A method for realizing interconnection and interoperation between field equipment data and cloud information

Abstract

The present invention discloses a method for realizing interconnection and interoperation between field equipment data and cloud information. On the one hand, the field equipment database and the automated production system database are accessed and the original data is stored. On the other hand, the PLC data of the field equipment is monitored in real time and pushed to the RabbitMQ message queue, and pushed to the MongoDB cluster to realize PLC data solidification storage, push the data that needs real-time visualization to WebSocket, push the data that needs calculation and / or analysis to the Redis cache database and store the original data; on the other hand, a three-layer data warehouse is established for the stored original data, which is used to store the original data, analyze the data and display the data respectively, so as to realize the collection, transmission and storage of multi-source heterogeneous data of the terminal, and combine calculation, analysis and message push to realize monitoring, early warning, visualization and the like of the field equipment, so as to realize all-round understanding and control of the field equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of automated docks, and in particular relates to a method for realizing interconnection and interoperation between on-site equipment data and cloud information. Background Art

[0002] Currently, many terminals have achieved automated operations, which has greatly liberated manpower and improved production efficiency. However, with the continuous increase in throughput demand, higher requirements are placed on the repair and maintenance of terminal equipment. Therefore, real-time understanding of the operation status of on-site equipment and the health status of each operating mechanism has become an important research topic in the field of terminal health monitoring.

[0003] There are many types of data interactions between the terminal automation system and various equipment. The accuracy of data interconnection and interoperation requires precision, and the real-time requirements of data are high. How to process the diversified data of terminal equipment has become the main research direction for solving the real-time interconnection and interoperation between terminal equipment and cloud platforms. Summary of the invention

[0004] The present invention proposes a method for realizing the interconnection and interoperation between field equipment data and cloud information, which collects, stores, analyzes, operates and visualizes the PLC data of field equipment at the terminal and the data of the production system, so that the terminal staff can grasp the dynamic information of the field equipment at any time and realize the real-time interconnection and interoperation between the field equipment at the terminal and the cloud.

[0005] The present invention is implemented by the following technical solutions:

[0006] A method for interconnecting and interoperating field equipment data with cloud information is proposed, including:

[0007] 1) Obtain data from the field equipment database and the automated production system database: Encapsulate the connection drivers of the field equipment database and the automated production system database respectively, obtain data from the field equipment database and the automated production system database according to access requirements, and transmit the obtained data to the cloud database according to the set period to achieve data synchronization of the original data storage layer;

[0008] 2) Use OPCDA and OPCHDA protocols to monitor PLC data sent back by field devices in real time, push the received PLC data to the RabbitMQ message queue, and classify the PLC data according to device type; push the data that needs to be visualized in real time to WebSocket to complete the real-time data display on the WEB side; push the data that needs to be calculated and / or analyzed to the Redis cache database, and store it in the cloud database according to the set period to achieve data synchronization at the original data storage layer; the RabbitMQ message queue pushes the collected PLC data to the MongoDB cluster in real time to achieve solidified storage of PLC historical data;

[0009] 3) Establish data for the field equipment database, automated production system database and Redis cache database to be transferred to the cloud database:

[0010] The original data storage layer is used to store the original data synchronized from the field equipment database, the automated production system database, and the Redis cache database to the cloud database;

[0011] The data analysis layer is used to analyze the raw data according to the statistical analysis and / or algorithm data standard requirements and store the data generated after the analysis;

[0012] The data presentation layer is used to extract and store data that needs to be displayed or required by the algorithm from the data analysis layer, and provide a data interface for statistical data visualization.

[0013] In some embodiments of the present invention, the original data is analyzed, including: creating a first scheduled synchronization process for each field device; when the first synchronization time is reached, extracting the accumulated operation time of the field device to the original data storage layer; making a first difference Δt1 between the current accumulated time value T1 and the accumulated time value T2 at the last synchronization, and storing the current accumulated time value T1 and the first difference Δt1 to the data display layer; calculating the second difference Δt2 between the operation end time and the operation start time of the field device; and accumulating the first difference Δt1 within the range of the second difference Δt2, so that the data display layer performs front-end display.

[0014] In some embodiments of the present invention, the original data is analyzed, including: creating a second scheduled synchronization process for each field device; when the second synchronization time is reached, extracting the field device operation instruction data to the original data storage layer; judging the operation box quantity and the operation box type according to the field device operation instruction; determining the standard box quantity according to the operation box quantity and the operation box type; calculating the third difference Δt3 between the operation end time and the operation start time of the field device; superimposing the standard box quantity calculated based on each field device operation instruction within the range of the third difference Δt3, and obtaining the operation standard box quantity of the field equipment within the range of the third difference Δt3.

[0015] In some embodiments of the present invention, the method includes: creating a third scheduled synchronization process; when the third synchronization time is reached, extracting ship planning data to the data display layer; obtaining and judging whether the end time of the whole ship operation exists based on the ship planning data within a set time range; if not, determining the ship loading and unloading time according to the difference between the current time and the start time of the whole ship operation; if so, determining the ship loading and unloading time according to the difference between the end time of the whole ship operation and the start time of the whole ship operation; wherein the start time of the whole ship operation is obtained according to the ship planning data.

[0016] In some embodiments of the present invention, the method also includes: acquiring data collected by field sensors; extracting features of the data collected by the field sensors and storing them in the original data storage layer; and establishing a field equipment health warning model by combining the data collected by the field sensors with the stand-alone PLC data of the field equipment.

[0017] In some embodiments of the present invention, the method further includes: establishing a permanent message interconnection and interoperation channel between the human-computer interaction terminal and the Redis cache database; when the data in the Redis cache database changes, pushing the changed data to the human-computer interaction terminal for display.

[0018] In some embodiments of the present invention, the method further includes: acquiring PLC historical data of a set time period from a MongoDB cluster; and displaying the operation trend of the on-site equipment in combination with the PLC historical data.

[0019] In some embodiments of the present invention, the method further includes: storing the field sensor data in an InfluxDB sequence database; and accessing the field sensor historical data from the InfluxDB sequence database to perform sequence icon display and operation of the sensor data.

[0020] In some embodiments of the present invention, the method further includes: implementing a query on the data display layer through a JDBC database driver; and transmitting the queried data to a human-computer interaction terminal in a JSON format for visual display.

[0021] In some embodiments of the present invention, the method further comprises: implementing a hyperlink to a webpage of a live camera device via HTML at the human-computer interaction end to remotely monitor the live device.

[0022] Compared with the prior art, the advantages and positive effects of the present invention are as follows: in the method for realizing the interconnection and interoperability between field equipment data and cloud information proposed in the present invention, on the one hand, the field equipment database and the automated production system database are accessed and the original data is stored; on the other hand, the PLC data of the field equipment is monitored in real time and pushed to the RabbitMQ message queue, and further pushed to the MongoDB cluster to realize the solidified storage of PLC historical data, the data that needs real-time visualization is pushed to WebSocket, the data that needs calculation and / or analysis is pushed to the Redis cache database, and the original data is stored; on the one hand, a three-layer data warehouse is established for the stored original data, which is used to store original data, analyze data and display data respectively, so as to realize the collection, transmission and storage of multi-source heterogeneous data of the terminal, and the combination of calculation, analysis and message push can realize the monitoring, early warning, operation and visualization of the field equipment, so that the equipment management personnel can have a comprehensive understanding of the field equipment, improve the work efficiency of the management personnel, and realize the storage and application of the terminal equipment data.

[0023] Other features and advantages of the present invention will become more apparent after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the system architecture proposed by the present invention to realize the interconnection and interoperation between field equipment data and cloud information;

[0025] Figure 2 This is a schematic diagram of the method proposed by the present invention for realizing the interconnection and interoperation between field device data and cloud information;

[0026] Figure 3 An example of data analysis in the method proposed by the present invention for realizing the interconnection and interoperation between field device data and cloud information;

[0027] Figure 4 An example of data analysis in the method proposed by the present invention for realizing the interconnection and interoperation between field device data and cloud information;

[0028] Figure 5 This is an example of data analysis in the method proposed by the present invention for realizing the interconnection and interoperability between field device data and cloud information. DETAILED DESCRIPTION

[0029] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0030] The method for realizing the interconnection and interoperation between field equipment data and cloud information proposed in the present invention is based on the following Figure 1 The field equipment data and cloud information interconnection and interoperation system shown is implemented, and the system includes:

[0031] The field device database 1 is used to store device data, operation data, etc. of the field devices.

[0032] In some embodiments of the present invention, field device data, for example:

[0033] Bridge crane data, including but not limited to main lifting operation time, gantry lifting operation time, pitching operation time, gantry trolley operation time, trolley operation time, sea side spreader pump operation time, land side spreader pump operation time, gantry spreader pump operation time, upper frame pump operation time, lifting / trolley operation time, control closing time, motor brake release times, spreader pump opening times, spreader rotation lock times, occurrence time of various mechanism failures, etc.

[0034] Rail crane data, including but not limited to crane start time, trolley operation time, hoisting operation time, spreader operation time, trolley operation time, crane operation time, reel braking times, trolley braking times, trolley braking times, hoisting braking times, rotary lock times, and the time when each mechanism failure occurs.

[0035] The automated production system database 2 is used to store the terminal production operation data.

[0036] In some embodiments of the present invention, production operation data includes, for example, bridge crane operation instructions, rail crane operation instructions, automatic guided vehicle operation instructions, container movement records, ship plans, bridge crane loading and unloading record information, etc.

[0037] The PLC data monitoring unit 3 of the field device is used to monitor the PLC data returned by the field device in real time through the OPCDA and OPCHDA protocols.

[0038] The RabbitMQ message queue 4 is used to receive the PLC data pushed by the PLC data monitoring unit 3 of the field device, and classify the PLC data according to the device type.

[0039] The field device sensor 5 is installed on the field device and is used to obtain the operation data, status data, etc. of the field device.

[0040] The computing center 6 is used to perform calculations, analyses, statistics, etc. on the sensor data of the field equipment and the PLC data of the field equipment.

[0041] The data storage platform 7 includes a cloud database 71, a Redis cache unit 72, an InfluxDB 73 and a MongoDB cluster 74; the cloud database 71 is used to store raw data, including field device data, automation system data and data that needs to be calculated and / or analyzed; the Redis cache unit 72 is used to cache data that needs to be calculated and / or analyzed, and push the data to the cloud database 71 according to a set period to be stored as raw data; the InfluxDB 73 is used to store field device sensor data; the MongoDB cluster 74 is used to store PLC data collected and pushed by the RabbitMQ message queue, so as to realize the solidified storage of PLC historical data.

[0042] The human-computer interaction terminal 8 is used for human-computer interaction, visualization, etc. of system operations.

[0043] Based on the above-mentioned field device data and cloud information interconnection and interoperation system, the present invention proposes a method for realizing the interconnection and interoperation of field device data and cloud information, such as Figure 2 As shown, including:

[0044] S1. Obtain data from the field device database and the automated production system database: respectively encapsulate the connection drivers of the field device database and the automated production system database, obtain data from the field device database and the automated production system database according to access requirements, and transmit the obtained data to the cloud database according to the set period.

[0045] S2. Obtain PLC data from on-site equipment: Use the OPCDA and OPCHDA protocols to monitor the PLC data sent back by on-site equipment in real time, push the received PLC data to the RabbitMQ message queue, and classify the PLC data according to the device type; push the data that needs to be visualized in real time to WebSocket to complete the real-time data display on the WEB side; push the data that needs to be calculated and / or analyzed to the Redis cache database, and store it in the cloud database according to the set period; the RabbitMQ message queue pushes the collected PLC data to the MongoDB cluster in real time to realize the solidified storage of PLC historical data.

[0046] S3, for the data transmitted from the field equipment database, automated production system database and Redis cache database to the cloud database:

[0047] The original data storage layer is used to store the original data synchronized from the field equipment database, the automated production system database, and the Redis cache database to the cloud database;

[0048] The data analysis layer is used to analyze the raw data according to the statistical analysis and / or algorithm data standard requirements and store the data generated after the analysis;

[0049] The data presentation layer is used to extract and store data that needs to be displayed or required by the algorithm from the data analysis layer, and provide a data interface for statistical data visualization.

[0050] Based on the above method, the multi-source heterogeneous data of the automated terminal field equipment can be collected, operated, transmitted and stored, which can meet the data query performance requirements of different functions of the system, as well as the equipment management personnel's comprehensive understanding of the field equipment, and realize the long-term storage and backup requirements of the terminal field equipment data.

[0051] Based on the above data acquisition and storage processing, combined with the computing center to perform streaming computing, analysis, message push, data storage, etc. on the collected data, it is possible to realize status detection and early warning analysis of on-site equipment.

[0052] Specifically, in some embodiments of the present invention, the control time is obtained from the bridge crane stand-alone database, the crane opening time is obtained from the rail crane stand-alone database, and the automatic guided vehicle obtains the vehicle driving time from the Redis cache database, etc., and the above time information is used as the original data such as the operating time of each field device for analysis, such as Figure 3 As shown, including:

[0053] 1) Create a first scheduled synchronization process for each field device and start the first scheduled synchronization process.

[0054] 2) Determine whether the first synchronization time has been reached. When the first synchronization time has been reached, extract the accumulated operation time of the on-site equipment to the original data storage layer.

[0055] 3) A first difference Δt1 is calculated between the current accumulated time value T1 and the accumulated time value T2 at the last synchronization, and the current accumulated time value T1 and the first difference Δt1 are stored in the data display layer.

[0056] 4) Calculate a second difference Δt2 between the operation end time and the operation start time of the field device.

[0057] If the system does not input the operation end time and the operation start time of the field device, the default time is used as the second difference Δt2.

[0058] 5) Accumulating the first difference Δt1 within the range of the second difference Δt2 so that the data display layer performs front-end display.

[0059] In some embodiments of the present invention, data from the automated production system database, including equipment number, box number, box size, instruction start time, instruction end time, etc. in the operation instructions of the bridge crane, rail crane, and automatic guided vehicle, can be used to count the standard box quantity of each on-site equipment operation within a certain period of time, such as Figure 4 As shown, including:

[0060] 1) Create a second timed synchronization process for each field device and start the second timed synchronization process.

[0061] 2) Determine whether the second synchronization time has been reached, and when the second synchronization time has been reached, extract the field equipment operation instruction data to the original data storage layer.

[0062] 3) Determine the quantity and type of operating boxes based on the on-site equipment operating instructions.

[0063] Taking the automatic guided vehicle as an example, it can carry two 20-foot containers, or one 40-foot or 45-foot container at a time. The quantity of 40-foot or 45-foot containers is recorded as two operating containers, and the quantity of one 20-foot container is recorded as one operating container.

[0064] 4) Determine the standard box quantity based on the operating box quantity and operating box type.

[0065] Container number 1 represents the quantity of a 20-foot container, which is one standard container quantity. Container number 2 represents the quantity of two 20-foot containers or a 40-foot or 45-foot container, which is two standard container quantities.

[0066] 5) Calculate a third difference Δt3 between the operation end time and the operation start time of the field device.

[0067] If the system does not input the operation end time and the operation start time of the field device, the default time is used as the third difference Δt3.

[0068] 6) The standard box quantities calculated based on each field equipment operation instruction within the third difference Δt3 are superimposed to obtain the operation standard box quantities of the field equipment within the third difference Δt3.

[0069] In some embodiments of the present invention, by obtaining the ship planning data in the automated production system database, including the ship name code, voyage number, ship operation start time, ship operation end time, etc., the loading and unloading time of each ship in a certain time period can be calculated, such as Figure 5 As shown, including:

[0070] 1) Create a third scheduled synchronization process and start the third scheduled synchronization process.

[0071] 2) When the third synchronization time is reached, the ship planning data is extracted to the data display layer.

[0072] 3) Obtain and determine whether the end time of the entire ship operation exists based on the ship planning data within the set time range.

[0073] If not, go to step 4, if not go to step 5.

[0074] Setting the time range provides equipment managers with values ​​that need to be input into the system or that the system defaults.

[0075] 4) Determine the loading and unloading time of the ship based on the difference between the current time and the start time of the entire ship operation.

[0076] 5) Determine the loading and unloading time of the ship based on the difference between the end time and the start time of the whole ship operation.

[0077] Among them, the start time of the whole ship operation is obtained based on the ship planning data.

[0078] In some embodiments of the present invention, the processing of the field device sensor by the computing center 6 specifically includes:

[0079] 1) Obtain data collected by field sensors.

[0080] The vibration, rotation speed, temperature and other data collected by field equipment sensors are transmitted to the computing center via optical cables or wirelessly.

[0081] 2) Extracting features of data collected by field sensors and storing them in the raw data storage layer.

[0082] The Spark Streaming streaming computing engine is used to transform the sensor data in the frequency domain and time domain, extract feature data, and store it in the original data storage layer.

[0083] 3) Combine the data collected by on-site sensors with the PLC data of the on-site equipment to establish a health warning model for on-site equipment.

[0084] The Spark memory computing module combines sensor feature data with PLC data to establish a deep learning prediction model to realize the on-site equipment health analysis and early warning model. The health early warning model here can be implemented according to the existing model building method.

[0085] In some embodiments of the present invention, when the human-machine interaction terminal 8 realizes the visualization of the on-site equipment operation, the processing performed includes but is not limited to:

[0086] 1. Establish a permanent message interconnection channel between the human-computer interaction terminal and the Redis cache database; when the data in the Redis cache database changes, push the changed data to the human-computer interaction terminal for display.

[0087] 2. Obtain PLC historical data for a set period of time from the MongoDB cluster; display the operating trends of on-site equipment in combination with PLC historical data.

[0088] 3. Store the field sensor data in the InfluxDB sequence database; access the field sensor historical data from the InfluxDB sequence database to display and operate the sensor data in sequence icons.

[0089] 4. Use the JDBC database driver to query the data display layer; transmit the queried data to the human-computer interaction end in JSON format for visual display.

[0090] 5. Use HTML to realize the hyperlink of the web page of the on-site camera equipment at the human-computer interaction end, and remotely monitor the on-site equipment.

[0091] It should be pointed out that the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for interconnecting and interoperating field equipment data with cloud information, characterized in that: include: 1) Obtain data from the field equipment database and the automated production system database: Encapsulate the connection drivers of the field equipment database and the automated production system database respectively, obtain data from the field equipment database and the automated production system database according to access requirements, and transmit the obtained data to the cloud database according to the set period; 2) Use OPCDA and OPCHDA protocols to monitor PLC data sent back by field devices in real time, push the received PLC data to the RabbitMQ message queue, and classify the PLC data according to device type; push the data that needs to be visualized in real time to WebSocket to complete the real-time data display on the WEB side; push the data that needs to be calculated and / or analyzed to the Redis cache database, and store it in the cloud database according to the set period; the RabbitMQ message queue pushes the collected PLC data to the MongoDB cluster in real time to realize the solidification storage of PLC historical data; 3) Data transmission from the field equipment database, automated production system database and Redis cache database to the cloud database is established: The original data storage layer is used to store the original data synchronized from the field equipment database, the automated production system database, and the Redis cache database to the cloud database; The data analysis layer is used to analyze the raw data according to the statistical analysis and / or algorithm data standard requirements and store the data generated after the analysis; The data display layer is used to extract and store data that needs to be displayed or required by the algorithm from the data analysis layer, and provide a data interface for statistical data visualization; Analyze the raw data, including: Creating a first timed synchronization process for each field device; When the first synchronization time is reached, the accumulated operation time of the field equipment is extracted to the original data storage layer; Compute a first difference Δt1 between the current accumulated time value T1 and the accumulated time value T2 at the last synchronization, and store the current accumulated time value T1 and the first difference Δt1 in the data display layer; Calculate a second difference Δt2 between the operation end time and the operation start time of the field device; Accumulating the first difference Δt1 within the range of the second difference Δt2 so that the data display layer performs front-end display; Analysis of the raw data also includes: Creating a second timed synchronization process for each field device; When the second synchronization time is reached, extracting the field equipment operation instruction data to the original data storage layer; Determine the quantity and type of operating boxes according to the on-site equipment operation instructions; Determine the standard box quantity based on the operating box quantity and operating box type; Calculating a third difference Δt3 between the operation end time and the operation start time of the field device; The standard box quantities calculated based on each field equipment operation instruction within the third difference Δt3 are superimposed to obtain the operation standard box quantities of the field equipment within the third difference Δt3.

2. The method for realizing interconnection and interoperation between field equipment data and cloud information according to claim 1, characterized in that: The method comprises: Create a third scheduled synchronization process; When the third synchronization time is reached, extracting the ship planning data to the data display layer; Obtain the ship planning data within the set time range to determine whether the end time of the whole ship operation exists; if not, determine the ship loading and unloading time according to the difference between the current time and the start time of the whole ship operation; if so, determine the ship loading and unloading time according to the difference between the end time of the whole ship operation and the start time of the whole ship operation; among which, the start time of the whole ship operation is obtained according to the ship planning data.

3. The method for realizing interconnection and interoperation between field equipment data and cloud information according to claim 1, characterized in that: The method further comprises: Obtain data collected by field sensors; Extracting features of data collected by field sensors and storing them in the raw data storage layer; A field equipment health warning model is established by combining the data collected by field sensors with the PLC data of the field equipment.

4. The method for realizing interconnection and interoperation between field equipment data and cloud information according to claim 1, characterized in that: The method further comprises: Establish a permanent message interconnection and interoperation channel between the human-computer interaction terminal and the Redis cache database; When the data in the Redis cache database changes, the changed data is pushed to the human-computer interaction terminal for display.

5. The method for realizing interconnection and interoperation between field equipment data and cloud information according to claim 1, characterized in that: The method further comprises: Get the PLC historical data of the set period from the MongoDB cluster; Combine PLC historical data to display the operating trends of on-site equipment.

6. The method for realizing interconnection and interoperation between field device data and cloud information according to claim 3, characterized in that: The method further comprises: Store field sensor data into the InfluxDB sequence database; Access field sensor historical data from the InfluxDB sequence database to display sensor data in sequence charts.

7. The method for realizing interconnection and interoperation between field equipment data and cloud information according to claim 1, characterized in that: The method further comprises: Implement query on data presentation layer through JDBC database driver; The queried data is transmitted to the human-computer interaction terminal in JSON format for visual display and operation.

8. The method for realizing interconnection and interoperation between field device data and cloud information according to claim 7, characterized in that: The method further comprises: On the human-computer interaction side, a hyperlink to the web page of the on-site camera equipment is realized through HTML, so that the on-site equipment can be remotely monitored.

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