CN116389229B - Self-healing ring network system based on RS485 bus - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G08B21/18—Status alarms
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- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/07—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems
- H04B10/075—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal
- H04B10/079—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
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Abstract
The invention discloses a self-healing ring network system based on an RS485 bus, which relates to the technical field of data information transmission and comprises a main equipment module, a slave equipment module, an equipment selection module, a data storage module, a temperature and humidity detection module, a bus transmission module, a self-healing ring network module and a diagnosis repair module.
Description
Technical Field
The invention relates to the technical field of data information transmission, in particular to a self-healing ring network system based on an RS485 bus.
Background
Today, with rapid development of network information technology, with great improvement of data information transmission rate and update iteration of data transmission technology, bus transmission technology is applied to more and more non-IT industries due to advantages of the bus transmission technology in transmission time, transmission space, transmission distance and transmission cost, bus transmission modes comprise serial transmission, parallel transmission, multiplexing transmission and data packet transmission, wherein serial transmission is widely used in various industries due to advantages of transmission line use saving in a communication process, especially in remote communication, an RS485 bus serial transmission protocol is a typical bus industry standard, but an existing ring network system based on an RS485 bus has the problems that failure data cannot be self-healed, normal data cannot be isolated from failure data, breakpoint failure occurs to a transmission link, division of the transmission link is not fine and the like.
Aiming at the defects of the prior art, such as the problems of fault repair, transmission blockage and the like of a ring network system based on an RS485 bus, the construction of a bus transmission system is not perfected, and therefore, a self-healing ring network system based on the RS485 bus is needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses a self-healing ring network system based on an RS485 bus, which improves the data diagnosis precision and diagnosis speed by adopting a data layering evaluation analysis encapsulation algorithm, increases the fusion effect of a characteristic space and improves the fault isolation rate by adopting a multi-characteristic fusion fault reconstruction isolation algorithm, reduces the possibility of generating single-point faults at the node connection part of a switch by adopting a multi-path full-duplex noise reduction system, does not miss effective information in a data stream when noise is filtered because of overlarge noise, and improves the precision degree of fault division of an optical fiber link by adopting a bionic fish swarm clustering algorithm fusion commonality difference comparison model on the basis of the existing bionic fish swarm algorithm.
The invention adopts the following technical scheme:
a self-healing ring network system based on an RS485 bus, the system comprising: the device comprises a main device module, a slave device module, a device selection module, a data storage module, a network interface module and a temperature and humidity detection module;
the main equipment module is used for controlling the whole self-healing ring network system, the main equipment module realizes the information receiving control of the slave equipment and completes the bus control function by sending an information transmission instruction to the slave equipment, the main equipment module comprises an optical fiber sending unit, a switch unit and a signal modulation unit, the optical fiber sending unit is used for converting a data electric signal into an optical signal and sending the optical signal, the switch unit is used for forwarding signals between network nodes of the main equipment module, the signal modulation unit is used for encrypting and encoding transmission data so as to improve the safety of the data transmission process, the output end of the optical fiber sending unit is connected with the input end of the switch unit, and the output end of the switch unit is connected with the input end of the signal modulation unit;
the slave device module is used for receiving the control instruction of the master device and the information data sent by the master device end and realizing the serial transmission of the data between the devices;
the device selection module is used for receiving a device selection instruction and selecting a slave device machine for receiving data, and comprises a priority judgment module, a hardware selection unit and a software selection unit;
the data storage module is used for storing data packet files in a link and comprises a storage receiving unit, a storage configuration unit and a cloud backup unit, wherein the storage receiving unit is used for receiving storage instructions, the storage configuration unit is used for configuring a file storage area, the output end of the storage receiving unit is connected with the input end of the storage configuration unit, and the output end of the storage configuration unit is connected with the input end of the cloud backup unit;
the temperature and humidity detection module is used for monitoring the internal temperature and humidity of the self-healing ring network system in real time, transmitting the system temperature and humidity data to the system main control unit in real time through the temperature and humidity sensor, and comprises an alarm module used for giving an alarm when the temperature and humidity exceeds a threshold set by a user;
the method is characterized in that: further comprises: the system comprises a bus transmission module, a self-healing ring network module and a diagnosis and repair module;
the bus transmission module is used for connecting internal computer equipment and external communication equipment and transmitting data information, the bus transmission module transmits data information, address information and operation instruction information of a main equipment module to a slave equipment module through an RS485 bus serial transmission protocol by differential transmission, the bus transmission module comprises a signal collection unit, a message serial transmission unit and a receiving terminal unit, the signal collection unit is used for collecting equipment node information, the message serial transmission unit is used for packaging the equipment node information into data blocks and carrying out serial output, and the receiving terminal unit is used for receiving data packets of an equipment terminal;
the output end of the signal collection unit is connected with the input end of the message serial transmission unit, and the output end of the message serial transmission unit is connected with the input end of the receiving terminal unit;
the self-healing ring network module is used for expanding a network interface and rapidly and autonomously recovering when network faults occur, the self-healing ring network module integrates failure fault data packet files of equipment terminals of different transmission nodes into an optical fiber transmission link for transmission and automatically recovers faults, the self-healing ring network module comprises a fault isolation unit, a data merging unit and an optical fiber link self-checking unit, the fault isolation unit is used for isolating fault data and normal data in a data transmission network, recording IP addresses of the fault data into an internal memory of the self-healing ring network module, the data merging unit is used for carrying out message data negotiation operation on the fault data packet files of different address links through a link aggregation control protocol and converging the fault data packet files into one optical fiber output transmission link, the optical fiber link self-checking unit is used for detecting the bidirectional transmission performance of the optical fiber link, the optical fiber link self-checking unit adopts an optical fiber detection sensor to acquire optical fiber transmission loop data, and transmits the optical fiber transmission loop data to an embedded microprocessor, and carries out optical fiber transmission fault self-checking through a bionic fish swarm clustering algorithm;
the output end of the fault isolation unit is connected with the input end of the data merging unit, and the output end of the data merging unit is connected with the input end of the optical fiber link self-checking unit;
the diagnosis repair module is used for automatically diagnosing fault data in the ring network and repairing and recording fault logs of the fault data in the whole ring network system, the diagnosis repair module comprises an automatic diagnosis unit, a fault classification unit and a fault repair unit, the automatic diagnosis unit is used for diagnosing fault reasons of the fault data, the fault classification unit is used for grouping the fault data according to transmission characteristics, the fault repair unit is used for modifying error code elements of the fault data and uploading repair records to the cloud storage end, the output end of the automatic diagnosis unit is connected with the input end of the fault classification unit, and the output end of the fault classification unit is connected with the input end of the fault repair unit;
the output end of the main equipment module is connected with the input ends of the bus transmission module, the equipment selection module and the self-healing ring network module, the output end of the equipment selection module is connected with the input end of the slave equipment module, and the output ends of the main equipment module and the slave equipment module are connected with the input ends of the diagnosis repair module, the data storage module and the temperature and humidity detection module.
As a further technical scheme of the invention, the automatic diagnosis unit adopts a data hierarchical evaluation analysis packaging algorithm ILEP to decompose the data block into a hierarchical matrixWherein m is the longitudinal depth of the message data matrix, n is the transverse width of the data matrix, and the characteristic value is extracted from the hierarchical matrix according to the characteristic decomposition and layering of the information entropy, and the characteristic value decomposition and layering extraction function is as follows:
（1）
in the case of the formula (1),for the hierarchical reference parameter, +.>Is the entropy average value>Prediction error for information entropy layering characteristics, +.>The feature value projection statistic is characterized in that the feature value decomposition hierarchical extraction function contains 5 sub-types, the sub-types represent feature value decomposition definition degree parameters, after the feature value decomposition hierarchical extraction processing of the data block is completed, feature value aggregation processing is carried out on the data group containing the feature value, and an aggregation data set is output, wherein the feature value aggregation processing function is as follows:
（2）
in the formula (2) of the present invention,for the eigenvalue aggregate processing function,/->For aggregating output density parameters, +.>Hierarchical statistical degrees of freedom for eigenvalues, +.>For the aggregate error correction parameter +.>Is the entropy average value>Prediction error for information entropy layering characteristics, +.>Representing error distribution confidence, ++>Data depth representing the output aggregate dataset, +.>The data representing the output data set is laterally extended wide.
As a further aspect of the inventionAccording to the technical scheme, the fault isolation unit adopts a multi-feature fusion fault reconstruction isolation algorithm muti-LFI to isolate fault data and normal data in a data transmission network, and a fault data block matrix is set asWherein->For a fault data block sub-matrix, the fault data block matrix is divided into 5 subsets comprising fault data block sub-matrices +.>Wherein->For the number of the fault weights, confidence self-adaptive fusion is carried out on each subset, and the self-adaptive fusion output formula is as follows:
（3）
in the formula (3) of the present invention,for adaptive fusion output function,/->For subset lateral nodes, ++>For subset longitudinal nodes, +.>Modifying parameters for the subset->For adaptive bias parameters +.>Threshold parameters for subset interactions, +.>And for the fault data block subset, performing feature global fusion processing on the self-adaptive fusion output value, wherein a feature global fusion formula is as follows:
（4）
in the formula (4) of the present invention,for feature global fusion function, ++>For minimum feature base parameter, ++>For global scan range, +.>Assigning a scale parameter for global->Is the minimum feature base upper limit,/-, for>Is the minimum feature base lower limit,/->For minimum feature base branch depth, +.>Fusing reliability parameters for features, +.>Dimension parameters are fused for the features.
As a further technical scheme of the invention, the fault repairing unit adopts the honeycomb big data autonomous cloud storage platform to realize the on-site cloud storage fault repairing and return correct code elements so as to reduce the data transmission bandwidth and improve the data transmission speed.
As a further technical scheme of the invention, the fault repairing unit adopts an artificial intelligent fault screening system to reduce the fault calling time and reduce the screening error of fault error code elements.
As a further technical scheme of the invention, a multichannel balanced check hybrid structure receiving device is adopted when the repair record is stored, and the data transmission leakage prevention is realized through a multipath full duplex noise reduction system.
As a further technical scheme of the invention, the bionic fish swarm clustering algorithm fuses the common difference comparison model to perform common difference comparison of the optimal solution set on the self-checking space of the optical fiber link, and improves the fine degree of fault division of the optical fiber link.
Has the positive beneficial effects that:
the invention discloses a self-healing ring network system based on an RS485 bus, which adopts a data layering evaluation analysis encapsulation algorithm to improve the data diagnosis precision and diagnosis speed, adopts a multi-feature fusion fault reconstruction isolation algorithm to increase the fusion effect of feature space and improve fault isolation rate, adopts a multi-path full duplex noise reduction system to reduce the possibility of generating single-point faults at the node connection position of a switch, does not miss effective information in data flow when noise is filtered because of overlarge noise, and adopts a bionic fish swarm clustering algorithm to fuse commonality difference comparison model to improve the fineness of optical fiber link fault division on the basis of the existing bionic fish swarm algorithm.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of a self-healing ring network system based on an RS485 bus;
fig. 2 is a schematic diagram of an ILEP (integrated circuit) of a self-healing ring network system data hierarchical evaluation analysis encapsulation algorithm based on an RS485 bus;
FIG. 3 is a muti-LFI architecture diagram of a multi-feature fusion fault reconstruction isolation algorithm of a self-healing ring network system based on an RS485 bus;
fig. 4 is a schematic diagram of a multipath full duplex noise reduction system of a self-healing ring network system based on an RS485 bus;
fig. 5 is a schematic diagram of a bionic fish swarm clustering algorithm of a self-healing ring network system based on an RS485 bus;
fig. 6 is a full duplex noise reduction circuit diagram of a self-healing ring network system based on an RS485 bus.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a self-healing ring network system based on an RS485 bus, the system includes: the device comprises a main device module, a slave device module, a device selection module, a data storage module, a network interface module and a temperature and humidity detection module;
the main equipment module is used for controlling the whole self-healing ring network system, the main equipment module realizes the information receiving control of the slave equipment and completes the bus control function by sending an information transmission instruction to the slave equipment, the main equipment module comprises an optical fiber sending unit, a switch unit and a signal modulation unit, the optical fiber sending unit is used for converting a data electric signal into an optical signal and sending the optical signal, the switch unit is used for forwarding signals between network nodes of the main equipment module, the signal modulation unit is used for encrypting and encoding transmission data so as to improve the safety of the data transmission process, the output end of the optical fiber sending unit is connected with the input end of the switch unit, and the output end of the switch unit is connected with the input end of the signal modulation unit;
the slave device module is used for receiving the control instruction of the master device and the information data sent by the master device end and realizing the serial transmission of the data between the devices;
the device selection module is used for receiving a device selection instruction and selecting a slave device machine for receiving data, and comprises a priority judgment module, a hardware selection unit and a software selection unit;
the data storage module is used for storing data packet files in a link and comprises a storage receiving unit, a storage configuration unit and a cloud backup unit, wherein the storage receiving unit is used for receiving storage instructions, the storage configuration unit is used for configuring a file storage area, the output end of the storage receiving unit is connected with the input end of the storage configuration unit, and the output end of the storage configuration unit is connected with the input end of the cloud backup unit;
the temperature and humidity detection module is used for monitoring the internal temperature and humidity of the self-healing ring network system in real time, transmitting the system temperature and humidity data to the system main control unit in real time through the temperature and humidity sensor, and comprises an alarm module used for giving an alarm when the temperature and humidity exceeds a threshold set by a user;
the method is characterized in that: further comprises: the system comprises a bus transmission module, a self-healing ring network module and a diagnosis and repair module;
the bus transmission module is used for connecting internal computer equipment and external communication equipment and transmitting data information, the bus transmission module transmits data information, address information and operation instruction information of a main equipment module to a slave equipment module through an RS485 bus serial transmission protocol by differential transmission, the bus transmission module comprises a signal collection unit, a message serial transmission unit and a receiving terminal unit, the signal collection unit is used for collecting equipment node information, the message serial transmission unit is used for packaging the equipment node information into data blocks and carrying out serial output, and the receiving terminal unit is used for receiving data packets of an equipment terminal;
the output end of the signal collection unit is connected with the input end of the message serial transmission unit, and the output end of the message serial transmission unit is connected with the input end of the receiving terminal unit;
the self-healing ring network module is used for expanding a network interface and rapidly and autonomously recovering when network faults occur, the self-healing ring network module integrates failure fault data packet files of equipment terminals of different transmission nodes into an optical fiber transmission link for transmission and automatically recovers faults, the self-healing ring network module comprises a fault isolation unit, a data merging unit and an optical fiber link self-checking unit, the fault isolation unit is used for isolating fault data and normal data in a data transmission network, recording IP addresses of the fault data into an internal memory of the self-healing ring network module, the data merging unit is used for carrying out message data negotiation operation on the fault data packet files of different address links through a link aggregation control protocol and converging the fault data packet files into one optical fiber output transmission link, the optical fiber link self-checking unit is used for detecting the bidirectional transmission performance of the optical fiber link, the optical fiber link self-checking unit adopts an optical fiber detection sensor to acquire optical fiber transmission loop data, and transmits the optical fiber transmission loop data to an embedded microprocessor, and carries out optical fiber transmission fault self-checking through a bionic fish swarm clustering algorithm;
the output end of the fault isolation unit is connected with the input end of the data merging unit, and the output end of the data merging unit is connected with the input end of the optical fiber link self-checking unit;
the diagnosis repair module is used for automatically diagnosing fault data in the ring network and repairing and recording fault logs of the fault data in the whole ring network system, the diagnosis repair module comprises an automatic diagnosis unit, a fault classification unit and a fault repair unit, the automatic diagnosis unit is used for diagnosing fault reasons of the fault data, the fault classification unit is used for grouping the fault data according to transmission characteristics, the fault repair unit is used for modifying error code elements of the fault data and uploading repair records to the cloud storage end, the output end of the automatic diagnosis unit is connected with the input end of the fault classification unit, and the output end of the fault classification unit is connected with the input end of the fault repair unit;
the output end of the main equipment module is connected with the input ends of the bus transmission module, the equipment selection module and the self-healing ring network module, the output end of the equipment selection module is connected with the input end of the slave equipment module, and the output ends of the main equipment module and the slave equipment module are connected with the input ends of the diagnosis repair module, the data storage module and the temperature and humidity detection module.
In the above embodiment, the automatic diagnostic unit decomposes the data block into a hierarchical matrix using a data hierarchical evaluation analysis encapsulation algorithm ILEPWherein m is the longitudinal depth of the message data matrix, n is the transverse width of the data matrix, and the characteristic value is extracted from the hierarchical matrix according to the characteristic decomposition and layering of the information entropy, and the characteristic value decomposition and layering extraction function is as follows:
（1）
in the case of the formula (1),for the hierarchical reference parameter, +.>Is the entropy average value>Prediction error for information entropy layering characteristics, +.>The feature value projection statistic is characterized in that the feature value decomposition hierarchical extraction function contains 5 sub-types, the sub-types represent feature value decomposition definition degree parameters, after the feature value decomposition hierarchical extraction processing of the data block is completed, feature value aggregation processing is carried out on the data group containing the feature value, and an aggregation data set is output, wherein the feature value aggregation processing function is as follows:
（2）
in the formula (2) of the present invention,for the eigenvalue aggregate processing function,/->For aggregating output density parameters, +.>Is of special interestThe degree of freedom of the hierarchical statistics of the sign values, < >>For the aggregate error correction parameter +.>Is the entropy average value>Prediction error for information entropy layering characteristics, +.>Representing error distribution confidence, ++>Data depth representing the output aggregate dataset, +.>The data representing the output data set is laterally extended wide.
In particular embodiments, when decomposing a data block into a hierarchical matrix, the matrixEach row represents a data block classification level, each column represents a data block classification item, matrix elements are distinguished by column-row independent subscripts in each cell, when the matrix elements are decomposed and layered according to information entropy characteristics, uncertainty of feature values is reduced by decomposing random variables in a level matrix and entropy information with uncertain measurement, the operation of carrying out aggregation treatment on the feature values is realized, direct access, filtration and searching by category of an original data set are realized, an ILEP model of a data layering evaluation analysis encapsulation algorithm is used, compared with an ILEP model without a traditional feature value encapsulation algorithm model, diagnosis precision and diagnosis speed can be improved, and application comparison effects of the two models are shown in a table 1;
TABLE 1 comparative statistics of accuracy and Rate for different diagnostic models
As shown in table 1, three diagnostic tests are set, each diagnostic test comprises a data layering evaluation analysis packaging algorithm and a characteristic value packaging algorithm comparison group, the accuracy percentage of each diagnosis and the time used for diagnosis are recorded, the comparison of the accuracy is a fault set manually, and as can be seen from the table, the automatic diagnosis unit can improve the data diagnosis accuracy and the diagnosis speed by adopting the data layering evaluation analysis packaging algorithm.
In the above embodiment, the fault isolation unit adopts a multi-feature fusion fault reconstruction isolation algorithm muti-LFI to isolate fault data and normal data in the data transmission network, and sets a fault data block matrix asWherein->For a fault data block sub-matrix, the fault data block matrix is divided into 5 subsets comprising fault data block sub-matrices +.>Wherein->For the number of the fault weights, confidence self-adaptive fusion is carried out on each subset, and the self-adaptive fusion output formula is as follows:
（3）
in the formula (3) of the present invention,for adaptive fusion output function,/->For subset lateral nodes, ++>For subset longitudinal nodes, +.>Modifying parameters for the subset->For adaptive bias parameters +.>Threshold parameters for subset interactions, +.>And for the fault data block subset, performing feature global fusion processing on the self-adaptive fusion output value, wherein a feature global fusion formula is as follows:
（4）
in the formula (4) of the present invention,for feature global fusion function, ++>For minimum feature base parameter, ++>For global scan range, +.>Assigning a scale parameter for global->Is the minimum feature base upper limit,/-, for>Is the minimum feature base lower limit,/->For minimum feature base branch depth, +.>Fusing reliability parameters for features, +.>Dimension parameters are fused for the features.
In a specific embodiment, firstly, constructing a multi-feature fusion big frame, constructing data blocks mixed with fault data and normal data into a subset of n rows and 5 columns, wherein n is the number of weights, namely the number of priorities of different data transmission nodes in each different data block matrix subset in a multi-feature fusion fault reconstruction isolation algorithm, the subset increases the fusion effect on a feature space in the self-adaptive fusion process, namely a feature space modal item is added in the traditional self-adaptive fusion process, and in the global fusion process, data packets isolated by class groups are obtained by carrying out feature weighted distribution on different group data, and the fault isolation result obtained by the multi-feature fusion fault reconstruction isolation algorithm is compared with the traditional fault isolation classification result as shown in table 2;
table 2 comparison statistics of different fault isolation algorithms isolation results
As shown in Table 2, by setting different groups and manually setting different fault number reference groups, fault classification simulation experiments are respectively carried out on hardware faults and software faults, and the obtained fault isolation results show that the multi-feature fusion fault reconstruction isolation algorithm used by the invention has higher fault isolation rate compared with the traditional fault isolation classification.
In the above embodiment, the fault repairing unit adopts the honeycomb big data autonomous cloud storage platform to realize the on-site cloud storage fault repairing and return correct code elements, so as to reduce the data transmission bandwidth and improve the data transmission speed.
In a specific embodiment, the honeycomb big data autonomous cloud storage platform is a network transmission ring based on a local area network, an innovative grabbing analysis engine is adopted after data cleaning and data integration are carried out on a big data bottom layer, and technologies such as data mining, machine learning, computer vision and the like of a center are combined to provide tools for products and services such as data grabbing analysis, data wind control and data mining, correct code elements and error code elements generated in a fault repairing unit are stored and backed up to the cloud storage platform, and the cloud storage platform uses a log automatic generation and refreshing instruction to carry out log refreshing every 30 seconds.
In the above embodiment, the fault repairing unit adopts an artificial intelligence fault screening system to reduce the fault calling time and reduce the screening error of fault error code elements.
In a specific embodiment, the artificial intelligent fault screening system internally comprises an artificial intelligent neural network, the network comprises a large number of processing units, the processing units are linked in pairs to form a complex fault screening network, the fault screening neural network has the functions of fault tolerance in principle, structural topological robustness, association, speculation, memory, self-adaption, self-learning, parallelism and complex mode processing, and can better cope with a plurality of faults and sudden faults in the operation of the current mechanical equipment, and monitor the operation process of a plurality of relatively huge machines or systems so as to discover fault problems in time and make diagnosis and ensure the healthy and stable operation of the fault screening network.
In the above embodiment, the multi-channel balanced check hybrid structure receiving device is adopted when the repair record is stored, and the data transmission leakage prevention is realized through the multi-path full duplex noise reduction system.
In a specific embodiment, the multi-path full duplex noise reduction system sets a plurality of transmission paths between a master device and a slave device storage array, and a plurality of switch devices can be contained between the transmission paths, so that the connection mode reduces the possibility of generating single-point faults at the node connection position of the switch, if the data is disconnected when passing through one transmission path, the data stream is transferred to another intact transmission path through a transfer transmission node closest to the disconnection point to continue transmission, the formation of network paralysis is avoided, and a noise reduction processing unit is arranged in the transmission path, so that effective information in the data stream is not missed when noise is filtered due to overlarge noise in the data stream transmission process.
In the above embodiment, the bionic fish swarm clustering algorithm fuses the common difference comparison model to perform the common difference comparison of the optimal solution set on the self-checking space of the optical fiber link, and improves the fine degree of the fault division of the optical fiber link.
As shown in fig. 5, in a specific embodiment, the bionic fish-swarm clustering algorithm includes the following steps:
initializing a fish swarm, generating a population structure with randomly distributed speed and position, and setting a movable range for the fish swarm;
setting an initial self-defined adaptive function according to the size of the optical fiber self-checking space, and evaluating the optimal solution position;
thirdly, calculating and updating the speed and the position of the fish shoal in real time, and predicting the speed change trend and the azimuth change rule of each fish;
setting an optimal solution judging function, and judging whether the optimal solution obtained in the step meets the fineness condition;
and fifthly, clustering the optimal solutions into an optimal solution set after the optimal solutions meet the fineness condition set by the user, and reading out commonalities and differences among the optimal solutions from the solution set.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are by way of example only, and that various omissions, substitutions, and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is limited only by the following claims.
Claims (6)
1. A self-healing ring network system based on an RS485 bus, the system comprising: the device comprises a main device module, a slave device module, a device selection module, a data storage module, a network interface module and a temperature and humidity detection module;
the main equipment module is used for controlling the whole self-healing ring network system, the main equipment module realizes the information receiving control of the slave equipment and completes the bus control function by sending an information transmission instruction to the slave equipment, the main equipment module comprises an optical fiber sending unit, a switch unit and a signal modulation unit, the optical fiber sending unit is used for converting a data electric signal into an optical signal and sending the optical signal, the switch unit is used for forwarding signals between network nodes of the main equipment module, the signal modulation unit is used for encrypting and encoding transmission data so as to improve the safety of the data transmission process, the output end of the optical fiber sending unit is connected with the input end of the switch unit, and the output end of the switch unit is connected with the input end of the signal modulation unit;
the slave device module is used for receiving the control instruction of the master device and the information data sent by the master device end and realizing the serial transmission of the data between the devices;
the device selection module is used for receiving a device selection instruction and selecting a slave device machine for receiving data, and comprises a priority judgment module, a hardware selection unit and a software selection unit;
the data storage module is used for storing data packet files in a link and comprises a storage receiving unit, a storage configuration unit and a cloud backup unit, wherein the storage receiving unit is used for receiving storage instructions, the storage configuration unit is used for configuring a file storage area, the output end of the storage receiving unit is connected with the input end of the storage configuration unit, and the output end of the storage configuration unit is connected with the input end of the cloud backup unit;
the temperature and humidity detection module is used for monitoring the internal temperature and humidity of the self-healing ring network system in real time, transmitting the system temperature and humidity data to the system main control unit in real time through the temperature and humidity sensor, and comprises an alarm module used for giving an alarm when the temperature and humidity exceeds a threshold set by a user;
the method is characterized in that: the system further comprises: the system comprises a bus transmission module, a self-healing ring network module and a diagnosis and repair module;
the bus transmission module is used for connecting internal computer equipment and external communication equipment and transmitting data information, the bus transmission module transmits data information, address information and operation instruction information of a main equipment module to a slave equipment module through an RS485 bus serial transmission protocol by differential transmission, the bus transmission module comprises a signal collection unit, a message serial transmission unit and a receiving terminal unit, the signal collection unit is used for collecting equipment node information, the message serial transmission unit is used for packaging the equipment node information into data blocks and carrying out serial output, and the receiving terminal unit is used for receiving data packets of an equipment terminal;
the output end of the signal collection unit is connected with the input end of the message serial transmission unit, and the output end of the message serial transmission unit is connected with the input end of the receiving terminal unit;
the self-healing ring network module is used for expanding a network interface and rapidly and autonomously recovering when network faults occur, the self-healing ring network module integrates failure fault data packet files of equipment terminals of different transmission nodes into an optical fiber transmission link for transmission and automatically recovers faults, the self-healing ring network module comprises a fault isolation unit, a data merging unit and an optical fiber link self-checking unit, the fault isolation unit is used for isolating fault data and normal data in a data transmission network, recording IP addresses of the fault data into an internal memory of the self-healing ring network module, the data merging unit is used for carrying out message data negotiation operation on the fault data packet files of different address links through a link aggregation control protocol and converging the fault data packet files into one optical fiber output transmission link, the optical fiber link self-checking unit is used for detecting the bidirectional transmission performance of the optical fiber link, the optical fiber link self-checking unit adopts an optical fiber detection sensor to acquire optical fiber transmission loop data, and transmits the optical fiber transmission loop data to an embedded microprocessor, and carries out optical fiber transmission fault self-checking through a bionic fish swarm clustering algorithm;
the output end of the fault isolation unit is connected with the input end of the data merging unit, and the output end of the data merging unit is connected with the input end of the optical fiber link self-checking unit;
the diagnosis repair module is used for automatically diagnosing fault data in the ring network and repairing and recording fault logs of the fault data in the whole ring network system, the diagnosis repair module comprises an automatic diagnosis unit, a fault classification unit and a fault repair unit, the automatic diagnosis unit is used for diagnosing fault reasons of the fault data, the fault classification unit is used for grouping the fault data according to transmission characteristics, the fault repair unit is used for modifying error code elements of the fault data and uploading repair records to the cloud storage end, the output end of the automatic diagnosis unit is connected with the input end of the fault classification unit, and the output end of the fault classification unit is connected with the input end of the fault repair unit;
the output end of the main equipment module is connected with the input ends of the bus transmission module, the equipment selection module and the self-healing ring network module, the output end of the equipment selection module is connected with the input end of the slave equipment module, and the output ends of the main equipment module and the slave equipment module are connected with the input ends of the diagnosis repair module, the data storage module and the temperature and humidity detection module;
the bionic fish swarm clustering algorithm fuses the commonality difference comparison model to perform commonality difference comparison of optimal solution sets on the self-checking space of the optical fiber link, and improves the fine degree of fault division of the optical fiber link;
the bionic fish swarm clustering algorithm comprises the following steps:
initializing a fish swarm, generating a population structure with randomly distributed speed and position, and setting a movable range for the fish swarm;
setting an initial self-defined adaptive function according to the size of the optical fiber self-checking space, and evaluating the optimal solution position;
thirdly, calculating and updating the speed and the position of the fish shoal in real time, and predicting the speed change trend and the azimuth change rule of each fish;
setting an optimal solution judging function, and judging whether the optimal solution obtained in the step meets the fineness condition;
and fifthly, clustering the optimal solutions into an optimal solution set after the optimal solutions meet the fineness condition set by the user, and reading out commonalities and differences among the optimal solutions from the solution set.
2. The self-healing ring network system based on the RS485 bus according to claim 1, wherein: the automatic diagnosis unit adopts a data layering evaluation analysis packaging algorithm ILEP to decompose the data block into a hierarchical matrixWherein m is the longitudinal depth of the message data matrix, n is the transverse width of the data matrix, and the characteristic value is extracted from the hierarchical matrix according to the characteristic decomposition and layering of the information entropy, and the characteristic value decomposition and layering extraction function is as follows:
（1）
in the case of the formula (1),for the hierarchical reference parameter, +.>Is the entropy average value>For the information entropy layered feature prediction error,the feature value projection statistic is characterized in that the feature value decomposition hierarchical extraction function contains 5 sub-types, the sub-types represent feature value decomposition definition degree parameters, after the feature value decomposition hierarchical extraction processing of the data block is completed, feature value aggregation processing is carried out on the data group containing the feature value, and an aggregation data set is output, wherein the feature value aggregation processing function is as follows:
（2）
in the formula (2) of the present invention,for the eigenvalue aggregate processing function,/->For aggregating output density parameters, +.>Hierarchical statistical degrees of freedom for eigenvalues, +.>For the aggregate error correction parameter +.>Is the entropy average value>For the information entropy layered feature prediction error,representing error distribution confidence, ++>Data depth representing the output aggregate dataset, +.>The data representing the output data set is laterally extended wide.
3. The self-healing ring network system based on the RS485 bus according to claim 1, wherein: the fault isolation unit adopts a multi-feature fusion fault reconstruction isolation algorithm muti-LFI to isolate fault data and normal data in a data transmission network, and sets a fault data block matrix asWherein->For a fault data block sub-matrix, the fault data block matrix is divided into 5 subsets comprising fault data block sub-matrices +.>Wherein->For the number of the fault weights, confidence self-adaptive fusion is carried out on each subset, and the self-adaptive fusion output formula is as follows:
（3）
in the formula (3) of the present invention,for adaptive fusion output function,/->For subset lateral nodes, ++>For subset longitudinal nodes, +.>Modifying parameters for the subset->For adaptive bias parameters +.>Threshold parameters for subset interactions, +.>And for the fault data block subset, performing feature global fusion processing on the self-adaptive fusion output value, wherein a feature global fusion formula is as follows:
（4）
in the formula (4) of the present invention,for feature global fusion function, ++>For minimum feature base parameter, ++>For global scan range, +.>Assigning a scale parameter for global->Is the minimum feature base upper limit,/-, for>Is the minimum feature base lower limit,/->For minimum feature base branch depth, +.>Fusing reliability parameters for features, +.>Dimension parameters are fused for the features.
4. The self-healing ring network system based on the RS485 bus according to claim 1, wherein: the fault repairing unit adopts the honeycomb big data autonomous cloud storage platform to realize the on-line cloud storage fault repairing and return correct code elements so as to reduce the data transmission bandwidth and improve the data transmission speed.
5. The self-healing ring network system based on the RS485 bus according to claim 1, wherein: the fault repairing unit adopts an artificial intelligent fault screening system to reduce the fault calling time and reduce the screening error of fault error code elements.
6. The self-healing ring network system based on the RS485 bus according to claim 5, wherein: and when the repair record is stored, a multichannel balanced check hybrid structure receiving device is adopted, and data transmission leakage prevention is realized through a multipath full duplex noise reduction system.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101022388A (en) * | 2007-03-23 | 2007-08-22 | 毛德操 | Method for constituting self-healing ring with ordinery Ethernet technique |
| CN101404599A (en) * | 2008-11-12 | 2009-04-08 | 华为技术有限公司 | Network fault detection method, primary device, slave device, control terminal and system |
| CN102208028A (en) * | 2011-05-31 | 2011-10-05 | 北京航空航天大学 | Fault predicting and diagnosing method suitable for dynamic complex system |
| CN103326911A (en) * | 2013-07-03 | 2013-09-25 | 富盛科技股份有限公司 | Self healing ring RS485 bus structure with short circuit and circuit break prevention |
| CN103973537A (en) * | 2014-05-09 | 2014-08-06 | 杭州同尊信息技术有限公司 | Ring network with fault protection function and fault protection method for ring network |
| CN108414896A (en) * | 2018-06-04 | 2018-08-17 | 西南交通大学 | A kind of electric network failure diagnosis method |
| CN110532341A (en) * | 2019-09-03 | 2019-12-03 | 华东师范大学 | Spatial information space-time big data constraint expression method |
| CN111022313A (en) * | 2019-12-30 | 2020-04-17 | 哈尔滨工程大学 | Ocean platform air compressor fault diagnosis method based on LSTM |
| CN111596604A (en) * | 2020-06-12 | 2020-08-28 | 中国科学院重庆绿色智能技术研究院 | Intelligent fault diagnosis and self-healing control system and method for engineering equipment based on digital twinning |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8264953B2 (en) * | 2007-09-06 | 2012-09-11 | Harris Stratex Networks, Inc. | Resilient data communications with physical layer link aggregation, extended failure detection and load balancing |
| US11659449B2 (en) * | 2020-07-31 | 2023-05-23 | Beegol Corporation | Machine learning-based network analytics, troubleshoot, and self-healing holistic telemetry system incorporating modem-embedded machine analysis of multi-protocol stacks |
-
2023
- 2023-06-07 CN CN202310664008.7A patent/CN116389229B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101022388A (en) * | 2007-03-23 | 2007-08-22 | 毛德操 | Method for constituting self-healing ring with ordinery Ethernet technique |
| CN101404599A (en) * | 2008-11-12 | 2009-04-08 | 华为技术有限公司 | Network fault detection method, primary device, slave device, control terminal and system |
| CN102208028A (en) * | 2011-05-31 | 2011-10-05 | 北京航空航天大学 | Fault predicting and diagnosing method suitable for dynamic complex system |
| CN103326911A (en) * | 2013-07-03 | 2013-09-25 | 富盛科技股份有限公司 | Self healing ring RS485 bus structure with short circuit and circuit break prevention |
| CN103973537A (en) * | 2014-05-09 | 2014-08-06 | 杭州同尊信息技术有限公司 | Ring network with fault protection function and fault protection method for ring network |
| CN108414896A (en) * | 2018-06-04 | 2018-08-17 | 西南交通大学 | A kind of electric network failure diagnosis method |
| CN110532341A (en) * | 2019-09-03 | 2019-12-03 | 华东师范大学 | Spatial information space-time big data constraint expression method |
| CN111022313A (en) * | 2019-12-30 | 2020-04-17 | 哈尔滨工程大学 | Ocean platform air compressor fault diagnosis method based on LSTM |
| CN111596604A (en) * | 2020-06-12 | 2020-08-28 | 中国科学院重庆绿色智能技术研究院 | Intelligent fault diagnosis and self-healing control system and method for engineering equipment based on digital twinning |
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