CN111507489B - Cloud-edge-coordinated amusement equipment fault prediction and health management system and method - Google Patents

Cloud-edge-coordinated amusement equipment fault prediction and health management system and method Download PDF

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CN111507489B
CN111507489B CN202010312802.1A CN202010312802A CN111507489B CN 111507489 B CN111507489 B CN 111507489B CN 202010312802 A CN202010312802 A CN 202010312802A CN 111507489 B CN111507489 B CN 111507489B
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卢晶琦
孟庆元
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University of Electronic Science and Technology of China Zhongshan Institute
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Abstract

The invention discloses a cloud-edge collaborative amusement equipment fault prediction and health management system and method. The data acquisition system which is distributed and deployed on the entertainment equipment can acquire various heterogeneous state data including operation parameters, mechanical system parameters, hydraulic and pneumatic system parameters, electrical system parameters and the like in real time; the edge computing device comprises five functional modules of edge data management, a prediction service orchestrator, edge model training, fault early warning and communication service, so that the data caching, device health diagnosis and early warning capabilities of an edge platform are realized. The cloud computing platform comprises four modules of a data warehouse, model training, health management and communication service, and data storage and model training capabilities of the cloud are achieved. The management and control center provides human-computer interaction for monitoring the operating parameters and health conditions of the amusement equipment and receiving fault prediction messages.

Description

Cloud-edge-coordinated amusement equipment fault prediction and health management system and method
Technical Field
The invention relates to the technical field of equipment management and Internet of things application, in particular to a cloud-edge collaborative amusement equipment fault prediction and health management system and method.
Background
With the development of the economic society and the improvement of the living standard of people in China, the tourism and entertainment industry is rapidly developed, amusement facilities are developed towards the directions of large scale, high parameter, novelty, high stimulation and the like, when joy is brought to people, the potential safety hazard is hidden in some large amusement facilities, the safety accident of extreme joy happens occasionally, the major property loss and the casualties are caused, and extremely bad social influence is generated.
Usually, amusement equipment is in a long-term safe operation state, enterprises and individuals often relax and manage, most of amusement industry equipment maintenance runs in forms, and inspection personnel can not check the equipment on site and write maintenance records in modes of advance, delay or fake making and the like; and the maintenance of the equipment is too dependent on personal experience, and the serviceability rate of the equipment is seriously influenced when personnel change or flow occurs. The device management method has the advantages that no professional management and monitoring means exist, a device manufacturing party, a device operating party and a device maintenance party cannot accurately know the specific conditions of devices and users in real time, and the possible faults of the devices are difficult to early warn.
With the development of the internet of things and intelligent manufacturing technology, remote operation and maintenance are more and more widely concerned. For the management of large-scale amusement equipment, in the process of state detection, the sensing layer collects a great deal of data, and if a traditional cloud computing center is still used for directly collecting and analyzing the data, the bandwidth occupation, network delay and data packet loss are large, and even the load of the cloud computing center is too high, the pressure is too high, and extra energy is consumed.
Disclosure of Invention
The invention aims to provide a cloud-edge collaborative amusement equipment fault prediction and health management system and method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a cloud-edge collaborative amusement device fault prediction and health management system, comprising:
the distributed data acquisition system is deployed on the entertainment equipment, acquires various heterogeneous state data in real time and transmits the state data to the edge computing equipment through the wireless communication module;
an edge computing device; the system comprises an edge data management module, a prediction service orchestrator and a model operation module, wherein the edge data management module is used for preprocessing and caching various heterogeneous data uploaded by an acquisition system; a configuration overloading module of the prediction service orchestrator acquires the configuration of a target equipment state prediction model of the cloud platform and the configuration of the equipment state prediction model trained by an edge model training module, a model operation module loads the latest state prediction model of the equipment, and the preprocessed data are used for calculation judgment;
the cloud computing platform is internally provided with a configuration management module, the configuration management module selects and triggers a model training module according to the number of the global data increments to update the state prediction training model of each entertainment equipment by using all global data in the data warehouse in a classification manner, and the model is issued to the edge computing equipment in a free time;
and the management and control center comprises a human-computer interaction interface and is used for monitoring the operating parameters and the health condition of the amusement equipment and receiving the fault prediction message.
As a further technical scheme of the invention: the distributed data acquisition system comprises acquisition data operation parameters, mechanical system parameters, hydraulic and pneumatic system parameters and electrical system parameters.
As a further technical scheme of the invention: the operation parameters comprise operation voltage, current, pressure, noise, speed, acceleration and operation period, and are obtained by monitoring of a sensor and an instrument.
As a further technical scheme of the invention: the detection means of the mechanical system parameters comprise fixed-point industrial three-proofing image acquisition, ultrasonic thickness measurement, ultrasonic and vibration test and acoustic emission.
As a further technical scheme of the invention: the parameters of the hydraulic and pneumatic system comprise pipeline pressure and voltage and current of a solenoid valve/steering valve coil, and are obtained by monitoring through a sensor and an instrument.
As a further technical scheme of the invention: the electrical system parameters include voltage and current monitoring of the PLC and core components.
As a further technical scheme of the invention: the edge computing device further comprises a fault early warning module and a communication service module.
As a further technical scheme of the invention: the cloud computing platform comprises four modules of a data warehouse, model training, health management and communication service.
As a further technical scheme of the invention: the data warehouse module stores perception data, historical data and a failure knowledge base uploaded by all connected edge computing devices, and the model training module is responsible for training a prediction model of the device state by using global data stored in the data warehouse.
As a further technical scheme of the invention: and the health management module performs fusion analysis and fault diagnosis on multiple information sources on the newly uploaded data by using the latest model of the model training module, and sends out a fault early warning signal to the edge computing equipment and the management control center when a preset threshold value is reached.
A cloud-edge collaborative amusement equipment fault prediction and health management method comprises the following specific steps: the data acquisition system which is distributed on the entertainment equipment acquires various heterogeneous state data in real time and transmits the data to the edge computing equipment through the wireless communication module, and an edge data management module of the edge computing equipment preprocesses and caches the acquired various heterogeneous data; the configuration overloading module of the prediction service orchestrator acquires the target equipment state prediction model configuration of the cloud platform and the equipment state prediction model configuration trained by the edge model training module, the model operation module loads the latest state prediction model of the equipment, the preprocessed data are used for calculation judgment, if the result reaches a preset fault risk threshold value, the fault early warning module is directly triggered, the configuration management module in the cloud platform selects the trigger model training module according to the number of the global data increments, the model training module is used for updating the state prediction training model of each game equipment by using all global data in the data warehouse in a classified mode, and the model is issued to the edge computing equipment when the model is idle.
As a further technical scheme of the invention: the model training module is responsible for training a predictive model of the plant state using global data stored in the data warehouse, wherein available diagnostic analysis methods include: the method comprises a structural fatigue life analysis method based on virtual simulation, a G acceleration determination method based on dynamic simulation and an artificial neural network evaluation method.
As a further technical scheme of the invention: the configuration overloading module requests the cloud platform to update the model configuration requirement when a task is idle, the request frequency is set by the management and control center according to requirements, and the task is idle, namely the configuration overloading task does not influence the equipment state prediction task which runs on the edge computing platform at present.
As a further technical scheme of the invention: the prediction service orchestrator needs to execute a timed device state prediction task and an instant device state prediction task; for a timed prediction task, the data reporting period of the acquisition system needs to be coordinated with the related timed task period on the edge computing device, so as to ensure that the edge computing device obtains the latest acquired data before the task starts; for the instant prediction task, the edge computing device issues a data uploading instruction to each terminal of the corresponding acquisition system, and each terminal can receive the upper layer instruction and respond to the upper layer instruction, so that the latest acquisition data is uploaded to the edge computing device.
Compared with the prior art, the invention has the beneficial effects that: the invention realizes the state monitoring and fault early warning of large-scale entertainment equipment, avoids manual inspection and flow in a form, overcomes the defect of early warning according to a single data source in the past, makes a maintenance plan in advance according to a data analysis result, prevents sudden faults, and can realize the remote monitoring and management of multiple entertainment equipment through the client of the control center. In addition, the cloud computing and edge computing servers are adopted for cooperation, abundant computing and storage resources of a cloud platform are utilized, the global data with stable increment is used for training a prediction model, efficient iteration of the model is achieved, and the accuracy of the model is effectively improved; the data in the service area are efficiently summarized through the near-end edge computing device, the state prediction and early warning maintenance of the target device are realized at the edge, the waiting time of a data uploading cloud platform is saved, and the efficiency of fault prediction and health management is improved.
Drawings
Fig. 1 is a general block diagram of the system.
Fig. 2 is a system data flow diagram.
FIG. 3 is a schematic diagram of an edge computing device.
Fig. 4 is a schematic diagram of a cloud computing platform.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-4, embodiment 1, a cloud-edge collaborative amusement equipment fault prediction and health management system,
a cloud-edge collaborative amusement equipment fault prediction and health management system comprises a distributed data acquisition system, edge computing equipment, a cloud computing platform and a management and control center.
1. The distributed data acquisition system is deployed on the entertainment equipment, acquires various heterogeneous state data in real time and transmits the state data to the edge computing equipment through the wireless communication module;
the distributed data acquisition system is mounted at each key part of the large-scale amusement equipment on a vehicle, and the acquired data specifically comprises but is not limited to: operating parameters, mechanical system parameters, hydraulic and pneumatic system parameters, electrical system parameters.
The operation parameters mainly refer to voltage, current, pressure, noise, speed, acceleration and operation period during operation, and can be obtained by monitoring through a sensor and an instrument;
the mechanical system parameter detection ensures the integrity of the structure, the flexibility of the mechanism action, the loss and looseness of parts, macroscopic deformation, the abrasion of easily damaged parts, running noise and the like, and comprises fixed-point industrial three-proofing image acquisition, ultrasonic thickness measurement, ultrasonic and vibration testing, sound emission and other means;
the parameters of the hydraulic and pneumatic system comprise pipeline pressure, voltage and current measurement of a solenoid valve/steering valve coil and the like, and can be obtained by monitoring through a sensor and an instrument;
the electrical system parameters comprise voltage and current monitoring of core components such as a PLC and a relay.
2. The edge computing device comprises five functional modules, namely an edge data management module, a prediction service orchestrator module, an edge model training module, a fault early warning module and a communication service module, so that the data caching and computing capacity of the edge platform is realized.
The edge data management module is responsible for data storage, data preprocessing and data uploading. The data storage can store heterogeneous data of one month uploaded by the connected acquisition system; the rules of data pre-processing may be set and updated by the management and control center, including but not limited to data deduplication and data cleansing operations, to reduce the amount of data uploaded. When the task is idle, the module uploads the uploaded preprocessed data to the cloud platform, and the task is idle when the overload task is configured, so that the current device state prediction task running on the edge computing platform is not influenced.
The prediction service orchestrator comprises a configuration overload submodule and a model operation submodule, the configuration overload module can receive a model configuration scheme sent from a cloud platform and a model configuration scheme trained by an edge model training module, the receiving mode can be request response receiving and passive receiving, the model operation module is responsible for loading the latest state prediction model configuration aiming at target equipment provided by the configuration overload module, calculation judgment is carried out by using preprocessed data, and if the result reaches a preset fault risk threshold value, the fault early warning module is directly triggered.
The edge model training module performs secondary training by using locally stored historical preprocessed data based on model configuration issued by the cloud platform so as to improve local adaptability of the state model of the target equipment, and a formed model configuration scheme is sent to the configuration overloading module.
And the fault early warning module is responsible for receiving early warning information output by the model operation module of the prediction service orchestrator and issuing the early warning information to a target equipment responsible person and a maintenance person.
The communication service module is mainly responsible for data interaction service of the edge computing device and other connection entities.
3. A configuration management module in the cloud platform selects a trigger model training module according to the number of the global data increments, updates a state prediction training model of each entertainment equipment by using all global data in a data warehouse in a classified manner, and issues the model to the edge computing equipment when the model is idle;
the cloud computing platform comprises four modules of a data warehouse, model training, health management and communication service, and data storage and computing capacity of a cloud are achieved.
The data warehouse module stores the perception data and other historical data uploaded by all connected edge computing devices and a failure knowledge base. And when the new increment of the global data reaches a certain amount, triggering the model training module to retrain a new model, and ensuring the accuracy of prediction.
The model training module is responsible for training a predictive model of the plant state using global data stored in the data warehouse. Among the diagnostic assays that may be used include, but are not limited to: the method comprises a structural fatigue life analysis method based on virtual simulation, a G acceleration determination method based on dynamic simulation and an artificial neural network evaluation method.
And the health management module performs fusion analysis and fault diagnosis on multiple information sources on the newly uploaded data by using the latest model of the model training module, and sends out a fault early warning signal to the edge computing equipment and the management control center when a preset threshold value is reached.
4. The management and control center provides a human-computer interaction interface for monitoring the operating parameters and health conditions of the amusement equipment and receiving fault prediction messages.
The invention also provides a cloud-edge collaborative amusement equipment fault prediction and health management method, which adopts the system and comprises the following specific steps: the data acquisition system distributed on the entertainment equipment acquires various heterogeneous state data in real time and transmits the various heterogeneous state data to the edge computing equipment through the wireless communication module, and an edge data management module of the edge computing equipment preprocesses and caches the various heterogeneous data; the configuration overloading module of the prediction service orchestrator acquires the target equipment state prediction model configuration of the cloud platform and the equipment state prediction model configuration trained by the edge model training module, the model operation module loads the latest state prediction model of the equipment, the preprocessed data are used for calculation judgment, if the result reaches a preset fault risk threshold value, the fault early warning module is directly triggered, the configuration management module in the cloud platform selects the trigger model training module according to the number of global data increments to update the state prediction training model of each entertainment equipment by using all global data in the data warehouse in a classified mode, and the model is issued to the edge computing equipment when the model is idle.
Embodiment 2, on the basis of embodiment 1, the fixed-point three-proofing image acquisition includes but is not limited to: the operation posture deformation diagram, the steel wire rope abrasion state diagram, the key anti-loose marker diagram, the screwing torque diagram, the key welding seam cracking and damage condition diagram and the like need to adopt machine learning or neural network algorithm to detect and judge the crack, abrasion, corrosion, looseness and abnormal degree of mechanical components.
The configuration overloading module requests the cloud platform to update the model configuration requirement when the task is idle, and the request frequency can be set by the management and control center according to the requirement. By task idle, it is meant that the configuration reload tasks do not affect the device state prediction tasks currently running on the edge computing platform.
The prediction service orchestrator needs to execute a timed device state prediction task and an instant device state prediction task; for a timed prediction task, the data reporting period of the acquisition system needs to be coordinated with the related timed task period on the edge computing device, so as to ensure that the edge computing device obtains the latest acquired data before the task starts; for the instant prediction task, the edge computing device issues a data uploading instruction to each terminal of the corresponding acquisition system, and each terminal can receive the upper layer instruction and respond, so that the latest acquired data is uploaded to the edge computing device.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.

Claims (14)

1. A cloud-edge collaborative amusement device fault prediction and health management system, comprising:
the distributed data acquisition system is deployed on the entertainment equipment, acquires various heterogeneous state data in real time and transmits the state data to the edge computing equipment through the wireless communication module;
an edge computing device; the system comprises an edge data management module, a prediction service orchestrator and a model operation module, wherein the edge data management module is used for preprocessing and caching various heterogeneous data uploaded by an acquisition system; a configuration overloading module of the prediction service orchestrator acquires the configuration of a target equipment state prediction model of the cloud platform and the configuration of the equipment state prediction model trained by an edge model training module, a model operation module loads the latest state prediction model of the equipment, and the preprocessed data are used for calculation judgment;
the cloud computing platform is internally provided with a configuration management module, the configuration management module selects and triggers a model training module according to the number of the global data increments to update the state prediction training model of each entertainment equipment by using all global data in the data warehouse in a classification manner, and the model is issued to the edge computing equipment in a free time;
and the management and control center comprises a human-computer interaction interface and is used for monitoring the operation parameters and the health condition of the amusement equipment and receiving the fault prediction message.
2. The cloud-edge collaborative amusement ride equipment fault prediction and health management system of claim 1, wherein the distributed data collection system comprises collected data operational parameters, mechanical system parameters, hydraulic and pneumatic system parameters, and electrical system parameters.
3. The cloud-edge collaborative amusement device fault prediction and health management system according to claim 2, wherein the operational parameters include operational voltage, current, pressure, noise, speed, acceleration, and operational period, and the operational parameters are obtained through sensor and meter monitoring.
4. The cloud-edge collaborative amusement ride equipment fault prediction and health management system according to claim 2, wherein the detection means of mechanical system parameters include fixed-point industrial three-proofing image acquisition, ultrasonic thickness measurement, ultrasonic and vibration testing, and acoustic emission.
5. The cloud-side collaborative amusement ride equipment fault prediction and health management system according to claim 2, wherein the hydraulic and pneumatic system parameters include pipeline pressure, solenoid/steering valve coil voltage current, obtained through sensors and instrumentation monitoring.
6. The cloud-edge collaborative amusement ride fault prediction and health management system according to claim 2, wherein the electrical system parameters include voltage and current monitoring of the PLC and core components.
7. The cloud-edge collaborative amusement device fault prediction and health management system of claim 1, wherein the edge computing device further comprises a fault pre-warning module and a communication service module.
8. The cloud-edge collaborative amusement device fault prediction and health management system according to claim 1, wherein the cloud computing platform comprises four modules of data warehouse, model training, health management, and communication services.
9. The cloud-edge collaborative amusement device fault prediction and health management system according to claim 8, wherein the data warehouse module stores perception data and historical data uploaded by all connected edge computing devices and a failure knowledge base, and the model training module is responsible for training a prediction model of device states using global data stored in the data warehouse.
10. The cloud-edge collaborative amusement equipment fault prediction and health management system according to claim 8, wherein the health management module performs fusion analysis and fault diagnosis on newly uploaded data through a latest model of the model training module, and sends out a fault early warning signal to the edge computing device and the management control center when a preset threshold value is reached.
11. A cloud-edge cooperative amusement equipment fault prediction and health management method is characterized in that the system of any one of claims 1-9 is adopted, and the specific steps are as follows: the data acquisition system distributed on the entertainment equipment acquires various heterogeneous state data in real time and transmits the various heterogeneous state data to the edge computing equipment through the wireless communication module, and an edge data management module of the edge computing equipment preprocesses and caches the various heterogeneous data; the configuration overloading module of the prediction service orchestrator acquires the target equipment state prediction model configuration of the cloud platform and the equipment state prediction model configuration trained by the edge model training module, the model operation module loads the latest state prediction model of the equipment, the preprocessed data are used for calculation judgment, if the result reaches a preset fault risk threshold value, the fault early warning module is directly triggered, the configuration management module in the cloud platform selects the trigger model training module according to the number of global data increments to update the state prediction training model of each entertainment equipment by using all global data in the data warehouse in a classified mode, and the model is issued to the edge computing equipment when the model is idle.
12. The cloud-edge collaborative amusement device fault prediction and health management method according to claim 11, wherein the model training module is responsible for training a predictive model of device states using global data stored in a data warehouse, wherein available diagnostic analysis methods include: the method comprises a structural fatigue life analysis method based on virtual simulation, a G acceleration determination method based on dynamic simulation and an artificial neural network evaluation method.
13. The method for cloud-edge collaborative amusement equipment fault prediction and health management according to claim 11, wherein the configuration reloading module requests the cloud platform to update model configuration requirements when a task is idle, the request frequency is set by the management and control center according to requirements, and the task is idle when the configuration reloading task does not affect an equipment state prediction task currently running on the edge computing platform.
14. The cloud-edge collaborative amusement device fault prediction and health management method of claim 11, wherein the prediction service orchestrator is required to perform timed device state prediction tasks and immediate device state prediction tasks; for a timed prediction task, the data reporting period of the acquisition system needs to cooperate with the related timed task period on the edge computing device, so as to ensure that the edge computing device obtains the latest acquired data before the task starts; for the instant prediction task, the edge computing device issues a data uploading instruction to each terminal of the corresponding acquisition system, and each terminal can receive the upper layer instruction and respond, so that the latest acquired data is uploaded to the edge computing device.
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