range data is obtained to obtain blood collection deviation-related data; Based on the blood collection deviation-related data, through the formula: δV = max(V actual - V max , V min - V actual , 0) Calculate the blood collection volume deviation δV to obtain the blood collection volume deviation evaluation result, where V actual is the actual blood collection volume of the animal, V min and V max are the minimum and maximum values of the standard blood collection volume range respectively, and δV is the blood collection volume deviation.

6. The real-time management system for livestock and veterinary blood collection data according to claim 5, characterized in that, The steps for obtaining the anomaly detection and processing result are: Based on the blood collection volume deviation evaluation result, through the formula: Calculate the abnormal level value L, where δV is the blood collection volume deviation, V min and V max are the minimum and maximum values of the standard range of blood collection volume respectively, and k L is the conversion coefficient. L min is the minimum abnormal level, and L is the abnormal level value; Based on the abnormal level value L, compare it with the preset abnormal level table, implement corresponding abnormal notification measures, notify the veterinarian to immediately change the data, and obtain the abnormal detection and processing result.

7. The real-time management system for livestock and veterinary blood sampling data according to claim 1, characterized in that The method for adjusting the synchronization interval of the data is as follows: Based on the abnormal detection and processing result and the sorted data, extract the data entry time points within a certain period of time to obtain the data entry time information; Based on the data entry time information, combined with the minimum synchronization time interval, through the formula: Calculate the adjusted synchronization time interval T sync , and obtain the synchronization interval adjustment result, where t i is the time point of the i-th data entry, n is the total number of data entries, T min is the minimum synchronization time interval, C T is the adjustment coefficient, and T sync is the adjusted synchronization time interval.

8. The real-time management system for livestock and veterinary blood sampling data according to claim 7, characterized in that The steps for obtaining the blood collection data backup management result are as follows: Based on the synchronization interval adjustment result, transmit the data to the cloud server, and collect the animal value information corresponding to the blood collection data to obtain the data backup association information; Based on the data backup association information, through the formula: Calculate the adjusted number of backup nodes N adjusted , where N base is the basic number of backup nodes, V economic is the economic value of the animal, C factor is the adjustment coefficient, P importance is the importance level of the data, and N adjusted is the adjusted number of backup nodes; Based on the adjusted number of backup nodes N adjusted , the differential blood collection data is backed up to obtain the management result of the blood collection data backup.

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