A transformer production control system based on big data

Abstract

The present invention discloses a transformer production control system based on big data, which specifically relates to the field of production control systems, including a data acquisition module: used for acquiring data information of target production in real time, collecting production equipment data, environmental data and production raw material data of each target, wherein the data include environmental impact fluctuation parameters, production equipment impact fluctuation parameters and production raw material impact fluctuation parameters; a data processing and analysis module: used for analyzing and processing the data collected by the data acquisition module to obtain the environmental impact fluctuation coefficient, the production equipment impact fluctuation coefficient and the production raw material impact fluctuation coefficient; the present invention collects product production data by setting a product data monitoring module, and uses this data to compare and analyze with a product specification sheet, thereby facilitating the improvement of the original production method and ensuring the comprehensiveness of data collection.

Description

Technical Field

[0001] The present invention relates to the technical field of production control systems, and more specifically, to a transformer production control system based on big data. Background Art

[0002] With the continuous growth of global electricity demand and the continuous development of the power industry, transformers, as indispensable power equipment in the power system, have received more and more attention. Their production efficiency and quality control are particularly important. The traditional transformer production control system has demonstrated unique advantages and applicability such as high reliability and simple maintenance in a specific historical context and practical applications. Big data technology, as a new technology, can help transformer production control to mine and analyze data, predict equipment status, optimize production processes, and improve production efficiency and product quality. Therefore, the invention of a transformer production control system based on big data to meet the needs of the transformer production control industry for intelligent production control has important practical significance.

[0003] At present, the traditional big data transformer production control system includes: a data acquisition module, a data storage module, a data analysis module and a control module, wherein the data acquisition module is responsible for collecting various data in the transformer production process, including equipment status, temperature, voltage, and current. These data are collected through sensors and instruments, and transmitted to the data storage module through a data transmission system; the data storage module is used to store the collected data and provide data query and retrieval functions. It adopts distributed storage technology and can process massive amounts of data to ensure data security and reliability; the data analysis module is responsible for deep mining and analysis of the stored data. This module adopts big data analysis technology to discover potential problems in the transformer production process and predict equipment status to provide optimization suggestions. The production equipment will also automatically adjust equipment parameters according to the data analysis results, optimize the production process, and improve production efficiency and product quality; the control module is responsible for controlling the transformer production process according to the results of the data analysis module, which can automatically adjust equipment parameters, optimize the production process, and ensure the production quality of the transformer. At the same time, it can also warn of equipment failures in advance according to the prediction results, reduce maintenance costs, so as to improve the continuity and stability of production and reduce risks in production.

[0004] However, it still has some shortcomings in actual use, such as inaccurate data collection. In the data acquisition module, the traditional big data transformer production control system relies on periodic monitoring rather than real-time monitoring. This method is inefficient and easily affected by human factors. It also causes a time misalignment between the collected data and data processing, and lacks real-time accuracy of the data; insufficient data collection. The traditional big data transformer production control system does not have a good way to monitor the product effects after transformer production and improve the transformer production method, and lacks comprehensiveness of the data.

[0005] Therefore, there is an urgent need to provide a transformer production control system based on big data to solve the problems of incomplete data collection and insufficient data accuracy in the existing big data transformer production control system. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a transformer production control system based on big data to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a transformer production control system based on big data, comprising:

[0008] Data acquisition module: used to obtain data information of target production in real time, collect production equipment data, environmental data and production raw material data of each target, where the data includes environmental impact fluctuation parameters, production equipment impact fluctuation parameters and production raw material impact fluctuation parameters;

[0009] Data processing and analysis module: used to analyze and process the data collected by the data acquisition module to obtain the environmental impact fluctuation coefficient, the production equipment impact fluctuation coefficient and the production raw material impact fluctuation coefficient;

[0010] Production method rationality index calculation module: used to bring the environmental impact fluctuation coefficient, production equipment impact fluctuation coefficient and production raw material impact fluctuation coefficient into the production method rationality index model to obtain the production method rationality index;

[0011] Production method rationality judgment module: used to compare the rationality of the production method with the preset rationality of the production method, calculate the difference between the rationality value of the production method and the preset rationality value of the production method, and when the difference is greater than the preset difference, transmit the difference to the data interaction transmission module, and transmit the production data to the production execution module;

[0012] Production execution module: used to receive the data sent by the production mode rationality judgment module and carry out production according to the production mode opinions provided by this data;

[0013] Product data monitoring module: used to perform sampling data monitoring on transformer products produced by the production execution module and collect product data parameters;

[0014] Product data feedback module: used to compare the data collected by the product data monitoring module with the data specified in the relevant specifications. If the product data value is greater than the value specified in the specifications, the production method data of the data will be optimized and transmitted to the data interaction transmission module; if the product data value is less than the value specified in the specifications, the data will be transmitted to the data interaction transmission module;

[0015] The data interaction transmission module is used to transmit the difference value calculated by the comprehensive booth lighting judgment module to the administrator's data terminal, and provide reference data for alerting the administrator to make adjustment measures.

[0016] Preferably, the environmental impact fluctuation parameters include environmental temperature, denoted as T; environmental humidity, denoted as H; and environmental dust content, denoted as Pm; the production equipment impact fluctuation parameters include production equipment voltage, denoted as U; production equipment current, denoted as I; production equipment resistance, denoted as R; production equipment temperature, denoted as T 设 , the operating speed of the production equipment, denoted as v; the fluctuation parameters affecting the production raw materials include the production raw material hardness HB; the production raw material tensile strength, denoted as Rm; the production raw material yield strength, denoted as τ; the production raw material impact toughness, denoted as Q.

[0017] Preferably, the data processing and analysis module includes a production equipment impact fluctuation coefficient calculation unit, an environment impact fluctuation coefficient calculation unit and a production raw material impact fluctuation coefficient calculation unit.

[0018] Preferably, the environmental impact fluctuation coefficient calculation unit is used to import the environmental impact fluctuation parameters into the environmental impact fluctuation coefficient model to obtain the environmental impact fluctuation coefficient; the production equipment impact fluctuation coefficient calculation unit is used to import the production equipment impact fluctuation parameters into the production equipment impact fluctuation coefficient model to obtain the production equipment impact fluctuation coefficient; the production raw material impact fluctuation coefficient calculation unit is used to import the production raw material impact fluctuation parameters into the production raw material impact fluctuation coefficient model to obtain the production raw material impact fluctuation coefficient.

[0019] Preferably, the environmental impact fluctuation coefficient model is specifically: The specific model of the fluctuation coefficient of production equipment is as follows: The specific model of fluctuation coefficient of production raw materials is: Among them, Φ represents the influencing factor of the production environment, specifically the light intensity; Γ represents the influencing factor of the production equipment, specifically the degree of equipment wear; υ represents the influencing factor of the production raw materials, specifically the shear strength.

[0020] Preferably, the production mode rationality index model is specifically: v 实 Indicates the actual operating rate of transformer production.

[0021] Preferably, the specific formula of the actual operation rate model of transformer production is: κ represents the influencing factor of the production mode rationality index, specifically the transformer production operation rate.

[0022] Preferably, the product data parameters include transformer material model parameters, transformer flying lead parameters, transformer core parameters, transformer size parameters, transformer high voltage bearing parameters, transformer secondary output voltage parameters and transformer coil parameters.

[0023] Preferably, the transformer material model parameters include the transformer material model; the transformer flying wire parameters include the transformer flying wire length and the transformer flying wire diameter; the transformer core parameters include the transformer core height; the transformer size parameters include the length, width and height of the transformer; the transformer high-voltage withstand parameters include the high-voltage withstand capacity of the transformer; the transformer secondary output voltage parameters include the output voltage of each secondary of the transformer; the transformer coil parameters include the number of turns, inductance, leakage inductance, phase, Q value and number of high-temperature glue layers of each coil of the transformer, the number of turns, wire diameter, material, winding direction and coil length of the coil.

[0024] Technical effects and advantages of the present invention:

[0025] 1. The present invention determines the rationality of the production method by setting the fluctuation coefficient of each monitored production equipment, the environmental fluctuation coefficient and the production raw material fluctuation coefficient in the target farmland area, thereby ensuring the comprehensiveness of data use and the rationality of the production method;

[0026] 2. The present invention ensures more accurate judgment of the rationality of the comprehensive production method and increases data accuracy by real-time monitoring of the influencing parameters of each production equipment, environmental influencing fluctuation parameters and production raw material influencing fluctuation parameters within the target production range in the production area.

[0027] 3. The present invention collects product production data by setting up a product data monitoring module, and uses this data to compare and analyze with the product specification table, thereby facilitating the improvement of the original production method and ensuring the comprehensiveness of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the attached embodiment of the present invention Figure 1, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] As attached Figure 1 A transformer production control system based on big data is shown, including a data acquisition module, a data processing and analysis module, a production method rationality index calculation module, a production method rationality index calculation module, a production method rationality judgment module, a production method execution module, a product data monitoring module, a product data feedback module, and a data interaction transmission module; the data processing and analysis module includes a production equipment impact fluctuation coefficient calculation unit, an environment impact fluctuation coefficient calculation unit, and a production raw material impact fluctuation coefficient calculation unit.

[0031] The output end of the data acquisition module is connected to the input end of the data processing and analysis module, the output end of the data processing and analysis module is connected to the input end of the production mode rationality index calculation module, the output end of the production mode rationality index calculation module is connected to the input end of the production mode rationality judgment module, the output end of the production mode rationality judgment module is connected to the input end of the data interactive transmission module, the output end of the production mode rationality judgment module is connected to the input end of the production mode execution module, the output end of the production execution module is connected to the input end of the product data monitoring module, the output end of the product data monitoring module is connected to the input end of the product data feedback module, and the output end of the product data feedback module is connected to the input end of the data interactive transmission module.

[0032] The data acquisition module is used to acquire the data of the target transformer production process in real time, and collect the production equipment data, environmental data and production raw material data of the target transformer production process, wherein the data includes environmental impact fluctuation parameters, production equipment impact fluctuation parameters and production raw material impact fluctuation parameters.

[0033] In this embodiment, it should be specifically noted that the environmental impact fluctuation parameters include the ambient temperature, denoted as T; the ambient humidity, denoted as H; and the ambient dust content, denoted as Pm; the production equipment impact fluctuation parameters include the production equipment voltage, denoted as U; the production equipment current, denoted as I; the production equipment resistance, denoted as R; the production equipment temperature, denoted as T 设 , the operating speed of the production equipment, denoted as v; the fluctuation parameters affecting the production raw materials include the production raw material hardness HB; the production raw material tensile strength, denoted as Rm; the production raw material yield strength, denoted as τ; the production raw material impact toughness, denoted as Q.

[0034] The data processing and analysis module is used to analyze and process the data collected by the data collection module to obtain the environmental impact fluctuation coefficient, the production equipment impact fluctuation coefficient and the production raw material impact fluctuation coefficient.

[0035] The environmental impact fluctuation coefficient calculation unit is used to import the environmental impact fluctuation parameters into the environmental impact fluctuation coefficient model to obtain the environmental impact fluctuation coefficient; the production equipment impact fluctuation coefficient calculation unit is used to import the production equipment impact fluctuation parameters into the production equipment impact fluctuation coefficient model to obtain the production equipment impact fluctuation coefficient; the production raw material impact fluctuation coefficient calculation unit is used to import the production raw material impact fluctuation parameters into the production raw material impact fluctuation coefficient model to obtain the production raw material impact fluctuation coefficient.

[0036] In this embodiment, it should be specifically noted that the environmental impact fluctuation coefficient model is specifically: The specific model of the fluctuation coefficient of production equipment is as follows: The specific model of fluctuation coefficient of production raw materials is: Among them, Φ represents the influencing factor of the production environment, specifically the light intensity; Γ represents the influencing factor of the production equipment, specifically the degree of equipment wear; υ represents the influencing factor of the production raw materials, specifically the shear strength.

[0037] The production method rationality index calculation module is used to bring the environmental impact fluctuation coefficient, the production equipment impact fluctuation coefficient and the production raw material impact fluctuation coefficient into the production method rationality index model to obtain the production method rationality index.

[0038] In this embodiment, it should be specifically noted that the specific formula of the production mode rationality index model is: in v 实 represents the actual operating rate of transformer production, and κ represents the influencing factor of the production mode rationality index, specifically the transformer production operating rate.

[0039] The production method rationality judgment module is used to compare the rationality of the production method with the preset production method rationality, calculate the difference between the production method rationality value and the preset production method rationality value, and when the difference is greater than the preset difference, transmit the difference to the data interaction transmission module, and transmit the production data to the production execution module.

[0040] What needs to be specifically explained in this embodiment is that the preset production method rationality value is the average of the previous production method rationalities, wherein the average removes the maximum value and the minimum value, both of which are extreme rationality values, and the preset difference value is the theoretical production method rationality value.

[0041] The production execution module is used to receive the data transmitted by the production method rationality judgment module, and perform production according to the production method opinions provided by the data.

[0042] The product data monitoring module is used to perform sampling data monitoring on the transformer products produced by the production execution module and collect product data, wherein the product data parameters include transformer material model parameters, transformer flying lead parameters, transformer core parameters, transformer size parameters, transformer high voltage bearing parameters, transformer secondary output voltage parameters and transformer coil parameters.

[0043] What needs to be specifically explained in this embodiment is: the transformer material model parameters include the transformer material model; the transformer flying wire parameters include the transformer flying wire length and the transformer flying wire diameter; the transformer core parameters include the transformer core height; the transformer size parameters include the length, width and height of the transformer; the transformer high-voltage withstand parameters include the high-voltage withstand capacity of the transformer; the transformer secondary output voltage parameters include the output voltage of each secondary of the transformer; the transformer coil parameters include the number of turns, inductance, leakage inductance, phase, Q value and number of high-temperature glue layers of each coil of the transformer, the number of turns, wire diameter, material, winding direction and coil length of the coil.

[0044] The product data feedback module is used to compare the data collected by the product data monitoring module with the data specified in the relevant specification. If the product data value is greater than the value specified in the specification, the production method data of the data is optimized and transmitted to the data interaction transmission module. If the product data value is less than the value specified in the specification, the data is transmitted to the data interaction transmission module.

[0045] The data interaction transmission module is used to transmit the difference value calculated by the comprehensive booth lighting judgment module to the administrator's data terminal, and provide reference data for alerting the administrator to make adjustment measures.

[0046] In this embodiment, it should be specifically noted that the administrator's data terminal is a common data receiver such as a mobile phone or a computer.

[0047] The working principle of this embodiment is specifically as follows:

[0048] The present invention collects production equipment data, environmental data and production raw material data through the data acquisition module. These data are crucial for production machines to plan production methods and production efficiency. By collecting production equipment data, environmental data and production raw material data in real time, production machines can dynamically adjust their production methods to improve production efficiency; the data processing and analysis module analyzes and interprets the production equipment data, environmental data and production raw material data to provide useful information about the production status so that the production machine can make more accurate decisions; the production method rationality index calculation module processes the calculated environmental impact fluctuation coefficient, production equipment impact fluctuation coefficient and production raw material impact fluctuation coefficient to obtain the production method rationality index, which helps to understand the factors affecting production efficiency and improve the accuracy of decision-making. The rationality judgment module of the production method compares the rationality of the production method with the rationality of the previous production method, intuitively obtains the difference in production data, and is convenient for optimizing production decisions; the production execution module systematically produces the received production method opinion data to obtain sufficient transformer production products for data analysis; the product data monitoring module conducts sampling data monitoring on the produced transformer products and collects product data. By collecting product data, it can dynamically adjust its production method to improve production efficiency; the product data monitoring module compares the collected data with the data specified in the relevant specifications to understand the shortcomings of its production results so as to improve the production plan during the next production; the data interaction and transmission module ensures that the production machine and the administrator have sufficient interaction by issuing control instructions.

[0049] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0050] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A transformer production control system based on big data, characterized in that: include: Data acquisition module: used to obtain data information of target production in real time, and collect production equipment data, environmental data and production raw material data of each target; The data include environmental impact fluctuation parameters, production equipment impact fluctuation parameters and production raw material impact fluctuation parameters; The environmental impact fluctuation parameters include the ambient temperature, denoted as T; the ambient humidity, denoted as H; and the ambient dust content, denoted as Pm; the production equipment impact fluctuation parameters include the production equipment voltage, denoted as U; the production equipment current, denoted as I; the production equipment resistance, denoted as R; the production equipment temperature, denoted as T 设 , the running speed of the production equipment, denoted as v; the fluctuation parameters affecting the production raw materials include the hardness of the production raw materials HB; The tensile strength of the raw material is recorded as Rm; the yield strength of the raw material is recorded as τ; the impact toughness of the raw material is recorded as Q; Data processing and analysis module: used to analyze and process the data collected by the data acquisition module to obtain the environmental impact fluctuation coefficient, the production equipment impact fluctuation coefficient and the production raw material impact fluctuation coefficient; The data processing and analysis module includes a production equipment impact fluctuation coefficient calculation unit, an environment impact fluctuation coefficient calculation unit, and a production raw material impact fluctuation coefficient calculation unit; The environmental impact fluctuation coefficient calculation unit is used to import the environmental impact fluctuation parameters into the environmental impact fluctuation coefficient model to obtain the environmental impact fluctuation coefficient; the production equipment impact fluctuation coefficient calculation unit is used to import the production equipment impact fluctuation parameters into the production equipment impact fluctuation coefficient model to obtain the production equipment impact fluctuation coefficient; the production raw material impact fluctuation coefficient calculation unit is used to import the production raw material impact fluctuation parameters into the production raw material impact fluctuation coefficient model to obtain the production raw material impact fluctuation coefficient; The environmental impact fluctuation coefficient model is specifically: The specific model of the fluctuation coefficient of production equipment is as follows: The specific model of fluctuation coefficient of production raw materials is: Where Φ represents the influencing factor of the production environment, specifically the light intensity; Γ represents the influencing factor of the production equipment, specifically the degree of equipment wear; υ represents the influencing factor of the production raw materials, specifically the shear strength; Production method rationality index calculation module: used to bring the environmental impact fluctuation coefficient, production equipment impact fluctuation coefficient and production raw material impact fluctuation coefficient into the production method rationality index model to obtain the production method rationality index; Production method rationality judgment module: used to compare the rationality of the production method with the preset rationality of the production method, calculate the difference between the rationality value of the production method and the preset rationality value of the production method, and when the difference is greater than the preset difference, transmit the difference to the data interaction transmission module, and transmit the production data to the production execution module; Production execution module: used to receive the data sent by the production mode rationality judgment module and carry out production according to the production mode opinions provided by this data; Product data monitoring module: used to perform sampling data monitoring on transformer products produced by the production execution module and collect product data parameters; Product data feedback module: used to compare the data collected by the product data monitoring module with the data specified in the relevant specifications. If the product data value is greater than the value specified in the specifications, the production method data of the data will be optimized and transmitted to the data interaction transmission module; if the product data value is less than the value specified in the specifications, the data will be transmitted to the data interaction transmission module; Data interaction transmission module: used to transmit the difference value calculated by the comprehensive booth lighting judgment module to the administrator's data terminal, and provide reference data for alerting the administrator to make adjustment measures.

2. The transformer production control system based on big data according to claim 1 is characterized in that: The product data parameters include transformer material model parameters, transformer flying lead parameters, transformer core parameters, transformer size parameters, transformer high voltage withstand parameters, transformer secondary output voltage parameters and transformer coil parameters.

3. A transformer production control system based on big data according to claim 2, characterized in that: The transformer material model parameters include the transformer material model; the transformer flying lead parameters include the transformer flying lead length and the transformer flying lead wire diameter; the transformer core parameters include the transformer core height; the transformer size parameters include the length, width and height of the transformer; the transformer high-voltage withstand parameters include the high-voltage withstand capacity of the transformer; the transformer secondary output voltage parameters include the output voltage of each secondary of the transformer; the transformer coil parameters include the number of turns, inductance, leakage inductance, phase, Q value and number of high-temperature glue layers of each coil of the transformer, the number of turns, wire diameter, material, winding direction and coil length of the coil.

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