CN114019946A - Monitoring data processing method and device of industrial control terminal - Google Patents
Monitoring data processing method and device of industrial control terminal Download PDFInfo
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- CN114019946A CN114019946A CN202111331235.5A CN202111331235A CN114019946A CN 114019946 A CN114019946 A CN 114019946A CN 202111331235 A CN202111331235 A CN 202111331235A CN 114019946 A CN114019946 A CN 114019946A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a method and a device for processing monitoring data of an industrial control terminal, relates to the technical field of industrial control, and aims to solve the problem of poor processing efficiency of the monitoring data of the conventional industrial control terminal. The method comprises the following steps: acquiring equipment information, control function information and process flow information matched with the industrial control terminals of at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information; each terminal node in the terminal control network is issued to an industrial control terminal, monitoring data generated by the industrial control terminal are collected, and the monitoring data collected by the industrial control terminal are subjected to prediction processing based on a prediction model to obtain at least one monitoring prediction result; determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network; and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, sending monitoring alarm information.
Description
Technical Field
The invention relates to the technical field of industrial control, in particular to a monitoring data processing method and device of an industrial control terminal.
Background
The industrial control system is a business process control system which is composed of various automatic control components and process control components for collecting and monitoring real-time data and ensures automatic operation, process control and monitoring of industrial infrastructure, wherein the industrial automatic control terminal is a terminal for realizing automatic operation based on the industrial control system, and is called an industrial control terminal for short. With the rapid development of network environment, the network security of the industrial control terminal is gradually exposed in the large environment of the internet, and the industrial control terminal needs to be monitored in real time according to special requirements in the industrial environment, such as material confidentiality, parameter security and the like, so as to ensure that the industrial control terminal is industrially produced in a safe and private environment.
At present, currently, monitoring the industrial control terminal usually includes acquiring operation data of each industrial control terminal, performing artificial intelligence prediction, or comparing according to specific indexes, so as to monitor various parameters in industrial production, but performing artificial intelligence prediction on operation data too much depends on an algorithm function, there may be situations where algorithm robustness is too high and precision is poor, and monitoring based on characteristic indexes causes that equipment of industrial production runs very rigidly, thereby affecting operation effectiveness of industrial production, and therefore, a monitoring data processing method of the industrial control terminal is urgently needed to solve the problems.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for processing monitoring data of an industrial control terminal, and mainly aims to solve the problem of poor efficiency in processing monitoring data of the existing industrial control terminal.
According to one aspect of the invention, a monitoring data processing method of an industrial control terminal is provided, which comprises the following steps:
acquiring equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information, wherein the terminal control network comprises at least three layer network structures, each layer network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model;
issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network;
and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, sending monitoring alarm information to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to perform monitoring abnormal processing on the industrial control terminals.
According to another aspect of the present invention, there is provided a monitoring data processing apparatus for an industrial control terminal, including:
the system comprises an acquisition module, a prediction module and a processing module, wherein the acquisition module is used for acquiring equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information, the terminal control network comprises at least three layers of network structures, each layer of network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and the physical models are embedded with prediction models matched with the process flow information;
the processing module is used for issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively carrying out prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
the determining module is used for determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, and the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network;
and the sending module is used for sending monitoring alarm information to the industrial control terminal and the industrial control terminal which is associated with the industrial control terminal if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value so as to monitor the industrial control terminal for abnormal processing.
According to another aspect of the present invention, a storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to execute operations corresponding to the monitoring data processing method of the industrial control terminal.
According to still another aspect of the present invention, there is provided a terminal including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the monitoring data processing method of the industrial control terminal.
By the technical scheme, the technical scheme provided by the embodiment of the invention at least has the following advantages:
the invention provides a monitoring data processing method and a device of an industrial control terminal, compared with the prior art, the embodiment of the invention establishes a terminal control network by acquiring the equipment information, the control function information and the process flow information matched with the industrial control terminals of at least two industrial control terminals and based on the equipment information, the control function information and the process flow information, wherein the terminal control network comprises at least three layers of network structures, each layer of network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model; issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result; determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network; if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, monitoring alarm information is sent to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to monitor the industrial control terminals and perform abnormal processing, the terminals of the whole industrial control network are monitored in an integrity mode, the monitoring effectiveness of each industrial control terminal in the whole network is determined, the flexibility of each equipment in the operation monitoring process is greatly improved, the rigidity and the singularity of operation monitoring on single equipment are avoided, and therefore the monitoring accuracy and the monitoring efficiency of the industrial control terminals are improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a monitoring data processing method of an industrial control terminal according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a terminal control network structure according to an embodiment of the present invention;
fig. 3 is a block diagram illustrating a monitoring data processing apparatus of an industrial control terminal according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a monitoring data processing method of an industrial control terminal, which comprises the following steps of:
101. the method comprises the steps of obtaining equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information.
In the embodiment of the invention, the industrial control terminal includes, but is not limited to, a control system terminal in the chemical industry, the steel industry, the petroleum industry and other industries, the industrial control terminal is used as a terminal device of an online production device, after receiving a control signal from an industrial control host, the production device is started to perform production operation in an industrial process based on the online production device, and various monitoring data in the production device are collected to be monitored or fed back to the industrial control host, wherein the monitoring data include, but are not limited to, time, inlet and outlet temperatures, inlet and outlet pressures, feeding amount, discharging amount and the like collected by different production devices. The industrial control server serving as the current execution main body can respectively perform data communication with different industrial control terminals so as to acquire monitoring data generated by each industrial control terminal. Before that, in order to accurately perform full-automatic monitoring data processing, device information, control function information and matched process flow information of at least two industrial control terminals are firstly acquired to establish a terminal control network. Specifically, the device information is the device model, the device manufacturer, and the like of the industrial control terminal, the control function information is the specific function of the industrial control terminal for executing the control operation, including but not limited to the heating temperature extreme range, the pressure adjustment extreme range, the refrigeration range, the distillation parameter, the filtering parameter, and the like, and the process flow information is the current industrial production flow produced by the industrial control terminal, including but not limited to the flow steps of compressing the material after heating, and then performing distillation refrigeration and the like. And after the acquisition of the equipment information, the control function information and the process flow information is finished, establishing a terminal control network. The terminal control network comprises at least three layer network structures, each layer network structure comprises at least three physical models of the industrial control terminal corresponding to the equipment information, a prediction model matched with the process flow information is embedded in the physical models, namely, a network with at least three layer network structures is constructed, each layer network comprises three physical models and three equipment nodes, so that the three equipment information is respectively embedded into the corresponding equipment nodes, and a corresponding physical model in one equipment node is explained. The physical model in the embodiment of the invention is a simulation model which is matched based on control function information or equipment information and is constructed in computer software by industrial control equipment, including but not limited to MATLAB software, chemical process software Aspenplus or HYSYS and the like, and can be used as a connection port through an open source interface between the simulation model and each piece of simulation software in a current execution end, so that the purpose of constructing the physical model based on each piece of simulation model software is realized, and the physical model is exported. Meanwhile, in the process of carrying out process operation simulation on physical models obtained based on different control function information and equipment information, in order to realize the complete process of process operation through vivid simulation, prediction models are embedded into the physical models, so that the physical models can carry out prediction transportation on transmitted material parameters and the like under the driving of the prediction models, and all predicted data in the process operation are obtained.
In an embodiment of the present invention, for further limitation and description, the establishing a terminal control network based on the equipment information, the control function information, and the process flow information includes: constructing a three-level ring network, and acquiring a physical model corresponding to each industrial control terminal, wherein each layer in the three-level ring network is distributed with at least three equipment nodes, so that the equipment nodes are distributed with physical models matched with each industrial control terminal according to the equipment information; and calling a prediction model which is trained and matched with the equipment information, and embedding the prediction model into a physical model matched with the equipment information to complete the construction of the terminal control network.
In order to implement the construction of the terminal control network, as shown in the schematic diagram of the terminal control network shown in fig. 2, the constructed terminal control network includes three layers of ring networks, at least three device nodes are respectively allocated in each layer, that is, at least 9 device nodes are allocated in the three layers of ring networks, and a physical model matched with each industrial control terminal is allocated at each device node according to device information, wherein, if the number of the industrial control terminals is less than 9, an energy detection sensor, such as a liquid flow sensor and a gas flow sensor, is allocated at the redundant device node, and the corresponding physical model corresponds to a liquid flow meter physical model and a gas flow meter physical model, at this time, a linear function of energy parameter micro-loss, which can be configured in advance by a prediction model, is used as a process flow passing through the liquid flow sensor and the gas flow sensor.
In addition, the prediction models embedded in the physical models are obtained by training based on training samples corresponding to the process flow information and the equipment information.
In an embodiment of the present invention, for further definition and illustration, the method further comprises: acquiring training samples corresponding to the process flow information and the equipment information, and establishing a three-layer convolutional neural network; and model training is carried out on the three-layer convolutional neural network based on the training samples to obtain a prediction model matched with different industrial control terminals, wherein the convolutional layer weight of the prediction model is obtained by converting according to the positions of nodes of the different industrial control terminals in the process flow.
For the specific model training process of the prediction model, the established three-layer convolutional neural network can be trained by acquiring process flow information and training samples corresponding to equipment information, wherein the training samples are marked with different process flow information and monitoring sample values corresponding to different equipment information and serve as training samples. Wherein, the convolutional layer weight of the prediction model is obtained by converting the node positions of different industrial control terminals in the process flow, that is, the convolutional layer weight of the three-layer convolutional network is limited, that is, the convolutional layer weight is configured by converting the sequence of the node positions of the industrial control terminals in the process flow into corresponding weight, for example, if all the process flow nodes are 5, the node position corresponding to the process flow information of the industrial control terminal to be embedded is 3, the convolutional layer weight corresponding to one layer in the prediction model is configured as 3/5, and the rest are respectively configured as (1-3/5)/2, meanwhile, the training method of the convolutional neural network is the same as the prior art, the embodiment of the invention is not specifically limited, so that the prediction models embedded in the physical model corresponding to each industrial control terminal are matched with the respective process flow node positions, the prediction accuracy of the industrial control terminal in the process flow and the simulation accuracy of the physical model in the terminal control network are greatly improved.
It should be noted that, because the terminal control network is a three-layer ring network, the outermost layer is three industrial control terminals that start operating in the process flow, the innermost layer is the last industrial control terminal that serves as the output, the output between each layer based on the current ring layer serves as the input of the next ring layer, and simultaneously, the input based on the current ring layer serves as the input auxiliary parameter of the next ring layer, the error caused by the damage of the intermediate equipment is reduced when the prediction model performs the prediction. Meanwhile, when the prediction model is trained, the training samples also comprise input auxiliary parameter sample values so as to finish the training of the prediction model, wherein the prediction model in one physical model connected with the previous layer can be selected from each layer to introduce the input auxiliary parameters, and the prediction models of other physical models can still be trained according to the normal monitoring sample values, so that the process simulation accuracy of the three-layer annular network is realized.
102. And issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result.
In the embodiment of the invention, in order to combine the constructed physical model with the corresponding prediction model, the industrial control terminal can predict based on the prediction model, reduce the data processing pressure in the current industrial control server, and issue each terminal node to the corresponding industrial control terminal, so that each industrial control terminal can predict and process the monitoring data while acquiring the monitoring data. The issuing process is that each physical model and the prediction model are issued to the corresponding industrial control terminal according to the matched device information, meanwhile, if the auxiliary parameters need to be input in the prediction model based on the terminal control network, the issued terminal nodes include source information for acquiring the input auxiliary parameters, that is, the output of other industrial control terminals is taken as the input auxiliary parameters, for example, if the industrial control terminal 4 belongs to a terminal at the second flow node position of the second layer in the three-layer ring network, the loaded prediction model needs the output parameter of the industrial control terminal 2 at the last flow node position of the first layer as the input auxiliary parameter of the prediction model in the physical model of the engineering terminal 4, so that the monitoring data is predicted based on the prediction model, and the monitoring prediction result is obtained.
103. And determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network.
In the embodiment of the invention, in order to ensure the security of the whole terminal control network, the industrial control server of the current execution main body acquires the monitoring prediction result from each industrial control terminal, and determines the monitoring state of each industrial control terminal relative to the whole terminal control network according to the network level and the network attribute, namely the monitoring state is used for limiting the security attribute of the industrial control terminal in the terminal control network.
In an embodiment of the present invention, for further limitation and description, the determining, according to the network level and the network attribute of the terminal control network, a monitoring state matched with at least one monitoring prediction result includes: analyzing the network level and the network attribute controlled by the terminal; and searching for the monitoring states corresponding to all monitoring prediction results matched with different network levels and network attributes in the terminal control network according to the corresponding relation of the preset monitoring states.
Specifically, the determination of the monitoring state specifically includes analyzing a network level and a network attribute of the terminal control, where the network level is used to represent a node position of each physical model in the terminal control network, and the network attribute is used to represent a functional role of each physical model in the terminal control network on the process flow information, for example, the larger the network attribute is, the stronger the physical model performs in the process flow is, the network attribute is a value between 0 and 1, the network attribute of a general physical model with a heating function is the largest, and a corresponding specific value may be preset by a technician. In addition, after the network levels and the network attributes corresponding to the industrial control terminals are determined, monitoring states corresponding to all monitoring prediction results matched with different network levels and network attributes in the terminal control network are searched according to the preset monitoring state corresponding relation, and the monitoring states corresponding to different monitoring prediction results of different network levels and different network attributes are recorded in the preset monitoring state corresponding relation, so that the monitoring states corresponding to the industrial control terminals, including abnormal states and normal states, can be accurately searched.
104. And if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, sending monitoring alarm information to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals.
In the embodiment of the invention, in order to ensure the safety of the whole terminal control network, based on the fact that the number of each industrial control terminal in an abnormal state is greater than a preset monitoring threshold value, the whole terminal control network is determined to be in the abnormal state, and monitoring alarm information is sent to the industrial control terminal and the industrial control terminals which are in association with the industrial control terminal, so that monitoring abnormity processing is carried out on the industrial control terminals.
In an embodiment of the present invention, for further limitation and description, sending monitoring alarm information to the industrial control terminal and the industrial control terminal having an association relationship with the industrial control terminal includes: searching matched adjacent industrial control terminals belonging to the same network level, and determining the industrial control terminal to be alarmed and the industrial control terminal with the incidence relation; and sending monitoring alarm information to the industrial control terminal and the industrial control terminal with the incidence relation.
In order to ensure the security of a terminal control network of the whole network, when monitoring alarm information is sent to industrial control terminals with an incidence relation, adjacent industrial control terminals which belong to the same network level and are matched with process flow information are specifically searched, the industrial control terminal to be alarmed and the industrial control terminals with the incidence relation are determined, namely, in the same ring layer, if one industrial control terminal has an abnormal monitoring state, the industrial control terminal is used as an object for sending the monitoring alarm information based on the matched adjacent industrial control terminals. The industrial control terminal to be alarmed and the industrial control terminal with the association relation are determined to be the industrial control terminal with the abnormal state, the industrial control terminal with the association relation is the adjacent industrial control terminal, so that the current execution main body sends monitoring alarm information to the industrial control terminal and the industrial control terminal with the association relation, and the specific form of the monitoring alarm information is not limited in the embodiment of the invention.
In addition, the embodiment of the invention also comprises: and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is less than or equal to a preset monitoring threshold value, recording the monitoring state of each industrial control terminal according to the network form in the terminal control network so as to extract and process according to a preset time interval. The network form is the node position where each industrial control terminal in the three-layer ring network is located so as to store the corresponding monitoring state, and the current execution main body can extract the historical data according to a preset time interval in order to analyze the historical data so as to manually perform secondary monitoring processing.
The invention provides a monitoring data processing method of an industrial control terminal, compared with the prior art, the embodiment of the invention establishes a terminal control network based on equipment information, control function information and process flow information matched with the industrial control terminal by obtaining the equipment information, the control function information and the process flow information of at least two industrial control terminals, wherein the terminal control network comprises at least three layers of network structures, each layer of network structure comprises at least three physical models of the industrial control terminal corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model; issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result; determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network; if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, monitoring alarm information is sent to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to monitor the industrial control terminals and perform abnormal processing, the terminals of the whole industrial control network are monitored in an integrity mode, the monitoring effectiveness of each industrial control terminal in the whole network is determined, the flexibility of each equipment in the operation monitoring process is greatly improved, the rigidity and the singularity of operation monitoring on single equipment are avoided, and therefore the monitoring accuracy and the monitoring efficiency of the industrial control terminals are improved.
Further, as an implementation of the method shown in fig. 1, an embodiment of the present invention provides a monitoring data processing apparatus for an industrial control terminal, and as shown in fig. 3, the apparatus includes:
an obtaining module 21, configured to obtain device information, control function information, and process flow information matched with at least two industrial control terminals, and establish a terminal control network based on the device information, the control function information, and the process flow information, where the terminal control network includes at least three layer network structures, each layer network structure includes at least three physical models of the industrial control terminals corresponding to the device information, and a prediction model matched with the process flow information is embedded in the physical model;
the processing module 22 is configured to issue each terminal node in the terminal control network to the industrial control terminal, collect monitoring data generated by the industrial control terminal, and perform prediction processing on the monitoring data collected by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
the determining module 23 is configured to determine a monitoring state matched with at least one monitoring prediction result according to a network hierarchy and a network attribute of the terminal control network, where the monitoring state is used to limit a security attribute of the industrial control terminal in the terminal control network;
and the sending module 24 is configured to send monitoring alarm information to the industrial control terminal and the industrial control terminal having an association relationship with the industrial control terminal if the number of the monitoring states of the industrial control terminals in the terminal control network that are abnormal states is greater than a preset monitoring threshold value, so as to perform monitoring exception handling on the industrial control terminal.
The invention provides a monitoring data processing device of an industrial control terminal, compared with the prior art, the embodiment of the invention establishes a terminal control network based on the equipment information, the control function information and the process flow information by acquiring the equipment information, the control function information and the process flow information matched with the industrial control terminal, wherein the terminal control network comprises at least three layers of network structures, each layer of network structure comprises at least three physical models of the industrial control terminal corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model; issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result; determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network; if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, monitoring alarm information is sent to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to monitor the industrial control terminals and perform abnormal processing, the terminals of the whole industrial control network are monitored in an integrity mode, the monitoring effectiveness of each industrial control terminal in the whole network is determined, the flexibility of each equipment in the operation monitoring process is greatly improved, the rigidity and the singularity of operation monitoring on single equipment are avoided, and therefore the monitoring accuracy and the monitoring efficiency of the industrial control terminals are improved.
According to an embodiment of the present invention, a storage medium is provided, where the storage medium stores at least one executable instruction, and the computer executable instruction may execute the monitoring data processing method of the industrial control terminal in any method embodiment described above.
Fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the terminal.
As shown in fig. 4, the terminal may include: a processor (processor)302, a communication Interface 304, a memory 306, and a communication bus 308.
Wherein: the processor 302, communication interface 304, and memory 306 communicate with each other via a communication bus 308.
A communication interface 304 for communicating with network elements of other devices, such as clients or other servers.
The processor 302 is configured to execute the program 310, and may specifically execute relevant steps in the monitoring data processing method embodiment of the industrial control terminal.
In particular, program 310 may include program code comprising computer operating instructions.
The processor 302 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The terminal comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 306 for storing a program 310. Memory 306 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 310 may specifically be configured to cause the processor 302 to perform the following operations:
acquiring equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information, wherein the terminal control network comprises at least three layer network structures, each layer network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model;
issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network;
and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, sending monitoring alarm information to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to perform monitoring abnormal processing on the industrial control terminals.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A monitoring data processing method of an industrial control terminal is characterized by comprising the following steps:
acquiring equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information, wherein the terminal control network comprises at least three layer network structures, each layer network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and a prediction model matched with the process flow information is embedded in each physical model;
issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively performing prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, wherein the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network;
and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value, sending monitoring alarm information to the industrial control terminals and the industrial control terminals which are associated with the industrial control terminals so as to perform monitoring abnormal processing on the industrial control terminals.
2. The method of claim 1, wherein the establishing a terminal control network based on the equipment information, the control function information, and the process flow information comprises:
constructing a three-level ring network, and acquiring a physical model corresponding to each industrial control terminal, wherein at least three equipment nodes are distributed in each layer of the three-level ring network, so that the physical model matched with each industrial control terminal is distributed at the equipment nodes according to the equipment information;
and calling a prediction model which is trained by the model and matched with the equipment information, embedding the prediction model into a physical model matched with the equipment information, and completing construction of a terminal control network, wherein the prediction model is obtained by training a training sample corresponding to the process flow information and the equipment information.
3. The method of claim 2, further comprising:
acquiring training samples corresponding to the process flow information and the equipment information, and establishing a three-layer convolutional neural network;
and model training is carried out on the three-layer convolutional neural network based on the training samples to obtain a prediction model matched with different industrial control terminals, wherein the convolutional layer weight of the prediction model is obtained by converting according to the positions of nodes of the different industrial control terminals in the process flow.
4. The method of claim 1, wherein the determining the monitoring state matching the at least one monitoring prediction according to the network level and the network attribute of the terminal control network comprises:
analyzing the network level and the network attribute of the terminal control, wherein the network level is used for representing the position information of each physical model in the terminal control network, and the network attribute is used for representing the functional action size of each physical model in the terminal control network on the process flow information;
and searching for monitoring states corresponding to all monitoring prediction results matched with different network levels and network attributes in the terminal control network according to a preset monitoring state corresponding relation, wherein the monitoring states corresponding to different monitoring prediction results of different network levels and different network attributes are recorded in the preset monitoring state corresponding relation.
5. The method according to claim 4, wherein the sending of the monitoring alarm information to the industrial control terminal and the industrial control terminal having an association relationship with the industrial control terminal comprises:
searching matched adjacent industrial control terminals belonging to the same network level, and determining the industrial control terminal to be alarmed and the industrial control terminal with the incidence relation;
monitoring alarm information is sent to the industrial control terminal and the industrial control terminal with the incidence relation;
the method further comprises the following steps:
and if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is less than or equal to a preset monitoring threshold value, recording the monitoring state of each industrial control terminal according to the network form in the terminal control network so as to extract and process according to a preset time interval.
6. The utility model provides a control data processing apparatus at industrial control terminal which characterized in that includes:
the system comprises an acquisition module, a prediction module and a processing module, wherein the acquisition module is used for acquiring equipment information, control function information and process flow information matched with at least two industrial control terminals, and establishing a terminal control network based on the equipment information, the control function information and the process flow information, the terminal control network comprises at least three layers of network structures, each layer of network structure comprises at least three physical models of the industrial control terminals corresponding to the equipment information, and the physical models are embedded with prediction models matched with the process flow information;
the processing module is used for issuing each terminal node in the terminal control network to the industrial control terminal, acquiring monitoring data generated by the industrial control terminal, and respectively carrying out prediction processing on the monitoring data acquired by the industrial control terminal based on the prediction model to obtain at least one monitoring prediction result;
the determining module is used for determining a monitoring state matched with at least one monitoring prediction result according to the network level and the network attribute of the terminal control network, and the monitoring state is used for limiting the safety attribute of the industrial control terminal in the terminal control network;
and the sending module is used for sending monitoring alarm information to the industrial control terminal and the industrial control terminal which is associated with the industrial control terminal if the number of the monitoring states of the industrial control terminals in the terminal control network which are abnormal states is larger than a preset monitoring threshold value so as to monitor the industrial control terminal for abnormal processing.
7. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to execute an operation corresponding to the monitoring data processing method of the industrial control terminal according to any one of claims 1-5.
8. A terminal, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the monitoring data processing method of the industrial control terminal as claimed in any one of claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115208307A (en) * | 2022-06-23 | 2022-10-18 | 国电投河南电力工程有限公司 | Data monitoring method and device based on distributed photovoltaic power station, medium and terminal |
Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101283539A (en) * | 2005-10-05 | 2008-10-08 | 拜尔斯安全公司 | Network security appliance |
| US20110060427A1 (en) * | 2009-09-09 | 2011-03-10 | Batke Brian A | Diagnostic Module For Distributed Industrial Network Including Industrial Control Devices |
| CN102438026A (en) * | 2012-01-12 | 2012-05-02 | 冶金自动化研究设计院 | Industrial control network security protection method and system |
| CN104460657A (en) * | 2014-11-14 | 2015-03-25 | 北京网御星云信息技术有限公司 | Method, device and system for achieving protection of mobile operation and maintenance of industrial control system |
| CN105208018A (en) * | 2015-09-09 | 2015-12-30 | 上海三零卫士信息安全有限公司 | Industrial control network information security monitoring method based on funnel type white list |
| CN107612733A (en) * | 2017-09-19 | 2018-01-19 | 杭州安恒信息技术有限公司 | A kind of network audit and monitoring method and its system based on industrial control system |
| CN108646722A (en) * | 2018-07-18 | 2018-10-12 | 杭州安恒信息技术股份有限公司 | A kind of industrial control system information security simulation model and terminal |
| US20180314240A1 (en) * | 2017-04-28 | 2018-11-01 | Honeywell International Inc. | Flexible hierarchical model for monitoring distributed industrial control systems |
| CN108769022A (en) * | 2018-05-29 | 2018-11-06 | 浙江大学 | A kind of industrial control system safety experiment platform for penetration testing |
| WO2019187155A1 (en) * | 2018-03-30 | 2019-10-03 | 日本電気株式会社 | Information processing device, information processing method, and program |
| CN110495138A (en) * | 2017-05-31 | 2019-11-22 | 西门子股份公司 | The monitoring method of industrial control system and its network security |
| CN110703161A (en) * | 2019-09-18 | 2020-01-17 | 国网江苏省电力有限公司淮安市洪泽区供电分公司 | Distribution lines fuse on-line monitoring system |
| CN111093007A (en) * | 2018-10-23 | 2020-05-01 | 辽宁石油化工大学 | Walking control method and device for biped robot, storage medium and terminal |
| CN111262722A (en) * | 2019-12-31 | 2020-06-09 | 中国广核电力股份有限公司 | Safety monitoring method for industrial control system network |
| CN112202736A (en) * | 2020-09-15 | 2021-01-08 | 浙江大学 | Industrial control system communication network abnormity classification method based on statistical learning and deep learning |
| CN112738063A (en) * | 2020-12-25 | 2021-04-30 | 山东钢铁集团日照有限公司 | Industrial control system network safety monitoring platform |
| CN112769796A (en) * | 2020-12-30 | 2021-05-07 | 华北电力大学 | Cloud network side collaborative defense method and system based on end side edge computing |
| CN112822291A (en) * | 2021-02-07 | 2021-05-18 | 国网福建省电力有限公司电力科学研究院 | Monitoring method and device for industrial control equipment |
| CN113064399A (en) * | 2021-03-11 | 2021-07-02 | 南京泠然信息科技有限公司 | Industrial monitoring software prediction maintenance system based on big data distributed programming framework |
| CN113079186A (en) * | 2021-06-07 | 2021-07-06 | 北京网藤科技有限公司 | Industrial network boundary protection method and system based on industrial control terminal feature recognition |
| CN213877036U (en) * | 2020-12-31 | 2021-08-03 | 南通隐印科技有限公司 | Remote monitoring device based on edge calculation |
| CN113612656A (en) * | 2021-07-26 | 2021-11-05 | 招商银行股份有限公司 | Network flow detection method and device, terminal equipment and storage medium |
| US20210382989A1 (en) * | 2018-11-20 | 2021-12-09 | Siemens Aktiengesellschaft | Multilevel consistency check for a cyber attack detection in an automation and control system |
-
2021
- 2021-11-11 CN CN202111331235.5A patent/CN114019946B/en active Active
Patent Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101283539A (en) * | 2005-10-05 | 2008-10-08 | 拜尔斯安全公司 | Network security appliance |
| US20110060427A1 (en) * | 2009-09-09 | 2011-03-10 | Batke Brian A | Diagnostic Module For Distributed Industrial Network Including Industrial Control Devices |
| CN102438026A (en) * | 2012-01-12 | 2012-05-02 | 冶金自动化研究设计院 | Industrial control network security protection method and system |
| CN104460657A (en) * | 2014-11-14 | 2015-03-25 | 北京网御星云信息技术有限公司 | Method, device and system for achieving protection of mobile operation and maintenance of industrial control system |
| CN105208018A (en) * | 2015-09-09 | 2015-12-30 | 上海三零卫士信息安全有限公司 | Industrial control network information security monitoring method based on funnel type white list |
| US20180314240A1 (en) * | 2017-04-28 | 2018-11-01 | Honeywell International Inc. | Flexible hierarchical model for monitoring distributed industrial control systems |
| US20200089204A1 (en) * | 2017-05-31 | 2020-03-19 | Siemens Aktiengesellschaft | Industrial control system and network security monitoring method therefor |
| CN110495138A (en) * | 2017-05-31 | 2019-11-22 | 西门子股份公司 | The monitoring method of industrial control system and its network security |
| CN107612733A (en) * | 2017-09-19 | 2018-01-19 | 杭州安恒信息技术有限公司 | A kind of network audit and monitoring method and its system based on industrial control system |
| WO2019187155A1 (en) * | 2018-03-30 | 2019-10-03 | 日本電気株式会社 | Information processing device, information processing method, and program |
| CN108769022A (en) * | 2018-05-29 | 2018-11-06 | 浙江大学 | A kind of industrial control system safety experiment platform for penetration testing |
| CN108646722A (en) * | 2018-07-18 | 2018-10-12 | 杭州安恒信息技术股份有限公司 | A kind of industrial control system information security simulation model and terminal |
| CN111093007A (en) * | 2018-10-23 | 2020-05-01 | 辽宁石油化工大学 | Walking control method and device for biped robot, storage medium and terminal |
| US20210382989A1 (en) * | 2018-11-20 | 2021-12-09 | Siemens Aktiengesellschaft | Multilevel consistency check for a cyber attack detection in an automation and control system |
| CN110703161A (en) * | 2019-09-18 | 2020-01-17 | 国网江苏省电力有限公司淮安市洪泽区供电分公司 | Distribution lines fuse on-line monitoring system |
| CN111262722A (en) * | 2019-12-31 | 2020-06-09 | 中国广核电力股份有限公司 | Safety monitoring method for industrial control system network |
| CN112202736A (en) * | 2020-09-15 | 2021-01-08 | 浙江大学 | Industrial control system communication network abnormity classification method based on statistical learning and deep learning |
| CN112738063A (en) * | 2020-12-25 | 2021-04-30 | 山东钢铁集团日照有限公司 | Industrial control system network safety monitoring platform |
| CN112769796A (en) * | 2020-12-30 | 2021-05-07 | 华北电力大学 | Cloud network side collaborative defense method and system based on end side edge computing |
| CN213877036U (en) * | 2020-12-31 | 2021-08-03 | 南通隐印科技有限公司 | Remote monitoring device based on edge calculation |
| CN112822291A (en) * | 2021-02-07 | 2021-05-18 | 国网福建省电力有限公司电力科学研究院 | Monitoring method and device for industrial control equipment |
| CN113064399A (en) * | 2021-03-11 | 2021-07-02 | 南京泠然信息科技有限公司 | Industrial monitoring software prediction maintenance system based on big data distributed programming framework |
| CN113079186A (en) * | 2021-06-07 | 2021-07-06 | 北京网藤科技有限公司 | Industrial network boundary protection method and system based on industrial control terminal feature recognition |
| CN113612656A (en) * | 2021-07-26 | 2021-11-05 | 招商银行股份有限公司 | Network flow detection method and device, terminal equipment and storage medium |
Non-Patent Citations (6)
| Title |
|---|
| LANG, X., LI, P., GUO, Y., CAO, J., & LU, S: "A multiple leaks’ localization method in a pipeline based on change in the sound velocity", 《IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT》, vol. 69, no. 7, pages 5010 - 5017, XP011791813, DOI: 10.1109/TIM.2019.2955774 * |
| SOMMER R,ZAMBON E,HARTEL P H: "Through the eye of the PLC:semantic security monitoring for industrial processes", 《COMPUTER SECURITY APPLICATIONS CONFERENCE.ACM》, pages 126 - 135 * |
| 孙易安;井柯;汪义舟;: "工业控制***安全网络防护研究", 《信息安全研究》, no. 02, pages 77 - 82 * |
| 田伟宏;李喜旺;司志坚;: "基于长短期记忆网络的工控网络异常流量检测", 《计算机***应用》, no. 09, pages 270 - 275 * |
| 赵贵成;程鹏;王文海;: "工控***现场控制网络异常检测技术浅析", 《信息技术与网络安全》, no. 03, pages 32 - 35 * |
| 郭颖等: "基于TSA多变量非线性RBF神经网络预测控制", 《控制工程》, vol. 26, no. 9, pages 1656 - 1659 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115208307A (en) * | 2022-06-23 | 2022-10-18 | 国电投河南电力工程有限公司 | Data monitoring method and device based on distributed photovoltaic power station, medium and terminal |
| CN115208307B (en) * | 2022-06-23 | 2024-05-28 | 国电投河南工程运维有限公司 | Data monitoring method and device based on distributed photovoltaic power station, medium and terminal |
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