Remote urine analysis and data management method and system

Abstract

The present invention relates to the field of remote data management technology, specifically a remote urine analysis and data management method and system, comprising the following steps: based on the data transmission starting node of remote urine analysis, extracting key parameters in the urine sample data group, identifying the load and frequency of each data group, performing preliminary priority division of data packets according to the data load and processing frequency, and generating a task priority processing structure. In the present invention, by analyzing the key parameters and their load and frequency in the data group, preliminary priority division is implemented for the urine sample data, greatly improving the processing efficiency and the real-time and accuracy of the data. In terms of resource allocation, sub-parameter requirements are matched and task segments are decomposed to optimize resource utilization and reduce delays. Batch scheduling and dynamic resource adjustment enable urine analysis to flexibly respond to actual needs, enhance processing capabilities, select the best transmission path to optimize data flow, and enhance transmission stability and security.

Description

Technical Field

[0001] The present invention relates to the technical field of remote data management, and in particular to a remote urine analysis and data management method and system. Background Art

[0002] The field of remote data management technology refers to a series of technologies and methods for collecting, transmitting, storing, analyzing and managing data through information technology. This technology field is widely used in various industries such as medicine, industry, and education, and is committed to achieving real-time, accurate, secure and convenient data. In remote data management, data can be collected through hardware devices such as smart devices and sensors, transmitted to a central database or cloud storage through the network, and combined with data analysis tools for classification processing and structured management. Remote data management can effectively improve data processing efficiency, support cross-regional data sharing, and adapt to the trend of information development.

[0003] Among them, the remote urine analysis and data management method refers to the process of using remote technology to collect and transmit urine analysis data, and processing and managing it through remote data management. The main purpose of this topic is to realize the remote collection and management of urine data, facilitate the real-time transmission and efficient management of relevant data, reduce data processing time, improve the convenience of data collection, and thus achieve a more efficient urine analysis data management model.

[0004] Existing technologies have been widely used in multiple industries, but in the field of urine analysis, existing technologies face problems such as insufficient resource allocation and inefficient data processing. For example, without careful distinction between data load and processing frequency, traditional data management methods cannot effectively and accurately allocate resources, resulting in resource waste and slow processing speed. The lack of a dynamic resource scheduling mechanism also makes it impossible to adjust resources in real time according to network conditions and task requirements during data transmission, increasing the uncertainty and security risks of data transmission, and limiting the real-time and accuracy of urine analysis data management. This deficiency leads to inefficient data management. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote urine analysis and data management method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote urine analysis and data management method, comprising the following steps:

[0007] S1: Based on the data transmission starting node of remote urine analysis, it extracts key parameters from the urine sample data set, identifies the load and frequency of each data set, performs preliminary priority division of data packets based on data load and processing frequency, and generates a task priority processing structure;

[0008] S2: Based on the task priority processing structure, decompose the task segments in the sample parameter group, match the resource requirement values ​​of the sub-parameters in the task segments, generate a task segment resource association table, allocate resources to the task segments in the task segment resource association table, and generate a task segment resource pairing list;

[0009] S3: Based on the task segment resource pairing list, batch scheduling is performed, batch task data sets are processed in batches according to the requirements of urine analysis, load is distributed for each batch task, task data on resource utilization is extracted and allocation is adjusted, and a batch task execution status table is generated. According to the batch task execution status table, resources after each batch task are completed are recycled to the resource pool to obtain the batch resource usage status;

[0010] S4: Based on the batch resource usage status, dynamically select a transmission path according to the data transmission node of the remote urine analysis, place the real-time data stream of the urine analysis on the designated transmission path according to priority, select a transmission path with a stable path status, and generate a remote urine data management profile.

[0011] As a further solution of the present invention, the steps for obtaining the task priority processing structure are specifically as follows:

[0012] S111: Based on the data transmission starting node of the remote urine analysis, key parameters in each set of urine sample data are extracted, load parameters of each set of data are identified, the extracted load parameters are called, and statistics are performed on each set of loads in combination with the extracted load parameters to generate a preliminary data set of load and frequency;

[0013] S112: Based on the preliminary data set of load and frequency, count the load value and processing frequency of each set of data, mark items that exceed a preset threshold, extract related data, and obtain a priority sorted list;

[0014] S113: According to the priority ranking list, extract the priority ranking of each group of data and divide them, and combine them according to the load value and frequency to generate a task priority processing structure.

[0015] As a further solution of the present invention, the steps of obtaining the task segment resource association table are specifically as follows:

[0016] S211: Based on the task priority processing structure, decompose the task segment into subtasks according to the task segment requirements of the sample parameter group, identify the resource requirement characteristics of each subtask, match the resource requirement type of the task segment, task priority and sample characteristics, and establish a task segment resource requirement classification table;

[0017] S212: Based on the task segment resource requirement classification table, analyze the demand proportion of the task segment in the differentiated resource type, and combine the resource type characteristics to use the formula:

[0018]

[0019] Calculate the total weight of the task segment resources and generate the task segment resource requirement weight table, where: is the total weight of the task segment resources, Indicates the demand value of a single resource. is the resource type parameter, is the task priority parameter, is the resource association coefficient, is the demand proportion parameter, is the demand adjustment coefficient, Indicates the total number of tasks;

[0020] S213: Call the demand proportion of each resource type in the task segment resource demand weight table, sort by demand proportion, determine the optimal sorting item of the task segment resource demand, and establish a task segment resource association table based on the demand proportion of the priority sorting item.

[0021] As a further solution of the present invention, the steps for obtaining the task segment resource pairing list are specifically as follows:

[0022] S221: extracting the resource type and quantity required for each task segment from the task segment resource association table, determining the resource requirement standard of the task segment by analyzing the task requirements, and generating a task segment resource requirement list;

[0023] S222: Analyze the resource requirement list and resource inventory of the task segment, perform resource matching analysis on each task segment, determine available resources through matching calculation of resource types and quantities, and obtain a resource matching list;

[0024] S223: Optimize resource allocation based on the resource matching list using the formula:

[0025]

[0026] Generate a task segment resource pairing list, where: A function representing the efficiency of computing resource pairing, Representative The resource matching degree of each task segment, is a parameter of resource availability, is the adjustment factor.

[0027] As a further solution of the present invention, the steps for obtaining the batch task execution status table are specifically as follows:

[0028] S311: Based on the task segment resource pairing list, perform data collection for each batch, including task type, execution time, and required resources, and preliminarily classify the data to generate a classified task data set;

[0029] S312: performing resource utilization analysis on the classified task dataset, performing resource allocation efficiency evaluation by calculating the ratio of resources required for each task to total resources, and obtaining an optimized resource allocation dataset based on the efficiency evaluation result;

[0030] S313: Based on the optimized resource allocation data set, perform resource reconfiguration using the formula:

[0031]

[0032] Generate a batch task execution status table, where: represents the resource adjustment efficiency, Indicates the total number of tasks, Indicates the The amount of resources used by the task, Indicates the The total amount of resources for each task.

[0033] As a further solution of the present invention, the step of obtaining the batch resource usage status is specifically as follows:

[0034] S321: extracting the tag data of completed tasks from the task execution status table, identifying tasks requiring resource recovery in the urine analysis data through remote urine analysis, and obtaining a list of pending tasks;

[0035] S322: Based on the list of pending tasks, analyze the types of resources used and duration of tasks in the remote urine analysis data, and calculate the average total resource usage time using the formula:

[0036]

[0037] Get resource usage time summary data, where: Indicates the average total resource usage time, Representative The usage time of resources in a task, Represents the number of tasks;

[0038] S323: Based on the resource usage duration summary data, the resource usage status in the urine analysis data is updated, and the batch resource usage status is obtained by analyzing the resource usage frequency and duration.

[0039] As a further embodiment of the present invention, the steps for obtaining the remote urine data management overview are specifically as follows:

[0040] S411: parsing the data transmission node information of the remote urine analysis according to the batch resource usage status, performing a preliminary screening of the transmission path status based on the real-time data flow and transmission stability parameters of each node, and generating a transmission path priority list;

[0041] S412: Based on the transmission path priority list and the flow level of the real-time urine data, a priority distribution strategy is set, and the formula is used to balance the flow and stability weights:

[0042]

[0043] Calculate the priority of the path and obtain real-time data of urine analysis, where Indicates the path priority. Indicates transmission stability, Indicates real-time traffic, and are stability weight and flow weight respectively;

[0044] S413: Mapping the urine analysis real-time data to the selected path according to the priority distribution strategy, adjusting the data flow distribution through path priority control, and generating a remote urine data management profile.

[0045] A remote urine analysis and data management system, the remote urine analysis and data management system is used to perform the remote urine analysis and data management method described above, the system comprising:

[0046] The sampling data screening module determines the data load value and transmission frequency based on the data transmission starting node of remote urine analysis, and screens the load and frequency data, filters the data items in the key load range, deletes the data groups with low load and low frequency, sorts and integrates them, and generates a list of screened data loads;

[0047] The task priority determination module analyzes the load and frequency of the data based on the screened data load list, identifies the load differences and divides the task priorities of the data groups, parses the task segments of the data groups, analyzes the load requirements of the sub-items in the task segments, organizes the sub-items and resource requirements, and establishes a sub-task resource requirement table;

[0048] The resource allocation module allocates resources and matches them with subtasks based on each resource requirement in the subtask resource requirement table, analyzes task segments and resource requirements, integrates resource groups, divides the matched resource allocation information into batches, records and organizes them, and obtains task resource matching results;

[0049] The batch data scheduling module allocates the resources and task data in the task resource pairing result to the batch task group in order of priority, schedules resources item by item, integrates batch data, captures idle resources after each batch task is completed, and forms a batch resource recycling state;

[0050] The dynamic transmission path management module determines the real-time load of the transmission path based on the resource information of the batch resource recovery status, places the urine data stream on the load-stable path according to the priority order, monitors the load of each path in real time according to the path status, updates the path stability status information, and generates a remote urine data management profile.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, by accurately analyzing the key parameters in the data group and the load and frequency of each group of data, the preliminary priority division of the data is achieved, the processing efficiency of the urine sample data is significantly improved, and the real-time and accuracy of the data are ensured. In terms of resource allocation, by matching the resource requirement values ​​of the sub-parameters and decomposing the task segments and pairing the resources according to the resource requirement values, the resource utilization is optimized, the processing delay is reduced, the batch scheduling and the dynamic adjustment of resource utilization are carried out, so that the urine analysis task can be flexibly processed according to actual needs, further enhancing the data processing capability of remote urine analysis, and the strategy of dynamically selecting the transmission path optimizes the data transmission process and improves the stability and security of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0054] Figure 2 A flowchart of the task priority processing structure in the present invention;

[0055] Figure 3 This is a flow chart of the task segment resource association table in the present invention;

[0056] Figure 4 A flowchart of the task segment resource pairing list in the present invention;

[0057] Figure 5 This is a flow chart of the batch task execution status table in the present invention;

[0058] Figure 6 This is a flow chart of the batch resource usage status in the present invention;

[0059] Figure 7 Flowchart showing an overview of remote urine data management in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0061] 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0062] Example 1

[0063] See also Figure 1 The present invention provides a technical solution: a remote urine analysis and data management method, comprising the following steps:

[0064] S1: Based on the data transmission starting node of remote urine analysis, it extracts key parameters from the urine sample data set, identifies the load and frequency of each data set, performs preliminary priority division of data packets based on data load and processing frequency, and generates a task priority processing structure;

[0065] S2: Based on the task priority processing structure, the task segments in the sample parameter group are decomposed, the resource requirement values ​​of the sub-parameters in the task segments are matched, a task segment resource association table is generated, resources are allocated to the task segments in the task segment resource association table, and a task segment resource pairing list is generated;

[0066] S3: Based on the task segment resource pairing list, batch scheduling is performed. The batch task dataset is divided into batches according to the requirements of urine analysis. The load is distributed to each batch task. Task data on resource utilization is extracted and the allocation is adjusted. A batch task execution status table is generated. Based on the batch task execution status table, the resources after each batch task are completed are recycled to the resource pool to obtain the batch resource usage status.

[0067] S4: Based on the batch resource usage status, the transmission path is dynamically selected according to the data transmission node of remote urine analysis, the real-time data stream of urine analysis is placed on the designated transmission path according to priority, the transmission path with stable path status is selected, and the remote urine data management overview is generated.

[0068] The task priority processing structure includes data load, processing frequency, and priority hierarchy. The task segment resource association table includes task segment identification, sub-parameter resource demand value, and resource pairing items. The task segment resource pairing list includes task segment number, allocated resources, and pairing records. The batch task execution status table includes load distribution status, resource utilization, and allocation records. The batch resource usage status includes resource recovery records, resource pool status, and resource occupancy ratio. The remote urine data management overview includes transmission path priority, path stability status, and real-time data stream placement.

[0069] See also Figure 2 ,The specific steps for obtaining the task priority processing structure are:

[0070] S111: Based on the data transmission starting node of the remote urine analysis, key parameters in each set of urine sample data are extracted, load parameters of each set of data are identified, the extracted load parameters are called, and statistics are performed on each set of loads in combination with the extracted load parameters to generate a preliminary data set of load and frequency;

[0071] Based on the data transmission starting node of remote urine analysis, a set of key parameters is extracted from the urine sample data of the remote terminal. The chemical component concentration, conductivity, pH value, etc. in each set of data are selected as the main basis for load calculation. The above parameters of each set of data are called one by one to calculate and record the load value. The calculation result of the load value is associated with the parameters of the sample. For each set of sample data, the load parameters are grouped and counted, and the load amount and frequency are used as the screening basis. On this basis, the processing frequency of each data group is recorded, and the number of times the data group is processed is determined item by item. For the processing frequency, according to the different classifications of the data source and the frequency of each parameter, the data items in different frequency intervals are preliminarily marked as different data packets, and the priority labels corresponding to the load conditions are set to generate a preliminary data set of load and frequency. This set contains the load value and processing frequency of each set of data, providing a data basis for subsequent priority sorting, so as to complete the preliminary priority data screening structure.

[0072] S112: Based on the preliminary data set of load and frequency, the load value and processing frequency of each set of data are counted, items exceeding a preset threshold are marked, and related data is extracted to obtain a priority sorted list;

[0073] Call the preliminary data set of load and frequency, extract the load value and processing frequency parameters of each data packet, use the product of frequency value and load parameter as the classification standard, perform weighted processing on each data item in the set, calculate the weighted average value, and compare the average value result with the set standard threshold value for judgment. To ensure the accuracy of screening, first calculate the priority factor of the data for the product of load and frequency of each group of data, compare the priority factor with the standard threshold, select the entries that exceed the threshold, and then call the filtered marked data packets, associate their load values ​​with frequency parameters, sort them from large to small according to the priority factor, and establish a priority sorting list, which contains the priority of each data packet and the corresponding load frequency value, and re-sort the entries with the same priority factor from large to small according to the load value.

[0074] S113: extracting and dividing the priority of each group of data according to the priority ranking list, and combining them according to load value and frequency to generate a task priority processing structure;

[0075] Call the priority sorting list to further extract the priority and load value of each group of data, and calculate the product of the load value and frequency of each group of data as the final sorting basis. In order to further complete the task priority arrangement, call the sorted priority list, use the load frequency and priority of each item as the calculation basis, and use the comprehensive weight of load and frequency as the priority score. The weighted final priority value score is used as the final task priority arrangement to obtain the load priority ranking of each data packet. The task priority processing structure is generated through the final sorting result, which includes the arrangement from data packet priority to the final task order.

[0076] See also Figure 3 ,The specific steps for obtaining the task segment resource association table are:

[0077] S211: Based on the task priority processing structure, the task segment is decomposed into subtasks according to the task segment requirements of the sample parameter group, and the resource requirement characteristics of each subtask are identified. The resource requirement type, task priority and sample characteristics of the task segment are matched to establish a task segment resource requirement classification table;

[0078] According to the requirements of the task segments within the sample parameter group, we first conduct an in-depth analysis of each task segment and decompose each task segment into several subtasks to ensure that the resource requirements of the subtasks are clear and divisible, and clarify the specific characteristics of each subtask in terms of resource requirements. We call the remote data management system to read the resource requirement value of each subtask, and analyze the demand characteristics of each type of resource by comparing the type, priority and other parameters of the resource requirement value. We perform detailed processing based on the specific characteristics of the sample parameter group, and further calculate the total resource demand based on the priority and demand characteristics of the task segment. According to the priority of the task segment, the demand value of each type of resource is summarized and classified into a set of resource demand parameters of differentiated types. According to the differences in resource requirements of each subtask, the resource classification information is recorded, and the demand values ​​are stored in a classification table according to priority. Through the hierarchical relationship of priority, a task segment resource demand classification table is obtained.

[0079] S212: Based on the task segment resource requirement classification table, analyze the demand proportion of the task segment in the differentiated resource type, and combine the characteristics of the resource type to use the formula:

[0080]

[0081] Calculate the total weight of the task segment resources and generate the task segment resource requirement weight table, where: is the total weight of the task segment resources, Indicates the demand value of a single resource. is the resource type parameter, is the task priority parameter, is the resource association coefficient, is the demand proportion parameter, is the demand adjustment coefficient, Indicates the total number of tasks;

[0082] The formula is beneficial in that it ensures the weighted summation of various resource requirements by incorporating resource type parameters, priority parameters, and demand weight coefficients. The square root and weight parameters improve the accuracy of the calculation, accurately reflecting the impact of different resource requirements on the overall weight of the task segment.

[0083] Setting parameters The value of a single resource requirement can be obtained by reading the real-time data provided by the resource acquisition system. The specific value is 45. Indicates the resource type parameter, which can be calculated by the resource type weight analysis method and the value is set to 12;

[0084] Priority parameter The correlation coefficient is 5 according to the characteristics of the sample data. Calculated based on the priority characteristics of the resource, the value is 0.8, setting the demand proportion parameter =4, and the adjustment coefficient is obtained by analyzing the total score of demand Set to 1;

[0085] First calculate the square root of the denominator, that is: ;

[0086] Substitute the denominator result into the numerator ;

[0087] Next, calculate the entire formula:

[0088]

[0089] The result shows that the total weight of the calculated task segment resource demand is 13.21, which reflects the comprehensive impact of different types of resource demands on the task segment weight and will serve as the key input parameter of the task segment resource weight table.

[0090] S213: Calling the demand proportion of each resource type in the task segment resource demand weight table, sorting by demand proportion, determining the optimal sorting item for the task segment resource demand, and establishing a task segment resource association table based on the demand proportion of the priority sorting item;

[0091] Call the demand proportion of each resource type in the task segment resource demand weight table, use the demand proportion data recorded in the weight table to sort the proportion of different resource types, conduct a holistic analysis of the resource demand of the task segment, and filter the resource type with the highest proportion as the priority item based on the sorting result of the resource demand proportion, confirm it in combination with the priority of the task segment, further analyze the priority items ranked by weight, and establish a specific association between the task segment and the resource demand based on the sorting item with the highest proportion as the main associated item. Integrate the weight sorting result into the task segment resource association table, establish a priority mapping relationship between resource demand and task segment, and associate different resource type requirements to each task segment in turn. And determine the associated sorting according to the priority of the task segment in the resource association table to ensure that the resource priority requirements of each task segment are clear.

[0092] See also Figure 4 ,The specific steps for obtaining the task segment resource pairing list are:

[0093] S221: extracting the resource type and quantity required for each task segment from the task segment resource association table, determining the resource requirement standard of the task segment through analysis of the task requirements, and generating a task segment resource requirement list;

[0094] The resource types and quantities required for each task segment are extracted from the task segment resource association table, and a detailed analysis of the resource requirement standards is conducted. The process involves comparing and trending the historical data recorded in the database, identifying the usage frequency and changing trends of various types of resources, and using statistical methods to derive the average demand and fluctuation range of each resource. The maximum and minimum resource requirement thresholds for each task segment are then determined. This threshold will be used in the subsequent resource matching and optimization allocation process to ensure the accuracy and efficiency of resource allocation. The generated task segment resource requirement list will list in detail the specific resource types and quantities required for each task segment.

[0095] S222: Analyze the task segment resource requirement list and resource inventory, perform resource matching analysis on each task segment, determine available resources through matching calculation of resource types and quantities, and obtain a resource matching list;

[0096] Resource matching analysis of the task segment resource requirement list involves not only an analysis of the utilization rate of existing inventory resources, but also a quantitative assessment of the degree of resource satisfaction. Resource matching algorithms are used to sort and prioritize various types of resources. Dynamic programming methods are used to optimize the resource allocation process and maximize resource utilization. The resulting resource matching list will indicate the specific types and quantities of available resources for each task segment, ensuring efficient resource allocation and the achievement of goals, thereby making resource use more accurate and purposeful.

[0097] S223: Based on the resource matching list, optimize resource allocation using the formula:

[0098]

[0099] Generate a task segment resource pairing list, where: A function representing the efficiency of computing resource pairing, Representative The resource matching degree of each task segment, is a parameter of resource availability, is the adjustment factor;

[0100] The benefit of the formula is that by adjusting the coefficients To optimize the resource allocation priority of key tasks, which makes resource allocation not only based on the current matching degree , also references the availability of resources , thereby achieving dynamic optimization of resource allocation;

[0101] There are three task segments, , , Indicates resource matching degree;

[0102] Set the resource availability parameters to , , ;

[0103] Adjustment factor , substituted into the formula to calculate:

[0104]

[0105]

[0106]

[0107] The results show that after considering the matching degree and availability of resources, the overall resource pairing efficiency reaches the standard of 0.9, which means that the overall resource utilization efficiency is high, ensuring the full utilization of resources and optimizing the priority allocation of resources in key task segments.

[0108] See also Figure 5 , the specific steps for obtaining the batch task execution status table are:

[0109] S311: Based on the task segment resource pairing list, perform data collection for each batch, including task type, execution time, and required resources, and perform preliminary classification of the data to generate a classified task data set;

[0110] In the detailed task data collection stage, based on historical data and real-time monitoring systems, the resource usage, start and end time of each batch of tasks are accurately recorded. The process covers data mining and classification processing of various types of tasks, and an efficient data classification algorithm is used to classify task types. Through database query and real-time data stream analysis, each task is marked with metadata of its resource utilization and execution time, which provides a basis for subsequent resource allocation and load balancing. The task data set classified by resource utilization efficiency and task execution time is obtained, which will be directly used to optimize the resource allocation strategy.

[0111] S312: Perform resource utilization analysis on the classified task datasets, perform resource allocation efficiency evaluation by calculating the ratio of resources required for each task to total resources, and obtain an optimized resource allocation dataset based on the efficiency evaluation results;

[0112] By performing resource utilization analysis and combining real-time monitoring with historical data analysis, the resource usage and completion time of each task are evaluated. Here, the peak and valley values ​​of resource usage are determined by comparing and analyzing historical data with real-time data in the database, and then the resource utilization rate of each task is calculated. The calculation process involves multiple resource parameters, such as CPU time, memory usage, and input / output operations. The analysis results will be used to adjust and optimize resource allocation strategies. By comparing the resource utilization of each task, an optimized resource allocation data set is generated. This data set lists optimization suggestions for the resource usage of each task, thereby improving the efficiency of the overall operation.

[0113] S313: Based on the optimized resource allocation data set, perform resource reconfiguration using the formula:

[0114]

[0115] Generate a batch task execution status table, where: represents the resource adjustment efficiency, Indicates the total number of tasks, Indicates the The amount of resources used by the task, Indicates the The total amount of resources for each task;

[0116] The formula provides a comprehensive resource adjustment efficiency assessment by combining the ratio of used resources to unused resources for each task. It dynamically adjusts resource allocation based on real-time data to optimize task execution status and resource utilization.

[0117] There are 3 tasks, and the resource usage of each task is as follows:

[0118] Task 1 has used 20 units, and the total available units are 50;

[0119] 30 units have been used in Task 2, and 60 units are available;

[0120] 25 units have been used in mission 3, and the total available units are 50;

[0121] In the formula is the number of tasks, here , It is The amount of resources used by the task, It is The total resource amount of a task is calculated as follows:

[0122]

[0123] The result shows that the calculated resource adjustment efficiency is 1, which means that resource utilization has reached the optimal state, the resource allocation of each task is within a reasonable range, and there is no resource waste, which further improves the task execution efficiency and the balance of resource utilization.

[0124] See also Figure 6 , the specific steps for obtaining batch resource usage status are:

[0125] S321: extracting the tag data of completed tasks from the task execution status table, identifying tasks requiring resource recovery in the urine analysis data through remote urine analysis, and obtaining a list of pending tasks;

[0126] It is crucial to extract completed analysis tasks from the task execution status table. This process ensures the accurate entry of data and the reliability of subsequent processing. The analysis tasks marked as "completed" are screened by automated software. The tasks represent the successful analysis of a series of urine samples. Key data will be collected from each completed task, such as urine component analysis results and related patient health information. This step is the key to resource optimization because it directly affects the quality of the data. The collected data is classified and summarized, and the common components and abnormal values ​​of various types of urine samples are analyzed. This not only optimizes data storage and processing, but also improves the efficiency and response speed of remote urine analysis, ultimately ensuring the accuracy and availability of each data.

[0127] S322: Based on the list of pending tasks, analyze the types of resources used and duration of tasks in the remote urine analysis data, and calculate the average total resource usage time using the formula:

[0128]

[0129] Get resource usage time summary data, where: Indicates the average total resource usage time, Representative The usage time of resources in a task, Represents the number of tasks;

[0130] The usefulness of the formula is that by aggregating the resource usage time in each task and distributing it evenly to each resource type, the average usage time of each resource is obtained, which helps to evaluate the efficiency and status of resources;

[0131] In a typical remote urine analysis center, there are three urine analysis tasks, which take 30 minutes, 45 minutes, and 25 minutes to complete, respectively. The total number of tasks is 3. The formula is as follows:

[0132]

[0133] The results show that the average processing time for each urine analysis task is 33.33 minutes. This data helps the management of the analysis center evaluate the efficiency of urine analysis equipment and determine whether the equipment needs to be adjusted or upgraded to increase processing speed and accuracy and improve resource utilization.

[0134] S323: Based on the resource usage duration summary data, the resource usage status in the urine analysis data is updated, and the batch resource usage status is obtained by analyzing the resource usage frequency and duration;

[0135] In the final stage of resource utilization, especially when urine analysis data is involved, it is particularly important to update and optimize the resource utilization status. After collecting and analyzing the detailed data of urine samples, it is necessary to evaluate the usage of various analytical equipment in the resource pool, including checking the frequency and duration of use of various equipment, as well as the efficiency of processing urine samples. For example, by tracking high-frequency analytical instruments and less frequently used equipment, managers can make adjustments and prioritize maintenance or replacement of the most frequently used equipment. The analysis quality of urine samples also needs to be considered to ensure that all equipment can provide accurate and reliable data. The operation not only improves the operating efficiency of the entire system, but also ensures the accuracy of patient test results and obtains batch resource usage status.

[0136] See also Figure 7 ,The steps for obtaining the remote urine data management profile are as follows:

[0137] S411: Analyze the data transmission node information of remote urine analysis based on the batch resource usage status, perform preliminary screening of the transmission path status based on the real-time data flow and transmission stability parameters of each node, and generate a transmission path priority list;

[0138] The data transmission node information of remote urine analysis is parsed. Based on the real-time status indicators of the nodes, including transmission stability and data flow rate, the stability indicator values ​​of each node are monitored in real time, high-volatility data are extracted and screening rules are established. The urine data transmission performance of each node is quantitatively analyzed, including packet loss rate, transmission delay and path availability. By classifying and counting the fluctuation amplitude of the stability data, nodes with low transmission stability are identified and marked as suboptimal nodes. A preliminary screening rule set is constructed according to the marking list. All nodes are filtered in combination with the screening rules to determine the node set to be used first. A transmission path priority list is formed by comprehensively analyzing the data flow direction and node priority.

[0139] S412: Based on the transmission path priority list and the flow level of the real-time urine data, a priority distribution strategy is set. The formula is used to balance the flow and stability weights:

[0140]

[0141] Calculate the priority of the path and obtain real-time data of urine analysis, where Indicates the path priority. Indicates transmission stability, Indicates real-time traffic, and are stability weight and flow weight respectively;

[0142] The benefit of this formula is that it combines the real-time stability of the path with the weight of the traffic distribution to achieve dynamic optimization of the urine data transmission path, improve the efficiency and stability of real-time data transmission, and avoid node overload or transmission delay problems.

[0143] Path transmission stability ,This value is obtained by real-time monitoring of urine packet loss rate (less than 1%) and network latency (less than 100ms);

[0144] Route real-time traffic ,calculated in real time by traffic analysis tools, based on the amount of data transferred per second (e.g. 20MB / s);

[0145] Stability weight , set according to the real-time requirements of key data in urine analysis;

[0146] Traffic weight , measured by data transmission volume and node load status;

[0147] Substituting the above values ​​into the formula:

[0148]

[0149] The results show that the selected path transmission priority is 0.77. The path priority reflects the balance between stability and flow distribution of the current path, providing an important basis for the dynamic selection of urine data transmission path.

[0150] S413: Mapping the urine analysis real-time data to the selected path according to the priority distribution strategy, adjusting the data flow distribution through path priority control, and generating a remote urine data management profile;

[0151] The real-time urine analysis data is mapped to the transmission path according to the priority distribution strategy. Combined with the determined path priority, a dynamic data allocation mechanism is constructed. By analyzing the matching degree between data flow and path load capacity, the distribution rules of different data packets are determined. The real-time urine analysis data is graded according to importance, including basic diagnostic data, detailed laboratory parameters and special marker data. Critical data is transmitted through high-priority paths, and non-core data is transmitted through low-priority paths. The real-time allocation ratio of priority paths is adjusted according to the actual status of path load data. Paths with high loads are marked as suboptimal and the data flow direction is dynamically adjusted. The priority optimization mapping of data flow is achieved through the real-time path priority update mechanism, and an overview of remote urine data management is generated.

[0152] The remote urine analysis and data management system is used to perform the remote urine analysis and data management method described above, and the system includes:

[0153] The sampling data screening module determines the data load value and transmission frequency based on the data transmission starting node of remote urine analysis, and screens the load and frequency data, filters the data items in the key load range, deletes the data groups with low load and low frequency, sorts and integrates them, and generates a list of screened data loads;

[0154] The task priority judgment module is based on screening the data load list, analyzing the data load and frequency, identifying the load differences to divide the task priority of the data group, parsing the task segments of the data group, analyzing the load requirements of the sub-items in the task segments, sorting out the sub-items and resource requirements, and establishing a sub-task resource requirement table;

[0155] The resource allocation module allocates resources and matches them with subtasks based on each resource requirement in the subtask resource requirement table, analyzes task segments and resource requirements, integrates resource groups, divides the matched resource allocation information into batches, records and organizes them, and obtains the task resource matching results;

[0156] The batch data scheduling module allocates resources and task data in the task resource matching results to batch task groups in order of priority, schedules resources item by item, integrates batch data, captures idle resources after each batch task is completed, and forms a batch resource recycling status;

[0157] The dynamic transmission path management module determines the real-time load of the transmission path based on the resource information of the batch resource recovery status, places the urine data stream on the load-stable path according to the priority order, monitors the load of each path in real time according to the path status, updates the path stability status information, and generates a remote urine data management profile.

[0158] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A remote urine analysis and data management method, characterized in that: The following steps are involved: Based on the data transmission starting node of remote urine analysis, key parameters in the urine sample data group are extracted, the load and frequency of each data group are identified, and the data packets are preliminarily prioritized according to the data load and processing frequency to generate a task priority processing structure; Based on the task priority processing structure, the task segments in the sample parameter group are decomposed, the resource requirement values ​​of the sub-parameters in the task segments are matched, a task segment resource association table is generated, resources are allocated to the task segments in the task segment resource association table, and a task segment resource pairing list is generated; Based on the task segment resource pairing list, batch scheduling is performed, batch task data sets are processed in batches according to the requirements of urine analysis, load is distributed for each batch task, task data on resource utilization is extracted and allocation is adjusted, and a batch task execution status table is generated. According to the batch task execution status table, resources after each batch task are completed are recycled to the resource pool to obtain the batch resource usage status; The steps for obtaining the batch task execution status table are specifically as follows: Based on the task segment resource pairing list, perform data collection for each batch, including task type, execution time, and required resources, and perform preliminary classification of the data to generate a classified task data set; Performing resource utilization analysis on the classified task data set, performing resource allocation efficiency evaluation by calculating the ratio of resources required for each task to total resources, and obtaining an optimized resource allocation data set based on the efficiency evaluation result; Based on the optimized resource allocation data set, resource reconfiguration is performed using the formula: ； Generate a batch task execution status table, where: represents the resource adjustment efficiency, Indicates the total number of tasks, Indicates the The amount of resources used by the task, Indicates the The total amount of resources for each task; The steps for obtaining the batch resource usage status are specifically as follows: Extracting the tag data of completed tasks from the task execution status table, identifying tasks requiring resource recovery in the urine analysis data through remote urine analysis, and obtaining a list of pending tasks; Based on the pending task list, the resource types and durations used by tasks in the remote urine analysis data were analyzed, and the average total resource usage time was calculated using the formula: ； Get resource usage time summary data, where: Indicates the average total resource usage time, Representative The usage time of resources in a task, Represents the number of tasks; Based on the resource usage duration summary data, the resource usage status in the urine analysis data is updated, and the batch resource usage status is obtained by analyzing the resource usage frequency and duration; Based on the batch resource usage status, a transmission path is dynamically selected according to the data transmission node of the remote urine analysis, the real-time data stream of the urine analysis is placed on the designated transmission path according to priority, a transmission path with a stable path status is selected, and a remote urine data management profile is generated.

2. The remote urine analysis and data management method according to claim 1, characterized in that: The steps for obtaining the task priority processing structure are specifically as follows: Based on the data transmission starting node of remote urine analysis, key parameters in each set of urine sample data are extracted, the load parameters of each set of data are identified, the extracted load parameters are called, and statistics are performed on each set of loads in combination with the extracted load parameters to generate a preliminary data set of load and frequency; Based on the preliminary data set of load and frequency, the load value and processing frequency of each set of data are counted, items exceeding a preset threshold are marked, and related data are extracted to obtain a priority sorted list; According to the priority ranking list, the priority ranking of each group of data is extracted and divided, and combined according to the load value and frequency to generate a task priority processing structure.

3. The remote urine analysis and data management method according to claim 2, characterized in that: The steps for obtaining the task segment resource association table are specifically as follows: Based on the task priority processing structure, the task segment is decomposed into subtasks according to the task segment requirements of the sample parameter group, and the resource requirement characteristics of each subtask are identified. The resource requirement type, task priority and sample characteristics of the task segment are matched to establish a task segment resource requirement classification table; Based on the task segment resource requirement classification table, analyze the demand proportion of task segments in differentiated resource types, and combine the characteristics of resource types to adopt the formula: ； Calculate the total weight of the task segment resources and generate the task segment resource requirement weight table, where: is the total weight of the task segment resources, Indicates the demand value of a single resource. is the resource type parameter, is the task priority parameter, is the resource association coefficient, is the demand proportion parameter, is the demand adjustment coefficient, Indicates the total number of tasks; The demand proportion of each resource type in the task segment resource demand weight table is called, sorted by demand proportion, and the optimal sorting item of the task segment resource demand is determined. The task segment resource association table is established through the demand proportion of the priority sorting item.

4. The remote urine analysis and data management method according to claim 3, characterized in that: The steps for obtaining the task segment resource pairing list are as follows: Extracting the resource type and quantity required for each task segment from the task segment resource association table, determining the resource requirement standard of the task segment by analyzing the task requirements, and generating a task segment resource requirement list; Analyze the resource requirement list and resource inventory of the task segment, perform resource matching analysis on each task segment, determine available resources through matching calculation of resource types and quantities, and obtain a resource matching list; Based on the resource matching list, optimize resource allocation using the formula: ； Generate a task segment resource pairing list, where: A function representing the efficiency of computing resource pairing, Representative The resource matching degree of each task segment, is a parameter of resource availability, is the adjustment factor.

5. The remote urine analysis and data management method according to claim 1, characterized in that: The steps for obtaining the remote urine data management overview are specifically as follows: According to the batch resource usage status, the data transmission node information of the remote urine analysis is parsed, and the transmission path status is preliminarily screened through the real-time data flow and transmission stability parameters of each node to generate a transmission path priority list; Combined with the transmission path priority list and the flow level of real-time urine data, a priority distribution strategy is set based on the balance between flow and stability weights, using the formula: ； Calculate the priority of the path and obtain real-time data of urine analysis, where Indicates the path priority. Indicates transmission stability, Indicates real-time traffic, and are stability weight and flow weight respectively; The urine analysis real-time data is mapped to the selected path according to the priority distribution strategy, and the data flow distribution is adjusted through the path priority control to generate a remote urine data management profile.

6. A remote urine analysis and data management system, characterized in that: The remote urine analysis and data management method according to any one of claims 1 to 5, wherein the system comprises: The sampling data screening module determines the data load value and transmission frequency based on the data transmission starting node of remote urine analysis, and screens the load and frequency data, filters the data items in the key load range, deletes the data groups with low load and low frequency, sorts and integrates them, and generates a list of screened data loads; The task priority determination module analyzes the load and frequency of the data based on the screened data load list, identifies the load differences and divides the task priorities of the data groups, parses the task segments of the data groups, analyzes the load requirements of the sub-items in the task segments, organizes the sub-items and resource requirements, and establishes a sub-task resource requirement table; The resource allocation module allocates resources and matches them with subtasks based on each resource requirement in the subtask resource requirement table, analyzes task segments and resource requirements, integrates resource groups, divides the matched resource allocation information into batches, records and organizes them, and obtains task resource matching results; The batch data scheduling module allocates the resources and task data in the task resource pairing result to the batch task group in order of priority, schedules resources item by item, integrates batch data, captures idle resources after each batch task is completed, and forms a batch resource recycling state; The dynamic transmission path management module determines the real-time load of the transmission path based on the resource information of the batch resource recovery status, places the urine data stream on the load-stable path according to the priority order, monitors the load of each path in real time according to the path status, updates the path stability status information, and generates a remote urine data management profile.

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