CN113391900A - Abnormal event processing method and system in discrete production environment - Google Patents
Abnormal event processing method and system in discrete production environment Download PDFInfo
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Abstract
The invention relates to an abnormal event processing method in a discrete production environment, which comprises the steps of determining a data structure of an abnormal event; receiving an abnormal event and forming an abnormal event receiving queue; judging the authenticity of the processing source of each abnormal event by adopting an SHA256 algorithm; distinguishing the level and priority of each abnormal event in the abnormal event receiving queue; dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm; adopting a driving calling method to perform early warning processing on the abnormal events after dynamic scheduling; processing result feedback is carried out on the abnormal event after the early warning processing, and the processing result and the processing state of the event log are updated; carrying out knowledge mining on the event log to form abnormal event processing knowledge; generating an exception handling strategy according to the exception event handling knowledge; the exception handling policy is used in determining the level and priority of the exception event next time. The method can effectively improve the production efficiency of the discrete workshop and improve the product quality.
Description
Technical Field
The invention relates to the field of processing of production abnormal events, in particular to a method and a system for processing the abnormal events in a discrete production environment.
Background
In the workshop production process of discrete manufacturing enterprises, various workshop production abnormalities inevitably occur, and once the workshop production abnormalities cannot be found and processed in time, the normal operation of the workshop production flow is directly influenced, and the economic loss is directly brought to the enterprises. Therefore, effective processing of abnormal events in a discrete production environment is helpful for avoiding production abnormalities, and efficient and systematic processing of abnormal processing defects in the production environment becomes one of the important problems to be solved urgently by the current manufacturing enterprises.
At present, some researches mainly focus on how to realize an efficient production abnormity early warning method aiming at an abnormal event in a production environment, and few researches are carried out on a processing method. The abnormal event processing method comprises environment monitoring early warning, production equipment energy consumption abnormity early warning, production equipment state abnormity early warning and the like, is only an abnormal event processing method aiming at a certain aspect, and cannot combine all abnormal event processing mechanisms together.
The existing abnormal event processing method mainly comprises two methods, the first method is an independent method based on multiple abnormal processing mechanisms, and the method carries out early warning aiming at that each part between enterprises contains independent abnormal events, so that the relevance of the abnormal events in each link cannot be found, the abnormal event processing is influenced, and the production efficiency and the product quality are influenced. The second method is based on the experience of exception handling, and the method is characterized in that after exception early warning of each part is performed, an experienced person of exception handling handles exceptions, a paper file is formed after handling, and exception occurrence time, exception description, a solving person, a solving method, solving time and the like are written. For enterprises with frequent personnel change, the paper experience can not be quickly found out and provide a basis for exception handling, so that the production efficiency and the product quality are influenced. Therefore, the existing abnormal event processing method cannot effectively process the abnormal event, and both the production efficiency and the product quality of enterprises are affected.
Therefore, in view of the discrete production environment, a new abnormal event processing method is needed to solve the problem of the reduction of production efficiency and product quality caused by the ineffective processing of the abnormal event.
Disclosure of Invention
The invention aims to provide an abnormal event processing method and system in a discrete production environment, which uses a clue binary tree to organize abnormal event numbers and distinguish the level and the processing priority of each abnormal event; solving the authenticity problem of the abnormal event processing source by using an SHA256 algorithm; a slow start algorithm is adopted to realize the dynamic scheduling of the abnormal events; and (3) excavating abnormal event states by using a clustering algorithm, establishing a state transition matrix by using a Markov chain theory, forming abnormal event processing knowledge, and generating an abnormal processing strategy, wherein the abnormal processing strategy can be used in the subsequent abnormal event processing process, can effectively process the abnormal events, and improves the production efficiency and the product quality.
In order to achieve the purpose, the invention provides the following scheme:
an abnormal event processing method in a discrete production environment comprises the following steps:
determining a data structure of the abnormal event;
receiving the abnormal event and forming an abnormal event receiving queue;
judging the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by adopting an SHA256 algorithm;
distinguishing the level of each abnormal event in the abnormal event receiving queue, putting each abnormal event into a sensitive queue with different levels, sequencing each abnormal event in the sensitive queues with different levels by using a clue binary tree, and determining the priority of each abnormal event in each sensitive queue;
dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm;
adopting a driving calling method to perform early warning processing on the abnormal event after dynamic scheduling;
processing result feedback is carried out on the abnormal event after early warning processing, and the processing result and the processing state of the event log are updated;
carrying out knowledge mining on the event log to form abnormal event processing knowledge;
generating an exception handling strategy according to the exception event handling knowledge; the exception handling policy is used in determining the level and priority of the exception event next time.
Optionally, the data structure of the abnormal event includes an event number, an event level, an event source, an occurrence time, an occurrence place, a reporter and an event description.
Optionally, the determining, by using the SHA256 algorithm, the authenticity of the processing source of each abnormal event in the abnormal event receiving queue specifically includes:
according to the data structure of the abnormal event, SHA256 encryption is carried out on the event number, the event source and the occurrence time;
the encrypted SHA256 event number, the encrypted event source and the encrypted occurrence time are used as proofreading items and transmitted to a receiver together with abnormal event data;
after the abnormal event data is successfully received, SHA256 encryption is carried out on the received event number, the event source and the occurrence time to form a verification item;
checking the verification item and the check item, and determining whether the processing source of the abnormal event is correct or not according to a check result; the successful verification indicates that the processing source of the abnormal event is correct, and the failed verification indicates that the processing source of the abnormal event is wrong;
and putting the abnormal events with correct processing sources into the abnormal event receiving queue, and removing the abnormal events with wrong processing sources from the abnormal event receiving queue.
Optionally, the distinguishing the level of each abnormal event in the abnormal event receiving queue, placing each abnormal event into a sensitive queue of a different level, sorting each abnormal event in the sensitive queues of the different levels by using a binary clue tree, and determining the priority of each abnormal event in each sensitive queue specifically includes:
distinguishing the sensitivity degree of each abnormal event according to the event level of each abnormal event in the abnormal event receiving queue and the priority of each abnormal event in the abnormal event processing strategy;
according to the sensitivity degree of each abnormal event, putting each abnormal event into a sensitive queue of a corresponding level one by one, and simultaneously recording an event log of each abnormal event; the sensitive queues comprise a high sensitive queue, a medium sensitive queue and a low sensitive queue;
and numbering each abnormal event in each sensitive queue in sequence by adopting a clue binary tree, and determining the priority of each abnormal event in each sensitive queue.
Optionally, the dynamically scheduling each abnormal event in the sensitive queues of different levels by using a slow start algorithm specifically includes:
detecting the network state when each abnormal event in the sensitive queues of different levels sends a data report by using the slow start algorithm;
when the network state is good, increasing the size of the congestion window in the order from small to large; otherwise, the congestion window is not adjusted.
Optionally, the performing, by using the drive calling method, early warning processing on the abnormal event after dynamic scheduling specifically includes:
determining a uniform control type of the early warning equipment and a driving interface of a control parameter; the early warning equipment comprises equipment for early warning by adopting an early warning mode of sound, light, electricity and notification;
and compiling drivers according to the control functions or sending functions of different manufacturers and different early warning equipment, and loading the compiled drivers into an operation program of the early warning equipment to realize the expansion of the driving forms of equipment independence and manufacturer independence.
Optionally, the feeding back the processing result of the abnormal event after the early warning processing and updating the processing result and the processing state of the event log specifically include:
selecting a processed abnormal event from the abnormal event receiving queue, feeding back a processing result of the processed abnormal event into the event log of the processed abnormal event, marking the processed abnormal event as a processed completion state, and updating the processing result and the processing state in the event log in real time.
Optionally, the knowledge mining is performed on the event log to form abnormal event processing knowledge, and the method specifically includes:
taking event log data which is not subjected to data processing in the event log as metadata, and performing data duplicate removal and data residue removal processing on the metadata to obtain available analysis data;
processing the available analysis data by a principal component analysis method, determining useful characteristics of the available analysis data, and reducing dimensionality to obtain the available analysis data in an invariant form;
clustering key features of the abnormal events by adopting a k-means clustering algorithm, determining an incidence relation among the abnormal events, and obtaining incidence abnormal events of the abnormal events;
and establishing a state transition matrix by adopting a Markov chain theory, predicting the occurrence probability of the associated abnormal event, and forming abnormal event processing knowledge.
Optionally, the generating an exception handling policy according to the exception event handling knowledge specifically includes:
generating an explicit exception handling strategy according to the exception event handling knowledge;
and applying the explicit exception handling policy to the step of distinguishing the level of each exception in the exception receiving queue, putting each exception into a sensitive queue with different levels, sequencing each exception in the sensitive queue with different levels by using a clue binary tree, and determining the priority of each exception in each sensitive queue, so as to determine the level and the priority of the exception in the next exception handling process.
An exception handling system in a discrete production environment, comprising:
the abnormal event data structure determining module is used for determining the data structure of the abnormal event;
an abnormal event receiving queue forming module, configured to receive the abnormal event and form an abnormal event receiving queue;
the source authenticity judging module is used for judging the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by adopting an SHA256 algorithm;
a level and priority distinguishing module, configured to distinguish the level of each abnormal event in the abnormal event receiving queue, place each abnormal event into a sensitive queue at a different level, sort each abnormal event in the sensitive queues at the different levels by using a binary clue tree, and determine the priority of each abnormal event in each sensitive queue;
the dynamic scheduling module is used for dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm;
the early warning processing module is used for carrying out early warning processing on the abnormal event after dynamic scheduling by adopting a driving calling method;
the processing result feedback module is used for feeding back the processing result of the abnormal event after the early warning processing and updating the processing result and the processing state of the event log;
the abnormal event processing knowledge forming module is used for carrying out knowledge mining on the event log to form abnormal event processing knowledge;
the abnormal processing strategy generating module is used for generating an abnormal processing strategy according to the abnormal event processing knowledge; the exception handling policy is used in determining the level and priority of the exception event next time.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides an abnormal event processing method and system in a discrete production environment, which are used for numbering abnormal events by using a clue binary tree method, thereby not only distinguishing the level and the processing priority of each abnormal event, but also improving the retrieval efficiency of a common event strategy and reducing the response time of abnormal event processing. The method solves the authenticity problem of the abnormal event processing source by using the SHA256 algorithm, ensures the safety of the abnormal event processing and solves the reliability problem of the abnormal event. The slow start algorithm is adopted to realize the dynamic scheduling of each abnormal event, and the event processing efficiency is improved. Finally, abnormal event processing knowledge is formed, and an abnormal processing strategy is generated, wherein the abnormal processing strategy can be used in the subsequent abnormal event processing process and can effectively process the abnormal event. Aiming at the production environment of the discrete workshop, the open, structured and intelligent abnormal event processing method is utilized to organically combine all parts forming the intelligent manufacturing, so that the processing capacity of the discrete workshop on the abnormal event is improved, and the production efficiency is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an abnormal event handling method in a discrete production environment according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of an abnormal event handling method in a discrete production environment according to embodiment 1 of the present invention;
fig. 3 is a schematic diagram of a storage structure of a binary clue tree according to embodiment 1 of the present invention;
fig. 4 is a schematic structural diagram of a binary linked list in a clue binary tree according to embodiment 1 of the present invention;
fig. 5 is a block diagram of an exception handling system in a discrete production environment according to embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an abnormal event processing method and system in a discrete production environment, which uses a clue binary tree to organize abnormal event numbers, distinguishes the level and processing priority of each abnormal event, improves the event strategy retrieval efficiency and reduces the event processing response time; the problem of authenticity of an abnormal event processing source is solved by using an SHA256 algorithm, and the safety of abnormal event processing is ensured; a slow start algorithm is adopted to realize dynamic scheduling, and the event processing efficiency is improved; and (3) excavating abnormal event states by using a clustering algorithm, establishing a state transition matrix by using a Markov chain theory, forming abnormal event processing knowledge, generating an abnormal processing strategy, and using the abnormal processing strategy in the step of distinguishing the level and the priority of the abnormal event when the abnormal event is processed next time, so that the abnormal event can be effectively processed, and the production efficiency and the product quality are improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1
As shown in fig. 1, the present embodiment provides a method for processing an abnormal event in a discrete production environment, which includes the following steps:
and step S1, determining the data structure of the abnormal event.
In this embodiment, the data structure of the abnormal event includes an event number, an event level, an event source, an occurrence time, an occurrence location, a reporter, an event description, and the like. And establishing a uniform representation mode of the abnormal event by determining the data structure of the abnormal event and standardizing the data structure of the abnormal event.
The abnormal event processing mechanism under the discrete production environment is composed of a mechanism system structure, an abnormal event unified representation method, an abnormal event processing algorithm and a scheduling algorithm based on a strategy, abnormal event knowledge mining and management and an abnormal event processing strategy definition. By defining a uniform representation mode of the abnormal event, the data structure of the abnormal event is specified, and the data structure comprises the contents of an event number, an event level, an event source, the occurrence time, the occurrence place, a reporter, an event description and the like. And mining and analyzing the abnormal information according to the abnormal time information, predicting the occurrence condition of the abnormal event by using a data mining algorithm, and inputting the abnormal event into an event processing driving component supporting abnormal processing. And the event processing driving component realizes the processing of the abnormal event in a driving control mode under the support of the operation planning and scheduling component and various abnormal event processing algorithms. FIG. 2 illustrates a schematic diagram of a method for exception handling in a discrete production environment.
It should be noted that, as shown in fig. 2, the abnormal event in the present invention refers to a real-time abnormal event obtained from a database or various sensors in a production facility. When an abnormal event is received, the accessed data is processed according to the abnormal data, whether the format and the source of the abnormal data accord with the abnormal event or not and whether the abnormal event is formed or not needs to be judged, and if the abnormal event does not accord with the abnormal event, the abnormal data is abandoned and is not processed. The event processing engine is used for dividing the abnormal events by utilizing a team scheduling mechanism according to the user-defined event level and a processing strategy, namely dividing the abnormal events into a high-sensitivity queue, a medium-sensitivity queue and a low-sensitivity queue according to the sensitivity degree, and realizing the division of the level and the priority of the abnormal events. The processing strategy is to divide each abnormal event in an abnormal event receiving queue formed by n abnormal events to provide a processing basis.
And step S2, receiving the abnormal event and forming an abnormal event receiving queue.
In this embodiment, a unified exception receiving mechanism is established, where the exception is a unified data structure, and the specific data structure is the data structure in step S1. And after receiving the abnormal event, entering an abnormal event receiving queue.
And step S3, judging the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by adopting an SHA256 algorithm.
Due to the problem of openness of the source of the abnormal event reception in step S2, the problem of authenticity of the source of the abnormal event processing cannot be determined. Therefore, in this embodiment, after receiving an exception and entering the exception receiving queue, the SHA256 algorithm is first used to determine the authenticity of the processing source of each exception in the exception receiving queue. The method comprises the following specific steps:
s3.1, according to the data structure of the abnormal event, the abnormal event transmitter carries out SHA256 encryption on the event number, the event source and the occurrence time;
s3.2, the event number, the event source and the occurrence time which are encrypted by the SHA256 are used as proofreading items and are transmitted to an abnormal event receiver together with abnormal event data;
s3.3, after the abnormal event receiver successfully receives the abnormal event data, carrying out SHA256 encryption on the received event number, the event source and the occurrence time to form a verification item;
s3.4, the verification item and the check item are checked, and whether the processing source of the abnormal event is correct or not is determined according to a check result; the successful verification indicates that the processing source of the abnormal event is correct, and the failed verification indicates that the processing source of the abnormal event is wrong;
and S3.5, putting the abnormal events with correct processing sources into the abnormal event receiving queue, and removing the abnormal events with wrong processing sources from the abnormal event receiving queue.
According to the invention, the source of the abnormal event processing is determined by SHA256 encryption, and the authenticity of the abnormal data is judged after the abnormal event processing is encrypted by a transmitting party and a receiving party, so that other malicious attacks or data which do not meet the requirements are prevented from entering. The SHA256 algorithm is adopted to solve the problem of the authenticity of the processing source of the abnormal event, and the safety of the processing of the abnormal event is ensured.
Step S4, distinguishing the level of each abnormal event in the abnormal event receiving queue, placing each abnormal event into a sensitive queue of different level, sorting each abnormal event in the sensitive queue of different level by using a binary clue tree, and determining the priority of each abnormal event in each sensitive queue. The method specifically comprises the following steps:
s4.1, distinguishing the sensitivity degree of each abnormal event according to the event level of each abnormal event in the abnormal event receiving queue and the priority of each abnormal event in the abnormal event processing strategy; wherein, the exception handling policy is the exception handling policy generated in the subsequent step S9 in the last exception event handling process.
S4.2, putting the abnormal events into a sensitive queue of a corresponding level one by one according to the sensitivity of each abnormal event, and simultaneously recording the event log of each abnormal event; the sensitive queues comprise a high sensitive queue, a medium sensitive queue and a low sensitive queue.
And S4.3, numbering each abnormal event in each sensitive queue in sequence by adopting a clue binary tree, and determining the priority of each abnormal event in each sensitive queue.
In this embodiment, the level and priority of the exception event are differentiated by using the event processing engine. The event processing engine is used for dividing the abnormal events by utilizing a team scheduling mechanism according to the user-defined event level and a processing strategy, namely dividing the abnormal events into a high-sensitivity queue, a medium-sensitivity queue and a low-sensitivity queue according to the sensitivity degree, so that the level and the priority of the abnormal events are divided. Firstly, accurately defining the sensitivity degree of the abnormal event according to the event level of the abnormal event and the processing priority of the abnormal event in the processing strategy, putting the abnormal event into corresponding level queues, namely a high-sensitivity queue, a medium-sensitivity queue and a low-sensitivity queue, and simultaneously recording an event log of the abnormal event. Because the types and the quantity of the devices in the discrete production environment are more and the number of the devices is huge, in order to solve the problem that the response after the large-scale abnormal events are generated is not timely, the abnormal events are organized by a clue binary tree to be numbered, so that the retrieval efficiency of the abnormal event strategy is improved, and the event processing response time is shortened.
The clue binary tree method is a binary tree of n nodes, n +1 empty chain domains are arranged in a binary chain storage structure, pointers of a predecessor node and a successor node of the node under a certain traversal order are stored by using the empty chain domains, the pointers are called clues, and the binary tree with the clues is called a clue binary tree. This binary linked list with the clues added is called the clue linked list, and the corresponding binary tree is called the clue binary tree.
In this embodiment, the method for implementing the thread binary tree is to change a null pointer in the binary linked list to point to a predecessor thread or a successor thread, and the storage structure is as shown in fig. 3. In fig. 3, lchild (leftchild) means "left child", rchild (rightchild) means "right child". In order to distinguish between the clue pointer and the child pointer, two flags ltag and rtag are set in each node, where ltag denotes the left flag and rtag denotes the right flag. When ltag and rtag are 0, lchild and rchild are pointers to "left child" and "right child", respectively; otherwise, lchild is the predecessor thread that points to the node and rchild is the successor thread that points to the node. Since the flag only occupies one binary bit, the storage space required by each node is saved greatly. The structure of the binary linked list is shown in figure 4. YC2104291000, YC2104291100, YC2104291200, YC2104291111, and the like in the middle frame indicated by arrows in fig. 4 are abnormal event numbers, and the values on the left and right sides of the numbers are 0 or 1, and there are two cases:
(1) when the ltag is 0, the left child of the node is pointed to, and when the ltag is 1, the precursor clue of the node is pointed to;
(2) the "right child" pointing to the node when rtag is 0 and the successor threads pointing to the node when rtag is 1.
I.e., lcaild points to "left child" when ltag ═ 0; leading clues pointing to nodes by lchild when ltag is 1; rchild points to "right child" when rtag ═ 0; rtag is 1. the successor thread to the node pointed to by rchild.
The thread linked list in the thread binary tree solves the problem that the predecessor and successor of the node in a certain traversal sequence cannot be directly found, and solves the problem that the left child and the right child are difficult to find in the binary linked list.
And step S5, dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm.
In order to deal with the large-scale abnormal events in the high-sensitivity queue, the medium-sensitivity queue and the low-sensitivity queue in the step S4 and prevent the problem that the router or the link in the network is overloaded due to excessive data injection, a mechanism of dynamic scheduling is realized by adopting a slow-start algorithm, and the event processing efficiency is improved. The method specifically comprises the following steps:
s5.1, detecting the network state when each abnormal event in the sensitive queues of different levels sends a data report by using the slow start algorithm;
s5.2, when the network state is good, increasing the size of the congestion window in the order from small to large; otherwise, the congestion window is not adjusted.
In this embodiment, the slow start algorithm is implemented by: when an event handling queue sends datagrams to an event handling driver, a problem of network congestion may occur if a large amount of data is injected into the network immediately. The slow start algorithm is to detect the network condition when the event processing queue just starts to send the datagram, and if the network condition is good, the sender can correctly receive the acknowledgement message segment every time the sender sends the message segment. The size of the congestion window, i.e. the size of the send window, is increased in order from small to large.
For example, the sender sets cwnd (congestion window) to 1, sends the first segment B1, after the receiver receives B1, the sender increases cwnd to 2 after receiving the acknowledgement from the receiver, then the sender sends B2 and B3, after the sender receives the acknowledgement from the receiver, cwnd is increased to 4, and the congestion window cwnd is doubled after the sender is considered to successfully receive the acknowledgement from the receiver every transmission round of the slow start algorithm.
Through a slow start algorithm, the size of the sent data is gradually increased when the data is sent, network congestion caused by injection of a large amount of data is prevented, and the efficiency of receiving and processing abnormal events by event processing driving is ensured.
And step S6, performing early warning processing on the abnormal event after dynamic scheduling by adopting a driving calling method. The method specifically comprises the following steps:
s6.1, determining a uniform control type and a driving interface of a control parameter; the mode based on driving meets the calling of different early warning devices, and early warning modes such as sound, light, electricity and notification are included, as shown in fig. 2. Namely, the early warning equipment comprises equipment for early warning by adopting an early warning mode of sound, light, electricity and notification;
and S6.2, compiling a driver according to the control function or the sending function of different manufacturers and different early warning equipment, and loading the compiled driver into an operation program of the early warning equipment to realize the expansion of the drive form of equipment independence and manufacturer independence.
The invention adopts the adapter mode to construct the communication protocol driver and dynamically load the communication protocol driver into the abnormal event processing application, the adapter mode is compatible with various communication protocols in a loading driving mode, and determines the driving interface of a uniform control type and a control parameter, thereby solving the problem of compatibility of equipment drivers of various different protocols of various manufacturers, equipment of different manufacturers with different protocols can be added after writing the driver, the program does not need to be changed, the problem of incompatibility of the event early warning equipment is solved, a uniform, simple and compatible event processing mechanism is provided, and the complexity and the cost of enterprise data abnormal event early warning are reduced.
And step S7, feeding back the processing result of the abnormal event after the early warning processing, and updating the processing result and the processing state of the event log. The method specifically comprises the following steps:
after the maintainer finishes processing the abnormal event, the processed abnormal event is selected from the abnormal event receiving queue, namely the abnormal event list of each device, the processing result of the processed abnormal event is fed back and written into the event log of the processed abnormal event, the processed abnormal event is marked as a processed state, and the processing result and the processing state in the event log are updated in real time.
And step S8, carrying out knowledge mining on the event log to form abnormal event processing knowledge. The method specifically comprises the following steps:
s8.1, taking event log data which is not subjected to data processing in the event log as metadata, and performing data duplication removal and data residue removal processing on the metadata to obtain available analysis data;
and S8.2, processing the available analysis data by using a principal component analysis method, determining useful characteristics of the available analysis data, and reducing dimensions to obtain the available analysis data in an invariant form.
The process of the principal component analysis method: sampling is carried out according to data of secondary abnormity in the event log, and sample content comprises content of event level, event source, occurrence time, occurrence place, event description, processing result and the like and serves as input data. Taking 4 samples as an example, a matrix is constructed, and the columns of the matrix comprise 7 columns of event level, event source, occurrence time, occurrence place, event description, processing result, duration and the like. Forming a matrix by taking each sample data as a column vector, subtracting the mean value of the row vector from each row vector of the matrix so as to enable the mean value of a new row vector to be 0, obtaining a new data set matrix with 4 rows and 7 columns, obtaining a covariance matrix through the new data set matrix, solving the eigenvalue and the unit eigenvector of the covariance matrix, arranging the unit eigenvector into the matrix according to the sequence of the eigenvalue from large to small, obtaining a conversion matrix, obtaining the first k principal components, and obtaining the key features of the secondary abnormal events reduced to the k dimension.
And S8.3, clustering the key features of the abnormal events by adopting a k-means clustering algorithm, and determining the incidence relation among the abnormal events to obtain the incidence abnormal events of the abnormal events.
The process of the k-means clustering algorithm: selecting key features of k secondary abnormal events as initial clustering centers, calculating distance division from other abnormal events to the clustering centers, calculating each clustering center again, calculating a standard measurement function, stopping until the maximum iteration times are reached, otherwise, continuing operation, and finally determining the optimal clustering center to obtain an association relation between the abnormal events so as to obtain an associated abnormal event associated with each abnormal event.
And S8.4, establishing a state transition matrix by adopting a Markov chain theory, predicting the occurrence probability of the associated abnormal event, and forming abnormal event processing knowledge.
Procedure of markov chain theory: calculating the occurrence probability of each secondary abnormal event state, calculating the transition probability of the next time according to the occurrence probability of the current time, forming the prediction of the occurrence probability of the secondary abnormal events of the next time, and forming event knowledge by associating the abnormal events and the prediction probability of the occurrence of the secondary abnormal events for processing decision support.
In this embodiment, based on the metadata of the event log, usable analysis data is formed by performing data processing and preprocessing such as deduplication and residue removal on the metadata, and useful features are searched by a Principal Component Analysis (PCA) method of the usable analysis data, dimensions are reduced, and an invariant representation form is formed. And clustering key features of the abnormal events by adopting a k-means clustering algorithm, excavating the association among the abnormal events, establishing a state transition matrix by adopting a Markov chain theory, predicting the occurrence probability of the associated abnormal events and forming abnormal event processing knowledge.
Step S9, generating an exception handling strategy according to the exception event handling knowledge; the exception handling policy is used in determining the level and priority of the exception event next time. The method specifically comprises the following steps:
s9.1, generating an explicit exception handling strategy according to the exception handling knowledge; the exception handling policy is used to support the event handling engine in step S4, and is used to determine the priority and level of the exception event.
And S9.2, applying the explicit exception handling strategy to the step of distinguishing the level of each exception in the exception receiving queue, putting each exception into a sensitive queue with different levels, sequencing each exception in the sensitive queue with different levels by using a clue binary tree, and determining the priority of each exception in each sensitive queue so as to determine the level and the priority of the exception in the next exception handling process.
For example, when an exception event with a low level of event level (i.e. an exception event in a low sensitive queue) is not processed quickly, a secondary exception occurs, and a high level exception is generated, through knowledge mining, the characteristics after the low level exception event occurs are compared with the characteristics of the high level secondary exception, when the similarity exceeds 80%, the possibility that the low level exception time is derived into a high level secondary exception is very high, and at this time, the processing priority of the low level exception event is the same as the processing priority of the high level exception event (i.e. an exception event in a high sensitive queue). It should be noted that the similarity of 80% in this embodiment is a preferred value, and is only used for illustration, and the specific threshold value of the similarity is not fixed and unique, and can be determined according to the actual situation.
The invention adopts a unified abnormal event processing mechanism, stores the abnormal event and processing feedback into an event log, excavates the state of the event and the association between the events on the basis of the abnormal event log, defines an abnormal processing strategy by referring to abnormal event knowledge on the basis of an abnormal event processing flow, and applies the abnormal processing strategy to the subsequent abnormal event processing process, thereby solving the abnormal event association and providing a basis for the abnormal event processing.
Example 2
As shown in fig. 5, the present embodiment shows an exception handling system in a discrete production environment, which includes:
an abnormal event data structure determining module M1 for determining the data structure of the abnormal event;
an exception event receiving queue forming module M2, configured to receive the exception event and form an exception event receiving queue;
the source authenticity judging module M3 is configured to judge authenticity of a processing source of each abnormal event in the abnormal event receiving queue by using an SHA256 algorithm;
a level and priority distinguishing module M4, configured to distinguish the level of each abnormal event in the abnormal event receiving queue, put each abnormal event into a sensitive queue at a different level, sort each abnormal event in the sensitive queue at the different level by using a binary clue tree, and determine the priority of each abnormal event in each sensitive queue;
the dynamic scheduling module M5 is configured to dynamically schedule each abnormal event in the sensitive queues of different levels by using a slow-start algorithm;
the early warning processing module M6 is configured to perform early warning processing on the dynamically scheduled abnormal event by using a drive calling method;
the processing result feedback module M7 is configured to perform processing result feedback on the abnormal event after the early warning processing, and update the processing result and the processing state of the event log;
an abnormal event processing knowledge forming module M8, configured to perform knowledge mining on the event log to form abnormal event processing knowledge;
an exception handling policy generating module M9, configured to generate an exception handling policy according to the exception event handling knowledge; the exception handling policy is used in determining the level and priority of the exception event next time.
The invention provides an abnormal event processing method and system in a discrete production environment, which are used for numbering abnormal events by using a clue binary tree method, thereby not only distinguishing the level and the processing priority of each abnormal event, but also improving the retrieval efficiency of a common event strategy and reducing the response time of abnormal event processing. The method solves the authenticity problem of the abnormal event processing source by using the SHA256 algorithm, ensures the safety of the abnormal event processing and solves the reliability problem of the abnormal event. The slow start algorithm is adopted to realize the dynamic scheduling of each abnormal event, and the event processing efficiency is improved. Finally, abnormal event processing knowledge is formed, and an abnormal processing strategy is generated, wherein the abnormal processing strategy can be used in the subsequent abnormal event processing process and can effectively process the abnormal event. Aiming at the production environment of the discrete workshop, the open, structured and intelligent abnormal event processing method is utilized to organically combine all parts forming the intelligent manufacturing, so that the processing capacity of the discrete workshop on the abnormal event is improved, and the production efficiency is greatly improved.
In the present specification, the emphasis points of the embodiments are different from those of the other embodiments, and the same and similar parts among the embodiments may be referred to each other. The principle and the implementation mode of the present invention are explained by applying specific examples in the present specification, and the above descriptions of the examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for processing abnormal events in a discrete production environment is characterized by comprising the following steps:
determining a data structure of the abnormal event;
receiving the abnormal event and forming an abnormal event receiving queue;
judging the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by adopting an SHA256 algorithm;
distinguishing the level of each abnormal event in the abnormal event receiving queue, putting each abnormal event into a sensitive queue with different levels, sequencing each abnormal event in the sensitive queues with different levels by using a clue binary tree, and determining the priority of each abnormal event in each sensitive queue;
dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm;
adopting a driving calling method to perform early warning processing on the abnormal event after dynamic scheduling;
processing result feedback is carried out on the abnormal event after early warning processing, and the processing result and the processing state of the event log are updated;
carrying out knowledge mining on the event log to form abnormal event processing knowledge;
generating an exception handling strategy according to the exception event handling knowledge; the exception handling policy is used in determining the level and priority of the exception event next time.
2. The method for processing the abnormal event in the discrete production environment as claimed in claim 1, wherein the data structure of the abnormal event comprises an event number, an event level, an event source, an occurrence time, an occurrence place, a reporter and an event description.
3. The method for processing the abnormal event under the discrete production environment according to claim 2, wherein the determining the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by using the SHA256 algorithm specifically comprises:
according to the data structure of the abnormal event, SHA256 encryption is carried out on the event number, the event source and the occurrence time;
the encrypted SHA256 event number, the encrypted event source and the encrypted occurrence time are used as proofreading items and transmitted to a receiver together with abnormal event data;
after the abnormal event data is successfully received, SHA256 encryption is carried out on the received event number, the event source and the occurrence time to form a verification item;
checking the verification item and the check item, and determining whether the processing source of the abnormal event is correct or not according to a check result; the successful verification indicates that the processing source of the abnormal event is correct, and the failed verification indicates that the processing source of the abnormal event is wrong;
and putting the abnormal events with correct processing sources into the abnormal event receiving queue, and removing the abnormal events with wrong processing sources from the abnormal event receiving queue.
4. The method according to claim 2, wherein the distinguishing of the levels of the abnormal events in the abnormal event receiving queue, placing the abnormal events in sensitive queues of different levels, sorting the abnormal events in the sensitive queues of different levels by using a binary clue tree, and determining the priority of each abnormal event in each sensitive queue specifically comprises:
distinguishing the sensitivity degree of each abnormal event according to the event level of each abnormal event in the abnormal event receiving queue and the priority of each abnormal event in the abnormal event processing strategy;
according to the sensitivity degree of each abnormal event, putting each abnormal event into a sensitive queue of a corresponding level one by one, and simultaneously recording an event log of each abnormal event; the sensitive queues comprise a high sensitive queue, a medium sensitive queue and a low sensitive queue;
and numbering each abnormal event in each sensitive queue in sequence by adopting a clue binary tree, and determining the priority of each abnormal event in each sensitive queue.
5. The method for processing the abnormal event under the discrete production environment according to claim 1, wherein the dynamically scheduling each abnormal event in the sensitive queues of different levels by using a slow start algorithm specifically comprises:
detecting the network state when each abnormal event in the sensitive queues of different levels sends a data report by using the slow start algorithm;
when the network state is good, increasing the size of the congestion window in the order from small to large; otherwise, the congestion window is not adjusted.
6. The method for processing the abnormal event under the discrete production environment according to claim 1, wherein the early warning processing of the abnormal event after the dynamic scheduling is performed by using a driving calling method specifically comprises:
determining a uniform control type of the early warning equipment and a driving interface of a control parameter; the early warning equipment comprises equipment for early warning by adopting an early warning mode of sound, light, electricity and notification;
and compiling drivers according to the control functions or sending functions of different manufacturers and different early warning equipment, and loading the compiled drivers into an operation program of the early warning equipment to realize the expansion of the driving forms of equipment independence and manufacturer independence.
7. The method for processing the abnormal event under the discrete production environment according to claim 1, wherein the step of feeding back the processing result of the abnormal event after the early warning processing and updating the processing result and the processing state of the event log comprises the following steps:
selecting a processed abnormal event from the abnormal event receiving queue, feeding back a processing result of the processed abnormal event into the event log of the processed abnormal event, marking the processed abnormal event as a processed completion state, and updating the processing result and the processing state in the event log in real time.
8. The method for processing the abnormal event under the discrete production environment according to claim 1, wherein the knowledge mining is performed on the event log to form abnormal event processing knowledge, and specifically comprises:
taking event log data which is not subjected to data processing in the event log as metadata, and performing data duplicate removal and data residue removal processing on the metadata to obtain available analysis data;
processing the available analysis data by a principal component analysis method, determining useful characteristics of the available analysis data, and reducing dimensionality to obtain the available analysis data in an invariant form;
clustering key features of the abnormal events by adopting a k-means clustering algorithm, determining an incidence relation among the abnormal events, and obtaining incidence abnormal events of the abnormal events;
and establishing a state transition matrix by adopting a Markov chain theory, predicting the occurrence probability of the associated abnormal event, and forming abnormal event processing knowledge.
9. The method for processing the abnormal event under the discrete production environment according to claim 1, wherein the generating the abnormal event processing policy according to the abnormal event processing knowledge specifically comprises:
generating an explicit exception handling strategy according to the exception event handling knowledge;
and applying the explicit exception handling policy to the step of distinguishing the level of each exception in the exception receiving queue, putting each exception into a sensitive queue with different levels, sequencing each exception in the sensitive queue with different levels by using a clue binary tree, and determining the priority of each exception in each sensitive queue, so as to determine the level and the priority of the exception in the next exception handling process.
10. An exception handling system in a discrete production environment, comprising:
the abnormal event data structure determining module is used for determining the data structure of the abnormal event;
an abnormal event receiving queue forming module, configured to receive the abnormal event and form an abnormal event receiving queue;
the source authenticity judging module is used for judging the authenticity of the processing source of each abnormal event in the abnormal event receiving queue by adopting an SHA256 algorithm;
a level and priority distinguishing module, configured to distinguish the level of each abnormal event in the abnormal event receiving queue, place each abnormal event into a sensitive queue at a different level, sort each abnormal event in the sensitive queues at the different levels by using a binary clue tree, and determine the priority of each abnormal event in each sensitive queue;
the dynamic scheduling module is used for dynamically scheduling each abnormal event in the sensitive queues of different levels by adopting a slow start algorithm;
the early warning processing module is used for carrying out early warning processing on the abnormal event after dynamic scheduling by adopting a driving calling method;
the processing result feedback module is used for feeding back the processing result of the abnormal event after the early warning processing and updating the processing result and the processing state of the event log;
the abnormal event processing knowledge forming module is used for carrying out knowledge mining on the event log to form abnormal event processing knowledge;
the abnormal processing strategy generating module is used for generating an abnormal processing strategy according to the abnormal event processing knowledge; the exception handling policy is used in determining the level and priority of the exception event next time.
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