CN115860508A

CN115860508A - Nuclear power storage engineering quantity measuring method and device, computer equipment and medium

Info

Publication number: CN115860508A
Application number: CN202211342427.0A
Authority: CN
Inventors: 李军; 朱德才; 袁德胜; 赵文灿; 欧阳小玉
Original assignee: China General Nuclear Power Corp; China Nuclear Power Engineering Co Ltd; CGN Power Co Ltd
Current assignee: China General Nuclear Power Corp; China Nuclear Power Engineering Co Ltd; CGN Power Co Ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-03-28

Abstract

The invention discloses a nuclear power storage engineering quantity measuring method, a device, computer equipment and a medium, wherein the nuclear power storage engineering quantity measuring method comprises the following steps: acquiring at least one workflow attribute index related to workload of nuclear power storage; analyzing all workflow attribute indexes to obtain attribute efficiency data; optimizing attribute efficiency data by adopting a task allocation algorithm based on at least one logistics stage to obtain a work decomposition structure chart corresponding to the logistics stage; and acquiring a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is the summary of the comprehensive engineering quantity involved in the logistics stage of each minimum item unit. The method can effectively realize the progress plan and the cost value calculation of the whole period and the whole stage of the engineering project, finally achieve the purposes of reducing the project management cost and improving the warehousing operation efficiency, and realize the result that the point value statistical result is matched with the actual comprehensive engineering quantity on site.

Description

Nuclear power storage engineering quantity measuring method and device, computer equipment and medium

Technical Field

The invention relates to the technical field of warehouse management, in particular to a nuclear power warehousing engineering quantity measuring method, a device, computer equipment and a medium.

Background

The cost of installation equipment and materials related to some domestic large-scale nuclear power engineering projects can reach billions, and the corresponding storage management cost can also reach billions of yuan. In view of the diversity of the warehousing management processes (logistics management, data system, loading and unloading operations, quality inspection, maintenance, etc.) and the complexity of the types (thousands of categories of complete equipment, electromechanical equipment, pipe valve materials, bulk materials, etc.), the warehousing management is difficult to manage the warehousing cost, and related calculation tools and methods are also lacking in similar industries in China.

The project amount of the existing building engineering project is mainly divided into three parts of labor quota, material consumption quota and mechanical shift quota. The engineering quantity metering mode of warehouse management is obviously different from the construction engineering, and the statistics can not be carried out according to the labor quota mode completely. The measurement mode of the warehouse management engineering quantity is generally carried out according to the labor quota and the machine shift quota by referring to the measurement method of the construction engineering project. However, the measurement result cannot feed back a real warehouse management engineering quantity statistical result, the calculation of the warehouse management engineering quantity is complex, the calculation cannot be simply carried out according to the manual input cost or the material price, and the measurement method of the warehouse management engineering quantity always has deviation from the actual business condition.

Disclosure of Invention

The embodiment of the invention provides a nuclear power warehousing engineering quantity measuring method, a device, computer equipment and a medium, and aims to solve the problems that the warehousing management engineering quantity is complex to calculate, cannot be simply counted according to the labor input cost or the material price, and the metering method of the warehousing management engineering quantity always deviates from the actual service condition.

A nuclear power storage engineering quantity measuring method comprises the following steps:

acquiring at least one workflow attribute index related to workload of nuclear power storage;

analyzing all workflow attribute indexes to obtain attribute efficiency data;

optimizing attribute efficiency data by adopting a task allocation algorithm based on at least one logistics stage to obtain a work decomposition structure chart corresponding to the logistics stage;

and acquiring a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is the summary of the comprehensive engineering quantity involved in the logistics stage of each minimum item unit.

A nuclear power storage engineering quantity metering device comprises:

the system comprises a work flow attribute index acquisition module, a work flow attribute index acquisition module and a data processing module, wherein the work flow attribute index acquisition module is used for acquiring at least one work flow attribute index related to the workload of nuclear power storage;

the efficiency data acquisition module is used for analyzing all workflow attribute indexes and acquiring attribute efficiency data;

the system comprises an acquisition decomposition structure chart module, a distribution analysis module and a distribution analysis module, wherein the acquisition decomposition structure chart module is used for optimizing attribute efficiency data by adopting a task distribution algorithm based on at least one logistics stage and acquiring a work decomposition structure chart corresponding to the logistics stage;

and the point value acquisition module is used for acquiring a point value corresponding to the logistics stage based on the work decomposition structure chart, wherein the point value is the summary of the comprehensive engineering quantity related to each minimum item unit in the logistics stage.

A computer device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the nuclear power warehousing engineering quantity measuring method.

A computer-readable medium, in which a computer program is stored which, when being executed by a processor, carries out the above-mentioned nuclear power warehousing project quantity method.

According to the nuclear power storage engineering quantity measuring method, device, computer equipment and medium, the point value corresponding to the nuclear power storage workload can be obtained by quantifying the workflow attribute indexes corresponding to the human, object, things, management and other elements related to nuclear power storage management, the progress plan and cost value calculation of the whole period and the whole stage of the engineering project can be effectively realized, the purposes of reducing project management cost and improving storage operation efficiency are finally achieved, and the result that the point value statistical result is matched with the actual comprehensive engineering quantity on site is realized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.

FIG. 1 is a schematic diagram of an application environment of a nuclear power warehousing project quantity measuring method according to an embodiment of the present invention;

FIG. 2 is a flowchart of a nuclear power warehousing project quantity measuring method according to an embodiment of the present invention;

FIG. 3 is a first flowchart of a method for measuring the quantity of nuclear power storage engineering according to an embodiment of the present invention;

FIG. 4 is a second flowchart of a nuclear power warehousing project quantity measuring method according to an embodiment of the present invention;

FIG. 5 is a work breakdown structure diagram of a certain flow stage in the nuclear power warehousing project quantity measuring method according to an embodiment of the present invention;

FIG. 6 is a third flowchart of a nuclear power warehousing project quantity measuring method according to an embodiment of the present invention;

FIG. 7 is a fourth flowchart of a method for measuring the quantity of nuclear power storage engineering according to an embodiment of the present invention;

FIG. 8 is a fifth flowchart of a method for measuring the quantity of nuclear power storage engineering according to an embodiment of the present invention;

FIG. 9 is a schematic view of a nuclear power storage engineering quantity metering device according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a computer device in an embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The nuclear power warehousing engineering quantity measuring method provided by the embodiment of the invention can be applied to the application environment shown in figure 1, and is applied to a nuclear power warehousing engineering quantity measuring system, wherein the nuclear power warehousing engineering quantity measuring system comprises a client and a server, and the client communicates with the server through a network. The client is also called a client, and refers to a program corresponding to the server and providing local services for the client. The client can be installed on computer equipment such as but not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable equipment. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.

The method solves the problem of engineering quantity statistics under the warehousing engineering project, and the main carrier of the engineering quantity is the external attribute and the logistics circulation process of the goods, and the main carrier is expressed in the actions of receiving, checking, storing, maintaining, issuing and the like of the goods, the attributes of the size, weight, type and the like of the goods, and the influence of a special operation mode or a management mode of a certain project is also involved. Therefore, the calculation of the warehousing management engineering quantity is complex, and the statistics cannot be simply carried out according to the manual input cost or the material price. Therefore, the application provides a point system (Dot Method Of The Cost Of The route Management), and a Method for obtaining a storage Management engineering quantity summary result through point quantization calculation Of a minimum item unit. The introduction of the application to the point system mainly relates to the analysis of the relevant properties of the engineering quantity and the point value calculation of the engineering quantity. See the examples below for details:

in an embodiment, as shown in fig. 2, a method for measuring nuclear power storage engineering quantity is provided, which is described by taking the application of the method to the server in fig. 1 as an example, and specifically includes the following steps:

s10, acquiring at least one workflow attribute index related to the workload of the nuclear power storage.

The workload of nuclear power warehousing relates to warehousing management, and the warehousing management is used for managing warehouses and materials stored in the warehouses. A Project Of shipping Management (Project Of shipping Management), a field warehousing Management work Project taking engineering construction as a carrier. The workload of the nuclear power storage, namely the comprehensive engineering quantity related to the workload of the nuclear power storage, is calculated by a certain quota or quantitative method according to a relevant rule, and is an entity amount represented by a physical metering unit or a natural metering unit.

The workflow Attribute Index (Attribute Index) is a label for classifying and explaining the Attribute of a certain flow or article based on the business guide principle.

In particular to a nuclear power storage engineering quantity measuring method, the main influencing factors are the attribute indexes of the logistics stage and the work flow,

3.2.3.1 definition of relevant factors in engineering volume statistics

(1) A logistics stage: referring to a warehousing management standard flow, the logistics stage relates to multiple stages of planning management, receiving management, unpacking inspection management, storage capacity arrangement management, maintenance management, issuing control management, data system management, resource preparation management and the like.

(2) Workflow attribute indexes:

the workflow attribute index mainly relates to the following two parts:

a) The operation attribute is as follows: man, machine, material, method, ring;

human-operator factors: managers, workers, special workers, non-production personnel;

machine-mode factors: forklift operation, traveling operation, crane operation and manual operation;

material-work tool factors: preparing a forklift, preparing a traveling crane, preparing a transport vehicle, preparing a warehousing operation and maintenance tool and the like;

law-job management factors: file preparation, information management, high risk control, quality assurance level, large equipment, package recovery and the like;

ring-work environment factors: indoor storage, outdoor storage, hazardous chemical management, tarpaulin protection and the like.

b) Carrier attribute: single bulk procurement package, single package box, single goods category attribute.

Therefore, the storage management engineering quantity statistical process involves more variables, the attribute matching is complex, and the actual condition of the storage management engineering quantity can be shown only by fusing and matching the two factors. In order to realize the optimal fusion matching result, an attribute assignment principle based on service direction needs to be established.

And S20, analyzing all the workflow attribute indexes to obtain attribute efficiency data.

Wherein the attribute efficiency data is data in accordance with an input format of a task allocation algorithm employed for further processing.

And S30, optimizing the attribute efficiency data by adopting a task allocation algorithm based on at least one logistics stage to obtain a work decomposition structure chart corresponding to the logistics stage.

The Logistics Stage (Logistics Stage) refers to different circulation stages of a certain goods or product in the warehousing link.

A Work Breakdown Structure (WBS) is similar to the factorization of Work, namely a project is broken down according to a certain principle, the project is broken down into tasks, the tasks are broken down into a piece of Work, and the piece of Work is distributed to the daily activities of each person until the Work is not broken down. Namely: project → task → work → daily activity. The work breakdown structure is directed to deliverables, grouping project elements that summarize and define the entire working scope of the project, each descending layer representing a more detailed definition of the project's work. WBS is always in the center of the planning process and is also an important basis for making schedule plans, resource requirements, cost budgets, risk management plans, procurement plans, and the like.

Specifically, in order to achieve accuracy of the point value statistical result and reduce the calculation amount in the point value statistical process, attribute assignment analysis based on service guidance is adopted for a logistics stage in combination with a workflow attribute index, and a task allocation algorithm is adopted to perform optimization analysis on the workflow attribute index. Because the storage management engineering quantity metering process involves more variables and the attribute matching is complex, the actual situation of the storage management engineering quantity can be shown only by fusing and matching the logistics stage and the workflow attribute indexes, and the method is used for realizing an optimized fusion and matching result.

And S40, acquiring a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is the summary of the comprehensive engineering quantity related to each minimum item unit in the logistics stage.

The point value is a comprehensive engineering quantity statistical result implemented by the workflow attribute indexes of each minimum item unit at a certain logistics stage. The comprehensive engineering quantity comprises four parts of mechanical cost input, labor cost input, warehousing operation mode cost input and manual standard operation time cost. Thus, the point value may also reflect the relationship of the schedule cost to the project price cost.

Specifically, the key three elements of the point value calculation are as follows: (1) a minimum item unit; (2) and (3) determining a summarizing mode of a specific business comprehensive engineering quantity and (3) summarizing and measuring the comprehensive engineering quantity. Wherein, the 'point value' is the summary of the specific business integrated engineering quantity of each minimum item unit in a certain logistics stage. Each minimum item unit may be defined as a specific equipment material, a packaged packing box of a certain type of equipment material, and the workload of a qualified standard worker in one hour. The comprehensive engineering quantity comprises cost input components of mechanical shift, personnel configuration, operation modes, management modes and project schedules in the warehousing management process. Therefore, the summary of the comprehensive engineering quantity can adopt various forms such as manpower cost measurement, progress cost measurement, material cost measurement and financial cost measurement.

Furthermore, the point value can be counted only according to the delivery plan and the purchase order without counting after the business is implemented, and the practical value and the plan value are high. According to the method, a point system concept of point values is introduced through correlation analysis of warehouse management logistics stages and working business attributes thereof, the factors of people, objects, things, management and the like of warehouse management are quantified, and a series of calculation methods are formulated to obtain a metering mode capable of truly reflecting warehouse management engineering quantities. The warehouse management engineering quantity statistics belongs to a combination of a progress value and a cost value, has great engineering application significance, and can solve a plurality of evaluation problems of warehouse management progress plan, warehouse planning, labor cost, personnel efficiency, price cost, management deviation and the like. The method can be applied to the engineering quantity statistics of different stages (receiving, acceptance, storage and release) in the storage management process.

According to the nuclear power storage engineering quantity measuring method provided by the embodiment, the real point value corresponding to the nuclear power storage workload can be obtained by quantifying the workflow attribute indexes corresponding to the human, object, event, management and other elements related to the nuclear power storage management and then carrying out statistical processing, the progress plan and cost value calculation of the whole period and the whole stage of the engineering project can be effectively realized, the purposes of reducing the project management cost and improving the storage operation efficiency are finally achieved, and the result that the point value statistical result is matched with the on-site actual comprehensive engineering quantity is realized.

In a specific embodiment, as shown in fig. 3, in step s20, analyzing all workflow attribute indexes to obtain attribute efficiency data specifically includes the following steps:

and S21, converting each workflow attribute index into data to obtain workflow attribute index data.

And S22, converting the workflow attribute index data into attribute efficiency data through set definition.

Specifically, the assignment of the workflow attribute indexes is the optimal matching calculation with business as the guide, and can be performed through Hungarian algorithm of operational research. Firstly, all workflow attribute indexes are converted into data, and the workflow attribute index data are converted into efficiency data in a task allocation algorithm in a set definition mode. Set definition refers to a given set, any given element, either belonging to or not belonging to the set, whichever is mandatory, and no ambiguity is allowed.

In a specific embodiment, as shown in fig. 4, in step s30, that is, the attribute efficiency data is optimized by using a task allocation algorithm, and the work breakdown structure diagram corresponding to the logistics stage is obtained, which specifically includes the following steps:

and S31, modularizing the workflow attribute indexes included in the common category task and the special category task.

And S32, optimizing and assigning the workflow attribute indexes in the common category task by adopting Hungarian principle to obtain a first optimization result.

And S33, carrying out secondary analysis on the first optimization result to obtain a second optimization result.

And S34, drawing a work decomposition structure diagram corresponding to the logistics stage based on the second optimization result.

Specifically, the matching of the work flow attribute indexes of the engineering quantity and the logistics stage is completed through the optimal assignment of the efficiency data, so that the subsequent engineering quantity statistical difficulty caused by the complexity of the work flow attribute indexes is simplified.

(1) The attribute index decomposition of the engineering quantity workflow related to the engineering quantity should consider all influence factors as much as possible, and the finer the index decomposition, the better the index decomposition;

(2) the engineering work flow attribute index is scientifically assigned according to the characteristics of the warehousing management work;

(3) the similarity principle is that the engineering quantity workflow process attribute indexes belonging to a single logistics process are classified as one as possible, and the single logistics process is matched with the single engineering quantity workflow process attribute indexes;

(4) according to the compatibility principle, a single logistics flow can contain the attribute indexes of the engineering work flows at different stages, and the single logistics flow is matched with the attribute indexes of the plurality of engineering work flows;

(5) the correlation principle is that for a plurality of logistics flows, the same engineering work flow attribute index can be correlated, and the plurality of logistics flows are matched with the single engineering work flow attribute index.

(6) And (4) an optimization rule, wherein all the engineering quantity workflow attribute indexes are assigned by adopting an optimization principle so as to reduce the calculation work of the engineering quantity.

After the processes of secondary analysis and the like of the project work flow attribute index optimization assignment result, a work decomposition structure diagram corresponding to the logistics stage can be obtained, as shown in fig. 5, and then the point value of the project is calculated according to the decomposition diagram.

In a specific embodiment, as shown in fig. 6, in step s32, namely, performing optimization assignment on workflow attribute indexes in a common category task by using hungarian law, and obtaining a first optimization result, the method specifically includes the following steps:

s321, adopting Hungarian principle, randomly acquiring a workflow attribute index as a tuning index, wherein the workflow attribute index is used for acquiring a logistics stage which is most matched with the tuning index, and increasing the matching times of the tuning index and the logistics stage once.

And S322, repeatedly executing the step of randomly acquiring a workflow attribute index as an optimization index by using Hungarian's law, and acquiring the logistics stage which is most matched with the optimization index, and increasing the matching times of the optimization index and the logistics stage once until the maximum matching times corresponding to each logistics stage is acquired.

And S323, matching the tuning indexes corresponding to the maximum matching times corresponding to the logistics stage to serve as a first optimization result.

Specifically, according to the hungarian law formula:

C _ij and the optimal efficiency distribution of the jth logistics process is completed by representing the ith work process attribute index for the coupling function.

X _ij In order to make a decision on a variable,

taking project A as an example, the calculation process of assigning optimization to workflow attribute indexes is introduced as follows:

a) Firstly, according to the warehousing management experience of the project A, the logistics stage is divided into four stages, namely, receiving management (A1), unpacking inspection management (A2), maintenance management (A3) and issuing control management (A4).

And secondly, dividing the workflow attribute indexes of the project A into a common task class and a special task class.

The common task category refers to the work of associating one or more workflow attribute indexes with one or more logistics stages, as shown in the common task category table of table 1 below, belonging to the assignment problem related to the compatibility principle and the correlation principle. The common task category of the A project can be divided into a plurality of categories (G1-Gn), the category is a non-detachable minimum workflow attribute index, the minimum workflow attribute index can fully express the work amount condition of a certain work or sub-work, and the common task category content can be automatically changed according to the condition of each project.

TABLE 1

The special task category refers to the work of associating a workflow attribute index with a logistics stage, and belongs to the assignment problem related to the similarity principle as shown in a special task category table in the following table 2. Each logistics stage has a workflow attribute index with own characteristics, the workflow attribute index is irrelevant to the optimization assignment of other processes, the special task category of the A project can be divided into receiving management (J1-J4), unpacking inspection (K1-K3), maintenance (W1-W4) and issuing control (F1-F4), and the category is the minimum workflow attribute index which cannot be split. The special task category content can be changed according to the condition of each project.

TABLE 2

b) Since the common task category is applicable to all logistics stages, it needs to be assigned optimally for workflow attribute indexes.

b) And (4) applying Hungarian's law to carry out workflow attribute index optimization assignment of the common category task.

According to Hungarian's law, C _ij The function calculation needs to consider the optimization matching of the logistics process and the work process attribute index assignment, the efficiency of completing a certain attribute assignment task by a certain logistics process is assigned, the assignment is set from 1 to 9 levels and represents the completion efficiency of the work, and the higher the level number is, the higher the efficiency is. If a certain workflow attribute index is not suitable for a certain logistics process, the efficiency value is marked as "-". The numerical value correspondence table for the attribute index optimization assignment of the common category task by applying the hungarian law is shown in table 4 below.

TABLE 4

The efficiency value calculation based on Hungarian's law is carried out on the table, initial condition assignment is carried out according to the characteristics of material management of nuclear power engineering, and the assignment should fully express the actual working condition as much as possible.

In a specific embodiment, as shown in fig. 7, in step S33, performing a secondary analysis on the first optimization result to obtain a second optimization result, which specifically includes the following steps:

and S331, collecting the matching times corresponding to each workflow attribute index in the logistics stage to form a matching time set.

S332, acquiring a corresponding normal curve distribution diagram, a corresponding mean value of the matching times set and a standard deviation based on the matching times set, and setting a confidence interval according with engineering practice, wherein the confidence interval comprises the maximum matching times corresponding to the logistics stage.

Specifically, one time of Hungarian algorithm matching is completed, one workflow attribute index is randomly selected from a plurality of attribute modules and is substituted into a calculated value, so that the best matching mode of the workflow attribute indexes and the logistics stage is obtained, multiple times of attribute module index circulating calculation can be adopted according to practical engineering experience to solve the problem of single assignment of the Hungarian algorithm, so that the total matching times of each workflow attribute index and the logistics process can be found, and finally the optimal assignment of the workflow attribute index matched with a certain logistics process is selected according to the maximization of the total matching times.

Step1: the following is a work flow attribute index combined with the efficiency assignment table of the logistics flow, as shown in tables 5 and 6 below:

	B1G1	B2G1	B3G1	B4G1	B5G1
						A1	8	5	8	-	8
A2	6	8	7	8	9
						A3	3	7	4	7	6
A4	1	4	-	4	7
						A5	0	0	0	0	0

TABLE 5

	B1G2	B2G1	B3G1	B4G1	B5G1
						A1	6	5	8	-	8
A2	4	8	7	8	9
						A3	2	7	4	7	6
A4	6	4	-	4	7
						A5	0	0	0	0	0

TABLE 6

Step2: the related set formula of the set operation of the logistics process and the workflow process attribute indexes is as follows:

the above collective operation solves the problem of optimal matching between the primary logistics stage and the attribute indexes, but cannot solve the problem of occurrence frequency of a plurality of process attribute indexes in each logistics stage, so that secondary analysis needs to be performed on results after optimized assignment in hungarian law.

Therefore, a set function can be established by the result of the secondary analysis of the B1G1 attribute optimization assignment, and is recorded as follows:

i =2-5,j = the number of module attribute indexes;

the set function may also be expressed as a maximum value for the number of occurrences of the different attribute indices after the best match for each process. Its expression can be written as follows:

W＝A1：BiGj＝max(K _A1：B1G1 、K _A1：B1G2 、K _A1：BiGj )

the principle of screening the maximized value after optimizing the attribute indexes is as follows:

1) And establishing a set of distribution times of all attribute indexes obtained based on Hungarian's law in a certain logistics stage.

2) And calculating the mean value and the standard deviation of the data of the set by adopting a normal curve distribution diagram, and setting a confidence interval which accords with engineering practice.

3) Each attribute module must retain at least one attribute index that occurs the highest number of times.

And the optimized and matched W set is the optimal matching relation between the logistics stage and the attribute indexes, and the matching relation determines the number of the attribute indexes needing point value operation.

According to the actual optimization operation result of the W set, the attribute index of the A item can be optimized by half. In the second stage (point value calculation) of the storage engineering quantity statistics, the number of items related to the attribute indexes can reach hundreds of thousands, and the optimization and reduction of the attribute indexes with low correlation degree with the engineering quantity statistics in a certain logistics stage are of great importance.

In an embodiment, as shown in fig. 8, in step S40, that is, based on the work breakdown structure diagram, the method obtains a point value corresponding to the logistics stage, and specifically includes the following steps:

s41, acquiring at least one task attribute module and task attribute module time based on the work decomposition structure chart.

In particular, since a common task class is applicable to all logistics stages, it needs to be assigned for attribute index optimization for this purpose.

(1) The common category task attribute is modularized.

The common task category list represents a workflow package of a complete warehousing process, and for this purpose, the common task category list of the A project is modularized according to workflow attributes, such as: the system comprises an operation mode module and an operation condition module, wherein each module represents the same type of work summary of a certain period of time in a warehouse management logistics stage, as shown in a common task type attribute index table in the following table 3, all the modules need to be simultaneously embodied in attribute assignment calculation of one logistics stage. The dedicated task class attribute index may also be performed in a homogeneous manner.

TABLE 3

S42, acquiring a point value corresponding to the logistics stage by adopting the following public indication:

point value = sum of all task attribute modules time job minimum number of people.

Specifically, the comprehensive engineering quantity comprises cost input components of mechanical shift, personnel configuration, operation modes, management modes and project schedules in the warehousing management process. Therefore, the summary of the comprehensive engineering quantity can adopt various forms such as manpower cost measurement, progress cost measurement, material cost measurement, financial cost measurement and the like. The key three elements of the point value calculation are as follows:

(1) a minimum item unit; (2) and (3) determining a summarizing mode of a specific business comprehensive engineering quantity and (3) summarizing and measuring the comprehensive engineering quantity. The available analysis methods for realizing the labor cost measurement mainly comprise the following steps:

the characteristic method comprises the following steps: analyzing the characteristics or characteristics of nuclear power engineering material management, and summarizing required engineering quantity statistical basic data;

the application method comprises the following steps: quoting a verified engineering quantity statistical method;

PERT method: and (5) performing point value assignment by using a work hour record of a specific working procedure operation process.

From the above analysis, the point value calculation can take two methods:

1) Point value = (technical file module time + job management module time + quality management module time + job condition module time + job mode module time) × minimum number of jobs.

2) Point value = ∑ (B1G 1; B1G2; B1G3; B1G4; B2G1; biGj), which is the point value summarizing all the logistics stage attribute indexes.

In a specific embodiment, after step S40, i.e. after obtaining the point value corresponding to the logistics stage, the following steps are further specifically included:

and S50, acquiring point efficiency based on the point value, and evaluating the matching condition of the labor hour investment and the engineering construction cost investment at a certain stage.

Specifically, "dot efficiency" can be simply expressed as: the ratio of the engineering quantity to the input manual quantity (single manual quantity: single engineering quantity [ point value ]), the better the point efficiency evaluation result is, the better the cost control and manual efficiency of the warehouse management are represented, the point efficiency under the optimal working state is '1', and the representative meaning is: the labor time investment of a certain stage of the project construction period is completely matched with the project amount, or the cost investment is completely matched with the project construction.

According to the nuclear power storage engineering quantity measuring method provided by the embodiment, the real point value corresponding to the nuclear power storage workload can be obtained by quantifying the workflow attribute indexes corresponding to the human, object, affair, management and other elements related to the nuclear power storage management and then carrying out statistical processing, the progress plan and the cost value calculation of the whole period and the whole stage of the engineering project can be effectively realized, the purposes of reducing the project management cost and improving the storage operation efficiency are finally achieved, and the result that the point value statistical result is matched with the actual comprehensive engineering quantity on site is realized.

In order to solve the problems, the problem of unified quantitative standard of warehousing management engineering quantity can be solved by providing the concept of a point system.

The method aims to establish a warehousing management engineering quantity calculation method under a project mode so as to realize the calculation of progress plans and cost values of the whole period and the whole stage of an engineering project and finally achieve the purposes of reducing project management cost and improving warehousing operation efficiency. The method aims to realize a point value statistical optimization calculation method based on service guidance, and pursues the highest performance goal of realizing the coincidence of a point value statistical result and the actual field engineering quantity.

After the point system concept is introduced, the problem of unified quantification of different equipment types in the warehousing management engineering quantity calculation process is solved, the problem of unified quantification calculation among different professionals and different personnel is solved, the problem of unified quantification analysis of equipment attribute and operation mode difference is solved, and the point system concept is embodied in the following aspects:

1. the statistics of the warehouse management engineering quantity is realized.

2. The organic unification of the warehousing management progress value and the cost value is realized.

3. And the attribute optimization assignment of the warehousing management engineering quantity is realized.

4. The contrast of external cost, efficiency, progress in the warehouse management has been realized, include:

(1) and the lateral comparison of the cost and the progress among different projects.

(2) And the longitudinal comparison of the cost and the progress between the same project organizations.

(3) Differential comparison of efficiency between different professionals.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

In an embodiment, a nuclear power storage engineering quantity metering device is provided, and the nuclear power storage engineering quantity metering device corresponds to the nuclear power storage engineering quantity metering method in the embodiment one to one. As shown in fig. 9, the nuclear power storage engineering quantity metering device includes a work flow attribute index obtaining module 10, an efficiency data obtaining module 20, a structure diagram obtaining module 30 and a point value obtaining module 40. The functional modules are explained in detail as follows:

a work flow attribute index obtaining module 10, configured to obtain at least one work flow attribute index related to a workload of nuclear power storage.

And an obtaining efficiency data module 20, configured to analyze all workflow attribute indexes and obtain attribute efficiency data.

And the acquisition decomposition structure chart module 30 is configured to optimize the attribute efficiency data by using a task allocation algorithm based on at least one logistics stage, and acquire a work decomposition structure chart corresponding to the logistics stage.

And the point value obtaining module 40 is used for obtaining a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is a summary of the comprehensive engineering quantity involved in the logistics stage of each minimum item unit.

Preferably, the acquisition efficiency data module 20 includes an acquisition indicator data sub-module and a conversion indicator data sub-module. The functional modules are explained in detail as follows:

and the acquisition index data submodule is used for digitizing each workflow attribute index to acquire workflow attribute index data.

And the conversion index data submodule is used for converting the workflow attribute index data into attribute efficiency data through set definition.

Preferably, the module 30 for obtaining a structure diagram includes a task sub-module for modularization category, a sub-module for obtaining a first result, a sub-module for obtaining a second result, and a sub-module for drawing a structure diagram for decomposition. The detailed description of each functional module is as follows:

and the modularization category task submodule is used for modularizing the workflow attribute indexes included in the common category task and the special category task.

And the obtaining first result submodule is used for optimizing and assigning the workflow attribute indexes in the common category task by adopting Hungarian principle to obtain a first optimization result.

And the second result obtaining submodule is used for carrying out secondary analysis on the first optimization result to obtain a second optimization result.

And the drawing decomposition structure chart submodule is used for drawing a work decomposition structure chart corresponding to the logistics stage based on the second optimization result.

Preferably, the sub-module for obtaining the first result includes a sub-module for obtaining the logistics stage, a sub-module for obtaining the maximum matching times, and a sub-module for obtaining the first result. The detailed description of each functional module is as follows:

and the logistics stage acquisition sub-module is used for randomly acquiring a workflow attribute index as an optimization index by adopting Hungarian's law, acquiring a logistics stage which is most matched with the optimization index, and increasing the matching times of the optimization index and the logistics stage once.

And the maximum matching time obtaining sub-module is used for repeatedly executing the step of randomly obtaining a workflow attribute index as an optimization index by using Hungarian's law, obtaining the logistics stage which is most matched with the optimization index, and increasing the matching times of the optimization index and the logistics stage once until the maximum matching times corresponding to each logistics stage is obtained.

And the first result sub-module is used for matching the tuning indexes corresponding to the maximum matching times corresponding to the logistics stage as a first optimization result.

Preferably, the obtain second result submodule includes a form matching times set submodule and a obtain normal curve submodule. The functional modules are explained in detail as follows:

and forming a matching times set sub-module, which is used for collecting the matching times corresponding to each workflow attribute index in the logistics stage to form a matching times set.

And the normal curve acquisition submodule is used for acquiring a corresponding normal curve distribution diagram, a corresponding mean value and a standard deviation of the matching times set based on the matching times set, and setting a confidence interval which accords with engineering practice, wherein the confidence interval comprises the maximum matching times corresponding to the logistics stage.

The get point value module 40 preferably includes an get module time submodule and an get point value submodule. The functional modules are explained in detail as follows:

and the module time acquisition sub-module is used for acquiring at least one task attribute module and task attribute module time based on the work decomposition structure chart.

The point value obtaining sub-module is used for obtaining a point value corresponding to the logistics stage by adopting the following notations:

point value = sum of all task attribute module times job minimum number of people.

Preferably, the nuclear power storage engineering quantity metering device further comprises an evaluation cost matching module. The functional modules are explained in detail as follows:

and the evaluation cost matching module is used for obtaining point efficiency based on the point value and evaluating the matching condition of the labor hour investment and the engineering construction cost investment at a certain stage.

For specific limitations of the nuclear power storage engineering quantity metering device, reference may be made to the above limitations of the nuclear power storage engineering quantity metering method, and details are not described herein again. All or part of each module in the nuclear power storage engineering quantity metering device can be realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile medium, an internal memory. The non-volatile medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile media. The database of the computer equipment is used for data related to the nuclear power storage engineering quantity measuring method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the nuclear power storage engineering quantity measuring method.

In an embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the nuclear power warehousing engineering quantity measuring method according to the above embodiments is implemented, for example, in S10 to S40 shown in fig. 2. Alternatively, the processor executes the computer program to implement the functions of the modules/units of the nuclear power storage engineering quantity metering device in the above embodiments, such as the functions of the modules 10 to 40 shown in fig. 9. To avoid repetition, the description is omitted here.

In an embodiment, a computer readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the nuclear power warehousing engineering quantity measuring method of the above embodiment, such as S10 to S40 shown in fig. 2. Alternatively, the computer program is executed by a processor to implement the functions of each module/unit in the nuclear power storage engineering quantity metering device in the above-mentioned embodiment, for example, the functions of the modules 10 to 40 shown in fig. 9. To avoid repetition, the description is omitted here.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer readable medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database, or other medium used in the embodiments of the present application may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.

Claims

1. A nuclear power storage engineering quantity measuring method is characterized by comprising the following steps:

analyzing all the workflow attribute indexes to obtain attribute efficiency data;

optimizing the attribute efficiency data by adopting a task allocation algorithm based on at least one logistics stage to obtain a work decomposition structure chart corresponding to the logistics stage;

and acquiring a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is a summary of the comprehensive engineering quantity involved in the logistics stage of each minimum item unit.

2. The nuclear power warehousing project quantity measuring method according to claim 1, wherein the analyzing all the workflow attribute indexes to obtain attribute efficiency data comprises:

digitizing each workflow attribute index to obtain workflow attribute index data;

and converting the workflow attribute index data into attribute efficiency data through set definition.

3. The nuclear power warehousing project quantity measuring method according to claim 1, wherein the optimizing the attribute efficiency data by using a task allocation algorithm to obtain a work decomposition structure diagram corresponding to the logistics stage comprises:

modularizing workflow attribute indexes included in the common category tasks and the special category tasks;

optimizing and assigning workflow attribute indexes in the common category task by adopting Hungarian's law to obtain a first optimization result;

performing secondary analysis on the first optimization result to obtain a second optimization result;

and drawing a work decomposition structure diagram corresponding to the logistics stage based on the second optimization result.

4. The nuclear power warehousing project quantity measuring method of claim 3, wherein the optimizing and assigning workflow attribute indexes in the common category task by Hungarian's law to obtain a first optimization result comprises:

randomly acquiring one workflow attribute index as an optimization index by using Hungarian's law, acquiring a logistics stage which is most matched with the optimization index, and increasing the matching times of the optimization index and the logistics stage once;

repeatedly executing the step of randomly acquiring one workflow attribute index as an optimization index by using Hungarian's law, and acquiring a logistics stage which is most matched with the optimization index, and increasing the matching times of the optimization index and the logistics stage once until the maximum matching time corresponding to each logistics stage is acquired;

and matching the tuning indexes corresponding to the maximum matching times corresponding to the logistics stage to serve as the first optimization result.

5. The nuclear power warehousing project quantity measuring method according to claim 4, wherein the performing the secondary analysis on the first optimization result to obtain a second optimization result comprises:

collecting the matching times corresponding to each workflow attribute index in the logistics stage to form a matching time set;

and acquiring a corresponding normal curve distribution diagram, a corresponding mean value of the matching times set and a standard deviation based on the matching times set, and setting a confidence interval according with engineering practice, wherein the confidence interval comprises the maximum matching times corresponding to the logistics stage.

6. The nuclear power warehousing project quantity measuring method according to claim 1, wherein the obtaining of the point value corresponding to the logistics stage based on the work breakdown structure diagram comprises:

based on the work decomposition structure chart, acquiring at least one task attribute module and task attribute module time;

obtaining a point value corresponding to the logistics stage by adopting the following notations:

7. The nuclear power warehousing project quantity measuring method according to claim 1, further comprising, after acquiring the point value corresponding to the logistics stage:

and based on the point value, obtaining the point efficiency for evaluating the matching condition of the labor hour investment and the engineering construction cost investment at a certain stage.

8. The utility model provides a nuclear power storage engineering volume metering device which characterized in that includes:

the acquisition workflow attribute index module is used for acquiring at least one workflow attribute index related to the workload of the nuclear power storage;

the efficiency data acquisition module is used for analyzing all the workflow attribute indexes and acquiring attribute efficiency data;

a decomposition structure chart acquisition module used for optimizing the attribute efficiency data by adopting a task allocation algorithm based on at least one logistics stage and acquiring a work decomposition structure chart corresponding to the logistics stage;

and the point value acquisition module is used for acquiring a point value corresponding to the logistics stage based on the work decomposition structure diagram, wherein the point value is the summary of the comprehensive engineering quantity related to each minimum item unit in the logistics stage.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the nuclear power warehousing engineering quantity method of any of claims 1 to 7 when executing the computer program.

10. A computer-readable medium, in which a computer program is stored which, when being executed by a processor, carries out the nuclear power warehousing project quantity method according to any one of claims 1 to 7.