CN117171551B - Large-scale industrial equipment data analysis and intelligent management method - Google Patents
Large-scale industrial equipment data analysis and intelligent management method Download PDFInfo
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Abstract
The invention relates to the field of data analysis and equipment management, in particular to a large-scale industrial equipment data analysis and intelligent management method. Firstly, an industrial equipment information model is established, industrial equipment data is deeply analyzed by adopting a big data analysis technology, the failure rate and the resource utilization rate of equipment are determined according to data analysis results, and the comprehensive operation efficiency of the equipment is estimated; secondly, constructing a collaborative operation model, acquiring a weight vector of equipment, acquiring a collaborative matrix by combining an interactive collaborative criterion, performing global evaluation on large-scale industrial Internet equipment based on the comprehensive operation efficiency of the equipment, generating a collaborative operation instruction, and performing intelligent management on the large-scale industrial Internet equipment. The method solves the problems that the prior art only pays attention to single equipment parameters, and causes blindness and inefficiency of optimization measures; the coordination between the devices is not efficient enough, and the resource use of the devices cannot be accurately estimated and optimized; and the problem that the accuracy and pertinence are lacking and measures cannot be taken in time when equipment fails.
Description
Technical Field
The invention relates to the field of data analysis and equipment management, in particular to a large-scale industrial equipment data analysis and intelligent management method.
Background
In the 4.0 era of industry, industrial equipment is rapidly evolving towards intellectualization, automation and digitization. A large number of sensors are integrated into various industrial devices, collecting operational data of the devices in real time. The data includes various parameters such as the operating state, operating efficiency, fault information, energy consumption, etc. of the device. With the rapid development of industrial internet and internet of things, the number and variety of industrial devices are also rapidly increasing, resulting in explosive growth of data volume. This presents a great opportunity for industrial production, but also presents challenges.
How to efficiently process and analyze such huge amounts of industrial equipment data to obtain valuable information and knowledge has become an urgent issue. The data may include critical information about the operation of the device, such as impending failures, potential efficiency improvement points, etc. The accurate and timely analysis of the data has key significance in ensuring the continuity of industrial production, improving the production efficiency and reducing the maintenance cost. Industrial equipment is typically not operated in isolation, and there are complex interactions and dependencies between them. How to ensure that these devices can work cooperatively to achieve optimal production results is also a problem to be solved in industrial production. With rapid updates of industrial equipment and changes in production environments, a more intelligent, adaptive method of equipment management is needed.
Chinese patent application number: CN202110364851.4, publication date: 2021.07.09 it discloses a terminal equipment for industrial equipment information management based on big data, including cyclic annular rack, a plurality of supporter have evenly been seted up to the outside of cyclic annular rack, the inside movable mounting of supporter has the server, the middle part groove has been seted up at the middle part of cyclic annular rack makes cyclic annular rack link up from top to bottom. This industrial equipment information management based on big data is with terminal equipment, through indulging the heat dissipation pipe and transversely winding pipe constitution echelonment water-cooling system, after not receiving rivers to strike one side temperature and rise, with the temperature transfer by the heat transfer strip to the fixed disk below for gas expansion between two lower rotating plates, promote the rotating plate and rotate then, make the rotating plate stir the toothed disc and rotate down, thereby will drive the gear and rotate when the toothed disc rotates, and form the negative pressure in mouth of pipe department through interior impeller, accelerate the water velocity in the radiating pipe, improve radiating efficiency then.
However, the above technology has at least the following technical problems: in the prior art, only single equipment parameters are concerned, and the one-sided analysis mode may lead to blindness and inefficiency of optimization measures; the synergy between the devices is not efficient enough and even conflicts and faults can occur; the resource usage of the device cannot be accurately estimated and optimized, resulting in waste of resources and inefficient operation of the device; the lack of accuracy and pertinence results in mismatching of the equipment and inefficiency of management, and when the equipment fails, the manager cannot take measures in time, so that the downtime and maintenance cost of the equipment are increased.
Disclosure of Invention
The embodiment of the application solves the problems in the prior art by providing a large-scale industrial equipment data analysis and intelligent management method: in the prior art, only single equipment parameters are concerned, and the one-sided analysis mode may lead to blindness and inefficiency of optimization measures; the synergy between the devices is not efficient enough and even conflicts and faults can occur; the resource usage of the device cannot be accurately estimated and optimized, resulting in waste of resources and inefficient operation of the device; the lack of accuracy and pertinence results in mismatching of the equipment and inefficiency of management, and when the equipment fails, the manager cannot take measures in time, so that the downtime and maintenance cost of the equipment are increased. Finally, the high-efficiency operation of the equipment can be ensured, and obvious economic and social benefits are brought to industrial production.
The application provides a large-scale industrial equipment data analysis and intelligent management method, which specifically comprises the following technical scheme:
the large-scale industrial equipment data analysis and intelligent management method comprises the following steps:
s1, establishing an industrial equipment information model, performing deep analysis on industrial equipment data by adopting a big data analysis technology, determining the failure rate and the resource utilization rate of equipment according to a data analysis result, and evaluating the comprehensive operation efficiency of the equipment;
s2, constructing a collaborative operation model, acquiring a weight vector of the equipment, acquiring a collaborative matrix by combining an interactive collaborative criterion, performing global evaluation on the large-scale industrial Internet equipment based on the comprehensive operation efficiency of the equipment, generating a collaborative operation instruction, and performing intelligent management on the large-scale industrial Internet equipment.
Preferably, the S1 specifically includes:
normalizing the original equipment data; the data is converted from the original high-dimensional space to the low-dimensional space by principal component analysis while key information of the data is maintained.
Preferably, the S1 further includes:
simulating the characteristics of a real world system and constructing a small world network model; in the small world network model, each industrial device acts as a node, and the connections between nodes are both highly clustered and have short path lengths.
Preferably, the S1 further includes:
a nonlinear differential equation is used to describe the dynamic behavior of the device.
Preferably, the S1 further includes:
predicting a failure of the device based on an entropy-based method, wherein entropy is used to describe a distribution of device states; a high entropy value means that the state distribution of the device is more diffuse; further, the relationship between the resource utilization of the device and the entropy is described using entropy.
Preferably, the S2 specifically includes:
and acquiring a weight vector of the equipment through a large-scale equipment linkage method, acquiring a collaboration matrix by combining an interactive collaboration criterion, and performing global evaluation on the large-scale industrial Internet equipment according to the collaboration matrix.
Preferably, the S2 further includes:
and acquiring a cooperation factor influencing the cooperation operation of the equipment, calculating a membership function of the cooperation factor, and calculating a weight vector of the equipment according to the membership.
Preferably, the S2 further includes:
determining a collaboration matrix through an interaction collaboration criterion, performing global evaluation on large-scale industrial Internet equipment according to the collaboration matrix based on the comprehensive operation efficiency of the equipment, determining the collaboration operation efficiency of the equipment, and selecting the optimal collaboration operation efficiency to generate a collaboration operation instruction.
Preferably, the S2 further includes:
and selecting a cooperative operation mode with highest operation efficiency based on the cooperative operation efficiency, thereby obtaining a device identifier needing cooperative operation, and sending a cooperative operation instruction to a corresponding edge gateway according to the device identifier.
The beneficial effects are that:
the technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
through normalization, principal component analysis, small world network modeling and nonlinear differential equation modeling, the invention ensures that data collected from large-scale industrial equipment is fully and deeply analyzed; the accuracy of data processing is improved, unnecessary redundant information is effectively reduced, and the efficiency of data analysis is improved; the method has the advantages that key parameters such as the operation efficiency, the failure rate, the resource utilization rate and the like of the industrial equipment can be comprehensively evaluated, so that an industrial equipment manager can acquire the operation state of the equipment at the first time, and corresponding measures are taken to optimize the performance of the equipment;
by constructing the cooperative operation model, the large-scale industrial equipment can realize efficient cooperative operation, so that the cooperative efficiency among the equipment is improved, and the equipment can be ensured to stably and efficiently operate in a complex production environment; through the relation between the resource utilization rate and the entropy in the invention, the resource use of the equipment is optimized, thereby realizing higher production efficiency and reducing resource waste;
based on the comprehensive efficiency index and the cooperative operation model, the large-scale industrial Internet equipment can be globally evaluated, so that an intelligent cooperative operation instruction is generated, and the intelligent and efficient management of the equipment in a large-scale and complex industrial production environment is ensured; the state distribution of the equipment is described by utilizing an entropy-based method, the potential faults of the equipment are accurately predicted, and preventive measures are taken, so that the downtime and the maintenance cost are reduced.
The technical scheme of the method and the device can effectively solve the problem that in the prior art, only single equipment parameters are concerned, and the blind and low-efficiency of optimization measures can be caused by a one-sided analysis mode; the synergy between the devices is not efficient enough and even conflicts and faults can occur; the resource usage of the device cannot be accurately estimated and optimized, resulting in waste of resources and inefficient operation of the device; the lack of accuracy and pertinence results in mismatching of the equipment and inefficiency of management, and when the equipment fails, the manager cannot take measures in time, so that the downtime and maintenance cost of the equipment are increased. Can ensure the high-efficiency operation of equipment and bring remarkable economic and social benefits to industrial production.
Drawings
FIG. 1 is a flow chart of a method for data analysis and intelligent management of large-scale industrial equipment described in the present application.
Detailed Description
By providing a large-scale industrial equipment data analysis and intelligent management method, the embodiment of the application solves the problem that the prior art only pays attention to single equipment parameters, and the one-sided analysis mode can lead to blindness and inefficiency of optimization measures; the synergy between the devices is not efficient enough and even conflicts and faults can occur; the resource usage of the device cannot be accurately estimated and optimized, resulting in waste of resources and inefficient operation of the device; the lack of accuracy and pertinence results in mismatching of the equipment and inefficiency of management, and when the equipment fails, the manager cannot take measures in time, so that the downtime and maintenance cost of the equipment are increased.
The technical scheme in the embodiment of the application aims to solve the problems, and the overall thought is as follows:
through normalization, principal component analysis, small world network modeling and nonlinear differential equation modeling, the invention ensures that data collected from large-scale industrial equipment is fully and deeply analyzed; the accuracy of data processing is improved, unnecessary redundant information is effectively reduced, and the efficiency of data analysis is improved; the method has the advantages that key parameters such as the operation efficiency, the failure rate, the resource utilization rate and the like of the industrial equipment can be comprehensively evaluated, so that an industrial equipment manager can acquire the operation state of the equipment at the first time, and corresponding measures are taken to optimize the performance of the equipment; by constructing the cooperative operation model, the large-scale industrial equipment can realize efficient cooperative operation, so that the cooperative efficiency among the equipment is improved, and the equipment can be ensured to stably and efficiently operate in a complex production environment; through the relation between the resource utilization rate and the entropy in the invention, the resource use of the equipment is optimized, thereby realizing higher production efficiency and reducing resource waste; based on the comprehensive efficiency index and the cooperative operation model, the large-scale industrial Internet equipment can be globally evaluated, so that an intelligent cooperative operation instruction is generated, and the intelligent and efficient management of the equipment in a large-scale and complex industrial production environment is ensured; the state distribution of the equipment is described by utilizing an entropy-based method, the potential faults of the equipment are accurately predicted, and preventive measures are taken, so that the downtime and the maintenance cost are reduced.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1, the method for analyzing and intelligently managing data of large-scale industrial equipment described in the application comprises the following steps:
s1, establishing an industrial equipment information model, performing deep analysis on industrial equipment data by adopting a big data analysis technology, determining the failure rate and the resource utilization rate of equipment according to a data analysis result, and evaluating the comprehensive operation efficiency of the equipment;
with the continuous development of the industrial internet, the application of big data technology in industrial production is also becoming more and more important. The invention provides a large-scale industrial equipment data analysis and intelligent management method based on a computer, which aims to realize efficient and intelligent data analysis, optimization and management of various intelligent industrial equipment, thereby improving production efficiency and management quality.
And establishing an industrial equipment information model, wherein the industrial equipment information model consists of equipment elements, and the equipment elements comprise component elements, attribute elements and operation elements. The component elements are descriptive information of various parts of the equipment; the attribute element is description information of physical attributes of equipment, and comprises equipment identification, equipment model, equipment name and the like; the operation element is descriptive information of operation of the equipment and parameters in the operation process, including input, output, state change and the like.
In order to facilitate the management of large-scale industrial internet equipment, edge computing technology is used to acquire various sensor data related to equipment operation, such as temperature, humidity, illumination and the like, and each edge gateway is responsible for acquiring equipment data in a corresponding range. And after the cloud server constructs the industrial equipment information model frame, transmitting the industrial equipment information model frame to each industrial equipment. The industrial equipment information model framework is an equipment element framework, the industrial equipment is filled with information corresponding to the equipment element framework, and equipment data is combined with the model to realize the digital representation of the equipment.
The method comprises the steps of acquiring industrial equipment information model parameters of each industrial equipment, wherein the industrial equipment information model parameters comprise various sensor data which are positioned on the edge side and related to the operation of the industrial equipment, and the sensors comprise a temperature sensor, a humidity sensor, an illumination sensor and the like. And uniformly converting the acquired industrial equipment information model and sensor data into intermediate protocol data from heterogeneous network data, and performing edge calculation to acquire heterogeneous information. The edge calculation is to compare the industrial equipment based on the information in the industrial equipment information model, wherein the comparison content comprises the component parts, equipment state, equipment failure rate, interfaces, transmission protocols and the like of the equipment; the heterogeneous information includes structural differences and resource differences among the industrial devices.
Further, the collected industrial equipment data is subjected to deep analysis by adopting a big data analysis technology, and key parameters such as the operation efficiency, the failure rate, the resource utilization rate and the like of the equipment are determined according to the data analysis result.
In modern industrial production, in order to achieve a continuous, efficient production flow and to reduce downtime, an in-depth analysis of the plant data is an essential step. Deep data analysis may reveal operating efficiency of the device, possible failures, and optimal use of resources. The following is a detailed method for industrial equipment data analysis based on high dimensional data processing, complex network structures and nonlinear dynamics systems.
Raw equipment data often contains much noise and irrelevant information, and normalization is required to ensure that all data is on a uniform scale. Ensuring that no bias is generated in subsequent analysis due to the original dimensions of the data. The normalization formula is:
，
wherein,representing normalized data, ++>Representing original device data, ++>Mean value of data>The standard deviation of the data is represented.
However, even with normalization, the high-dimensional data may still contain a large amount of redundant information. Principal component analysis is introduced into the process in order to extract key features in the data and reduce the dimensionality of the data. The purpose of principal component analysis is to transform data from an original high-dimensional space to a new, lower-dimensional space while preserving as much as possible the critical information of the data. The data is converted into by principal component analysis:
，
wherein,representing the converted data +.>Is a matrix of the first k principal components, representing the k most significant directions in the data.
Interactions and dependencies between devices are another key part of the analysis. Simulating the characteristics of a large number of real world systems and constructing a small world network model. In small world networks, each industrial device acts as a node, and the connections between nodes are both highly clustered and have a short path length. Through mathematical modeling, the following steps are obtained:
，
，
wherein,and->Respectively representing the path length and the cluster coefficient of the network, < >>Representing the probability of a random connection in the network,,/>and->Is a characteristic parameter of the network.
In order to understand the behavior of the device more deeply and predict its future state, the dynamic behavior of the device is described by a nonlinear differential equation, specifically expressed as:
，
wherein,time derivative representing the i-th device state, is->Representing the status of the ith device, +.>Representing the internal dynamic behaviour of the device,/->Is a matrix element describing the interaction between the ith device and the jth device, +.>Is a nonlinear response function describing the device state +.>Status of the device>How the influence of (a) varies with the differences between them. />Is a modulation function which further adjusts the interaction between the devices,/>Is a nonlinear kinetic parameter for controlling the internal kinetic behavior of the device,/i>Is the intensity of the interaction between the devices, +.>Is an offset parameter for adjusting the baseline or center point of interaction between devices, +.>NonlinearKinetic parameters.
Predicting the failure of a device is another critical analysis step. The state distribution of the device is described using an entropy-based method:
，
wherein,entropy (entropy)>Is the device status +.>Probability density function of (a). An entropy-based method predicts a failure of a device, where entropy is used to describe a distribution of device states. A high entropy value means that the state distribution of the device is more diffuse and therefore more likely to fail. Further, the relationship between the resource utilization and entropy of the device is described using entropy:
，
wherein,representing the resource utilization of the device, +.>Is a regularization parameter, balances the relation between the resource utilization rate of the device and the entropy of the device, and is->Is a sensitivity parameter->Is a preset entropy threshold, < ->Is an efficiency parameter, +.>Is a time constant that describes how device status changes over time to affect resource utilization.
Integrating all of these information to form an integrated efficiency index to evaluate the integrated operating efficiency of the device:
，
wherein,is the overall operating efficiency of the device,/->Is the weight coefficient of the i-th device, < ->Is the maximum value of the device state, +.>And->Is a parameter describing the influence of the external environment. Thereby determining the overall operating efficiency of the device, possible failures and optimal use of resources.
S2, constructing a collaborative operation model, acquiring a weight vector of the equipment, acquiring a collaborative matrix by combining an interactive collaborative criterion, performing global evaluation on the large-scale industrial Internet equipment based on the comprehensive operation efficiency of the equipment, generating a collaborative operation instruction, and performing intelligent management on the large-scale industrial Internet equipment.
In order to ensure that large-scale industrial equipment realizes collaborative operation and improve equipment operation efficiency, comprehensive intelligent management of industrial Internet equipment is required. And constructing a cooperative operation model, obtaining a cooperative operation instruction of the industrial equipment through the cooperative operation model, and improving the industrial production efficiency through the interactive cooperation of the equipment.
Further, a large-scale equipment linkage method is used for acquiring weight vectors of equipment, a collaboration matrix is obtained by combining with an interactive collaboration criterion, and then global evaluation is carried out on large-scale industrial Internet equipment according to the collaboration matrix. The method comprises the following specific steps:
and acquiring a historical equipment cooperative operation record and a corresponding industrial equipment information model, and manually setting or acquiring a cooperative factor influencing the cooperative operation of equipment from the historical cooperative operation record. Representing the collaboration factor as，/>Indicating the kind of collaboration factor. Let r be the value of a certain collaboration factor at any moment, calculate its membership function:
，
wherein,representing membership functions, +.>Represents the standard deviation of the collaboration factor>Representing the mean of the collaboration factors. Computing a weight vector for the device:
，
wherein,weight vector representing device, +.>Indicating the number of devices currently operating in cooperation with the current device,/-for the current period>Indicating the efficiency of operation of the current device compared to other devices.
Determining a collaboration matrix of the real-time state of the equipment in a certain period through an interaction collaboration criterion, wherein a specific calculation formula is as follows:
，
wherein,representation->And->Collaboration matrix between->And->For two devices that cooperate interactively, +.>Represents an attenuation factor->And->Respectively represent device->And->Weight vector of>Representation->And->The larger of (2), ->The preset threshold value can be obtained from expert experience.
Based on the comprehensive operation efficiency of the equipment, global evaluation is carried out on the large-scale industrial Internet equipment according to the collaboration matrix, the collaboration operation efficiency of the equipment is determined, and the best collaboration operation efficiency is selected to generate a collaboration operation instruction. The collaborative operation efficiency is as follows:
，
wherein,representing the operating efficiency of the current collaborative operating mode, +.>Indicating the overall operating efficiency of the device,/->And->Representing the minimum and maximum threshold values of the collaborative operation, respectively,/->And->The thresholds for the local and global evaluations are represented, respectively. And selecting a cooperative operation mode with highest operation efficiency based on the cooperative operation efficiency, thereby obtaining equipment identifiers needing cooperative operation, and sending a cooperative operation instruction to a corresponding edge gateway according to the equipment identifiers to realize intelligent management of large-scale industrial equipment.
In summary, the method for analyzing and intelligently managing the data of the large-scale industrial equipment is completed.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages:
through normalization, principal component analysis, small world network modeling and nonlinear differential equation modeling, the invention ensures that data collected from large-scale industrial equipment is fully and deeply analyzed; the accuracy of data processing is improved, unnecessary redundant information is effectively reduced, and the efficiency of data analysis is improved; the method has the advantages that key parameters such as the operation efficiency, the failure rate, the resource utilization rate and the like of the industrial equipment can be comprehensively evaluated, so that an industrial equipment manager can acquire the operation state of the equipment at the first time, and corresponding measures are taken to optimize the performance of the equipment;
by constructing the cooperative operation model, the large-scale industrial equipment can realize efficient cooperative operation, so that the cooperative efficiency among the equipment is improved, and the equipment can be ensured to stably and efficiently operate in a complex production environment; through the relation between the resource utilization rate and the entropy in the invention, the resource use of the equipment is optimized, thereby realizing higher production efficiency and reducing resource waste;
based on the comprehensive efficiency index and the cooperative operation model, the large-scale industrial Internet equipment can be globally evaluated, so that an intelligent cooperative operation instruction is generated, and the intelligent and efficient management of the equipment in a large-scale and complex industrial production environment is ensured; the state distribution of the equipment is described by utilizing an entropy-based method, the potential faults of the equipment are accurately predicted, and preventive measures are taken, so that the downtime and the maintenance cost are reduced.
Effect investigation:
the technical scheme of the method and the device can effectively solve the problem that in the prior art, only single equipment parameters are concerned, and the blind and low-efficiency of optimization measures can be caused by a one-sided analysis mode; the synergy between the devices is not efficient enough and even conflicts and faults can occur; the resource usage of the device cannot be accurately estimated and optimized, resulting in waste of resources and inefficient operation of the device; the lack of accuracy and pertinence results in mismatching of the equipment and inefficiency of management, and when the equipment fails, the manager cannot take measures in time, so that the downtime and maintenance cost of the equipment are increased. Moreover, the system or the method has a series of effect researches, and through verification, the high-efficiency operation of equipment can be ensured, and obvious economic and social benefits are brought to industrial production.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. The large-scale industrial equipment data analysis and intelligent management method is characterized by comprising the following steps of:
s1, an industrial equipment information model is established, sensor data related to equipment operation is acquired by using an edge computing technology, and each edge gateway is responsible for acquiring equipment data in a corresponding range; uniformly converting the acquired industrial equipment information model and sensor data into intermediate protocol data from heterogeneous network data, and performing edge calculation to acquire heterogeneous information; carrying out deep analysis on industrial equipment data by adopting a big data analysis technology, determining the operation efficiency, failure rate and resource utilization rate of the equipment according to the data analysis result, and evaluating the comprehensive operation efficiency of the equipment; simulating the characteristics of a real world system and constructing a small world network model; in the small-world network model, each industrial device serves as a node, and the connection between the nodes has high clustering property and short path length; through mathematical modeling, the following steps are obtained:
，
，
wherein,and->Representing the path length and the clustering coefficient of the small world network, respectively,/->Representing the probability of random connections in the small world network, +.>,/>And->Is a characteristic parameter of the small world network;
and a nonlinear differential equation is adopted to describe the dynamic behavior of the equipment, and the specific formula is as follows:
，
wherein,time derivative representing the i-th device state, is->Representing the status of the ith device, +.>Representing the internal dynamic behaviour of the device,/->Is a matrix element describing the interaction between the ith device and the jth device, +.>A nonlinear response function; />Is a modulation function; />Nonlinear kinetic parameters; />Is the strength of the interaction between the devices;/>is an offset parameter; />Nonlinear kinetic parameters;
s2, constructing a collaborative operation model, acquiring a weight vector of equipment through a large-scale equipment linkage method, and acquiring a collaboration matrix by combining an interactive collaboration criterion, wherein the specific calculation formula is as follows:
，
wherein,representation->And->Collaboration matrix between->And->For two devices that cooperate interactively, +.>Represents an attenuation factor->And->Respectively represent device->And->Weight vector of>Representation->And->Maximum value of>A preset threshold value;represents the standard deviation of the collaboration factor;
based on the comprehensive operation efficiency of the equipment, performing global evaluation on the large-scale industrial Internet equipment according to the collaboration matrix, determining the collaboration operation efficiency of the equipment, selecting the optimal collaboration operation efficiency to generate a collaboration operation instruction, and performing intelligent management on the large-scale industrial Internet equipment; the collaborative operation efficiency is as follows:
，
wherein,representing the operating efficiency of the current collaborative operating mode, +.>Indicates the kind of collaboration factor, ++>Indicating the overall operating efficiency of the device,/->And->Representing the minimum and maximum threshold values of the collaborative operation, respectively,/->And->The thresholds for the local and global evaluations are represented, respectively.
2. The method for analyzing and intelligently managing data of large-scale industrial equipment according to claim 1, wherein S1 specifically comprises:
normalizing the original equipment data; the data is converted from the original high-dimensional space to the low-dimensional space by principal component analysis while key information of the data is maintained.
3. The method for analyzing and intelligently managing data of large-scale industrial equipment according to claim 1, wherein S1 further comprises:
predicting a failure of the device based on an entropy-based method, wherein entropy is used to describe a distribution of device states; a high entropy value means that the state distribution of the device is more diffuse; further, the relationship between the resource utilization of the device and the entropy is described using entropy.
4. The method for analyzing and intelligently managing data of large-scale industrial equipment according to claim 1, wherein S2 specifically comprises:
and acquiring a cooperation factor influencing the cooperation operation of the equipment, calculating a membership function of the cooperation factor, and calculating a weight vector of the equipment according to the membership.
5. The method for analyzing and intelligently managing data of large-scale industrial equipment according to claim 1, wherein S2 further comprises:
and selecting a cooperative operation mode with highest operation efficiency based on the cooperative operation efficiency of the equipment, thereby obtaining equipment identification needing cooperative operation, and sending a cooperative operation instruction to a corresponding edge gateway according to the equipment identification.
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