CN115525327A - Method and device for gray level tangential flow production and emergency - Google Patents

Method and device for gray level tangential flow production and emergency Download PDF

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CN115525327A
CN115525327A CN202211251904.2A CN202211251904A CN115525327A CN 115525327 A CN115525327 A CN 115525327A CN 202211251904 A CN202211251904 A CN 202211251904A CN 115525327 A CN115525327 A CN 115525327A
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emergency
service
flow
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transaction data
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秦闻
黄光宇
胡育基
吴延生
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The application provides a gray level tangential flow commissioning and emergency method and device, relates to the field of system operation and maintenance, and can be applied to the financial field and other fields, and the method comprises the following steps: constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements; calling a corresponding commissioning strategy through an RPA robot according to a service flow switching process to perform service flow switching processing, and acquiring first transaction data and second transaction data generated before and after the service flow switching processing; analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain an operation state after service flow switching; and calling the emergency strategy according to the running state to execute corresponding emergency treatment.

Description

Gray level tangential flow production and emergency method and device
Technical Field
The application relates to the field of system operation and maintenance, can be applied to the financial field and other fields, and particularly relates to a gray level tangential flow production and emergency method and device.
Background
For an enterprise which is built in a traditional way in an informationized way, under the wave of rapid development of the internet, the IT architecture transformation of a legacy system is very important for future development of the enterprise. In the framework transformation process, parallel trial points of a new system and an old system are a necessary loop in the project construction process, complex service flow switching and emergency scenes exist in the process, the related applications and services are more, and execution modes of flow switching schemes are different. At present, a general flow switching emergency scheme depends on manual operation of operation and maintenance personnel, namely, the operation and maintenance personnel manually set parameters in an operation and maintenance system according to a flow switching scheme provided by a developer. For non-systematic errors, such as data loss when system parameters are synchronized through a message queue, transaction failure caused by inconsistency of service logic and original functions, and the like, the problems can be found only by manually checking logs or service outgoing, the problem exposure time is not timely, and the back-switching operation is also manually executed by operation and maintenance personnel.
At present, non-systematic errors depend on manual checking, the influence surface of system errors is easily enlarged due to untimely problem identification, meanwhile, when a service switching flow has problems and needs emergency switching, an operation and maintenance worker needs to be contacted to manually execute a switching scheme, the risk caused by misoperation or inaccurate execution process exists in manual operation, if special conditions such as a morning time period are met, the influence surface can be enlarged due to the fact that the service cannot be switched in time, the flow quality of operation and maintenance management and the accuracy of service processing are influenced to a certain extent, and the management cost of enterprises is increased when people are on duty at night.
Disclosure of Invention
The application aims to provide a gray level cut-flow production and emergency method and device, which are used for reducing manual intervention, improving the back cutting efficiency and ensuring stable production operation.
To achieve the above object, the present application provides a method for gray-scale tangential flow commissioning and emergency, the method comprising: constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements; calling a corresponding commissioning strategy through an RPA (resilient packet access) robot according to a service flow switching process to perform service flow switching processing, and acquiring first transaction data and second transaction data generated before and after the service flow switching processing; analyzing the first transaction data and the second transaction data according to a Naive Bayes Classifier (Naive Bayes Classifier) to obtain an operation state after service cut; and calling the emergency strategy according to the running state to execute corresponding emergency treatment.
In the above method for gray-scale tangential flow commissioning and emergency, optionally, the method further includes: and monitoring the running state of the RPA robot, and starting another RPA robot to take over the current flow when the flow is interrupted due to the abnormality of the RPA robot.
In the above grayscale switching commissioning and emergency method, optionally, invoking a corresponding commissioning policy by the RPA robot according to the service switching process to perform service switching processing includes: obtaining flow switching time according to the service flow switching process, and calling a corresponding production strategy through an RPA robot according to a comparison result of the flow switching time and the current time; and executing corresponding service flow switching processing according to the production strategy and storing an execution log to a preset position.
In the above method for bringing gray scale tangential flow into production and for emergency, optionally, the method further includes: acquiring service historical operating state data, and analyzing the service historical operating state data to obtain a discrete value attribute; and training by a naive Bayes classifier algorithm through the discrete value attribute to obtain a naive Bayes classifier.
In the above grayscale cut-flow commissioning and emergency method, optionally, the discrete value attribute includes cut-flow data, transaction success data, error code data, service continuity number, and response time.
In the above method for commissioning and emergency of gray-scale tangential flow, optionally, analyzing the first transaction data and the second transaction data according to a naive bayes classifier to obtain the operation state after service tangential flow comprises: analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain the discrete value attributes before and after service cut-flow processing to obtain a health probability and an abnormal probability; and obtaining the running state after the service flow cut according to the comparison result of the health probability and the abnormal probability.
In the above method for bringing the gray-scale tangential flow into production and performing emergency, optionally, invoking the emergency policy according to the operating state to perform corresponding emergency processing includes: when the health probability is larger than the abnormal probability, the emergency strategy is called through an RPA robot; and carrying out emergency back-switching processing on the service according to the emergency strategy.
In the above method for production and emergency of gray-scale cut stream, optionally, the production strategy includes a gray-scale cut stream script; the emergency strategy comprises an emergency type, a back-cut support category and an emergency statement.
The application also provides a gray scale tangential flow production and emergency device, which comprises a setting module, an acquisition module, an analysis module and a processing module; the setting module is used for constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements; the acquisition module is used for calling a corresponding commissioning strategy through the RPA robot according to the service flow switching process to perform service flow switching processing, and acquiring first transaction data and second transaction data generated before and after the service flow switching processing; the analysis module is used for analyzing the first transaction data and the second transaction data according to a naive Bayesian classifier to obtain the operation state after service flow switching; and the processing module is used for calling the emergency strategy according to the running state to execute corresponding emergency treatment.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.
The present application also provides a computer-readable storage medium storing a computer program for executing the above method.
The present application also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method.
The beneficial technical effect of this application lies in: the method effectively reduces the manual operation steps when the service system is put into production, reduces the operation risk and the manual execution cost of the switching among systems when a plurality of systems are jointly completed when the service system is put into production, and avoids the system risk caused by misoperation. The problem of long time effectiveness of manual intervention emergency schemes when the service system is abnormal at night is solved, the back-switching time after the service system is abnormal is effectively reduced, and high availability guarantee is provided for the service system. The quality of the operation and maintenance process and the accuracy of service processing are improved, and the operation and maintenance process is more compliant and safer.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this application, and are not intended to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a gray-scale tangential flow commissioning and emergency method according to an embodiment of the present application;
fig. 2 is a flowchart illustrating a service switching process according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a naive Bayes classifier training process according to an embodiment of the present application;
fig. 4 is a schematic view of an operation status analysis process provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of an emergency process according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a grayscale tangential flow commissioning and emergency device according to an embodiment of the present application;
fig. 7 is a schematic view of an application structure of a grayscale tangential flow commissioning and emergency device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following detailed description will be given with reference to the accompanying drawings and examples to explain how to apply the technical means to solve the technical problems and to achieve the technical effects. It should be noted that, as long as there is no conflict, the embodiments and the features in the embodiments in the present application may be combined with each other, and the technical solutions formed are all within the scope of the present application.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Referring to fig. 1, a gray-scale tangential flow commissioning and emergency method provided by the present application includes:
s101, establishing a service flow switching process, a production strategy and an emergency strategy according to production requirements;
s102, calling a corresponding commissioning strategy through an RPA robot according to a service flow switching process to perform service flow switching processing, and collecting first transaction data and second transaction data generated before and after the service flow switching processing;
s103, analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain the operation state after service flow switching;
s104, calling the emergency strategy according to the running state to execute corresponding emergency treatment.
Wherein the production strategy comprises a gray level cut flow script; the emergency strategy comprises an emergency type, a back-cut support category and an emergency statement. Therefore, a gray level cut flow production strategy and an emergency strategy are introduced into the system before each application is put into production, the content of the production strategy mainly comprises a gray level cut flow script, and the emergency strategy mainly comprises emergency types, whether automatic back cut is supported or not, emergency statements and the like; the RPA robot can collect transaction data before and after gray level flow cutting, compare the transaction success rate or abnormal error codes before and after flow cutting, and automatically switch back according to emergency types. Manual intervention is reduced, back cutting efficiency is improved, and stable production operation is guaranteed. In actual work, the RPA robots comprise a plurality of RPA robots, the running states of the RPA robots can be monitored when the RPA robots run, and when any one of the RPA robots is abnormal and causes flow interruption, another RPA robot in an idle state is started to take over the current flow to continue to complete processing tasks.
Referring to fig. 2, in an embodiment of the present application, the invoking the corresponding commissioning policy by the RPA robot according to the service flow switching process to perform the service flow switching process includes:
s201, obtaining flow cutting time according to the service flow cutting process, and calling a corresponding production strategy through an RPA robot according to a comparison result of the flow cutting time and the current time;
s202, executing corresponding service flow switching processing according to the production strategy and saving an execution log to a preset position.
Specifically, in actual work, a test point service list shown in table 1 below, that is, a service flow switching process, may be referred to, screening may be performed in sequence according to the test point service list, and it is determined whether a flow switching identifier exists and whether a flow switching time is equal to a current system time, if all the flow switching identifiers are met, a corresponding commissioning policy is performed to complete service flow switching, and an execution log is retained to a preset storage location.
TABLE 1
Figure BDA0003888378700000051
Referring to fig. 3, in an embodiment of the present application, the method may further include:
s301, acquiring service historical operating state data, and analyzing according to the service historical operating state data to acquire a discrete value attribute;
s302, a naive Bayes classifier is obtained through the discrete value attribute and the naive Bayes classifier algorithm training.
Wherein the discrete value attribute comprises cut stream data, transaction success data, error code data, service continuity number and response time. Further, referring to fig. 4, in an embodiment of the present application, analyzing the first transaction data and the second transaction data according to a naive bayes classifier to obtain an operation state after the service cut flow includes:
s401, analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain the discrete value attributes before and after service cut flow processing to obtain a health probability and an abnormal probability;
s402, obtaining the running state after the service flow cut according to the comparison result of the health probability and the abnormal probability.
Specifically, the information can be read in the actual work to store a stored system log, the first transaction data and the second transaction data are obtained according to the system log, the error code between the first transaction data and the second transaction data is calculated through a naive Bayesian classifier according to the transaction data, and whether the service is in a healthy state or not is judged; the principle is as follows:
assuming that a service is healthy or not as a random event c, x denotes a random event-related factor having a variety of attribute values, i.e., x = (x) 1 ,x 2 ,x 3 ,...,x n )；
P (c | x): the probability of a random event occurring in case of c under the condition of x. (posterior probability)
P (c): probability of occurrence of a random event c. (prior probability)
P (x | c): the probability of the occurrence of condition x is known, given the occurrence of event c. (posterior probability)
P (x): the probability of x occurring. (prior probability)
According to a naive Bayes classifier algorithm, assuming that the attribute condition of a random event x is independent, the probability of each value of x is independent and irrelevant to the values of other attributes, the formula can be expressed as follows:
Figure BDA0003888378700000061
p (x) is set as the probability of the attribute x being independent 1 ,x 2 ,x 3 ,...,x n | c) the calculation is simplified to:
Figure BDA0003888378700000062
the classification criterion is formulated as follows, i.e. the most likely case c, y, for a given factor of x, is the set of values of c, where the output result is c such that
Figure BDA0003888378700000063
The calculated result value of (2) is maximum.
Figure BDA0003888378700000064
Each condition x, assuming whether the service is healthy, is a discrete-value attribute, D c Representing a set formed by class c samples of a training set D, | | | represents the element number of the set, D c,xi Is shown by D c And (4) a set consisting of samples with the value xi on the ith attribute value. The ratio of the number of each attribute value in all samples is the value:
Figure BDA0003888378700000065
if x i For continuous value attributes, then probability density functions are used, assuming
Figure BDA0003888378700000066
Wherein mu c,i And
Figure BDA0003888378700000067
the mean and variance of the values of the class c sample on the ith attribute are respectively
Figure BDA0003888378700000068
Assuming that the data set D is the service health condition counted according to the historical condition, training a classifier through the data set D; wherein D can be referred to as shown in the following Table 2:
TABLE 2
Figure BDA0003888378700000069
Figure BDA0003888378700000071
Based on the above data set, assuming that the following characteristics exist for the current service { whether to cut flow = yes, number of service connections =300, response time =160, transaction success = yes, error code =1113}, a prior probability can be calculated:
p (healthy = yes) 0.6923;
p (health = no) ≈ 0.3077;
the discrete value attribute has cut-flow, successful transaction and error code, the continuous value attribute has service connection number and response time, and the following formula can be obtained:
a = P (health = yes) × P Whether or not the tangential flow is *P Number of service connections: 300| is *P Response time: 160| is *P Transaction success is *P Error code 1113 No ；
b = P (health = No). P Whether or not to shear flow *P Number of service connections: 300| No *P Response time: 160| no *P Success of transaction | no *P Error code: 11113| NO ；
Wherein, a is the health probability, and b is the abnormal probability.
Referring to fig. 5, in an embodiment of the present application, invoking the emergency policy according to the operating status to execute a corresponding emergency process includes:
s501, when the health probability is larger than the abnormal probability, the emergency strategy is called through the RPA robot;
s502, performing emergency back-switching processing on the service according to the emergency strategy.
Specifically, if a is larger than b, returning to health, and after completion, registering an execution log to a preset storage position; and if b is larger than a, inquiring whether an emergency strategy exists in the emergency scheme, if so, executing the emergency back-cut script in the operation and maintenance system, and after completion, registering an execution log to a preset storage position. Of course, in actual work, the emergency policy may also be selected and set according to actual needs, and the application is not further limited herein.
Referring to fig. 6, the present application further provides a gray-scale tangential flow commissioning and emergency device, which includes a setting module, an acquisition module, an analysis module, and a processing module; the setting module is used for constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements; the acquisition module is used for calling a corresponding production strategy through the RPA robot according to the service flow cutting process to perform service flow cutting processing and acquiring first transaction data and second transaction data generated before and after the service flow cutting processing; the analysis module is used for analyzing the first transaction data and the second transaction data according to a naive Bayesian classifier to obtain the operation state after service flow switching; and the processing module is used for calling the emergency strategy according to the running state to execute corresponding emergency treatment.
Specifically, in actual work, please refer to fig. 7 in principle, an intelligent processing module may be added to the RPA robot, recognition of the probability of occurrence of the transaction error code is realized through a naive bayes distributor, and it is determined whether the program is in a healthy state after being subjected to stream-cutting, and determination processing capability is added on the basis of automatic execution. The whole structure can comprise a flow designer, a controller and an intelligent robot (Smart robot, which is abbreviated as sbot hereinafter). The following description takes a general production operation and maintenance system as an example, and includes a commissioning policy setting module, a change processing module, an emergency policy module, a system log module, and the like.
1. And the flow designer designs the whole flow through the flow designer, for the condition of service flow switching, the sbot logs in the production operation and maintenance system, acquires a trial service list from the production strategy setting module, screens the service list needing flow switching by the intelligent robot, executes a service flow switching strategy in the change processing module, and saves an operation log. For the emergency back-cut condition, the sbot logs in an emergency scheme management module of the production operation and maintenance system to obtain an emergency scheme, logs in a system log module to obtain an execution log, calculates whether the service is in a healthy state or not through an intelligent module, returns to be normal if the service is healthy, and executes an automatic back-cut notification according to the emergency scheme if the service is not in the healthy state.
2. And a controller. The centralized control center monitors the running states of the plurality of sbots, and if a certain sbot has an abnormal condition in the execution process to cause flow interruption, other sbots can be started to take over the tasks and continue to execute the tasks.
3. Intelligent robot (sbot). Comprises a flow control module 21, an information processing module 22, an information storage module 23 and an artificial intelligence processing module 24. And the flow control module 21 records the execution flow set by the flow controller and controls the sbot to execute the flow in sequence. The information processing module 22 carries a mouse and keyboard event simulation method and a screen capture method, obtains an interface layout and a menu through the screen capture method after logging in a production operation and maintenance system, and clicks to enter specific modules such as production strategy setting through the mouse and keyboard event simulation method to realize non-invasive access. The information storage module 23 is configured to store a List of test point services, for example, information such as List < Map < String, string > serviceList, emergency plan, processing result, and training set is stored, so as to implement interactive access of the artificial intelligence module. And the artificial intelligence module 24 executes the service flow cutting script according to the pointing service list, carries a naive Bayes classifier execution algorithm, calculates according to error codes in a system log, and returns whether the service is healthy at present.
The beneficial technical effect of this application lies in: the method effectively reduces the manual operation steps when the service system is put into production, reduces the operation risk and the manual execution cost of the switching among systems when a plurality of systems are jointly completed when the service system is put into production, and avoids the system risk caused by misoperation. The problem of long time effectiveness of manual intervention emergency schemes when the service system is abnormal at night is solved, the back-switching time after the service system is abnormal is effectively reduced, and high availability guarantee is provided for the service system. The quality of the operation and maintenance process and the accuracy of service processing are improved, and the operation and maintenance process is more compliant and safer.
The present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method is implemented.
The present application also provides a computer-readable storage medium storing a computer program for executing the above method.
The present application also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method.
As shown in fig. 8, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 8; in addition, the electronic device 600 may also include components not shown in fig. 8, which may be referred to in the prior art.
As shown in fig. 8, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable devices. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used for displaying display objects such as images and characters. The display may be, for example, but is not limited to, an LCD display.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142 for storing application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (12)

1. A gray scale tangential flow commissioning and contingency method, comprising:
constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements;
calling a corresponding commissioning strategy through an RPA robot according to a service flow switching process to perform service flow switching processing, and acquiring first transaction data and second transaction data generated before and after the service flow switching processing;
analyzing the first transaction data and the second transaction data according to a naive Bayesian classifier to obtain an operation state after service cut flow;
and calling the emergency strategy according to the running state to execute corresponding emergency treatment.
2. The gray-scale tangential flow commissioning and contingency method of claim 1, further comprising:
and monitoring the running state of the RPA robot, and starting another RPA robot to take over the current flow when the RPA robot is abnormal to cause flow interruption.
3. The gray scale cut flow commissioning and emergency method according to claim 1, wherein invoking a corresponding commissioning policy for service cut flow processing by an RPA robot according to a service cut flow process comprises:
obtaining flow switching time according to the service flow switching process, and calling a corresponding production strategy through an RPA robot according to a comparison result of the flow switching time and the current time;
and executing corresponding service flow switching processing according to the production strategy and storing an execution log to a preset position.
4. The gray-scale tangential flow commissioning and contingency method of claim 1, further comprising:
acquiring service historical operating state data, and analyzing the service historical operating state data to acquire a discrete value attribute;
and training by a naive Bayes classifier algorithm through the discrete value attribute to obtain a naive Bayes classifier.
5. The gray-scale cut-stream commissioning and contingency method of claim 4, wherein said discrete-value attributes comprise cut-stream data, transaction success data, error code data, number of service continuity, and response time.
6. The gray-scale tangential flow commissioning and emergency method of claim 4, wherein analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain the operating state after the service tangential flow comprises:
analyzing the first transaction data and the second transaction data according to a naive Bayes classifier to obtain the discrete value attributes before and after the service cut flow processing to obtain a health probability and an abnormal probability;
and obtaining the running state after the service flow cut according to the comparison result of the health probability and the abnormal probability.
7. The gray-scale tangential flow commissioning and emergency method according to claim 6, wherein invoking the emergency policy according to the operating state to execute a corresponding emergency treatment comprises:
when the health probability is larger than the abnormal probability, the emergency strategy is called through an RPA robot;
and carrying out emergency back-switching processing on the service according to the emergency strategy.
8. The gray-scale cut-flow commissioning and contingency method according to any one of claims 1 to 7, wherein the commissioning strategy comprises a gray-scale cut-flow script; the emergency strategy comprises an emergency type, a back-switch support category and an emergency statement.
9. A gray scale tangential flow production and emergency device is characterized by comprising a setting module, an acquisition module, an analysis module and a processing module;
the setting module is used for constructing a service flow switching process, a production strategy and an emergency strategy according to production requirements;
the acquisition module is used for calling a corresponding production strategy through the RPA robot according to the service flow cutting process to perform service flow cutting processing and acquiring first transaction data and second transaction data generated before and after the service flow cutting processing;
the analysis module is used for analyzing the first transaction data and the second transaction data according to a naive Bayesian classifier to obtain the operation state after service flow switching;
and the processing module is used for calling the emergency strategy according to the running state to execute corresponding emergency treatment.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 8 when executing the computer program.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 8 by a computer.
12. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 8.
CN202211251904.2A 2022-10-13 2022-10-13 Method and device for gray level tangential flow production and emergency Pending CN115525327A (en)

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CN202211251904.2A CN115525327A (en) 2022-10-13 2022-10-13 Method and device for gray level tangential flow production and emergency

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211251904.2A CN115525327A (en) 2022-10-13 2022-10-13 Method and device for gray level tangential flow production and emergency

Publications (1)

Publication Number Publication Date
CN115525327A true CN115525327A (en) 2022-12-27

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