Die-casting machine equipment monitoring, management and transmission system and method based on data analysis

Abstract

The present invention discloses a die-casting machine equipment monitoring, management and transmission system and method based on data analysis, which relates to the technical field of die-casting machine equipment data transmission, including: a database, a multi-source data acquisition module, a data preprocessing and feature extraction module, a process switching intelligent decision module, a template optimization module and a data transmission and cloud platform interaction module. The database stores historical die-casting product information and matches the initial process parameter template according to the received features; the multi-source data acquisition module collects the die-casting machine operation data and transmits it to the data preprocessing and feature extraction module; the process switching intelligent decision module collects the product features of new orders, calls the template to input the die-casting machine, and analyzes the error of the operation data features; the template optimization module optimizes the template; the data transmission and cloud platform interaction module transmits data features. When there is a new order, the present invention accurately calls the product template and analyzes the error, continuously optimizes the process parameters, ensures the accurate adaptation of the process to the diverse products, and flexibly responds to market demand.

Description

Technical Field

[0001] The present invention relates to the technical field of die-casting machine equipment data transmission, and in particular to a die-casting machine equipment monitoring, management and transmission system and method based on data analysis. Background Art

[0002] Throughout the development of the die-casting industry, traditional die-casting machine equipment management has faced numerous difficulties, including complex production processes and challenges in quality control. The die-casting process involves numerous parameters, such as die-casting temperature, injection pressure, and hold time, all of which influence each other and must be precisely matched to product characteristics. Traditional production relies on manual experience to set initial process parameters, and different operators have varying understandings and control of these parameters, resulting in unstable product quality and high scrap rates. Once product orders change, adjusting process parameters becomes time-consuming and labor-intensive.

[0003] Lag in equipment operating status monitoring: Die-casting machines operate in high-temperature, high-pressure, and high-speed environments for extended periods, making key components susceptible to potential failures. In the past, equipment monitoring was done through regular manual inspections, but these inspections were time-consuming and difficult to detect. Consequently, repairs were often performed only after a failure occurred, resulting in long periods of downtime.

[0004] Inefficiency in data management and collaboration: As die-casting companies expand, workshops generate massive amounts of production data, including product characteristics and equipment operation data. However, this data is stored in a decentralized manner and lacks unified and effective management, making it impossible to form data assets to support production decision-making. Furthermore, information flow between different levels and departments in the workshop is poor, making it difficult to provide timely feedback of production site data to process R&D and management, hindering overall collaborative efficiency improvements.

[0005] Therefore, people need a die-casting machine equipment monitoring, management and transmission system and method based on data analysis to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a die-casting machine equipment monitoring, management and transmission system and method based on data analysis to solve the problems raised in the above background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a die-casting machine equipment monitoring, management and transmission system based on data analysis, the system comprising: a database, a multi-source data acquisition module, a data preprocessing and feature extraction module, a process switching intelligent decision module, a template optimization module and a data transmission and cloud platform interaction module;

[0008] The database stores the product feature information of historical die-cast products, and when receiving the product feature information, the database can match the initial process parameter template according to the product feature information;

[0009] The multi-source data acquisition module collects the die-casting machine operation data and transmits it to the data preprocessing and feature extraction module;

[0010] The die-casting machine operation data is preprocessed by the data preprocessing and feature extraction module, and the equipment operation data features are extracted and transmitted to the process switching intelligent decision module;

[0011] The process switching intelligent decision module collects new product feature information when the die-casting machine receives a new order, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operation data feature and the template operation data feature;

[0012] Analyze the die-casting results through the template optimization module and optimize the initial process parameter template;

[0013] The data transmission and cloud platform interaction module adopts a communication method combining wired and wireless communication to transmit the real operation data characteristics and the template operation data characteristics.

[0014] The database stores the characteristic information of historical die-cast products and the initial process parameter template. The product characteristic information includes: product material, product size and product shape complexity. By analyzing the three-dimensional model of the product, the main curvature of each point on the surface is calculated, and then the range and frequency of curvature change are statistically analyzed. For example, a product with a smooth surface has a relatively small curvature change; while a product with multiple protrusions, depressions or complex surface transitions has a large curvature change and a high shape complexity. Gaussian curvature and average curvature can be used to quantify this change. By integrating or statistically analyzing the distribution of these curvature values ​​across the entire surface, an indicator reflecting the degree of surface change is obtained. The initial process parameter template includes: die-casting temperature, injection pressure, injection speed setting value, holding time, mold opening and closing speed and stroke, template operation data characteristics and product completion results. The template operation data characteristics are obtained through data preprocessing and feature extraction modules when the die-casting machine processes historical die-cast products. The product completion result is the average deviation of the product size.

[0015] The database features intelligent classification and retrieval capabilities, matching the most similar initial process parameter templates based on product characteristics. This data is then used to deeply analyze the extensive production data accumulated by the company's own past die-casting production processes. This data includes the process parameters used in the actual production of different products, as well as corresponding product quality test results, production efficiency data, and other information. Using data mining techniques, process parameter combinations that ensure stable production processes and excellent product quality are identified. For example, for a die-cast housing for a particular electronic product, historical data is used to identify parameters such as die-casting temperature and injection speed that correspond to production batches with scrap rates below a certain threshold (e.g., 5%). These parameters are then organized into a parameter template relevant to that product in the database.

[0016] The multi-source data acquisition module collects raw data through sensors, and the sensors include a furnace temperature sensor, a hydraulic pressure sensor, a mold pressure sensor, a mold temperature sensor, a transmission vibration sensor, a transmission speed sensor, and a molten metal flow sensor;

[0017] A furnace temperature sensor is installed in the furnace of the die-casting machine to monitor the temperature of the molten metal in real time. A hydraulic sensor is arranged in the hydraulic system to measure the injection pressure and clamping force in real time. Mold temperature sensors and mold pressure sensors are installed inside and around the mold to monitor the temperature distribution and stress of the mold during the die-casting process in real time. Transmission vibration sensors and transmission speed sensors are equipped on the transmission mechanism to detect the operating status of mechanical components in real time. A molten metal flow sensor is installed in the molten metal circulation area to monitor the molten metal flow rate. The data acquisition module has a high sampling frequency and data accuracy, and can continuously and stably collect data during the process switching process and the entire die-casting production cycle. The collected data is preliminarily verified and sorted, and obvious abnormal data points are removed to ensure data reliability and availability.

[0018] The data preprocessing and feature extraction module preprocesses the collected raw data. The preprocessing includes: using low-pass filtering to remove pressure data fluctuations caused by high-frequency electromagnetic interference, and using a smoothing algorithm to correct temperature data jumps caused by environmental factors. The die-casting machine working environment is surrounded by many other devices, such as nearby furnaces, heating devices, or cooling devices. If there are heating devices such as furnaces nearby, the heat they emit may be conducted to the temperature sensor, causing the temperature detected by the sensor to increase and causing data jumps. Therefore, a lossless compression algorithm is used to compress the data.

[0019] The data preprocessing and feature extraction module extracts the equipment operation data features, and the operation data features include: extracting vibration frequency, amplitude and kurtosis from the data collected by the transmission vibration sensor; extracting temperature features and the time nodes of data occurrence from the data collected by the furnace temperature sensor, and the temperature features include: temperature change rate, maximum temperature and minimum temperature; extracting pressure peak value, pressure rise rate and pressure stability in the holding stage from the data collected by the mold pressure sensor, and the pressure stability analysis method in the holding stage is: calculating statistics such as the mean, variance or standard deviation of the pressure data, and obtaining the pressure stability in the holding stage based on the pressure fluctuation; it also includes hydraulic feature data, mold temperature feature data, transmission speed feature data and metal liquid flow feature data extracted from the data collected by the hydraulic sensor, mold temperature sensor, transmission speed sensor and metal liquid flow sensor. The hydraulic feature data includes the hydraulic peak value and the interval time of the hydraulic peak value, the mold temperature feature data includes the mold temperature change rate, the mold maximum temperature and the mold minimum temperature, the transmission speed feature data includes the average speed of the transmission system; the metal liquid flow feature data includes the average flow rate of the metal liquid in the equipment.

[0020] When the die-casting machine receives a new order, the process switching intelligent decision module collects new product feature information of the new order, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and uses the average deviation α0 of the product size in the initial process parameter template as the current production prediction result. The template operation data feature obtained by the process switching intelligent decision module through the database is Z={Z1,Z2,…,Z n The die-casting machine runs according to the template operation data features. The real operation data features obtained in real time by the data preprocessing and feature extraction module during the operation of the die-casting machine are z={z1,z2,…,z n}, where n represents the number of real running data features and template running data features, and then the error coefficient C between the real running data features and the template running data features is calculated:

[0021] ;

[0022] where Z e represents the e-th real running data feature, z e Represents the e-th real running data feature, e=1,2,…,m T Relying on the rich historical data stored in the database, the system can quickly match initial process templates for new products, eliminating differences in manual experience, ensuring stable product quality, and reducing scrap rates. When a new order arrives, the process switching intelligent decision-making module accurately calls the template and deeply analyzes the discrepancies between the actual and template operating data characteristics. It continuously optimizes process parameters, helping companies flexibly respond to diverse orders and ensure precise adaptation of products and processes.

[0023] The template optimization module obtains the average deviation α of the product size of this die casting. When α>α0, the real operation data features are deleted; when α≤α0, Z is compared. e and z e The size of, if (1-C)*Z e <z e <(1+C)*Z e , the real running data feature z e Replace Z in the template run data feature e , replace the template operation data features in the template with the real operation data features that meet the conditions, realize the dynamic optimization of the process parameter template, make the die-casting process continuously close to the actual optimal state, ensure the stable improvement of product quality, and flexibly adopt response strategies according to different product deviations, so that the die-casting machine system can quickly self-adjust according to real-time production feedback. Whether it is responding to regular production fluctuations or occasional abnormal conditions, it can optimize the process in an orderly manner, greatly enhancing the system's adaptability to complex and changing production environments, helping enterprises to efficiently and stably produce high-quality die-cast products;

[0024] The data transmission and cloud platform interaction module transmits the real operation data characteristics to the cloud platform; the data transmission and cloud platform interaction module adopts a communication method that combines wired and wireless, and industrial Ethernet is preferentially used for data transmission within the workshop. When data backup is required, mobile communication technology is used to upload the data to the cloud server.

[0025] The die-casting machine equipment monitoring, management and transmission method based on data analysis is characterized by comprising the following steps:

[0026] S1: Store product feature information of historical die-cast products, and when receiving the product feature information, match the initial process parameter template according to the product feature information;

[0027] S2: Collect die-casting machine operation data;

[0028] S3: Preprocess the die-casting machine operation data and extract the equipment operation data features;

[0029] S4: When the die-casting machine receives a new order, it collects new product feature information, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operating data features and the template operating data features;

[0030] S5: Analyze die casting results and optimize the initial process parameter template;

[0031] S6: A communication method combining wired and wireless is used to transmit the real operation data characteristics and the template operation data characteristics.

[0032] Compared with the existing technology, the present invention has the following beneficial effects: on the one hand, the system's database stores historical product characteristics and process parameter templates. When receiving new product characteristics, it can quickly match the initial process template, avoid differences in manual experience, stabilize product quality, and reduce scrap rates. When a new order arrives, the process switching intelligent decision module accurately calls the template and analyzes errors, continuously optimizing process parameters, enabling enterprises to quickly adapt to order changes, ensure accurate adaptation of processes to diverse products, and flexibly respond to market demand.

[0033] On the one hand, the multi-source data acquisition module, in conjunction with the data preprocessing and feature extraction modules, uses a variety of sensors to collect real-time operating data from the die-casting machine, closely monitoring key information such as furnace temperature and mold pressure. When abnormal data fluctuations occur, it can provide early warning of potential faults, eliminating the lag of manual inspections.

[0034] The database, meanwhile, centrally stores scattered data and intelligently categorizes and retrieves it, transforming massive amounts of product and equipment information into the data foundation for a resume template. The data transmission and cloud platform interaction modules utilize a combination of wired and wireless methods to break down information barriers between workshops and departments, enabling real-time data exchange and improving overall efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 It is a structural diagram of the die-casting machine equipment monitoring, management and transmission system based on data analysis of the present invention;

[0037] Figure 2 It is a flow chart of the die-casting machine equipment monitoring, management and transmission method based on data analysis of the present invention. DETAILED DESCRIPTION

[0038] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0039] See also Figure 1 and Figure 2 The present invention provides a technical solution: a die-casting machine equipment monitoring, management and transmission system based on data analysis, the system comprising: a database, a multi-source data acquisition module, a data preprocessing and feature extraction module, a process switching intelligent decision module, a template optimization module and a data transmission and cloud platform interaction module;

[0040] The database stores the product feature information of historical die-cast products, and when receiving the product feature information, the database can match the initial process parameter template according to the product feature information;

[0041] The multi-source data acquisition module collects the die-casting machine operation data and transmits it to the data preprocessing and feature extraction module;

[0042] The die-casting machine operation data is preprocessed by the data preprocessing and feature extraction module, and the equipment operation data features are extracted and transmitted to the process switching intelligent decision module;

[0043] The process switching intelligent decision module collects new product feature information when the die-casting machine receives a new order, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operation data feature and the template operation data feature;

[0044] Analyze the die-casting results through the template optimization module and optimize the initial process parameter template;

[0045] The data transmission and cloud platform interaction module adopts a communication method combining wired and wireless communication to transmit the real operation data characteristics and the template operation data characteristics.

[0046] Furthermore, the database stores characteristic information of historical die-cast products and initial process parameter templates. The product characteristic information includes: product material, product size, and product shape complexity. By analyzing the product's three-dimensional model, the principal curvature of each surface point is calculated, and then the range and frequency of curvature variation are statistically analyzed. For example, a product with a smooth surface has relatively small curvature variation; whereas, a product with multiple protrusions, depressions, or complex surface transitions has large curvature variation and high shape complexity. This variation can be quantified using Gaussian curvature and mean curvature. An indicator reflecting the degree of surface variation is obtained by integrating or statistically analyzing the distribution of these curvature values ​​across the entire surface. The initial process parameter template includes: die-casting temperature, injection pressure, injection speed setting, holding time, mold opening and closing speed and stroke, template operation data features, and product completion results. The template operation data features are obtained through data preprocessing and feature extraction modules when the die-casting machine processes historical die-cast products. The product completion result is the average deviation of product dimensions. The database features intelligent classification and retrieval capabilities, matching the most similar initial process parameter templates based on product characteristics. This data is then used to deeply analyze the extensive production data accumulated by the company's own past die-casting production processes. This data includes the process parameters used in the actual production of different products, as well as corresponding product quality test results, production efficiency data, and other information. Using data mining techniques, process parameter combinations that ensure stable production processes and excellent product quality are identified. For example, for a die-cast housing for a particular electronic product, historical data is used to identify parameters such as die-casting temperature and injection speed that correspond to production batches with scrap rates below a certain threshold (e.g., 5%). These parameters are then organized into a parameter template relevant to that product in the database.

[0047] Furthermore, the multi-source data acquisition module collects raw data through sensors, and the sensors include a furnace temperature sensor, a hydraulic pressure sensor, a mold pressure sensor, a mold temperature sensor, a transmission vibration sensor, a transmission speed sensor, and a molten metal flow sensor;

[0048] A furnace temperature sensor is installed in the furnace of the die-casting machine to monitor the temperature of the molten metal in real time. A hydraulic sensor is arranged in the hydraulic system to measure the injection pressure and clamping force in real time. Mold temperature sensors and mold pressure sensors are installed inside and around the mold to monitor the temperature distribution and stress of the mold during the die-casting process in real time. Transmission vibration sensors and transmission speed sensors are equipped on the transmission mechanism to detect the operating status of mechanical components in real time. A molten metal flow sensor is installed in the molten metal circulation area to monitor the molten metal flow rate. The data acquisition module has a high sampling frequency and data accuracy, and can continuously and stably collect data during the process switching process and the entire die-casting production cycle. The collected data is preliminarily verified and sorted, and obvious abnormal data points are removed to ensure data reliability and availability.

[0049] Furthermore, the data preprocessing and feature extraction module preprocesses the collected raw data. The preprocessing includes: using low-pass filtering to remove pressure data fluctuations caused by high-frequency electromagnetic interference, and using a smoothing algorithm to correct temperature data jumps caused by environmental factors. There are many other devices in the working environment of the die-casting machine, such as nearby furnaces, heating devices or cooling equipment. If there are heating devices such as furnaces around, the heat emitted by them may be conducted to the temperature sensor, causing the temperature detected by the sensor to rise, resulting in data jumps. A lossless compression algorithm is used to compress the data.

[0050] Furthermore, the data preprocessing and feature extraction module extracts equipment operation data features, and the operation data features include: extracting vibration frequency, amplitude and kurtosis from data collected by the transmission vibration sensor; extracting temperature features and the time nodes of data occurrence from data collected by the furnace temperature sensor, and the temperature features include: temperature change rate, maximum temperature and minimum temperature; extracting pressure peak, pressure rise rate and pressure stability in the holding stage from data collected by the mold pressure sensor, and the pressure stability analysis method in the holding stage is: calculating statistics such as the mean, variance or standard deviation of the pressure data, and obtaining the pressure stability in the holding stage based on the pressure fluctuation; it also includes hydraulic feature data, mold temperature feature data, transmission speed feature data and metal liquid flow feature data extracted from data collected by the hydraulic sensor, mold temperature sensor, transmission speed sensor and metal liquid flow sensor, the hydraulic feature data includes the hydraulic peak and the interval time of the hydraulic peak, the mold temperature feature data includes the mold temperature change rate, the highest mold temperature and the lowest mold temperature, the transmission speed feature data includes the average speed of the transmission system; the metal liquid flow feature data includes the average flow rate of the metal liquid in the equipment.

[0051] Furthermore, when the die-casting machine receives a new order, the process switching intelligent decision module collects new product feature information of the new order, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and uses the product size average deviation α0 in the initial process parameter template as the current production prediction result. The template operation data feature obtained by the process switching intelligent decision module through the database is Z={Z1,Z2,…,Z n The die-casting machine runs according to the template operation data features. The real operation data features obtained in real time by the data preprocessing and feature extraction module during the operation of the die-casting machine are z={z1,z2,…,z n}, where n represents the number of real running data features and template running data features, and then the error coefficient C between the real running data features and the template running data features is calculated:

[0052] ;

[0053] where Z e represents the e-th real running data feature, z e Represents the e-th real running data feature, e=1,2,…,m T Relying on the rich historical data stored in the database, the system can quickly match initial process templates for new products, eliminating differences in manual experience, ensuring stable product quality, and reducing scrap rates. When a new order arrives, the process switching intelligent decision-making module accurately calls the template and deeply analyzes the discrepancies between the actual and template operating data characteristics. It continuously optimizes process parameters, helping companies flexibly respond to diverse orders and ensure precise adaptation of products and processes.

[0054] Furthermore, the template optimization module obtains the average deviation α of the product size of this die casting. When α>α0, the real operation data features are deleted; when α≤α0, Z is compared. e and z e The size of, if (1-C)*Z e <z e <(1+C)*Z e , the real running data feature z e Replace Z in the template run data feature e , replace the template operation data features in the template with the real operation data features that meet the conditions, realize the dynamic optimization of the process parameter template, let the die-casting process continue to be close to the actual optimal state, ensure the stable improvement of product quality, and flexibly adopt response strategies according to different product deviations, so that the die-casting machine system can quickly adjust itself according to real-time production feedback. Whether it is responding to routine production fluctuations or occasional abnormal conditions, it can optimize the process in an orderly manner, greatly enhancing the system's adaptability to complex and changeable production environments, and helping enterprises to efficiently and stably produce high-quality die-casting products.

[0055] Furthermore, the data transmission and cloud platform interaction module transmits the real operation data characteristics to the cloud platform; the data transmission and cloud platform interaction module adopts a communication method that combines wired and wireless, and preferentially uses industrial Ethernet for data transmission within the workshop. When data backup is required, mobile communication technology is used to upload the data to the cloud server.

[0056] The die-casting machine equipment monitoring, management and transmission method based on data analysis includes the following steps:

[0057] S1: Store product feature information of historical die-cast products, and when receiving the product feature information, match the initial process parameter template according to the product feature information;

[0058] S2: Collect die-casting machine operation data;

[0059] S3: Preprocess the die-casting machine operation data and extract the equipment operation data features;

[0060] S4: When the die-casting machine receives a new order, it collects new product feature information, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operating data features and the template operating data features;

[0061] S5: Analyze die casting results and optimize the initial process parameter template;

[0062] S6: A communication method combining wired and wireless is used to transmit the real operation data characteristics and the template operation data characteristics.

[0063] Example 1: In a die-casting workshop, multiple die-casting machines are operating, producing various precision die-casting parts. A data-analysis-based die-casting machine equipment monitoring, management, and transmission system is deployed here, with the core process switching intelligent decision-making module playing a key role.

[0064] After the system is started, the process switching intelligent decision module retrieves the template operation data characteristics of a specific die-casting product from the database. Assuming that a certain automobile part is produced this time, its template operation data characteristics Z={Z1,Z2,…,Z n These data cover the die-casting machine's pressure setting value, temperature curve, mold opening and closing speed and other parameters, and the die-casting machine starts running according to these precise settings.

[0065] At the same time, the data preprocessing and feature extraction module collects the real operation data features z={z1,z2,…,z n For example, during the die-casting process, data such as the actual pressure value and the real-time temperature of the mold are collected every 0.1 seconds.

[0066] Next, the system calculates the error coefficient C between the actual operating data characteristics and the template operating data characteristics. For example, suppose at a certain moment, the third actual operating data characteristic, Z3, represents a standard mold temperature of 200°C, while the real-time z3 is 203°C. Using a predetermined formula, the error coefficient C is calculated to measure the degree of deviation between the die-casting machine's operating status and the ideal template.

[0067] When entering the template optimization phase, the template optimization module obtains the average deviation of the product size of this die-casting. If α>α0, for example, the size deviation of a batch of parts produced is too large, exceeding the allowable α0 value, the system determines that the current operation data is unreliable and directly deletes the corresponding real operation data features to avoid interference with subsequent template optimization. If α≤α0, if the size of the produced parts is qualified, the system compares Z e and z e The size of, if (1-C)*Z e <ze <(1+C)*Z e , the real running data feature z e Replace Z in the template run data feature e , dynamic optimization of the template is realized to ensure the continuous, efficient and stable production of the die-casting machine. The production accuracy and stability of the die-casting machine are significantly improved, and the scrap rate is greatly reduced, providing enterprises with a higher level of product intelligent production process.

[0068] Finally, it should be noted that the above description is merely 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, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. The die-casting machine equipment monitoring, management and transmission system based on data analysis is characterized by: The system includes: a database, a multi-source data acquisition module, a data preprocessing and feature extraction module, a process switching intelligent decision module, a template optimization module, and a data transmission and cloud platform interaction module; The database stores the product feature information of historical die-cast products, and when receiving the product feature information, the database can match the initial process parameter template according to the product feature information; The multi-source data acquisition module collects the die-casting machine operation data and transmits it to the data preprocessing and feature extraction module; The die-casting machine operation data is preprocessed by the data preprocessing and feature extraction module, and the equipment operation data features are extracted and transmitted to the process switching intelligent decision module; The process switching intelligent decision module collects new product feature information when the die-casting machine receives a new order, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operation data feature and the template operation data feature; Analyze the die-casting results through the template optimization module and optimize the initial process parameter template; The data transmission and cloud platform interaction module adopts a communication method combining wired and wireless communication to transmit the real operation data characteristics and the template operation data characteristics.

2. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 1 is characterized in that: The database stores product feature information and initial process parameter templates of historical die-cast products, wherein the product feature information includes: product material, product size, and product shape complexity; the initial process parameter template includes: die-casting temperature, injection pressure, injection speed setting value, holding time, mold opening and closing speed and stroke, template operation data features, and product completion results; the template operation data features are obtained through data preprocessing and feature extraction modules when the die-casting machine processes historical die-cast products; and the product completion result is the average deviation of product dimensions; The database has intelligent classification and retrieval functions, and can match the initial process parameter template according to the characteristic information of the product.

3. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 2 is characterized in that: The multi-source data acquisition module collects raw data through sensors, and the sensors include a furnace temperature sensor, a hydraulic pressure sensor, a mold pressure sensor, a mold temperature sensor, a transmission vibration sensor, a transmission speed sensor, and a molten metal flow sensor; A furnace temperature sensor is installed in the furnace of the die-casting machine to monitor the temperature of the molten metal in real time; a hydraulic sensor is arranged in the hydraulic system to measure the injection pressure and clamping force in real time; a mold temperature sensor and a mold pressure sensor are installed inside and around the mold to monitor the temperature distribution and force of the mold during the die-casting process in real time; a transmission vibration sensor and a transmission speed sensor are installed on the transmission mechanism to detect the operating status of the mechanical components in real time; a molten metal flow sensor is installed in the molten metal circulation area to monitor the molten metal flow.

4. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 3 is characterized in that: The data preprocessing and feature extraction module preprocesses the collected raw data. The data preprocessing includes: using low-pass filtering to remove pressure data fluctuations caused by high-frequency electromagnetic interference, using a smoothing algorithm to correct temperature data jumps caused by environmental factors, and using a lossless compression algorithm to compress the data monitored by the sensors. The pressure data includes data monitored by the mold pressure sensor, and the temperature data includes data monitored by the mold temperature sensor and the furnace temperature sensor. The data preprocessing and feature extraction module extracts equipment operation data features, and the operation data features include: extracting vibration frequency, amplitude and kurtosis from data collected by the transmission vibration sensor; extracting molten metal temperature features and the time nodes of data occurrence from data collected by the furnace temperature sensor, and the molten metal temperature features include: molten metal temperature change rate, maximum molten metal temperature and minimum molten metal temperature; extracting pressure peak value, pressure rise rate and pressure stability in the holding stage from data collected by the mold pressure sensor; and also including hydraulic feature data, mold temperature feature data, transmission speed feature data and molten metal flow feature data extracted from data collected by the hydraulic sensor, mold temperature sensor, transmission speed sensor and molten metal flow sensor.

5. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 4 is characterized in that: When the die-casting machine receives a new order, the process switching intelligent decision module collects the new product feature information of the new order, calls the initial process parameter template in the database to input the die-casting machine equipment, and uses the average product size deviation α0 in the initial process parameter template as the production prediction result for this time.

6. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 5 is characterized in that: The template operation data characteristics obtained by the process switching intelligent decision module through the database are Z={Z1, Z2, ..., Z n The die-casting machine runs according to the template operation data features. The real operation data features obtained in real time by the data preprocessing and feature extraction module during the operation of the die-casting machine are z={z1,z2,…,z n }, where n represents the number of real operation data features and template operation data features, and then the error coefficient C between the real operation data features and the template operation data features is calculated.

7. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 6 is characterized in that: The template optimization module obtains the average deviation α of the product size of this die casting. When α>α0, the real operation data features are deleted; when α≤α0, Z is compared. e and z e The size of, if (1-C)*Z e <z e <(1+C)*Z e , the real running data feature z e Replace Z in the template run data feature e .

8. The die-casting machine equipment monitoring, management and transmission system based on data analysis according to claim 7 is characterized in that: The data transmission and cloud platform interaction module transmits the real operation data characteristics to the cloud platform; the data transmission and cloud platform interaction module adopts a communication method that combines wired and wireless, and industrial Ethernet is preferentially used for data transmission within the workshop. When data backup is required, mobile communication technology is used to upload the data to the cloud server.

9. A method for monitoring, managing, and transmitting die-casting machine equipment based on data analysis, the method being applied to a system for monitoring, managing, and transmitting die-casting machine equipment based on data analysis as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: S1: Store product feature information of historical die-cast products, and when receiving the product feature information, match the initial process parameter template according to the product feature information; S2: Collect die-casting machine operation data; S3: Preprocess the die-casting machine operation data and extract the equipment operation data features; S4: When the die-casting machine receives a new order, it collects new product feature information, calls the initial process parameter template in the database and inputs it into the die-casting machine equipment, and analyzes the error between the actual operating data features and the template operating data features; S5: Analyze die casting results and optimize the initial process parameter template; S6: A communication method combining wired and wireless is used to transmit the real operation data characteristics and the template operation data characteristics.

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