Operation method and system for monitoring health indexes

Abstract

The invention discloses an operation method and system for monitoring health indexes, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the data collection through a collector, and carrying out the primary processing of the data; monitoring analysis and troubleshooting are carried out on the system according to the collected data; health degree analysis visual thematic analysis is carried out, and system service is optimized. According to the method, the operation state of the system is monitored in all directions, detailed data support is provided for troubleshooting and performance optimization, in-depth insight of all levels of the system is achieved, potential risks and problems of the system can be monitored and processed in time, a targeted solution is provided for system optimization, and the method is suitable for popularization and application. And the stability and the reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and in particular to an operation method and system for monitoring health indicators. Background Art

[0002] With the improvement of global health awareness and the rapid development of the Internet of Things and big data technologies, health monitoring technology has become a research hotspot in the field of medical health. Current health monitoring devices such as smart watches, health bracelets, wearable ECG monitors, etc. have been widely introduced into ordinary households and have become an indispensable health management tool in people's daily lives. By integrating sensors, data processing algorithms and visualization technologies, modern health monitoring systems can collect and analyze individual physiological parameters in real time and provide users with personalized health management services. These systems have played an important role in improving the efficiency and accuracy of medical services and have gradually become an indispensable part of people's daily lives.

[0003] With the aging of the population and the increase in the number of patients with chronic diseases, the demand for real-time, continuous and non-invasive health monitoring is becoming more and more urgent. However, although the existing health monitoring technology has met the basic needs of users to a certain extent, it still has many shortcomings in data processing, monitoring accuracy, system response speed and user experience. In the data collection process, due to the limitations of sensor performance and environmental factors, the accuracy of the data is often not fully guaranteed, resulting in deviations in subsequent analysis results. There is a lack of unified data collection standards between devices of different brands and types. These factors directly affect the reliability of the monitoring results. The current system lacks depth in monitoring analysis and troubleshooting, and cannot effectively identify complex health patterns and predict health risks. The existing monitoring system has defects in human-computer interaction design, the visual display of health analysis is not intuitive enough, and there is a lack of special analysis for specific health problems, which makes it difficult for users to take effective health management measures based on the monitoring results, resulting in difficulty for users to obtain and understand key health information and provide effective health advice for specific situations of users. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing health monitoring technology has insufficient data collection accuracy, limited system monitoring and analysis and troubleshooting capabilities, insufficient health analysis visualization and thematic analysis, as well as how to achieve high-precision data collection, in-depth monitoring and analysis and optimization of user health management services.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for monitoring the operation of health indicators, including collecting data through a collector and performing preliminary processing on the data; monitoring and analyzing the system and troubleshooting based on the collected data; performing health analysis and visualization thematic analysis to optimize system services.

[0007] As a preferred solution of the operation method of monitoring health indicators described in the present invention, the data collection includes monitoring indicators, network traffic, call links and log data.

[0008] Monitoring indicators include CPU usage, memory usage, and disk I / O performance monitoring indicators; network traffic includes the amount of network packets sent and received, network delay, and packet loss rate.

[0009] The call link involves collecting detailed data by tracking the call relationship between microservices, including call time, number of calls, and call failures.

[0010] Log data includes system logs, application logs, and error logs.

[0011] As a preferred solution of the operation method for monitoring health indicators described in the present invention, the preliminary processing of data includes classified storage of data, storing indicator data and traffic log data nearby in the data storage node cluster of the local area, and storing SCAPY traffic packet capture data in the data nodes of the local area and the exclusive server type collector.

[0012] As a preferred solution of the operation method for monitoring health indicators described in the present invention, the preliminary processing of data also includes performing a data integrity test and a data consistency test.

[0013] Data integrity testing includes testing whether data is lost or damaged during transmission and storage through verification mechanisms and redundant data storage methods.

[0014] Data consistency testing includes testing whether data is consistent between different nodes and systems through consistency verification and synchronization mechanisms.

[0015] As a preferred solution of the operation method of monitoring health indicators described in the present invention, the monitoring, analysis and troubleshooting include comprehensive monitoring and analysis of the system operation status through a monitoring platform and a data detection mechanism.

[0016] As a preferred scheme of the operation method of monitoring health indicators described in the present invention, the comprehensive monitoring and analysis of the system operation status includes setting system monitoring thresholds and anomaly detection algorithms, analyzing and processing abnormal data through data detection and alarm mechanisms, and responding to and processing various abnormal situations in the system.

[0017] As a preferred solution of the operation method of monitoring health indicators described in the present invention, the health analysis visualization thematic analysis includes network thematic analysis, transaction thematic analysis, service thematic analysis, and SQL thematic analysis.

[0018] The network thematic analysis includes collecting data based on network traffic between nodes, between PODs, and between hosts, analyzing the upstream and downstream rates, latency, throughput, and abnormal indicators of application network links, screening out high-latency, high-throughput, and abnormal network links through preset thresholds, and conducting targeted analysis of network loads to troubleshoot network problems.

[0019] Transaction-specific analysis includes counting the URL access of each business request, the number of business requests, the number of slow responses, and the number of abnormal response transactions based on business requests, screening out frequently used business requests in the application, slow response business requests, and business requests that often have abnormal responses, performing full-link tracking for problematic business requests, locating problem nodes or PODs through service codes, and screening out potential application problems.

[0020] Service-specific analysis includes counting the number of service calls and the average response time based on microservice requests, filtering out slow-responding and abnormal-responding services in the application through preset thresholds, horizontally scaling services with high loads, and analyzing problematic service calls in combination with the full-link call situation, capturing service exception logs, and troubleshooting.

[0021] SQL special analysis screens out SQL statements with long response time and frequent errors in the application based on the reasons for slow and abnormal response of database analysis services and transactions, analyzes the SQL call status, and optimizes SQL statements.

[0022] Another object of the present invention is to provide an operating system for monitoring health indicators, which can monitor, analyze and troubleshoot the system based on collected data, thereby solving the problem of delayed fault response in current health monitoring technology.

[0023] As a preferred solution of the operating system for monitoring health indicators described in the present invention, it includes: a data acquisition module, a system monitoring and analysis module, and a service optimization module; the data acquisition module is used to collect data through a collector and perform preliminary processing on the data; the system monitoring and analysis module is used to monitor, analyze and troubleshoot the system based on the collected data; the service optimization module is used to perform health analysis, visualized thematic analysis, and optimize system services.

[0024] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an operating method for monitoring health indicators.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for monitoring health indicators.

[0026] Beneficial effects of the present invention: The operating method for monitoring health indicators provided by the present invention collects data through a collector and performs preliminary processing on the data, thereby realizing all-round monitoring of the system operation status, and providing detailed data support for subsequent troubleshooting and performance optimization. Based on the collected data, the system is monitored, analyzed and troubleshooted, and potential risks and problems of the system can be discovered in a timely manner, thereby improving the stability and reliability of the system, performing health analysis and visual thematic analysis, optimizing system services, achieving in-depth insights into all levels of the system, and providing targeted solutions for system optimization. The present invention achieves better results in improving system stability, reducing the occurrence of failures and enhancing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 An overall flow chart of an operating method for monitoring health indicators provided in the first embodiment of the present invention.

[0029] Figure 2 An overall flow chart of an operating system for monitoring health indicators provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0031] Example 1, reference Figure 1 , as an embodiment of the present invention, provides an operation method for monitoring health indicators, comprising:

[0032] S1: Collect data through the collector and perform preliminary data processing.

[0033] Furthermore, data collection includes monitoring metrics, network traffic, call links, and log data.

[0034] Monitoring indicators include CPU usage, memory usage, and disk I / O performance monitoring indicators.

[0035] Network traffic includes the amount of network packets sent and received, network delay, and packet loss rate.

[0036] The call link involves collecting detailed data by tracking the call relationship between microservices, including call time, number of calls, and call failures.

[0037] Log data includes system logs, application logs, and error logs.

[0038] It should be noted that the preliminary processing of data includes classified storage of data, storing indicator data and traffic log data in the data storage node cluster in the area, and storing SCAPY traffic packet capture data in the data nodes and dedicated server-type collectors in the area.

[0039] It should also be noted that preliminary processing of data also includes data integrity testing and data consistency testing.

[0040] Data integrity testing includes testing whether data is lost or damaged during transmission and storage through verification mechanisms and redundant data storage methods.

[0041] Data consistency testing includes testing whether data is consistent between different nodes and systems through consistency verification and synchronization mechanisms.

[0042] It should also be noted that by collecting data through the collector and performing preliminary processing on the data, all-round monitoring of the system operation status is achieved, providing detailed data support for subsequent troubleshooting and performance optimization, improving the efficiency of data retrieval and analysis, and ensuring the accuracy and reliability of the data, thereby making subsequent analysis and decision-making more accurate.

[0043] S2: Monitor, analyze and troubleshoot the system based on the collected data.

[0044] Furthermore, monitoring analysis and troubleshooting include comprehensive monitoring and analysis of system operation status through monitoring platforms and data detection mechanisms.

[0045] It should be noted that comprehensive monitoring and analysis of system operation status includes setting system monitoring thresholds and anomaly detection algorithms, analyzing and processing abnormal data through data detection and alarm mechanisms, and responding to and handling various abnormal situations in the system.

[0046] It should also be noted that through the monitoring platform and data detection mechanism, the system operation status is comprehensively monitored and analyzed, potential risks and problems in the system are discovered in time, the stability and reliability of the system are improved, the system monitoring thresholds and anomaly detection algorithms are set, and abnormal data is analyzed and processed through data detection and alarm mechanisms, which enables rapid response and processing of system abnormalities and reduces the impact of system failures.

[0047] S3: Perform health analysis and visualized thematic analysis to optimize system services.

[0048] Furthermore, health analysis visualization thematic analysis includes network thematic analysis, transaction thematic analysis, service thematic analysis, and SQL thematic analysis.

[0049] The network thematic analysis includes collecting data based on network traffic between nodes, between PODs, and between hosts, analyzing the upstream and downstream rates, latency, throughput, and abnormal indicators of application network links, screening out high-latency, high-throughput, and abnormal network links through preset thresholds, and conducting targeted analysis of network loads to troubleshoot network problems.

[0050] Transaction-specific analysis includes counting the URL access of each business request, the number of business requests, the number of slow responses, and the number of abnormal response transactions based on business requests, screening out frequently used business requests in the application, slow response business requests, and business requests that often have abnormal responses, performing full-link tracking for problematic business requests, locating problem nodes or PODs through service codes, and screening out potential application problems.

[0051] Service-specific analysis includes counting the number of service calls and the average response time based on microservice requests, filtering out slow-responding and abnormal-responding services in the application through preset thresholds, horizontally scaling services with high loads, and analyzing problematic service calls in combination with the full-link call situation, capturing service exception logs, and troubleshooting.

[0052] SQL special analysis screens out SQL statements with long response time and frequent errors in the application based on the reasons for slow and abnormal response of database analysis services and transactions, analyzes the SQL call status, and optimizes SQL statements.

[0053] It should be noted that high-latency, high-throughput and abnormal network links are screened out through preset thresholds. The specific plan is as follows: use a network data collection tool (Wireshark or a custom collector) to collect network traffic data, including data packet transmission time, size, and flow direction information; analyze the collected data to calculate the average response time and data transmission rate performance indicators of each network link; set latency and throughput thresholds based on the performance history data and business needs of the network link; use standard deviation analysis to identify data points that exceed the normal distribution; train a machine learning model to identify abnormal patterns; finally, compare the calculated performance indicators with the preset thresholds to screen out links whose average response time exceeds the latency threshold and whose data transmission rate exceeds the throughput threshold.

[0054] Count the number of business requests, the number of slow responses, and the number of abnormal response transactions, and filter out frequently used business requests, slow response business requests, and business requests with frequent abnormal responses in the application. The specific plan is as follows: collect and analyze application logs, record the URL, response time, and status code of each business request; count the number of occurrences, average response time, and failure rate of each business request; set thresholds for request frequency, response time, and failure rate based on business needs and performance goals; thereby filter out business requests whose number of requests exceeds the set frequency threshold, whose average response time exceeds the set response time threshold, and whose failure rate exceeds the set failure rate threshold.

[0055] Through the preset thresholds, we can filter out the slow-responding and abnormal-responding services in the application. The specific plan is as follows: monitor the number of service calls, response time, and failure conditions in real time; calculate the average response time and failure rate of each service; set thresholds for response time and failure rate based on the performance requirements and business needs of the service; and then filter out the services whose average response time exceeds the set threshold.

[0056] According to the reasons for slow response and abnormality of database analysis services and transactions, filter out SQL statements with long response time and frequent errors in the application. The specific plan is as follows: deploy database monitoring tools, including MySQL slow query logs and SQL Server Profiler, to collect relevant data on SQL execution; ensure that the application server log records the execution time and results of all SQL statements, including error information; analyze monitoring data to calculate the average execution time, number of executions, and maximum execution time of each SQL statement; count the number and type of errors for each SQL statement; set SQL statement execution time thresholds (such as more than 2 seconds is considered a slow query) and error frequency thresholds (such as more than 10 errors per hour is considered a frequent error) according to business needs and performance standards; thereby filter out SQL statements whose average execution time exceeds the set threshold; find out SQL statements whose number of errors exceeds the set threshold.

[0057] It should be noted that through network-themed analysis, transaction-themed analysis, service-themed analysis and SQL-themed analysis, we have achieved in-depth insights into all aspects of the system, provided targeted solutions for system optimization, helped to troubleshoot network problems, optimize network configuration, improve network performance, and improve user experience by accurately locating problems in business requests. Through horizontal expansion and troubleshooting, we have improved service stability and responsiveness, optimized database performance, reduced response time and error rates, and thus improved overall system performance.

[0058] Example 2 is an embodiment of the present invention, which provides an operating method for monitoring health indicators. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0059] First, the experiment selected six different computer systems (system A to system F) as research objects. Each system was equipped with a standard data collector to collect CPU usage, memory usage, disk I / O performance, network traffic (including network delay and packet loss rate), call link and log data. The first stage of the experiment was data collection. In this stage, the data collector was configured to extract data from each system once a minute for one week. The collected data included monitoring indicators, network traffic, call links and log data. The monitoring indicators paid special attention to the performance monitoring indicators of CPU usage, memory usage and disk I / O. The network traffic data included the amount of network packets sent and received, network delay and packet loss rate. The call link data tracked the call relationship between microservices and recorded the call time, number of calls and call failures in detail. Log data covers system logs, application logs and error logs. The second stage is the preliminary processing of data. In this stage, we classify and store the collected data to ensure that the indicator data and flow log data are stored in the data storage node cluster of this area, while the SCAPY flow packet capture data is stored in the data node and dedicated server type collector of this area, and perform data integrity test and data consistency test to ensure the accuracy and consistency of data during transmission and storage. The third stage is monitoring analysis and troubleshooting. Through the monitoring platform and data detection mechanism, the system operation status is comprehensively monitored and analyzed, the system monitoring threshold and anomaly detection algorithm are set, and the abnormal data is analyzed and processed through the data detection and alarm mechanism to respond to and handle various abnormal situations of the system. In the last stage, a visual thematic analysis of health analysis is carried out, including network thematic analysis, transaction thematic analysis, service thematic analysis and SQL thematic analysis, which effectively identifies high latency, high throughput and abnormal situations in network links, and conducts full-link tracking and analysis for problematic business requests and services to optimize system services. It can be seen from the experimental results that the present invention shows creativity and novelty, and provides strong support for the performance optimization and service stability of the health monitoring system.

[0060] Example 3, reference Figure 2 , as an embodiment of the present invention, provides an operation system for monitoring health indicators, including a data acquisition module, a system monitoring and analysis module, and a service optimization module.

[0061] The data acquisition module is used to collect data through the collector and perform preliminary data processing; the system monitoring and analysis module is used to monitor, analyze and troubleshoot the system based on the collected data; the service optimization module is used to perform health analysis, visualized thematic analysis and optimize system services.

[0062] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0064] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0065] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring health indicators, characterized in that: include: Collect data through the collector and process the data preliminarily; Monitor, analyze and troubleshoot the system based on the collected data; Conduct health analysis and visualized thematic analysis to optimize system services.

2. The method for monitoring health indicators according to claim 1, characterized in that: The data collection includes monitoring indicators, network traffic, call links and log data; Monitoring indicators include CPU usage, memory usage, and disk I / O performance monitoring indicators; Network traffic includes the amount of network packets sent and received, network delay, and packet loss rate; The call link includes collecting detailed data by tracking the call relationship between microservices, including call time, call count, and call failures; Log data includes system logs, application logs, and error logs.

3. The method for monitoring health indicators according to claim 2, characterized in that: The preliminary processing of the data includes classifying and storing the data, storing the indicator data and the flow log data in the data storage node cluster of the local area, and storing the SCAPY flow packet capture data in the data nodes and dedicated server type collectors of the local area.

4. The method for monitoring health indicators according to claim 3, characterized in that: The preliminary processing of the data also includes performing a data integrity test and a data consistency test; Data integrity testing includes testing whether data is lost or damaged during transmission and storage through verification mechanisms and redundant data storage methods; Data consistency testing includes testing whether data is consistent between different nodes and systems through consistency verification and synchronization mechanisms.

5. The method for monitoring health indicators according to claim 4, characterized in that: The monitoring, analysis and troubleshooting include comprehensive monitoring and analysis of system operation status through a monitoring platform and data detection mechanism.

6. The method for monitoring health indicators according to claim 5, characterized in that: The comprehensive monitoring and analysis of the system operation status includes setting system monitoring thresholds and anomaly detection algorithms, analyzing and processing abnormal data through data detection and alarm mechanisms, and responding to and processing various abnormal situations in the system.

7. The method for monitoring health indicators according to claim 6, characterized in that: The health analysis visualization thematic analysis includes network thematic analysis, transaction thematic analysis, service thematic analysis, and SQL thematic analysis; The network thematic analysis includes collecting data based on network traffic between nodes, between PODs, and between hosts, analyzing the uplink and downlink rates, latency, throughput, and abnormal indicators of application network links, screening out latency, throughput, and abnormal network links through preset thresholds, and specifically analyzing network loads to troubleshoot network problems. Transaction-specific analysis includes counting the URL access of each business request, the number of business requests, the number of slow responses, and abnormal response transactions based on business requests, screening out frequently used business requests, slow response business requests, and business requests that often have abnormal responses in the application, and tracking the entire chain of problematic business requests. The problem node or POD is located through the service code, and potential application problems are screened out. Service-specific analysis includes counting the number of service calls and average response time based on microservice requests, filtering out slow-responding services and abnormal-responding services in the application through preset thresholds, horizontally scaling services with high loads, and analyzing problematic service nodes based on full-link call conditions for problematic service calls, capturing service abnormality logs, and troubleshooting. SQL special analysis screens out SQL statements with long response time and frequent errors in the application based on the reasons for slow and abnormal response of database analysis services and transactions, analyzes the SQL call status, and optimizes SQL statements.

8. A system using the operation method for monitoring health indicators according to any one of claims 1 to 7, characterized in that: Including data acquisition module, system monitoring and analysis module, and service optimization module; The data acquisition module is used to collect data through a collector and perform preliminary processing on the data; The system monitoring and analysis module is used to monitor and analyze the system and troubleshoot faults based on the collected data; The service optimization module is used to perform health analysis, visualize thematic analysis, and optimize system services.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the operating method for monitoring health indicators described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the operating method for monitoring health indicators described in any one of claims 1 to 7 are implemented.

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