Data storage and backtracking method for automatic carrying equipment

Abstract

The invention relates to the technical field of computers, and discloses a data storage and backtracking method for automatic carrying equipment, which comprises the following steps of: detecting and acquiring data change, detecting and acquiring data of the automatic carrying equipment by adopting a trigger, and immediately recording the currently changed data by the trigger when the data change; when the data does not change, the data acquisition frequency is reduced; data storage: carrying out compression processing on the collected data through a GZIP DEFLATE algorithm, and storing the data in a JSON format, so that the data model can be recovered even if the data model changes; and data interaction: based on the stored data, displaying the data of the automatic carrying equipment, and providing an interaction function, so that a user can control the speed and direction of data backtracking. According to the method, the aspects of data visualization effect, interactivity, data storage and the like are remarkably improved. And more convenient and efficient historical data is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data storage and backtracking method for automated handling equipment. Background Art

[0002] In the warehousing and logistics industry, the wafer box automatic handling storage system (Stocker, hereinafter referred to as STK) is a multi-layer storage space for automatically storing, retrieving and conveying boxes of various specifications. It is widely used in various automated warehouse scenarios, such as large-scale automated stereoscopic warehouses, semiconductor wafer manufacturing plants, and PCB (printed circuit board) production lines. In these scenarios, STK is not only responsible for the automatic storage and circulation of goods, but also meets the needs of specific industries through its efficient data processing and storage mechanism, as well as customized design. For example, in semiconductor wafer manufacturing, STK works in collaboration with other automated equipment to build a high-cleanliness automated warehousing system to ensure the quality of wafers during storage and circulation. With its high efficiency, intelligence and precision, STK has promoted the automation and intelligent development of the warehousing and logistics industry, and plays an irreplaceable role in different industry scenarios.

[0003] With the expansion of warehouse scale and the improvement of operating efficiency, the amount of data generated by STK system in the operation process has increased dramatically, including location information, transport commands, sensor signals, fault alarms, etc. These data are crucial for real-time monitoring, scheduling optimization, troubleshooting and long-term performance evaluation of warehouses, so monitoring equipment and recording operating data are becoming more and more important.

[0004] In the semiconductor wafer manufacturing industry, the amount of data generated by STK during operation is huge, complex and diverse. High-frequency data requires efficient data processing and storage mechanisms. However, existing playback technology can only record fixed-frequency signal information. When the device frequently changes the signal, some data may be lost, and it is easy to cause data storage redundancy. Specifically, the existing technology has the following deficiencies:

[0005] 1. Limitations of fixed frequency recording lead to loss of signal details:

[0006] During equipment operation, the speed and pattern of signal changes may vary depending on a variety of factors. Fixed-frequency recording methods cannot be dynamically adjusted based on the actual rate of change of the signal, so when the signal changes rapidly or there are high-frequency fluctuations, it is easy to miss key signal details. This loss of signal details may lead to inaccurate subsequent analysis, and it is impossible to fully restore the actual situation of equipment operation, affecting the effectiveness of fault diagnosis and performance optimization. When faced with sudden changes in key signals, they may not be captured in time due to long recording intervals. These key signals are often important indicators of equipment abnormalities, fault warnings, or performance bottlenecks.

[0007] 2. The visualization effect is not obvious:

[0008] When reviewing the data playback later, the data display is not obvious, and multiple data will change at the same time, which makes it impossible to correctly judge the order of data transformation, which may lead to misjudgment of some key information. Due to the limitation of recording frequency, some important information may be omitted, resulting in the inability to observe it in the visual interface and the inability to discover and deal with potential problems in time, increasing the risk of equipment failure and operating costs.

[0009] 3. Redundant records during idle periods increase storage and management costs:

[0010] When the equipment is idle or performing non-critical operations, continuous recording will generate a large amount of redundant data. This data not only takes up valuable storage space, but also increases the difficulty of data management and maintenance. As the amount of data continues to accumulate, storage costs and management costs will also increase accordingly. In addition, redundant data may also interfere with subsequent data analysis and decision-making processes, reducing work efficiency.

[0011] To address this problem, the present invention will avoid signal loss and the problem of excessive storage space by optimizing the data acquisition frequency. Summary of the invention

[0012] In order to solve the above technical problems, the present invention provides a data storage and backtracking method for automated handling equipment.

[0013] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0014] A data storage and backtracking method for automated handling equipment, comprising:

[0015] Data change detection and collection: triggers are used to detect and collect data from automated handling equipment. When data changes, the trigger immediately records the changed data. When data does not change, the data collection frequency is reduced, and data is collected and saved at least once within a set minimum data sampling period.

[0016] Data preservation: The collected data is compressed using the GZIP DEFLATE algorithm and saved in JSON format, so that the data model can be restored even if it changes, and can be jumped to a specified time point for playback as needed;

[0017] Data interaction: Based on the saved data, a front-end visual interface is built to display the data of automated handling equipment and provide interactive functions so that users can control the speed and direction of data tracing.

[0018] Furthermore, the collected data is compressed by the GZIP DEFLATE algorithm and saved in JSON format, so that the data model can be restored even if it changes, and can jump to a specified time point for playback as needed, specifically including:

[0019] Select the GZIP DEFLATE algorithm to compress the data to be saved: first read the data to be compressed, preprocess the data, use the LZ77 algorithm to compress the preprocessed data, then perform Huffman coding on the compression result of the LZ77 algorithm to achieve further compression, and finally encrypt the compressed data and store it in JSON format, and store it in the database by time; when playing, search for files according to the time of the file name, decompress, decode and read the search results.

[0020] Furthermore, when the data does not change, the data collection frequency is reduced, and data is collected at least once within a set cycle time, specifically including:

[0021] Set feedback control to collect statistics on the actual frequency of changes in the data of the automated handling equipment when the automated handling equipment is operating normally, and set a frequency threshold based on the actual frequency of changes;

[0022] When the actual data change frequency of the automated handling equipment is lower than the frequency threshold, it is considered that the data of the automated handling equipment has not changed, and the data collection frequency is reduced at this time; and a minimum data sampling period is set. Within the minimum data sampling period, even if the data has not changed, a data collection and data saving operation is performed once.

[0023] Compared with the prior art, the beneficial technical effects of the present invention include:

[0024] 1. Timely recording: When the data of the equipment changes, it will be recorded immediately to ensure that all data will be captured. This will avoid data loss that may occur when collecting data at a fixed frequency.

[0025] 2. Scheduled recording: In the absence of data changes, the system will adaptively reduce the frequency and set the minimum data sampling period to automatically record the current status once to maintain data continuity while reducing unnecessary recording.

[0026] 3. Improved troubleshooting efficiency: Through the historical backtracking function, you can gain an in-depth understanding of the equipment's operating conditions and performance, and view the data of the equipment at the time when the problem occurred, thereby improving the overall efficiency and capacity of the production line.

[0027] 4. Visualization: Through the visualization display function, you can intuitively understand the status and location of the equipment, accurately find the events that occurred at the equipment at past time points, facilitate monitoring and control of equipment operation, and improve the convenience and accuracy of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the process of data collection for the present invention;

[0029] Figure 2 It is a data acquisition principle diagram in the prior art;

[0030] Figure 3 It is a schematic diagram of the data substrate of the present invention. DETAILED DESCRIPTION

[0031] A preferred embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0032] The data of automated handling equipment mainly includes video images, sensor signals, control instructions, IO signals and other data. The present invention supports free selection of historical backtracking time points that need to be viewed, generates corresponding timelines, displays historical data of corresponding time points on the front-end page, and supports users to view detailed status information of automated handling equipment at each backtracking time point, such as the detailed content of the commands received by the automated handling equipment, the ID information of the carrier being transported, the status information of the port, alarm information and alarm codes, etc.

[0033] The data storage and backtracking method of the automated handling equipment in the present invention includes: data change detection and collection, data interaction, and data preservation.

[0034] The following is divided into several parts to introduce the technical solution of the present invention in detail.

[0035] 1. Data change detection and collection:

[0036] The present invention communicates with the automated handling equipment through the connection interface and bus and obtains IO signals in real time. It no longer uses the method of periodically saving data, but monitors the changes of data in real time. Whenever there is a change in data, the data is collected and stored, and the stored data will cover the current state of the equipment, such as the state of the input and output ports (Port) of the wafer box, the wafer box (Carrier) information, the current specific position of the wafer box, IO signals, whether there is an alarm at present, and the specific content of the alarm information. These data are compressed, segmented and saved through an algorithm, and jumped to a specified time point for playback as needed.

[0037] Specifically, the present invention uses a trigger to record the data changes of the automated handling equipment in real time. Whenever the data changes, the trigger will trigger the event of signal recording, thereby avoiding data loss; and the present invention adopts an adaptive control mechanism, such as Figure 1 As shown, when the automated handling equipment is used at high performance, the data collection frequency is increased. When the automated handling equipment is in low performance, the sampling frequency is adjusted according to the current sampling result and the preset error threshold. When the actual data change frequency of the automated handling equipment is lower than the frequency threshold, it is considered that the data of the automated handling equipment does not change, and the data collection frequency is reduced at this time.

[0038] 2. Data interaction:

[0039] The present invention uses an intuitive and dynamic timeline combined with a behavior progress bar to convert the historical data of automated handling equipment into easy-to-understand image information. The Realtime Layout is used to optimize the display of the screen, making it clearer for the user, and the position of the wafer box handling robot, the status of the wafer box input and output ports (Port), IO signals, etc. are changed in real time according to the stored data, so that the user's data query request can be responded to immediately, providing the latest and most accurate data results.

[0040] On the front-end page, you can enter the query time point to query the status of the device at the exact time point and other information. The front-end visual interface (UI) will accurately display the device's playback to the user, and you can freely control buttons such as fast forward, rewind, speed, and pause. Clicking on the corresponding device can easily query the status of the current device.

[0041] 3. Data preservation:

[0042] The present invention adopts a storage method combining database and local files. The files in the database are saved in JSON format, which can be restored even if the data model changes, and the GZIP DEFLATE algorithm is used to compress and split the data for storage. When restoring data or playing back, you can jump to a specified time point for playback as needed. The local playback file converts the data into binary data (0, 1, 0, 1, ...) in real time and compresses and stores it, thereby saving storage space.

[0043] like Figure 2 and Figure 3 As shown, the present invention reduces the resources occupied by data and the generation of redundant data while ensuring that no data is lost, while ensuring that all important data is recorded in a timely manner to reduce the risk of data loss; and displays dynamic data changes to users through a clearly visible visual interface.

[0044] Example

[0045] 1. Implementation of data change monitoring:

[0046] To achieve instant notification of any changes in playback data, the present invention will use the INotifyPropertyChanged interface in the .NET framework. This interface allows the Playback class (or any class containing playback data) to automatically notify components that monitor these changes when its properties change.

[0047] Define the Playback class and implement the INotifyPropertyChanged interface: In the Playback class, the present invention will add setter methods for all properties that need to be monitored, and call the OnPropertyChanged method (an auxiliary method used to trigger the PropertyChanged event) in these setters. This method passes the property name as a parameter to ensure that the listener subscribed to the event can identify which property has changed.

[0048] Registering a PropertyChanged event handler: In the class that monitors Playback data, the present invention will instantiate an instance of the Playback class and register an event handler for its PropertyChanged event. The event handler will be responsible for responding to any property changes and executing subsequent data processing logic.

[0049] 2. Capturing and processing data changes

[0050] Data change reception and processing: When the properties of the Playback class change, the PropertyChanged event will be triggered, and the registered processor will receive this event. In the event processor, the present invention will check whether the changed property is a data item that needs to be recorded. If so, the next step of processing will be carried out.

[0051] Data compression and preservation: For data changes that need to be recorded, the present invention will first compress them (to reduce storage space and improve loading efficiency), select Gzip and DEFLATE algorithm for compression, and use chunking to process large-scale data files. First, read the original data to be compressed, pre-process the data, use the LZ77 algorithm to compress the processed data, and then perform Huffman coding on the output of the LZ77 algorithm to achieve further compression. Finally, encrypt the compressed data and store it in JSON format. Store it in the database by time, find the time of the file name when playing, search the file, decompress, decode, and read the data.

[0052] 3. Data Collection Cycle and Integrity Assurance

[0053] Set the data collection cycle: Set feedback control. When the automated handling equipment is operating normally, the actual frequency of change of the data of the automated handling equipment is counted, and a frequency threshold is set according to the actual frequency of change counted. When the actual frequency of change of the data of the automated handling equipment is lower than the frequency threshold, it is considered that the data of the automated handling equipment does not change, and the sampling frequency is reduced to reduce the waste of storage space. In order to ensure the continuity and integrity of the data, the present invention will set a minimum data sampling cycle. During this period, even if the data does not change significantly, the present invention will perform a data collection and storage operation. This can avoid data loss due to unexpected situations (such as program crashes).

[0054] 4. Data playback and integration

[0055] Data loading and playback: When the Playback data needs to be played back, the user will specify a time period. The system will load the corresponding data segment from the stored compressed data according to this time period. Then, the loaded data is integrated with the current data of the Playback class (or the entire backup data, depending on the needs) to prepare for playback. The present invention uses CSS and HTML languages ​​to build a framework, uses Microsoft SignalR to achieve real-time data interaction, and uses Angular14 technology to build a Layout visualization interface in the front-end UI interface. The UI will contain all component information of the device, and can read the stored data in real time according to the time selected by the user, and decompress and decode it to dynamically display it on the UI screen.

[0056] Through the above specific implementation scheme, the present invention can effectively realize the change notification, capture, processing, storage and playback functions of Playback data, ensuring the integrity and availability of data.

[0057] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0058] In addition, it should be understood that although the present specification is described in terms of implementation modes, not every implementation mode contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A data storage and backtracking method for automated handling equipment, characterized in that: include: Data change detection and collection: triggers are used to detect and collect data from automated handling equipment. When data changes, the trigger immediately records the changed data. When data does not change, the data collection frequency is reduced, and data is collected and saved at least once within a set minimum data sampling period. Data preservation: The collected data is compressed using the GZIP DEFLATE algorithm and saved in JSON format, so that the data model can be restored even if it changes, and can be jumped to a specified time point for playback as needed; Data interaction: Based on the saved data, a front-end visual interface is built to display the data of automated handling equipment and provide interactive functions so that users can control the speed and direction of data tracing.

2. The data storage and backtracking method for automated handling equipment according to claim 1, characterized in that: The collected data is compressed by the GZIP DEFLATE algorithm and saved in JSON format, so that the data model can be restored even if it changes, and can jump to a specified time point for playback as needed, specifically including: Select the GZIP DEFLATE algorithm to compress the data to be saved: first read the data to be compressed, preprocess the data, use the LZ77 algorithm to compress the preprocessed data, then perform Huffman coding on the compression result of the LZ77 algorithm to achieve further compression, and finally encrypt the compressed data and store it in JSON format, and store it in the database by time; when playing, search for files according to the time of the file name, decompress, decode and read the search results.

3. The data storage and backtracking method for automated handling equipment according to claim 1, characterized in that: When the data does not change, the data collection frequency is reduced, and data is collected at least once within a set cycle time, specifically including: Set feedback control to collect statistics on the actual frequency of changes in the data of the automated handling equipment when the automated handling equipment is operating normally, and set a frequency threshold based on the actual frequency of changes; When the actual data change frequency of the automated handling equipment is lower than the frequency threshold, it is considered that the data of the automated handling equipment has not changed, and the data collection frequency is reduced at this time; and a minimum data sampling period is set. Within the minimum data sampling period, even if the data has not changed, a data collection and data saving operation is performed once.

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