A server intelligent error reporting system

Abstract

The application relates to the technical field of device management, in particular to a server intelligent error reporting system, which comprises a monitoring module for monitoring server operation key indicators; the monitoring module is internally and subordinately provided with a sub-module, which comprises a control unit for controlling the operation frequency of the monitoring module; and a storage unit for synchronously operating with the monitoring module, receiving the server operation key indicators monitored by the monitoring module, and storing the server operation key indicators; through the collection of the server operation key indicators and the reference of historical operation indicators, the busy weight of the server in each period is measured, the service state health is further predicted according to the server operation key indicators, the server health state prediction result is corrected in combination with the busy weight of the server in each period, the final correction result is used for judging the server state safety, and the daily operation management of the server can be predicted to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, and more specifically to a server intelligent error reporting system. Background Technology

[0002] Server management is crucial for ensuring the stable and efficient operation of servers. This includes hardware maintenance, such as monitoring equipment status and promptly handling faults; and software management, such as installing and updating system and applications. Simultaneously, it involves optimizing performance, allocating resources rationally, and ensuring data security to meet business needs and improve service quality.

[0003] Patent application number 202010110187.6 discloses a method for detecting errors in a server diagnostic system, characterized by the following steps: deploying a monitoring server and connecting the monitoring server to a website where the server diagnostic system publishes information; having the monitoring server acquire order information from the website every threshold time interval; analyzing the order information to calculate error information based on the order information, and determining the logical relationship between a first error threshold and a second error threshold in the monitoring server; issuing a warning message to the administrator in response to the logical relationship being such that the error information satisfies both the first and second error thresholds; and issuing a warning message to the administrator in response to the logical relationship being such that the error information satisfies either the first or the second error threshold.

[0004] The application aims to address the problem that "during server production, a system diagnostic process is required, during which batches of orders may report errors. Currently, the main methods for detecting batches of error orders are either through production line personnel or by system diagnostic engineers constantly checking websites that publish information about the server diagnostic system. Both methods require personnel to continuously monitor production line information, which is time-consuming and labor-intensive, and carries the risk of failing to detect problems in a timely manner."

[0005] However, during daily operation, the probability of server failures changes in real time due to changes in usage scenarios and users. If the server's operating status cannot be monitored in real time, it is impossible to formulate a series of predictive protection strategies in response to server failures.

[0006] To address this, a server intelligent error reporting system is proposed. Summary of the Invention

[0007] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a server intelligent error reporting system, which solves the technical problems mentioned in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A server intelligent error reporting system includes:

[0010] The monitoring module is used to monitor key server operation indicators. The monitoring module includes sub-modules, including: a control unit for controlling the operating frequency of the monitoring module; a storage unit for synchronously operating with the monitoring module, receiving and storing the key server operation indicators monitored by the monitoring module; an analysis module for uploading historical server operation indicators and analyzing the server's busy weight at different times based on these historical indicators; and a prediction module for receiving the key server operation indicators monitored by the monitoring module and predicting the server's health status based on these indicators. The prediction module includes sub-modules, including: a decision-making unit for determining the prediction accuracy of the prediction module's prediction of the server's health status. The system comprises the following modules: a retrieval unit (receiving prediction accuracy decision results from the decision-making unit, retrieving key server operation indicators based on these results, and forwarding them to the prediction module); a prediction module (receiving these key server operation indicators forwarded by the retrieval unit and predicting server health status); a correction module (receiving server security prediction results from the prediction module and the corresponding busy weights from the analysis module, and applying these busy weights to correct the server security prediction results); a judgment module (setting security judgment thresholds, synchronously acquiring corrected server security prediction results from the correction module, comparing the corrected prediction results with the security judgment thresholds, and determining whether the server has security issues); and a feedback module (receiving judgment results from the judgment module and outputting them).

[0011] The monitoring module is internally connected to a control unit via a wireless network. The monitoring module is also internally connected to a storage unit via a wireless network. The monitoring module is also internally connected to an analysis module and a prediction module via a wireless network. The prediction module is internally connected to a decision unit and a retrieval unit via a wireless network. The prediction module is also internally connected to the storage unit via a wireless network. The prediction module is also internally connected to a correction module and a judgment module via a wireless network. The judgment module is internally connected to a feedback module via a wireless network.

[0012] Furthermore, the key performance indicators of the server include: server CPU utilization, load, and temperature; server memory utilization and bandwidth utilization; server disk space utilization, I / O utilization, and read / write speed; server network bandwidth utilization, number of connections, latency, and packet loss rate.

[0013] Before the storage operation is performed in the storage unit, the key indicators of server operation are simultaneously marked with a monitoring timestamp. The storage unit distinguishes and stores the key indicators of server operation based on the timestamp of each key indicator.

[0014] Furthermore, the control unit is equipped with monitoring module operating frequency control logic. Based on this logic, the control unit controls the monitoring module to operate continuously, performing monitoring of key server operating indicators. The monitoring module operating frequency control logic within the control unit is expressed as follows:

[0015] Logic1: The time interval between two consecutive runs of the monitoring module shall not exceed five seconds;

[0016] Logic2: If the difference between any of the values ​​in the latest set of server operation key indicators and the corresponding values ​​in the first set of server operation key indicators exceeds ±5%, the monitoring module will run once.

[0017] The initial interval between two runs of the monitoring module is no less than five seconds. When the monitoring module is not running for five seconds, it is controlled to run based on Logic1.

[0018] Furthermore, the historical server operation metrics uploaded by the analysis module correspond to a time domain of no less than seven days, and no operational errors occurred on the server within the time domain corresponding to the historical server operation metrics uploaded by the analysis module. When analyzing the server's busy weight for each time period, the analysis module analyzes the server's busy weight for each hour on an hourly basis. The analysis logic is as follows:

[0019]

[0020] In the formula: ω is the busy weight; n is the total time domain corresponding to the server's historical operating indicators; This represents the average CPU utilization. This represents the average memory usage. This represents the average disk I / O utilization. This represents the average network bandwidth utilization.

[0021] in, Let represent the normalized average CPU utilization, average memory utilization, average disk I / O utilization, and average network bandwidth utilization for the same time period on day i. Based on the above formula, we have ω1, ω2, ..., ω 23 ω 24 The subscript of each busy weight indicates the corresponding time period.

[0022] Furthermore, the decision prediction module in the decision unit operates on the prediction accuracy of server health status, that is, the decision on the number of key indicators of server operation to be retrieved. The decision unit is operated by the system user. The higher the required accuracy of server health status prediction, the more key indicators of server operation are retrieved. When retrieving key indicators of server operation, the retrieval unit uses all the key indicators of server operation collected by the monitoring module in one run as a set of key indicators of server operation.

[0023] Furthermore, the prediction logic for the server's health status in the prediction module is expressed as follows:

[0024] S=a×SCPU+b×SMEM+c×SDISK+d×SNET;

[0025] In the formula: S is the server's health status value; a, b, c, and d are weights; S CPU For server CPU health status; S MEM For the health status of the server's memory; S DISK For server disk-level health; S NET For the health status of the server network layer;

[0026] Among them, the larger the server health performance value S, the healthier the server is, and vice versa. The weights a, b, c, and d are user-defined by the system side and the sum of weights a, b, c, and d is 1, and a ≥ b ≥ d > c.

[0027] The formulas for calculating the server health status at each level in the server health prediction logic are as follows:

[0028]

[0029] In the formula: U CPU CPU utilization; L CPU C represents server load; T represents the number of server cores; C represents the number of server cores; T represents the number of server cores CPU T represents the current CPU temperature. max T min The maximum and minimum temperatures for safe CPU operation; U MEM Memory usage; For memory bandwidth utilization; Disk space usage; U IO Disk I / O utilization; This represents the current average disk read / write speed. This represents the maximum read / write speed of the disk. For network bandwidth utilization; C NET C is the number of connections; max C minD represents the maximum and minimum allowed range of the number of connections. NET For delay; D max D min P represents the maximum and minimum allowable delay range; NET This refers to the packet loss rate.

[0030] Furthermore, the correction operation for the server state security prediction result in the correction module is as follows:

[0031]

[0032] In the formula: S′ is the corrected server health performance value; q is the total amount of the key server operation indicators used for server health prediction during the corresponding time period; ω p Let be the busy weight corresponding to the p-th time period;

[0033] in, Indicates to Find the average.

[0034] Furthermore, the feedback module connects to a mobile computer held by the system user via a wireless network. The feedback module transmits the judgment result of the judgment module and the corrected server status security prediction result in the correction module to the mobile computer. The system user reads the judgment result and the corrected server status security prediction result on the mobile computer.

[0035] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0036] This invention provides a server intelligent error reporting system. During operation, the system collects key server operation indicators and references historical operation indicators to measure the busy weight of the server at different times. It further predicts the health status of the service based on the key server operation indicators, and then corrects the server health status prediction results by combining the busy weight of the server at different times. Finally, it determines the server's security status based on the corrected results. This allows the daily operation management of the server to achieve a certain degree of predictive monitoring effect. Based on the monitoring information of the system, predictive protection strategies can be configured for the server to ensure the long-term stable operation of the service. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0038] Figure 1 This is a schematic diagram of the structure of a server intelligent error reporting system. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0040] The present invention will be further described below with reference to embodiments.

[0041] Example 1:

[0042] This embodiment provides a server intelligent error reporting system, such as... Figure 1 As shown, it includes:

[0043] The monitoring module is used to monitor key performance indicators of the server.

[0044] The key performance indicators (KPIs) for server operation include: server CPU utilization, load, and temperature; server memory utilization and bandwidth utilization; server disk space utilization, I / O utilization, and read / write speed; server network bandwidth utilization, number of connections, latency, and packet loss rate. The monitoring module contains sub-modules, including:

[0045] The control unit is used to control the operating frequency of the monitoring module;

[0046] The storage unit is used to run synchronously with the monitoring module, receive key server operation indicators monitored by the monitoring module, and store the key server operation indicators.

[0047] Before the storage operation is performed in the storage unit, the key indicators of server operation are simultaneously marked with a monitoring timestamp. The storage unit distinguishes and stores the key indicators of server operation based on the timestamp of each key indicator.

[0048] The analysis module is used to upload historical server performance metrics and analyze the server's busy weight for different time periods based on these metrics.

[0049] The historical server performance metrics uploaded by the analysis module correspond to a time period of no less than seven days, and no operational errors occurred on the server within the time period corresponding to the historical server performance metrics uploaded by the analysis module. When analyzing the server's busy weight for each time period, the analysis module analyzes the server's busy weight for each hour on an hourly basis. The analysis logic is as follows:

[0050]

[0051] In the formula: ω is the busy weight; n is the total time domain corresponding to the server's historical operating indicators; This represents the average CPU utilization. This represents the average memory usage. This represents the average disk I / O utilization. This represents the average network bandwidth utilization.

[0052] in, Let represent the normalized average CPU utilization, average memory utilization, average disk I / O utilization, and average network bandwidth utilization for the same time period on day i. Based on the above formula, we have ω1, ω2, ..., ω 23 ω 24 The subscript of each busy weight indicates the corresponding time period of the busy weight;

[0053] The prediction module is used to receive key server operation indicators monitored by the monitoring module and predict the server's health status based on these key indicators.

[0054] The prediction module has sub-modules at its lower level, including:

[0055] The decision unit is used to determine the prediction accuracy of the server health status prediction module.

[0056] The retrieval unit is used to receive the prediction accuracy decision results from the decision-making unit, retrieve the key indicators of server operation based on the prediction accuracy decision results, and forward them to the prediction module. The prediction module predicts the health status of the server based on the key indicators of server operation forwarded by the retrieval unit.

[0057] Among them, the decision prediction module in the decision unit operates on the prediction accuracy of server health status, that is, the decision on the number of key indicators of server operation to be retrieved. The decision unit is operated by the system user. The higher the required accuracy of server health status prediction, the more key indicators of server operation are retrieved. When retrieving key indicators of server operation, the retrieval unit takes all the key indicators of server operation collected by the monitoring module in one run as a set of key indicators of server operation.

[0058] The prediction logic for server health in the prediction module is expressed as follows:

[0059] S=a×SCPU+b×SMEM+c×SDISK+d×SNET;

[0060] In the formula: S is the server's health status value; a, b, c, and d are weights; SCPU For server CPU health status; S MEM For the health status of the server's memory; S DISK For server disk-level health; S NET For the health status of the server network layer;

[0061] Among them, the larger the server health performance value S, the healthier the server is, and vice versa. The weights a, b, c, and d are user-defined by the system side and the sum of weights a, b, c, and d is 1, and a ≥ b ≥ d > c.

[0062] The formulas for calculating the server health status at each level in the server health prediction logic are as follows:

[0063]

[0064] In the formula: U CPU CPU utilization; L CPU C represents server load; T represents the number of server cores; C represents the number of server cores; T represents the number of server cores CPU T represents the current CPU temperature. max T min The maximum and minimum temperatures for safe CPU operation; U MEM Memory usage; For memory bandwidth utilization; Disk space usage; U IO Disk I / O utilization; This represents the current average disk read / write speed. This represents the maximum read / write speed of the disk. For network bandwidth utilization; C NET C is the number of connections; max C min D represents the maximum and minimum allowed range of the number of connections. NET For delay; D max D min P represents the maximum and minimum allowable delay range; NET Packet loss rate;

[0065] The correction module is used to receive the server status security prediction results from the prediction module and the busy weight of the corresponding time period from the prediction results from the analysis module, and to correct the server status security prediction results using the busy weight.

[0066] The correction operation for the server state security prediction result in the correction module is as follows:

[0067]

[0068] In the formula: S′ is the corrected server health performance value; q is the total amount of the key server operation indicators used for server health prediction during the corresponding time period; ω p Let be the busy weight corresponding to the p-th time period;

[0069] in, Indicates to Find the average;

[0070] The judgment module is used to set the security judgment threshold, synchronously obtain the server status security prediction results that have been corrected in the correction module, and compare the corrected prediction results with the security judgment threshold to determine whether the server has a security problem.

[0071] The feedback module is used to receive the judgment results from the judgment module and output the judgment results.

[0072] The monitoring module is internally connected to a control unit via a wireless network. The monitoring module is also internally connected to a storage unit via a wireless network. The monitoring module is also internally connected to an analysis module and a prediction module via a wireless network. The prediction module is internally connected to a decision unit and a retrieval unit via a wireless network. The prediction module is also internally connected to the storage unit via a wireless network. The prediction module is also internally connected to a correction module and a judgment module via a wireless network. The judgment module is internally connected to a feedback module via a wireless network.

[0073] In this embodiment, the monitoring module monitors key indicators of server operation, the control unit synchronously controls the operating frequency of the monitoring module, the storage unit follows the monitoring module and operates synchronously, receiving the key indicators of server operation monitored by the monitoring module and storing them, the analysis module uploads historical indicators of server operation and analyzes the busy weight of the server in different time periods based on the historical indicators of server operation, the prediction module further receives the key indicators of server operation monitored by the monitoring module and predicts the health status of the server based on the key indicators of server operation, the decision unit makes real-time decisions on the prediction accuracy of the prediction module when predicting the health status of the server, and the retrieval unit receives the prediction accuracy decision results from the decision unit in real-time, retrieves the key indicators of server operation based on the prediction accuracy decision results, and forwards them to the prediction module.

[0074] At this point, the prediction module predicts the server's health status based on the key server operation indicators forwarded by the receiving and retrieving unit. Then, the correction module receives the server status security prediction results from the prediction module and the busy weight of the corresponding time period from the analysis module's prediction results. It applies the busy weight to correct the server status security prediction results. Finally, the judgment module sets a security judgment threshold and synchronously obtains the corrected server status security prediction results from the correction module. Based on the comparison between the corrected prediction results and the security judgment threshold, it determines whether the server has a security problem. The feedback module receives the judgment results from the judgment module and outputs the judgment results.

[0075] Based on the above system operation, the analysis of key indicators for daily service operation enables effective prediction of server operational status and security, thus maintaining the server and ensuring that backend management users can make more proactive service management decisions based on the system's output, providing effective assurance for the long-term stable operation of the service.

[0076] Example 2

[0077] At the implementation level, based on Example 1, this example refers to... Figure 1 A further detailed description of the server intelligent error reporting system in Example 1 is provided below:

[0078] The control unit is equipped with monitoring module operating frequency control logic. Based on this logic, the control unit controls the monitoring module to run continuously, performing monitoring of key server operating indicators. The monitoring module operating frequency control logic in the control unit is expressed as follows:

[0079] Logic1: The time interval between two consecutive runs of the monitoring module shall not exceed five seconds;

[0080] Logic2: If the difference between any of the values ​​in the latest set of server operation key indicators and the corresponding values ​​in the first set of server operation key indicators exceeds ±5%, the monitoring module will run once.

[0081] The initial interval between two runs of the monitoring module is no less than five seconds. When the monitoring module is not running for five seconds, it is controlled to run based on Logic1.

[0082] The above settings further limit the operating frequency control logic of the monitoring module in the system of Embodiment 1, ensuring the stable and continuous operation of the monitoring module and providing necessary operating data support for the operation of subsequent modules in the system.

[0083] like Figure 1As shown, the feedback module connects to the mobile computer held by the system user via a wireless network. The feedback module transmits the judgment result of the judgment module and the server status security prediction result after correction processing in the correction module to the mobile computer. The system user reads the judgment result and the server status security prediction result after correction processing on the mobile computer.

[0084] The above settings further define the operation logic for feeding back the system's output results to the user in Embodiment 1.

[0085] In summary, during operation, the system in the above embodiments measures the busy weight of the server at different times by collecting key server operation indicators and referring to historical operation indicators. It then predicts the health status of the service based on the key server operation indicators, and corrects the server health status prediction results by combining the busy weight of the server at different times. Finally, it determines the server's security status based on the corrected results. This allows the daily operation management of the server to achieve a certain degree of predictive monitoring effect. Based on the monitoring information of the system, predictive protection strategies are configured for the server to ensure the long-term stable operation of the service.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A server intelligent error reporting system, characterized in that, include: The monitoring module is used to monitor key performance indicators of the server. The analysis module is used to upload historical server performance metrics and analyze the server's busy weight for different time periods based on these metrics. The historical server performance metrics uploaded by the analysis module correspond to a time period of no less than seven days, and no operational errors occurred on the server within the time period corresponding to the historical server performance metrics uploaded by the analysis module. When analyzing the server's busy weight for each time period, the analysis module analyzes the server's busy weight for each hour on an hourly basis. The analysis logic is as follows: ； In the formula: Weighting based on busyness; This represents the total time-domain values ​​corresponding to the server's historical performance metrics. This represents the average CPU utilization. This represents the average memory usage. This represents the average disk I / O utilization. This represents the average network bandwidth utilization. in, Let represent the normalized average CPU utilization, average memory utilization, average disk I / O utilization, and average network bandwidth utilization for the same time period on day i. Based on the above formula, we have: The subscript of each busy weight indicates the corresponding time period of the busy weight; The prediction module is used to receive key server operation indicators monitored by the monitoring module and predict the server's health status based on these key indicators. The correction module is used to receive the server status security prediction results from the prediction module and the busy weight of the corresponding time period from the prediction results from the analysis module, and to correct the server status security prediction results using the busy weight. The correction operation for the server state security prediction result in the correction module is as follows: ； In the formula: The corrected server health status value; This represents the server's health status. The total amount of key server operation indicators used for server health prediction within the corresponding time period; Let be the busy weight corresponding to the p-th time period; in, Indicates to Find the average; The judgment module is used to set the security judgment threshold, synchronously obtain the server status security prediction results that have been corrected in the correction module, and compare the corrected prediction results with the security judgment threshold to determine whether the server has a security problem. The feedback module is used to receive the judgment results from the judgment module and output the judgment results.

2. The server intelligent error reporting system according to claim 1, characterized in that, The key performance indicators (KPIs) for server operation include: server CPU utilization, load, and temperature; server memory utilization and bandwidth utilization; server disk space utilization, I / O utilization, and read / write speed; server network bandwidth utilization, number of connections, latency, and packet loss rate. The monitoring module contains sub-modules, including: The control unit is used to control the operating frequency of the monitoring module; The storage unit is used to run synchronously with the monitoring module, receive key server operation indicators monitored by the monitoring module, and store the key server operation indicators. Before the storage operation is performed in the storage unit, the key indicators of server operation are simultaneously marked with a monitoring timestamp. The storage unit distinguishes and stores the key indicators of server operation based on the timestamp of each key indicator.

3. The server intelligent error reporting system according to claim 2, characterized in that, The control unit is equipped with monitoring module operating frequency control logic. Based on this logic, the control unit controls the monitoring module to run continuously, performing monitoring of key server operating indicators. The monitoring module operating frequency control logic in the control unit is expressed as follows: Logic1: The time interval between two consecutive runs of the monitoring module shall not exceed five seconds; Logic2: If the difference between any of the values ​​in the latest set of server operation key indicators and the corresponding values ​​in the first set of server operation key indicators exceeds ±5%, the monitoring module will run once. The initial interval between two runs of the monitoring module is no less than five seconds. When the monitoring module is not running for five seconds, it is controlled to run based on Logic1.

4. The server intelligent error reporting system according to claim 1, characterized in that, The prediction module has sub-modules at its lower level, including: The decision unit is used to determine the prediction accuracy of the server health status prediction module. The retrieval unit is used to receive the prediction accuracy decision results from the decision-making unit, retrieve the key indicators of server operation based on the prediction accuracy decision results, and forward them to the prediction module. The prediction module predicts the health status of the server based on the key indicators of server operation forwarded by the retrieval unit. Among them, the decision prediction module in the decision unit operates on the prediction accuracy of server health status, that is, the decision on the number of key indicators of server operation to be retrieved. The decision unit is operated by the system user. The higher the required accuracy of server health status prediction, the more key indicators of server operation are retrieved. When retrieving key indicators of server operation, the retrieval unit uses all the key indicators of server operation collected by the monitoring module in one run as a set of key indicators of server operation.

5. A server intelligent error reporting system according to claim 1, characterized in that, The prediction logic for server health in the prediction module is expressed as follows: ； In the formula: This represents the server's health status. , , , As weight; This refers to the health status of the server's CPU. To assess the health of the server's memory. Assess the health of the server's disk. For the health status of the server network layer; Among them, the server health status performance value The larger the value, the healthier the server; conversely, the smaller the value, the less healthy the server. (Weight) , , , The value is user-defined on the system side and has a weight. , , , The sum is 1, and ≥ ≥ > .

6. A server intelligent error reporting system according to claim 5, characterized in that, The formulas for calculating the server health status at each level in the server health prediction logic are as follows: ； In the formula: CPU utilization; For server load; This refers to the number of server cores. The current temperature of the CPU; The highest and lowest temperatures required for safe CPU operation; Memory usage; For memory bandwidth utilization; Disk space usage; Disk I / O utilization; This represents the current average disk read / write speed. This represents the maximum read / write speed of the disk. Network bandwidth utilization; This represents the number of connections. These represent the maximum and minimum allowed range of the number of connections. For delay; The maximum and minimum values ​​of the allowed delay range; This refers to the packet loss rate.

7. The server intelligent error reporting system according to claim 1, characterized in that, The feedback module connects to the mobile computer held by the system user via a wireless network. The feedback module transmits the judgment result of the judgment module and the corrected server status security prediction result in the correction module to the mobile computer. The system user reads the judgment result and the corrected server status security prediction result on the mobile computer.

8. The server intelligent error reporting system according to claim 1, characterized in that, The monitoring module is internally connected to a control unit via a wireless network. The monitoring module is also internally connected to a storage unit via a wireless network. The monitoring module is also internally connected to an analysis module and a prediction module via a wireless network. The prediction module is internally connected to a decision unit and a retrieval unit via a wireless network. The prediction module is also internally connected to the storage unit via a wireless network. The prediction module is also internally connected to a correction module and a judgment module via a wireless network. The judgment module is internally connected to a feedback module via a wireless network.

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