Application operation and maintenance human cost evaluation method and device, storage medium and equipment

Abstract

The invention discloses an application operation and maintenance manpower cost assessment method and device, a storage medium and equipment, and adopts the scheme that software system application operation and maintenance manpower is regarded as being composed of a plurality of target attributes, each target attribute has a certain weight and value, and then the weight of each target attribute is calculated by adopting an analytic hierarchy process; target attribute scores of the selected software are obtained, normalization processing is carried out, normalization results serve as attribute scores of the target attributes, and final scores of the to-be-tested software applications are obtained through calculation based on the weight values of the target attributes and the attribute scores of the target attributes corresponding to the to-be-tested software applications. And finally, based on the ratio of the final score of each to-be-tested software application to the total score of all the to-be-tested software applications and the total manpower consumed by all the to-be-tested software applications every year, calculating to obtain the amount of manpower consumed every year corresponding to each to-be-tested software application, so that the manpower cost required by each to-be-tested software application can be estimated.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, storage medium and equipment for evaluating application operation and maintenance manpower costs. Background Art

[0002] Estimation of workload in operation and maintenance work is the estimation of the manpower and time required for operation and maintenance of software systems - manpower cost is the main cost. The workload is usually measured in the form of man-days, man-months, and man-years. Doing a good job of workload estimation can better allocate budgets, better deploy human resources, and improve work efficiency. In operation and maintenance work, there are several common workload estimation models: function point analysis model, manpower input model, experience model, and use case point model.

[0003] Function Point Analysis Model: This model estimates the workload based on counting the function points of the software system. It divides the functions of the software system into different function points, and then calculates the workload based on the complexity and weight of each function point. It is suitable for large and complex software systems. However, the number of software systems we operate and maintain is large, and the types and complexities vary greatly. This model may be applicable to some systems, but it cannot be used as a general solution for all software systems.

[0004] Manpower Estimation Model: This model estimates the workload based on the data and experience of operating similar software systems in the past. By analyzing the similarity of historical data and software systems, the required human resources and workload can be predicted. This model relies too much on the amount of historical data and the similarity of software systems. In actual application operation and maintenance, there is usually not enough historical data support, and there will be no software systems with similar functions.

[0005] Empirical Model: This model is jointly participated by a variety of people with relevant experience. Each person makes an estimate, and then summarizes and discusses, and finally reaches a negotiated result. The disadvantage of this model is that the judgment result is affected by personal experience and bias. It may be applicable to the software he operates but not to other software. Moreover, this judgment method may not be applicable to new software systems.

[0006] Use Case Points Model: This model estimates the workload based on the number and complexity of system use cases. It divides the system's use cases into different types and calculates the workload based on the weight and complexity of each type. However, this model requires an accurate definition and understanding of the concept of use cases, which undoubtedly adds an extra manpower investment before application operation and maintenance, and this model is not very suitable for smaller software systems.

[0007] A comprehensive comparison of the existing function point analysis models, human input models, experience models, and use case point models shows that they are limited by the scale of the software system, rely on the amount of historical data and the similarity of the software system, are limited by the number of software systems, and cannot be applied to completely new software systems. They can only be used as a reference and cannot be used for actual application operation and maintenance human cost estimation. Summary of the invention

[0008] In view of this, an embodiment of the present invention provides an application operation and maintenance manpower cost assessment method, apparatus, storage medium and device to achieve the estimation of software application operation and maintenance manpower cost.

[0009] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0010] A method for evaluating application operation and maintenance labor costs, comprising:

[0011] Obtaining scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes;

[0012] Normalizing the scores of the target attributes of the software applications to be tested;

[0013] Based on the weight of each target attribute and the normalized score of each target attribute of each software application to be tested, the final score of each software application to be tested is calculated, wherein the weight of each target attribute is obtained according to the hierarchical analysis method;

[0014] Based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested, and the total manpower consumed annually by all software applications to be tested, the number of manpower consumed annually corresponding to each software application to be tested is calculated.

[0015] Optionally, in the above-mentioned application operation and maintenance manpower cost evaluation method, the target attributes include change level, software maturity and average number of work orders per week.

[0016] Optionally, in the above-mentioned application operation and maintenance manpower cost evaluation method, the normalization of the scores of each target attribute of each software application to be tested includes:

[0017] Based on the benefit-based normalization algorithm, the change level and software maturity score are normalized;

[0018] Based on the cost-based normalization algorithm, the weekly average work order score is normalized.

[0019] Optionally, in the above-mentioned application operation and maintenance manpower cost evaluation method, the weight value corresponding to the software maturity is less than the weight value corresponding to the change level;

[0020] The weight value corresponding to the change level is less than the weight value corresponding to the weekly average number of work orders.

[0021] Optionally, in the above-mentioned application operation and maintenance manpower cost evaluation method, the weight values ​​of the change level, software maturity and average weekly work order number are: 0.30899564, 0.10945229 and 0.58155207 respectively.

[0022] Optionally, in the above-mentioned application operation and maintenance manpower cost evaluation method, obtaining the score of the target attribute of each software application to be tested includes:

[0023] The scores of the target attributes of each software application to be tested are obtained from the operation and maintenance white paper.

[0024] Optionally, in the above-mentioned application operation and maintenance labor cost evaluation method, the total labor consumed by all the software applications to be tested each year is for: =(number of junior engineers*0.62+number of mid-level engineers*1+number of senior engineers*1.46+number of experienced engineers*2.02)*12.

[0025] An application operation and maintenance labor cost assessment device, comprising:

[0026] A data collection unit, used to obtain scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes;

[0027] A normalization unit, used for normalizing the scores of each target attribute of each software application to be tested;

[0028] A final score calculation unit, used to calculate the final score of each software application to be tested based on the weight of each target attribute and the score of each target attribute of each software application to be tested after normalization, wherein the weight of each target attribute is obtained according to the hierarchical analysis method;

[0029] The manpower cost calculation unit is used to calculate the number of manpower consumed each year for each software application to be tested based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested and the total manpower consumed each year by all software applications to be tested.

[0030] A storage medium stores a computer program, which, when executed by a processor, implements each step of the application operation and maintenance manpower cost assessment method as described in any one of the above.

[0031] An application operation and maintenance labor cost assessment device includes a memory and a processor;

[0032] The memory is used to store programs;

[0033] The processor is used to execute the program to implement each step of the application operation and maintenance manpower cost evaluation method described in any one of the above items.

[0034] Based on the above technical scheme, the above scheme provided by the embodiment of the present invention adopts target attribute decision and hierarchical analysis method as decision algorithms, and uses the principle of target attribute decision to regard the software system application operation and maintenance manpower as composed of multiple target attributes, each target attribute has a certain weight and value, and then uses the hierarchical analysis method to calculate the weight of each target attribute, obtain the target attribute score of each selected software, and perform normalization processing. The normalization result is used as the attribute score of the target attribute, and then the final score of the software application to be tested is calculated based on the weight value of each target attribute and the attribute score of the target attribute corresponding to each software application to be tested. Finally, based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested, and the total manpower consumed annually by all software applications to be tested, the number of manpower consumed annually corresponding to each software application to be tested is calculated, so that the manpower cost required for each software application to be tested can be estimated, so as to better and more efficiently plan the human resource allocation plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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 only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0036] Figure 1 A flowchart of the application operation and maintenance labor cost evaluation method disclosed in the embodiment of the present application;

[0037] Figure 2 A hierarchical model diagram of the application operation and maintenance manpower cost evaluation method disclosed in the embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of the application operation and maintenance manpower cost evaluation device disclosed in an embodiment of the present application;

[0039] Figure 4 This is an architecture diagram of the application operation and maintenance human cost evaluation device disclosed in an embodiment of the present application;

[0040] Figure 5 This is a schematic diagram of the structure of the application operation and maintenance labor cost assessment device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0042] The technical solution disclosed in this application adopts multi-attribute decision-making and hierarchical analysis as decision-making algorithms, and uses the data of the overall software evaluation table in the operation and maintenance white paper as the data source. The principle of multi-attribute decision-making is used to regard the software system application operation and maintenance manpower as composed of multiple components, each factor has a certain weight and value, and then the hierarchical analysis method is used to calculate the weight of each factor, and the scores of each selected software component factor are obtained from the software application operation and maintenance white paper, and normalized, and the normalized result is used as the attribute score of the attribute, and then the proportion of each software in the overall software is calculated based on the attribute score of each attribute, and then the human cost required for each software is estimated based on the total number of human resources; before taking on a new software system, this method can also be used to estimate the required human cost, so as to better and more efficiently plan human resource allocation.

[0043] For details, see Figure 1 The application operation and maintenance manpower cost evaluation method disclosed in the embodiment of the present application may include:

[0044] Step S101: obtaining scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes.

[0045] In the technical solution disclosed in the present application, the scores of the target attributes of each software application to be tested can be obtained from the operation and maintenance white paper. The content of the operation and maintenance white paper can be configured according to user needs. In this solution, the scores of the attributes of each software application need to be pre-configured in the operation and maintenance white paper, and can be directly retrieved when needed.

[0046] In the process of multi-attribute decision-making, it is usually necessary to have multiple options, multiple attribute evaluations, and attribute weight assignments. However, software applications have many attributes and the importance of these attributes varies greatly, which is exactly suitable for decision-making methods. The output result of the model constructed by this method is labor cost, which does not involve the choice of options. Therefore, the decision-making process is optimized, appropriate attributes are selected and reasonable weight values ​​are assigned, and then the weighted arithmetic average operator is used to calculate the labor cost required for each software system as the decision result. In the selection of software system attribute values, the software system attributes that affect human resource allocation are ranked and selected. Among the many attributes, at least 2 attributes can be selected as the target attributes in this step. For example, see Figure 2 In this solution, three relatively independent attributes that have a greater impact on human resource consumption can be selected as the target attributes in this application: change level, software maturity, and average weekly work order number. In terms of weight allocation, in order to make the weight value of each target attribute more reasonable, the judgment matrix method in the hierarchical analysis method can be used to calculate the weight value of the target attribute, and then finally obtain the human cost required for each target attribute.

[0047] Step S102: normalizing the scores of the target attributes of the software applications to be tested.

[0048] The scores of the target attributes of each software application can be obtained from the operation and maintenance white paper, such as the score of the change level, the score of the software maturity, and the score of the average number of weekly work orders. Since the numerical differences between the scores of different attributes are large, in order not to affect the reliability of the calculation results, it is also necessary to normalize the scores of each target attribute of all the software applications to be tested.

[0049] Step S103: based on the weights of the target attributes and the normalized scores of the target attributes of the software applications to be tested, the final scores of the software applications to be tested are calculated, wherein the weights of the target attributes are obtained according to the analytic hierarchy process.

[0050] Each target attribute has a certain weight value. Different attributes have different importance and their corresponding attribute weight values ​​are different. In this scheme, the hierarchical analysis method can be used to calculate the weight value of each target attribute. After determining the weight value of each target attribute, a corresponding judgment matrix can be constructed. The reliability of the weight value of each target attribute is judged based on the judgment matrix. For example, when the target attributes include change level, software maturity and weekly average number of work orders, the change level is slightly more important than the software maturity, the change level is slightly more important than the weekly average number of work orders, and the weekly average number of work orders is significantly more important than the software maturity. Therefore, when configuring the weight value, the weight value corresponding to the software maturity is smaller than the weight value corresponding to the change level; the weight value corresponding to the change level is smaller than the weight value corresponding to the weekly average number of work orders. When configuring the weight values ​​of the change level, software maturity, and average weekly work order number, combined with empirical data, a judgment matrix [(1,3,1 / 2)(1 / 3,1,1 / 5)(2,5,1)] can be constructed based on the importance of the change level, software maturity, and average weekly work order number. Based on the judgment matrix [(1,3,1 / 2)(1 / 3,1,1 / 5)(2,5,1)], the weight vector is calculated and the consistency check is performed, and the consistency ratio is 0.0031850, 0.0031850 < 0.1, so the judgment matrix [(1,3,1 / 2)(1 / 3,1,1 / 5)(2,5,1)] satisfies the consistency check, which proves that the judgment matrix is ​​reasonable. The normalized result of the eigenvector corresponding to the maximum eigenvalue can be used as the weight value of the three attributes of change level, software maturity, and average weekly work order number to process the judgment matrix [(1,3,1 / 2)(1 / 3,1,1 / 5)(2,5,1)], and finally the weight values ​​of the change level, software maturity, and average weekly work order number are 0.30899564, 0.10945229, and 0.58155207, respectively.

[0051] After determining the weight value of the target attribute, the product of the weight value of each target attribute and the normalized score of the target attribute is calculated, and then the products corresponding to each attribute are summed to obtain the final score of each software application to be tested.

[0052] Step S104: Based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested and the total manpower consumed annually by all software applications to be tested, the manpower consumed annually by each software application to be tested is calculated.

[0053] After calculating the final score of each software application to be tested, the final scores of these software applications to be tested are summed up to calculate the total score of all software applications to be tested. Then the total manpower consumed by all software applications to be tested each year is obtained. The total manpower consumed by all software applications to be tested each year is calculated based on the number of staff in the actual scenario, and the unit is man-month / year. In this embodiment, the total manpower consumed by all software applications to be tested each year is: (number of junior engineers * 0.62 + number of intermediate engineers * 1 + number of senior engineers * 1.46 + number of senior engineers * 2.02) * 12. Based on the above algorithm, the total manpower consumed by all software applications to be tested each year can be calculated. Then based on the formula The number of manpower consumed each year for each software application to be tested can be calculated, where: represents the number of manpower consumed each year by the jth software application to be tested. represents the final score of the jth software application to be tested, It is the total score of all software applications to be tested. The coefficients corresponding to the number of junior engineers, mid-level engineers, senior engineers, and experienced engineers can be evaluated based on the empirical model according to the annual work output and operation and maintenance contribution of engineers of different levels.

[0054] The technical solution disclosed in this embodiment adopts target attribute decision and hierarchical analysis method as decision algorithms, and uses the principle of target attribute decision to regard the software system application operation and maintenance manpower as composed of multiple target attributes, each of which has a certain weight and value. Then, the hierarchical analysis method is used to calculate the weight of each target attribute, obtain the target attribute score of each selected software, and perform normalization processing. The normalization result is used as the attribute score of the target attribute, and then the final score of the software application to be tested is calculated based on the weight value of each target attribute and the attribute score of the target attribute corresponding to each software application to be tested. Finally, based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested, and the total manpower consumed by all software applications to be tested each year, the number of manpower consumed each year corresponding to each software application to be tested is calculated, so that the manpower cost required for each software application to be tested can be estimated, so as to better and more efficiently plan the human resource allocation plan.

[0055] If the change level, software maturity, and average weekly work order number are used as the target attributes, the change level, software maturity, and average weekly work order number of each software application to be tested can be directly obtained from the operation and maintenance white paper. Since the numerical differences between the scores of different target attributes are large, in order to improve the accuracy of the calculation results, the above three target attribute values ​​of all software applications to be tested are normalized separately. Among them, the change level and the average weekly work order number are positively correlated with the operation and maintenance labor cost. The higher the change level and the larger the average weekly work order number, the higher the corresponding operation and maintenance labor cost. The software maturity is negatively correlated with the operation and maintenance labor cost. The higher the software maturity, the lower the corresponding maintenance labor cost. Therefore, the change level score and the average weekly work order number are processed using a benefit-based normalization algorithm, and the software maturity score is processed using a cost-based normalization algorithm.

[0056] When the benefit-based normalization algorithm is used to process the change grade score and the average weekly work order number, the benefit-based normalization algorithm (Formula 1), normalize the change level and software maturity score;

[0057] In the formula, subscript j represents the jth target attribute, subscript i represents the ith software application to be tested, and subscript ij represents the jth target attribute of the ith software application to be tested. represents the score of the jth target attribute of the i-th software application under test, represents the minimum score of the jth target attribute of all the software applications under test among the i software applications under test, represents the maximum value of the scores of the jth target attribute of all the software applications under test among the i software applications under test. represents the score of the jth target attribute of the i-th software application to be tested obtained by normalization. When the scores corresponding to the change level are normalized, i corresponds to the change level, and when the scores corresponding to the software maturity are normalized, i corresponds to the software maturity.

[0058] When the software maturity score is processed by the cost-based normalization algorithm, the cost-based normalization algorithm (Formula 2) Normalize the software maturity score; subscript j represents the jth target attribute, subscript i represents the i-th software application to be tested, and subscript ij represents the j-th target attribute of the i-th software application to be tested. represents the score of the jth target attribute of the i-th software application under test, represents the minimum score of the jth target attribute of all the software applications under test among the i software applications under test, represents the maximum value of the score of the jth target attribute of all the software applications under test among the i software applications under test, represents the score of the jth target attribute of the i-th software application to be tested obtained by normalization. The target attribute in Formula 2 refers to software maturity, that is, when the software maturity score is processed by the cost-based normalization algorithm using Formula 2, the subscript j corresponds to the software maturity.

[0059] Further, in the technical solution disclosed in this embodiment, after obtaining the target attribute score corresponding to the software application to be tested, the target attribute score can be verified first, and the subsequent steps can be continued after the verification is passed. If the verification fails, the corresponding prompt information is fed back. For example, the verification process can include: comparing each target attribute score based on a pre-set reasonable range to determine whether the target attribute score falls within the corresponding reasonable range. If it falls within the corresponding reasonable range, the target attribute score verification passes. If it does not fall within the reasonable range, the corresponding error prompt is returned. For another example, the verification process can include: constructing a judgment matrix based on the obtained target attribute score, each data in the judgment matrix can be represented by Amn, and Amn is the importance of target attribute n compared with target attribute m, calculating the single-layer weight vector of the judgment matrix and performing a consistency check. The purpose of the consistency check is to judge whether the design of the judgment matrix is ​​reasonable, and the single-layer weight vector is the weight value of each attribute. If the design is unreasonable, a corresponding error prompt is returned.

[0060] This embodiment discloses a device for evaluating the manpower cost of application operation and maintenance. For the specific working contents of each unit in the device, please refer to the contents of the above method embodiment.

[0061] The following describes an application operation and maintenance manpower cost assessment device provided by an embodiment of the present invention. The application operation and maintenance manpower cost assessment device described below and the application operation and maintenance manpower cost assessment method described above can be referenced to each other.

[0062] See also Figure 3 , the application operation and maintenance manpower cost evaluation device disclosed in the embodiment of the present application may include:

[0063] A data collection unit 10, which matches step S101 in the above method, is used to obtain the score of the target attribute of each software application to be tested, and the target attribute is not less than 2 attributes;

[0064] A normalization unit 20, which matches step S102 in the above method, and is used to normalize the scores of each target attribute of each software application to be tested;

[0065] A final score calculation unit 30, which matches step S103 in the above method, is used to calculate the final score of each software application to be tested based on the weight of each target attribute and the score of each target attribute of each software application to be tested after normalization, wherein the weight of each target attribute is obtained according to the hierarchical analysis method;

[0066] The manpower cost calculation unit 40 matches step S104 in the above method and is used to calculate the number of manpower consumed each year corresponding to each software application to be tested based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested and the total manpower consumed each year by all software applications to be tested.

[0067] In order to better use the device, a python program can be written to implement the data acquisition unit 10, the normalization unit 20, the final score calculation unit 30 and the labor cost calculation unit 40, and the data acquisition unit 10, the normalization unit 20, the final score calculation unit 30 and the labor cost calculation unit 40 are integrated into the data analysis model, which can be integrated into Figure 4 In the application service based on the Django framework shown, all existing software applications of the operation and maintenance team are stored in MySQL as software applications to be tested. The application service can read the change level, software maturity, and average weekly work order number of each software application to be tested from MySQL. The input of the data analysis model is the change level, software maturity, and average weekly work order number of each software application to be tested in MySQL. The output results of the data analysis model are the labor costs required to operate and maintain the software, that is, the number of manpower consumed each year for each software application to be tested. This solution estimates the labor cost of taking on a new software application. It will be combined with the existing sample space in the database (each software application to be tested in MySQL) to form a new software sample space, and will recalculate the final score of each software. Figure 4In the program architecture shown, the user can send an HTTP-POST request through the browser of the user terminal. The parameters of the POST request are the software change level, software maturity, and average weekly work order number in JSON format. uWSGI receives the HTTP-POST request as the gateway layer, processes the request into a format acceptable to Django, and sends it to the application service for processing. In the process of processing data, the application service needs to read the data in the MySQL database based on the request (the data in the database is original data, written in advance, and the data to be read is the change level, software maturity, average weekly work order number of each software application to be tested, and the total manpower consumed annually by all software applications to be tested). The application service performs comprehensive calculations based on the read data, and finally returns the calculation results (the number of manpower consumed annually corresponding to each software application to be tested) to uWSGI in JSON format. uWSGI formats the returned results and returns them to the browser for display.

[0068] Corresponding to the above method, the present application also discloses a storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the application operation and maintenance labor cost assessment method as described in any of the above embodiments is implemented.

[0069] Figure 5 For a hardware structure diagram of the application operation and maintenance labor cost evaluation device provided in an embodiment of the present invention, see Figure 5 As shown, it may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0070] In the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 communicate with each other through the communication bus 400; obviously, Figure 5 The communication connections shown for the processor 100, the communication interface 200, the memory 300, and the communication bus 400 are merely optional;

[0071] Optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module;

[0072] The processor 100 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0073] The memory 300 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory, which stores a preset program corresponding to the above method embodiment.

[0074] The processor 100 is specifically used to execute the preset program to implement each step of the application operation and maintenance manpower cost evaluation method as described in any one of the above items.

[0075] For example, the processor 100 is used to:

[0076] Obtaining scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes;

[0077] Normalizing the scores of the target attributes of the software applications to be tested;

[0078] Based on the weight of each target attribute and the normalized score of each target attribute of each software application to be tested, a final score of each software application to be tested is calculated;

[0079] Based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested, and the total manpower consumed annually by all software applications to be tested, the number of manpower consumed annually corresponding to each software application to be tested is calculated.

[0080] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0081] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0083] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0084] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0085] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0086] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating application operation and maintenance labor costs, characterized in that: include: Obtaining scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes; Normalizing the scores of the target attributes of the software applications to be tested; Based on the weight of each target attribute and the normalized score of each target attribute of each software application to be tested, the final score of each software application to be tested is calculated, wherein the weight of each target attribute is obtained according to the hierarchical analysis method; Based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested, and the total manpower consumed annually by all software applications to be tested, the number of manpower consumed annually corresponding to each software application to be tested is calculated.

2. The method for evaluating application operation and maintenance labor costs according to claim 1, characterized in that: The target attributes include change level, software maturity, and average number of work orders per week.

3. The method for evaluating application operation and maintenance labor costs according to claim 2, characterized in that: The normalizing of the scores of the target attributes of the software applications to be tested includes: Based on the benefit-based normalization algorithm, the change level and software maturity score are normalized; Based on the cost-based normalization algorithm, the weekly average work order score is normalized.

4. The method for evaluating application operation and maintenance labor costs according to claim 2, characterized in that: The weight value corresponding to the software maturity is less than the weight value corresponding to the change level; The weight value corresponding to the change level is less than the weight value corresponding to the weekly average number of work orders.

5. The method for evaluating application operation and maintenance labor costs according to claim 4, characterized in that: The weight values ​​of the change level, software maturity and average weekly number of work orders are 0.30899564, 0.10945229 and 0.58155207 respectively.

6. The method for evaluating application operation and maintenance manpower cost according to claim 1, characterized in that: The step of obtaining the scores of the target attributes of each software application to be tested includes: The scores of the target attributes of each software application to be tested are obtained from the operation and maintenance white paper.

7. The method for evaluating application operation and maintenance labor costs according to claim 1, characterized in that: Total manpower consumed annually by all software applications under test for: =(number of junior engineers*0.62+number of mid-level engineers*1+number of senior engineers*1.46+number of experienced engineers*2.02)*12.

8. An application operation and maintenance labor cost assessment device, characterized in that: include: A data collection unit, used to obtain the scores of target attributes of each software application to be tested, wherein the target attributes are no less than 2 attributes; A normalization unit, used for normalizing the scores of each target attribute of each software application to be tested; A final score calculation unit, used to calculate the final score of each software application to be tested based on the weight of each target attribute and the score of each target attribute of each software application to be tested after normalization, wherein the weight of each target attribute is obtained according to the hierarchical analysis method; The manpower cost calculation unit is used to calculate the number of manpower consumed each year for each software application to be tested based on the ratio of the final score of each software application to be tested to the total score of all software applications to be tested and the total manpower consumed each year by all software applications to be tested.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the application operation and maintenance manpower cost evaluation method as described in any one of claims 1 to 7 is implemented.

10. An application operation and maintenance labor cost assessment device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the application operation and maintenance manpower cost evaluation method as described in any one of claims 1-8.

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