Performance pressure measurement method, device and equipment of clothing hanging system based on Internet of Things, and medium

Abstract

The invention discloses a performance pressure measurement method, device and equipment of a garment hanging system based on the Internet of Things, and a medium, and relates to the technical field of the Internet of Things technology and garment manufacturing equipment testing, and the method comprises the steps: monitoring a current scene of the system; the current scene comprises a target load scene and a target comprehensive scene; the target comprehensive scene is a combined scene of the target load scene and the temporary change scene; recording hardware operation data acquired by the data acquisition sensor and software operation data acquired by the data monitoring platform in the current scene when the software environment and the hardware environment cooperate through a self-defined pressure measurement tool; and determining a key performance numerical value of the preset key performance according to the software and hardware operation data through the data analysis tool, determining the target key performance with the performance bottleneck according to the key performance numerical value, and triggering bottleneck position positioning operation to determine the bottleneck position and bottleneck reason of the performance bottleneck of the target key performance. And comprehensive performance pressure measurement and analysis can be carried out.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) technology and garment manufacturing equipment testing technology, and particularly to a performance stress testing method, apparatus, equipment and medium for garment hanging systems based on IoT. Background Technology

[0002] Currently, with the widespread adoption of IoT technology in the garment manufacturing industry, IoT-based garment hanging systems are gradually becoming a core component of garment manufacturing production lines. This system integrates hardware devices (such as hanging tracks, smart hangers, infrared sensors, card readers, and PLCs (Programmable Logic Controllers)) with software systems (such as data acquisition software, equipment control software, and data transmission and analysis software) to achieve automated transportation, positioning, and management of cut pieces and semi-finished products during garment production, significantly improving the efficiency and accuracy of garment manufacturing.

[0003] However, in practical applications, software systems face increasing bottlenecks due to the need to simultaneously process large amounts of real-time data uploaded from hardware devices (such as hanger position data, production process data, equipment operating status data, and production progress data) and perform real-time control of these devices. Specifically, as the number of hardware devices increases, data transmission frequency rises, or production workload increases, the software system is prone to data processing delays, data loss, and untimely response to control commands. This can lead to sluggish operation of the entire garment hanging system, decreased production efficiency, and even equipment failure.

[0004] To address these issues, comprehensive performance testing and analysis of the IoT-based garment hanging system are necessary to identify and optimize software bottlenecks. However, existing testing solutions yield results that deviate significantly from real-world scenarios and cannot adapt to changes in actual conditions.

[0005] In summary, how to conduct comprehensive performance testing and analysis is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a performance stress testing method, apparatus, equipment, and medium for an IoT-based garment hanging system, capable of comprehensive performance stress testing and analysis. The specific solution is as follows:

[0007] In a first aspect, this application discloses a performance stress testing method for an IoT-based garment hanging system, comprising:

[0008] Monitor the current scenario of the IoT-based garment hanging system; the current scenario includes the target load scenario and the target combined scenario; the target load scenario includes the preset normal load scenario and the preset peak load scenario; the target combined scenario is a combined scenario of the target load scenario and the temporary change scenario;

[0009] A custom stress testing tool is used to record the hardware and software operation data corresponding to the current scenario when the software and hardware environments work together. The hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform.

[0010] Key performance values ​​for preset critical performance are determined using data analysis tools and based on software and hardware operating data.

[0011] By using data analysis tools and based on key performance values, the target key performance that has a performance bottleneck in the current scenario is identified, and a bottleneck location operation is triggered to determine the location of the bottleneck and output the corresponding bottleneck cause.

[0012] Optionally, the step of determining the target key performance that has a performance bottleneck in the current scenario based on the key performance value, and triggering a bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reason includes:

[0013] The key performance characteristics that are not less than the corresponding preset performance threshold are defined as the target key performance characteristics that have performance bottlenecks in the current scenario.

[0014] By combining software and hardware operation data, a bottleneck location operation is triggered to determine the location of the bottleneck where the target key performance is constrained, and the corresponding bottleneck cause is output.

[0015] Optionally, the step of determining the target key performance that has a performance bottleneck in the current scenario based on the key performance value, and triggering a bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reason includes:

[0016] The key performance values ​​of preset key performance indicators are statistically analyzed within a preset time range based on the time-varying variables.

[0017] If the changes meet the preset fault conditions corresponding to the preset key performance, then the preset key performance will be taken as the target key performance that has a performance bottleneck in the current scenario.

[0018] The preset locations and reasons corresponding to the target key performance are used as the bottleneck locations and reasons for the performance bottlenecks of the preset key performance.

[0019] Optionally, the temporary change scenarios include equipment failure scenarios, network fluctuation scenarios, production order switching scenarios, and load change scenarios;

[0020] Accordingly, when the temporary change scenario is a device failure scenario, the hardware operation data corresponding to the target number of racks in the simulation hardware device is set to empty or erroneous data; when the temporary change scenario is a network fluctuation scenario, random delays or random packet loss are periodically injected into a specified proportion of the simulation gateways; when the temporary change scenario is a production order switching scenario, the virtual cut piece type carried by all or some preset racks in the simulation hardware device is dynamically switched; when the temporary change scenario is a load change scenario, the usage of racks in the simulation hardware device is adjusted.

[0021] Optionally, the software operation data includes CPU utilization, memory usage, data transmission latency, and instruction response time; the hardware operation data includes the rack movement speed and the recognition status of data acquisition sensors in the simulation hardware device.

[0022] Optionally, the preset key performance indicators corresponding to the software operation data include peak and average CPU utilization, peak and average memory usage, average and maximum data transmission latency, average and maximum instruction response time, and data loss rate; the preset key performance indicators corresponding to the hardware operation data include the recognition success rate of data acquisition sensors in the simulated hardware device.

[0023] Optionally, before determining the key performance values ​​of the preset key performance based on software operation data and hardware operation data, the method further includes:

[0024] Based on the timestamps associated with the software and hardware operation data, the software and hardware operation data are categorized and stored in the data storage software.

[0025] Secondly, this application discloses a performance stress testing device for an IoT-based garment hanging system, comprising:

[0026] The scene monitoring module is used to monitor the current scene of the IoT-based garment hanging system; the current scene includes the target load scene and the target comprehensive scene; the target load scene includes the preset normal load scene and the preset peak load scene; the target comprehensive scene is a combined scene of the target load scene and the temporary change scene;

[0027] The data recording module is used to record the hardware operation data and software operation data corresponding to the current scenario when the software environment and hardware environment work together, using a custom stress testing tool; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform.

[0028] The key performance value calculation module is used to determine the key performance values ​​of preset key performances through data analysis tools and based on software operation data and hardware operation data.

[0029] The performance analysis module is used to identify the target key performance that has a performance bottleneck in the current scenario by using data analysis tools and based on key performance values, and to trigger the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reasons.

[0030] Thirdly, this application discloses an electronic device, including:

[0031] Memory, used to store computer programs;

[0032] A processor is used to execute the computer program to implement the aforementioned performance stress testing method for an IoT-based garment hanging system.

[0033] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned performance stress testing method for an IoT-based garment hanging system.

[0034] As can be seen, this application monitors the current scenario of an IoT-based garment hanging system; the current scenario includes a target load scenario and a target comprehensive scenario; the target load scenario includes a preset normal load scenario and a preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and a temporary change scenario; a custom load testing tool records the hardware operation data and software operation data corresponding to the current scenario when the software and hardware environments work together; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the garment hanging software collected by the data monitoring platform; the data analysis tool determines the key performance values ​​of preset key performance based on the software operation data and hardware operation data; the data analysis tool determines the target key performance with performance bottlenecks in the current scenario based on the key performance values, and triggers a bottleneck location operation to determine the bottleneck location of the target key performance with performance bottlenecks, and outputs the corresponding bottleneck reasons. Therefore, this application records software and hardware operation data when the software and hardware environments work together, rather than testing only the software system or hardware device individually. It takes into account the mutual influence between software and hardware working together, which leads to a large deviation between the stress test results and the actual application scenario. The mutual influence between software and hardware working together makes the stress test results closer to the actual application scenario. In addition, this application fully considers the target load scenario and the target comprehensive scenario, which can comprehensively detect the system's performance under extreme conditions. For target key performance with performance bottlenecks, it is also necessary to analyze the bottleneck location and the cause of the bottleneck in order to achieve in-depth performance analysis. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0036] Figure 1 This application discloses a flowchart of a performance stress testing method for an IoT-based garment hanging system.

[0037] Figure 2 This is a schematic diagram of the performance stress testing device for an IoT-based garment hanging system disclosed in this application;

[0038] Figure 3 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] In practical applications, software systems face increasing bottlenecks due to the need to simultaneously process large amounts of real-time data uploaded from hardware devices (such as hanger position data, production process data, equipment operating status data, and production progress data) and perform real-time control of these devices. Specifically, as the number of hardware devices increases, data transmission frequency rises, or production workload expands, the software system is prone to data processing delays, data loss, and untimely response to control commands. This can lead to system lag, decreased production efficiency, and even equipment failure.

[0041] To address these issues, comprehensive performance testing and analysis of the IoT-based garment hanging system are necessary to identify and optimize software bottlenecks. However, existing testing solutions yield results that deviate significantly from real-world scenarios and cannot adapt to changes in actual conditions.

[0042] Therefore, this application proposes a performance stress testing scheme for an IoT-based garment hanging system, which can perform comprehensive performance stress testing and analysis.

[0043] This application discloses a performance stress testing method for an IoT-based garment hanging system. See [link to relevant documentation]. Figure 1 As shown, the method includes:

[0044] Step S11: Monitor the current scenario of the IoT-based garment hanging system; the current scenario includes the target load scenario and the target comprehensive scenario; the target load scenario includes the preset normal load scenario and the preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and the temporary change scenario.

[0045] In this embodiment, the hardware and software environments for the IoT-based garment hanging system must first be set up: 1. Hardware Environment Setup: A hardware environment consistent with an actual garment manufacturing production line is set up for software operation, such as a Lenovo server. Hardware configuration requirements include an i5 13th generation CPU, 48GB of RAM, and an SSD (Solid State Drive); Windows 10 / 11 operating system. 2. Software Environment Setup: The IoT-based garment hanging system software to be tested is deployed on the server. Simultaneously, load testing tools (self-developed custom load testing tools ihs-troubleshooting-server and ihs-troubleshooting-mock) and data analysis tools (such as Python data analysis libraries and Tableau (a self-service business intelligence and data visualization platform)) and a data monitoring platform are set up to monitor the software system's operating status in real time (such as CPU utilization, memory usage, data transmission latency, and command response time). The ihs-troubleshooting-server implements core functions such as test cases, data collection, data analysis, and test reports. The ihs-troubleshooting-mock implements data, status, and protocols for various hardware devices, simulating different versions of the same hardware and their operational differences. Examples of simulations include: simulating the main rail's operating status, full rail status, simulating RFID (Radio Frequency Identification) sensors, simulating infrared sensors, and the uplink and downlink communication protocols, operating results, operating status, and alarms of the electronic control motherboard; simulating the communication interaction between RFID sensors and the software system (such as simulating data frame format, transmission frequency, and error code types); simulating the infrared sensor sending simulated data; simulating the downlink command execution data and action changes of the electronic control motherboard; simulating the reporting protocol of the electronic control motherboard; and simulating the air pressure status of the pneumatic equipment.

[0046] In this embodiment, after setting up the hardware and software environments, it is also necessary to configure the scene parameters of the current scenario. The current scenario includes the target load scenario and the target comprehensive scenario. Specifically, based on the actual operation scenario of the garment manufacturing production line, the ihs-troubleshooting-mock is configured with data to simulate the core hardware equipment of the garment hanging system, such as hanging rails, intelligent hangers (the number of which can be adjusted according to test requirements, ranging from 100 to 1000), RFID identification sensors, infrared positioning sensors, equipment controllers, and gateway devices; and to simulate the operating data of the hardware equipment (such as hanger movement speed, position coordinates, sensor recognition success rate, equipment current and voltage, air pressure status, etc.).

[0047] In this embodiment, the temporary change scenarios include equipment failure scenarios, network fluctuation scenarios, production order switching scenarios, and load transformation scenarios. Accordingly, when the temporary change scenario is an equipment failure scenario, the hardware operation data corresponding to the target number of racks in the simulation hardware device is set to empty or erroneous data. When the temporary change scenario is a network fluctuation scenario, random delays or random packet loss are periodically injected into a specified proportion of the simulation gateways. When the temporary change scenario is a production order switching scenario, the virtual cut-out type carried by all or a preset portion of the racks in the simulation hardware device is dynamically switched. When the temporary change scenario is a load transformation scenario, the usage of racks in the simulation hardware device is adjusted.

[0048] It should be noted that the design logic of the dynamic scenario combines the actual working conditions of garment production (such as morning peak production and equipment failure probability) to build a load fluctuation model that is close to reality, rather than a stress test with fixed parameters.

[0049] It should be noted that the specific adjustment scenarios are achieved by configuring the data in ihs-troubleshooting-mock. For example, the usage of brackets in the simulation hardware device can be adjusted by adjusting the usage data of the brackets in ihs-troubleshooting-mock.

[0050] It should be noted that the target comprehensive scenario is a combined scenario of the target load scenario and the temporary change scenario, such as: normal load + random failure or peak load + continuous network fluctuation.

[0051] It should be noted that temporary change scenarios can include personnel leave / absence: simulating the absence of an operator at a specific workstation. This is not simply removing a rack, but causing all production tasks (corresponding smart racks) flowing through that workstation to enter a waiting or queue blockage state. In the scenario configuration, one or more simulated sensors (representing workstations) can be specified to continuously send fault or busy signals, causing all simulated smart racks passing through that point to stop moving forward and forming a task backlog queue in the system background. Other expandable dynamic scenario settings: Production order switching scenario: simulating a factory changing production styles. Different styles have different process paths and processing times. The scenario can be configured to: dynamically switch the type of "virtual cut piece" carried by all or some of the simulated smart racks during stress testing, thereby changing their predetermined trajectory path and the simulated processing time at each workstation, testing the system scheduling algorithm and real-time adjustment capabilities. Network fluctuation scenario: simulating network interference common in industrial settings. Configurable: Periodically inject random latency (e.g., 50-200ms) or random packet loss (e.g., 1%-5%) into a specified proportion of the simulated gateway to test the data transmission resilience and retry mechanism of the software system under unstable network conditions.

[0052] Step S12: Record the hardware operation data and software operation data corresponding to the current scenario when the software environment and hardware environment work together using a custom stress testing tool; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform.

[0053] It should be noted that the implementation of software and hardware co-simulation is achieved through a self-developed simulation protocol, which enables the software to control and interact with virtual hardware in real time, rather than simply simulating data input, thus avoiding the actual investment in large-scale testing equipment.

[0054] It should be noted that when performing performance stress testing, the test should first be initialized: start the hardware simulation platform, hardware stress testing platform, and software system; confirm through the data monitoring platform that all simulated hardware devices are connected to the network normally, the software system is running normally, and the data acquisition and transmission functions are normal; reset the smart rack to its initial position, clear the historical data in the data storage software, and ensure that the test environment is in its initial state. Then, set the corresponding scenarios to execute performance stress tests under different scenarios in sequence. The test duration for each scenario is set to 1-2 hours. The specific process is as follows: 1. Normal load scenario test (standard load scenario): control 50%-70% of the smart rack to run along the preset track, the data acquisition software collects the device operation data at a set frequency (10 times / second), and the device control software sends control commands at a set frequency; record the CPU utilization, memory usage, data transmission latency (time from data acquisition to storage), and command response time (time from command to device execution) of the software system through the stress testing tool, and at the same time record the operation data of the simulated hardware devices (such as the stability of the rack movement speed, sensor recognition success rate). ); 2. Peak load scenario test (standard load scenario): Control 80%-100% of the smart racks to run, repeat the above test process, and focus on monitoring the data processing capability and stability of the software system under high load, and whether data loss, instruction timeout, etc. occur; 3. Fault load scenario test (a target comprehensive scenario): Based on the peak load scenario, randomly select 5%-10% of the smart racks to simulate faults, stop data uploading or send erroneous data, test the software system's ability to handle abnormal data (such as data verification, error prompts, fault device isolation), and whether the overall system performance is significantly affected. Among them, for idle racks that are not participating in the current operation, their faults can be simulated as being unable to be woken up or failing self-test.

[0055] Step S13: Determine the key performance values ​​of the preset key performance using data analysis tools and based on software and hardware operation data.

[0056] In this embodiment, the software operation data includes CPU utilization, memory usage, data transmission latency, and instruction response time; the hardware operation data includes the rack movement speed and the recognition status of data acquisition sensors in the simulation hardware device.

[0057] In this embodiment, the preset key performance indicators corresponding to the software operation data include peak and average CPU utilization, peak and average memory usage, average and maximum data transmission latency, average and maximum instruction response time, and data loss rate; the preset key performance indicators corresponding to the hardware operation data include the recognition success rate of the data acquisition sensors in the simulated hardware device.

[0058] It should be noted that the software operation data, hardware operation data, and corresponding preset key performance parameters can also be adjusted according to the actual situation.

[0059] It should be noted that software and hardware operation data require preprocessing before use. Specifically, data preprocessing involves using data analysis tools to preprocess the stored test data, including data cleaning (removing outliers and missing values, such as instantaneous data jumps caused by network fluctuations), data standardization (converting data of different magnitudes to a uniform magnitude, such as converting CPU utilization and memory usage into percentages of 0-100%), and data classification (classifying data according to test scenarios and data types, such as software performance data under normal load scenarios and simulated hardware operation data under fault scenarios).

[0060] In this embodiment, before determining the key performance values ​​of the preset key performance through data analysis tools and based on software operation data and hardware operation data, the method further includes: classifying and storing the software operation data and hardware operation data in data storage software according to the timestamps bound to the software operation data and hardware operation data.

[0061] It's important to note that during load testing, the simulation hardware's operational data and the software system's performance data are recorded in real-time via data acquisition terminals and a data monitoring platform. All test data is categorized by timestamp and stored in data storage software (such as MySQL (My Structured Query Language) or MongoDB (MongoDatabase)) to ensure data integrity and traceability. It's crucial to understand that timestamps are used for subsequent classification and problem analysis. Different times simply represent the state of the device at a specific moment. For example, scenario phase backtracking allows for precise extraction of all data from peak load periods or fault injection phases for independent phase analysis.

[0062] Step S14: Using data analysis tools and based on key performance values, determine the target key performance that has a performance bottleneck in the current scenario, and trigger the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reason.

[0063] In this embodiment, the step of determining the target key performance that has a performance bottleneck in the current scenario based on the key performance value, and triggering the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reason includes: determining the preset key performance with a key performance value not less than the corresponding preset performance threshold as the target key performance that has a performance bottleneck in the current scenario; and triggering the bottleneck location operation by combining software operation data and hardware operation data to determine the bottleneck location of the target key performance and output the corresponding bottleneck reason.

[0064] It should be noted that during performance index analysis, key performance indicators (key performance values) are calculated for the preprocessed test data and compared with preset performance thresholds (set according to the actual needs of the garment manufacturing production line, such as data transmission latency ≤ 500ms, instruction response time ≤ 1s, and sensor recognition success rate ≥ 99.5%): 1. Software performance index analysis: including peak and average CPU utilization, peak and average memory usage, average and maximum data transmission latency, average and maximum instruction response time, and data loss rate (amount of lost data / total amount of data); if any index exceeds the preset threshold, such as the maximum data transmission latency reaching 800ms under peak load scenarios, it is determined that the software system has a performance bottleneck in that scenario; 2. Hardware operation index analysis: including the stability of the intelligent hanger's moving speed (speed fluctuation range / average speed), sensor recognition success rate, and fault equipment handling efficiency (time from the occurrence of a fault to isolation of the faulty equipment); if the simulated hardware index is abnormal, such as the sensor recognition success rate being lower than 98%, further analysis is needed to determine whether it is caused by software data processing errors (such as data parsing deviation).

[0065] It should be noted that the correlation analysis method for bottleneck location establishes a mapping relationship between software performance indicators (such as CPU utilization) and hardware simulation data (such as sensor data volume) to achieve automatic correlation determination of bottleneck causes.

[0066] It should be noted that for metrics exceeding performance thresholds, correlation analysis should be performed using both software and simulation hardware data (e.g., using native algorithms in Pandas (Python Data Analysis Library) such as merge and join to create data associations), and Matplotlib (Mathematical Plotting...). The library (a mathematical plotting library) is used to draw line graphs and other graphs from collected data, and to trace changes and trends in CPU, memory, and I / O (Input / Output) for trend comparison. It accurately pinpoints bottlenecks and their causes: 1. If the software system's CPU usage is consistently too high (e.g., exceeding 90%), and data transmission latency increases, while the simulated hardware device's operating data is normal (e.g., stable rack movement speed, normal sensor recognition), then the bottleneck is determined to be the software's data processing module (e.g., inefficient data parsing algorithm); 2. If the data loss rate is high, and the simulated hardware device's network connection status data shows frequent disconnections (e.g., fluctuating network signal strength of the gateway device), then the bottleneck is determined to be the software's data transmission module (e.g., poor communication protocol compatibility) or the hardware's network device (e.g., insufficient gateway performance); 3. If the overall system performance drops significantly under fault scenarios, and the software's exception handling log shows a large amount of unresponsive error data, then the bottleneck is determined to be the software's exception handling module (e.g., incomplete fault handling logic).

[0067] In this embodiment, the step of determining the target key performance that has a performance bottleneck in the current scenario based on the key performance value, and triggering the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck cause, includes: statistically analyzing the key performance value of the preset key performance according to the time variable within a preset time range; if the change meets the preset fault condition corresponding to the preset key performance, then the preset key performance is taken as the target key performance that has a performance bottleneck in the current scenario; and the preset location and cause corresponding to the target key performance are taken as the bottleneck location and bottleneck cause of the preset key performance.

[0068] It's important to note that performance analysis isn't limited to threshold methods; other approaches can also be used: 1. Introducing time-series pattern recognition: Performance trend analysis – This involves not only examining the instantaneous peak values ​​of metrics but also analyzing their trends throughout the entire load testing period. For example, observing whether memory usage slowly increases as the load testing time progresses (signs of memory leaks), or whether CPU usage exhibits periodic fluctuations under sustained high load (potentially related to background garbage collection or scheduled tasks). 2. Pre- and post-event comparative analysis: Within the time window before and after a fault injection or load change, perform fine-grained (e.g., second-level) comparisons of key performance indicators. This clearly reveals the system's response to disturbances and recovery time.

[0069] It should be noted that, based on the performance analysis results, a performance stress test report for the IoT-based garment hanging system can be generated. The report includes: test environment configuration (number of simulation hardware devices, software version, scene parameters), an overview of the test process in each scene, a comparison table of key performance indicators (actual test values ​​and preset thresholds), bottleneck location and cause analysis results, and data visualization charts (such as software CPU usage change curves in different scenes and bar charts comparing the recognition success rates of simulation hardware sensors).

[0070] It should be noted that corresponding optimization suggestions can be proposed based on the performance analysis results: For the identified software bottlenecks, and considering the actual needs of the garment manufacturing production line, specific optimization suggestions can be made, such as: if the data processing module has a bottleneck, it is recommended to optimize the data parsing algorithm (e.g., adopt a distributed computing architecture) and add a software caching mechanism (to reduce redundant data processing); if the data transmission module has a bottleneck, it is recommended to optimize the IoT communication protocol and add data compression functionality (to reduce data packet size); if the exception handling module has a bottleneck, it is recommended to improve the fault handling logic (e.g., add a fault early warning mechanism) and optimize the equipment isolation algorithm (to shorten fault handling time).

[0071] In summary, the beneficial effects of this application are as follows: 1. Enables collaborative testing of software and simulation hardware: By building a software and hardware testing environment consistent with actual scenarios, the collaborative working process of software and simulation hardware in a garment manufacturing production line is simulated, avoiding test result deviations caused by testing a single software or hardware, and comprehensively reflecting the actual performance of the system; 2. Comprehensive scenario simulation: Various dynamic scenarios such as normal load, peak load, and fault load are set, covering common scenarios and extreme situations in the garment manufacturing process, which can detect the performance stability of the system under different conditions, improving the comprehensiveness and reliability of the test; 3. In-depth data analysis: Through data preprocessing, performance index analysis, and correlation analysis, the specific location and cause of software bottlenecks can be accurately located, avoiding the shortcomings of traditional testing schemes that can only find problems but cannot locate the root cause, providing accurate data support for system optimization; 4. High practicality: This method can flexibly adjust the number of simulation hardware devices and scenario parameters according to the testing needs of garment industry equipment providers, and is applicable to IoT garment hanging system testing of different scales. Moreover, the generated test reports and optimization suggestions can directly guide the subsequent optimization of the system, significantly improving the system's operating efficiency and stability, and reducing the risk of equipment failure in the garment manufacturing process.

[0072] As can be seen, this application monitors the current scenario of an IoT-based garment hanging system; the current scenario includes a target load scenario and a target comprehensive scenario; the target load scenario includes a preset normal load scenario and a preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and a temporary change scenario; a custom load testing tool records the hardware operation data and software operation data corresponding to the current scenario when the software and hardware environments work together; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the garment hanging software collected by the data monitoring platform; the data analysis tool determines the key performance values ​​of preset key performance based on the software operation data and hardware operation data; the data analysis tool determines the target key performance with performance bottlenecks in the current scenario based on the key performance values, and triggers a bottleneck location operation to determine the bottleneck location of the target key performance with performance bottlenecks, and outputs the corresponding bottleneck reasons. Therefore, this application records software and hardware operation data when the software and hardware environments work together, rather than testing only the software system or hardware device individually. It takes into account the mutual influence between software and hardware working together, which leads to a large deviation between the stress test results and the actual application scenario. The mutual influence between software and hardware working together makes the stress test results closer to the actual application scenario. In addition, this application fully considers the target load scenario and the target comprehensive scenario, which can comprehensively detect the system's performance under extreme conditions. For target key performance with performance bottlenecks, it is also necessary to analyze the bottleneck location and the cause of the bottleneck in order to achieve in-depth performance analysis.

[0073] Accordingly, this application also discloses a performance stress testing device for an IoT-based garment hanging system, see [link to relevant documentation]. Figure 2 As shown, the device includes:

[0074] Scene monitoring module 11 is used to monitor the current scene of the IoT-based garment hanging system; the current scene includes a target load scene and a target comprehensive scene; the target load scene includes a preset normal load scene and a preset peak load scene; the target comprehensive scene is a combined scene of the target load scene and the temporary change scene;

[0075] The data recording module 12 is used to record the hardware operation data and software operation data corresponding to the current scenario when the software environment and hardware environment work together through a custom stress testing tool; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform.

[0076] The key performance value calculation module 13 is used to determine the key performance values ​​of preset key performances through data analysis tools and based on software operation data and hardware operation data.

[0077] The performance analysis module 14 is used to determine the target key performance that has a performance bottleneck in the current scenario by using data analysis tools and based on key performance values, and to trigger the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reasons.

[0078] The more specific working process of each of the above modules can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0079] As can be seen, this application monitors the current scenario of an IoT-based garment hanging system; the current scenario includes a target load scenario and a target comprehensive scenario; the target load scenario includes a preset normal load scenario and a preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and a temporary change scenario; a custom load testing tool records the hardware operation data and software operation data corresponding to the current scenario when the software and hardware environments work together; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the garment hanging software collected by the data monitoring platform; the data analysis tool determines the key performance values ​​of preset key performance based on the software operation data and hardware operation data; the data analysis tool determines the target key performance with performance bottlenecks in the current scenario based on the key performance values, and triggers a bottleneck location operation to determine the bottleneck location of the target key performance with performance bottlenecks, and outputs the corresponding bottleneck reasons. Therefore, this application records software and hardware operation data when the software and hardware environments work together, rather than testing only the software system or hardware device individually. It takes into account the mutual influence between software and hardware working together, which leads to a large deviation between the stress test results and the actual application scenario. The mutual influence between software and hardware working together makes the stress test results closer to the actual application scenario. In addition, this application fully considers the target load scenario and the target comprehensive scenario, which can comprehensively detect the system's performance under extreme conditions. For target key performance with performance bottlenecks, it is also necessary to analyze the bottleneck location and the cause of the bottleneck in order to achieve in-depth performance analysis.

[0080] Furthermore, embodiments of this application also provide an electronic device. Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0081] Figure 3This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the performance stress testing method for the IoT-based clothing hanging system disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be a computer.

[0082] In this embodiment, the power supply 26 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 25 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 24 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0083] Furthermore, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored thereon may include computer programs 221, and the storage method may be temporary storage or permanent storage. The computer programs 221 may include, in addition to computer programs capable of performing the performance stress testing method for the IoT-based garment hanging system executed by the electronic device 20 as disclosed in any of the foregoing embodiments, computer programs capable of performing other specific tasks.

[0084] Furthermore, embodiments of this application also disclose a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned performance stress testing method for an IoT-based garment hanging system.

[0085] The specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0086] The various embodiments in this application are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. For the same or similar parts between the various embodiments, refer to each other. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0087] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0089] Finally, it should be noted that in this document, relational terms such as "first" and "first" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The above provides a detailed description of the performance stress testing method, apparatus, equipment, and storage medium for an IoT-based garment hanging system provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A performance stress testing method for an IoT-based garment hanging system, characterized in that, include: Monitor the current scene of the IoT-based garment hanging system; The current scenario includes the target load scenario and the target comprehensive scenario; The target load scenario includes a preset normal load scenario and a preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and the temporary change scenario. A custom stress testing tool is used to record the hardware and software operation data corresponding to the current scenario when the software and hardware environments work together. The hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform. Key performance values ​​for preset critical performance are determined using data analysis tools and based on software and hardware operating data. By using data analysis tools and based on key performance values, the target key performance that has a performance bottleneck in the current scenario is identified, and a bottleneck location operation is triggered to determine the location of the bottleneck and output the corresponding bottleneck cause.

2. The performance stress testing method for the IoT-based garment hanging system according to claim 1, characterized in that, The process involves determining the target key performance indicator that represents a performance bottleneck in the current scenario based on key performance values, triggering a bottleneck location operation to pinpoint the bottleneck location, and outputting the corresponding bottleneck cause, including: The key performance characteristics that are not less than the corresponding preset performance threshold are defined as the target key performance characteristics that have performance bottlenecks in the current scenario. By combining software and hardware operation data, a bottleneck location operation is triggered to determine the location of the bottleneck where the target key performance is constrained, and the corresponding bottleneck cause is output.

3. The performance stress testing method for the IoT-based garment hanging system according to claim 1, characterized in that, The process involves determining the target key performance indicator that represents a performance bottleneck in the current scenario based on key performance values, triggering a bottleneck location operation to pinpoint the bottleneck location, and outputting the corresponding bottleneck cause, including: The key performance values ​​of preset key performance indicators are statistically analyzed within a preset time range based on the time-varying variables. If the changes meet the preset fault conditions corresponding to the preset key performance, then the preset key performance will be taken as the target key performance that has a performance bottleneck in the current scenario. The preset locations and reasons corresponding to the target key performance are used as the bottleneck locations and reasons for the performance bottlenecks of the preset key performance.

4. The performance stress testing method for the IoT-based garment hanging system according to claim 1, characterized in that, The temporary change scenarios include equipment failure scenarios, network fluctuation scenarios, production order switching scenarios, and load change scenarios; Accordingly, when the temporary change scenario is a device failure scenario, the hardware operation data corresponding to the target number of racks in the simulation hardware device is set to empty or erroneous data; when the temporary change scenario is a network fluctuation scenario, random delays or random packet loss are periodically injected into a specified proportion of the simulation gateways; when the temporary change scenario is a production order switching scenario, the virtual cut piece type carried by all or some preset racks in the simulation hardware device is dynamically switched; when the temporary change scenario is a load change scenario, the usage of racks in the simulation hardware device is adjusted.

5. The performance stress testing method for the IoT-based garment hanging system according to claim 1, characterized in that, The software operation data includes CPU utilization, memory usage, data transmission latency, and instruction response time; The hardware operation data includes the moving speed of the mounting bracket and the identification status of the data acquisition sensors in the simulation hardware device.

6. The performance stress testing method for the IoT-based garment hanging system according to claim 1, characterized in that, The preset key performance parameters corresponding to the software operation data include peak and average CPU utilization, peak and average memory usage, average and maximum data transmission latency, average and maximum instruction response time, and data loss rate. The preset key performance parameters corresponding to the hardware operation data include the recognition success rate of the data acquisition sensors in the simulated hardware device.

7. The performance stress testing method for an IoT-based garment hanging system according to any one of claims 1 to 6, characterized in that, Before determining the key performance values ​​of the preset key performance based on software operation data and hardware operation data, the process also includes: Based on the timestamps associated with the software and hardware operation data, the software and hardware operation data are categorized and stored in the data storage software.

8. A performance stress testing device for a clothing hanging system based on the Internet of Things, characterized in that, include: The scene monitoring module is used to monitor the current scene of the IoT-based garment hanging system; The current scenario includes the target load scenario and the target comprehensive scenario; The target load scenario includes a preset normal load scenario and a preset peak load scenario; the target comprehensive scenario is a combined scenario of the target load scenario and the temporary change scenario. The data recording module is used to record the hardware operation data and software operation data corresponding to the current scenario when the software environment and hardware environment work together, using a custom stress testing tool; the hardware operation data is the hardware operation data of the simulated hardware device collected by the data acquisition sensor, and the software operation data is the software operation data of the clothing hanging software collected by the data monitoring platform. The key performance value calculation module is used to determine the key performance values ​​of preset key performances through data analysis tools and based on software operation data and hardware operation data. The performance analysis module is used to identify the target key performance that has a performance bottleneck in the current scenario by using data analysis tools and based on key performance values, and to trigger the bottleneck location operation to determine the bottleneck location of the target key performance and output the corresponding bottleneck reasons.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the performance stress testing method for an IoT-based garment hanging system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the performance stress testing method for an IoT-based garment hanging system as described in any one of claims 1 to 7.

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