CN113779402B - Novel system capacity demand generation method - Google Patents

Abstract

The invention discloses a new system capacity demand generation method, which belongs to the field of system capacity analysis, and comprises the following specific steps: (1) The staff inputs the system name and collects the corresponding capacity requirement; (2) Constructing a capacity demand table and sequencing the importance of the capacity demand table; (3) Constructing an analysis model, and calculating the duty ratio of each capacity requirement; (4) Generating a conclusion report, and feeding back to a worker for analysis and adjustment; (5) Uploading the conclusion report to a cloud server, and enabling a user to select and view the conclusion report; the invention can analyze the capability requirements of different types of systems, reduce the use limitation, improve the working efficiency of staff, save the time of the staff, explain the terminology in the theoretical report, reduce the reading limitation, improve the experience of common users for consulting, strengthen the popularity of knowledge and improve the correctness of the theoretical report.

Description

A new method for generating system capability requirements

Technical field

The present invention relates to the field of system capability analysis, and in particular to a new system capability requirement generation method.

Background technique

With the rapid development of information technology, people can analyze the corresponding capability requirements of various systems and rationally allocate the capabilities of the system based on the analysis results. The analysis methods mainly include analytic hierarchy process, mathematical programming, and data envelopment. Analysis, artificial neural network method, entropy value method; fuzzy comprehensive evaluation method, etc. Capacity demand analysis is conducive to real-time scheduling and elimination of deviations generated in the system. If such deviations cannot be eliminated, it may lead to chaos and disorder in production; therefore, It becomes particularly important to invent a new method for generating system capability requirements;

After searching, Chinese patent number CN105069696A discloses an avionics system capability requirement analysis method based on an architecture framework. Although this invention can shorten the development cycle and save development costs, it can only analyze a single system and cannot satisfy the staff. The need to analyze different systems reduces the work efficiency of staff; in addition, the conclusion report generated by the existing new system capability requirement generation method can only be understood by relevant staff, which has a large audience limit and affects the viewing experience of ordinary users; for this reason , we propose a new system capability requirements generation method.

Contents of the invention

The purpose of the present invention is to propose a new method for generating system capability requirements in order to solve the defects existing in the existing technology.

In order to achieve the above objects, the present invention adopts the following technical solutions:

A new method for generating system capability requirements. The specific steps of this generation method are as follows:

(1) The staff inputs the system name and collects the corresponding capability requirements: the staff inputs the system name into the computing device, the computing device communicates with the Internet, and collects and filters the relevant capability requirements;

(2) Construct a capability requirement table and rank it by importance: Intelligently construct a capability requirement table and enter the screened capability requirements into the table. At the same time, a convolutional neural network is constructed, and the capability requirement table is processed by the convolutional neural network. The Pythagorean fuzzy number converted from the corresponding evaluation by a certain semantic conversion standard is calculated, and the importance is sorted based on the calculation results;

(3) Build an analysis model and calculate the proportion of each capability requirement: Build an analysis model based on the sorted capability requirement table, import each capability requirement in sequence according to the order in the table, and weight each capability requirement. Classify and calculate, and enter the calculation results into the capacity demand table;

(4) Generate a conclusion report and feed it back to the staff for analysis and adjustment: intelligently generate a conclusion report based on the calculation results, and feed the generated conclusion report back to the staff, who will review the conclusion report and analyze and adjust it at the same time;

(5) Upload the conclusion report to the cloud server, and the user can select and view it: the cloud server receives the conclusion report and stores it in an orderly manner. At the same time, the user can communicate with the cloud server through smart devices and review the conclusion report. Make a selection to view.

Further, the specific steps for collection and screening described in step (1) are as follows:

Step 1: The computing device receives the system name N, conducts an accurate search on the Internet based on N, and collects the relevant task intelligence and technical data found;

Step 2: Extract the capability requirements from the collected relevant task intelligence and technical data, and divide them into 26 groups according to the initials A to Z. At the same time, each capability requirement in each group of data is deduplicated and processed. Feedback completed capability requirements to staff;

Step 3: Staff review various capability requirements and make manual adjustments.

Further, the specific steps of importance ranking described in step (2) are as follows:

The first step: the convolutional neural network grabs the reference system from the Internet, and uses certain semantic conversion standards to convert the corresponding evaluation into Pythagorean fuzzy numbers to evaluate the reference system's ability to complete the corresponding task, and at the same time generate a fuzziness evaluation;

Step 2: Calculate the importance of each capability requirement. The specific calculation formula is as follows:

Among them, V(p _n ) represents the sorted value of p _n , p _n represents the Pythagorean fuzzy number, and n is a natural number, and n is not greater than the total task amount;

Step 3: Start comparing the sizes of V(p _n ) of each group, sort them by importance from large to small, and update the capability demand table based on the sorting results.

Further, the specific steps of the classification calculation described in step (3) are as follows:

S1: The analysis model automatically captures the solution set and attribute set corresponding to the capability requirements, and generates a decision matrix. The specific decision matrix is as follows:

Among them, R represents the decision matrix, r _ij is the Pythagorean fuzzy number, and r _ij =＜μ _ij ,v _ij >, μ _ml and v _ml represent the degree of membership and non-membership respectively;

S2: Calculate the corresponding weight of each group of capability requirements. The specific calculation formula is as follows:

Among them, ω _y represents the weight of the corresponding ability demand, and π _ij represents the hesitation degree of the Pythagorean fuzzy number;

S3: Conduct a hierarchical analysis on the weight of each capability requirement, generate the result data, draw a pie chart, and mark the proportion of each capability requirement.

Further, the specific steps for analysis and adjustment described in step (4) are as follows:

SS1: The staff reviews the relevant conclusion reports, annotates the professional terms in them, and makes modifications and notes on the erroneous data in the conclusion reports;

SS2: The convolutional neural network receives the conclusion report completed by the staff annotation, and replaces and adjusts the annotation position. At the same time, the convolutional neural network is connected to the Internet communication, retrieves the professional terms in the conclusion report, and replaces the sentences at the same time;

SS3: Collect modification note locations, record error causes, and generate data repair logs.

Further, the specific steps for selecting and viewing described in step (5) are as follows:

P1: The user inputs the conclusion report number N that needs to be found through the smart device, and the cloud server receives N and performs data retrieval based on N;

P2: The cloud server sends the retrieved conclusion report to the user's smart device for the user to view.

Further, the smart devices described in step (5) include smart phones, tablet computers and laptop computers.

Compared with the existing technology, the beneficial effects of the present invention are:

1. Compared with the previous method of generating capability requirements for a single system, this new system capability requirement generation method requires the staff to input the system name into the computing device, the computing device is connected to the Internet, and an accurate search is performed from the Internet based on the system name, and Collect the searched relevant task intelligence and technical data, extract the capability requirements of the collected relevant task intelligence and technical data, and classify them. At the same time, the system capability requirements are analyzed through the convolutional neural network, which can analyze different types of tasks. Analyze system capability requirements to reduce usage limitations, improve staff efficiency, and save staff time;

2. This new system capability requirement generation method generates a conclusion report based on the weight of each capability requirement and feeds it back to the staff. The staff review the relevant conclusion report and annotate the professional terms in it, and at the same time correct the erroneous data in the conclusion report. To make modification notes, the convolutional neural network receives the conclusion report completed by the staff annotation, and replaces and adjusts the position of the annotation. At the same time, the convolutional neural network searches for the professional terms in the conclusion report, and replaces the sentences at the same time, and adjusts the position of the modification remarks. Collect, record the causes of errors, and generate data repair logs, which can explain professional terms in the conclusion report, reduce reading limitations, improve the viewing experience for ordinary users, enhance knowledge popularization, and improve the accuracy of the conclusion report. .

Description of drawings

The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

Figure 1 is a flow chart of a new system capability requirement generation method proposed by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments.

In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inner", " The orientation or positional relationship indicated by "outside" and so on is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation. Specific orientations of construction and operation are therefore not to be construed as limitations of the invention.

Example 1

Referring to Figure 1, a new system capability demand generation method is shown. This embodiment specifically discloses a collection and screening method:

The staff inputs the system name and collects the corresponding capability requirements: The staff inputs the system name into the computing device, which communicates with the Internet and collects and filters the relevant capability requirements.

Specifically, the computing device receives the system name N, conducts an accurate search in the Internet based on N, collects the relevant task intelligence and technical data that have been searched, extracts the capability requirements from the collected relevant task intelligence and technical data, and performs a search according to the first The letters A to Z are divided into 26 groups. At the same time, each capability requirement in each group of data is deduplicated and the completed capability requirements are fed back to the staff. The staff checks each capability requirement and conducts a review on it. Manual adjustment.

Among them, it should be further explained that computing devices include desktop computers and laptop computers.

Construct a capability requirement table and rank it by importance: Intelligently construct a capability requirement table and enter the screened capability requirements into the table. At the same time, a convolutional neural network is constructed, and the convolutional neural network sorts the capabilities requirements in the capability requirement table. The corresponding evaluation is calculated based on the Pythagorean fuzzy number converted from a certain semantic conversion standard, and the importance is ranked based on the calculation results.

Specifically, the convolutional neural network grabs the reference system from the Internet, and uses certain semantic conversion standards to convert the corresponding evaluation into Pythagorean fuzzy numbers to evaluate the reference system's ability to complete the corresponding tasks. At the same time, it generates a fuzzy evaluation to evaluate each Calculate the importance of each capability requirement, start to compare the sorting values of each group, and sort the importance from large to small, and update the capability requirement table based on the sorting results.

What needs further explanation is that the specific calculation formula for its ranking value is as follows:

Among them, V(p _n ) represents the sorted value of p _n , p _n represents the Pythagorean fuzzy number, and n is a natural number, and n is not greater than the total task amount.

Build an analysis model and calculate the proportion of each capability requirement: Build an analysis model based on the sorted capability requirement table, import each capability requirement in sequence according to the order in the table, and classify and calculate the weight of each capability requirement. At the same time, the calculation results are entered into the capacity demand table.

Specifically, the analysis model automatically captures the solution set and attribute set corresponding to the capability requirements, and generates a decision matrix. At the same time, it calculates the corresponding weights of each group of capability requirements, performs hierarchical analysis on the weight of each capability requirement, and generates the result data. , draw a pie chart at the same time, and mark the proportion of each capability requirement.

What needs further explanation is that its specific decision matrix is as follows:

Among them, R represents the decision matrix, r _ij is the Pythagorean fuzzy number, and r _ij =＜μ _ij ,v _ij >, μ _ml and v _ml represent the degree of membership and non-membership respectively;

The specific calculation formula of its weight is as follows:

Among them, ω _y represents the weight of the corresponding ability demand, and π _ij represents the hesitation degree of the Pythagorean fuzzy number;

In this embodiment, a convolutional neural network is used to receive the conclusion report completed by the staff annotation, and the annotation position is replaced and adjusted. At the same time, the convolutional neural network searches for the professional terms in the conclusion report, and at the same time replaces the sentences and makes modifications. Collect note locations, record the cause of the error, and generate a data repair log, which can explain professional terms in the conclusion report, reduce reading limitations, improve the viewing experience for ordinary users, enhance knowledge popularization, and at the same time improve the accuracy of the conclusion report. Correctness.

Example 2

Referring to Figure 1, a new method for generating system capability requirements is shown. In addition to the same structure as above, this embodiment specifically discloses an analysis and adjustment method:

Generate a conclusion report and feed it back to the staff for analysis and adjustment: Intelligently generate a conclusion report based on the calculation results, and feed the generated conclusion report back to the staff. The staff will review the conclusion report and analyze and adjust it at the same time.

Specifically, the staff reviewed the relevant conclusion report and annotated the professional terms in it, and at the same time modified and commented on the erroneous data in the conclusion report. The convolutional neural network received the conclusion report completed by the staff annotation and replaced and adjusted the position of the annotation. At the same time, the convolutional neural network is connected to the Internet, retrieves professional terms in the conclusion report, replaces the sentences, collects modification note locations, records the cause of the error, and generates a data repair log.

Upload the conclusion report to the cloud server, and the user can select and view it: the cloud server receives the conclusion report and stores it in an orderly manner. At the same time, the user can communicate with the cloud server through smart devices and select and view the conclusion report. .

Specifically, the user inputs the number N of the conclusion report that needs to be searched through the smart device. The cloud server receives N and performs data retrieval based on N. The cloud server sends the retrieved conclusion report to the user's smart device for the user to view.

It should be further explained that smart devices include smartphones, tablets and laptops;

In this embodiment, the computing device is connected to the Internet, and the system name is accurately retrieved from the Internet, and the relevant task intelligence and technical data collected are collected, and the capability requirements are extracted from the collected relevant task intelligence and technical data. , and perform classification processing. At the same time, the system capability requirements are analyzed through the convolutional neural network, which can analyze the capability requirements of different types of systems, reduce usage limitations, improve staff work efficiency, and save staff time.

The above are only preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person familiar with the technical field can, within the technical scope disclosed in the present invention, implement the technical solutions of the present invention. Equivalent substitutions or changes of the inventive concept thereof shall be included in the protection scope of the present invention.

Claims (7)

1. A new method for generating system capability requirements, which is characterized in that the specific steps of the generation method are as follows: (1) The staff inputs the system name and collects the corresponding capability requirements: the staff inputs the system name into the computing device, the computing device communicates with the Internet, and collects and filters the relevant capability requirements; (2) Construct a capability requirement table and rank it by importance: Intelligently construct a capability requirement table and enter the screened capability requirements into the table. At the same time, a convolutional neural network is constructed, and the capability requirement table is processed by the convolutional neural network. The Pythagorean fuzzy number converted from the corresponding evaluation by a certain semantic conversion standard is calculated, and the importance is sorted based on the calculation results; (3) Build an analysis model and calculate the proportion of each capability requirement: Build an analysis model based on the sorted capability requirement table, import each capability requirement in sequence according to the order in the table, and weight each capability requirement. Classify and calculate, and enter the calculation results into the capacity demand table; (4) Generate a conclusion report and feed it back to the staff for analysis and adjustment: intelligently generate a conclusion report based on the calculation results, and feed the generated conclusion report back to the staff, who will review the conclusion report and analyze and adjust it at the same time; (5) Upload the conclusion report to the cloud server, and the user can select and view it: the cloud server receives the conclusion report and stores it in an orderly manner. At the same time, the user can communicate with the cloud server through smart devices and review the conclusion report. Make a selection to view. 2. A new system capability demand generation method according to claim 1, characterized in that the specific steps of collection and screening described in step (1) are as follows: Step 1: The computing device receives the system name N, conducts an accurate search on the Internet based on N, and collects the relevant task intelligence and technical data found; Step 2: Extract the capability requirements from the collected relevant task intelligence and technical data, and divide them into 26 groups according to the initials A to Z. At the same time, each capability requirement in each group of data is deduplicated and processed. Feedback completed capability requirements to staff; Step 3: Staff review various capability requirements and make manual adjustments. 3. A new system capability demand generation method according to claim 1, characterized in that the specific steps of importance ranking in step (2) are as follows: The first step: the convolutional neural network grabs the reference system from the Internet, and uses certain semantic conversion standards to convert the corresponding evaluation into Pythagorean fuzzy numbers to evaluate the reference system's ability to complete the corresponding task, and at the same time generate a fuzziness evaluation; Step 2: Calculate the importance of each capability requirement. The specific calculation formula is as follows: Among them, V(p _n ) represents the sorted value of p _n , p _n represents the Pythagorean fuzzy number, and n is a natural number, and n is not greater than the total task amount; Step 3: Start comparing the sizes of V(p _n ) of each group, sort them by importance from large to small, and update the capability demand table based on the sorting results. 4. A new system capability demand generation method according to claim 1, characterized in that the specific steps of classification calculation in step (3) are as follows: S1: The analysis model automatically captures the solution set and attribute set corresponding to the capability requirements, and generates a decision matrix. The specific decision matrix is as follows: Among them, R represents the decision matrix, r _ij is the Pythagorean fuzzy number, and r _ij =＜μ _ij ,v _ij >, μ _ml and v _ml represent the degree of membership and non-membership respectively; S2: Calculate the corresponding weight of each group of capability requirements. The specific calculation formula is as follows: Among them, ω _y represents the weight of the corresponding ability demand, and π _ij represents the hesitation degree of the Pythagorean fuzzy number; S3: Conduct a hierarchical analysis on the weight of each capability requirement, generate the result data, draw a pie chart, and mark the proportion of each capability requirement. 5. A new system capability demand generation method according to claim 4, characterized in that the specific steps of analysis and adjustment described in step (4) are as follows: SS1: The staff reviews the relevant conclusion reports, annotates the professional terms in them, and makes modifications and notes on the erroneous data in the conclusion reports; SS2: The convolutional neural network receives the conclusion report completed by the staff annotation, and replaces and adjusts the annotation position. At the same time, the convolutional neural network is connected to the Internet communication, retrieves the professional terms in the conclusion report, and replaces the sentences at the same time; SS3: Collect modification note locations, record error causes, and generate data repair logs. 6. A new system capability demand generation method according to claim 1, characterized in that the specific steps for selecting and viewing described in step (5) are as follows: P1: The user inputs the conclusion report number N that needs to be found through the smart device, and the cloud server receives N and performs data retrieval based on N; P2: The cloud server sends the retrieved conclusion report to the user's smart device for the user to view. 7. A new method for generating system capability requirements according to claim 1, characterized in that the smart devices in step (5) include smart phones, tablet computers and notebook computers.