Automatic data acquisition device for production equipment

Abstract

The invention relates to the technical field of industrial automation, and discloses a production equipment data automatic acquisition device, which comprises a sensor module used for monitoring the operation state of production equipment in real time; the data acquisition module is responsible for collecting data from the sensor module and converting an analog signal into a digital signal; the data processing module is used for carrying out primary processing on the collected data; the communication module is used for transmitting the processed data to a cloud platform or a control center; the central control module is responsible for managing coordination work among the modules; the intelligent prediction and maintenance module is used for judging the health state of the production equipment by predicting and analyzing the operation data of the production equipment; and the energy efficiency optimization module is used for optimizing energy consumption parameters of the production equipment through intelligent analysis. According to the invention, through the real-time decision-making capability of the central control module, the operation of the equipment is automatically controlled, so that the equipment is always kept in an optimal operation state, and the dependence on manual operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to an automatic data acquisition device for production equipment. Background Art

[0002] As the manufacturing industry is gradually transforming towards intelligence and automation, especially in the field of production equipment management and maintenance, the demand for efficient and accurate data collection and analysis is growing. Traditional production equipment management relies on manual inspection and regular maintenance, which is inefficient and often has delays, and cannot meet the needs of modern efficient production. The operating status data of production equipment, such as temperature, pressure, vibration, speed, current and voltage, can fully reflect the working status of the equipment, and real-time monitoring and analysis of these data is an important means to ensure the stable and efficient operation of production equipment. Therefore, how to effectively collect and use these equipment data has become the key to realizing intelligent production management.

[0003] However, traditional production management relies on manual monitoring and data entry, which is complex, time-consuming, labor-intensive, and prone to errors. Since it is impossible to achieve automated data collection and analysis, the daily management and status monitoring of equipment are highly dependent on the skill level of operators, and the automation level of the production process is low, making it difficult to meet the needs of intelligent manufacturing. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an automatic data collection device for production equipment, aiming to improve the problems that traditional production management relies on manual monitoring and data entry, the daily management and status monitoring of equipment are highly dependent on the skill level of the operator, and the degree of automation of the production process is low.

[0005] In a first aspect, the present invention provides the following technical solution: a device for automatically collecting data of production equipment, comprising:

[0006] Sensor module, used to monitor the operating status of production equipment in real time, including temperature, pressure, vibration, speed, current and voltage parameters, to monitor whether the production equipment is operating under normal conditions, and to provide basic data for subsequent data processing and analysis;

[0007] The data acquisition module is responsible for collecting data from the sensor module, converting analog signals into digital signals, providing multi-channel data acquisition functions, and responsible for the comprehensive collection of various parameters;

[0008] The data processing module is used to perform preliminary processing on the collected data, including denoising, filtering and anomaly detection. The data processing module analyzes and preprocesses the data locally to reduce the amount of data transmission;

[0009] The communication module is used to transmit the processed data to the cloud platform or control center, supporting wired and wireless communication methods and encrypting the data;

[0010] The central control module is responsible for managing the coordination between modules, centrally processing and analyzing the collected data, and making decisions based on the data. The central control module can realize real-time control, data storage, and interaction with operators;

[0011] The intelligent predictive maintenance module predicts and analyzes the operating data of production equipment to determine the health status of production equipment, predict existing faults, and provide maintenance suggestions in advance;

[0012] The energy efficiency optimization module is used to monitor the energy consumption data of production equipment and optimize the energy consumption parameters of production equipment through intelligent analysis.

[0013] Preferably, the sensor module includes a temperature sensor, a pressure sensor, a vibration sensor, a rotation speed sensor, and a current and voltage sensor;

[0014] The temperature sensor is based on thermocouple or thermistor technology and is used in conjunction with an adaptive temperature compensation algorithm to monitor the temperature during the operation of the production equipment;

[0015] The pressure sensor adopts a strain gauge or piezoelectric pressure sensor, combined with a fuzzy control algorithm, to monitor the pressure changes during the production process, through a data-driven dynamic pressure threshold adjustment mechanism;

[0016] The vibration sensor uses an accelerometer or a velocity sensor to determine the wear and failure of the production equipment by detecting the vibration frequency and amplitude of the production equipment, and combines fast Fourier transform and wavelet transform to dually analyze and identify mechanical anomalies;

[0017] The speed sensor is based on the Hall effect or photoelectric encoder, combined with an intelligent abnormal mode detection algorithm, and is used to monitor the speed of production equipment in real time;

[0018] The current and voltage sensor uses a Hall sensor and a voltage transformer, and cooperates with a power factor measurement function to monitor the electrical parameters of the production equipment.

[0019] Preferably, the data acquisition module includes a multi-channel data acquisition card and an analog-to-digital converter;

[0020] The multi-channel data acquisition card is based on FPGA or high-performance microcontroller, supports multiple sensor inputs, and can simultaneously collect temperature, pressure, vibration, speed and current parameters;

[0021] The analog-to-digital converter adopts a high-precision, multi-channel analog-to-digital converter to convert the analog signal collected by the sensor into a digital signal, introduces a signal calibration mechanism, and can dynamically adjust the sampling rate and resolution.

[0022] Preferably, the data processing module includes an embedded microprocessor, an algorithm processing unit and a storage unit;

[0023] The embedded microprocessor is used to process the collected data in real time, including data cleaning, denoising and filtering;

[0024] The algorithm processing unit uses the machine learning algorithm SVM or random forest to further analyze the data, including anomaly detection and trend analysis, and combined with the self-learning ability of the neural network, it can continuously optimize the accuracy of anomaly detection based on historical data;

[0025] The storage unit uses a high-speed flash memory or an SD card to temporarily store the collected data, and the data storage method adopts a ring cache technology.

[0026] Preferably, the communication module includes a wired communication interface unit, a wireless communication unit and a data encryption unit;

[0027] The wired communication interface unit adopts an industrial-grade Ethernet PHY chip, supports the TCP / IP protocol stack, and adds a redundant link mechanism;

[0028] The wireless communication unit is based on ESP32 or NB-IoT module, suitable for long-distance data transmission, supports multiple communication path selection, and automatically selects the path with the best signal for data transmission;

[0029] The data encryption unit uses the AES-256 encryption algorithm to ensure the security of data during transmission. The communication module is equipped with a hardware encryption unit to encrypt the transmitted data to prevent data leakage and adopt a double-layer encryption strategy.

[0030] Preferably, the central control module includes a data management unit, a historical data storage unit, a control logic unit and a human-computer interaction interface unit;

[0031] The data management unit uses an embedded Linux system, is responsible for receiving and managing data transmitted by each module, coordinating the workflow between modules, and using database cache technology to process data requests;

[0032] The historical data storage unit uses a solid-state hard drive or cloud storage to store historical data, supports large capacity and high reading speed, and stores data through a time series database, which facilitates data query and analysis over a long span;

[0033] The control logic unit is based on a programmable logic controller or a real-time operating system, analyzes the operating status of the production equipment according to the collected data, and issues an alarm signal or a control instruction;

[0034] The human-machine interaction interface unit is based on touch screen technology and provides a user interface. The operating status and data analysis results of the production equipment can be viewed through the touch screen, and related settings and controls can be performed. It supports graphical data display and multi-language support, and introduces a voice interaction function.

[0035] Preferably, the intelligent predictive maintenance module includes an adaptive health management unit and a fault diagnosis and maintenance suggestion unit;

[0036] The adaptive health management unit combines the deep learning model LSTM neural network with historical production equipment operation data to predict the fault points of production equipment through time series analysis;

[0037] The fault diagnosis and maintenance suggestion unit uses a knowledge graph-based production equipment fault model to collect data from sensor modules and map it to known production equipment fault modes to provide intelligent diagnosis results;

[0038] The energy efficiency optimization module includes an energy monitoring and analysis unit and an energy consumption control unit:

[0039] The energy monitoring and analysis unit is based on power monitoring sensors and energy consumption data collection, combined with Bayesian optimization of energy consumption analysis algorithm, to evaluate the energy efficiency performance of production equipment in real time;

[0040] The energy consumption control unit is based on an AI-driven controller that automatically adjusts the operating parameters of the production equipment and adaptively adjusts the state of the production equipment through a multivariable control strategy.

[0041] In a second aspect, the present invention provides the following technical solution, a method for operating a production equipment data automatic collection device, comprising the following steps:

[0042] S1. Data monitoring

[0043] During the operation of production equipment, various sensors collect the operation data of production equipment in real time, including temperature, pressure, vibration, speed, current and voltage parameters;

[0044] S2. Data Collection

[0045] The analog signal is converted into a digital signal through an analog-to-digital converter and collected synchronously in a multi-channel manner;

[0046] S3. Data Processing

[0047] Preprocess the collected data, including data cleaning, filtering, and anomaly detection. At the same time, use edge AI technology to perform preliminary analysis and processing of the data locally.

[0048] S4. Data transmission

[0049] The processed data is uploaded to the central control module through the communication module, supporting wired and wireless communication;

[0050] S5. Data storage

[0051] After the data is transmitted to the central control module, the management unit stores the data, and the historical data storage unit saves the data for subsequent query and analysis, using solid-state drives or cloud storage for large-capacity and high-reading-speed data management;

[0052] S6. Intelligent analysis and maintenance

[0053] Based on historical data and real-time collected data, adaptive health management and deep learning model LSTM are used for analysis to predict the health status and fault points of production equipment. If an abnormality or potential fault of production equipment is detected, the fault diagnosis and maintenance suggestion unit will provide corresponding maintenance measures and suggestions;

[0054] S7, Energy efficiency monitoring and optimization

[0055] The energy monitoring and analysis unit monitors the energy consumption data of production equipment, identifies the optimal parameter configuration for energy efficiency performance by analyzing the load, operating time and production efficiency factors of production equipment, and the energy consumption control unit automatically adjusts the operating parameters of production equipment.

[0056] In a third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the operating method of the above-mentioned automatic production equipment data collection device when executing the computer program.

[0057] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the operating method of the above-mentioned automatic production equipment data collection device.

[0058] The present invention has the following beneficial effects:

[0059] 1. In the present invention, the real-time decision-making ability of the central control module is used to automatically control the operation of the equipment, so that the equipment is always kept in the best operating state, reducing the dependence on manual operation. The intelligent human-computer interaction interface not only provides visual data of the equipment status, but also introduces a voice interaction function, so that the operator can view the equipment status in real time through the voice or touch interface, monitor the data analysis results, and perform remote or local operations, which greatly improves the convenience of operation and the intelligence level of the production process.

[0060] 2. In the present invention, the energy efficiency optimization module can monitor the energy consumption of production equipment in real time, and combine AI and optimization algorithms to automatically adjust the operating parameters of the equipment according to the equipment load, operating time and production efficiency, so as to achieve low-energy and high-efficiency operation. This function not only ensures that the equipment reduces energy consumption while meeting production requirements, but also meets the needs of green production and sustainable development. By continuously optimizing the energy efficiency performance of the equipment, the factory can reduce energy costs in the production process and meet the goals of energy conservation and emission reduction.

[0061] 3. In the present invention, the data support module saves the equipment's operating data for a long time through a time series database, realizes the centralized storage of equipment's historical data, and provides a reliable basis for data analysis, trend prediction, and fault tracing. The accumulation and analysis of historical data make the equipment's usage and operating status more transparent, and provide a scientific basis for the factory's equipment maintenance plan and production management decision-making.

[0062] 4. In the present invention, the intelligent predictive maintenance function is combined with edge AI processing to monitor and analyze the operating status of the equipment in real time, identify potential problems of the equipment in advance, and effectively avoid unplanned downtime caused by sudden equipment failures. At the same time, multi-channel synchronous data acquisition and high-speed data processing ensure real-time updating of equipment operation data, accelerate the system's response speed to the equipment status, and significantly improve the utilization rate and operating efficiency of the equipment, thereby greatly improving the stability and efficiency of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a system architecture diagram of the automatic data collection device for production equipment proposed by the present invention;

[0064] Figure 2 This is a sensor module architecture diagram of the automatic data collection device for production equipment proposed by the present invention;

[0065] Figure 3 This is a data acquisition module architecture diagram of the automatic data acquisition device for production equipment proposed by the present invention;

[0066] Figure 4 This is a data processing module architecture diagram of the automatic data collection device for production equipment proposed by the present invention;

[0067] Figure 5 This is a communication module architecture diagram of the automatic data collection device for production equipment proposed by the present invention;

[0068] Figure 6 This is a diagram of the central control module architecture of the automatic data collection device for production equipment proposed by the present invention;

[0069] Figure 7 This is an architecture diagram of the intelligent predictive maintenance module of the automatic data collection device for production equipment proposed by the present invention;

[0070] Figure 8 This is a diagram of the energy efficiency optimization module architecture of the automatic data collection device for production equipment proposed by the present invention;

[0071] Fig. 9 This is a method flow chart of the operating method of the automatic data collection device for production equipment proposed by the present invention. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0073] Embodiment 1

[0074] Reference Figure 1-Figure 8 In a first embodiment of the present invention, the present invention provides an automatic data collection device for production equipment, comprising:

[0075] Sensor module, used to monitor the operating status of production equipment in real time, including temperature, pressure, vibration, speed, current and voltage parameters, to monitor whether the production equipment is operating under normal conditions, and to provide basic data for subsequent data processing and analysis;

[0076] The data acquisition module is responsible for collecting data from the sensor module, converting analog signals into digital signals, providing multi-channel data acquisition functions, and responsible for the comprehensive collection of various parameters;

[0077] The data processing module is used to perform preliminary processing on the collected data, including denoising, filtering and anomaly detection. The data processing module analyzes and preprocesses the data locally to reduce the amount of data transmission;

[0078] The communication module is used to transmit the processed data to the cloud platform or control center, supporting wired and wireless communication methods and encrypting the data;

[0079] The central control module is responsible for managing the coordination between modules, centrally processing and analyzing the collected data, and making decisions based on the data. The central control module can realize real-time control, data storage, and interaction with operators;

[0080] The intelligent predictive maintenance module predicts and analyzes the operating data of production equipment to determine the health status of production equipment, predict existing faults, and provide maintenance suggestions in advance;

[0081] The energy efficiency optimization module is used to monitor the energy consumption data of production equipment and optimize the energy consumption parameters of production equipment through intelligent analysis.

[0082] Specifically, the realization of intelligent production is an important innovation of the automatic data collection device for production equipment, which aims to improve the automation and intelligence level of production equipment through efficient data collection and analysis. The device integrates automatic control, real-time decision-making and intelligent interaction functions through the central control module. In terms of data collection, the device collects multiple key parameters of the equipment, such as temperature, pressure, vibration, speed, current, voltage, etc., and integrates these data into the central control module for real-time analysis to form a comprehensive understanding of the equipment status. The control module automatically optimizes the equipment parameters through adaptive adjustment based on the equipment's operating data and the preset operating strategy to ensure that the production equipment always operates under the best working conditions. In addition, the device is equipped with an intelligent human-machine interaction (HMI) interface and voice interaction function, which can more intuitively view the equipment status and data analysis results, and conveniently control and adjust the equipment. The equipment parameters can be adjusted through the touch screen interface or voice commands, and the equipment status trends and maintenance prompts can be viewed, making the production process more automated and efficient.

[0083] The sensor module includes a temperature sensor, a pressure sensor, a vibration sensor, a speed sensor, and a current and voltage sensor;

[0084] Temperature sensors are based on thermocouple or thermistor technology and are used with adaptive temperature compensation algorithms to monitor the temperature during the operation of production equipment;

[0085] The pressure sensor uses a strain gauge or piezoelectric pressure sensor, combined with a fuzzy control algorithm, to monitor pressure changes during the production process, through a data-driven dynamic pressure threshold adjustment mechanism;

[0086] The vibration sensor uses an accelerometer or velocity sensor to determine the wear and failure of the production equipment by detecting the vibration frequency and amplitude of the production equipment, and combines fast Fourier transform and wavelet transform to dually analyze and identify mechanical anomalies;

[0087] Speed ​​sensors based on Hall effect or photoelectric encoders, combined with intelligent abnormal pattern detection algorithms, are used to monitor the speed of production equipment in real time;

[0088] Current and voltage sensors use Hall sensors and voltage transformers with power factor measurement to monitor electrical parameters of production equipment.

[0089] Specifically, the sensor module is the data source of the entire system. It integrates various types of sensors to monitor various operating parameters of the equipment, including temperature, pressure, vibration, speed, current and voltage. Each sensor type has its specific function. For example, the temperature sensor can monitor the heating of the equipment, and the vibration sensor can detect abnormal vibration of the equipment to prevent potential mechanical failures. Through reasonable layout, each sensor can achieve accurate monitoring of key parts of the equipment, and adopt an adaptive calibration mechanism to eliminate the interference of the external environment on data collection.

[0090] Beneficial Effects

[0091] High-precision data collection: By accurately monitoring the key parts of the equipment, the system can capture subtle changes in the equipment's operating status and ensure the accuracy of the data.

[0092] Real-time response: The sensor module can provide a response in seconds or even milliseconds to ensure real-time safety monitoring of the equipment.

[0093] Multi-dimensional information coverage: Multiple parameters such as temperature, pressure, vibration, etc. are collected simultaneously to form a multi-dimensional data perspective, supporting a more comprehensive equipment status assessment.

[0094] The data acquisition module includes a multi-channel data acquisition card and an analog-to-digital converter;

[0095] Multi-channel data acquisition cards are based on FPGA or high-performance microcontrollers, support multiple sensor inputs, and can simultaneously collect temperature, pressure, vibration, speed, and current parameters;

[0096] The analog-to-digital converter uses a high-precision, multi-channel analog-to-digital converter to convert the analog signal collected by the sensor into a digital signal, and introduces a signal calibration mechanism that can dynamically adjust the sampling rate and resolution.

[0097] Specifically, the data acquisition module is responsible for converting the analog signals collected by the sensor into digital signals and performing synchronous acquisition, using a high-performance analog-to-digital converter (ADC) to ensure high data accuracy and high sampling rate. The multi-channel data acquisition card is designed to integrate multiple signal input ports and support synchronous data acquisition of multiple different types of sensors. In addition, the module has a modular design that allows flexible addition or reduction of acquisition channels, supporting the transformation and expansion of different types of production lines.

[0098] Beneficial Effects

[0099] Flexible expansion capability: Through modular design, the module can quickly adapt to changes in different equipment or processes, reducing the trouble of redeploying the acquisition system due to equipment upgrades.

[0100] Efficient data collection: Multi-channel collection ensures the synchronous acquisition of status information of each part of the equipment, providing accurate input for subsequent joint analysis.

[0101] Reliability and durability: Industrial-grade design ensures that the data acquisition module can operate stably and for a long time even under complex working conditions.

[0102] The data processing module includes an embedded microprocessor, an algorithm processing unit and a storage unit;

[0103] The embedded microprocessor is used to process the collected data in real time, including data cleaning, denoising and filtering;

[0104] The algorithm processing unit uses machine learning algorithms such as SVM or random forest to further analyze the data, including anomaly detection and trend analysis. Combined with the self-learning ability of neural networks, it can continuously optimize the accuracy of anomaly detection based on historical data.

[0105] The storage unit uses high-speed flash memory or SD card to temporarily store the collected data, and the data storage method adopts ring cache technology.

[0106] Specifically, the data processing module uses an embedded microprocessor to perform preliminary processing on the data obtained from the data acquisition module, including noise filtering, data cleaning, and anomaly detection. The module also combines edge computing capabilities to perform preliminary analysis of the data locally, reduce the impact of signal noise through the Kalman filter, and use machine learning models to achieve preliminary judgment of the device status. This local data processing reduces the amount of data transmitted to the central system and optimizes data transmission efficiency.

[0107] Beneficial Effects

[0108] Improved data quality: Through data cleaning and denoising operations, the accuracy and availability of data are significantly improved, ensuring the effectiveness of subsequent analysis.

[0109] Improved real-time performance: Edge processing capabilities enable some analysis to be completed directly on the device side, shortening the data analysis response time and achieving early warning of faults.

[0110] Intelligent processing: Machine learning models are applied to data processing, which improves the intelligence level of data anomaly detection and lays the foundation for predictive maintenance.

[0111] The communication module includes a wired communication interface unit, a wireless communication unit and a data encryption unit;

[0112] The wired communication interface unit adopts industrial-grade Ethernet PHY chip, supports TCP / IP protocol stack, and adds redundant link mechanism;

[0113] The wireless communication unit is based on ESP32 or NB-IoT module, suitable for long-distance data transmission, supports multiple communication path selection, and automatically selects the path with the best signal for data transmission;

[0114] The data encryption unit uses the AES-256 encryption algorithm to ensure the security of data during transmission. The communication module is equipped with a hardware encryption unit to encrypt the transmitted data to prevent data leakage and adopt a double-layer encryption strategy.

[0115] Specifically, the communication module is responsible for data transmission and supports multiple communication methods to ensure data transmission reliability and flexibility. The industrial-grade Ethernet interface provides a high-bandwidth and high-stability transmission path, which is suitable for real-time data transmission within the industry. At the same time, wireless communication modules such as Wi-Fi, 4G / 5G, and LoRa are used to achieve remote monitoring functions, which are especially suitable for environments where wiring is inconvenient. The communication module also integrates hardware encryption functions, and uses the AES-256 encryption algorithm to ensure the security and tamper-proofness of data during transmission.

[0116] Beneficial Effects

[0117] Diverse communication options: Whether it is wireless transmission in a remote environment or using a wired network within the factory, the communication module can provide a suitable solution.

[0118] High reliability: The communication module has a redundant link mechanism to ensure that in the event of a single-path communication interruption, the system can automatically switch to the backup path to achieve seamless data transmission.

[0119] Security assurance: Use high-level encryption algorithms to ensure the confidentiality of data during transmission and prevent leakage of sensitive production data.

[0120] The central control module includes a data management unit, a historical data storage unit, a control logic unit and a human-computer interaction interface unit;

[0121] The data management unit uses an embedded Linux system, which is responsible for receiving and managing the data transmitted by each module, coordinating the workflow between modules, and using database cache technology to process data requests;

[0122] The historical data storage unit uses a solid-state drive or cloud storage to save historical data, supports large capacity and high reading speed, and stores data through a time series database, which facilitates data query and analysis over a long span of time;

[0123] The control logic unit is based on a programmable logic controller or real-time operating system. It analyzes the operating status of the production equipment based on the collected data and issues alarm signals or control instructions.

[0124] The human-machine interaction interface unit is based on touch screen technology and provides a user interface. It can view the operating status and data analysis results of production equipment through the touch screen, and perform related settings and controls. It supports graphical data display and multi-language support, and introduces voice interaction function.

[0125] Specifically, the central control module is equivalent to the "brain" of the system, responsible for receiving data from each module and performing centralized processing and decision-making. It includes functions such as data management, storage, real-time analysis, and human-computer interaction. The data management unit coordinates the work of each module, and the historical data storage unit uses a time series database to save long-term operating data to provide data support for trend analysis and equipment optimization. The control logic unit makes automatic control decisions by performing real-time analysis on the collected data to ensure safe and efficient operation of the equipment.

[0126] Beneficial Effects

[0127] Centralized management: Through the coordination of the central control module, each module works more efficiently and uniformly, achieving seamless integration of the overall system.

[0128] Data traceability and analysis: Centralized storage and management of historical data provides a basis for equipment failure analysis and energy efficiency optimization, and supports in-depth analysis and equipment improvement.

[0129] Intelligent control: Through data-driven decision-making mechanisms, equipment automation control is achieved, which reduces manual involvement and the possibility of operational errors.

[0130] The intelligent predictive maintenance module includes an adaptive health management unit and a fault diagnosis and maintenance suggestion unit;

[0131] The adaptive health management unit combines the deep learning model LSTM neural network with historical production equipment operation data to predict the fault points of production equipment through time series analysis;

[0132] The fault diagnosis and maintenance suggestion unit uses the knowledge graph-based production equipment fault model to collect data from sensor modules and map it to known production equipment failure modes to provide intelligent diagnosis results.

[0133] The energy efficiency optimization module includes an energy monitoring and analysis unit and an energy consumption control unit:

[0134] The energy monitoring and analysis unit is based on power monitoring sensors and energy consumption data collection, combined with Bayesian optimization of energy consumption analysis algorithms, to evaluate the energy efficiency performance of production equipment in real time;

[0135] The energy consumption control unit is based on an AI-driven controller that automatically adjusts the operating parameters of production equipment and adaptively adjusts the status of production equipment through a multivariable control strategy.

[0136] Specifically, the intelligent predictive maintenance module uses the adaptive health management unit and the fault diagnosis and maintenance suggestion unit to evaluate the health status of the equipment. Through deep learning algorithms such as LSTM neural networks, time series analysis of historical data and real-time collected data is performed to predict the potential failure risk of the equipment. When an abnormality occurs, the module can provide specific maintenance suggestions, including parts that need maintenance, recommended maintenance methods, and optimization measures.

[0137] Beneficial Effects

[0138] Early maintenance warning: Through equipment health prediction, the risk of downtime caused by sudden failures is reduced, so that equipment maintenance can be carried out within the planned time.

[0139] Maintenance cost optimization: Accurate prediction and diagnosis make maintenance actions more targeted, reduce unnecessary downtime for maintenance, and save maintenance costs.

[0140] Extend equipment life: Effective predictive maintenance reduces the operation of equipment in a sub-healthy state, thereby extending the service life of the equipment.

[0141] The energy efficiency optimization module monitors the energy consumption data of the equipment through the energy monitoring unit, and analyzes the energy efficiency of the equipment by combining AI algorithm and Bayesian optimization method. The energy consumption control unit automatically adjusts the operating parameters of the equipment according to the analysis results so that the equipment can operate at the best efficiency. This module can not only reduce the energy consumption of a single device, but also comprehensively optimize the energy efficiency of the entire production line, helping the factory to achieve green manufacturing.

[0142] Beneficial Effects

[0143] Real-time energy efficiency monitoring: Real-time monitoring of equipment energy consumption helps identify energy inefficiencies and optimize them in a timely manner.

[0144] Optimize production costs: Through intelligent energy efficiency management, energy waste in equipment operation can be effectively reduced, thereby reducing production costs.

[0145] Green and environmentally friendly: Energy efficiency optimization helps reduce energy consumption, meet environmental protection requirements, and achieve the goal of sustainable production.

[0146] Embodiment 2:

[0147] Reference Fig. 9 In a second embodiment of the present invention, the present invention provides an operation method of an automatic data collection device for production equipment, comprising the following steps:

[0148] S1. Data monitoring

[0149] During the operation of production equipment, various sensors collect the operation data of production equipment in real time, including temperature, pressure, vibration, speed, current and voltage parameters. Temperature sensors can accurately monitor the temperature of key parts of the equipment to ensure that the equipment is not damaged due to overheating; pressure sensors monitor the pressure changes inside the equipment or hydraulic system to ensure that the equipment operates within a safe range; vibration sensors are used to detect mechanical vibrations and detect wear and abnormalities of mechanical parts through changes in frequency and amplitude; speed sensors detect the rotation state of the equipment in real time to ensure the stable operation of the equipment; current and voltage sensors monitor the electrical system to ensure the stability of the power supply of the equipment and the normal electrical parameters. Through the collaborative work of these sensors, the system can fully and real-time grasp the status of the equipment, providing an accurate data basis for subsequent data processing and analysis.

[0150] S2. Data Collection

[0151] The analog signal is converted into a digital signal through an analog-to-digital converter (ADC) and collected synchronously in a multi-channel manner. This process uses a high-precision, multi-channel analog-to-digital converter to convert analog signals from different sensors into digital signals to ensure the digitization accuracy of the signal. At the same time, the multi-channel data acquisition card can collect data from multiple sensors at the same time to ensure that the status information of each part of the equipment is updated synchronously. Each channel is specially calibrated to ensure the accuracy and consistency of the data. The data acquisition module also has modular expansion capabilities, and can flexibly increase the acquisition channels according to the type and quantity of equipment.

[0152] S3. Data Processing

[0153] The collected data is preprocessed, including data cleaning, filtering, and anomaly detection. Data cleaning ensures the authenticity and reliability of the data by removing noise and outliers; filtering uses methods such as low-pass filters or Kalman filters to remove high-frequency noise in the signal and obtain a smooth signal. The anomaly detection module can use rule algorithms and preliminary statistical analysis to identify potential anomalies in the data. At the same time, edge AI technology is used to perform preliminary analysis and processing of the data locally. These analyses include preliminary classification of device status, simple trend prediction, and other operations, thereby reducing the burden of data transmission and improving system response speed.

[0154] S4. Data transmission

[0155] The processed data is uploaded to the central control module through the communication module, which supports wired and wireless communication. The wired communication interface uses industrial Ethernet to ensure stable data transmission, which is suitable for short-distance data transmission within the factory. Wireless communication modules include Wi-Fi, 4G, 5G, LoRa, etc., which are suitable for scenarios that require remote monitoring. The communication module uses the AES-256 encryption algorithm to ensure data security during data transmission, ensuring that sensitive data will not be attacked or tampered with during transmission. In addition, the communication module has a redundant link function, which can automatically switch to the backup link when there is a problem with the main link to ensure uninterrupted data transmission.

[0156] S5. Data storage

[0157] After the data is transmitted to the central control module, the management unit stores the data, and the historical data storage unit saves the data for subsequent query and analysis. Solid-state drives (SSDs) or cloud storage are used for large-capacity and high-reading speed data management. Solid-state drives are used for local data storage, with high-speed read and write characteristics, suitable for rapid processing and analysis of real-time data. Cloud storage is used to store large amounts of historical data and supports long-term data storage and query. The storage module uses a time series database to manage the historical operation data of the equipment, which facilitates long-term data query and trend analysis, and provides data support for equipment health status assessment and future maintenance decisions.

[0158] S6. Intelligent analysis and maintenance

[0159] Based on historical data and real-time collected data, adaptive health management and deep learning models LSTM are used for analysis to predict the health status and fault points of production equipment. LSTM (Long Short-Term Memory Network) is a deep learning model suitable for time series data. It can identify abnormal patterns and potential risks in the operation status of equipment by analyzing historical operation data. When an abnormality or potential failure of the equipment is detected, the fault diagnosis and maintenance suggestion unit will generate a maintenance report and provide corresponding maintenance measures and suggestions. For example, the report will point out the parts that need maintenance, possible causes of failure, and recommended maintenance time and method, so as to help maintenance personnel perform maintenance work more targeted and reduce unnecessary downtime and maintenance costs.

[0160] S7, Energy efficiency monitoring and optimization

[0161] The energy monitoring and analysis unit monitors the energy consumption data of production equipment, and analyzes the relationship between energy consumption and production efficiency through the Bayesian optimization method in combination with the operating status of the production equipment. The analysis process will consider factors such as the load, operating time and production efficiency of the production equipment to identify the optimal parameter configuration for energy efficiency performance. Based on the analysis results, the energy consumption control unit automatically adjusts the operating parameters of the production equipment (such as operating speed, heating temperature, etc.) to minimize energy consumption while ensuring that production efficiency is not affected. This module is not only suitable for the energy efficiency optimization of a single device, but also can achieve comprehensive energy efficiency optimization of the entire production line, thereby helping the factory achieve the goals of energy conservation and green manufacturing. Through real-time energy efficiency monitoring and automatic adjustment, the factory can reduce energy consumption and production costs and improve overall production efficiency.

[0162] Finally, based on the data analysis and optimization results, the central control module controls the equipment in real time through the control logic unit, automatically adjusts the operating status of the equipment, and ensures that the equipment works under the best operating conditions.

[0163] In addition, operators can view the real-time status, energy efficiency performance and analysis results of the equipment through the HMI interface, and set parameters and manually control the equipment as needed. The HMI also provides voice interaction function to help operators operate more conveniently.

[0164] Embodiment 3

[0165] The third embodiment of the present invention is based on the same inventive concept and proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the operating method of the automatic data collection device for production equipment of the above embodiment are implemented.

[0166] Embodiment 4

[0167] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer device, the terminal includes: a processor, a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the operation method of the automatic data collection device for production equipment of the above embodiment.

[0168] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0169] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. 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. The automatic data collection device for production equipment is characterized by: include: Sensor module, used to monitor the operating status of production equipment in real time, including temperature, pressure, vibration, speed, current and voltage parameters, to monitor whether the production equipment is operating under normal conditions, and to provide basic data for subsequent data processing and analysis; The data acquisition module is responsible for collecting data from the sensor module, converting analog signals into digital signals, providing multi-channel data acquisition functions, and responsible for the comprehensive collection of various parameters; The data processing module is used to perform preliminary processing on the collected data, including denoising, filtering and anomaly detection. The data processing module analyzes and preprocesses the data locally to reduce the amount of data transmission; The communication module is used to transmit the processed data to the cloud platform or control center, supporting wired and wireless communication methods and encrypting the data; The central control module is responsible for managing the coordination between modules, centrally processing and analyzing the collected data, and making decisions based on the data. The central control module can realize real-time control, data storage, and interaction with operators; The intelligent predictive maintenance module predicts and analyzes the operating data of production equipment to determine the health status of production equipment, predict existing faults, and provide maintenance suggestions in advance; The energy efficiency optimization module is used to monitor the energy consumption data of production equipment and optimize the energy consumption parameters of production equipment through intelligent analysis.

2. The automatic data collection device for production equipment according to claim 1, characterized in that: The sensor module includes a temperature sensor, a pressure sensor, a vibration sensor, a rotation speed sensor, and a current and voltage sensor; The temperature sensor is based on thermocouple or thermistor technology and is used in conjunction with an adaptive temperature compensation algorithm to monitor the temperature during the operation of the production equipment; The pressure sensor adopts a strain gauge or piezoelectric pressure sensor, combined with a fuzzy control algorithm, to monitor the pressure changes during the production process, through a data-driven dynamic pressure threshold adjustment mechanism; The vibration sensor uses an accelerometer or a velocity sensor to determine the wear and failure of the production equipment by detecting the vibration frequency and amplitude of the production equipment, and combines fast Fourier transform and wavelet transform to dually analyze and identify mechanical anomalies; The speed sensor is based on the Hall effect or photoelectric encoder, combined with an intelligent abnormal mode detection algorithm, and is used to monitor the speed of production equipment in real time; The current and voltage sensor uses a Hall sensor and a voltage transformer, and cooperates with a power factor measurement function to monitor the electrical parameters of the production equipment.

3. The automatic data collection device for production equipment according to claim 1, characterized in that: The data acquisition module includes a multi-channel data acquisition card and an analog-to-digital converter; The multi-channel data acquisition card is based on FPGA or high-performance microcontroller, supports multiple sensor inputs, and can simultaneously collect temperature, pressure, vibration, speed and current parameters; The analog-to-digital converter adopts a high-precision, multi-channel analog-to-digital converter to convert the analog signal collected by the sensor into a digital signal, introduces a signal calibration mechanism, and can dynamically adjust the sampling rate and resolution.

4. The automatic data collection device for production equipment according to claim 1, characterized in that: The data processing module includes an embedded microprocessor, an algorithm processing unit and a storage unit; The embedded microprocessor is used to process the collected data in real time, including data cleaning, denoising and filtering; The algorithm processing unit uses the machine learning algorithm SVM or random forest to further analyze the data, including anomaly detection and trend analysis, and combined with the self-learning ability of the neural network, it can continuously optimize the accuracy of anomaly detection based on historical data; The storage unit uses a high-speed flash memory or an SD card to temporarily store the collected data, and the data storage method adopts a ring cache technology.

5. The automatic data collection device for production equipment according to claim 1, characterized in that: The communication module includes a wired communication interface unit, a wireless communication unit and a data encryption unit; The wired communication interface unit adopts an industrial-grade Ethernet PHY chip, supports the TCP / IP protocol stack, and adds a redundant link mechanism; The wireless communication unit is based on ESP32 or NB-IoT module, suitable for long-distance data transmission, supports multiple communication path selection, and automatically selects the path with the best signal for data transmission; The data encryption unit uses the AES-256 encryption algorithm to ensure the security of data during transmission. The communication module is equipped with a hardware encryption unit to encrypt the transmitted data to prevent data leakage and adopt a double-layer encryption strategy.

6. The automatic data collection device for production equipment according to claim 1, characterized in that: The central control module includes a data management unit, a historical data storage unit, a control logic unit and a human-computer interaction interface unit; The data management unit uses an embedded Linux system, is responsible for receiving and managing data transmitted by each module, coordinating the workflow between modules, and using database cache technology to process data requests; The historical data storage unit uses a solid-state hard drive or cloud storage to store historical data, supports large capacity and high reading speed, and stores data through a time series database, which facilitates data query and analysis over a long span; The control logic unit is based on a programmable logic controller or a real-time operating system, analyzes the operating status of the production equipment according to the collected data, and issues an alarm signal or a control instruction; The human-machine interaction interface unit is based on touch screen technology and provides a user interface. The operating status and data analysis results of the production equipment can be viewed through the touch screen, and related settings and controls can be performed. It supports graphical data display and multi-language support, and introduces a voice interaction function.

7. The automatic data collection device for production equipment according to claim 1, characterized in that: The intelligent predictive maintenance module includes an adaptive health management unit and a fault diagnosis and maintenance suggestion unit; The adaptive health management unit combines the deep learning model LSTM neural network with historical production equipment operation data to predict the fault points of production equipment through time series analysis; The fault diagnosis and maintenance suggestion unit uses a knowledge graph-based production equipment fault model to collect data from sensor modules and map it to known production equipment fault modes to provide intelligent diagnosis results; The energy efficiency optimization module includes an energy monitoring and analysis unit and an energy consumption control unit: The energy monitoring and analysis unit is based on power monitoring sensors and energy consumption data collection, combined with Bayesian optimization of energy consumption analysis algorithm, to evaluate the energy efficiency performance of production equipment in real time; The energy consumption control unit is based on an AI-driven controller that automatically adjusts the operating parameters of the production equipment and adaptively adjusts the state of the production equipment through a multivariable control strategy.

8. The operation method of the automatic data collection device for production equipment is characterized in that: The automatic data collection device for production equipment according to any one of claims 1 to 7 comprises the following steps: S1. Data monitoring During the operation of production equipment, various sensors collect real-time operating data of production equipment, including temperature, pressure, vibration, speed, current and voltage parameters; S2. Data Collection The analog signal is converted into a digital signal through an analog-to-digital converter and collected synchronously in a multi-channel manner; S3. Data Processing Preprocess the collected data, including data cleaning, filtering, and anomaly detection. At the same time, use edge AI technology to perform preliminary analysis and processing of the data locally. S4. Data transmission The processed data is uploaded to the central control module through the communication module, supporting wired and wireless communication; S5. Data storage After the data is transmitted to the central control module, the management unit stores the data, and the historical data storage unit saves the data for subsequent query and analysis, using solid-state drives or cloud storage for large-capacity and high-reading-speed data management; S6. Intelligent analysis and maintenance Based on historical data and real-time collected data, adaptive health management and deep learning model LSTM are used for analysis to predict the health status and fault points of production equipment. If an abnormality or potential fault of production equipment is detected, the fault diagnosis and maintenance suggestion unit will provide corresponding maintenance measures and suggestions; S7, Energy efficiency monitoring and optimization The energy monitoring and analysis unit monitors the energy consumption data of production equipment, identifies the optimal parameter configuration for energy efficiency performance by analyzing the load, operating time and production efficiency factors of production equipment, and the energy consumption control unit automatically adjusts the operating parameters of production equipment.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the operating method of the automatic data collection device for production equipment as claimed in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the operating method of the automatic data collection device for production equipment as claimed in claim 8 is implemented.

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