Production line equipment data processing method and device, equipment and storage medium

Abstract

The invention discloses a production line equipment data processing method and device, equipment and a storage medium. The method comprises the following steps: receiving current equipment data sent by at least one production line equipment, wherein the current equipment data comprises equipment temperature, equipment humidity, equipment yield and equipment state; determining production line data based on the current equipment data, wherein the production line data comprises production line efficiency, production line energy consumption, production line operation time, production line failure rate and production line rejection rate; and sending the production line data to a workshop gateway, wherein the production line data is sent to a cloud platform for data processing. According to the scheme, the real-time data of each production line device is acquired and processed in a centralized manner, so that the real-time monitoring and accurate evaluation of the overall operation state of the production line are realized. Through calculation and analysis of key indexes such as production line efficiency, energy consumption and failure rate, data support can be provided for production scheduling, equipment maintenance and energy consumption optimization, the manual intervention cost is reduced, and the overall management efficiency of the production line is improved.

Description

Technical Field

[0001] This application relates to the field of industrial internet, and in particular to a method, apparatus, equipment and storage medium for processing data of production line equipment. Background Technology

[0002] With the continuous development of intelligent operation and maintenance of food processing production lines and the deepening of industrial internet technology, enterprises are increasingly demanding real-time monitoring of the status of key production equipment, collaborative optimization of production processes, and data security protection. Traditional production management models mostly rely on independent data collection by local OT equipment in the workshop or centralized analysis by IT systems. This not only makes it difficult to achieve cross-production line and multi-operating-condition sharing of production experience, but also presents an inherent contradiction between data integration efficiency and equipment operation safety, making it difficult to meet the high standards of food processing in terms of production efficiency, quality control, and safety management.

[0003] However, existing food processing production lines generally face two major constraints in terms of OT and IT system collaboration and high-performance data processing: On the one hand, there is a clear separation between OT and IT systems. Real-time production data such as temperature, humidity, output, production speed, and equipment operating status generated by OT equipment such as PLCs, sensors, and frequency converters are difficult for IT systems such as ERP, MES, and cloud platforms to acquire efficiently, resulting in data flow obstruction and production management decisions failing to respond promptly to changes on-site. On the other hand, traditional centralized data processing architectures are prone to data fragmentation, data silos caused by inconsistent communication protocols, and insufficient computing power and high processing latency when faced with large amounts of real-time data generated by scattered and diverse production line equipment. This restricts the upgrading of food processing production lines towards higher reliability, real-time performance, and intelligence. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for processing production line equipment data. It can dynamically perceive the production line's operational status based on real-time status data uploaded by the equipment and generate key production indicators, thereby achieving intelligent monitoring and optimization of the production process. This method supports dynamic perception of production line operational status, automatic generation of key indicators, and efficient transmission of production data. It can significantly improve the real-time performance, accuracy, and intelligence level of production line management, and is particularly suitable for food processing or industrial production environments that require multi-production line collaboration, data sensitivity, and high response speeds.

[0005] In a first aspect, this application provides a method for processing production line equipment data, including: Receive current equipment data sent by at least one production line device, the current equipment data including equipment temperature, equipment humidity, equipment output and equipment status; Production line data is determined based on the current equipment data, including production line efficiency, production line energy consumption, production line running time, production line failure rate, and production line scrap rate. The production line data is sent to the workshop gateway for transmission to the cloud platform for data processing.

[0006] Secondly, this application provides a production line equipment data processing device, comprising: The receiving module is used to receive current equipment data sent by at least one production line device, the current equipment data including equipment temperature, equipment humidity, equipment output and equipment status; The determination module is used to determine production line data based on the current equipment data, wherein the production line data includes production line efficiency, production line energy consumption, production line running time, production line failure rate, and production line scrap rate; The sending module is used to send the production line data to the workshop gateway, and to send it to the cloud platform for data processing.

[0007] Thirdly, this application provides a production line equipment data processing device, comprising: One or more processors; A memory that stores one or more programs that, when executed by one or more processors, cause the one or more processors to implement the production line equipment data processing method as described in the first aspect.

[0008] Fourthly, this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the production line equipment data processing method as described in the first aspect.

[0009] This application constructs a multi-stage processing mechanism covering production line equipment data acquisition, key indicator generation, and centralized cloud analysis. Combined with real-time data perception and indicator calculation matching strategies, it achieves automated and high-precision execution of production line equipment data processing. First, sensors or controllers deployed on each production line device collect current equipment data such as equipment temperature, humidity, output, and operating status, and upload this data to the workshop gateway for aggregation. Based on the aggregated real-time equipment data, production line data is calculated and generated, including production line efficiency, energy consumption, operating time, failure rate, and scrap rate. Subsequently, the production line data is sent to the cloud platform for centralized processing and analysis, achieving unified management and real-time monitoring of key production line indicators. This method, by combining production line data acquisition with cloud analysis, ensures the efficiency, accuracy, and stability of the data processing process, effectively improving the perception of production line operating status and the ability to manage the production process. It is particularly suitable for food processing or industrial production environments with multi-production line collaboration, data sensitivity, and high real-time requirements. Attached Figure Description

[0010] Figure 1 This is a flowchart of a production line equipment data processing method provided in an embodiment of this application; Figure 2 This is a flowchart of the device data receiving method provided in the embodiments of this application; Figure 3 This is a flowchart of the sampling frequency adjustment method provided in the embodiments of this application; Figure 4 This is a flowchart of the production line data determination method provided in the embodiments of this application; Figure 5 This is a flowchart of the cached data breakpoint resume method provided in the embodiments of this application; Figure 6 This is a flowchart of the cache adjustment method provided in the embodiments of this application; Figure 7 This is a system architecture diagram of a production line equipment data processing method provided in an embodiment of this application; Figure 8 This is a structural block diagram of a production line equipment data processing device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a production line equipment data processing device provided in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as being processed sequentially, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0013] With the rapid advancement of intelligent and digital transformation in food processing production lines, existing data management methods for production line equipment generally rely on independent operation of local OT systems or centralized analysis by IT systems, which is insufficient to meet the high requirements of multi-data source collaboration, real-time production monitoring, and data security management. However, existing solutions generally suffer from problems such as hindered data flow, system separation contradictions, delayed indicator calculations, and insufficient real-time decision-making capabilities in terms of OT and IT system collaboration, data integration efficiency, and real-time production monitoring capabilities. This not only makes it difficult to achieve immediate perception of key production line indicators but also limits the improvement of intelligent and digital management in food processing production lines.

[0014] The production line equipment data processing method proposed in this application enables automatic collection, key indicator calculation, and unified management of production line equipment status data while ensuring the integrity and security of production data. By integrating an IoT sensing platform, the method can collect real-time data on the temperature, humidity, output, and operating status of each production line device, and upload this data to the workshop gateway for initial aggregation, generating local production line data. Based on the aggregation of multi-source equipment data, the cloud platform-driven analysis module processes the aggregated data, calculating key indicators such as production line efficiency, production line energy consumption, production line operating time, production line failure rate, and production line scrap rate, and generating unified production line data reports. Subsequently, the cloud platform distributes the processed production line data to various management systems, enabling dynamic monitoring and optimization of the production line's operating status, thereby improving overall production line management efficiency, real-time response capabilities, and production process transparency.

[0015] The production line equipment data processing method provided in this embodiment can be executed by a production line equipment data processing device. This production line equipment data processing device can be implemented by software and / or hardware. The production line equipment data processing device can consist of two or more physical entities, or it can consist of a single physical entity. For example, the production line equipment data processing device can be an edge server for production line data processing.

[0016] The production line equipment data processing device is equipped with at least one type of operating system, including but not limited to Android, Linux, and Windows. The production line equipment data processing device can install at least one application based on the operating system. This application can be a built-in application of the operating system or an application downloaded from a third-party device or server. In this embodiment, the production line equipment data processing device has at least one application capable of executing production line equipment data processing methods.

[0017] For ease of understanding, this embodiment uses the operation and maintenance server as the main body for executing the production line equipment data processing method as an example for description.

[0018] Figure 1 A flowchart of a production line equipment data processing method provided in an embodiment of this application is given. (Reference) Figure 1 The data processing method for this production line equipment specifically includes: S110. Receive current equipment data sent by at least one production line device. The current equipment data includes equipment temperature, equipment humidity, equipment output, and equipment status.

[0019] In some embodiments, current equipment data sent by at least one production line device is received. This current equipment data represents various status information collected by the production line device during real-time operation, reflecting the device's operating status and production status. The current equipment data includes device temperature, device humidity, device output, and device status. Device temperature represents the temperature information of the production line device under its current operating condition; device humidity represents the humidity information of the environment in which the device is located; device output represents the production quantity of the production line device within the current time period; and device status represents the specific operating conditions or abnormal information of the device.

[0020] In one embodiment, the current device data can be received by: collecting data in real time through device sensors and uploading it to the edge gateway in a unified data format to provide basic data for subsequent data analysis, monitoring and optimization.

[0021] Optionally, Figure 2 A flowchart of the device data receiving method provided in an embodiment of this application is given. (Reference) Figure 2 The data receiving method of this device specifically includes: S1101. Obtain the equipment type of the production line equipment, and match the corresponding driver protocol of the production line equipment in the preset mapping table according to the equipment type.

[0022] For example, the equipment type of the production line equipment is obtained, where the equipment type represents the specific category or model of each piece of equipment in the production line, used to identify the functional characteristics and communication requirements of the equipment. After obtaining the equipment type, the corresponding driver protocol of the production line equipment is matched in a preset mapping table according to the equipment type. The driver protocol represents the data exchange rules used for communication and control with the equipment, including instruction format, data acquisition cycle, communication interface type, etc., to ensure that production line data can be correctly acquired and control instructions can be correctly issued.

[0023] In one embodiment, the matching of the driving protocol can be achieved by querying a pre-established mapping table of device types and driving protocols through the device management module, obtaining the corresponding protocol information based on the device type, and using the protocol information for subsequent device data acquisition and command issuance operations to ensure the compatibility and reliability between the production line equipment and the data processing system.

[0024] S1102. Receive current device data sent by the corresponding production line equipment using the driver protocol.

[0025] For example, the driver protocol is used to receive the current equipment data sent by the corresponding production line equipment. The current equipment data represents various status information collected by the production line equipment during real-time operation, including equipment temperature, equipment humidity, equipment output and equipment status, which are used to reflect the equipment's operating status and production status.

[0026] In one embodiment, the method of receiving current device data can be: establishing data interaction with the device through the communication module according to the instruction format defined by the driving protocol, periodically or in real time acquiring the data uploaded by the device, and encapsulating the data into a unified format for subsequent production line data calculation and monitoring analysis.

[0027] Optionally, Figure 3 A flowchart of the sampling frequency adjustment method provided in an embodiment of this application is given. (Reference) Figure 3 The sampling frequency adjustment method specifically includes: S111. Obtain the cycle equipment data of the production line equipment within the preset cycle.

[0028] For example, the system acquires periodic equipment data for production line equipment within a preset cycle. The preset cycle represents a time interval used for statistical analysis of production line operation, such as a minute-level, hourly, or shift-level cycle, used to periodically summarize equipment data. The periodic equipment data represents a summary of the operating status and production data of the production line equipment throughout the entire cycle, including the cycle average, total, or changes in equipment temperature, humidity, output, and status, used for calculating indicators such as production line efficiency, energy consumption, and fault rates.

[0029] In one embodiment, the method for obtaining cycle equipment data can be: summarizing and statistically calculating the current equipment data collected in real time within the cycle through the data acquisition module, generating the average value, total amount or change range of each equipment within the cycle, and providing basic data for subsequent production line data calculation and monitoring analysis.

[0030] S112. Calculate the standard deviation of the periodic device data and determine the sampling frequency based on the standard deviation.

[0031] For example, the standard deviation of the periodic equipment data is calculated, where the standard deviation represents the degree of fluctuation in the periodic equipment data and is used to quantify the stability of equipment operation and the magnitude of data change. After calculating the standard deviation, the sampling frequency is determined based on the standard deviation, where the sampling frequency represents the time interval for collecting production line equipment data in future periods, used to balance data accuracy and acquisition resource overhead.

[0032] In one embodiment, the sampling frequency can be determined as follows: if the standard deviation of the periodic device data is large, it indicates that the device data fluctuates rapidly, so the sampling frequency is increased to capture more real-time changes; if the standard deviation is small, it indicates that the device data is relatively stable, so the sampling frequency is reduced to reduce data acquisition and transmission overhead, thereby achieving dynamic adjustment of the sampling strategy.

[0033] S113. Send the sampling frequency to the production line equipment so that the production line equipment can collect equipment data at the sampling frequency.

[0034] For example, a sampling frequency is sent to the production line equipment. Sending the sampling frequency means that the adjusted sampling interval parameter is sent to the production line equipment through the communication interface to guide the equipment to collect data at the new frequency. After receiving the sampling frequency, the production line equipment collects equipment data at that sampling frequency. The collected data includes equipment temperature, equipment humidity, equipment output, and equipment status, which are used for subsequent periodic equipment data updates and production line data calculations.

[0035] In one embodiment, the sampling frequency can be sent by encapsulating the sampling frequency parameters into control commands through a standardized communication protocol and sending them to the device control module, so that the device can automatically adjust the data acquisition frequency according to the new sampling strategy, thereby achieving dynamic and intelligent data acquisition management.

[0036] S120. Determine production line data based on current equipment data. Production line data includes production line efficiency, production line energy consumption, production line running time, production line failure rate, and production line scrap rate.

[0037] In some embodiments, production line data is determined based on current equipment data. This production line data represents a comprehensive evaluation of the entire production line's operation, reflecting production efficiency, energy consumption levels, operating status, and product quality. Production line data includes production line efficiency, production line energy consumption, production line operating time, production line failure rate, and production line scrap rate. Production line efficiency represents the ratio of production output to equipment capacity per unit time; production line energy consumption represents the energy consumed by the production line per unit time or production task; production line operating time represents the actual operating time of the equipment within a specific cycle; production line failure rate represents the frequency of equipment failures; and production line scrap rate represents the proportion of defective products to total output during the production process.

[0038] In one embodiment, the production line data can be determined by: calculating and statistically analyzing the current equipment data through a data processing module, integrating the status, output, and abnormal information of each piece of equipment to generate production line efficiency, energy consumption, and fault indicators, thereby achieving a quantitative evaluation of the overall operation of the production line.

[0039] In one embodiment, the production line efficiency can be calculated by: counting the number of qualified products completed by the production line in the current time period, comparing it with the theoretical output that the production line equipment should complete according to the design capacity in the same time period, calculating the ratio of actual output to theoretical capacity, and thus obtaining the production line efficiency, which is used to evaluate the production performance of the production line.

[0040] In one embodiment, the method for calculating production line energy consumption may be: statistically analyzing the power consumption of all equipment on the production line in the current cycle, and accumulating the total energy consumption based on the actual operating time of each piece of equipment, thereby obtaining the total energy consumption of the production line in that cycle, which is used to analyze the energy usage of the production line.

[0041] In one embodiment, the production line running time can be calculated by: statistically analyzing the actual running time of each piece of equipment in the current cycle, and summing up the running times of all equipment to obtain the overall running time of the production line, which is used to measure the utilization rate and workload of the production line.

[0042] In one embodiment, the production line failure rate can be calculated by: counting the number of failures that occur in the production line equipment during the current cycle, comparing the number of failures with the total number of times the equipment runs or the total running time during the cycle, calculating the proportion of failures, and thus obtaining the production line failure rate, which is used to evaluate equipment reliability.

[0043] In one embodiment, the production line scrap rate can be calculated by: counting the number of products that do not meet quality standards produced in the current cycle, comparing them with the total number of products produced in the cycle, calculating the proportion of defective products to the total output, and thus obtaining the production line scrap rate, which reflects the product quality of the production line.

[0044] Optionally, Figure 4 A flowchart of the production line data determination method provided in an embodiment of this application is given. (Reference) Figure 4 The specific methods for determining the production line data include: S1201. Calculate the magnitude of change between the current device data and the reference device data from the previous sampling.

[0045] For example, the change amplitude of the current device data and the reference device data of the previous sampling is calculated, where the change amplitude represents the degree of change of various indicators of the device in two consecutive sampling periods, which is used to reflect the dynamic fluctuation of the device's operating status.

[0046] In one embodiment, the method for calculating the change amplitude can be: calculating the difference between the current value and the baseline value for each indicator such as equipment temperature, equipment humidity, equipment output, and equipment status, and performing absolute value or relative percentage processing as needed, thereby obtaining the change amplitude of each indicator, providing a basis for subsequent anomaly monitoring or production line data analysis.

[0047] S1202: Filter the current equipment data whose change amplitude is less than the preset amplitude threshold to obtain valid equipment data, and determine the production line data based on the valid equipment data.

[0048] For example, current equipment data with fluctuations less than a preset threshold is filtered to obtain valid equipment data. Valid equipment data represents equipment status information that has changed significantly within the current cycle, used to eliminate minor fluctuations or noise data, ensuring the accuracy of production line data calculations. After obtaining valid equipment data, production line data is determined based on it. Production line data represents a comprehensive evaluation of the entire production line's operation, including production line efficiency, energy consumption, operating time, failure rate, and scrap rate, reflecting the production line's performance and quality status.

[0049] In one embodiment, the production line data can be determined by: statistically summarizing and calculating the effective equipment data through a data processing module, integrating the effective operating information of each piece of equipment to generate production line indicators, thereby providing an accurate data foundation for production line monitoring, optimization and decision-making.

[0050] S130: Send production line data to the workshop gateway for transmission to the cloud platform for data processing.

[0051] In some embodiments, production line data is sent to the workshop gateway. Sending production line data means transmitting the production line data to the workshop gateway via a data communication interface within the workshop, for subsequent data aggregation and processing. Production line data is also sent to a cloud platform for data processing. The cloud platform refers to a data processing and analysis system deployed on a remote server, which provides decision support for production line monitoring, production optimization, and equipment maintenance by storing, statistically analyzing, and visualizing the production line data.

[0052] In one embodiment, production line data can be sent by encapsulating it into a unified data format and sending it to the workshop gateway through a standardized data interface and communication protocol. The gateway then uploads the data to the cloud platform, thus realizing a closed-loop process from local data collection to remote analysis.

[0053] Optionally, production line data is sent to the workshop gateway for transmission to the cloud platform for data processing, including: Production line data is sent to the workshop gateway, where it is cached in the gateway's cache area. This cached data is then sent to the cloud platform for data processing.

[0054] For example, production line data is sent to the workshop gateway. Sending production line data means transmitting the production line data to the workshop gateway via the internal data communication interface for local caching and subsequent processing. The production line data generates cached data in the workshop gateway's cache area. This cached data represents a set of production line data temporarily stored locally on the gateway, used to improve data transmission efficiency, ensure data integrity, and support data retransmission in case of anomalies. After generating the cached data, the workshop gateway sends it to a cloud platform for data processing. The cloud platform represents a data processing and analysis system deployed on a remote server, which provides decision support for production line monitoring, production optimization, and equipment maintenance by storing, statistically analyzing, and visualizing the production line data.

[0055] In one embodiment, the method of sending production line data and generating cached data can be as follows: through standardized data interfaces and communication protocols, the production line data is encapsulated into a unified format and sent to the workshop gateway. After receiving the data, the gateway generates a cached data set and uploads it to the cloud platform periodically or in real time according to a preset strategy, thereby realizing a closed loop of data collection, caching and remote processing.

[0056] Optionally, Figure 5 A flowchart of the cached data breakpoint resume method provided in an embodiment of this application is given. (Reference) Figure 5 The method for resuming interrupted downloads of cached data specifically includes: S13011. In the event of a network transmission interruption between the workshop gateway and the cloud platform, the workshop gateway marks the transmission interruption point of the cached data.

[0057] For example, when the workshop gateway detects a network transmission interruption with the cloud platform, the workshop gateway marks the transmission breakpoint of the cached data. The transmission breakpoint indicates the position where the cached data has been successfully transmitted during the upload process, which is used to resume the data transmission after the network is restored, avoiding duplicate uploads or data loss.

[0058] In one embodiment, marking transmission breakpoints can be achieved by recording the amount of data or data identifiers that have been successfully uploaded through a cache management module, associating this information with cached data, and then continuing to upload the remaining data from the breakpoint when the network recovers, thereby achieving efficient and reliable data transmission.

[0059] S13012. When the network transmission between the workshop gateway and the cloud platform is restored, the workshop gateway sends the cached data to the cloud platform for data processing based on the transmission interruption.

[0060] For example, when the workshop gateway detects that network transmission with the cloud platform has resumed, the gateway sends cached data to the cloud platform for processing based on the transmission interruption point. Sending based on the transmission interruption point means that data uploads begin from the point after the interruption, avoiding duplicate transmissions of already transmitted data and improving data upload efficiency and reliability. After the cached data is uploaded to the cloud platform, the cloud platform processes it. The cloud platform refers to a data processing and analysis system deployed on a remote server. By storing, statistically analyzing, and visualizing production line data, it provides decision support for production line monitoring, production optimization, and equipment maintenance.

[0061] In one embodiment, the method of uploading cached data based on transmission breakpoints can be as follows: the workshop gateway obtains the breakpoint location through the cache management module, encapsulates the remaining data from that location, and sends the data to the cloud platform in batches or in a streaming manner according to the standard communication protocol, thereby achieving breakpoint resumption and efficient and reliable data synchronization.

[0062] Optionally, Figure 6 A flowchart of the cache adjustment method provided in an embodiment of this application is given. (See reference...) Figure 6 The specific methods for adjusting the cache include: S131. Obtain the current network status parameters and baseline network status parameters between the workshop gateway and the cloud platform.

[0063] For example, the current network status parameters between the workshop gateway and the cloud platform are first obtained. These parameters represent network performance metrics measured in real time during data transmission, including network bandwidth, latency, packet loss rate, and transmission rate, used to assess the quality of the current network connection. After obtaining the current network status parameters, baseline network status parameters are also obtained. These parameters represent network performance metrics recorded under normal communication conditions and are used to compare with the current network status to identify network anomalies or performance degradation.

[0064] In one embodiment, network status parameters can be obtained by periodically collecting network transmission performance data through a network monitoring module, matching and storing the collected current parameters with pre-set baseline parameters, and providing a basis for subsequent network anomaly judgment, transmission optimization, or breakpoint resume strategy.

[0065] S132. Calculate the network factor based on the current network state parameters and the baseline network state parameters.

[0066] For example, a network factor is calculated based on the current network state parameters and the baseline network state parameters. The network factor represents the degree of deviation between the current network connection quality and the baseline network state, and is used to quantify the reliability and performance level of the network transmission environment.

[0067] In one embodiment, the network factor can be calculated by comparing key indicators such as current network bandwidth with baseline bandwidth, current latency with baseline latency, and current packet loss rate with baseline packet loss rate, and generating a single value using a weighted or comprehensive algorithm to represent a comprehensive evaluation of network transmission capabilities, providing a basis for decision-making regarding subsequent data upload strategies, cache management, and breakpoint resume.

[0068] S133. Calculate the target cache capacity using network factors and a preset baseline cache capacity, and adjust the cache area based on the target cache capacity.

[0069] For example, the target cache capacity is calculated using network factors and a preset baseline cache capacity. The target cache capacity represents the cache size dynamically adjusted based on the current network status, ensuring that cached data can be completely stored and support subsequent uploads even under network fluctuations or transmission constraints. After obtaining the target cache capacity, the cache area is adjusted based on it. Adjusting the cache area means modifying the capacity or allocation strategy of the workshop gateway cache area through the cache management module, enabling the cache area to adapt to network transmission conditions and achieve efficient matching between data storage and uploading.

[0070] In one embodiment, the target cache capacity can be calculated by: appropriately increasing the cache capacity based on the decrease in network factor to cope with transmission delays or interruptions, and appropriately reducing the cache capacity when the network condition is good to optimize resource utilization, thereby achieving dynamic management of the cache area and ensuring the stability of data transmission.

[0071] Optionally, Figure 7 A system architecture diagram of a production line equipment data processing method provided in an embodiment of this application is given. (Reference) Figure 7 The system architecture specifically includes: production line equipment 11, eXware gateway 12, Nexus gateway 13, and IT platform 14.

[0072] For example, production line equipment 11 is the production line equipment in this application, eXware gateway 12 is a specific embodiment of edge gateway in this application, Nexus gateway 13 is a specific embodiment of workshop gateway in this application, and IT platform 14 is a specific embodiment of cloud platform in this application.

[0073] In one embodiment, production line equipment 11 belongs to the production execution layer of the enterprise's production line, including various instruments, meters, and production equipment in the food processing production line. This part is responsible for collecting key operational data in the production process in real time, such as temperature, humidity, output, production speed, and equipment operating status, and providing access to the upper-layer gateway through an open data interface, thereby providing a basic data source for subsequent data analysis and production optimization.

[0074] In one embodiment, eXware gateway devices 12 are deployed at multiple nodes of the food processing production line. The eXware gateway 12 integrates drivers for over 200 industrial equipment protocols, including OPC UA, Modbus RTU, Modbus TCP, EtherNet / IPCIP, EtherCAT, Profinet, Powerlink, 485, 232, 422, TCP, UDP, CAN, CANopen, J1939, NMEA2000, BACnet, KNX, Hitachi, and mainstream PLC protocols from Siemens, AB, Schneider Electric, ABB, Mitsubishi, Omron, Beckhoff, CODESYS, GE, Panasonic, Emerson, Rexroth, Keyence, and others. It also supports expanding local I / O modules to collect field I / O data, and is compatible with 99.99% of PLCs, inverters, sensors, smart meters, valves, thermometers, flow meters, concentration meters, and other instruments and equipment on the OT side. The eXware Gateway 12 can accurately collect key operational data from different types of processing equipment, including temperature, pressure, output, energy consumption, and equipment status. This enables unified access and management of multi-source devices, significantly reducing data collection difficulty and preventing data silos. The eXware Gateway 12 has a built-in JavaScript execution engine, allowing users to run custom data analysis code at the edge. This code performs real-time calculations and filtering on the collected raw data, generating high-level metrics such as overall production line equipment efficiency, energy consumption statistics, equipment runtime, number of failures, and scrap rate. This improves data quality and utilization efficiency, reducing the analysis burden on the IT side.

[0075] In one embodiment, the NeXus gateway 13 is deployed across various production workshops to receive processed data from multiple eXware gateways 12. One end interacts with the eXware gateway 12 via the OPC UA protocol, while the other end connects to the IT platform 14 via IT communication protocols such as MQTT, HTTP, and WebSocket, as well as database drivers such as MySQL, SQL Server, and InfluxDB, enabling centralized integration and forwarding of multi-source data. The NeXus gateway 13 possesses rich IT platform protocol adaptation capabilities, compatible with mainstream systems such as MES, ERP, IoT, and cloud platforms, with a protocol coverage rate of 99.99%. The NeXus gateway 13 facilitates efficient integration between OT-side data and IT systems, promoting data exchange between the enterprise's production and management layers, and providing highly reliable data support for production scheduling and business decisions. The NeXus gateway 13 has a built-in configurable cache for temporarily storing critical data in case of network anomalies or communication interruptions. After network recovery, the system automatically executes a data retransmission process to ensure upload integrity, prevent data loss, and significantly improve the stability and reliability of system operation. The NeXus Gateway 13 is based on a 32-bit quad-core 800 MHz ARM processor, equipped with 2GB RAM and 8GB FLASH storage. A single device can achieve a transmission rate of up to 3000 points / second, meeting the real-time transmission needs of high-frequency data acquisition and large-scale production line scenarios. The entire OT / IT converged system adopts a distributed deployment approach, allowing for horizontal scaling to food processing production lines of varying sizes. The system architecture is flexible, easy to deploy, and has low maintenance costs, enabling rapid adaptation to production line expansion and functional upgrades without additional hardware investment.

[0076] In one embodiment, the IT platform 14 belongs to the enterprise information management layer, mainly including the MES system, ERP system, IoT platform, cloud platform, and database. This layer receives centralized data uploaded by the NeXus gateway 13 and performs applications such as data visualization, AI analysis, predictive maintenance, and production strategy optimization based on business needs to achieve intelligent management and continuous improvement of the production process.

[0077] In one embodiment, after the food processing production line starts up and enters normal production, the production line equipment 11 establishes a communication connection with the eXware gateway 12 via fieldbus or industrial Ethernet. The eXware gateway 12 accesses the device register address through its built-in protocol driver, reads real-time operating data, and performs cache processing. Subsequently, the JavaScript engine executes analysis algorithms on the cached data, calculates high-order index data, and transmits the results to the NeXus gateway 13 via the OPC UA protocol. After receiving the aggregated data from multiple eXware gateways 12, the NeXus gateway 13 first writes the data into its internal cache and establishes a connection with the upper-layer IT platform 14 based on the IT communication protocol. When the network is accessible, the NeXus gateway 13 uploads the cached data to the IT platform 14 in real time. After the upload is complete, it automatically clears the cache to receive new device data. If there is a network failure or the IT platform 14 is temporarily unavailable, the NeXus gateway 13 retains all data in the cache and re-uploads it in batches after the connection is restored, ensuring the integrity and continuity of production data. After receiving data from the NeXus gateway 13, the IT platform 14 performs subsequent processing according to the enterprise's business logic, including data visualization, big data statistics, AI predictive maintenance, energy consumption analysis, and production optimization decisions. This process achieves a closed-loop data flow and intelligent collaboration across the entire data chain from the OT to the IT end. Through the above structure and working mechanism, the distributed integration of the OT / IT systems in the food processing production line is realized, eliminating the data silos problem in traditional architectures, significantly improving the system's real-time performance, reliability, and scalability, and providing efficient and stable technical support for the digital transformation of the food processing industry.

[0078] Based on the above embodiments, Figure 8 This is a structural block diagram of a production line equipment data processing device provided in an embodiment of this application. (Reference) Figure 8 The production line equipment data processing device provided in this embodiment specifically includes: a receiving module 21, a determining module 22, and a sending module 23.

[0079] The receiving module 21 is configured to receive current equipment data sent by at least one production line device, the current equipment data including equipment temperature, equipment humidity, equipment output, and equipment status; the determining module 22 is configured to determine production line data based on the current equipment data, the production line data including production line efficiency, production line energy consumption, production line running time, production line failure rate, and production line scrap rate; the sending module 23 is configured to send the production line data to the workshop gateway for transmission to the cloud platform for data processing.

[0080] Based on the above embodiments, the receiving module 21 includes: a protocol unit configured to obtain the device type of the production line equipment and match the corresponding driving protocol of the production line equipment in a preset mapping table according to the device type; and a receiving unit configured to receive the current device data sent by the corresponding production line equipment using the driving protocol.

[0081] Based on the above embodiments, the production line equipment data processing device further includes: a periodic data module configured to acquire periodic equipment data of the production line equipment within a preset period; a sampling frequency module configured to calculate the standard deviation of the periodic equipment data and determine the sampling frequency based on the standard deviation; and a sampling control module configured to send the sampling frequency to the production line equipment, so that the production line equipment can collect equipment data at the sampling frequency.

[0082] Based on the above embodiments, the determining module 22 includes: a change amplitude unit, configured to calculate the change amplitude between the current equipment data and the previously sampled reference equipment data; and a production line data unit, configured to filter current equipment data with change amplitudes less than a preset amplitude threshold to obtain valid equipment data, and determine production line data based on the valid equipment data.

[0083] Based on the above embodiments, the sending module 23 includes: a cached data unit, configured to send the production line data to the workshop gateway, for caching the cached data in the cache area of ​​the workshop gateway, and the cached data is used to send to the cloud platform for data processing.

[0084] Based on the above embodiments, the cached data unit includes: a transmission breakpoint subunit, configured to mark the transmission breakpoint of the cached data when the network transmission between the workshop gateway and the cloud platform is interrupted; and a power outage resumption subunit, configured to send the cached data to the cloud platform for data processing based on the transmission breakpoint when the network transmission between the workshop gateway and the cloud platform is restored.

[0085] Based on the above embodiments, the production line equipment data processing device further includes: a network status module, configured to acquire the current network status parameters and baseline network status parameters between the workshop gateway and the cloud platform; a network factor module, configured to calculate a network factor based on the current network status parameters and the baseline network status parameters; and a cache capacity module, configured to calculate a target cache capacity using the network factor and a preset baseline cache capacity, and adjust the cache area based on the target cache capacity.

[0086] The production line equipment data processing device provided in this application embodiment, by integrating core functional modules such as data receiving, data parsing, and data sending, constructs a data processing architecture centered on equipment status acquisition, production line indicator calculation, and cloud data interaction. This device is composed of functional units such as a receiving module, a determining module, and a sending module working together to form a complete closed-loop link from production line equipment data acquisition and production line operation indicator generation to data reporting to the cloud platform, effectively improving the real-time performance, reliability, and information interaction efficiency of production line data processing.

[0087] The production line equipment data processing device provided in this application embodiment can be used to execute the production line equipment data processing method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0088] Figure 9 This is a schematic diagram of the structure of a data processing device for a production line provided in an embodiment of this application. (Refer to...) Figure 9 The data processing equipment of this production line includes: a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 and the number of memories 32 in this data processing equipment can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 of this data processing equipment can be connected via a bus or other means.

[0089] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the production line equipment data processing method in any embodiment of this application (e.g., the receiving module 21, determining module 22, and sending module 23 in the production line equipment data processing apparatus). The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 32 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0090] The communication device 33 is used for data transmission.

[0091] The processor 31 executes various functional applications and data processing of the equipment by running software programs, instructions and modules stored in the memory 32, thereby realizing the above-mentioned production line equipment data processing method.

[0092] Input device 34 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 35 may include display devices such as a display screen.

[0093] The production line equipment data processing equipment provided above can be used to execute the production line equipment data processing method provided in the above embodiments, and has corresponding functions and beneficial effects.

[0094] This application also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform a production line equipment data processing method. The production line equipment data processing method includes: receiving current equipment data sent by at least one production line device, the current equipment data including equipment temperature, equipment humidity, equipment output, and equipment status; determining production line data based on the current equipment data, the production line data including production line efficiency, production line energy consumption, production line running time, production line failure rate, and production line scrap rate; and sending the production line data to a workshop gateway for transmission to a cloud platform for data processing.

[0095] Storage medium—any type of memory device or storage device. The term "storage medium" is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which a program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0096] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the production line equipment data processing method described above, but can also execute related operations in the production line equipment data processing method provided in any embodiment of this application.

[0097] The production line equipment data processing device, storage medium, and production line equipment data processing equipment provided in the above embodiments can execute the production line equipment data processing method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the production line equipment data processing method provided in any embodiment of this application.

[0098] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application. The scope of this application is determined by the scope of the claims.

Claims

1. A method for processing data of a production line equipment, for a production line gateway, characterized in that, The method comprises the following steps: receiving current device data sent by at least one production line device, wherein the current device data comprises device temperature, device humidity, device yield and device state; determining production line data based on the current device data, wherein the production line data comprises production line efficiency, production line energy consumption, production line running time, production line failure rate and production line scrap rate; sending the production line data to a plant gateway for sending to a cloud platform for data processing.

2. The line equipment data processing method of claim 1, wherein, The receiving current device data sent by at least one production line device comprises: obtaining the device type of the production line device, and matching the driving protocol corresponding to the production line device in a preset mapping table according to the device type; receiving the current device data sent by the corresponding production line device by using the driving protocol.

3. The line equipment data processing method of claim 1, wherein, After receiving the current device data sent by at least one production line device, the method comprises the following steps: obtaining periodic device data of the production line device within a preset period; calculating the standard deviation of the periodic device data, and determining a sampling frequency based on the standard deviation; sending the sampling frequency to the production line device, so that the production line device collects device data at the sampling frequency.

4. The line equipment data processing method of claim 1, wherein, The determining production line data based on the current device data comprises: calculating the change amplitude of the current device data and the last sampled reference device data; filtering the current device data with an amplitude smaller than a preset amplitude threshold to obtain effective device data, and determining the production line data based on the effective device data.

5. The line equipment data processing method of claim 1, wherein, The sending the production line data to a plant gateway for sending to a cloud platform for data processing comprises: sending the production line data to the plant gateway for caching in a cache area of the plant gateway to obtain cache data, wherein the cache data is used for sending to the cloud platform for data processing.

6. The line equipment data processing method of claim 5, wherein, The cache data used for sending to the cloud platform for data processing comprises: in the case that the network transmission between the plant gateway and the cloud platform is interrupted, the plant gateway marks a transmission breakpoint of the cache data; in the case that the network transmission between the plant gateway and the cloud platform is restored, the plant gateway sends the cache data to the cloud platform for data processing based on the transmission breakpoint.

7. The line equipment data processing method of claim 1, wherein, After sending the production line data to the plant gateway, the method further comprises the following steps: obtaining a current network state parameter and a reference network state parameter between the plant gateway and the cloud platform; calculating a network factor according to the current network state parameter and the reference network state parameter; calculating a target cache capacity by using the network factor and a preset reference cache capacity, and adjusting the cache area based on the target cache capacity.

8. A production line equipment data processing apparatus characterized by comprising: The method comprises the following steps: a receiving module, configured to receive current device data sent by at least one production line device, wherein the current device data comprises device temperature, device humidity, device yield and device state; a determining module, configured to determine production line data based on the current device data, wherein the production line data comprises production line efficiency, production line energy consumption, production line running time, production line failure rate and production line scrap rate; a sending module, configured to send the production line data to a plant gateway for sending to a cloud platform for data processing.

9. A production line equipment data processing apparatus, characterized by, The method comprises the following steps: one or more processors; memory storing one or more programs, when executed by the one or more processors, cause the one or more processors to implement the line equipment data processing method as claimed in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, wherein: The computer executable instructions, when executed by a computer processor, perform the line equipment data processing method as claimed in any one of claims 1-7.

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