Production data optimization motor design type selection method and system

Abstract

The invention discloses a production data optimization motor design type selection method and system, and the method comprises the steps: state monitoring, data recording, abnormity analysis, energy consumption analysis, and equipment selection for energy consumption data analysis, and the energy consumption analysis can be similar equipment in the same environment, or similar equipment in different factories under different working conditions; recording historical operation data of the equipment; the system comprises a data preparation module, a data label definition module and a function application module. According to the invention, the digital factory in the construction period is pushed to the new construction stage of the intelligent cement factory in the operation period, the data application value collected by the energy management and control system is improved, and the design level is improved by enabling equipment; cross-project and cross-equipment comparison analysis is supported, and the function of analyzing the motor power load condition and the yield is achieved; the method has high expansibility, and provides powerful guarantee for intelligent cement factories in the layout operation period.

Description

Technical Field

[0001] The present invention relates to the fields of digitization, informatization and intelligence, and in particular to a more stable and efficient method and system for optimizing motor design and selection using production data. Background Art

[0002] The industrial Internet platform has generated significant value in manufacturing enterprises. Based on emerging technologies such as the Industrial Internet of Things, big data, and artificial intelligence, it has built an enabling platform that includes storage, computing, analysis, integration, access, publishing, and management, and provides cement companies with industry-leading innovative applications and services through various industrial apps.

[0003] Power consumption is a key performance metric for factory production operations, and motor selection plays a crucial role in controlling factory equipment procurement costs, production energy costs, and operation and maintenance costs. Design institutes typically consider a certain margin when performing motor selection calculations to ensure normal production operations under extreme conditions. Long-term practice has shown that many production equipment does not reach extreme operating limits during long-term operation, leaving room for optimization in motor design and selection. Optimizing design theory calculations based on actual on-site production data will help improve motor selection accuracy. Prior art discloses a method for analyzing operating conditions and selecting motors for electric buses based on vehicle networking technology. The method includes collecting vehicle driving data; analyzing the driving data to obtain operating condition data; the operating condition data reflects the vehicle's kinematic characteristics under specific road conditions; and deriving vehicle motor selection parameters based on the operating condition data and information on regional urban road conditions. However, data acquisition is significantly affected by environmental factors (such as weather, road conditions, and passenger load), resulting in poor stability. Real-time data collection is also difficult, hindering real-time data analysis. Therefore, it is necessary to develop a more stable and efficient method and system for optimizing motor design and selection based on production data. Summary of the Invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a more stable and efficient method and system for optimizing motor design and selection based on production data.

[0005] Technical solution: The production data optimization motor design and selection method of the present invention includes the following steps:

[0006] (1) Status monitoring: The system accesses the operating data of on-site equipment. Each type of data has a normal range value. The operating parameters of the monitoring equipment are considered abnormal if they exceed the data range.

[0007] (2) Data recording: Record the monitored abnormal data, including the time and parameter values ​​of the abnormality;

[0008] (3) Analyze anomalies: Based on the equipment operation and maintenance knowledge base, preliminarily analyze the causes of anomalies;

[0009] (4) Energy consumption analysis: record the energy consumption of the equipment during operation, including energy consumption when abnormalities occur;

[0010] (5) Select equipment for energy consumption data analysis. The energy consumption analysis can be of similar equipment in the same environment or of similar equipment in different factories and under different working conditions.

[0011] (6) Record the historical operating data of the equipment.

[0012] The production data optimization motor design and selection system described in the present invention includes a data preparation module, a data label definition module and a function application module. The data preparation module models the indicator data, the data label definition module uniformly divides the equipment in different factories into sections and processes and classifies the equipment, and the function application module monitors the equipment, analyzes energy consumption and records data.

[0013] Furthermore, the data preparation module includes establishing data models and environmental requirements based on the production characteristics of the cement plant and system application requirements.

[0014] Furthermore, the data model established includes five dimensions: factory, work section, process, equipment, indicator, and time. The data comes from five application systems: energy consumption, production, operation, quality, and equipment management, and the indicator data is modeled.

[0015] Furthermore, the data tag definition module divides the factory structure based on the process flow, and divides it into factory-work section-process-sub-item. Through data tag definition, data classification standards are established. The system automatically associates tags based on the acquired work section, process, equipment name, equipment number, etc., and supports manual association and modification.

[0016] Furthermore, the functional application module is used for equipment monitoring, energy consumption analysis, data recording and equipment power consumption query.

[0017] Furthermore, the equipment monitoring displays the latest operating status of each on-site equipment through multiple selection of items, multiple selection of equipment types, multiple selection of equipment names, fuzzy retrieval of equipment codes, and equipment model selection, and monitors the energy consumption data of some selected equipment in real time by checking the items.

[0018] Furthermore, the energy consumption analysis includes viewing the latest energy consumption and output relationship data of equipment across projects, equipment types or models, and comparing parameters by selecting different equipment.

[0019] Furthermore, the data record query not only supports multiple projects and multiple devices but also supports time span query. The record results are displayed in the form of switching historical records of power data, current data, frequency conversion data, feeding amount, fan, and water pump; the data can be filtered by search conditions, and the operating status of different devices can be viewed by clicking on current data, frequency converter historical data, etc.

[0020] Furthermore, when the power consumption information interface for the device power consumption query is opened, the system does not query data from 0:00 on the day to the current time by default; after the operator sets the query conditions and clicks query, the system will query the power consumption information of the devices that meet the conditions and support export.

[0021] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: it pushes the digital factory in the construction period to a new stage of intelligent cement factory construction in the operation period, improves the application value of data collected by the energy management and control system, and enables equipment to improve the design level; supports cross-project and cross-equipment comparison and analysis, and has the analysis function between motor power load conditions and output; has high scalability, whether it is to accommodate more project site data in the future, or to expand support for process equipment analysis; can view the operation status of on-site equipment in real time, and the refined data application can reversely empower the owner unit, enhance the service value in the operation period, and provide strong guarantees for the layout of intelligent cement factories in the operation period. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention;

[0023] Figure 2 It is a system structure diagram of the present invention;

[0024] Figure 3 Flowchart for modeling data;

[0025] Figure 4 Exploded structure diagram for the plant;

[0026] Figure 5 It is the encoding format of mechanical equipment. DETAILED DESCRIPTION

[0027] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, the production data optimization motor design selection method of the present invention includes the following steps:

[0029] (1) Status monitoring: The system accesses the operating data of on-site equipment. Each type of data has a normal range value. The operating parameters of the monitoring equipment are considered abnormal if they exceed the data range.

[0030] (2) Data recording: Record the monitored abnormal data, including the time and parameter values ​​of the abnormality;

[0031] (3) Analyze anomalies: Based on the equipment operation and maintenance knowledge base, preliminarily analyze the causes of anomalies;

[0032] (4) Energy consumption analysis: record the energy consumption of the equipment during operation, including energy consumption when abnormalities occur;

[0033] (5) Select equipment for energy consumption data analysis. The energy consumption analysis can be of similar equipment in the same environment or of similar equipment in different factories and under different working conditions.

[0034] (6) Record the historical operating data of the equipment.

[0035] like Figure 2 As shown, the production data optimization motor design and selection system described in the present invention includes a data preparation module, a data label definition module and a function application module. The data preparation module models the index data, the data label definition module divides the equipment of different factories into unified work sections and processes and classifies the equipment, and the function application module monitors the equipment, analyzes energy consumption and records data.

[0036] 1. Data Preparation

[0037] 1.1 Data Modeling

[0038] like Figure 3 As shown in the figure, a data model is constructed based on the production characteristics and system application requirements of cement plants. It includes five dimensions: plant, work section, process, equipment, indicator, and time. Data comes from five application systems: energy consumption, production, operation, quality, and equipment management. By modeling indicator data, a standard data foundation is established for subsequent business applications. Table 1 includes the plant dimension table, Table 2 the work section dimension table, Table 3 the process dimension table, Table 4 the equipment dimension table, Table 5 the indicator dimension table, Table 6 the time dimension table, Table 7 the energy consumption indicator fact table, Table 8 the production indicator fact table, Table 9 the operation indicator fact table, Table 10 the quality inspection indicator fact table, and Table 11 the equipment diagnosis fact table.

[0039] Table 1 Factory dimension table

[0040]

[0041] Table 2 Work section dimension table

[0042]

[0043] Table 3 Process dimension table

[0044]

[0045] Table 4 Equipment dimension table

[0046]

[0047] Table 5 Indicator dimension table

[0048]

[0049] Table 6 Time dimension table

[0050]

[0051]

[0052] Table 7 Energy consumption index fact sheet

[0053]

[0054]

[0055] Table 8 Production indicator fact table

[0056]

[0057]

[0058] Table 9 Operation indicator fact table

[0059]

[0060] Table 10 Quality inspection indicator fact table

[0061]

[0062]

[0063] Table 11 Equipment Diagnosis Fact Sheet

[0064]

[0065]

[0066] 1.2 Environmental Requirements

[0067] Environmental requirements include the Industrial Internet version, server operating system, database, and encrypted transmission. The Industrial Internet version selects the non-clustered Industrial Internet platform, the server operating system selects CentOS 6-5 (64-bit, UTF-8 encoding, recommended file system), the database selects MySQL, and encrypted transmission. Party A provides the SSL certificate applied for by the company for encrypted transmission between the client and the server.

[0068] 2. Data Preparation

[0069] Based on the characteristics of cement plants, the plant structure is divided according to the process flow, with a categorization of plant, process, section, process, and sub-item. Data classification standards are established through data tag definition. The system automatically associates tags based on acquired process sections, processes, equipment names, and equipment numbers, and supports manual association and modification. Through tag definition, equipment from different plants can be uniformly divided into sections and processes, as well as categorized, providing the necessary conditions for subsequent data retrieval and horizontal comparison.

[0070] 2.1 Factory structure definition

[0071] like Figure 4 The figure shows the factory breakdown structure diagram. The factory breakdown structure definition is shown in Table 12:

[0072] Table 12 Factory breakdown structure definition

[0073]

[0074]

[0075] 2.2 Equipment number definition

[0076] The device number consists of a five- or seven-segment code, such as Figure 5 As shown, " "1" represents the project code, consisting of letters and numbers. The letter N represents a domestic project, F represents an international project, and the number is the project number. "2" represents the region code, indicating the sub-item to which the equipment belongs. "3" represents the professional code, "4" and "6" represent the equipment code, and "5" and "7" represent the equipment serial number. If there is no auxiliary equipment, the auxiliary equipment code and serial number in fields 6 and 7 can be left blank. For example, the equipment code for a crusher in a certain project is N001_211_PC_CR_01, where N001 is the project code, 211 is the region code, indicating that the equipment belongs to the crushing workshop, PC is the process professional code, CR is the crusher code, and 01 is the serial number. The code for the auxiliary motor of this crusher is N001_211_PC_R_01MT_01, where MT is the code for the auxiliary equipment motor.

[0077] 2.3 Device Tag Definition

[0078] Cement plant design is based on specific specifications, and host equipment is typically numbered consistently across different plants. To better categorize and identify equipment, a library of commonly used equipment tags is established. Tags are created based on device name. For example, power blowers can be categorized as Roots blowers, chute blowers, and so on. The system verifies the acquired device name and code, automatically adding device tags for manual verification. Tags can also be modified. Table 13 shows the device tag definitions.

[0079] Table 13 Equipment label definition

[0080] Device Name Equipment English name Equipment code Roots blower Roots blower RB Chute fan Air slide fan FA cooling fan Cooling fan FN Axial flow fans Axial fan FN

[0081] 3 Functional Application

[0082] 3.1 Equipment Monitoring

[0083] The latest operation status of each on-site equipment can be displayed through multiple selection of projects, multiple selection of equipment types, multiple selection of equipment names, fuzzy retrieval of equipment codes, and equipment model selection. Energy consumption data of some selected equipment can be monitored in real time by checking the boxes.

[0084] Filter the data by screening conditions, check the data and click [Start Monitoring] to monitor the data, and click [Close Monitoring] to cancel the data monitoring function.

[0085] 3.2 Energy consumption analysis

[0086] You can view the latest energy consumption and output relationship data of equipment across projects, equipment types or models.

[0087] By checking different devices to compare parameters, select the data and click [Compare] to pop up the time comparison analysis pop-up window.

[0088] Enter the start and end time, both of which cannot exceed 24 hours. Select the time interval and click Compare. A table view tab and a chart analysis tab will pop up.

[0089] Click on the chart analysis to display the comparison effect through icons.

[0090] 3.3 Data Recording

[0091] In addition to supporting multiple projects and multiple devices, data record query also supports time span query. The record results are displayed in a switchable manner in the form of power data, current data, frequency conversion data, feeding amount, fan, and water pump historical records.

[0092] Filter data by search criteria and click on current data, inverter historical data, etc. to view the operating status of different devices.

[0093] 3.4 Equipment power consumption query

[0094] When the device power consumption information interface is opened, the default setting is from 0:00 today to the current time, and the system does not query data;

[0095] After the operator sets the query conditions and clicks query, the system will query the power consumption information of the devices that meet the conditions and support export.

Claims

1. A method for optimizing motor design and selection based on production data, characterized in that: The steps include: (1) Status monitoring: The system accesses the operating data of on-site equipment. Each type of data has a normal range value. The operating parameters of the monitoring equipment are considered abnormal if they exceed the data range. (2) Data recording: Record the monitored abnormal data, including the time and parameter values ​​of the abnormality; (3) Analyze anomalies: Based on the equipment operation and maintenance knowledge base, preliminarily analyze the causes of anomalies; (4) Energy consumption analysis: record the energy consumption of the equipment during operation, including energy consumption when abnormalities occur; (5) Select equipment for energy consumption data analysis. The energy consumption analysis can be of similar equipment in the same environment or of similar equipment in different factories and under different working conditions. (6) Record the historical operating data of the equipment.

2. A production data optimization motor design and selection system, characterized in that: It includes data preparation module, data label definition module and function application module. The data preparation module models the indicator data. The data label definition module divides the equipment of different factories into unified work sections and processes and classifies the equipment. The function application module monitors the equipment, analyzes energy consumption and records data.

3. The production data optimization motor design and selection system according to claim 2, characterized in that: The data preparation module includes establishing data models and environmental requirements based on the production characteristics of the cement plant and system application requirements.

4. The production data optimization motor design and selection system according to claim 3 is characterized in that: The data model established includes five dimensions: factory, work section, process, equipment, indicator, and time. The data comes from five application systems: energy consumption, production, operation, quality, and equipment management, and the indicator data is modeled.

5. The production data optimization motor design and selection system according to claim 2, characterized in that: The data tag definition module divides the factory structure based on the process flow, and divides it into factory-section-process-sub-item. Through data tag definition, data classification standards are established. The system automatically associates tags based on the acquired section, process, equipment name, equipment number, etc., and supports manual association and modification.

6. The production data optimization motor design and selection system according to claim 2, characterized in that: The functional application module is used for equipment monitoring, energy consumption analysis, data recording and equipment power consumption query.

7. The production data optimization motor design and selection system according to claim 6, characterized in that: The equipment monitoring displays the latest operating status of each on-site equipment through multiple selection of items, multiple selection of equipment types, multiple selection of equipment names, fuzzy retrieval of equipment codes, and equipment model selection, and monitors the energy consumption data of some selected equipment in real time by checking the boxes.

8. The production data optimization motor design and selection system according to claim 6, characterized in that: The energy consumption analysis includes viewing the latest energy consumption and output relationship data of equipment across projects, equipment types or models, and comparing parameters by selecting different equipment.

9. The production data optimization motor design and selection system according to claim 6, characterized in that: In addition to supporting multiple projects and multiple devices, the data record query also supports time span query. The record results are displayed in the form of switching historical records of power data, current data, frequency conversion data, feeding amount, fan, and water pump; data can be filtered by search conditions, and the operating status of different devices can be viewed by clicking on current data, frequency converter historical data, etc.

10. The production data optimized motor design and selection system according to claim 6, characterized in that: When the power consumption information interface of the device power consumption query is opened, the system does not query data from 0:00 on the day to the current time by default; after the operator sets the query conditions and clicks query, the system will query the power consumption information of the devices that meet the conditions and support export.

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