Livestock blood sampling data management system

Abstract

The invention relates to the technical field of data management, in particular to a livestock blood sampling data management system which comprises a health state evaluation module, a blood sampling plan making module, a resource allocation management module and a blood sampling task monitoring module. According to the invention, through continuous monitoring and comparison of animal blood biochemical indexes, accurate assessment of animal health conditions is realized, a real-time health monitoring framework is provided, timely discovery of abnormal conditions, rapid identification of animal groups needing to be concerned and improvement of epidemic prevention and treatment efficiency are ensured, and through health risk grade division, the health risk level of the animal group can be rapidly identified. The system accurately formulates a blood sampling plan, optimizes resource allocation, ensures optimal allocation and use efficiency of resources, realizes real-time monitoring of resource use and task progress through blood sampling data management, optimizes task management, ensures smooth completion of a blood sampling task by flexibly adjusting the resource allocation and execution plan, reduces the operation cost, and improves the efficiency of blood sampling. And the overall efficiency and management level of animal husbandry business are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a livestock blood sampling data management system. Background Art

[0002] The field of data management technology involves the processes and methods used by information systems to collect, store, manage and analyze data. In the application of livestock blood collection data management, this technical field focuses on optimizing data flow and ensuring data accuracy and security, including data input, verification, storage, protection, query, update and visualization. Data management technology achieves rapid retrieval and effective management of data through efficient data processing algorithms and database management systems (DBMS). The technology supports processing needs in a big data environment and ensures the consistency and accuracy of data integration, thereby providing users with reliable data access and analysis capabilities.

[0003] Among them, the livestock blood collection data management system is a technical solution designed specifically for animal husbandry, aimed at managing and processing blood sample data collected from livestock farms. The main purpose of the system is to improve data accessibility and analysis efficiency by efficiently managing blood sample information. By using this system, livestock workers can track and record animal blood collection records, sample status and result data, support decision-making processes and livestock management, thereby improving the operational efficiency and management level of livestock business. The system is an optimization tool for data and information flow to support a wider range of livestock business management.

[0004] Existing technologies mainly focus on the data collection and storage process, but are obviously insufficient in dynamic data analysis and real-time health monitoring, which results in insufficient response to animal health status and inability to effectively identify high-risk animal groups, thus affecting the timeliness and effectiveness of disease prevention and control. The resource allocation of existing systems is static and one-time, lacking flexibility and adaptability, resulting in insufficient response or effectiveness in the face of emergencies or unplanned events. Existing technologies also show limitations in task monitoring and adjustment, and cannot effectively make real-time adjustments or optimizations, resulting in uneven resource utilization and inefficiency, increasing operating costs and risks, and lacking effective real-time monitoring and blood collection data management mechanisms, which also affects the overall level and operational efficiency of animal husbandry business management, and ultimately affects the economic benefits and sustainable development of the animal husbandry industry. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a livestock blood sampling data management system.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A livestock blood sampling data management system comprises: The health status assessment module calculates and compares multiple key parameters in the blood based on the changes in animal blood biochemical indicators, compares the abnormality of blood indicators with the baseline value, generates health status difference results, analyzes the animal health risk level by evaluating the health status difference results, and generates a health risk level classification table; The blood collection plan preparation module calculates the time interval and frequency of the blood collection tasks based on the health risk level classification table and the animal groups with differentiated risk levels, generates a blood collection schedule, sets the blood collection execution order of multiple types of animals according to the blood collection schedule, and generates a blood collection task execution table; The resource allocation management module allocates blood collection personnel and equipment based on the blood collection task execution table and the current blood collection task requirements, generates a resource allocation table, adjusts the task execution process according to the resource allocation table and the real-time status of personnel and equipment, and obtains a real-time resource allocation status table; The blood collection task monitoring module records the execution progress and resource usage of the blood collection task based on the real-time resource allocation table, generates a blood collection task execution record, analyzes the duration and resource utilization of multiple tasks according to the blood collection task execution record, makes resource allocation adjustments, and generates an optimized task execution plan.

[0007] As a further solution of the present invention, the steps for obtaining the health risk level classification table are specifically as follows: Based on the changes in animal blood biochemical indicators, the blood indicator data of animals in the database are extracted, and the difference between the preset health benchmark values ​​and the multiple key parameters of each sample are calculated to obtain the health status difference data set; The multiple data in the health status difference data set are standardized using the formula: ; Calculate the standardized health difference value and generate a standardized health status difference result set, where: represents the unstandardized health disparity value, and Represent the minimum and maximum values ​​of the difference values, respectively. represents the standardized health difference value; The standardized health status difference result set is analyzed, and the animals are classified into differentiated health risk levels according to a set risk threshold, so as to obtain a health risk level classification table.

[0008] As a further solution of the present invention, the steps for obtaining the blood collection schedule are specifically as follows: Extracting animal population information of each level from the health risk level classification table, including the number of animals and health status of each level, and generating a data set associated with risk level and animal number through screening and matching; Based on the risk level and animal number association data set, a composite calculation model is used to determine the time interval and frequency of blood sampling in combination with the risk level and number of animals, using the formula: ; Generate a blood sampling frequency data set, where The number of animals indicating the risk level, is the risk level index, and represents the adjustment factor, Indicates the blood sampling time interval, represents a constant; The blood sampling frequency data set and real-time operation resources are integrated to schedule the date and time of each blood sampling, match the priority and resources of animal health monitoring, and combine the availability of personnel and equipment to obtain a blood sampling schedule.

[0009] As a further solution of the present invention, the steps of obtaining the blood sampling task execution table are specifically as follows: Extracting blood collection demand data from the blood collection schedule, determining blood collection priorities according to animal species and health status indexes, evaluating the blood collection frequency and schedule for each type of animal, and generating a preliminary blood collection task priority list based on health status scores; Based on the preliminary blood sampling task priority list, combined with the original blood sampling data and animal health monitoring records, the blood sampling frequency is assigned to each animal using the formula: ; Calculate and obtain animal blood collection cycle data, where: Indicates animal blood collection cycle data, Represents the time from the last blood sampling to the present time. is the health risk level, Indicates the total number of blood draws; Integrate the blood collection cycle data and real-time medical resources, specify the blood collection time, location and responsible medical team for the animal, and refer to the availability of medical resources, including personnel and equipment, to generate a blood collection task execution table.

[0010] As a further solution of the present invention, the step of obtaining the resource allocation table is specifically as follows: Analyze the blood collection task execution table, extract the requirements of each task, including the blood collection time, location and animal type, update the animal health status and blood collection requirement database in real time, determine the resource type and quantity of each task, and generate a preliminary resource requirement list; Based on the said preliminary resource requirement list, resource matching and optimization are performed, using the formula; ; Generate an optimized resource allocation plan, where: Representative tasks The urgency score of Represents the amount of resources required, represents the safety margin factor, Represents the total amount of tasks, Indicates the resource optimization value; The optimized resource allocation scheme is matched with the blood collection personnel and equipment resources, and the optimal personnel and equipment are allocated to each task with reference to the skill level of the personnel and the performance indicators of the equipment, so as to generate a resource allocation table.

[0011] As a further solution of the present invention, the step of obtaining the real-time resource allocation status table is specifically as follows: Extracting personnel and equipment allocation from the resource allocation table, monitoring the current status of each resource, including whether personnel are on duty and the operating status of equipment, and generating a resource status list; Through the resource status list, the resource availability and demand matching analysis is performed using the formula: ; Calculate the resource priority allocation index and generate resource priority analysis results, where: Indicates the priority allocation index of resources. Indicates Real-time availability of resources, Indicates The priority weight of the resource. Indicates the total number of resource items; According to the resource priority analysis results, the task execution process is adjusted, task allocation is updated in real time to match resource changes, the task execution sequence and resource utilization efficiency are optimized, and a real-time resource allocation status table is generated.

[0012] As a further solution of the present invention, the steps of obtaining the blood sampling task execution record are specifically as follows: Based on the real-time resource allocation table, the current number of times each resource is used, the usage duration and the remaining available time are captured, and the resource usage of each task is compared with the corresponding scheduled duration. Resource items with high usage frequency and usage duration are selected, and the formula is used: ; Create an initial resource usage data table, where: Indicates the current utilization of resources. Indicates The number of times a resource is used. Indicates The duration of resource usage, Indicates the total allocation time of resources. is the remaining available time of the resource, is the total available time of the resource; Calling the resource utilization rate data in the resource utilization rate initial data table, extracting the current progress of multiple blood sampling tasks, comparing the scheduled completion time of the tasks, and adjusting the real-time utilization rate according to the resource utilization, comparing the task delay rate with the real-time resource demand, and generating a task execution progress update data table; The data table is updated according to the task execution progress, the real-time execution records and resource allocation information are arranged on a timeline, the resource usage during task execution is compared with the initial allocation, and the progress records are merged into the monitoring database to obtain the blood collection task execution record.

[0013] As a further solution of the present invention, the steps of obtaining the optimized task execution plan are specifically as follows: Based on the duration and resource usage rate of each task in the blood collection task execution record, the resource usage of the task is calculated item by item to obtain the usage duration ratio of each resource in the task, using the formula: ; Generates resource usage results, where Indicates resource utilization, Respectively represent the number of times each resource is used. is the usage weight of the corresponding resource, is the total duration of the task; According to the resource usage results, the resource usage rate data of the task is combined with the task execution priority data, the tasks are sorted according to the utilization value, the tasks with the best resource utilization rate are screened, and a resource priority task list is obtained; Based on the resource priority task list, by comparing the resource demand and usage of each task, adjusting resource allocation, allocating resources to key priority tasks in priority, and generating an optimized task execution plan.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by continuously monitoring and comparing the changes of animal blood biochemical indicators, an accurate assessment of the health status of animals is achieved, providing a real-time and dynamic health monitoring framework for animal husbandry, ensuring the timely discovery of abnormal conditions, being able to quickly identify animal groups that need special attention, improving the pertinence and efficiency of epidemic prevention and treatment, and formulating specific blood collection plans according to the classification of animal health risk levels, optimizing the allocation and use of resources, including personnel and equipment, to ensure the optimal allocation and utilization efficiency of resources. Through blood collection data management, real-time monitoring of resource usage and task execution progress is achieved to achieve transparency and efficiency optimization of task management, and by adjusting resource allocation and execution plans to adapt to actual conditions, the smooth completion of blood collection tasks is ensured and the flexibility of overall operations is improved, which not only reduces operating costs, but also greatly improves the overall efficiency and management level of animal husbandry business through precise data management and real-time feedback mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the health risk level classification table in the present invention; Figure 3 A flow chart of the blood sampling schedule in the present invention; Figure 4 It is a flow chart of the blood sampling task execution table in the present invention; Figure 5 It is a flow chart of the resource allocation table in the present invention; Figure 6 It is a flow chart of the real-time resource allocation table in the present invention; Figure 7 A flowchart of the blood sampling task execution record in the present invention; Figure 8 The figure is a flow chart of the optimized task execution plan in 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 , a livestock blood sampling data management system comprises: The health status assessment module calculates and compares multiple key parameters in the blood based on the changes in animal blood biochemical indicators, compares the abnormality of blood indicators with the baseline value, generates health status difference results, analyzes the animal health risk level by evaluating the health status difference results, and generates a health risk level classification table; The blood collection plan preparation module calculates the time interval and frequency of blood collection tasks based on the health risk level classification table and animal groups with differentiated risk levels, generates a blood collection schedule, sets the blood collection execution order for multiple types of animals according to the blood collection schedule, and generates a blood collection task execution table; The resource allocation management module allocates blood collection personnel and equipment based on the blood collection task execution table and the current blood collection task requirements, generates a resource allocation table, adjusts the task execution process based on the resource allocation table and the real-time status of personnel and equipment, and obtains a real-time resource allocation table; The blood collection task monitoring module records the execution progress and resource usage of the blood collection task based on the real-time resource allocation table, generates the blood collection task execution record, analyzes the duration and resource utilization of multiple tasks according to the blood collection task execution record, makes resource allocation adjustments, and generates an optimized task execution plan.

[0019] The health status difference results include blood parameter differences, abnormal indicator comparison values, and health risk levels. The health risk level classification table includes low risk level, medium risk level, and high risk level. The blood collection schedule includes blood collection time, task priority, and task frequency. The blood collection task execution table includes execution order, animal grouping, and time nodes. The resource allocation table includes personnel allocation, equipment allocation, and task scheduling. The real-time resource allocation status table includes personnel status, equipment usage status, and task progress. The blood collection task execution record includes task start time, task end time, and resource utilization rate. The optimized task execution plan includes resource adjustment plan, optimized task sequence, and revised time schedule.

[0020] See also Figure 2, the specific steps for obtaining the health risk level classification table are: Based on the changes in animal blood biochemical indicators, the blood indicator data of animals in the database are extracted, and the difference between the preset health benchmark values ​​and the multiple key parameters of each sample are calculated to obtain the health status difference data set; Data is extracted from the animal blood biochemical index database, and multiple key parameters are analyzed and compared with the preset health benchmark values. First, the key parameters in each sample are extracted, including red blood cell count, white blood cell count, blood sugar and cholesterol, and then the degree of deviation of each parameter from its normal range is calculated. This calculation not only involves simple subtraction operations, but also includes data normalization to ensure fair comparison between differentiated indicators. The resulting health status difference result set is an intuitive representation of the health status of each animal, reflecting health problems.

[0021] The multiple data in the health status difference data set are standardized using the formula: ; Calculate the standardized health difference value and generate a standardized health status difference result set, where: represents the unstandardized health disparity value, and Represent the minimum and maximum values ​​of the difference values, respectively. represents the standardized health difference value; The benefit of the formula is that it can convert the deviation values ​​of various blood biochemical indicators into a unified proportional scale, which helps in the subsequent classification of health risk levels and simplifies the comparison process of multivariate data; Set an animal's blood sugar level mg / dL, the minimum blood sugar value of the animal group mg / dL, max mg / dL, substitute into the formula to get: ; This result indicates that the blood sugar levels of the animals are in a lower risk range within the group, and the corresponding health risk level is lower.

[0022] Analyze the standardized health status difference result set, classify the animals into differentiated health risk levels according to the set risk threshold, and obtain a health risk level classification table; Based on the standardized health status difference result set, animals are classified according to health risks by setting differentiated risk thresholds. Each threshold defines a differentiated risk level, such as mild, moderate and severe, and the classification is based on the standardized health difference value of each animal. The process not only involves setting the threshold, but also includes statistics on the animals in each threshold interval, and finally generates a health risk level classification table, which directly reflects the animal health status corresponding to each risk level.

[0023] See also Figure 3 , the specific steps for obtaining the blood collection schedule are: Extract animal population information of each level from the health risk level classification table, including the number of animals and health status of each level, and generate a data set associated with risk level and animal number through screening and matching; Extract animal population information at each level from the health risk level classification table, and analyze the number of animals and specific health conditions at each level in detail. The information collection process involves data extraction and preliminary processing, using efficient matching algorithms to screen eligible data entries from large-scale databases, processing data to ensure its accuracy and applicability, analyzing data to identify the number of animals at each risk level, and ensuring that the basic data for subsequent calculations is accurate. Through dynamic database queries, extract data for specific conditions from numerous records, such as classifying according to animal species, historical health records, and recent medical examinations. Use advanced data processing techniques, such as data cleaning and conversion, to ensure that the extracted data set is the latest and most relevant. Further analyze and process the data to generate a data set that associates risk levels with animal numbers. This data set will be directly used to calculate the frequency of blood collection, provide necessary data support for the implementation of blood collection plans, and assist decision makers in understanding the resource allocation and time management strategies required under differentiated risk levels.

[0024] Based on the risk level and animal number correlation data set, a composite calculation model is used to determine the time interval and frequency of blood sampling in combination with the risk level and number of animals. The formula is: ; Generate a blood sampling frequency data set, where The number of animals indicating the risk level, is the risk level index, and represents the adjustment factor, Indicates the blood sampling time interval, represents a constant; The benefit of the formula is that it can flexibly adjust the frequency of blood sampling according to the number of animals and the level of health risk, ensuring that resources are allocated reasonably and more attention is paid to high-risk groups; is a constant whose value depends on resource availability and historical response data. It is used to adjust the frequency of blood collection in order to balance and adapt to resource requirements and operational realities under differentiated risk levels. This constant reflects the basic frequency setting of blood collection operations after comprehensive reference to human, equipment, financial and key resources; Number of animals to set a risk level The risk level index is 50. is 0.8, adjustment coefficient and are set to 1 and 0.5 respectively, and the constant Based on the resource availability set to 200, the formula is substituted to obtain: ; The results show that animals at this risk level need to have their blood drawn once every 4.47 days to effectively monitor their health status and detect health problems in a timely manner. Through this calculation method, the frequency of blood sampling can be flexibly adjusted according to changes in the number and health status of animals, which not only improves the efficiency of resource utilization, but also ensures the quality of animal welfare and health monitoring.

[0025] Integrate blood collection frequency data sets and real-time operation resources, schedule the date and time of each blood collection, match the priority and resources of animal health monitoring, and combine the availability of personnel and equipment to obtain a blood collection schedule; Integrate the blood collection frequency data set and actual operation resources, and make detailed plans for the specific date and time of each blood collection. Scheduling work includes matching available medical personnel and equipment, arranging blood collection schedules according to the blood collection frequency data set, and ensuring that all arrangements are in line with the priorities of animal health monitoring and the optimal use of resources. The process includes coordination with multiple departments, such as animal management departments, medical teams, and logistics support, as well as reference to various external factors, such as weather conditions, equipment availability, and animal behavior status. Detailed planning includes ensuring that blood collection activities do not conflict with the normal feeding and care activities of animals, using advanced scheduling software to optimize schedules so that blood collection tasks are not interfered with. Through a series of operations, determine and publish the blood collection schedule, which details the blood collection schedule and resource allocation to efficiently perform blood collection tasks while ensuring animal welfare and data accuracy.

[0026] See also Figure 4 , the specific steps for obtaining the blood collection task execution table are: Extract blood collection demand data from the blood collection schedule, determine the priority of blood collection according to animal species and health status index, evaluate the blood collection frequency and schedule for each type of animal, and generate a preliminary blood collection task priority list based on health status scores; Data on blood collection needs for multiple types of animals are extracted from the blood collection schedule. This data includes the type of animal, health status and the time of the last blood collection, as well as a detailed score for the health status of each type of animal. The score is updated by the veterinarian based on the health monitoring results after the last blood collection. Based on the data, it can be determined which animals need to be given priority for blood collection, and how the frequency and time interval of blood collection should be adjusted, to generate a preliminary blood collection priority list. This list will provide a basis for the order of execution of the next tasks, ensuring that animals in poor health or in the recovery period of illness can receive timely attention and treatment.

[0027] Based on the preliminary blood sampling task priority list, combined with the original blood sampling data and animal health monitoring records, the blood sampling frequency is assigned to each animal, applying the formula: ; Calculate and obtain animal blood collection cycle data, where: Indicates animal blood collection cycle data, Represents the time from the last blood sampling to the present time. is the health risk level, Indicates the total number of blood draws; The benefit of the formula is that it dynamically adjusts the frequency of blood collection by combining the time interval and health level, so that management decisions are more in line with the actual health needs of animals, including optimizing the use of resources and improving the efficiency and effectiveness of blood collection tasks under conditions of limited resources; In this formula, Represents the time (days) from the last blood collection to the present. The health risk level of the animal is collected and updated in real time through the animal health monitoring system. For example, if there are three animals, 5 days, 10 days and 15 days respectively. They are 2, 3 and 4 respectively, and are substituted into the formula for calculation: The results indicate that the overall blood sampling frequency should be adjusted to approximately 1.189 times per cycle based on the current health risk level and time interval, ensuring that focus is placed on animals with high health risks or those that have not had blood drawn for a long time.

[0028] Integrate blood collection cycle data and real-time medical resources, specify the time, location and responsible medical team for animal blood collection, and generate a blood collection task execution table based on the availability of medical resources, including personnel and equipment; After combining the calculated blood collection cycle and actual medical resources, the blood collection task execution table is further refined and finally formed. This table lists in detail the blood collection time, location and responsible medical personnel for each animal, and refers to the actual health status of the animals and the allocation of medical resources, such as the personnel arrangement of the medical team and the allocation of blood collection equipment, to ensure that the arrangements in the table are both reasonable and feasible, and the blood collection task proceeds smoothly as planned. After each blood collection, the related data will be recorded and fed back to the system for optimization of the blood collection arrangement for the next cycle. Such a cyclic update ensures the continuous optimization of the blood collection plan and the precision of animal health management.

[0029] See also Figure 5 , the specific steps for obtaining the resource allocation table are: Analyze the blood collection task execution table, extract the requirements of each task, including blood collection time, location and animal type, update the animal health status and blood collection requirements database in real time, determine the resource type and quantity for each task, and generate a preliminary resource requirement list; Detailed data required for the current task is extracted from the blood collection task execution table, including the required blood collection type, quantity, and corresponding blood collection time and location. The data is obtained through a real-time dynamic monitoring system to ensure the timeliness and accuracy of the information. Then, based on the urgency and complexity of the blood collection task, the required personnel and equipment resources are evaluated, and a preliminary match between personnel and equipment is performed. During the matching process, special attention is paid to matching the skill level of personnel with the functional characteristics of the equipment to ensure that each task can be covered by appropriate resources. Through a series of meticulous operations, a preliminary resource demand allocation plan is generated.

[0030] Based on the preliminary resource requirement list, resource matching and optimization are performed using formulas; ; Generate an optimized resource allocation plan, where: Representative tasks The urgency score of Represents the amount of resources required, represents the safety margin factor, Represents the total amount of tasks, Indicates the resource optimization value; The benefit of the formula is that it not only takes into account the urgency of the task and the amount of resources required, but also introduces a safety margin factor, which increases the complexity and adaptability of the calculation, making resource allocation more reasonable and scientific; There are three tasks, urgency rating The required resources are 10, 20, and 15 respectively. The safety margin factors are 5, 10, and 7 respectively. is 3, substitute it into the formula to calculate : , calculated ; This shows that based on the given urgency score and resource demand, the overall resource optimization value is 4.29. This result shows that the resource allocation optimized by the formula is more efficient and ensures maximum resource utilization.

[0031] Match the optimized resource allocation plan with the blood collection personnel and equipment resources, refer to the personnel's skill level and the equipment's performance indicators, allocate the best personnel and equipment for each task, and generate a resource allocation table; According to the optimized resource allocation plan, the final resource matching is carried out, which involves detailed resource inventory checks and personnel scheduling to ensure that each task is assigned to appropriate personnel and equipment. In particular, with reference to the operational complexity of the equipment and the work experience of the personnel, advanced scheduling algorithms are used to optimize the configuration of personnel and equipment. For example, for high-risk tasks, experienced technicians and the latest equipment are given priority. In this way, the smooth completion of the task is ensured, and a resource allocation table is generated. The table lists in detail the personnel list and equipment arrangement for each task, providing a detailed execution guide for the implementation of blood collection tasks.

[0032] See also Figure 6 , the specific steps for obtaining the real-time resource allocation table are: Extract personnel and equipment allocation from the resource allocation table, monitor the current status of each resource, including whether personnel are on duty and the operating status of equipment, and generate a resource status list; On the basis of realizing the resource allocation table, we first conduct a comprehensive check on the real-time status of current personnel and equipment, collect data through sensors installed in each work area, and record the operating status of the equipment and the on-the-job status of personnel in real time, and then transmit the data to the central database through the wireless network. The central processing system will analyze the data, evaluate the availability of each resource, and generate a real-time updated resource status report. The process includes rapid detection of equipment failures, scheduling priority determination of maintenance personnel, and resource reconfiguration. The system will also evaluate the peak hours of resource use, predict potential shortages and make arrangements in advance to ensure sufficient resources and continuity of work at critical moments. The entire process aims to improve the efficiency and response speed of resource management through technical means, and ensure the timeliness and accuracy of all operations.

[0033] Through the resource status list, perform resource availability and demand matching analysis, using the formula: ; Calculate the resource priority allocation index and generate resource priority analysis results, where: Indicates the priority allocation index of resources. Indicates Real-time availability of resources, Indicates The priority weight of the resource. Indicates the total number of resource items; In the formula, Representative Real-time availability of resources, Represents the priority weight of the resource. The formula is useful in that it can quantify the efficiency of allocating each resource and provide a mathematical method for dynamically adjusting resource allocation to ensure that critical tasks can obtain the required resources first. Referring to three key resources: manpower, equipment and materials, the real-time availability is set to 0.8, 0.9 and 0.85 respectively, and the priority weights are set to 1, 0.5 and 1.5 respectively. First, the real-time availability of each resource is multiplied by its priority weight to obtain the weighted availability of each resource, and then the values ​​are added to obtain the total resource priority allocation index. The specific calculation is as follows: ; This result reflects the comprehensive allocation efficiency of all available resources at a given point in time, which helps managers adjust task allocation in real time, optimize resource utilization, reduce waiting time and improve task completion rate.

[0034] According to the results of resource priority analysis, adjust the task execution process, update task allocation in real time and match resource changes, optimize task execution sequence and resource utilization efficiency, and generate a real-time resource allocation status table; In the process of generating real-time resource allocation, it is key to analyze in detail how each resource affects the task, extract the latest equipment status and staffing information from the resource database, and use optimization algorithms to match and prioritize resources according to project requirements and task urgency. For example, if a device is under maintenance, the system will automatically query the list of spare devices and evaluate whether it is necessary to adjust the task schedule or reallocate personnel to make up for the lack of equipment. In addition, the system will monitor resource usage trends, predict future resource needs through data analysis, and automatically adjust resource allocation to cope with changes. Through complex data processing and resource allocation, a detailed real-time resource allocation report is finally formed. This report not only improves the transparency of resource use, but also enhances the organization's ability to respond to emergencies, ensuring that the project can be completed on time with sufficient resources.

[0035] See also Figure 7 ,The specific steps for obtaining the blood collection task execution record are: Based on the real-time resource allocation table, capture the current usage times, usage duration, and remaining available time of each resource, compare the resource usage of each task with the corresponding scheduled duration, and filter out resource items with high usage frequency and usage duration, using the formula: ; Create an initial resource usage data table, where: Indicates the current utilization of resources. Indicates The number of times a resource is used. Indicates The duration of resource usage, Indicates the total allocation time of resources. is the remaining available time of the resource, is the total available time of the resource; The benefit of the formula is that it effectively evaluates the actual utilization of resources by combining the frequency, duration, and remaining available time of resources, making it easier to optimize resource allocation in task scheduling; Set the specific value of each parameter, set Indicates that a resource has been used 5 times. Indicates that the duration of a single use is 3 units of time. Indicates that the total allocated time is 50 units of time, Indicates that the remaining available time is 10 units of time. Indicates that the total available time is 60 units of time; First calculate , substituting the numerical value into ; Recalculate the numerator ; Calculate the remaining usage item ; Finally, the resource utilization rate ; The result shows that the actual utilization rate of the resource is 0.4667, which indicates the usage of the resource in the overall allocation, provides an effective basis for resource usage, and generates an initial data table of resource utilization.

[0036] Call the resource utilization data in the resource utilization initial data table, extract the current progress of multiple blood collection tasks, compare the scheduled completion time of the tasks, adjust the real-time utilization rate according to the resource utilization, compare the task delay rate with the real-time resource demand, and generate a task execution progress update data table; Based on the resource utilization data in the initial resource utilization data table, the current execution progress of each blood collection task is extracted, and the collected resource utilization is compared with the scheduled completion time of the task. The expected delay rate of the task is calculated and screened in combination with the resource demand. Resource items with utilization rates higher than the predetermined limit are marked as high-utilization items, and all resource items are recombined into the current task execution dynamic record table. Combined with the time period and progress changes of each task, a task execution progress update data table including task progress updates is formed.

[0037] Update the data table according to the task execution progress, arrange the real-time execution records and resource allocation information on the timeline, compare the resource usage during task execution with the initial allocation, merge the progress records into the monitoring database, and obtain the blood collection task execution record; On the basis of the task execution progress update data table, the actual resource usage of each task is summarized based on the timeline, and the initial resource allocation and usage are compared. The resource demand status of the task at each execution stage is extracted, and the task records of each stage are added to the monitoring module database in turn. The execution records of all tasks are integrated and associated with the resource usage status of each task. The record of resource allocation and task execution is completed, and the blood collection task execution record is generated.

[0038] See also Figure 8 , the specific steps for obtaining the optimized task execution plan are: Based on the duration and resource usage rate of each task in the blood collection task execution record, the resource usage of the task is calculated item by item to obtain the usage time ratio of each resource in the task using the formula: ; Generates resource usage results, where Indicates resource utilization, Respectively represent the number of times each resource is used. is the usage weight of the corresponding resource, is the total duration of the task; The benefit of the formula is that it improves the efficiency of task resource allocation by reasonably allocating the weights of resource usage time and task priority, thereby optimizing the overall task execution plan; Set the resource usage count of Task 1 to 5 ( ), with a weight of 0.5 ( ), the total mission duration is 100 hours ( ), the resource usage of Task 2 is 3 ( ), with a weight of 0.3 ( ), the resource usage count of Task 3 is 2 ( ), with a weight of 0.2 ( ), substitute the value into the formula to calculate: ; The result shows that based on the current resource usage and weight settings, the overall resource utilization is 3.8%, pointing out the optimization space for resource usage.

[0039] According to the resource usage results, the resource utilization data of the task is combined with the task execution priority data, the tasks are sorted according to the utilization value, the tasks with the best resource utilization are selected, and the resource priority task list is obtained; The generation process of the resource priority task list first conducts a detailed analysis of the resource usage of each task, such as analyzing the resource occupancy rate of each task in differentiated time periods, and determining which resources are overused and which are underused. By collecting and organizing data, statistical analysis methods can be used to identify imbalances in resource allocation and compare the data with the priority of the task. For example, whether high-priority tasks have sufficient resource support and whether any resources are overused by low-priority tasks can be used. A resource priority task list based on actual data and specific task priorities can be developed to provide a basis for adjusting resource allocation, thereby optimizing task execution efficiency and resource utilization.

[0040] Based on the resource priority task list, by comparing the resource requirements and usage of each task, adjust the resource allocation, allocate resources to key priority tasks first, and generate an optimized task execution plan; In the process of adjusting the resource allocation plan, it is necessary to refer not only to the priority of the task and the usage of resources, but also to the interdependence between tasks and their unique needs for resources. For example, a task has higher resource requirements because it relies on specific technical support or equipment. When adjusting resources, simulation technology can be used to predict the impact of differentiated resource allocation plans on task execution efficiency. By establishing a simulation model, simulating differentiated resource allocation plans, and performing predictive analysis through actual data-driven models, the optimal resource allocation plan can be found. This method can not only ensure that high-priority tasks obtain the necessary resources, but also avoid waste of resources and ensure that all tasks can run under optimal conditions.

[0041] 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. A livestock blood sampling data management system, characterized in that: The system comprises: The health status assessment module calculates and compares multiple key parameters in the blood based on the changes in animal blood biochemical indicators, compares the abnormality of blood indicators with the baseline value, generates health status difference results, analyzes the animal health risk level by evaluating the health status difference results, and generates a health risk level classification table; The blood collection plan preparation module calculates the time interval and frequency of the blood collection tasks based on the health risk level classification table and the animal groups with differentiated risk levels, generates a blood collection schedule, sets the blood collection execution order of multiple types of animals according to the blood collection schedule, and generates a blood collection task execution table; The resource allocation management module allocates blood collection personnel and equipment based on the blood collection task execution table and the current blood collection task requirements, generates a resource allocation table, adjusts the task execution process according to the resource allocation table and the real-time status of personnel and equipment, and obtains a real-time resource allocation status table; The blood collection task monitoring module records the execution progress and resource usage of the blood collection task based on the real-time resource allocation table, generates a blood collection task execution record, analyzes the duration and resource utilization of multiple tasks according to the blood collection task execution record, makes resource allocation adjustments, and generates an optimized task execution plan.

2. The livestock blood sampling data management system according to claim 1, characterized in that: The steps for obtaining the health risk level classification table are specifically as follows: Based on the changes in animal blood biochemical indicators, the blood indicator data of animals in the database are extracted, and the difference between the preset health benchmark values ​​and the multiple key parameters of each sample are calculated to obtain the health status difference data set; The multiple data in the health status difference data set are standardized using the formula: ； Calculate the standardized health difference value and generate a standardized health status difference result set, where: represents the unstandardized health disparity value, and Represent the minimum and maximum values ​​of the difference values, respectively. represents the standardized health difference value; The standardized health status difference result set is analyzed, and the animals are classified into differentiated health risk levels according to a set risk threshold, thereby obtaining a health risk level classification table.

3. The livestock blood sampling data management system according to claim 2, characterized in that: The steps for obtaining the blood collection schedule are specifically as follows: Extracting animal population information of each level from the health risk level classification table, including the number of animals and health status of each level, and generating a data set associated with risk level and animal number through screening and matching; Based on the risk level and animal number association data set, a composite calculation model is used to determine the time interval and frequency of blood sampling in combination with the risk level and number of animals, using the formula: ； Generate a blood sampling frequency data set, where The number of animals representing the risk level, is the risk level index, and represents the adjustment factor, Indicates the blood sampling time interval, represents a constant; The blood sampling frequency data set and real-time operation resources are integrated to schedule the date and time of each blood sampling, match the priority and resources of animal health monitoring, and combine the availability of personnel and equipment to obtain a blood sampling schedule.

4. The livestock blood sampling data management system according to claim 3, characterized in that: The steps for obtaining the blood sampling task execution table are specifically as follows: Extracting blood collection demand data from the blood collection schedule, determining blood collection priorities according to animal species and health status indexes, evaluating the blood collection frequency and schedule for each type of animal, and generating a preliminary blood collection task priority list based on health status scores; Based on the preliminary blood sampling task priority list, combined with the original blood sampling data and animal health monitoring records, the blood sampling frequency is assigned to each animal using the formula: ； Calculate and obtain animal blood collection cycle data, where: Indicates animal blood collection cycle data, Represents the time from the last blood sampling to the present time. is the health risk level, Indicates the total number of blood draws; Integrate the blood collection cycle data and real-time medical resources, specify the blood collection time, location and responsible medical team for the animal, and refer to the availability of medical resources, including personnel and equipment, to generate a blood collection task execution table.

5. The livestock blood sampling data management system according to claim 4, characterized in that: The steps for obtaining the resource allocation table are specifically as follows: Analyze the blood collection task execution table, extract the requirements of each task, including the blood collection time, location and animal type, update the animal health status and blood collection requirement database in real time, determine the resource type and quantity of each task, and generate a preliminary resource requirement list; Based on the said preliminary resource requirement list, resource matching and optimization are performed, using the formula; ； Generate an optimized resource allocation plan, where: Representative tasks The urgency score of Represents the amount of resources required, represents the safety margin factor, Represents the total amount of tasks, Indicates the resource optimization value; The optimized resource allocation scheme is matched with the blood collection personnel and equipment resources, and the optimal personnel and equipment are allocated to each task with reference to the skill level of the personnel and the performance indicators of the equipment, so as to generate a resource allocation table.

6. The livestock blood sampling data management system according to claim 5, characterized in that: The steps for obtaining the real-time resource allocation status table are specifically as follows: Extracting personnel and equipment allocation from the resource allocation table, monitoring the current status of each resource, including whether personnel are on duty and the operating status of equipment, and generating a resource status list; Through the resource status list, the resource availability and demand matching analysis is performed using the formula: ； Calculate the resource priority allocation index and generate resource priority analysis results, where: Indicates the priority allocation index of resources. Indicates Real-time availability of resources, express The priority weight of the resource. Indicates the total number of resource items; According to the resource priority analysis results, the task execution process is adjusted, task allocation is updated in real time to match resource changes, the task execution sequence and resource utilization efficiency are optimized, and a real-time resource allocation status table is generated.

7. The livestock blood sampling data management system according to claim 6, characterized in that: The steps for obtaining the blood sampling task execution record are specifically as follows: Based on the real-time resource allocation table, the current number of times each resource is used, the usage duration and the remaining available time are captured, and the resource usage of each task is compared with the corresponding scheduled duration. Resource items with high usage frequency and usage duration are selected, and the formula is used: ； Create an initial resource usage data table, where: Indicates the current utilization of resources. Indicates The number of times a resource is used. Indicates The duration of resource usage, Indicates the total allocation time of resources. is the remaining available time of the resource, is the total available time of the resource; Calling the resource utilization rate data in the resource utilization rate initial data table, extracting the current progress of multiple blood sampling tasks, comparing the scheduled completion time of the tasks, and adjusting the real-time utilization rate according to the resource utilization, comparing the task delay rate with the real-time resource demand, and generating a task execution progress update data table; The data table is updated according to the task execution progress, the real-time execution records and resource allocation information are arranged on a timeline, the resource usage during task execution is compared with the initial allocation, and the progress records are merged into the monitoring database to obtain the blood collection task execution record.

8. The livestock blood sampling data management system according to claim 7, characterized in that: The steps for obtaining the optimized task execution plan are as follows: Based on the duration and resource usage rate of each task in the blood collection task execution record, the resource usage of the task is calculated item by item to obtain the usage duration ratio of each resource in the task, using the formula: ； Generates resource usage results, where Indicates resource utilization, Respectively represent the number of times each resource is used. is the usage weight of the corresponding resource, is the total duration of the task; According to the resource usage results, the resource usage rate data of the task is combined with the task execution priority data, the tasks are sorted according to the utilization value, the tasks with the best resource utilization rate are screened, and a resource priority task list is obtained; Based on the resource priority task list, by comparing the resource demand and usage of each task, adjusting resource allocation, allocating resources to key priority tasks in priority, and generating an optimized task execution plan.

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