CN116846987A - Interactive interface generation method and system of industrial Internet - Google Patents
Interactive interface generation method and system of industrial Internet Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/08—Protocols for interworking; Protocol conversion
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/22—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The invention relates to the technical field of interactive interface generation, in particular to an interactive interface generation method and system of an industrial Internet, comprising the following steps: and a data processing module: for collecting, collating and analyzing data of industrial equipment; and an interface design module: automatically generating an interactive interface according to the analyzed data; interaction logic module: for interactive control between the interface and the industrial equipment; and a communication module: for enabling communication between industrial equipment and systems; and the real-time data stream processing module is used for: the method is used for capturing and processing a large amount of real-time data in real time, reducing delay and providing more real-time interaction experience; and the equipment health monitoring and predicting module is used for: predicting potential equipment failure or maintenance requirements through continuous monitoring and machine learning algorithms; cross-device intelligent collaboration module: a unified communication protocol and adaptive interaction logic are used. According to the invention, by creating the unified communication protocol adapter, seamless communication among different devices is realized, and the problem of diversity of the device communication protocol is effectively solved.
Description
Technical Field
The invention relates to the technical field of interactive interface generation, in particular to an interactive interface generation method and system of an industrial Internet.
Background
The industrial Internet is a key part in modern industry 4.0, intelligent coordination among equipment can be realized by connecting industrial equipment with the Internet, the production flow is optimized, and the working efficiency and quality are improved. However, existing industrial internet technologies still have some challenges and deficiencies in terms of:
device protocol diversity problem: devices on industrial sites often use different communication protocols, resulting in difficult communication between the devices and failure to achieve true intelligent collaboration.
The real-time data stream is not processed enough: the real-time data stream generated by a large number of devices is quite complex to process, and needs powerful data processing capability, so that the technical requirements on time sequence analysis, data mining and the like are high.
Lack of equipment health monitoring and prediction: the prior art is not mature enough in the aspects of equipment health monitoring and fault prediction, and the omnibearing monitoring and accurate prediction of the equipment state are difficult to realize.
Cross-device intelligent collaboration is limited: even with unified communication protocols, accurate task scheduling, optimization algorithms, and learning mechanisms are required to achieve efficient coordination between devices.
Therefore, the novel interactive interface generation system of the industrial Internet can solve the problems, and a comprehensive, flexible and intelligent solution is provided to meet the complex and diversified requirements of the modern industrial Internet. The system and the method provide a more efficient and intelligent operation mode for an industrial field by integrating various advanced technologies including communication protocol adaptation, real-time data flow processing, equipment health monitoring and prediction, cross-equipment intelligent coordination and the like.
Disclosure of Invention
Based on the above purpose, the invention provides a method and a system for generating an interactive interface of an industrial Internet.
An interactive interface generation system for an industrial internet, comprising:
and a data processing module: for collecting, collating and analyzing data of industrial equipment;
and an interface design module: automatically generating an interactive interface according to the analyzed data;
interaction logic module: for interactive control between the interface and the industrial equipment;
and a communication module: for enabling communication between industrial equipment and systems;
and the real-time data stream processing module is used for: the method is used for capturing and processing a large amount of real-time data in real time, reducing delay and providing more real-time interaction experience;
and the equipment health monitoring and predicting module is used for: predicting potential equipment failure or maintenance requirements through continuous monitoring and machine learning algorithms;
cross-device intelligent collaboration module: and the intelligent cooperative work among different devices is realized by using a unified communication protocol and self-adaptive interaction logic.
Further, the data processing module comprises a data acquisition unit, a data analysis unit and a data storage unit,
the data acquisition unit is used for acquiring original data from industrial equipment;
the data analysis unit is used for preprocessing and analyzing the original data;
the data storage unit is responsible for storing the analyzed data so as to adapt to different types of industrial equipment and application scenes.
Further, the interface design module comprises an interface layout unit, a component selection unit and a style adjustment unit,
the interface layout unit automatically arranges the layout structure of the interface according to the analyzed data;
the component selection unit is used for selecting interactive components suitable for industrial equipment;
the style adjustment unit adjusts the visual style of the interface according to the specific requirements of the industrial scene, and generates an interactive interface which is friendly to users and has comprehensive functions.
Further, the interaction logic module specifically includes:
an instruction conversion unit: converting the operation on the user interface into a specific instruction which can be understood by industrial equipment, and supporting various instruction formats and equipment types;
feedback processing unit: receiving feedback information of industrial equipment in real time;
an abnormality management unit: is responsible for identifying and handling abnormal conditions during the interaction process;
user behavior analysis unit: by analyzing the user's operational behavior and historical data, the user's intent and needs are presumed to provide more accurate interaction support;
context awareness unit: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic to adapt to different working conditions and requirements;
intelligent assistance unit: providing intelligent prompt and operation suggestion assistance functions by utilizing natural language processing;
multimode interaction unit: supporting other interaction modes except the traditional touch control;
safety control unit: ensuring that all interactive operations accord with security policies and standards, and preventing illegal access and manipulation;
an adaptation layer: the interactive logic module can be seamlessly integrated with various industrial equipment and systems, so that the compatibility problem is reduced;
performance monitoring and optimizing unit: and the interactive response speed and accuracy are monitored in real time, and the interaction response speed and accuracy are automatically optimized to ensure smooth user experience.
Further, the communication module comprises a communication protocol conversion unit, a data encryption unit and a connection management unit,
the communication protocol conversion unit is responsible for uniformly converting communication protocols of different industrial equipment;
the data encryption unit ensures the security of data transmission;
the connection management unit is used for stably connecting and managing industrial equipment and ensuring real-time and accurate transmission of data.
Further, the real-time data stream processing module includes:
a multi-source data integration unit: integrating real-time data streams from different devices, sensors and systems to ensure consistency and integrity of the data;
dynamic data filter: the irrelevant or redundant data is dynamically filtered through a predefined rule and a pattern recognition technology, so that the processing efficiency is improved;
parallel computing unit: the parallel computing capability is utilized to process a plurality of data streams simultaneously, so that the performance of the system under high load is ensured;
real-time alarm and notification unit: automatically triggering an alarm or notification according to the real-time analysis result, such as detected abnormality or key event;
a temporal analysis unit: analyzing the time sequence characteristics of the data, and identifying and predicting potential trends and periodic patterns;
a data privacy protection unit: adopting encryption and anonymization technology to ensure privacy protection of data in the process of processing and transmitting;
resource scheduling and optimizing unit: dynamically scheduling and optimizing computing resources according to real-time requirements of the system, and ensuring stable operation of the system;
edge calculation support unit: partial data processing at the edge of the device is supported, reducing delay and relieving the burden on the central system.
Further, the device health monitoring and predicting module includes:
the equipment state sensing unit acquires temperature, pressure and vibration parameters of equipment through a built-in or external sensor;
the health evaluation unit adopts a random forest evaluation model to evaluate the health condition of the equipment in real time, wherein the random forest evaluation model is specifically as follows:
bootstrap sampling for a given training data setEach tree is from +.>A subset of the same size is selected (with ground back), provided +.>Samples, then the training set for each tree is ；
Feature selection: at each split node of each decision tree, randomly selecting from all featuresOptimal splitting of the feature subsets, for classification problems, < ->, wherein />Is the total number of features;
training a decision tree: for each tree, training a decision tree based on the selected sample and feature subsets, the output function of each tree is expressed as:
wherein ,is->Output of tree->Is->Decision function of tree->Is a parameter thereof;
overall prediction:
for the regression problem, the output of the random forest is the average of all tree outputs:
for classification problems, the output of random forests is the mode in all tree outputs:
；
the fault prediction unit predicts the type and time of the possible fault of the device by analyzing the historical data through a time sequence, wherein the time sequence is based on an autoregressive moving average model (ARMA), and specifically comprises the following steps:
autoregressive moving average model is used to describe non-seasonal time series, and an ARMA model of p-order and q-order is expressed as:
the model considers the time dependence and random noise of the equipment parameters at the same time, and provides more comprehensive analysis for real-time monitoring and fault early warning;
a maintenance suggestion generation unit for providing specific suggestions and schemes for maintenance and optimization according to the health condition and the prediction result of the equipment;
the integration of the module enables the operation and maintenance management of the industrial equipment to be more intelligent and efficient, so that problems can be found and processed in time, and fault risks and operation cost can be reduced through prediction and optimization.
Further, the cross-device intelligent collaboration module includes:
protocol identification and mapping: first, the collaboration module will identify the communication protocols used by the various devices in the industrial field, including Modbus, profinet, etherCAT, and then create a protocol mapping table to define the conversion rules between the different protocols;
dynamic adapter generation: designing a dynamic adapter framework, allowing to generate specific protocol conversion adapters according to a protocol mapping table, wherein each adapter is responsible for converting data of one protocol into a universal format or another protocol, and registering and managing in an adapter registry after the adapter is generated;
the data conversion flow is as follows: when data is transmitted from the source device, the adapter firstly decodes the data into an intermediate general format by using a decoding rule of the source protocol, then the adapter converts the data into a format of the target protocol from the intermediate format by using a conversion rule defined in a protocol mapping table, and finally the adapter encodes the converted data by using an encoding rule of the target protocol and transmits the encoded data to the target device.
Furthermore, the system comprises a cloud cooperation module, so that the system and cloud resources cooperate to realize remote access and management of an interaction interface and promote cooperative interaction and control of multiple devices and multiple scenes.
An interactive interface generation method of an industrial Internet comprises the following steps:
s1: and a data processing stage:
s11, data acquisition: obtaining raw data from an industrial device;
s12, data analysis: preprocessing and analyzing the original data;
s13, data storage: storing the analyzed data, and adapting to different types of industrial equipment and application scenes;
s2: interface design stage:
s21 interface layout: automatically arranging the layout structure of the interface according to the analyzed data;
s22, selecting a component: selecting an interactive component suitable for industrial equipment;
s23 style adjustment: according to the specific requirements of the industrial scene, the visual style of the interface is adjusted, and an interactive interface which is friendly to users and has comprehensive functions is generated;
s3: interaction logic stage:
s31 instruction conversion: converting operations on the user interface into specific instructions;
s32 feedback processing: receiving feedback information of industrial equipment in real time and presenting the feedback information in a visual mode;
s33, anomaly management: identifying and handling abnormal conditions and providing a recovery strategy;
s34, user behavior analysis: analyzing user behavior and historical data, and speculating intent and demand;
s35 context awareness: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic;
s36 intelligent assistance: providing intelligent prompts and operation suggestions by natural language processing;
s37 multi-modal interaction: different interaction modes are supported;
s38, safety control: ensuring that all operations meet safety standards;
s39 adaptation and optimization: integrating with various industrial equipment and systems, and optimizing user experience in real time;
s4: and a communication stage:
s41, communication protocol conversion: uniformly converting communication protocols of different industrial equipment;
s42, data encryption: the safety of data transmission is ensured;
s43 connection management: ensuring stable connection and management with industrial equipment;
s5: real-time data stream processing stage:
s51, data integration, filtering, parallel computing, alarming and notification, temporal analysis, data privacy protection, resource scheduling and optimization and edge computing links;
s6: device health monitoring and prediction phase:
s61 state awareness, health assessment (including using a random forest assessment model), fault prediction (based on ARMA model), maintenance recommendation generation;
s7: cross-device intelligent collaboration phase:
s71, identifying and mapping a protocol;
s72, generating a dynamic adapter;
s73, data conversion flow.
The invention has the beneficial effects that:
according to the invention, by creating the unified communication protocol adapter, seamless communication among different devices is realized, the problem of diversity of the communication protocol of the devices is effectively solved, and the real-time data stream is processed by adopting advanced algorithms such as time sequence analysis, random forest and the like, so that the state change of the devices can be responded rapidly, and the accuracy and the efficiency of data analysis are improved.
The invention improves the production efficiency and reduces the maintenance cost: by realizing intelligent coordination among the devices and monitoring and maintaining the devices in time, the invention not only can improve the production efficiency, but also can reduce the maintenance cost, thereby bringing long-term economic benefit to industrial production.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of the present invention;
fig. 2 is a schematic diagram of system logic according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1-2, an interactive interface generating system of an industrial internet includes:
and a data processing module: for collecting, collating and analyzing data of industrial equipment;
and an interface design module: automatically generating an interactive interface according to the analyzed data;
interaction logic module: for interactive control between the interface and the industrial equipment;
and a communication module: for enabling communication between industrial equipment and systems;
and the real-time data stream processing module is used for: the method is used for capturing and processing a large amount of real-time data in real time, reducing delay and providing more real-time interaction experience;
and the equipment health monitoring and predicting module is used for: predicting potential equipment failure or maintenance requirements through continuous monitoring and machine learning algorithms;
cross-device intelligent collaboration module: and the intelligent cooperative work among different devices is realized by using a unified communication protocol and self-adaptive interaction logic.
The data processing module comprises a data acquisition unit, a data analysis unit and a data storage unit,
the data acquisition unit is used for acquiring original data from industrial equipment;
the data analysis unit is used for preprocessing and analyzing the original data;
the data storage unit is responsible for storing the analyzed data so as to adapt to different types of industrial equipment and application scenes.
The interface design module comprises an interface layout unit, a component selection unit and a style adjustment unit,
the interface layout unit automatically arranges the layout structure of the interface according to the analyzed data;
the component selection unit is used for selecting interactive components suitable for industrial equipment;
the style adjustment unit adjusts the visual style of the interface according to the specific requirements of the industrial scene, and generates an interactive interface which is friendly to users and has comprehensive functions.
The interactive logic module specifically comprises:
an instruction conversion unit: converting the operation on the user interface into a specific instruction which can be understood by industrial equipment, and supporting various instruction formats and equipment types;
feedback processing unit: receiving feedback information of the industrial equipment in real time, including state update, warning and error information, and visually presenting the feedback information to a user;
an abnormality management unit: the method is responsible for identifying and processing abnormal conditions in the interaction process, including equipment faults, communication interruption and the like, and providing corresponding recovery strategies;
user behavior analysis unit: by analyzing the user's operational behavior and historical data, the user's intent and needs are presumed to provide more accurate interaction support;
context awareness unit: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic to adapt to different working conditions and requirements;
intelligent assistance unit: providing intelligent prompt and operation suggestion assistance functions by utilizing natural language processing;
multimode interaction unit: other interaction modes except traditional touch control are supported, including voice and gestures, and richer and natural interaction experience is provided;
safety control unit: ensuring that all interactive operations accord with security policies and standards, and preventing illegal access and manipulation;
an adaptation layer: the interactive logic module can be seamlessly integrated with various industrial equipment and systems, so that the compatibility problem is reduced;
performance monitoring and optimizing unit: and the interactive response speed and accuracy are monitored in real time, and the interaction response speed and accuracy are automatically optimized to ensure smooth user experience.
The communication module comprises a communication protocol conversion unit, a data encryption unit and a connection management unit,
the communication protocol conversion unit is responsible for uniformly converting communication protocols of different industrial devices;
the data encryption unit ensures the security of data transmission;
the connection management unit is used for stably connecting and managing industrial equipment and ensuring real-time and accurate transmission of data.
The real-time data stream processing module comprises:
a multi-source data integration unit: integrating real-time data streams from different devices, sensors and systems to ensure consistency and integrity of the data;
dynamic data filter: the irrelevant or redundant data is dynamically filtered through a predefined rule and a pattern recognition technology, so that the processing efficiency is improved;
parallel computing unit: the parallel computing capability is utilized to process a plurality of data streams simultaneously, so that the performance of the system under high load is ensured;
real-time alarm and notification unit: automatically triggering an alarm or notification according to the real-time analysis result, such as detected abnormality or key event;
a temporal analysis unit: analyzing the time sequence characteristics of the data, and identifying and predicting potential trends and periodic patterns;
a data privacy protection unit: adopting encryption and anonymization technology to ensure privacy protection of data in the process of processing and transmitting;
resource scheduling and optimizing unit: dynamically scheduling and optimizing computing resources according to real-time requirements of the system, and ensuring stable operation of the system;
edge calculation support unit: partial data processing at the edge of the device is supported, reducing delay and relieving the burden on the central system.
The device health monitoring and predicting module comprises:
the equipment state sensing unit acquires temperature, pressure and vibration parameters of equipment through a built-in or external sensor;
the health evaluation unit adopts a Random Forest evaluation model to evaluate the health condition of the equipment in real time, and a Random Forest (Random Forest) is an integrated learning method, and the prediction accuracy is improved and the overfitting is controlled by combining the predictions of a plurality of decision trees. The basic idea of random forest is to construct a plurality of decision trees by self-sampling (Bootstrap Sampling) and to average or majority vote the prediction results of these trees, and the random forest assessment model is specifically as follows:
bootstrap sampling for a given training data setEach tree is from +.>A subset of the same size is selected (with ground back), provided +.>Samples, then the training set for each tree is ；
Feature selection: at each split node of each decision tree, randomly selecting from all featuresOptimal splitting of the feature subsets, for classification problems, < ->, wherein />Is the total number of features;
training a decision tree: for each tree, training a decision tree based on the selected sample and feature subsets, the output function of each tree is expressed as:
wherein ,is->Output of tree->Is->Decision function of tree->Is a parameter thereof;
overall prediction:
for the regression problem, the output of the random forest is the average of all tree outputs:
for classification problems, the output of random forests is the mode in all tree outputs:
in the invention, bootstrap sampling and feature selection: in an industrial environment, operational data of a device may include a number of dimensions and characteristics, such as temperature, pressure, vibration, rotational speed, and the like. Bootstrap sampling of random forests allows a representative set of subsamples to be selected from a large amount of data monitored in real time. By randomly selecting the feature subset, the model can be ensured to capture complex relationships between different features, and the generalization capability of the model can be enhanced.
Training a decision tree: by training multiple decision trees on sub-samples and feature subsets, the device health monitoring and prediction module can learn and understand the multifaceted aspects of the device's operating state. Each tree may be concerned with different aspects of device health, enabling the entire random forest model to comprehensively evaluate device status.
Overall prediction and application:
device health assessment: in the regression problem, the random forest model can predict the health score or the rating of the equipment, and the current health condition of the equipment is accurately reflected by averaging the prediction results of a plurality of trees.
Fault prediction and early warning: in the classification problem, a random forest can identify and predict the type of fault that a device may be experiencing. The model can reliably early warn potential faults of equipment through majority voting, and timely information is provided for maintenance personnel.
In connection with the scheme of the invention, these characteristics of random forests make it an ideal choice for equipment health monitoring and prediction modules. The method can extract useful health and fault information from complex and multidimensional data of industrial equipment, and can realize accurate and steady assessment of equipment states by integrating judgment of a plurality of trees. In addition, the self-adaption and the extensible characteristic of the random forest also enable the module to adapt to industrial equipment of different types and scales, and provide strong support for a modern industrial Internet interactive interface generation system;
the fault prediction unit predicts the type and time of the possible fault of the device by analyzing the historical data through a time sequence, wherein the time sequence is based on an autoregressive moving average model (ARMA), and specifically comprises the following steps:
autoregressive moving average model is used to describe non-seasonal time series, and an ARMA model of p-order and q-order is expressed as:
the model considers the time dependence and random noise of the equipment parameters at the same time, and provides more comprehensive analysis for real-time monitoring and fault early warning;
a maintenance suggestion generation unit for providing specific suggestions and schemes for maintenance and optimization according to the health condition and the prediction result of the equipment;
the integration of the module enables the operation and maintenance management of the industrial equipment to be more intelligent and efficient, so that problems can be found and processed in time, and fault risks and operation cost can be reduced through prediction and optimization.
The cross-device intelligent collaboration module comprises:
protocol identification and mapping: firstly, a collaboration module identifies communication protocols used by various devices in an industrial field, including Modbus, profinet, etherCAT, and then creates a protocol mapping table to define conversion rules between different protocols, wherein the mapping table includes field correspondence between source protocols and target protocols, data type conversion rules and the like;
dynamic adapter generation: designing a dynamic adapter framework, allowing to generate specific protocol conversion adapters according to a protocol mapping table, wherein each adapter is responsible for converting data of one protocol into a general format or another protocol, registering and managing the adapter after the adapter is generated in an adapter registry, and dynamically loading and unloading the adapter framework to adapt to the change of equipment and the protocol;
the data conversion flow is as follows: when data is transmitted from the source device, the adapter firstly decodes the data into an intermediate general format by using a decoding rule of the source protocol, then the adapter converts the data into a format of the target protocol from the intermediate format by using a conversion rule defined in a protocol mapping table, and finally the adapter encodes the converted data by using an encoding rule of the target protocol and transmits the encoded data to the target device.
The system comprises a cloud cooperation module, so that the system and cloud resources cooperate to realize remote access and management of an interactive interface and promote cooperative interaction and control of multiple devices and multiple scenes.
An interactive interface generation method of an industrial Internet comprises the following steps:
s1: and a data processing stage:
s11, data acquisition: obtaining raw data from an industrial device;
s12, data analysis: preprocessing and analyzing the original data;
s13, data storage: storing the analyzed data, and adapting to different types of industrial equipment and application scenes;
s2: interface design stage:
s21 interface layout: automatically arranging the layout structure of the interface according to the analyzed data;
s22, selecting a component: selecting an interactive component suitable for industrial equipment;
s23 style adjustment: according to the specific requirements of the industrial scene, the visual style of the interface is adjusted, and an interactive interface which is friendly to users and has comprehensive functions is generated;
s3: interaction logic stage:
s31 instruction conversion: converting operations on the user interface into specific instructions;
s32 feedback processing: receiving feedback information of industrial equipment in real time and presenting the feedback information in a visual mode;
s33, anomaly management: identifying and handling abnormal conditions and providing a recovery strategy;
s34, user behavior analysis: analyzing user behavior and historical data, and speculating intent and demand;
s35 context awareness: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic;
s36 intelligent assistance: providing intelligent prompts and operation suggestions by natural language processing;
s37 multi-modal interaction: different interaction modes are supported;
s38, safety control: ensuring that all operations meet safety standards;
s39 adaptation and optimization: integrating with various industrial equipment and systems, and optimizing user experience in real time;
s4: and a communication stage:
s41, communication protocol conversion: uniformly converting communication protocols of different industrial equipment;
s42, data encryption: the safety of data transmission is ensured;
s43 connection management: ensuring stable connection and management with industrial equipment;
s5: real-time data stream processing stage:
s51, data integration, filtering, parallel computing, alarming and notification, temporal analysis, data privacy protection, resource scheduling and optimization, edge computing and other links;
s6: device health monitoring and prediction phase:
s61 state awareness, health assessment (including using a random forest assessment model), fault prediction (based on ARMA model), maintenance recommendation generation;
s7: cross-device intelligent collaboration phase:
s71, identifying and mapping a protocol;
s72, generating a dynamic adapter;
s73, data conversion flow.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (10)
1. An interactive interface generation system of an industrial internet, comprising:
and a data processing module: for collecting, collating and analyzing data of industrial equipment;
and an interface design module: automatically generating an interactive interface according to the analyzed data;
interaction logic module: for interactive control between the interface and the industrial equipment;
and a communication module: for enabling communication between industrial equipment and systems;
and the real-time data stream processing module is used for: the method is used for capturing and processing a large amount of real-time data in real time, reducing delay and providing more real-time interaction experience;
and the equipment health monitoring and predicting module is used for: predicting potential equipment failure or maintenance requirements through continuous monitoring and machine learning algorithms;
cross-device intelligent collaboration module: and the intelligent cooperative work among different devices is realized by using a unified communication protocol and self-adaptive interaction logic.
2. The interactive interface generation system of claim 1, wherein the data processing module comprises a data acquisition unit, a data analysis unit and a data storage unit,
the data acquisition unit is used for acquiring original data from industrial equipment;
the data analysis unit is used for preprocessing and analyzing the original data;
the data storage unit is responsible for storing the analyzed data so as to adapt to different types of industrial equipment and application scenes.
3. The interactive interface generation system of claim 2, wherein the interface design module comprises an interface layout unit, a component selection unit and a style adjustment unit,
the interface layout unit automatically arranges the layout structure of the interface according to the analyzed data;
the component selection unit is used for selecting interactive components suitable for industrial equipment;
the style adjustment unit adjusts the visual style of the interface according to the specific requirements of the industrial scene, and generates an interactive interface which is friendly to users and has comprehensive functions.
4. The interactive interface generation system of the industrial internet according to claim 3, wherein the interactive logic module specifically comprises:
an instruction conversion unit: converting the operation on the user interface into a specific instruction which can be understood by industrial equipment, and supporting various instruction formats and equipment types;
feedback processing unit: receiving feedback information of industrial equipment in real time;
an abnormality management unit: is responsible for identifying and handling abnormal conditions during the interaction process;
user behavior analysis unit: by analyzing the user's operational behavior and historical data, the user's intent and needs are presumed to provide more accurate interaction support;
context awareness unit: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic to adapt to different working conditions and requirements;
intelligent assistance unit: providing intelligent prompt and operation suggestion assistance functions by utilizing natural language processing;
multimode interaction unit: supporting other interaction modes except the traditional touch control;
safety control unit: ensuring that all interactive operations accord with security policies and standards, and preventing illegal access and manipulation;
an adaptation layer: the interactive logic module can be seamlessly integrated with various industrial equipment and systems, so that the compatibility problem is reduced;
performance monitoring and optimizing unit: and the interactive response speed and accuracy are monitored in real time, and the interaction response speed and accuracy are automatically optimized to ensure smooth user experience.
5. The interactive interface generation system of claim 4, wherein the communication module comprises a communication protocol conversion unit, a data encryption unit and a connection management unit,
the communication protocol conversion unit is responsible for uniformly converting communication protocols of different industrial equipment;
the data encryption unit ensures the security of data transmission;
the connection management unit is used for stably connecting and managing industrial equipment and ensuring real-time and accurate transmission of data.
6. The interactive interface generation system of claim 5, wherein the real-time data stream processing module comprises:
a multi-source data integration unit: integrating real-time data streams from different devices, sensors and systems to ensure consistency and integrity of the data;
dynamic data filter: the irrelevant or redundant data is dynamically filtered through a predefined rule and a pattern recognition technology, so that the processing efficiency is improved;
parallel computing unit: the parallel computing capability is utilized to process a plurality of data streams simultaneously, so that the performance of the system under high load is ensured;
real-time alarm and notification unit: automatically triggering an alarm or notification according to the real-time analysis result, such as detected abnormality or key event;
a temporal analysis unit: analyzing the time sequence characteristics of the data, and identifying and predicting potential trends and periodic patterns;
a data privacy protection unit: adopting encryption and anonymization technology to ensure privacy protection of data in the process of processing and transmitting;
resource scheduling and optimizing unit: dynamically scheduling and optimizing computing resources according to real-time requirements of the system, and ensuring stable operation of the system;
edge calculation support unit: partial data processing at the edge of the device is supported, reducing delay and relieving the burden on the central system.
7. The industrial internet interactive interface generation system of claim 6, wherein the device health monitoring and prediction module comprises:
the equipment state sensing unit acquires temperature, pressure and vibration parameters of equipment through a built-in or external sensor;
the health evaluation unit adopts a random forest evaluation model to evaluate the health condition of the equipment in real time, wherein the random forest evaluation model is specifically as follows:
bootstrap sampling for a given training data setEach tree is from +.>A subset of the same size is selected at random, provided +.>Samples, then training set for each tree is +.> ；
Feature selection: at each split node of each decision tree, randomly selecting from all featuresOptimal splitting of the feature subsets, for classification problems, < ->, wherein />Is the total number of features;
training a decision tree: for each tree, training a decision tree based on the selected sample and feature subsets, the output function of each tree is expressed as:
wherein ,is->Output of tree->Is->Decision function of tree->Is a parameter thereof;
overall prediction:
for the regression problem, the output of the random forest is the average of all tree outputs:
for classification problems, the output of random forests is the mode in all tree outputs:
the fault prediction unit is used for predicting the possible fault type and time of the equipment by analyzing the historical data through a time sequence, wherein the time sequence is based on an autoregressive moving average model and specifically comprises the following steps:
autoregressive moving average model is used to describe non-seasonal time series, and an ARMA model of p-order and q-order is expressed as:
the model considers the time dependence and random noise of the equipment parameters at the same time, and provides more comprehensive analysis for real-time monitoring and fault early warning;
and the maintenance suggestion generation unit is used for providing specific suggestions and schemes for maintenance and optimization according to the health condition and the prediction result of the equipment.
8. The interactive interface generation system of claim 7, wherein the cross-device intelligent collaboration module comprises:
protocol identification and mapping: first, the collaboration module will identify the communication protocols used by the various devices in the industrial field, including Modbus, profinet, etherCAT, and then create a protocol mapping table to define the conversion rules between the different protocols;
dynamic adapter generation: designing a dynamic adapter framework, allowing to generate specific protocol conversion adapters according to a protocol mapping table, wherein each adapter is responsible for converting data of one protocol into a universal format or another protocol, and registering and managing in an adapter registry after the adapter is generated;
the data conversion flow is as follows: when data is transmitted from the source device, the adapter firstly decodes the data into an intermediate general format by using a decoding rule of the source protocol, then the adapter converts the data into a format of the target protocol from the intermediate format by using a conversion rule defined in a protocol mapping table, and finally the adapter encodes the converted data by using an encoding rule of the target protocol and transmits the encoded data to the target device.
9. The interactive interface generation system of the industrial internet according to claim 8, wherein the system comprises a cloud cooperation module, so that the system cooperates with cloud resources to realize remote access and management of an interactive interface and promote cooperative interaction and control of multiple devices and multiple scenes.
10. The interactive interface generation method of the industrial Internet is characterized by comprising the following steps of:
s1: and a data processing stage:
s11, data acquisition: obtaining raw data from an industrial device;
s12, data analysis: preprocessing and analyzing the original data;
s13, data storage: storing the analyzed data, and adapting to different types of industrial equipment and application scenes;
s2: interface design stage:
s21 interface layout: automatically arranging the layout structure of the interface according to the analyzed data;
s22, selecting a component: selecting an interactive component suitable for industrial equipment;
s23 style adjustment: according to the specific requirements of the industrial scene, the visual style of the interface is adjusted, and an interactive interface which is friendly to users and has comprehensive functions is generated;
s3: interaction logic stage:
s31 instruction conversion: converting operations on the user interface into specific instructions;
s32 feedback processing: receiving feedback information of industrial equipment in real time and presenting the feedback information in a visual mode;
s33, anomaly management: identifying and handling abnormal conditions and providing a recovery strategy;
s34, user behavior analysis: analyzing user behavior and historical data, and speculating intent and demand;
s35 context awareness: sensing environmental changes and equipment states in an industrial scene, and automatically adjusting interaction logic;
s36 intelligent assistance: providing intelligent prompts and operation suggestions by natural language processing;
s37 multi-modal interaction: different interaction modes are supported;
s38, safety control: ensuring that all operations meet safety standards;
s39 adaptation and optimization: integrating with various industrial equipment and systems, and optimizing user experience in real time;
s4: and a communication stage:
s41, communication protocol conversion: uniformly converting communication protocols of different industrial equipment;
s42, data encryption: the safety of data transmission is ensured;
s43 connection management: ensuring stable connection and management with industrial equipment;
s5: real-time data stream processing stage:
s51, data integration, filtering, parallel computing, alarming and notification, temporal analysis, data privacy protection, resource scheduling and optimization and edge computing links;
s6: device health monitoring and prediction phase:
s61, state sensing, health assessment, fault prediction and maintenance suggestion generation;
s7: cross-device intelligent collaboration phase:
s71, identifying and mapping a protocol;
s72, generating a dynamic adapter;
s73, data conversion flow.
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