An intelligent planning method for mechanical design projects

Abstract

The present invention discloses an intelligent planning method for mechanical design projects. The steps include: annotating historical mechanical design project planning data and training an intelligent recommendation algorithm model for project planning; establishing a multi-objective collaborative optimization algorithm model with the time, cost, resources, and quality of the project as optimization objectives; constructing a project intelligent planning system that can output an optimized project planning scheme; collecting the characteristic information of the mechanical design project to be planned and importing it into the project intelligent planning system; judging the similarity between the time, cost, resources invested, and quality of the planning scheme of the recommended project and the result of multi-objective collaborative optimization, and outputting an optimized recommendation or scheme. The present invention can reduce the project management planning time, cost, etc., and effectively improve the quality and efficiency of project planning, and is applicable to multi-dimensional information interaction and multi-objective collaborative optimization.

Description

Technical Field

[0001] The present invention relates to computer big data processing technology, and in particular to an intelligent planning method for a mechanical design project. Background Art

[0002] Mechanical equipment design not only involves mechanical design work, but also involves information interaction evaluation and verification with simulation, process, manufacturing, assembly and other links. Therefore, project management in mechanical equipment design faces various challenges, especially with the development of computer technology today, which has gradually integrated simulation, process, computer-aided manufacturing, assembly and other aspects into the actual mechanical design work process. The amount of information interaction in each task dimension in the design has increased dramatically, and the difficulty and workload of project management planning caused by this have also doubled, seriously affecting the cycle, quality and cost of the design project.

[0003] At present, project management in mechanical equipment design is mostly planned for the work content and time of each dimension. For example, CN202010998971 discloses a real-time progress management system for engineering construction projects. The system organizes and marks the collected relevant data through a data processing unit, and the progress calculation unit calculates the relevant data after organization and marking, so as to obtain the impact value of the corresponding project duration, and calculates the estimated number of days for the duration based on the impact value, saving the time consumed by analyzing the data. However, this patent only considers the key nodes of the project duration, and does not integrate the key dimensions that affect the project progress, such as the material demand supply at each stage and the quality inspection interaction, into the project management process, and the goal is relatively single. In addition, in the actual project management process, multiple goals such as resources, cost, and quality are also involved. How to quickly and reasonably plan the various task dimensions of the project, the interactive information between the various task dimensions, and the coordinated optimization under multiple goals to obtain the optimal project management planning scheme is a problem that urgently needs to be solved in today's project management, especially in project management planning in manufacturing fields such as mechanical equipment design. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides an intelligent planning method for mechanical design projects that can reduce project management planning time and cost, effectively improve the quality and efficiency of project planning, and is suitable for multi-dimensional information interaction and multi-objective collaborative optimization.

[0005] The purpose of the present invention is achieved through the following technical solutions.

[0006] A method for intelligent planning of mechanical design projects, comprising the following steps:

[0007] 1) Annotate historical mechanical design project planning data and train the project planning intelligent recommendation algorithm model;

[0008] 2) Establish a multi-objective collaborative optimization algorithm model with the time, cost, resources and quality of the calculation project as the optimization objectives;

[0009] 3) Build a project intelligent planning system that can output an optimized project planning scheme;

[0010] 4) Collect the characteristic information of the mechanical design project to be planned and import it into the project intelligent planning system;

[0011] 5) Judge the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the results of multi-objective collaborative optimization, and output recommended suggestions or schemes.

[0012] The intelligent recommendation algorithm model in step 1) is a fully connected neural network algorithm model, and the fully connected neural network recommendation algorithm is used to calculate the planning scheme of the recommended project.

[0013] Step 1) specifically includes the following steps:

[0014] 1.1) Collect historical mechanical design project planning management data, where the historical mechanical design project planning management data includes project characteristic information and planning information. The project characteristic information includes task dimension, key time nodes, cost requirements, quality requirements, planned number of personnel to be invested, and project workload; the planning information includes the work task arrangements in each dimension, time plan, and interaction plan of information and tasks;

[0015] 1.2) Establish a mapping relationship between the project characteristic information and the planning information to form data annotation of historical project management;

[0016] 1.3) Import the annotated data into the fully connected neural network algorithm model to train the model.

[0017] The planning scheme of the recommended project in step 1) is the work task arrangements in each dimension of the project planning, time plan, and interaction plan of tasks in each dimension.

[0018] The multi-objective collaborative optimization algorithm model in step 2) is a dynamic multi-objective optimization algorithm using multi-particle swarm collaboration. Taking the project work task volume as a constraint condition, and project time, cost, invested resources, and quality as optimization objectives, input the work task volume, time, cost, invested resources, and quality data of historical projects into the dynamic multi-objective optimization algorithm of multi-particle swarm collaboration to obtain the relevant parameters of the multi-objective collaborative optimization algorithm model and establish the multi-objective collaborative optimization algorithm model.

[0019] In step 3), the project intelligent planning system includes: an information collection module, a calculation module, and a project planning output module; the calculation module includes a fully connected neural network recommendation algorithm sub-module and a dynamic multi-objective optimization algorithm sub-module of multi-particle swarm collaboration.

[0020] When the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme in step 5) and the result of multi-objective collaborative optimization is greater than or equal to the set value, the project planning scheme is directly output; when the similarity between the two results is less than the set value, suggestions for modifying the project feature information are output, and the modified project feature information is input into the fully connected neural network recommendation algorithm sub-module to re-output the recommended planning scheme.

[0021] The set value of the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the result of multi-objective collaborative optimization is 80%.

[0022] The suggestions for modifying the project feature information output in step 5) are calculated based on the calculation results of the dynamic multi-objective optimization algorithm of multi-particle swarm collaboration combined with the set similarity value.

[0023] The recommendation result in step 5) is to use the output module of the system to display the project planning result recommended by the calculation module in the form of an interactive graph or table.

[0024] Compared with the prior art, the advantages of the present invention are as follows: The present invention utilizes the big data of historical mechanical design project management, deep learning, and dynamic multi-objective collaboration technology, integrates factors such as multi-task dimensions, information interaction in complex project management, and optimization of the relationship between project time, cost, and quality into project planning, and gives a reasonable project planning scheme according to the actual project conditions. The method of the present invention can simply and efficiently recommend a project management planning scheme, which is easy to promote and apply in project-related management planning such as engineering and manufacturing. Brief Description of the Drawings

[0025] Figure 1 Flowchart of the present invention.

[0026] Figure 2 Overall schematic diagram of the two-dimensional project planning for the design and simulation of automotive welding fixtures.

[0027] Figure 3 Two-dimensional project planning diagram for the design and simulation of automotive welding fixtures under the access status (selecting devices #14 - 20, 23 - 26).

[0028] Figure 4 When adding designers Figure 3 of the two-dimensional project planning diagram.

[0029] Figure 5 Overall three-dimensional project planning diagram for the design, simulation, and process of automotive welding fixtures. Detailed Description of the Invention

[0030] The present invention will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0031] As Figure 1As shown in the figure, an intelligent planning method for mechanical design projects includes the following steps:

[0032] 1) Label the historical mechanical design project planning data and train the intelligent recommendation algorithm model for project planning;

[0033] 2) Establish a multi-objective collaborative optimization algorithm model with the time, cost, resources, and quality of the project as the optimization objectives;

[0034] 3) Build an intelligent project planning system that can output an optimized project planning scheme;

[0035] 4) Collect the characteristic information of the mechanical design project to be planned and import it into the intelligent project planning system;

[0036] 5) Judge the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the results of multi-objective collaborative optimization, and output the recommendation suggestions or schemes.

[0037] The intelligent recommendation algorithm model in step 1) is a fully connected neural network algorithm model, and the fully connected neural network recommendation algorithm is used to calculate the planning scheme of the recommended project.

[0038] Step 1) specifically includes the following steps:

[0039] 1.1) Collect the historical mechanical design project planning management data, where the historical mechanical design project planning management data includes project characteristic information and planning information. The project characteristic information includes task dimension, key time nodes, cost requirements, quality requirements, planned number of personnel to be invested, and project workload; the planning information includes the work task arrangements in each dimension, time plan, information and task interaction plan;

[0040] 1.2) Establish the mapping relationship between the project characteristic information and the planning information to form the data annotation of historical project management;

[0041] 1.3) Import the annotated data into the fully connected neural network algorithm model and train the model.

[0042] The planning scheme of the recommended project in step 1) is the work task arrangements in each dimension of the project planning, time plan, and the interaction plan of each dimension task.

[0043] The multi-objective collaborative optimization algorithm model in step 2) is a dynamic multi-objective optimization algorithm using multi-particle swarm collaboration. Taking the project workload as the constraint condition, and the project time, cost, invested resources, and quality as the optimization objectives, input the workload, time, cost, invested resources, and quality data of historical projects into the dynamic multi-objective optimization algorithm of multi-particle swarm collaboration to obtain the relevant parameters of the multi-objective collaborative optimization algorithm model and establish the multi-objective collaborative optimization algorithm model.

[0044] The project intelligent planning system in step 3) includes: an information collection module, a calculation module, and a project planning output module; the calculation module includes a fully connected neural network recommendation algorithm sub-module and a dynamic multi-objective optimization algorithm sub-module with multi-particle swarm collaboration.

[0045] The cost, quality, and time of the planning result recommended by the fully connected neural network recommendation algorithm sub-module in step 5) are calculated based on the recommended planning scheme. Among them, the time is obtained based on the input time node, the quality is obtained based on the number of personnel inputs and time, and can be quantified by the number of problems of each device. The cost is calculated based on the number of people invested, labor costs, and company operation costs, while the calculation results of the dynamic multi-objective optimization algorithm sub-module with multi-particle swarm collaboration are directly the cost, quality, and time of the project plan.

[0046] When the similarity between the time, cost, resources invested, and quality of the project planning scheme recommended in step 5) and the result of multi-objective collaborative optimization is greater than or equal to the set value, the project planning scheme is directly output; when the similarity between the two results is less than the set value, suggestions for modifying project feature information are output, and the modified project feature information is input into the fully connected neural network recommendation algorithm sub-module to re-output the recommended planning scheme.

[0047] The set value of the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the result of multi-objective collaborative optimization is 80%, preferably 90%.

[0048] The suggestions for modifying project feature information output in step 5) are calculated based on the calculation results of the dynamic multi-objective optimization algorithm with multi-particle swarm collaboration combined with the set similarity value.

[0049] The recommendation result in step 5) is to use the output module of the system to display the project planning result recommended by the calculation module in the form of an interactive graph or table.

[0050] Example 1: Two-dimensional project management recommendation for fixture structure design and simulation in automotive welding fixture design

[0051] S1: Historical project management data collection and training of the recommended model for planning solutions. In Example 1, 830 sets of historical management data of automotive welding fixture design and simulation projects were collected. Each set of data contains project feature information and planning information. The specific feature information includes the workload, task dimension, key time nodes, cost requirements, quality requirements, and planned number of personnel input for each of the design and simulation projects; the project planning information (based on the actual implementation at the end of the project) includes the respective work plans, interactive work plans, and actual personnel input under the two dimensions of design and simulation. Then, establish the mapping relationship between project features and planning to form data labels for historical project management. Import the above-mentioned 830 sets of labeled project feature information and planning information labels into the established fully connected neural network model to train the model.

[0052] S2: Establishment of a multi-objective optimization algorithm model with multi-particle swarm collaboration. Take the project workload as a constraint condition, and project time, cost, input resources, and quality as optimization objectives to establish a preliminary multi-objective optimization algorithm model with multi-particle swarm collaboration. Then, input the data of workload, time, cost, input resources, and quality of the two dimensions of 830 historical projects into the model to obtain the relevant parameters of the model and obtain the multi-objective optimization algorithm model. Set the time as a dynamic parameter in the model. When the time changes, use Cauchy mutation for each parameter set to mutate its optimal set. By reasonably adjusting the compensation of Cauchy mutation, not only can the set obtain greater diversity, but also the search information of the previous time can be retained.

[0053] S3: Package the above-trained fully connected neural network model and the established multi-objective optimization algorithm model with multi-particle swarm collaboration into corresponding modules respectively, and construct a project intelligent planning system as shown in Figure 1 as shown.

[0054] S4: Import of information of the project to be planned. Import the new two-dimensional key feature information of the project management for which the recommended planning solution is to be recommended into the project intelligent planning system constructed in S3. The key feature information is as follows:

[0055] a. Key time nodes: Project start time: October 10, 2021; First review time: October 20, 2021; Second review time: November 10, 2021; Third review time: November 25, 2021; 3D freeze time: December 5, 2021; 2D drawing: December 10, 2021; Pneumatic circuit diagram and various reports: December 25, 2021;

[0056] b. The number of problem points for each version of data of a single set of equipment does not exceed 10;

[0057] c. The number of designers is 6, and the number of simulation personnel is 2;

[0058] d. The number of designed devices is 40 sets, among which the device numbers #1-5, #7-10, #14-20, #26-31, #35-36 have high priority;

[0059] e. The similarity is set to 90%.

[0060] S5: Judge the similarity between the time, cost, resources invested, and quality of the planning scheme of the recommended project and the result of multi-objective collaborative optimization, and output the recommended suggestions or schemes. According to the planning scheme recommended by the fully neural network algorithm module, calculate the project time, cost, resources invested, and quality. Since time and invested resources have a serious impact on cost and quality, the two items of invested resources and quality are weighted respectively, and the weighting coefficients are n and m respectively, forming a vector [T, C, nR, mQ]. In this embodiment, the project time calculated by the recommended scheme is 76 days; the cost is calculated according to the number of people invested, the labor cost per person, and the company's operating cost, and after calculation, it is about 400,000 yuan; the quality is evaluated by the number of problem points in the design scheme when there is a certain design workload. The number of design problem points is directly related to time when the personnel ability and experience are certain. In this embodiment, the number of quality problem points is 10. Among them, the weighting coefficients of invested resources and the number of quality problem points are n = 8 and m = 4, then the vector obtained from the planning scheme recommended by the fully neural network algorithm module is [76, 40, 64, 40]. Similarly, the calculation results of the particle swarm collaborative dynamic multi-objective optimization algorithm: time 62 days, cost 402,000 yuan, number of problems 6, number of designers 8, number of simulation people 3, forming a vector of [62, 40.2, 88, 24].

[0061] Calculating the similarity between the two vectors is to use the cosine value between the vector [76, 40, 64, 40] obtained from the recommended plan and the vector [62, 40.2, 88, 24] obtained from multi-objective optimization as the similarity. The closer the cosine value is to 1, the greater the similarity. After calculation, the similarity between the two vectors in Embodiment 1 is 96.20%, which meets the requirement that the value range in the embodiment is greater than or equal to 90%. Therefore, directly output the fixture design, simulation, and the interactive planning scheme including the project. The result is shown in Figure 2 shown. In Figure 2 you can select any specific device in the project to view the overall plan, such as Figure 3 is the plan for device numbers 14-20 and 23-26.

[0062] If the number of designers invested is changed to 7, the project recommended planning scheme is as shown in Figure 4 shown (the selected device numbers for viewing are still 14-20 and 23-26). Among them, the similarity between the time, cost, resources invested, and quality of the planning scheme recommended by the fully connected neural network recommendation algorithm sub-module and the calculation results of the multi-objective optimization algorithm is 99.2%.

[0063] Example 2: 3D project management for fixture structure design, simulation, and process planning in automotive welding fixture design.

[0064] S1: Historical project management data collection and training of a recommended model for planning solutions. The historical management data of automotive welding fixture design, simulation, and process planning projects collected in Example 2 is 950 sets. Each set of data contains project feature information and planning information. The specific feature information includes the workload, task dimension, key time nodes, cost requirements, quality requirements, and planned number of personnel input for each of the design, simulation, and process projects; the project planning information (based on the actual implementation at the end of the project) includes: the respective work plans, interactive work plans, and actual personnel input under the three dimensions of design, simulation, and process. Then, establish the mapping relationship between project features and planning to form data tags for historical project management. Import the above-labeled 950 sets of project feature information and planning information tags into the established fully connected neural network model to train the model.

[0065] S2: Establishment of a multi-objective optimization algorithm model with multi-particle swarm cooperation. Taking the project workload as a constraint condition and project time, cost, input resources, and quality as optimization objectives, establish a preliminary multi-objective optimization algorithm model with multi-particle swarm cooperation. Then, input the data of workload, time, cost, input resources, and quality in the three dimensions of 950 historical projects into the model to obtain relevant model parameters and obtain the multi-objective optimization algorithm model. The time in the model is set as a dynamic parameter. When the time changes, Cauchy mutation is used to mutate its optimal set for each parameter set. By reasonably adjusting the compensation of Cauchy mutation, not only can the set obtain greater diversity, but also the search information of the previous time can be retained.

[0066] S3: Package the above-trained fully connected neural network model and the established multi-objective optimization algorithm model with multi-particle swarm cooperation into corresponding modules respectively, and construct a project intelligent planning system similar to that in Example 1.

[0067] S4: Import of information of the project to be planned. Import the key feature information of the new 3D project management for which a recommended planning solution is to be recommended into the project intelligent planning system constructed in S3. The key feature information is as follows:

[0068] a. Key time nodes: Project start time: October 10, 2021; First review time: October 20, 2021; Second review time: November 10, 2021; Third review time: November 25, 2021; 3D freeze time: December 5, 2021; 2D drawing: December 10, 2021; Pneumatic circuit diagram and various reports: December 25, 2021;

[0069] b. The number of problem points for each version of data of a single set of equipment does not exceed 10;

[0070] c. The number of designers is 6, the number of simulation engineers is 2, and the number of process engineers is 2;

[0071] d. The number of design devices is 40, and among them, the device numbers #1-5, #7-10, #14-20, #26-31, #35-36 have high priority;

[0072] e. The similarity is set to 90%.

[0073] S5: Judge the similarity between the time, cost, resources invested, and quality of the planning scheme of the recommended project and the result of multi-objective collaborative optimization, and output the recommended suggestions or schemes. According to the planning scheme recommended by the full neural network algorithm module, calculate the project time, cost, resources invested, and quality. In this embodiment, the project time calculated by the recommended scheme is 76 days; the cost is calculated based on the number of people invested, the labor cost per person, and the company's operating cost, and after calculation, it is about 510,000 yuan; the quality is evaluated by the number of problem points in the design scheme when there is a certain amount of design work. The design problem points are directly related to time when the personnel capabilities and experience are certain. In this embodiment, the number of quality problem points is 10. Among them, the weighted coefficients n for the invested resources and quality problems are 8, and m is 4. Then the vector obtained from the planning scheme recommended by the full neural network algorithm module is [76, 51, 80, 40]. Similarly, the calculation results of the particle swarm collaborative dynamic multi-objective optimization algorithm: time 70 days, cost 540,000 yuan, number of problems 6, number of designers 8, number of simulation engineers 2, number of process engineers 2, and the formed vector is [70, 54, 96, 24].

[0074] Calculating the similarity between the two vectors is to use the cosine value between the vector [76, 51, 80, 40] obtained from the recommended plan and the vector [70, 54, 96, 24] obtained from the multi-objective optimization as the similarity. The similarity between the two vectors is 98.60%, which meets the requirement that the value range in the embodiment is greater than or equal to 90%. Therefore, directly output the fixture design, simulation, and the interactive planning scheme between design and simulation including the project, and the result is shown in Figure 5 as follows.

[0075] Embodiment 3: Fixture structure design and simulation two-dimensional project management in automotive welding fixture design.

[0076] Embodiment 3 adopts the same steps and conditions as Embodiment 1. Only due to insufficient designers, the project feature information is adjusted as follows:

[0077] a. Key time nodes: Project start time: October 10, 2021; first review time: October 20, 2021; second review time: November 10, 2021; third review time: November 25, 2021; 3D freeze time: December 5, 2021; 2D drawing output: December 10, 2021; pneumatic circuit diagram and various reports: December 25, 2021;

[0078] b. The number of problem points in each version of data for a single set of equipment does not exceed 10;

[0079] c. The number of designers is 3 and the number of simulation personnel is 2;

[0080] d. The number of designed equipment is 40 sets, among which the equipment numbers #1-5, #7-10, #14-20, #26-31, #35-36 have a high priority;

[0081] e. The similarity is set to 90%.

[0082] S5: Compare the planning scheme of the recommended project with the similarity of the time, cost, input resources and quality optimization results of the project to output the recommended result. In the intelligent planning system, the recommended scheme of the fully connected neural network model is [76, 22, 40, 64], while the vector formed by the calculation results of the particle swarm collaborative dynamic multi-objective optimization algorithm is [62, 40.2, 88, 24]. Calculate the similarity between the two vectors as 83.60%. In terms of cost, quality and the number of people, especially the number of quality problem points and the number of people, the similarity with the actual work requirements is small and cannot meet the requirement that the value range is greater than or equal to 90%. Therefore, the recommended scheme is recommended according to the actual project requirements, and the result is Figure 2 consistent, and it is recommended to increase the number of personnel to 11.

[0083] The above are the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent planning method for mechanical design projects, characterized in that the steps include: 1) Mark the historical mechanical design project planning data and train the intelligent recommendation algorithm model for project planning; 2) Establish a multi-objective collaborative optimization algorithm model with the time, cost, resources, and quality of the project as the optimization objectives; 3) Build an intelligent project planning system that can output an optimized project planning scheme; 4) Collect the characteristic information of the mechanical design project to be planned and import it into the intelligent project planning system; 5) Judge the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the results of multi-objective collaborative optimization, and output an optimized recommendation or scheme; The specific steps of step 1) are as follows: 1.1) Collect the historical mechanical design project planning management data, where the historical mechanical design project planning management data includes project characteristic information and planning information. The project characteristic information includes task dimension, key time nodes, cost requirements, quality requirements, the planned number of personnel to be invested, and project workload; the planning information includes the work task arrangements in each dimension, time plans, and information and task interaction plans; 1.2) Establish the mapping relationship between project characteristic information and planning information to form the data annotation of historical project management; 1.3) Import the annotated data into the fully connected neural network algorithm model and train the model; The multi-objective collaborative optimization algorithm model in step 2) is a dynamic multi-objective optimization algorithm using multi-particle swarm collaboration. Taking the project work task volume as a constraint condition, and the project time, cost, invested resources, and quality as optimization objectives, input the work task volume, time, cost, invested resources, and quality data of historical projects into the dynamic multi-objective optimization algorithm using multi-particle swarm collaboration to obtain the relevant parameters of the multi-objective collaborative optimization algorithm model and establish the multi-objective collaborative optimization algorithm model; The intelligent project planning system in step 3) includes: an information collection module, a calculation module, and a project planning output module; The calculation module includes a fully connected neural network recommendation algorithm sub-module and a dynamic multi-objective optimization algorithm sub-module using multi-particle swarm collaboration; When the similarity between the time, cost, resources invested, and quality of the recommended project planning scheme and the results of multi-objective collaborative optimization is greater than or equal to the set value in step 5), directly output the project planning scheme; when the similarity of the two results is less than the set value, output a suggestion to modify the project characteristic information, and input the modified project characteristic information into the fully connected neural network recommendation algorithm sub-module to re-output the recommended planning scheme.

2. An intelligent planning method for mechanical design projects according to claim 1, characterized in that the intelligent recommendation algorithm model in step 1) is a fully connected neural network algorithm model, and the fully connected neural network recommendation algorithm is used to calculate the planning scheme of the recommended project.

3. An intelligent planning method for mechanical design projects according to claim 2, characterized in that the planning scheme of the recommended project is the work task arrangements in each dimension of the project planning, time plans, and the interaction plans of tasks in each dimension.

4. An intelligent planning method for mechanical design projects according to claim 1, characterized in that The set value of the similarity between the time, cost, resources invested, and quality of the planning scheme of the recommended project and the result of multi-objective collaborative optimization is 80%.

5. A method for intelligent planning of a mechanical design project according to claim 1, characterized in that the recommended modification project feature information is calculated based on the calculation result of the dynamic multi-objective optimization algorithm of multi-particle swarm collaboration combined with the similarity set value.

6. A method for intelligent planning of a mechanical design project according to claim 1, characterized in that the recommended suggestion or scheme in step 5) is to display the project planning result recommended by the calculation module in the form of an interactive graph or table by using the output module of the system.

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