CN113849369B - Scoring method, scoring device, scoring equipment and scoring storage medium - Google Patents
Scoring method, scoring device, scoring equipment and scoring storage medium Download PDFInfo
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Abstract
The invention discloses a scoring method, a scoring device, scoring equipment and a scoring storage medium. The method comprises the following steps: acquiring first attribute information of a system to be scored, wherein the first attribute information comprises: monitoring the number of subclasses, the weights of the monitoring subclasses and the weights of the monitoring indexes; determining the monitoring index score of the system to be scored according to the number of the monitoring subclasses of the system to be scored and the weight of the monitoring subclasses of the system to be scored; and determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored. According to the technical scheme, a set of more perfect system scoring system is designed, the score of the existing system can be measured and calculated, and an administrator can intuitively and rapidly know the advantages and disadvantages of the existing system.
Description
Technical Field
The embodiment of the invention relates to the field of system monitoring, in particular to a scoring method, a scoring device, scoring equipment and a scoring storage medium.
Background
With the development of banking and information science technologies, banking increasingly depends on the stability of the system. Whether the service can be successfully developed is necessarily based on the stable operation of the system, so that the attention of application operation and maintenance personnel to system monitoring is brought.
Currently, system evaluation mainly focuses on both monitoring coverage and policy effectiveness. In the face of a sharply growing number of systems, although a large number of effective tools can monitor all indexes of the systems comprehensively, the monitoring of the systems still faces a bottleneck which is difficult to break through: the operation and maintenance personnel cannot effectively evaluate whether the setting of the monitoring of the system is reasonable.
Disclosure of Invention
The embodiment of the invention provides a scoring method, a scoring device, scoring equipment and a scoring storage medium, which designs a set of more perfect system scoring system, can calculate the score of the existing system, and is used for an administrator to intuitively and quickly know the advantages and disadvantages of the existing system.
In a first aspect, an embodiment of the present invention provides a scoring method, including:
Acquiring first attribute information of a system to be scored, wherein the first attribute information comprises: the monitoring subclass, the weight of the monitoring subclass and the weight of the monitoring index;
Determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored;
And determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored.
In a second aspect, an embodiment of the present invention further provides a scoring apparatus, where the scoring apparatus includes:
The system comprises an acquisition module, a scoring module and a scoring module, wherein the acquisition module is used for acquiring first attribute information of a system to be scored, and the first attribute information comprises: the monitoring subclass, the weight of the monitoring subclass and the weight of the monitoring index;
The first determining module is used for determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored;
And the second determining model is used for determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the scoring method according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a scoring method according to any one of the embodiments of the present invention.
The embodiment of the invention obtains the first attribute information of the system to be scored; determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored; and determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored. The scoring of the existing system can be calculated, so that an administrator can intuitively and rapidly know the advantages and disadvantages of the existing system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a scoring method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a scoring method according to a second embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a scoring device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer readable storage medium containing a computer program according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings. Furthermore, embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like. Furthermore, embodiments of the invention and features of the embodiments may be combined with each other without conflict.
The term "comprising" and variants thereof as used herein is intended to be open ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment".
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
Fig. 1 is a flowchart of a scoring method provided in an embodiment of the present invention, where the embodiment is applicable to scoring, the method may be performed by a scoring device in the embodiment of the present invention, and the device may be implemented in software and/or hardware, as shown in fig. 1, and the method specifically includes the following steps:
s101, acquiring first attribute information of a system to be scored.
In this embodiment, the system to be scored is any system in the current stock system set. The current stock system set may be understood as a set of currently existing system components. There may be a plurality of systems in the current stock system set, for example, 500 systems, which is not limited in this embodiment. It should be noted that the system to be scored may be one or more, which is not limited in this embodiment.
Wherein the first attribute information includes: the monitoring subclass, the weight of the monitoring subclass, and the weight of the monitoring index.
The obtaining mode of the monitoring subclass may be: the monitoring device arranged in the system collects monitoring subclasses in the system, for example, the monitoring device collects the monitoring subclasses existing in the current system and comprises: off baseline and superfluidic monitoring.
The method for obtaining the weight of the monitoring subclass may be: inquiring a database according to a monitoring subclass existing in the system to obtain a weight corresponding to the monitoring subclass, wherein a corresponding relation list of the weights of the monitoring subclass and the monitoring subclass is stored in the database, for example, if the monitoring subclass existing in the current system is: and if the base line is deviated and the flow control monitoring is performed, inquiring the weight and the flow control monitoring deviating from the base line.
The obtaining manner of the weight of the monitoring index may be: determining the monitoring index of the system according to the monitoring subclass of the system, and inquiring the database according to the monitoring index of the system to obtain the weight of the monitoring index of the system, for example, if the monitoring subclass of the current system is: and (3) deviating from the baseline and performing superfluity monitoring, wherein the monitoring indexes corresponding to the monitoring subclasses of 'deviating from the baseline' and 'superfluity monitoring' are 'transaction amount monitoring', and the weights of the monitoring indexes existing in the system can be determined by inquiring the monitoring index weight table in the database.
In the actual operation process, the systems are classified into 5 types according to the characteristics of the systems: financial core class, channel-to-customer class, platform hub class, business management class, and enterprise management class.
The characteristics of the class 5 system are shown in table 1:
TABLE 1
In general, the monitoring of the system can be divided into two types, transaction quality monitoring and technical monitoring. Transaction quality monitoring is generally divided into three types, namely transaction amount monitoring, transaction success rate and transaction time consumption monitoring. According to different technologies used by the system, the method can be divided into three types of state monitoring, existence monitoring and custom monitoring. The two-stage classification of transaction amount monitoring, transaction success rate, transaction time consumption monitoring, state monitoring, existence monitoring and custom monitoring is refined and divided into monitoring subclasses.
The monitoring index "transaction amount monitoring" can comprise less than X times IN the past X minutes, same-period deviation baseline monitoring, superfluity monitoring (superfluity of p_IN end/superfluity of c_IN end/superfluity of p_OUT end) and the like.
The monitoring index of transaction success rate can comprise system success rate, system response rate, same-period offset baseline monitoring, business success rate and past X minutes of failure X strokes.
The monitoring index "trade time-consuming monitoring" may include trade time-consuming jitter monitoring, which may be, for example, over X seconds for the past X minutes, off-baseline monitoring with the same period, and overall time-consuming monitoring.
The monitoring index "state monitoring" may include processes, containers, ports, threads, weblogic and database connection states.
The monitoring index "presence monitoring" may include processes, ports, and file systems, among others.
The monitoring index 'custom monitoring' is generally realized through log keywords, and mainly focuses on database connection abnormality, high or overflow of an application process memory, jdbc connection pool connection number monitoring and thread use conditions.
The weights of the monitoring indexes in the 5-class system are shown in table 2:
TABLE 2
| Monitoring index | Financial core class | Channel class for guests | Platform hinge | Service management class | Enterprise management class |
| Transaction amount monitoring | 0.2 | 0.2 | 0.15 | 0 | 0 |
| Transaction success rate | 0.3 | 0.2 | 0.15 | 0 | 0 |
| Transaction time consuming monitoring | 0.15 | 0.2 | 0.2 | 0.2 | 0 |
| State monitoring | 0.1 | 0.1 | 0.2 | 0.4 | 0.5 |
| Presence monitoring | 0.1 | 0.1 | 0.2 | 0.4 | 0.5 |
| Custom monitoring | 0.15 | 0.2 | 0.1 | 0 | 0 |
The financial core system carries the transaction of all core business of the bank, serves various business of the bank and is the core of the bank system. The transaction amount is large, and the stability and the availability requirements on the system are extremely high. And thus the emphasis on monitoring is on transaction quality monitoring. The monitoring of the success rate of the transaction is particularly important, whether the transaction is successful or not directly determines the result of the customer transaction, and if the success rate is low, unstable and the like, important attention is required, so that weight of 0.3 is given.
The system is a source for business initiation for the passenger channel system, and the transaction amount is extremely large. Meanwhile, the method is the most direct contact with an in-line system when a user initiates a transaction, and the image of a bank is affected if the user experience is poor, so that monitoring of transaction quality, namely time consumption, success rate and transaction amount is particularly important. In addition, custom monitoring is also important to the channel-based system based on the conventional monitoring.
The platform hub system is an intermediate platform of one service and is responsible for completing the serial connection and forwarding of each transaction in the one service. The availability of the system directly affects whether a service can be successfully, completely and quickly completed, so that the monitoring of the state, the existence and the time consumption of the transaction is particularly important.
The service management system is an in-pair service system, meets the service requirements of service personnel for processing customer information, transaction management and the like, has service time of generally workdays, has higher requirements on system fluency and availability, and has higher tolerance degree on faults compared with an important external transaction system. Thus, business management class systems should be more concerned with technical monitoring, such as status, progress, etc., and should also be concerned with transaction time consumption to ensure system fluency.
The enterprise management system is mainly responsible for the management in the enterprise, has low requirements on the operation smoothness of the system, and mainly focuses on availability. And is therefore primarily concerned with status, presence monitoring of the system.
The weights of the monitoring subclasses in the class 5 system are shown in table 3:
TABLE 3 Table 3
S102, determining the monitoring index score of the system to be scored according to the number of the monitoring subclasses of the system to be scored and the weight of the monitoring subclasses of the system to be scored.
The monitoring index score of the system to be scored can be understood as a result of scoring various monitoring indexes of the system to be scored according to a percentile.
Optionally, determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored includes:
And determining the score of the monitoring subclass of the system to be scored according to the monitoring subclass of the system to be scored.
The method for determining the score of the monitoring subclass of the system to be scored according to the monitoring subclass of the system to be scored may be as follows: if the monitoring subclass exists, the score of the monitoring subclass is 100, and if the monitoring subclass does not exist, the score of the monitoring subclass is 0.
Illustratively, during actual operation, the score A of the monitoring subclass of the system to be scored may be calculated according to the following manner:
Where C represents the number of actual deployments of the monitoring subclass of the system to be scored.
And determining the monitoring index score of the system to be scored according to the score of the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored.
In the actual operation process, the monitoring index score S of the system to be scored can be obtained by summing the products of the scores of all monitoring subclasses and the weights of the monitoring subclasses, and the following formula is shown:
S＝∑Ai*ωi；
Where A i represents the monitoring subclass score and ω i represents the weight of each monitoring subclass.
And S103, determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored.
In the actual operation process, the total score T of the system to be scored can be obtained by summing the results of multiplying the various monitoring index scores S i by the weights W i, as shown in the following formula:
T＝∑Wi*Si；
wherein T represents the total score of the system to be scored, S i represents the scores of various monitoring indexes, and W i represents the weights of the various monitoring indexes.
The embodiment of the invention obtains the first attribute information of the system to be scored; determining the score of the monitoring subclass of the system to be scored according to the monitoring subclass of the system to be scored; determining the monitoring index score of the system to be scored according to the score of the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored; and determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored. The scoring of the existing system can be calculated, so that an administrator can intuitively and rapidly know the advantages and disadvantages of the existing system.
Example two
Fig. 2 is a flowchart of a scoring method according to a second embodiment of the present invention, where, based on the foregoing embodiment, the system to be scored is a current system. Further, in the embodiment of the present invention, determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored includes: and determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system.
As shown in fig. 2, the method specifically includes the following steps:
s201, acquiring first attribute information of a current system.
In this embodiment, the current system is a system currently used by the user.
Wherein the first attribute information includes: the monitoring subclass, the weight of the monitoring subclass, and the weight of the monitoring index.
S202, determining the monitoring index score of the current system according to the monitoring subclass of the current system and the weight of the monitoring subclass of the current system.
The monitoring index score of the current system can be understood as a result of scoring various monitoring indexes of the current system according to a percentile.
Specifically, the manner of determining the monitoring index score of the current system according to the monitoring subclass of the current system and the weight of the monitoring subclass of the current system may be: determining the sum of the products of the weights of all the monitoring subclasses and the scores of the monitoring subclasses contained in the monitoring index as the score of the monitoring index of the current system, for example, if the monitoring index of the current system includes: transaction amount monitoring and transaction success rate, wherein the transaction amount monitoring comprises the following monitoring subclasses: off-baseline and superfluidic monitoring, transaction success rates include the following monitoring subclasses: the method comprises the steps of determining the sum of the product of the weight deviating from a base line and the score deviating from the base line and the product of the weight of the superfluity monitoring and the score of the superfluity monitoring as the score of the transaction amount monitoring, and determining the product of the weight of the system success rate and the score of the service success rate and the product of the score of the service success rate as the score of the transaction success rate.
Optionally, determining the monitoring index score of the current system according to the monitoring subclass of the current system and the weight of the monitoring subclass of the current system includes:
determining the score of the monitoring subclass of the current system according to the monitoring subclass of the current system;
and determining the monitoring index score of the current system according to the score of the monitoring subclass of the current system and the weight of the monitoring subclass of the current system.
Specifically, the manner of determining the score of the monitoring subclass of the current system according to the monitoring subclass of the current system may be: if the monitoring subclass exists, determining that the score of the monitoring subclass is 100, and if the monitoring subclass does not exist, determining that the score of the monitoring subclass is 0.
Illustratively, during actual operation, the score A' for the monitoring subclass of the current system may be calculated as follows:
Where C' represents the number of actual deployments of the monitoring subclass of the current system.
For example, in the actual operation process, the monitor indicator score S' of the current system may be obtained by summing the products of the monitor subclass scores and the weights thereof, as shown in the following formula:
S′＝∑A′i*ω′i；
Where A 'i represents the monitor subclass score and ω' i represents the weight of each monitor subclass.
S203, determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system.
Specifically, the manner of determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system may be: the sum of the products of the scores of all the monitoring indexes and the weights of the monitoring indexes in the current system is determined as the score of the current system, and can be: if the current system includes the following monitoring indexes: transaction amount monitoring, transaction success rate, and transaction time consuming monitoring, then the score of the current system is equal to the sum of the product of the transaction amount monitoring score and the transaction amount monitoring weight, the product of the transaction success rate score and the transaction success rate weight, and the product of the transaction time consuming monitoring score and the transaction time consuming monitoring weight.
Illustratively, in the actual operation process, the total score T ' of the current system can be obtained by summing the results obtained by multiplying the various monitoring index scores S ' i by the weights W ' i thereof, as shown in the following formula:
T′＝∑W′i*S′i；
Where T ' represents the total score of the current system, S ' i represents the scores of the various monitoring indicators, and W ' i represents the weights of the various monitoring indicators.
Optionally, after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further includes:
and acquiring second attribute information of the current system and the current stock system set.
Wherein the second attribute information includes: system category, monitoring index, monitoring subclass, weight of monitoring subclass, and number of monitoring subclass.
The manner of obtaining the number of the monitoring subclasses may be: the monitoring device arranged in the system collects the number of monitoring subclasses in the system, for example, the monitoring device can collect the monitoring subclasses existing in the current system, including: the method comprises the steps of deviating from a base line, performing superfluity monitoring, deviating from the base line and achieving service success rate, wherein the number of existing monitoring subclasses of the current system is two, the number of existing monitoring subclasses of the current system is 1, and the number of existing monitoring subclasses of the current system is 1.
In this embodiment, the system class refers to that the current system specifically belongs to at least one of a financial core class, a channel-to-customer class, a platform hub class, a business management class, and an enterprise management class.
It should be noted that the current stock system set may be understood as a set of currently existing system components. There may be a plurality of systems in the current stock system set, for example, 500 systems, which is not limited in this embodiment.
A first set is determined based on the system class of the current system and the current inventory system set.
It should be noted that, the first set may be understood as a set formed by taking out, from the current stock system set, a system having the same class as the system class of the current system after determining the system class of the current system.
A score is obtained for each system in the first set.
In this embodiment, the method for obtaining the score of each system in the first set is the same as the method for obtaining the score of the current system, and will not be described in detail herein.
The systems in the first set are ranked according to the score of each system.
In this embodiment, a heap sort algorithm (no sort is required, and the header element is acquired with minimum time complexity) is selected to sort the systems in the first set according to the score of each system.
And acquiring second attribute information of the target system of N before sequencing.
It should be noted that, the target system refers to a system N before the system in the first set is ranked according to the score of each system. Wherein N is a positive integer greater than or equal to 1.
In the actual operation process, between systems classified by the same system, a target system is determined by a TopK (a disordered array, length of N, and output of minimum/maximum K numbers) method.
And acquiring second attribute information of the target system, wherein the second attribute information comprises a system category of the target system, a monitoring index of the target system, monitoring subclasses of the target system, the number of the monitoring subclasses of the target system and the weight of the monitoring subclasses of the target system.
And generating a comparison graph according to the second attribute information of the current system, the score of the current system, the second attribute information of the target system and the score of the target system, and displaying the comparison graph.
The comparison graph may be, for example, a graph for illustrating a difference between the current system and the target system, so as to complement the current system monitoring scheme according to the target system targeted optimization, for example, a radar graph. And generating a comparison graph and displaying the difference between the monitoring indexes of the target system and the current system, so as to optimize and improve in the specific monitoring index direction.
The standard recommendation of the system monitoring is focused on the system category of prior classification, the total score T ' of the system in a scoring system is mainly used in various systems respectively through the predefined system classification, a heap ordering algorithm is selected, and the total score T ' of the target system is calculated to be recommended by the first three systems (because the total score T ' combines 6 monitoring indexes and the scores of 16 monitoring subclasses, a fuzzy general condition exists in the aspect of recommendation opinion, and a Top3 system is selected). The advantage monitoring of the short plates of the current system and the target system is specifically shown by using the visualization method of the contrast diagram, so that the target system can be conveniently subjected to targeted optimization and complement monitoring schemes.
In the standardized recommendation scenario, there are two risk points that are not applicable to the actual situation, which are respectively: the prior condition of system classification is utilized to artificially divide the system into different types, but the systems with different service types can also refer to the optimized content in the monitoring angle, so that recommendation deviation exists in the artificial division; and calculating dimension deviation. The evaluation model contains weights and scores of 5 system classifications, 6 monitoring indexes and 16 monitoring subclasses, and the monitoring performance of the system cannot be completely displayed by using the system score T feature. In addition, the weight of the scoring system lacks consideration on the monitoring volume, such as a financial core system, in the application monitoring, the attention to the transaction quality is highest, and a plurality of systems are additionally supplemented with a plurality of microparticle monitoring under the monitoring volume meeting the scoring weight requirement, so that the monitoring model of the system is recommended to be emphasized.
Therefore, another way of determining the recommendation system is presented below. The personalized recommendation considers the training model with the prior condition removed, and additionally enriches the application monitoring feature dimension provided by the scoring system, so that better recommendation capability is met.
Optionally, after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further includes:
and inputting the second attribute information of the current system and the score of the current system into a recommendation model, and determining a recommendation system identification corresponding to the current system.
The recommendation model is obtained by iteratively training a large-scale piecewise linear model through a target sample set.
In this embodiment, the recommendation model may be a model for a monitoring scheme of the current system, and the recommendation model can be used to obtain a more optimized recommendation system through training.
The recommendation system identifier may be, for example, a score of the recommendation system corresponding to the current system, a name of the recommendation system, or other identification information capable of characterizing the recommendation system, which is not limited in the embodiment of the present invention.
The target sample set refers to a set formed by determining one or more target systems for the current system and taking all selected target systems as samples.
It should be noted that the large-scale piecewise linear Model (LARGE SCALE PIECE-WISE LINER Model, LS-PLM), also called the hybrid logistic regression Model. For different groups, the model adopts a mode of combining classification and logistic regression, and different logistic regression models are applied to predict.
And obtaining second attribute information corresponding to the recommendation system and the score of the recommendation system according to the recommendation system identification.
The second attribute information corresponding to the recommendation system includes: the system category of the recommendation system, the monitoring index of the recommendation system, the monitoring subclass of the recommendation system, the weight of the monitoring subclass of the recommendation system and the number of the monitoring subclasses.
And generating a comparison graph according to the second attribute information corresponding to the recommendation system, the score of the recommendation system, the second attribute information of the current system and the score of the current system, and displaying the comparison graph.
Optionally, iteratively training the large-scale piecewise linear model through the set of target samples includes:
and establishing a large-scale piecewise linear model.
And inputting the second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain a prediction recommendation system.
Wherein the target sample set comprises: the system sample and the recommended system sample corresponding to the system sample.
It should be noted that, the acquisition mode of the target sample set may be: and randomly extracting a system sample from the current stock system set, acquiring a recommended system sample corresponding to the system sample, and constructing a target sample set according to the system sample and the recommended system sample.
Where a system sample refers to a system in a target sample set as a sample. A predictive recommendation system may be understood as one or more systems that make improvement suggestions for the weaknesses of the current system predicted by extensive piecewise linear model training.
And training parameters of the large-scale piecewise linear model according to the recommendation system samples corresponding to the system samples and the objective function formed by the prediction recommendation system.
And returning to execute the operation of inputting the second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain the prediction recommendation system until the recommendation model is obtained.
Optionally, the second attribute information further includes: the system monitors at least one of the total amount, the monitored amount of the monitored index, the monitored amount of the monitored subclass, the monitored amount of the monitored index and the change amount of the actual monitored amount and the monitored amount of the monitored grade.
Specifically, the system monitoring total amount refers to the sum of the number of each monitoring index and monitoring subclass actually existing. The monitoring amount of the monitoring index refers to the sum of the number of monitoring indexes. The monitoring amount of the monitoring subclass refers to the sum of the number of the monitoring subclasses.
The passing monitoring amount of the monitoring index is understood to be the amount that each monitoring index should reach. The variation of the actual monitored quantity and the passing monitored quantity refers to the difference between the actual monitored quantity and the passing monitored quantity, and the difference can be a positive value or a negative value. For example, the actual monitored quantity may be 2, and the passing monitored quantity may be 6, and then the variation between the actual monitored quantity and the passing monitored quantity is-4.
The scoring system of the embodiment provides the system category, the monitoring index (category, weight and score), the monitoring subclass (category, weight and score), the system total score and other multidimensional feature vectors, and additionally adds the feature of the monitoring volume, including the interpretable feature vectors of the system monitoring total volume, the monitoring volume of the monitoring index, the monitoring volume of the monitoring subclass and the like of the system, and derives the passing monitoring volume of the monitoring index and the variation (positive value/negative value) of the actual monitoring volume and the passing monitoring volume. For the numerical value type characteristics, the characteristics are directly used without normalization processing; for non-valued features such as category, a one-hot method (a form that converts category variables into a form that is readily available to machine learning algorithms) is used for processing. In conclusion, the feature vectors used by the recommendation model are constructed.
In this embodiment, the recommendation model may select a large-scale piecewise linear model, and for different groups, a mode of combining classification and logistic regression is adopted, and different logistic regression models are applied to predict. The input data source is the grading characteristic of the current system, and the reference system with the highest correlation is output as the recommendation system and is visually displayed for an administrator to refer to the recommendation system and optimize and improve the monitoring configuration of the current system.
The embodiment of the invention designs a recommendation system aiming at the weak links of the current system by taking the system to be scored as the current system and further determining the score of the current system according to the monitoring index score of the current system and the weight of the monitoring index of the current system. By combining an artificial intelligence algorithm, a standardized and personalized recommendation system is provided for weak links of current system monitoring, an intuitive and effective monitoring optimization scheme is provided for an administrator, and the system monitoring work efficiency is improved.
Example III
Fig. 3 is a schematic structural diagram of a scoring device according to a third embodiment of the present invention, where the scoring device according to the third embodiment of the present invention may perform the scoring method according to any one of the embodiments of the present invention, and has functional modules and beneficial effects corresponding to the performing method. The apparatus may be implemented in software and/or hardware, and the apparatus may be integrated in any device that provides a function of updating a scene library, as shown in fig. 3, where the evaluation apparatus specifically includes: an acquisition module 401, a first determination module 402 and a second determination module 403.
The obtaining module 401 is configured to obtain first attribute information of a system to be scored, where the first attribute information includes: the monitoring subclass, the weight of the monitoring subclass, and the weight of the monitoring index.
A first determining module 402, configured to determine a monitoring indicator score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored.
And a second determining module 403, configured to determine a score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored.
Further, the first determining module 402 may include:
and the first determining unit is used for determining the scores of the monitoring subclasses of the system to be scored according to the monitoring subclasses of the system to be scored.
And the second determining unit is used for determining the monitoring index score of the system to be scored according to the score of the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored.
Further, the determining, in the second determining module 403, that the system to be scored is the current system, and accordingly, determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored includes:
And determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system.
Further, after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further comprises:
Acquiring second attribute information of a current system and a current stock system set, wherein the second attribute information comprises: system category, monitoring index, monitoring subclass, weight of monitoring subclass and number of monitoring subclass;
determining a first set according to the system category of the current system and the current stock system set;
obtaining a score for each system in the first set;
Ranking the systems in the first set according to the score of each system;
Acquiring second attribute information of N target systems before sequencing, wherein N is a positive integer greater than or equal to 1;
And generating a comparison graph according to the second attribute information of the current system, the score of the current system, the second attribute information of the target system and the score of the target system, and displaying the comparison graph.
Further, after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further comprises:
Inputting second attribute information of the current system and scores of the current system into a recommendation model, and determining a recommendation system identifier corresponding to the current system, wherein the recommendation model is obtained by iteratively training a large-scale piecewise linear model through a target sample set;
acquiring second attribute information corresponding to the recommendation system and scores of the recommendation system according to the recommendation system identification;
and generating a comparison graph according to the second attribute information corresponding to the recommendation system, the score of the recommendation system, the second attribute information of the current system and the score of the current system, and displaying the comparison graph.
Further, iteratively training a large-scale piecewise linear model through the set of target samples, comprising:
establishing a large-scale piecewise linear model;
Inputting second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain a prediction recommendation system;
Training parameters of the large-scale piecewise linear model according to a recommendation system sample corresponding to the system sample and an objective function formed by the prediction recommendation system;
And returning to the operation of inputting the second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain a prediction recommendation system until a recommendation model is obtained.
Further, the second attribute information further includes: the system monitors at least one of the total amount, the monitored amount of the monitored index, the monitored amount of the monitored subclass, the monitored amount of the monitored index and the change amount of the actual monitored amount and the monitored amount of the monitored grade.
The product can execute the scoring method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
The embodiment of the invention obtains the first attribute information of the system to be scored; determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored; and determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored. The scoring of the existing system can be calculated, so that an administrator can intuitively and rapidly know the advantages and disadvantages of the existing system.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Fig. 4 shows a block diagram of an electronic device 312 suitable for use in implementing embodiments of the invention. The electronic device 312 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. Device 312 is a computing device of typical scoring functionality.
As shown in FIG. 4, the electronic device 312 is in the form of a general purpose computing device. Components of electronic device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that connects the different system components (including the storage device 328 and the processor 316).
Bus 318 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA bus, video electronics standards association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus.
Electronic device 312 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 312 and includes both volatile and nonvolatile media, removable and non-removable media.
Storage 328 may include computer system-readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 330 and/or cache memory 332. The electronic device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a Compact Disc-Read Only Memory (CD-ROM), digital versatile Disc (Digital Video Disc-Read Only Memory, DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 318 through one or more data medium interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
Programs 336 having a set (at least one) of program modules 326 may be stored, for example, in storage 328, such program modules 326 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 326 generally perform the functions and/or methods in the described embodiments of the invention.
The electronic device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), one or more devices that enable a user to interact with the electronic device 312, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 312 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 322. Also, the electronic device 312 may communicate with one or more networks (e.g., a local area network (Local Area Network, LAN), wide area network Wide Area Network, WAN) and/or a public network, such as the internet) via the network adapter 320. As shown in fig. 4, the network adapter 320 communicates with other modules of the electronic device 312 over the bus 318. It should be appreciated that although not shown in fig. 4, other hardware and/or software modules may be used in connection with electronic device 312, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk array (Redundant Arrays of INDEPENDENT DISKS, RAID) systems, tape drives, data backup storage systems, and the like.
The processor 316 executes various functional applications and data processing by running programs stored in the storage 328, for example, to implement the scoring method provided by the above-described embodiments of the present invention.
Example five
Fig. 5 is a schematic structural diagram of a computer readable storage medium containing a computer program according to a fifth embodiment of the present application. The present application provides a computer readable storage medium 61 having stored thereon a computer program 610 which, when executed by one or more processors, implements a scoring method as provided by all inventive embodiments of the present application:
Acquiring first attribute information of a system to be scored, wherein the first attribute information comprises: the monitoring subclass, the weight of the monitoring subclass and the weight of the monitoring index;
Determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored;
And determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A scoring method, comprising:
Acquiring first attribute information of a system to be scored, wherein the first attribute information comprises: the monitoring subclass, the weight of the monitoring subclass and the weight of the monitoring index;
Determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored;
Determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored;
The system to be scored is a current system, and correspondingly, the determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored comprises the following steps:
determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system;
after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further comprises the following steps:
Acquiring second attribute information of a current system and a current stock system set, wherein the second attribute information comprises: system category, monitoring index, monitoring subclass, weight of monitoring subclass and number of monitoring subclass;
determining a first set according to the system category of the current system and the current stock system set;
obtaining a score for each system in the first set;
Ranking the systems in the first set according to the score of each system;
Acquiring second attribute information of N target systems before sequencing, wherein N is a positive integer greater than or equal to 1;
And generating a comparison graph according to the second attribute information of the current system, the score of the current system, the second attribute information of the target system and the score of the target system, and displaying the comparison graph.
2. The method of claim 1, wherein determining the monitored metrics score of the system to be scored based on the monitored subclass of the system to be scored and the weight of the monitored subclass of the system to be scored comprises:
Determining the score of the monitoring subclass of the system to be scored according to the monitoring subclass of the system to be scored;
And determining the monitoring index score of the system to be scored according to the score of the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored.
3. The method of claim 1, further comprising, after determining the score of the current system based on the monitored metrics score of the current system and the weights of the monitored metrics of the current system:
Inputting second attribute information of the current system and scores of the current system into a recommendation model, and determining a recommendation system identifier corresponding to the current system, wherein the recommendation model is obtained by iteratively training a large-scale piecewise linear model through a target sample set;
acquiring second attribute information corresponding to the recommendation system and scores of the recommendation system according to the recommendation system identification;
and generating a comparison graph according to the second attribute information corresponding to the recommendation system, the score of the recommendation system, the second attribute information of the current system and the score of the current system, and displaying the comparison graph.
4. A method according to claim 3, wherein iteratively training the large-scale piecewise linear model through the set of target samples comprises:
establishing a large-scale piecewise linear model;
Inputting second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain a prediction recommendation system;
Training parameters of the large-scale piecewise linear model according to a recommendation system sample corresponding to the system sample and an objective function formed by the prediction recommendation system;
And returning to the operation of inputting the second attribute information of the system samples in the target sample set into the large-scale piecewise linear model to obtain a prediction recommendation system until a recommendation model is obtained.
5. The method of claim 3, wherein the second attribute information corresponding to the recommendation system further comprises: the system monitors at least one of the total amount, the monitored amount of the monitored index, the monitored amount of the monitored subclass, the monitored amount of the monitored index and the change amount of the actual monitored amount and the monitored amount of the monitored grade.
6. A scoring apparatus, comprising:
The system comprises an acquisition module, a scoring module and a scoring module, wherein the acquisition module is used for acquiring first attribute information of a system to be scored, and the first attribute information comprises: the monitoring subclass, the weight of the monitoring subclass and the weight of the monitoring index;
The first determining module is used for determining the monitoring index score of the system to be scored according to the monitoring subclass of the system to be scored and the weight of the monitoring subclass of the system to be scored;
The second determining module is used for determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored;
The system to be scored in the second determining module is a current system, and correspondingly, the determining the score of the system to be scored according to the score of the monitoring index of the system to be scored and the weight of the monitoring index of the system to be scored includes:
determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system;
after determining the score of the current system according to the score of the monitoring index of the current system and the weight of the monitoring index of the current system, the method further comprises the following steps:
Acquiring second attribute information of a current system and a current stock system set, wherein the second attribute information comprises: system category, monitoring index, monitoring subclass, weight of monitoring subclass and number of monitoring subclass;
determining a first set according to the system category of the current system and the current stock system set;
obtaining a score for each system in the first set;
Ranking the systems in the first set according to the score of each system;
Acquiring second attribute information of N target systems before sequencing, wherein N is a positive integer greater than or equal to 1;
And generating a comparison graph according to the second attribute information of the current system, the score of the current system, the second attribute information of the target system and the score of the target system, and displaying the comparison graph.
7. An electronic device, comprising:
One or more processors;
A memory for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the processor to implement the method of any of claims 1-5.
8. A computer readable storage medium containing a computer program, on which the computer program is stored, characterized in that the program, when executed by one or more processors, implements the method according to any of claims 1-5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110659800A (en) * | 2019-08-15 | 2020-01-07 | 平安科技（深圳）有限公司 | Risk monitoring processing method and device, computer equipment and storage medium |
| CN111402017A (en) * | 2018-12-29 | 2020-07-10 | 顺丰科技有限公司 | Credit scoring method and system based on big data |
| CN112700270A (en) * | 2020-12-29 | 2021-04-23 | 中国移动通信集团江苏有限公司 | Grading data processing method, device, equipment and storage medium |
| CN112989621A (en) * | 2021-03-31 | 2021-06-18 | 建信金融科技有限责任公司 | Model performance evaluation method, device, equipment and storage medium |
| CN113190426A (en) * | 2020-07-02 | 2021-07-30 | 北京睿知图远科技有限公司 | Stability monitoring method for big data scoring system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111402017A (en) * | 2018-12-29 | 2020-07-10 | 顺丰科技有限公司 | Credit scoring method and system based on big data |
| CN110659800A (en) * | 2019-08-15 | 2020-01-07 | 平安科技（深圳）有限公司 | Risk monitoring processing method and device, computer equipment and storage medium |
| CN113190426A (en) * | 2020-07-02 | 2021-07-30 | 北京睿知图远科技有限公司 | Stability monitoring method for big data scoring system |
| CN112700270A (en) * | 2020-12-29 | 2021-04-23 | 中国移动通信集团江苏有限公司 | Grading data processing method, device, equipment and storage medium |
| CN112989621A (en) * | 2021-03-31 | 2021-06-18 | 建信金融科技有限责任公司 | Model performance evaluation method, device, equipment and storage medium |
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