Cloud-based intelligent analysis methods and systems for industrial big data

Abstract

This invention belongs to the field of industrial big data and involves data analysis technology. It addresses the problem in existing technologies that cannot build targeted data collection networks for different types of industrial workshops. Specifically, it is a cloud-based intelligent analysis method and system for industrial big data, including a data collection end, a processing end, and an analysis end. The data collection end includes a data collection network construction module and a data acquisition server. The processing end includes a data preprocessing module and a cloud computing performance testing module. The analysis end includes a cloud computing performance evaluation module and an optimization analysis module. This invention builds data collection networks for industrial systems by statistically analyzing various data collection tendency parameters of production workshops. By combining these parameters with those of all production workshops, a targeted data collection network can be built for each workshop. Production workshops with different data collection tendencies can build data collection networks that match their needs, improving the efficiency of data collection and transmission.

Description

Technical Field

[0001] This invention belongs to the field of industrial big data and involves data analysis technology, specifically a cloud-based intelligent analysis method and system for industrial big data. Background Technology

[0002] Industrial big data refers to the collective term for all types of data and related technologies and applications generated in the industrial field around the intelligent manufacturing model, encompassing all stages of the entire product lifecycle, from customer needs to sales, orders, planning, R&D, design, manufacturing, procurement, supply, inventory, shipping and delivery, after-sales service, operation and maintenance, scrapping and recycling remanufacturing.

[0003] For example, the invention patent with announcement number CN118626555A discloses an industrial big data analysis method based on Spark. It adopts a distributed storage approach, which, while ensuring high data reliability and availability, leverages Spark's advantages in parallel computing and in-memory computing to significantly improve the speed and efficiency of data processing.

[0004] However, this industrial big data analysis method cannot build targeted data collection networks for different types of industrial workshops, resulting in poor matching between data collection and transmission methods and workshop types and functions, and low data collection and transmission efficiency. At the same time, it cannot evaluate and optimize the computing performance of cloud computing platforms, resulting in the inability to improve cloud computing performance. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud computing-based intelligent analysis method and system for industrial big data, which solves the problem in the existing technology that it is impossible to build a targeted data collection network for different types of industrial workshops.

[0006] The technical problem to be solved by this invention is: how to provide a cloud computing-based intelligent analysis method and system for industrial big data that can be used to build targeted data collection networks for different types of industrial workshops.

[0007] The objective of this invention can be achieved through the following technical solution: an industrial big data intelligent analysis system based on cloud computing, comprising a data acquisition end, a processing end, and an analysis end. The data acquisition end includes a data acquisition network construction module and a data acquisition server. The processing end includes a data preprocessing module and a cloud computing performance testing module. The analysis end includes a cloud computing performance evaluation module and an optimization analysis module.

[0008] The data acquisition network construction module is used to build a data acquisition network for industrial systems: the production workshop of the industrial system is marked as the analysis object, the real-time demand value, equipment type value and acquisition accuracy value of the analysis object are obtained, and sensor acquisition network, communication gateway acquisition network and network communication acquisition network are built for the analysis object based on the real-time demand value, equipment type value and acquisition accuracy value.

[0009] The data acquisition server is used to receive industrial monitoring data from the acquisition network and send it to the processing terminal;

[0010] The data preprocessing module is used to preprocess industrial monitoring data: the preprocessing process includes data cleaning, data integration, data transformation, and data cleanup;

[0011] The cloud computing performance detection module is used to perform performance detection and analysis on the cloud computing platform: generate an analysis period, set several detection time points with equal time intervals within the analysis period, acquire the cloud computing platform's occupancy data ZY, response data XY, and throughput data TT at the detection time points, perform numerical calculations to obtain the cloud computing platform's performance coefficient XN, and send the cloud computing platform's performance coefficient XN to the analysis terminal.

[0012] The cloud computing performance evaluation module is used to perform performance evaluation and analysis on the cloud computing platform;

[0013] The optimization analysis module is used to perform optimization analysis on the cloud computing platform.

[0014] Furthermore, the specific process of building the acquisition network for the analysis objects includes: arranging all analysis objects in ascending order of real-time demand values ​​to obtain a real-time demand sequence; arranging all analysis objects in descending order of equipment type values ​​to obtain a equipment type sequence; arranging all analysis objects in ascending order of acquisition accuracy values ​​to obtain an acquisition accuracy sequence; marking the sequence number of the analysis object in the real-time demand sequence, equipment type sequence, and acquisition accuracy sequence as the real-time priority value, equipment priority value, and accuracy priority value, respectively; marking the sequence corresponding to the smallest value among the real-time priority value, equipment priority value, and accuracy priority value of the analysis object as the construction sequence; and marking the acquisition network construction type of the analysis object according to the construction sequence.

[0015] Furthermore, the specific process of marking the data acquisition network construction type of the analysis object includes: if the construction sequence is a real-time demand sequence, then a network communication data acquisition network is built for the analysis object; if the construction sequence is a device type sequence, then a communication gateway data acquisition network is built for the analysis object; if the construction sequence is a data acquisition accuracy sequence, then a sensor data acquisition network is built for the analysis object; when the real-time priority value, device priority value, and accuracy priority value are the same, the order of priority of data acquisition network construction is sensor data acquisition network - communication gateway data acquisition network - network communication data acquisition network.

[0016] Furthermore, the CPU utilization data ZY represents the CPU utilization rate of the cloud computing platform, the response data XY represents the time from the request to the result returned from the cloud computing platform, and the throughput data TT represents the number of tasks completed by the cloud computing platform per unit time.

[0017] Furthermore, the specific process of the cloud computing performance evaluation module in performing performance evaluation and analysis on the cloud computing platform includes: at the end of the analysis period, numerically calculating the performance coefficient XN for all detection time points to obtain the performance evaluation value, and comparing the performance evaluation value with the preset performance evaluation threshold: if the performance evaluation value is greater than the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform meets the requirements during the analysis period; if the performance evaluation value is less than or equal to the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform does not meet the requirements during the analysis period, generating an optimization analysis signal and sending the optimization analysis signal to the optimization analysis module.

[0018] Furthermore, the specific process of the optimization analysis module to perform optimization analysis on the cloud computing platform includes: marking the M1 detection time points with the largest performance coefficient XN values ​​as analysis time points; marking the analysis objects corresponding to the data processing and computing tasks of the cloud computing platform at the analysis time points as optimization objects; marking the number of times the analysis object is marked as an optimization object as the optimization value of the analysis object; calculating the variance of the optimization values ​​of all analysis objects to obtain the performance optimization coefficient; and marking the computing performance optimization decision of the cloud computing platform through the performance optimization coefficient.

[0019] Furthermore, the specific process of marking the computing performance optimization decision of the cloud computing platform includes: comparing the performance optimization coefficient with the preset performance optimization threshold; if the performance optimization coefficient is less than the performance optimization threshold, a hardware optimization signal is generated and sent to the administrator's mobile terminal; if the performance optimization coefficient is greater than or equal to the performance optimization threshold, the M2 analysis objects with the largest optimization values ​​are marked as update objects, a collection update signal is generated and sent to the administrator's mobile terminal, and after receiving the collection update signal, the administrator updates the collection parameters, collection method and data transmission method in the update object.

[0020] This invention also proposes a cloud computing-based intelligent analysis method for industrial big data, comprising the following steps:

[0021] Step 1: Establish a data acquisition network for the industrial system;

[0022] Step 2: Receive industrial monitoring data from the data acquisition network through the data acquisition server and send it to the processing terminal;

[0023] Step 3: Preprocess the industrial monitoring data;

[0024] Step 4: Perform performance testing and analysis on the cloud computing platform;

[0025] Step 5: Perform performance evaluation and analysis on the cloud computing platform. If the computing performance does not meet the requirements, proceed to Step 6.

[0026] Step Six: Optimize and analyze the cloud computing platform.

[0027] The present invention has the following beneficial effects:

[0028] The data acquisition network building module can be used to build data acquisition networks for industrial systems. It can statistically analyze various data acquisition tendency parameters of production workshops, and then combine the data acquisition tendency parameters of all production workshops to build targeted data acquisition networks for each production workshop. Production workshops with different data acquisition tendencies can build data acquisition networks that match their needs, thereby improving the efficiency of data acquisition and transmission.

[0029] The cloud computing performance testing module can perform performance testing and analysis on the cloud computing platform, statistically calculate various computing performance parameters of the cloud computing platform to obtain performance coefficients, provide feedback on the computing performance of the cloud computing platform based on the performance coefficients, evaluate its computing performance in conjunction with the performance evaluation module, and perform optimization analysis when necessary.

[0030] The optimization analysis module can perform optimization analysis on the cloud computing platform, filter optimization objects based on performance coefficients, calculate the optimization values ​​of all optimization objects to obtain performance optimization coefficients, and mark the optimization decisions of the cloud computing platform based on the performance optimization coefficients, thereby improving the efficiency of cloud computing platform computing performance optimization. Attached Figure Description

[0031] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0033] Figure 2 This is a schematic diagram of the acquisition and transmission of industrial big data in Embodiment 1 of the present invention;

[0034] Figure 3 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0036] Example 1: As Figure 1 As shown, the cloud-based industrial big data intelligent analysis system includes a data acquisition end, a processing end, and an analysis end. The data acquisition end includes a data acquisition network construction module and a data acquisition server. The processing end includes a data preprocessing module and a cloud computing performance testing module. The analysis end includes a cloud computing performance evaluation module and an optimization analysis module.

[0037] The data acquisition network construction module is used to build a data acquisition network for industrial systems: the production workshop of the industrial system is marked as the analysis object, and the real-time demand value, equipment type value and acquisition accuracy value of the analysis object are obtained. The real-time demand value of the analysis object is the minimum value of the transmission delay requirement of all equipment acquisition parameters within the analysis object. The equipment type value of the analysis object is the number of all equipment types within the analysis object. The acquisition accuracy value of the analysis object is the minimum value of the acquisition accuracy requirement of all equipment acquisition parameters within the analysis object.

[0038] All analysis objects are arranged in ascending order of real-time demand value to obtain the real-time demand sequence; all analysis objects are arranged in descending order of equipment type value to obtain the equipment type sequence; and all analysis objects are arranged in ascending order of acquisition accuracy value to obtain the acquisition accuracy sequence. The sequence numbers of the analysis objects in the real-time demand sequence, equipment type sequence, and acquisition accuracy sequence are marked as real-time priority value, equipment priority value, and accuracy priority value, respectively. The sequence corresponding to the smallest real-time priority value, equipment priority value, and accuracy priority value of the analysis object is marked as the construction sequence.

[0039] If the sequence is a real-time demand sequence, then a network communication acquisition network is built for the analysis object; if the sequence is a device type sequence, then a communication gateway acquisition network is built for the analysis object; if the sequence is an acquisition accuracy sequence, then a sensor acquisition network is built for the analysis object.

[0040] When real-time priority, device priority, and accuracy priority values ​​are the same, the priority order for building the acquisition network is sensor acquisition network - communication gateway acquisition network - network communication acquisition network. Statistics are compiled on various data acquisition tendency parameters of the production workshops, and then, based on these parameters, a targeted acquisition network is built for each workshop. Production workshops with different data acquisition tendencies can build acquisition networks that match their needs, improving the efficiency of data acquisition and transmission.

[0041] like Figure 2 As shown, the data acquisition server is used to receive industrial monitoring data from the acquisition network and send it to the processing end: When the acquisition network of the object being analyzed is a network communication acquisition network, the data acquisition server directly acquires industrial monitoring data and forwards it via Ethernet or serial communication; when the acquisition network of the object being analyzed is a communication gateway acquisition network, the industrial communication gateway performs message conversion between various network protocols, converting different types of device communication protocols into a standard protocol, and the data acquisition server directly acquires industrial monitoring data and forwards it via the industrial communication gateway; when the acquisition network of the object being analyzed is a sensor acquisition network, the sensors measure and acquire various physical quantities and convert them into electrical signals, and the data acquisition server directly acquires industrial monitoring data and forwards it via wireless communication.

[0042] The data preprocessing module is used to preprocess industrial monitoring data. The preprocessing process includes data cleaning, data integration, and data transformation. Data cleaning includes handling missing values ​​and smoothing noisy data. Methods for handling missing values ​​include ignoring them, manually filling them in, and filling them with global variables or averages. Noisy data can be smoothed using methods such as binning, clustering, and regression. Data integration integrates data from multiple data sources into a consistent storage, solving data inconsistency and redundancy problems. Data transformation normalizes the data, eliminates redundant attributes, and projects the data into smaller subspaces for more efficient processing and analysis.

[0043] The cloud computing performance testing module is used to perform performance testing and analysis on the cloud computing platform: It generates an analysis period, sets several equally spaced testing time points within the analysis period, and acquires the cloud computing platform's CPU utilization data ZY, response data XY, and throughput data TT at each testing time point. ZY represents the cloud computing platform's CPU utilization rate, XY represents the time from request to result return from the cloud computing platform, and TT represents the number of tasks completed by the cloud computing platform per unit time. The performance coefficient XN of the cloud computing platform is obtained using the formula XN = w1 × TT - w2 × ZY - w3 × XY, where w1, w2, and w3 are proportionality coefficients, and w1 > w2 > w3 > 1. The performance coefficient XN of the cloud computing platform is then sent to the analysis terminal.

[0044] The cloud computing performance evaluation module is used to perform performance evaluation and analysis on the cloud computing platform. At the end of the analysis period, the performance coefficient XN of all detection time points is numerically calculated to obtain the performance evaluation value. The performance evaluation value is compared with the preset performance evaluation threshold. If the performance evaluation value is greater than the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform meets the requirements during the analysis period. If the performance evaluation value is less than or equal to the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform does not meet the requirements during the analysis period. An optimization analysis signal is generated and sent to the optimization analysis module. The module also statistically calculates and obtains performance coefficients for various computing performance parameters of the cloud computing platform. Based on the performance coefficients, the module provides feedback on the computing performance of the cloud computing platform. Combined with the performance evaluation module, the module evaluates the computing performance and performs optimization analysis when necessary.

[0045] The optimization analysis module is used to perform optimization analysis on the cloud computing platform. It marks the M1 detection time points with the largest performance coefficient XN as analysis time points, marks the analysis objects corresponding to the data processing tasks of the cloud computing platform at the analysis time points as optimization objects, and marks the number of times an analysis object is marked as an optimization object as its optimization value. The variance of the optimization values ​​of all analysis objects is calculated to obtain the performance optimization coefficient. This performance optimization coefficient is compared with a preset performance optimization threshold. If the performance optimization coefficient is less than the performance optimization threshold, a hardware optimization signal is generated and sent to the administrator's mobile terminal. If the performance optimization coefficient is greater than or equal to the performance optimization threshold, the M2 analysis objects with the largest optimization values ​​are marked as update objects, a collection update signal is generated and sent to the administrator's mobile terminal. Upon receiving the collection update signal, the administrator updates the collection parameters, collection methods, and data transmission methods within the update objects. Optimization objects are filtered based on the performance coefficient, and then the optimization values ​​of all optimization objects are calculated to obtain the performance optimization coefficient. The performance optimization coefficient is used to mark the optimization decisions of the cloud computing platform, improving the efficiency of cloud computing platform performance optimization.

[0046] Example 2: Figure 3 As shown, this invention also proposes a cloud computing-based intelligent analysis method for industrial big data, comprising the following steps:

[0047] Step 1: Establish a data acquisition network for the industrial system: Based on real-time demand values, equipment type values, and acquisition accuracy values, establish a sensor acquisition network, a communication gateway acquisition network, and a network communication acquisition network.

[0048] Step 2: Receive industrial monitoring data from the data acquisition network through the data acquisition server and send it to the processing terminal;

[0049] Step 3: Preprocessing industrial monitoring data: The preprocessing process includes data cleaning, data integration, and data transformation;

[0050] Step 4: Perform performance testing and analysis on the cloud computing platform: Generate an analysis period, set several test time points with equal time intervals within the analysis period, and obtain the performance coefficient XN of the cloud computing platform at each test time point;

[0051] Step 5: Perform performance evaluation and analysis on the cloud computing platform. If the computing performance does not meet the requirements, proceed to Step 6.

[0052] Step Six: Optimize and analyze the cloud computing platform.

[0053] In operation, this invention establishes a sensor acquisition network, a communication gateway acquisition network, and a network communication acquisition network based on real-time demand values, equipment type values, and acquisition accuracy values ​​as the analysis objects. It receives industrial monitoring data from the acquisition network through a data acquisition server and sends it to the processing end. The industrial monitoring data undergoes preprocessing, including data cleaning, data integration, and data transformation. An analysis cycle is generated, with several equally spaced detection time points set within the analysis cycle. At each detection time point, the performance coefficient XN of the cloud computing platform is obtained. The cloud computing platform's performance is evaluated and analyzed; if the computing performance does not meet the requirements, optimization analysis is performed on the cloud computing platform.

[0054] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0055] The above formulas are all derived from software simulation using a large amount of data, and are selected to be close to the true values. The coefficients in the formulas are set by those skilled in the art based on the actual situation; for example: formula XN=w1×TT-w2×ZY-w3×XY; those skilled in the art collect multiple sets of sample data and set corresponding performance optimization coefficients for each set of sample data; substitute the set performance optimization coefficients and the collected sample data into the formulas, any three formulas form a system of three linear equations, filter the calculated coefficients and take the average, and obtain the values ​​of w1, w2 and w3 as 4.25, 2.84 and 2.33 respectively;

[0056] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the performance optimization coefficient initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantized value, such as the performance optimization coefficient being proportional to the throughput data value.

[0057] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cloud-based industrial big data intelligent analysis system, characterized in that: It includes a data acquisition end, a processing end, and an analysis end. The data acquisition end includes a data acquisition network construction module and a data acquisition server. The processing end includes a data preprocessing module and a cloud computing performance testing module. The analysis end includes a cloud computing performance evaluation module and an optimization analysis module. The data acquisition network construction module marks the production workshop of the industrial system as the analysis object, obtains the real-time demand value, equipment type value and acquisition accuracy value of the analysis object, and builds a sensor acquisition network, a communication gateway acquisition network and a network communication acquisition network based on the real-time demand value, equipment type value and acquisition accuracy value of the analysis object. The data acquisition server receives industrial monitoring data from the acquisition network and sends it to the processing terminal. The data preprocessing module preprocesses the industrial monitoring data sent by the data acquisition server. The cloud computing performance detection module generates an analysis period and sets several detection time points with equal time intervals within the analysis period. At each detection time point, it acquires the cloud computing platform's usage data ZY, response data XY, and throughput data TT and performs numerical calculations to obtain the cloud computing platform's performance coefficient XN. The cloud computing platform's performance coefficient XN is then sent to the analysis terminal. The cloud computing performance evaluation module performs performance evaluation and analysis on the cloud computing platform based on the performance coefficient XN at all detection time points. The specific process of the cloud computing performance evaluation module in performing performance evaluation and analysis on the cloud computing platform includes: at the end of the analysis period, the performance coefficient XN of all detection time points is numerically calculated to obtain the performance evaluation value, and the performance evaluation value is compared with the preset performance evaluation threshold: if the performance evaluation value is greater than the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform meets the requirements during the analysis period; if the performance evaluation value is less than or equal to the performance evaluation threshold, it is determined that the computing performance of the cloud computing platform does not meet the requirements during the analysis period, an optimization analysis signal is generated, and the optimization analysis signal is sent to the optimization analysis module. The optimization analysis module performs optimization analysis when the computing performance of the cloud computing platform does not meet the requirements. The specific process of the optimization analysis module for optimizing the cloud computing platform includes: marking the M1 detection time points with the largest performance coefficient XN values ​​as analysis time points; marking the analysis objects corresponding to the data processing and computing tasks of the cloud computing platform at the analysis time points as optimization objects; marking the number of times the analysis object is marked as an optimization object as the optimization value of the analysis object; calculating the variance of the optimization values ​​of all analysis objects to obtain the performance optimization coefficient; and marking the computing performance optimization decision of the cloud computing platform through the performance optimization coefficient.

2. The cloud-based industrial big data intelligent analysis system according to claim 1, characterized in that, The specific process of building a data acquisition network for the analysis objects includes: arranging all analysis objects in ascending order of real-time demand values ​​to obtain a real-time demand sequence; arranging all analysis objects in descending order of device type values ​​to obtain a device type sequence; and arranging all analysis objects in ascending order of data acquisition accuracy values ​​to obtain a data acquisition accuracy sequence. The sequence numbers of the analysis objects in the real-time demand sequence, device type sequence, and data acquisition accuracy sequence are respectively marked as real-time priority values, device priority values, and accuracy priority values. The sequence corresponding to the smallest real-time priority value, device priority value, and accuracy priority value of the analysis object is marked as the construction sequence. The data acquisition network construction type of the analysis object is then marked according to the construction sequence.

3. The cloud-based industrial big data intelligent analysis system according to claim 2, characterized in that, The specific process of marking the data acquisition network construction type of the analysis object includes: if the construction sequence is a real-time demand sequence, then a network communication data acquisition network is built for the analysis object; if the construction sequence is a device type sequence, then a communication gateway data acquisition network is built for the analysis object; if the construction sequence is a data acquisition accuracy sequence, then a sensor data acquisition network is built for the analysis object; when the real-time priority value, device priority value, and accuracy priority value are the same, the order of priority of data acquisition network construction is sensor data acquisition network - communication gateway data acquisition network - network communication data acquisition network.

4. The cloud computing-based industrial big data intelligent analysis system according to claim 3, characterized in that, The CPU utilization data ZY represents the CPU utilization rate of the cloud computing platform, the response data XY represents the time from when the request is sent to when the result is returned from the cloud computing platform, and the throughput data TT represents the number of tasks completed by the cloud computing platform per unit time.

5. The cloud-based industrial big data intelligent analysis system according to claim 1, characterized in that, The specific process of marking the computing performance optimization decision of the cloud computing platform includes: comparing the performance optimization coefficient with the preset performance optimization threshold; if the performance optimization coefficient is less than the performance optimization threshold, a hardware optimization signal is generated and sent to the administrator's mobile terminal; if the performance optimization coefficient is greater than or equal to the performance optimization threshold, the M2 analysis objects with the largest optimization values ​​are marked as update objects, a collection update signal is generated and sent to the administrator's mobile terminal, and after receiving the collection update signal, the administrator updates the collection parameters, collection method and data transmission method in the update object.

6. A cloud-based intelligent analysis method for industrial big data, employing the cloud-based intelligent analysis system for industrial big data as described in claim 1, characterized in that, Includes the following steps: Step 1: Establish a data acquisition network for the industrial system; Step 2: Receive industrial monitoring data from the data acquisition network through the data acquisition server and send it to the processing terminal; Step 3: Preprocess the industrial monitoring data; Step 4: Perform performance testing and analysis on the cloud computing platform; Step 5: Perform performance evaluation and analysis on the cloud computing platform. If the computing performance does not meet the requirements, proceed to Step 6. Step Six: Optimize and analyze the cloud computing platform.

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