CN115437307A - Early warning method and device based on Thingworx platform and computer-storable medium - Google Patents
Early warning method and device based on Thingworx platform and computer-storable medium Download PDFInfo
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- G05B19/00—Programme-control systems
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Abstract
The disclosure relates to an early warning method and device based on a Thingworx platform and a computer-storable medium, and relates to the technical field of Internet platforms and automation. The early warning method based on the ThingWorx platform comprises the following steps: respectively acquiring historical data and real-time data from a historical database and a real-time database of an SCADA (supervisory control and data acquisition) system by utilizing a Kepware data acquisition server of a Thingworx platform; acquiring data acquisition data from a Programmable Logic Controller (PLC) system of an electric control layer by using a Kepware data acquisition server; and performing prenatal verification and prenatal early warning on the current production according to the historical data, the real-time data and the acquired data. According to the method and the device, the early warning accuracy in the production process can be improved.
Description
Technical Field
The disclosure relates to the technical field of internet platforms and automation, in particular to a warning method and device based on a thinworx platform and a computer-storable medium.
Background
An SCADA (Supervisory Control And Data Acquisition) system is a production process Control And scheduling automation system based on a bottom PLC (Programmable Logic Controller), and has the functions of monitoring And controlling field operating equipment so as to realize various functions of Data Acquisition, equipment Control, measurement, parameter adjustment, various signal alarms And the like.
Disclosure of Invention
The SCADA system is responsible for data acquisition and monitoring of all processing sections such as cut tobacco, leaves, blending, stems and the like in a cut tobacco manufacturing workshop, and the system has the problems of various alarm types, large alarm quantity, manual browsing of historical alarms and large workload for checking historical trends. The alarm limit is difficult to set in time due to the fact that the number of monitoring point positions is large, so that the alarm accuracy of the centralized control system is affected, unnecessary alarms are increased, and the manual confirmation error rate is increased. Meanwhile, in the existing production mode, the central control personnel manually browse the historical trend is a remedy mode for the insufficient alarm efficiency of the system. In order to ensure accurate monitoring, central control personnel need to monitor the historical trend chart of key point digit acquisition in real time, roughly judge the field condition from the abnormal form of the historical trend, and inform field inspection personnel of the implementation situation. The production mode has the defects that the number of alarm types of abnormal conditions is various, the speed is low, the period is long, the timeliness for finding accidents is poor and the response capability is poor by manually paying attention to the historical trend in real time.
In order to solve the technical problem, the early warning accuracy in the production process can be improved by the aid of the solution.
According to a first aspect of the disclosure, an early warning method based on a thinworx platform is provided, which includes: respectively acquiring historical data and real-time data from a historical database and a real-time database of an SCADA (supervisory control and data acquisition) system by utilizing a Kepware data acquisition server of a thinwork platform; acquiring data acquisition data from a Programmable Logic Controller (PLC) system of an electric control layer by using a Kepware data acquisition server; and performing prenatal verification and prenatal early warning on the current production according to the historical data, the real-time data and the acquired data.
In some embodiments, performing prenatal verification and prenatal warning on the current production based on the historical data, the real-time data, and the data collected includes: performing data cleaning on historical data, real-time data and data acquisition; performing data modeling according to historical data, real-time data and data acquisition data after data cleaning to obtain a data model; and according to the data model, performing prenatal verification and prenatal early warning on the current production.
In some embodiments, the prenatal verification of the current production includes: according to the data model, at least one of formula verification, energy verification, equipment state verification and process parameter verification is carried out on the current production; and displaying the checking result of the prenatal checking of the current production.
In some embodiments, displaying the verification results of the prenatal verification for the current production comprises: and displaying a verification result of the current prenatal verification through a Mashup front end of the thingwox platform.
In some embodiments, the in-production warning of the current production comprises: comparing the equipment state and the execution condition of the process parameters in the current production process on line in real time according to the data model; and generating and pushing early warning information under the condition that the comparison result is abnormal.
In some embodiments, pushing the early warning information comprises: and transferring the connection between the REST application program interface API and the enterprise WeChat interface based on the state of the presentation layer, and pushing the early warning information to the related enterprise WeChat.
In some embodiments, the in-production warning of the current production further comprises: and coupling equipment state analysis data in the batch production process based on a multidimensional data assimilation algorithm to generate and push an alarm message.
In some embodiments, the method further comprises: displaying the current production historical trend through a Mashup front end according to the historical data; and displaying the current production real-time data through a Mashup front end.
According to a second aspect of the present disclosure, there is provided an early warning device based on a thinworx platform, including: the acquisition module is configured to acquire historical data and real-time data from a historical database and a real-time database of the SCADA system through data acquisition and monitoring control of the centralized control layer by using a Kepware data acquisition server of a thinwork platform, and acquire data acquisition data from a PLC system of the electric control layer by using the Kepware data acquisition server; and the early warning module is configured to perform prenatal verification and prenatal early warning on the current production according to the historical data, the real-time data and the data acquired.
According to a third aspect of the present disclosure, an early warning device based on a thinworx platform is provided, including: a memory; and a processor coupled to the memory, the processor being configured to execute the thinwork platform-based early warning method according to any of the embodiments based on instructions stored in the memory.
According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method for a forewarning based on a thinwork platform according to any one of the embodiments described above.
In the embodiment, the early warning accuracy in the production process can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flow chart illustrating an early warning method according to some embodiments of the present disclosure;
fig. 2 is a block diagram illustrating an early warning device according to some embodiments of the present disclosure;
fig. 3 is a block diagram illustrating an early warning device according to some embodiments of the present disclosure;
FIG. 4 is a schematic diagram illustrating a system for implementing production process early warning in a fully centralized control mode based on a thinnwox platform according to some embodiments of the present disclosure;
FIG. 5 is a schematic diagram showing a system data logic flow for implementing production process early warning in a fully centralized control mode based on a Thingworx platform according to some embodiments of the present disclosure;
FIG. 6 is an illustration showing a system pre-production verification for production process early warning in a fully centralized control mode based on a thinnwox platform according to some embodiments of the present disclosure;
FIG. 7 is an illustration showing a systematic in-production warning for production process warning in a fully centralized control mode based on a thinnwox platform according to some embodiments of the present disclosure;
FIG. 8 is a block diagram illustrating a computer system for implementing some embodiments of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 is a flow chart illustrating an early warning method according to some embodiments of the present disclosure.
As shown in fig. 1, the early warning method based on the thinworx platform includes steps S110 to S130.
In step S110, a Kepware data acquisition server of a thinwork platform is used to acquire historical data and real-time data from a historical database and a real-time database of the SCADA system for data acquisition and monitoring control of the centralized control layer, respectively.
In step S120, the Kepware data acquisition server is used to acquire data acquisition data from the PLC system of the electrical control layer.
In step S130, according to the historical data, the real-time data and the data collected, prenatal verification and prenatal early warning are performed on the current production.
In the embodiment, the multidimensional data are obtained based on the thinwork platform, the prenatal verification and the prenatal early warning are carried out on the multidimensional data, a multidimensional early warning mechanism is carried out, and the accuracy of the early warning can be improved.
In some embodiments, performing prenatal verification and prenatal warning on the current production based on the historical data, the real-time data, and the data collected includes: performing data cleaning on historical data, real-time data and data acquisition; performing data modeling according to historical data, real-time data and data acquisition data after data cleaning to obtain a data model; and according to the data model, performing prenatal verification and prenatal early warning on the current production.
In some embodiments, performing the prenatal check on the current production includes: according to the data model, at least one of formula verification, energy verification, equipment state verification and process parameter verification is carried out on the current production; and displaying the checking result of the prenatal checking of the current production.
In some embodiments, displaying the verification results of the prenatal verification for the current production comprises: and displaying the verification result of the prenatal verification of the current production through a Mashup front end of a thingwox platform.
In some embodiments, the in-production warning of the current production comprises: comparing the equipment state and the execution condition of the process parameters in the current production process on line in real time according to the data model; and generating and pushing early warning information under the condition that the comparison result is abnormal.
In some embodiments, pushing the early warning information comprises: based on the connection between the REST (Representational State Transfer) API (Application Programming Interface) and the enterprise WeChat Interface, the early warning information is pushed to the related enterprise WeChat.
In some embodiments, the in-production warning of the current production further comprises: and coupling equipment state analysis data in the batch production process based on a multidimensional data assimilation algorithm to generate and push an alarm message.
In some embodiments, the warning method further comprises: displaying the current production historical trend through a Mashup front end according to the historical data; and displaying the current production real-time data through a Mashup front end.
Fig. 2 is a block diagram illustrating an early warning device according to some embodiments of the present disclosure.
As shown in fig. 2, the forewarning device 2 based on the thinwork platform includes an acquisition module 21 and a forewarning module 22.
The obtaining module 21 is configured to obtain historical data and real-time data from a historical database and a real-time database of the SCADA system for data collection and monitoring control of the centralized control layer by using a Kepware data collecting server of the thinwork platform, and obtain data collection data from a PLC system of the electronic control layer by using the Kepware data collecting server, for example, execute steps S110 and S120 shown in fig. 1.
The pre-alarm module 22 is configured to perform a prenatal check and an in-production pre-alarm for the current production based on the historical data, the real-time data and the collected data, for example, perform step S130 shown in fig. 1.
Fig. 3 is a block diagram illustrating an early warning apparatus according to some embodiments of the present disclosure.
As shown in fig. 3, the early warning apparatus 3 based on the thingwox platform includes a memory 31; and a processor 32 coupled to the memory 31. The memory 31 is used for storing instructions for executing the corresponding embodiment of the warning method. The processor 32 is configured to perform the warning method in any of the embodiments of the present disclosure based on instructions stored in the memory 31.
In order to facilitate understanding of the technical solutions of the present disclosure, the technical solutions of the present disclosure will be described below with reference to fig. 4 and 5.
FIG. 4 is a schematic diagram illustrating a system for implementing production process early warning in a fully centralized control mode based on a thinnwox platform according to some embodiments of the present disclosure.
Fig. 5 is a schematic diagram illustrating a system data logic flow for implementing production process early warning in a fully centralized control mode based on a thinworx platform according to some embodiments of the present disclosure.
As shown in fig. 4, the thinworx platform 1 is integrated with the SCADA server 4 of the centralized control system through the industrial data gateway 3 and the Kepware data acquisition server 2 to acquire the real-time database 7 and the historical database 8 of the SCADA system of the centralized control layer and the data acquisition data 6 of the PLC 5 of the electrical control layer.
The thinworx platform 1 performs data early warning modeling analysis 12 on key data acquired by the SCADA server, performs modeling according to related data early warning rules, and stores the data in a database 13 for storage. The early warning real-time data 10 and the historical trend data 11 after the data early warning modeling can be displayed through Mashup front end 9. The thinworx platform can push 14 the relevant early warning information to the enterprise WeChat interface 16 through the REST API 15, and then push the relevant early warning information to the relevant workshop section person 17 as an early warning reminder.
The data early warning modeling analysis module 12 in the thinworx platform 1 comprises prenatal verification data and prenatal early warning data.
As shown in fig. 5, the prenatal verification 21 data includes a formula verification 23, an energy verification 25, a device status verification 27, and a process parameter verification 29. Pre-production verification requires pre-production, post-line start-up manual triggering 22 to be performed. When the verification process is abnormal, the Mashup front-end interface can display the abnormal details 24, 26, 28 and 30 which fail to pass the verification in time, and inform the accurate abnormal point positions and abnormal reasons which fail to pass the verification in time. The verification process is completely passed, the required conditions of production are met, verification completion is displayed 31, and an operator is informed that production can be started. The early warning in production is mainly divided into two types: an in-production pre-alarm information push 14 and an in-production alarm message push 19. When the comparison of the early warning rules 18 in production is abnormal, the early warning rules are pushed 14 to an enterprise WeChat interface 16 through the REST API 15 as early warning information, and then the relevant early warning information is pushed to a relevant section person in charge 17 for enterprise WeChat to serve as early warning reminding. Meanwhile, when the comparison abnormality occurs in the early warning mechanism in production, the detail request of the abnormality inconsistent in comparison in early warning in production is displayed 32 through a Mashup front end 9 interface and an alarm box in time, and the accurate abnormal point position and the abnormal reason in the early warning point position in production are informed in time.
In some embodiments, a system for realizing production process early warning in a full centralized control mode based on a thinnwox platform may perform the following steps:
The data early warning rule module carries out data assimilation algorithm modeling on real-time data needing attention before and during the production, assimilation data in the batch production process are collected through an industrial data gateway and uploaded to a thindwex platform in a data transmission mode through the industrial data gateway, the thindwex platform is connected with a Kepware data acquisition server and an SCADA (supervisory control and data acquisition) and PLC (programmable logic controller) of a centralized control layer through a communication network to carry out data interaction, the Kepware data acquisition server adopts Grafana data visualization and Mashup front end display technology, batch production index data needing attention of operators in the production before are acquired through a real-time database and a historical database, and the acquired data information carries out data early warning mechanism modeling through a data early warning rule modeling analysis module to analyze the execution condition of batch formula parameter standards of a production field in the batch production process.
All data are stored in a data storage database, and early warning real-time data and historical trend data after data early warning modeling is carried out through a data early warning modeling analysis module can be displayed through Mashup front end data early warning. When the data running state in the batch production process of the early warning data monitoring after the data early warning modeling is carried out through the data early warning modeling analysis module is abnormal, the warning box display is also carried out in time through the Mashup front end, and a person responsible for a workshop section is reminded of finding abnormal warning information and processing the abnormal warning information.
When the workshop section responsible person issues a batch task, the thinworx platform automatically acquires information such as the batch recipe parameter standard through the task issuing process of the SCADA system. Before production, a workshop section person can start on line to check whether the running state of the equipment is normal or not, and whether the current formula standard, energy power, the running state of the equipment and the execution condition of process parameters can meet the production requirement or not. The manual checking process can be completed through a thinwork platform, wherein the manual checking process comprises formula checking, energy checking, equipment state checking and process parameter checking. When the verification process is abnormal, the detailed request of the abnormal condition which is not passed by the verification can be timely displayed through a Mashup front-end interface, and the accurate abnormal point position which is not passed by the verification and the abnormal reason can be timely informed.
The early warning in production is mainly divided into two categories: and pushing the early warning information in delivery and the alarm information in delivery. And if the comparison of the early warning rules in the production is abnormal, the early warning rules are used as early warning information to be pushed to an enterprise WeChat interface through REST API, and then the related early warning information is pushed to enterprise WeChat of a person responsible for a related workshop section to be used as early warning reminding. Meanwhile, when the comparison abnormality occurs in the in-production early warning mechanism, the detail of the abnormality inconsistent in-production early warning is displayed in time through the Mashup front-end interface, and the accurate abnormal point position and the abnormal reason in the in-production early warning point position are informed in time.
Referring to fig. 5, the logic flow chart of the system data for realizing the production process early warning of the system for realizing the production process early warning in the full centralized control mode based on the thingwox platform further includes the following steps.
Step 2: the industrial data gateway is internally provided with a wireless communication module and a wired communication module, supports the access of a 4G/5G network and an Ethernet, is connected with a thinworx platform in an MQTT + SSL/TLS and ttps mode, supports the access of a Modbus RTU/Modbus TCP communication protocol to a batch production process equipment data early warning data modeling analysis module, and realizes the collection, analysis, forwarding and pushing of real-time early warning data in the batch production process.
And step 3: the Thingworx platform also comprises a gateway authentication module and a standard and private protocol access authority management module, data of a real-time database and a historical database of the SCADA system are subjected to data cleaning, modeling, analysis, mining and storage, early warning capability and data tracing support in batch production are provided, an alarm bullet frame is displayed, and early warning information is timely presented to persons responsible for working sections through the alarm bullet frame display at the front end of the Mashup. Based on the connection of the REST API and the enterprise WeChat interface, an automatic early warning pushing mechanism in production is achieved, the backstage of the Thingworx platform supports the function that the microservice automatically pushes early warning information to enterprise WeChat of a person responsible for a relevant workshop section, has the function of calling remote and timely to find abnormality, and provides the capability of batch production prenatal verification and prenatal early warning.
And 4, step 4: a Kepware data acquisition Server of a functional module of a Thingworx platform is one of OPC servers, is used as a soft gateway and is mainly used for connecting a real-time database and a historical database of an SCADA (supervisory control and data acquisition) system. Mashup front-end is a thinworx web page, and Mashup can combine data services provided in thinworx with a set of visual components called gadgets to create a diversified front-end interface capable of combining multi-source data. The Mashup front end can display early-warning real-time data, historical trend data and automatic frame popping alarm data in real time.
The production process early warning system based on the Thingworx platform under the full centralized control mode is mature in technology of connecting the SCADA system real-time database by the Thingworx platform, can intelligently analyze real-time alarm data in the production process, and achieves intelligent auxiliary setting of alarm parameters according to the historical trend of a data source. The mode can improve the working efficiency of setting the alarm limit value and the accuracy and reliability of setting, reduces the alarm quantity of false alarm and missed alarm, rationalizes the alarm quantity area, lightens the workload of central control personnel, and improves the monitoring efficiency of the central control personnel.
Meanwhile, the flexible script processing function is closely combined with the original alarm of the SCADA, the alarm information is compared with the historical trend, the accuracy of alarm confirmation is greatly improved, the production mode of automatic early warning in the production process under the full centralized control mode of 'checking before delivery, early warning in delivery and data analysis after delivery' is realized on the basis of the production mode of batch logs, and the original civil defense mode can be replaced by the mechanical defense mode which can achieve active early warning in a way of pushing and driving things.
In some embodiments, the system for realizing production process early warning in a full centralized control mode based on a thinworx platform comprises a centralized control layer SCADA system, an enterprise WeChat interface, a real-time database, a historical database, an industrial data gateway, the thinworx platform, a Kepware data acquisition server, a Mashup front end, an electric control layer PLC system, a data modeling analysis and data storage database.
And the Thingworx platform acquires and fuses data of the SCADA server of the centralized control layer and the real-time database and the historical database of the SCADA system of the centralized control layer and the PLC data acquisition data of the electric control layer through an industrial data gateway and a Kepware data acquisition server. The Thingworx platform can be used as early warning information to be pushed to an enterprise WeChat interface through REST API, and then the related early warning information is pushed to a related workshop section person responsible to be used as early warning reminding.
The Thingworx platform carries out data early warning modeling analysis on the early warning data before and during the production acquired by the SCADA server of the centralized control layer, carries out modeling according to related data early warning rules, and stores the data in a database for storage.
The data early warning rule module carries out data assimilation algorithm modeling on real-time data needing attention before and during the production, assimilation data in the batch production process are collected through an industrial data gateway and uploaded to a thindwex platform in a data transmission mode through the industrial data gateway, the thindwex platform is connected with a Kepware data acquisition server and an SCADA (supervisory control and data acquisition) and PLC (programmable logic controller) of a centralized control layer through a communication network to carry out data interaction, the Kepware data acquisition server adopts Grafana data visualization and Mashup front end display technology, batch production index data needing attention of operators in the production before are acquired through a real-time database and a historical database, and the acquired data information carries out data early warning mechanism modeling through a data early warning rule modeling analysis module to analyze the execution condition of batch formula parameter standards of a production field in the batch production process.
All data are stored in a data storage database, and early warning real-time data and historical trend data after data early warning modeling is carried out through a data early warning modeling analysis module can be displayed through data early warning at the Mashup front end. When the data running state in the batch production process of early warning data monitoring after data early warning modeling is carried out through the data early warning modeling analysis module is abnormal, alarm box display is carried out in time through the Mashup front end, and a workshop section responsible person is reminded of finding abnormal alarm information and processing the abnormal alarm information.
In some embodiments, the data early warning modeling analysis module includes prenatal verification data and prenatal early warning data. The prenatal verification data comprises formula verification, energy verification, equipment state verification and process parameter verification. The prenatal verification needs to be executed by manual triggering after the line of equipment before production is started.
When the workshop section responsible person issues a batch task, the thinworx platform automatically acquires information such as the batch recipe parameter standard through the task issuing process of the SCADA system. Before production, a workshop section person can start on line to check whether the running state of the equipment is normal or not, and whether the current formula standard, energy power, the running state of the equipment and the execution condition of process parameters can meet the production requirement or not. The manual checking process can be completed through a Thingworx platform, wherein the manual checking process comprises formula checking, energy checking, equipment state checking and process parameter checking. When the verification process is abnormal, the detail of the abnormal condition which cannot pass the verification can be timely displayed through a Mashup front-end interface, and the accurate abnormal point position and the abnormal reason which cannot pass the verification can be timely informed.
In some embodiments, the data early warning modeling analysis module includes prenatal verification data and prenatal early warning data.
The early warning in production is mainly divided into two types: and pushing the early warning information in delivery and the alarm information in delivery. Early warning information in the batch production process, such as the blending and perfuming working section, mainly comprises the weight of various blended materials, the material flow, the motor frequency during the operation of the roller, the perfuming proportion, the injection pressure of a perfuming nozzle, the opening degree of a moisture exhaust air door, the moisture content of cut tobacco at an outlet and the like.
The weight early warning mechanism of each type of blending material refers to the real-time comparison of the weight and storage time of expanded cut tobacco, cut stems and cut tobacco of each module in the batch production process with the formula weight and storage time of the batch process standard. And if the comparison of the early warning rules in production is abnormal, the early warning rules are used as early warning information to be pushed to an enterprise WeChat interface through the REST API, and then the relevant early warning information is pushed to enterprise WeChats of responsible persons in relevant working sections to be used as early warning reminding. Meanwhile, when the comparison of the early warning mechanism in production is abnormal, the detail of the abnormality which is not always compared in the early warning in production can be displayed in time through a Mashup front-end interface, and the accurate abnormal point position and the abnormal reason in the early warning point position in production can be informed in time.
In some embodiments, the data early warning modeling analysis module includes prenatal verification data and prenatal early warning data.
The early warning in production is mainly divided into two types: and pushing the early warning information in delivery and the alarm information in delivery. The source of the alarm message in the batch production process refers to that the data early warning rule modeling analysis module applies a multidimensional data assimilation algorithm and is coupled with equipment state analysis data in the batch production process based on the multidimensional data assimilation algorithm, so that the equipment operation process and formula parameter change in the batch production process can be fully considered, and the equipment operation state early warning capability in the batch production process is enhanced.
In some embodiments, the industrial data gateway is internally provided with a wireless communication module and a wired communication module, supports the access of a 4G/5G network and an Ethernet, is connected with a thinworx platform in an MQTT + SSL/TLS and ttps mode, supports the access of a Modbus RTU/Modbus TCP communication protocol to a batch production process equipment data early warning data modeling analysis module, and realizes the collection, analysis, forwarding and pushing of real-time early warning data in the batch production process.
In some embodiments, the thingwox platform further comprises a gateway authentication module and a standard and private protocol access authority management module, data of a real-time database and a historical database of the SCADA system are subjected to data cleaning, modeling, analysis, mining and storage, early warning capacity and data tracing support in batch production are provided, an alarm bullet frame is displayed, and early warning messages are displayed in front of persons responsible for work sections in time through the alarm bullet frame display at the front end of Mashup. Based on the connection of the REST API and the enterprise WeChat interface, an automatic early warning pushing mechanism in production is achieved, the backstage of the Thingworx platform supports the function that the micro-service automatically pushes early warning information to enterprise WeChats of persons responsible for relevant work sections, the function of calling remote and timely to discover abnormity is achieved, and the capabilities of batch production prenatal verification and prenatal early warning are provided.
In some embodiments, a Kepware data acquisition Server of a function module of a Thingworx platform is one of OPC servers, and is used as a soft gateway and mainly used for connecting a real-time database and a historical database of a SCADA system. Mashup front-end is a thinworx web page window, and Mashup can combine data services provided in thinworx with a set of visual components called gadgets to create a diversified front-end interface capable of combining multi-source data. The Mashup front end can display early-warning real-time data, historical trend data and automatic frame-flipping warning data in real time.
The invention also provides a method for realizing production process early warning under the full centralized control mode based on the thingwox platform, which comprises the following steps:
(1) Data acquisition: the data source of the ThingWorx platform is a real-time database, a historical database and PLC data acquisition data of an electric control layer of an SCADA system which is connected with a centralized control layer through a Kepware data acquisition server and an industrial data gateway.
(2) Modeling and displaying data early warning: the data early warning rule modeling tool carries out data cleaning, modeling, analysis and storage on multidimensional original data acquired by the Kepware data acquisition server into a data storage database. The early warning data after data modeling is carried out through the data early warning rule modeling module can be presented through Mashup front end, and the display comprises real-time data, historical data and automatic frame popping warning data.
(3) Checking before delivery: the prenatal checking process comprises formula checking, energy checking, equipment state checking and process parameter checking, intelligent error-preventing quality risk management and control are carried out on the 4 aspects of the prenatal checking, checking abnormity of each module is carried out, the detail of the abnormity please be displayed in a classified mode, and specific abnormal point positions and reasons of workshop section responsible persons are informed in time.
(4) Pushing the early warning message in labor: the data model is modeled based on the data early warning rule, the execution conditions of equipment states and process parameters in the production process are compared on line in real time, an automatic early warning pushing mechanism is used when the comparison is abnormal, and the rear end of the Thingworx platform supports the function of automatically pushing early warning information to enterprise WeChat of a person responsible for a relevant work section;
in some embodiments, in the steps (1) to (4), the backend of the thinworx platform automatically accesses the enterprise WeChat interface through a micro-service architecture, so as to realize an automatic early warning pushing mechanism of the early warning message, display the early warning information by combining with an automatic frame flipping at the Mashup front end, and timely inform a person responsible for the workshop section of the abnormal situation.
The method comprises the steps of carrying out data acquisition on data source points of a centralized control SCADA system and an electric control PLC, analyzing historical trends and early warning parameter characteristics of relevant equipment operation state data and process parameter standard execution data, and establishing a data early warning rule model; and storing the data in a database; selecting a blending and perfuming section as a test section, checking program effectiveness and system stability of prenatal check and prenatal early warning, and after program debugging and testing are successful, carrying out popularization and application of other sections.
The invention provides a production process early warning system based on a thinworx platform, a centralized control SCADA system and an electric control PLC (programmable logic controller), and provides a system for realizing production process early warning under a full centralized control mode based on the thinworx platform, so that data acquisition, fusion and disposal of equipment running states and process parameter execution conditions in a batch production process, data early warning modeling analysis and an automatic frame popping technology of a Mashup front-end interface are realized, a pre-production verification and in-production early warning mechanism in the batch production process is realized, an enterprise WeChat automatic pushing early warning mode in a micro-service architecture is combined, and a machine prevention mode of automatic early warning in the production process under the full centralized control mode is realized.
The source of the alarm message in the batch production process refers to that the data early warning rule modeling analysis module applies a multidimensional data assimilation algorithm and couples the equipment state analysis data in the batch production process based on the multidimensional data assimilation algorithm, so that the equipment operation process and the process formula parameter change in the batch production process can be fully considered, and the equipment operation state early warning capability in the batch production process is enhanced.
Mashup can combine the data services provided within thinworx with a set of visualization components called gadgets to create a diverse front-end interface that can combine multi-source data. The Mashup front end can display early-warning real-time data, historical trend data and automatic frame popping alarm data in real time.
Based on the connection of the REST API and the enterprise WeChat interface, an automatic early warning pushing mechanism in production is achieved, the backstage of the Thingworx platform supports the function that the microservice automatically pushes early warning information to enterprise WeChat of a person responsible for a relevant workshop section, has the function of calling remote and timely to find abnormality, and provides the capability of batch production prenatal verification and prenatal early warning. The workload of operators is greatly reduced, so that the production monitoring process under the full centralized control mode is more scientific, normative and efficient. Meanwhile, the pushing mode of alarming is more intelligent, the sensing capability of operators to the field production process is improved, and the field problems are timely processed through alarming triggering, so that timeliness and accuracy are higher. The Mashup interface is fresh, convenient to use, strong in practicability, convenient and better meets the requirements of staff in a workshop production monitoring room.
Fig. 6 is an illustration showing a system pre-production verification for production process early warning in a fully centralized control mode based on a thinnwox platform according to some embodiments of the present disclosure.
Fig. 7 is an illustration showing a systematic in-production warning for realizing production process warning in a fully centralized control mode based on a thinwork platform according to some embodiments of the present disclosure.
FIG. 8 is a block diagram illustrating a computer system for implementing some embodiments of the present disclosure.
As shown in FIG. 8, computer system 80 may take the form of a general purpose computing device. Computer system 80 includes a memory 810, a processor 820, and a bus 800 that connects the various system components.
The memory 810 may include, for example, system memory, non-volatile storage media, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), and other programs. The system memory may include volatile storage media, such as Random Access Memory (RAM) and/or cache memory. The non-volatile storage medium, for instance, stores instructions to perform corresponding embodiments of at least one of the early warning methods. Non-volatile storage media include, but are not limited to, magnetic disk storage, optical storage, flash memory, and the like.
The processor 820 may be implemented as discrete hardware components, such as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gates or transistors, or the like. Accordingly, each of the modules such as the judging module and the determining module may be implemented by a Central Processing Unit (CPU) executing instructions in a memory to perform the corresponding steps, or may be implemented by a dedicated circuit to perform the corresponding steps.
The bus 800 may use any of a variety of bus architectures. For example, bus architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.
The computer system 80 may also include an input-output interface 830, a network interface 840, a storage interface 850, and the like. These interfaces 830, 840, 850 and the memory 810 and the processor 820 may be connected by a bus 800. The input/output interface 830 may provide a connection interface for input/output devices such as a display, a mouse, and a keyboard. The network interface 840 provides a connection interface for various networking devices. The storage interface 850 provides a connection interface for external storage devices such as a floppy disk, a usb disk, and an SD card.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable apparatus to produce a machine, such that the execution of the instructions by the processor results in an apparatus that implements the functions specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer-readable memory that can direct a computer to function in a particular manner, such that the instructions cause an article of manufacture to be produced, including instructions which implement the function specified in the flowchart and/or block diagram block or blocks.
The present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
By the early warning method and device based on the Thingworx platform and the computer-storable medium in the embodiment, the early warning accuracy in the production process can be improved.
So far, the early warning method and device based on the thinngworx platform and the computer-storable medium according to the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Claims (11)
1. An early warning method based on a Thingworx platform comprises the following steps:
respectively acquiring historical data and real-time data from a historical database and a real-time database of an SCADA (supervisory control and data acquisition) system by utilizing a Kepware data acquisition server of a thinwork platform;
acquiring data acquisition data from a Programmable Logic Controller (PLC) system of an electric control layer by using a Kepware data acquisition server;
and performing prenatal verification and prenatal early warning on the current production according to the historical data, the real-time data and the acquired data.
2. The method of claim 1, wherein the pre-delivery verification and mid-production pre-warning of the current production based on the historical data, real-time data, and data collected comprises:
performing data cleaning on historical data, real-time data and data acquisition;
performing data modeling according to historical data, real-time data and data acquisition data after data cleaning to obtain a data model;
and performing prenatal verification and prenatal early warning on the current production according to the data model.
3. The warning method of claim 2, wherein the pre-production verification of the current production comprises:
according to the data model, at least one of formula verification, energy verification, equipment state verification and process parameter verification is carried out on the current production;
and displaying the checking result of the prenatal checking of the current production.
4. The early warning method of claim 3, wherein displaying the verification results of the current prenatal verification comprises:
and displaying the verification result of the prenatal verification of the current production through a Mashup front end of a thingwox platform.
5. The warning method of claim 2, wherein the in-production warning of the current production comprises:
comparing the equipment state and the execution condition of the process parameters in the current production process on line in real time according to the data model;
and generating and pushing early warning information under the condition that the comparison result is abnormal.
6. The warning method of claim 5, wherein pushing warning information comprises:
and transferring the connection between the REST application program interface API and the enterprise WeChat interface based on the state of the presentation layer, and pushing the early warning information to the related enterprise WeChat.
7. The warning method of claim 5, wherein the in-production warning of the current production further comprises:
and coupling equipment state analysis data in the batch production process based on a multidimensional data assimilation algorithm to generate and push an alarm message.
8. The early warning method of claim 1, further comprising:
displaying the current production historical trend through a Mashup front end according to the historical data;
and displaying the current production real-time data through a Mashup front end.
9. An early warning device based on a thingwox platform, comprising:
the acquisition module is configured to acquire historical data and real-time data from a historical database and a real-time database of an SCADA (supervisory control and data acquisition) system of a centralized control layer by using a Kepware data acquisition server of a Thingworx platform, and acquire data acquisition data from a PLC (programmable logic controller) system of an electric control layer by using the Kepware data acquisition server;
and the early warning module is configured to carry out prenatal verification and prenatal early warning on the current production according to the historical data, the real-time data and the data acquisition.
10. An early warning device based on a thingwox platform, comprising:
a memory; and
a processor coupled to the memory, the processor configured to execute the thinworx platform based alert method of any of claims 1 to 8 based on instructions stored in the memory.
11. A computer-storable medium having stored thereon computer program instructions which, when executed by a processor, implement a Thingworx platform based early warning method as claimed in any of claims 1 to 8.
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| CN116304250A (en) * | 2023-05-18 | 2023-06-23 | 广州卓勤信息技术有限公司 | Intelligent rule engine management method and system based on visual operation |
| CN116304250B (en) * | 2023-05-18 | 2023-07-21 | 广州卓勤信息技术有限公司 | Intelligent rule engine management method and system based on visual operation |
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