Implementation method and device for supporting model to improve project operation and maintenance capability

Abstract

The invention relates to the field of the IT industry, and particularly provides an implementation method and device for supporting a model to improve the project operation and maintenance ability, and the method comprises the steps: collecting historical data accumulated in each stage of event response support of each cloud center, and making preparations for the analysis of the event response support; then, according to the collected data, reasons influencing the indexes in each step are analyzed, and root reasons influencing the indexes are determined through a statistical tool; finally, a statistical result is analyzed, improvement is carried out aiming at a root cause, and customer satisfaction is improved. Compared with the prior art, the method can continuously improve and optimize the model by analyzing the condition of historical event response support, identify all potential factors influencing the event response support, formulate targeted schemes and measures, and reasonably balance resource input and output.

Description

Technical Field

[0001] The present invention relates to the field of IT industry, and specifically provides a method and device for implementing a support model to improve project operation and maintenance capabilities. Background Art

[0002] With the advent of the cloud computing era, data centers are concentrating on cloud service providers. Due to the different scales and demands of data centers, the efficiency of event response support in daily operation and maintenance is low. Under the premise of ensuring SLA, it is urgent to comprehensively identify the key factors that affect the efficiency of event response support in each cloud center, improve the efficiency of event response, optimize processes and working methods, and improve customer satisfaction.

[0003] Traditional incident response support methods cannot adapt to the current rapidly developing environment and cannot effectively guide the development of company operations and maintenance. With the update of service technology, the expansion of service objects, and the diversification of service requirements, the incident response support requirements of each cloud center are constantly changing. The company's development direction, the allocation of limited resources, how to ensure service satisfaction, and how to develop business conditions healthily, all these issues need to rely on incident response support analysis. Summary of the invention

[0004] The present invention aims at the above-mentioned deficiencies of the prior art and provides a method for implementing a support model with strong practicability to improve project operation and maintenance capabilities.

[0005] A further technical task of the present invention is to provide a device for implementing a support model that is reasonably designed, safe and applicable to improve project operation and maintenance capabilities.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] A method for implementing a support model to improve project operation and maintenance capabilities, first, collect historical data accumulated in each cloud center incident response support phase to prepare for incident response support analysis;

[0008] Then, analyze the reasons affecting the indicators at each step based on the collected data, and determine the root causes affecting the indicators through statistical tools;

[0009] Finally, the statistical results are analyzed and improvements are made to the root causes to improve customer satisfaction.

[0010] Furthermore, when collecting historical data accumulated in each phase of incident response support for each cloud center, including the acceptance and recording phase, initial support and dispatch phase, diagnosis and troubleshooting, resolution and recovery, and ticket closure;

[0011] The data indicators of the acceptance and recording stage are the connection rate, timely order creation rate, form filling standard and repeated order creation;

[0012] The data indicators of the initial support and dispatch stage are classification accuracy, work order recognition rate, knowledge base case matching rate, first-time resolution rate and first-time order transfer accuracy rate;

[0013] The data indicators of the diagnosis and investigation stage are the average number of order transfers, knowledge base case matching rate and second-line resolution rate;

[0014] The data indicators of the solution recovery phase are solution standardization rate and solution efficiency;

[0015] The data indicators of the work order closing stage are the closing ratio and the successful closing ratio.

[0016] Furthermore, the connection rate is the user successfully contacting the support via telephone;

[0017] The timely order creation rate is a work order created within a specified time after receiving a response request;

[0018] The form filling specification is that the contents of the work order are filled in reasonably and described clearly;

[0019] The duplicate orders are work orders with the same content.

[0020] Furthermore, the classification accuracy is whether the accepted event matches the event classification and level;

[0021] The work order recognition rate is the proportion of work orders automatically recognized by the system;

[0022] The knowledge base case matching rate is the coverage of the existing knowledge base;

[0023] The first-time resolution rate mentioned above is that the NOC resolves the issue on its first acceptance;

[0024] The accuracy rate of the first order transfer is the accuracy rate of NOC work order distribution.

[0025] Furthermore, the average number of order transfers is the number of work order transfers; the knowledge base case matching rate is the coverage of the second-line knowledge base; the second-line resolution rate is the number of problems resolved within the second-line scope;

[0026] The standardization rate of the solution is that the content of the solution is clear, the description is reasonable and highly relevant; the effectiveness rate of the solution is that the incident is successfully handled according to the solution;

[0027] The closed order ratio is the ratio of the total number of work orders to the number of closed work orders within a period of time; the successful closed order ratio is the ratio of the number of successfully closed work orders to the total number of closed work orders within a period of time.

[0028] Furthermore, the root causes that affect the indicators include problems with the telephone system or work order system for the connection rate, insufficient manpower, and inability to respond in a timely manner; timely order creation, weak awareness of personnel process specifications, unreasonable workload distribution, and too many orders for some personnel; unclear personnel specifications, lack of business proficiency, and weak awareness of personnel process specifications for form filling specifications; and repeated order creation, accuracy of system identification of repeated work orders, and personnel process specification awareness;

[0029] For classification accuracy, the reason is that personnel have a good grasp of the classification regulations for event processing; for work order recognition rate, the system's performance in assigning work orders needs to be improved; for knowledge base case matching, the reason is that the total number of knowledge base cases is small, and the case keywords, summaries, and tags are not clear, the classification is not clear, and it is not easy to retrieve; for the first resolution rate, the reason is that the knowledge case base has no matching solution and the NOC cannot solve it, and the personnel do not pay attention to the case base and directly upgrade the work order; for the first order transfer accuracy rate, the reason is that personnel are not familiar with the technical architecture and organizational structure.

[0030] Furthermore, for the number of work order transfers, the workload of personnel is too saturated, there are problems with work attitude, and the content of the work order is difficult to classify; for the knowledge case library matching rate, the accumulation of the second-line knowledge base is weak, the case keywords, summaries, and tags are unclear, the classification is unclear, and it is not easy to retrieve; for the second-line solution rate, the skill shortage, the difficulty of the problem, and the difficulty of handling at the second-line level;

[0031] For the reasonableness of the plan, the writing standards of the plan documents are incomplete and the degree of attention paid by the personnel; for the effectiveness of the plan, the problems are plan problems and execution problems;

[0032] For the order closing rate, it means that the personnel failed to close the order in time according to the process, which is a problem of event handling efficiency. For the successful order closing rate, it means that the problems are event handling efficiency, technical capabilities, communication and coordination, and resources.

[0033] Furthermore, when analyzing the statistical results, we set indicators for each link for the many data generated in the process of incident response support for each cloud center, review the indicator status every cycle, use historical average indicators or established indicators as a reference, pay attention to abnormal indicators, analyze the root causes that affect the indicators and make improvements, and ultimately improve the efficiency of incident response work, optimize processes and working methods, and improve customer satisfaction.

[0034] A device for implementing a support model to improve project operation and maintenance capabilities, comprising: at least one memory and at least one processor;

[0035] The at least one memory is used to store a machine-readable program;

[0036] The at least one processor is used to call the machine-readable program to execute a method for implementing a support model to improve project operation and maintenance capabilities.

[0037] Compared with the prior art, the method and device for implementing a support model to improve project operation and maintenance capabilities of the present invention have the following outstanding beneficial effects:

[0038] (1) The present invention prepares for incident response support analysis by collecting data from various stages of incident response support: acceptance and recording stage (connection rate, timely order creation rate, form filling standard, simple repetition), initial support and dispatch stage (classification accuracy, work order recognition rate, knowledge base case matching rate, first-time resolution rate, first-time order transfer accuracy rate), diagnosis and investigation (average number of order transfers, knowledge base case matching rate, second-line resolution rate), resolution and recovery (solution standardization rate, solution efficiency), and work order closure (order closure ratio, successful closure ratio).

[0039] (2) The present invention analyzes the possible reasons that affect incident response support through the collected data, and confirms the root causes that affect incident response support for each cloud center through various statistical analysis tools.

[0040] (3) The present invention analyzes the statistical results to determine how each cloud center improves the efficiency of event response, optimizes processes and working methods, and improves customer satisfaction.

[0041] (4) The present invention analyzes the historical event response support, continuously improves and optimizes the model, identifies all potential factors affecting event response support, formulates targeted plans and measures, and reasonably balances resource input and output. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Attached Figure 1 It is a flowchart diagram of an implementation method of a support model to improve project operation and maintenance capabilities. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] A best embodiment is given below:

[0046] like Figure 1 As shown, a method for implementing a support model to improve project operation and maintenance capabilities in this embodiment, first, collect historical data accumulated in each stage of event response support for each cloud center, including the acceptance and recording stage (connection rate, timely order creation rate, form filling specifications, simple repetition), preliminary support and dispatch stage (classification accuracy, work order recognition rate, knowledge base case matching rate, first resolution rate, first order transfer accuracy), diagnosis and investigation (average number of order transfers, knowledge base case matching rate, second-line resolution rate), solution recovery (solution specification rate, solution efficiency), work order closure (order closure ratio, successful closure ratio), to prepare for incident response support analysis.

[0047] Then, based on the collected data, the possible reasons affecting the indicators at each step are analyzed, and the root causes affecting the indicators are determined through statistical tools.

[0048] Finally, the statistical results are analyzed and improvements are made based on the root causes to improve customer satisfaction.

[0049] The specific implementation of each stage is as follows:

[0050] When collecting historical data accumulated in each phase of incident response support for each cloud center, including acceptance and recording phase, initial support and dispatch phase, diagnosis and troubleshooting, resolution and recovery, and ticket closure, as shown in the following table:

[0051]

[0052]

[0053] The root causes affecting the indicators include: the connection rate is due to problems with the telephone system or the work order system, insufficient manpower, and inability to respond in a timely manner; timely order creation is due to weak awareness of personnel process specifications, unreasonable workload distribution, and some personnel taking too many orders; form filling specifications are due to unclear personnel specifications, lack of business proficiency, and weak awareness of personnel process specifications; duplicate order creation, the accuracy of the system in identifying duplicate work orders, and personnel awareness of process specifications.

[0054] For classification accuracy, the reason is that personnel have a good grasp of the classification regulations for event processing; for work order recognition rate, the system's performance in assigning work orders needs to be improved; for knowledge base case matching, the reason is that the total number of knowledge base cases is small, and the case keywords, summaries, and tags are not clear, the classification is not clear, and it is not easy to retrieve; for the first resolution rate, the reason is that the knowledge case base has no matching solution and the NOC cannot solve it, and the personnel do not pay attention to the case base and directly upgrade the work order; for the first order transfer accuracy rate, the reason is that personnel are not familiar with the technical architecture and organizational structure.

[0055] For the number of work order transfers, the workload of personnel is too saturated, there are problems with work attitude, and the content of work orders is difficult to classify; for the knowledge case library matching rate, the second-line knowledge base accumulation is weak, the case keywords, summaries, and tags are unclear, the classification is not clear, and it is not easy to retrieve; for the second-line solution rate, the skill shortage, the difficulty of the problem is high, and it is difficult to handle at the second-line level.

[0056] For the reasonableness of the plan, the writing standards of the plan documents are incomplete and the degree of attention paid by the personnel; for the effectiveness of the plan, the problems are plan problems and execution problems;

[0057] For the closure rate, it means that personnel fail to close orders in a timely manner according to the process, which is a problem of event handling efficiency. For the successful closure rate, it means that the event handling efficiency is a problem. The indicators that the technical capabilities, communication and coordination, and resource incident response support work focuses on include "total number of work orders", "proportion of major incidents", "handling time", etc.

[0058] The analytical tools and methods used were fishbone diagram, 5-why analysis, tree diagram, scatter plot and focus group;

[0059] When analyzing statistical results, pay periodic attention to indicators, perform root cause analysis on abnormal indicators, formulate improvement plans based on the conclusions, implement them, observe the effects, and run them on a rolling basis and continuously improve.

[0060] Through the above method, indicators are formulated in each link for the many data generated in the process of incident response support for each cloud center, and the indicator status is reviewed every cycle. Historical average indicators or formulated indicators are used as references, and attention is paid to abnormal indicators. The root causes affecting the indicators are analyzed and improved, ultimately improving the efficiency of incident response work, optimizing processes and working methods, and improving customer satisfaction.

[0061] Based on the above method, a device for implementing a support model to improve project operation and maintenance capabilities in this embodiment includes: at least one memory and at least one processor;

[0062] The at least one memory is used to store a machine-readable program;

[0063] The at least one processor is used to call the machine-readable program to execute a method for implementing a support model to improve project operation and maintenance capabilities.

[0064] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing a support model to improve project operation and maintenance capabilities, characterized in that: First, collect historical data accumulated at each stage of incident response support for each cloud center to prepare for incident response support analysis; Then, analyze the reasons affecting the indicators at each step based on the collected data, and determine the root causes affecting the indicators through statistical tools; Finally, the statistical results are analyzed and improvements are made to the root causes to improve customer satisfaction.

2. According to claim 1, a method for implementing a support model to improve project operation and maintenance capabilities is characterized in that: When collecting historical data accumulated at each stage of incident response support for each cloud center, including the acceptance and recording stage, initial support and dispatch stage, diagnosis and troubleshooting, resolution and recovery, and ticket closure; The data indicators of the acceptance and recording stage are the connection rate, timely order creation rate, form filling standard and repeated order creation; The data indicators of the initial support and dispatch stage are classification accuracy, work order recognition rate, knowledge base case matching rate, first-time resolution rate and first-time order transfer accuracy rate; The data indicators of the diagnosis and investigation stage are the average number of order transfers, knowledge base case matching rate and second-line resolution rate; The data indicators of the solution recovery phase are solution standardization rate and solution efficiency; The data indicators of the work order closing stage are the closing ratio and the successful closing ratio.

3. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 2, characterized in that: The connection rate is the number of users who successfully contact support via phone; The timely order creation rate is a work order created within a specified time after receiving a response request; The form filling specification is that the contents of the work order are filled in reasonably and described clearly; The duplicate orders are work orders with the same content.

4. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 3 is characterized in that: The classification accuracy rate is whether the accepted event matches the event classification and level; The work order recognition rate is the proportion of work orders automatically recognized by the system; The knowledge base case matching rate is the coverage of the existing knowledge base; The first-time resolution rate mentioned above is that the NOC resolves the issue on its first acceptance; The accuracy rate of the first order transfer is the accuracy rate of NOC work order distribution.

5. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 4 is characterized in that: The average number of order transfers is the number of work order transfers; the knowledge base case matching rate is the coverage of the second-line knowledge base; the second-line resolution rate is the number of problems resolved within the second-line scope; The standardization rate of the solution is that the content of the solution is clear, the description is reasonable and highly relevant; the effectiveness rate of the solution is that the incident is successfully handled according to the solution; The closed order ratio is the ratio of the total number of work orders to the number of closed work orders within a period of time; the successful closure ratio is the ratio of the number of successfully closed work orders to the total number of closed work orders within a period of time.

6. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 5, characterized in that: The root causes affecting the indicators include: the connection rate is caused by problems with the telephone system or the work order system, insufficient manpower, and inability to respond in a timely manner; timely order creation is caused by weak awareness of personnel process specifications, unreasonable workload distribution, and some personnel receiving too many orders; form filling specifications are caused by unclear personnel specifications, lack of business proficiency, and weak awareness of personnel process specifications; duplicate order creation is caused by the accuracy of system identification of duplicate work orders and personnel process specification awareness; For classification accuracy, the reason is that personnel have a good grasp of the classification regulations for event processing; for work order recognition rate, the system's performance in assigning work orders needs to be improved; for knowledge base case matching, the reason is that the total number of knowledge base cases is small, and the case keywords, summaries, and tags are not clear, the classification is not clear, and it is not easy to retrieve; for the first resolution rate, the reason is that the knowledge case base has no matching solution and the NOC cannot solve it, and the personnel do not pay attention to the case base and directly upgrade the work order; for the first order transfer accuracy rate, the reason is that personnel are not familiar with the technical architecture and organizational structure.

7. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 6, characterized in that: The number of work order transfers is due to the oversaturation of personnel workload, work attitude problems, and the difficulty in classifying the work order content; the knowledge case library matching rate is the weak accumulation of the second-line knowledge base, unclear case keywords, summaries, and tags, unclear classification, and difficult retrieval; the second-line solution rate is the shortage of skills, the difficulty of the problem is high, and it is difficult to handle at the second-line level; For the reasonableness of the plan, the writing standards of the plan documents are incomplete and the degree of attention paid by the personnel; for the effectiveness of the plan, the problems are plan problems and execution problems; For the order closing rate, it means that the personnel failed to close the order in time according to the process, which is a problem of event handling efficiency. For the successful order closing rate, it means that the problems are event handling efficiency, technical capabilities, communication and coordination, and resources.

8. The method for implementing a support model to improve project operation and maintenance capabilities according to claim 7, characterized in that: When analyzing statistical results, we set indicators for each link for the many data generated in the process of incident response support for each cloud center, review the indicator status every cycle, use historical average indicators or established indicators as a reference, pay attention to abnormal indicators, analyze the root causes that affect the indicators and make improvements, and ultimately improve the efficiency of incident response work, optimize processes and working methods, and improve customer satisfaction.

9. A device for implementing a support model to improve project operation and maintenance capabilities, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to execute the method according to any one of claims 1 to 8.

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