CN113298326B - Intelligent electronic event supervision method, equipment and storage medium - Google Patents
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Abstract
The invention discloses an intelligent electronic event supervision method, equipment and a storage medium, aiming at solving the problems of low efficiency and unscientific nature of artificial verification existing in a service supervision mode in the prior art, wherein the supervision method mainly comprises the following steps: red light event reply and manual verification; acquiring historical red light events of a plurality of monitoring points and corresponding red light replies, and performing semantic recognition on red light reply contents by using a semantic recognition algorithm to generate a label; sorting, layering and sampling; checking the samples and optimizing labels; establishing a classification model; marking is automatically carried out by using the classification model. The invention provides an intelligent electronic event supervision method, equipment and a storage medium.
Description
Technical Field
The present invention relates to a data processing system or method specially adapted for administrative, commercial, financial, management, supervision or forecasting purposes, and more particularly to an intelligent electronic event supervision method, apparatus and storage medium.
Background
In the rail transit industry, due to the fact that the types of the services are various, how to effectively control the service risk becomes an important and urgent task in front of enterprises. In order to control the core service, an enterprise generally sets a plurality of monitoring points and monitoring conditions to monitor the service operation, and automatically judges whether to trigger abnormity and early warning by butting monitoring service system data, so that the real-time monitoring of the service field is realized.
However, the existing supervision method has the following defects:
1. aiming at the red light events with obvious quantity and the red light reply, the workers are required to check one by one, so that the checking work is not scientific and the efficiency is low.
2. After the red light event is triggered, the contact abnormity can be realized through the red light reply, but the validity of the red light reply cannot be verified, and the text data replied by the red light is not well utilized.
Disclosure of Invention
The invention aims to solve the problems of low efficiency and no scientificity of manual verification in a service monitoring mode in the prior art, and provides an intelligent electronic event monitoring method, equipment and a storage medium.
The technical scheme adopted by the invention is as follows:
an intelligent electronic event surveillance method, the method comprising the steps of:
step S1, setting a monitoring point and a monitoring condition, and triggering a red light event if the monitoring point and the monitoring condition are abnormal;
step S2, replying to the red light event;
step S3, performing under-line check on the red light recovery;
step S4, acquiring historical red light events of a plurality of monitoring points and corresponding red light replies, and recognizing red light reply contents by using a semantic recognition algorithm to generate labels;
step S5, sorting the red light replies of the monitoring point according to a preset rule;
step S6, layering the red light replies sequenced by each monitoring point;
step S7, carrying out layered sampling on each monitoring point;
step S8, checking the samples and optimizing labels;
step S9, using the sampled data as a training set and the sampled red light reply as a verification set to establish a classification model;
and step S10, marking automatically by the classification model.
Further, in step S6, the monitoring point red light returns to the following state in accordance with the data volume 4: 3: the 3-ratio division was A, B, C trilayer.
Further, in step S9, a machine learning and classification algorithm is used in training the classification model.
Based on the same inventive concept, the invention also discloses a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the intelligent electronic event supervision method when executing the computer program.
Based on the same inventive concept, the present invention also discloses a computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, realizes the steps of the aforementioned intelligent electronic event supervision method.
The invention has the beneficial effects that:
1. the invention provides an intelligent electronic event supervision method, equipment and a storage medium, which are used for solving the problems of low efficiency and unscientific nature of artificial verification existing in a service supervision mode in the prior art. By adopting the method, the red light event and the red light reply are verified manually in the early stage, and the classification model is constructed on the basis of the accumulated red light event and the red light reply data in the later stage to perform automatic marking, so that the workload of manual verification in the later stage is greatly reduced, and the efficiency and the scientificity of the supervision and verification work in the later stage are improved.
2. By adopting the method, the abnormality of the red light excess phenomenon is analyzed from the root through the form of tagging the red light reply data, the reason of the red light is searched, and a deeper business risk control method is explored from the reverse thinking, for example, the method is applied to business risk control in the rail transit industry. Through the analysis of different business bodies, the deep root cause causing the business risk triggering is finally more scientifically excavated, and the method has guiding significance for the subsequent specific business work management.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of an embodiment.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
The following disclosure provides many different embodiments or examples for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention.
Embodiments of the invention are described in detail below with reference to the accompanying drawings.
In order to control the core service, enterprises generally adopt the arrangement of a monitoring point and monitoring conditions to monitor the service operation, and automatically judge whether to trigger abnormity and early warning by butting the data of a monitoring service system, thereby realizing the real-time monitoring in the service field. However, the existing supervision method has the following defects: 1. aiming at the red light events with obvious quantity and the red light reply, the workers are required to check one by one, so that the checking work is not scientific and the efficiency is low. 2. After the red light event is triggered, the contact abnormity can be realized through the red light reply, but the validity of the red light reply cannot be verified, and the text data replied by the red light is not well utilized.
In order to solve the problems of low efficiency and non-scientific manual verification in the service supervision mode in the prior art, the embodiment of the invention provides an intelligent electronic event supervision method, which replies to a red light event and a red light through manual verification in the early stage, and establishes a classification model based on accumulated red light event and red light reply data in the later stage to automatically mark and classify, wherein the implementation process is shown in fig. 1.
An intelligent electronic event supervision method, comprising the following steps:
and step S1, setting a monitoring point and a monitoring condition, and triggering a red light event if the monitoring point and the monitoring condition are judged to be abnormal. The red light event indicates that an abnormal condition exists in the event.
For example, in the rail transit construction project supervision, a list project supplement supervision point, a list project quantity supervision point, a list item price combination supervision point, a payment list quantity and price supervision point and the like are respectively arranged.
If the list item supplement monitoring point list item supplement is larger than 10, the abnormity is judged, and the red light event is triggered. I.e. there is an exception to the list item additions.
In step S2, a red light event is replied to. The red light recovery is an explanation of the cause of the abnormal condition.
For example, if the list item supplement monitoring point list item supplement is greater than 10, it is determined to be abnormal, and a red light event is triggered to perform red light recovery. That is, the list item addition has an abnormal condition, and the cause of the abnormal condition and the like will be described.
In step S3, the red light is manually checked again. The purpose of the verification is to further confirm the validity of the red light reply.
And step S4, acquiring historical red light events of a plurality of monitoring points and corresponding red light replies, and recognizing red light reply contents by using a semantic recognition algorithm to generate labels.
For example, after semantic recognition, a "class AB change" label, a "design change" label, a "list item under consideration" label, etc. are generated.
And step S5, sorting the red light replies of the monitoring points according to the preset regulations according to the incidence relation among the monitoring points.
For example, the supervision point red light replies are sorted according to the following rules:
and sorting the list item supplement monitoring point red light replies according to the quantity of supplement list items from large to small.
And sorting the red light replies of the supervision points of the clear project amount from large to small according to the percentage of the project amount exceeding.
And sorting the clear item contract price monitoring point red light replies according to the descending of the clear item contract price.
And sequencing the payment sum of the clear payment amount and the price monitoring point red light reply according to the sequence from large to small of the exceeding accumulated metering payment sum and the engineering amount.
And step S6, layering the red light replies sequenced by each monitoring point.
For example, the red light of the monitoring point returns to the original red light according to the quantity ratio of 4: 3: the 3-ratio division was A, B, C trilayer. The method comprises the following steps of: 3: the ratio of 3 is to focus more on red light events that are more severely verified, with the most even distribution possible.
Step S7, hierarchically sampling each monitoring point.
For example, if the inventory items total 8743 items, accounting for about 85%, 426 items are expected to be sampled.
And step S8, checking the samples and optimizing the labels.
Because a label set dictionary is initially created through a semantic recognition technology before verification, and the condition that labels cannot meet the actual working requirements can be met in the verification process in the later period, specific problems need to be specifically analyzed, and the label set is continuously optimized through adding and modifying labels.
Step S9, building a classification model by using the sampled data (including red light event, red light reply and label) as a training set and using the non-sampled (non-sampled) red light reply data as a verification set.
The classification model adopts machine learning and a classification algorithm, takes sampled and marked red light reply data as a training set, trains a classifier, and enables the classifier to automatically classify other unknown samples.
The classification algorithm belongs to a supervised learning. The classification process of classification algorithms is to build a classification model describing a predetermined set of data or concepts, the model being constructed by analyzing database tuples described by attributes. The purpose of classification is to use classification to partition a new data set, which mainly involves accuracy of classification rules, overfitting, choice of contradictory partitions, and the like.
And establishing a corresponding relation between the red light event and the label through a classification model.
Step S10, automatically marking new red light events with the classification model.
The new red light event is intelligently generated into a label, and convenience is brought to follow-up manual offline check.
Taking the supervision of rail transit construction projects as an example, a list project supervision point is set. If the list item is supplemented by more than 5, it is determined to be abnormal, and a red light event is triggered. During the monitoring, the total number of anomalies triggered the red light event 8743 times. After each red light event, 8743 red light replies were obtained. Semantic recognition is carried out on the red light reply data, and a dictionary formed by a plurality of labels is recognized in advance, such as labels of 'AB class change', labels of 'design change', and labels of 'list item under consideration'. And sorting the red light replies of the project amount monitoring points of the list items according to the percentage exceeding the project amount from large to small. The sorted red light responses are layered, wherein 3497 layers are arranged on the A layer, 2623 layers are arranged on the B layer, and 2623 layers are arranged on the C layer. By hierarchical sampling, it is confirmed that 170 samples are extracted from the a layer, 128 samples are extracted from the B layer, 127 samples are extracted from the C layer, and 426 samples are extracted. And (4) performing verification and marking work by an offline organization service expert and optimizing the label. And then, the machine takes 426 marking data as a training set to train a label classifier, the label classifier is applied to the remaining unmarked data 8317 to automatically mark, and finally the purpose that all red light reply data are provided with labels is achieved, namely a classification model is established. The classification model is adopted to automatically mark in the later period, so that the later-period worker verification efficiency and scientificity are improved.
By adopting the method in the embodiment, the red light event and the red light are replied through manual verification in the early stage, and the classification model is constructed on the basis of the accumulated red light event and the red light reply data in the later stage, so that the text data replied by the red light is fully utilized, automatic marking is performed, the workload of the manual verification in the later stage is greatly reduced, and the efficiency and the scientificity of supervision and verification work are improved.
Meanwhile, after the classification model operates for a certain time, new data such as red light events, red light replies and labels can be brought into the database to be sampled, and the classification model is further optimized and adjusted so as to improve the accuracy and the scientificity of marking.
The embodiment also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the intelligent electronic event supervision method when executing the computer program.
The present embodiment also provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the foregoing intelligent electronic event monitoring method.
In the electronic event supervision method in the embodiment, the red light event and the red light reply are verified manually in the early stage, and the classification model is established based on the accumulated red light event and the red light reply data in the later stage to automatically mark and classify, so that the workload of later-stage manual verification is greatly reduced, and the efficiency and the scientificity of later-stage supervision and verification work are improved. And by searching the reason of the red light event, the problem of excessive red light event is solved, the abnormity is analyzed from the root, and a deeper business risk management and control method is explored from the reverse thinking. Through the analysis of different business bodies, the deep root cause causing the business risk triggering is finally more scientifically excavated, and the method has guiding significance for the subsequent specific business work management.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. An intelligent electronic event supervision method, characterized in that the method comprises the following steps:
step S1, setting a monitoring point and a monitoring condition, and triggering a red light event if the monitoring point and the monitoring condition are abnormal;
step S2, replying to the red light event;
step S3, performing under-line check on the red light recovery;
step S4, acquiring historical red light events of a plurality of monitoring points and corresponding red light replies, and recognizing red light reply contents by using a semantic recognition algorithm to generate labels;
step S5, sorting the red light replies of the monitoring point according to a preset rule;
step S6, layering the red light replies sequenced by each monitoring point;
step S7, carrying out layered sampling on each monitoring point;
step S8, checking the samples and optimizing labels;
step S9, establishing a classification model by taking the sampled data as a training set and the sampled outer red light reply data as a verification set;
and step S10, marking the new red light event automatically by the classification model, and generating a label of the new red light event.
2. The intelligent electronic event supervision method according to claim 1, wherein in step S6, the red light of the supervision point returns to the state that the ratio of the number of the supervision point to the number of the supervision point is 4: 3: the 3-ratio division was A, B, C trilayer.
3. The intelligent electronic event supervision method according to claim 1, wherein in step S9, machine learning and classification algorithm is adopted when the classification model is established.
4. A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of the intelligent electronic event supervision method according to claim 1, 2 or 3.
5. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the intelligent electronic event supervision method according to claim 1, 2 or 3.
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Inventor after: Zhang Yi Inventor after: Su Zuquan Inventor after: Liu Hongjiang Inventor after: Wang Hongshuang Inventor after: Zhao Chengting Inventor after: Liu Kun Inventor before: Zhang Yi |
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Denomination of invention: Intelligent and electronic event monitoring method, equipment and storage medium Effective date of registration: 20221011 Granted publication date: 20211026 Pledgee: Bank of Chengdu science and technology branch of Limited by Share Ltd. Pledgor: Chengdu Xichen Software Co.,Ltd. Registration number: Y2022980017780 |