CN112232719A - Index quantitative scoring method, computer equipment and storage medium - Google Patents
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Abstract
The application relates to an index quantitative scoring method, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring an index value of an index, wherein the index represents the performance or user experience of a system; judging the size relation between the index value and a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value; when the index value is between the first threshold value and the second threshold value, the probability density integral value of the index value is determined according to the probability density function of the index, and the score corresponding to the index value is determined according to the probability density integral value, wherein the probability density function is obtained by performing kernel density estimation according to historical data of the index. By the method and the device, more detailed evaluation criteria are realized to quantitatively evaluate the performance of the system or the user experience, and the score can be given for each index value.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method for quantitatively scoring an index, a computer device, and a storage medium.
Background
And the index is used for representing the performance of the system or the user experience. For example, the system response time is usually in the range of 0 to + ∞, and the smaller the system response time value is, the better the system response performance to the client request is and the better the user experience is. As another example, availability, which typically ranges from 0 to 100, is higher, the lower the system error incidence is, the higher the availability, and the better the user experience.
In the related art, a standard is usually set to qualitatively evaluate the performance or user experience of the system, which is illustrated by the response time of the system: (0, 10): excellent; (10, 25): good; (25, 40): the method is good; (40, 50): generally; (50, 70): a difference; (70, + ∞): it is very poor. This standard is typically set dynamically based on industry level, and historical data statistics. For example, the response time evaluation value of the same industry system is 30, and based on historical data, a standard deviation is calculated, assuming that 10 is obtained, and an industry average value is used as a reference, and N standard deviations are vertically floated, so that one standard can be defined.
However, the method in the related art scores the index slightly, and the detailed condition of the index is difficult to reflect.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, the present application provides an index quantitative scoring method, a computer device and a storage medium.
In a first aspect, the present application provides a quantitative index scoring method, including: acquiring an index value of an index, wherein the index represents the performance or user experience of a system; judging the size relation between the index value and a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value; when the index value is between the first threshold value and the second threshold value, the probability density integral value of the index value is determined according to the probability density function of the index, and the score corresponding to the index value is determined according to the probability density integral value, wherein the probability density function is obtained by performing kernel density estimation according to historical data of the index.
In some embodiments, the score corresponding to the index value is linear with the probability density integral value.
In some embodiments, the quantitative scoring method for the index further includes: when the index value is smaller than or equal to a first threshold value, determining the score corresponding to the index value as a first score; and when the index value is greater than or equal to the second threshold value, determining the score corresponding to the index value as a second score.
In some embodiments, determining a score corresponding to the index value according to the probability density integral value includes: when the index is a negative correlation index, determining the score corresponding to the index value according to the following mode: h- (H-L) c; where H denotes a first score, L denotes a second score, and c denotes a probability density integral value of the index value, and the first score is larger than the second score.
In some embodiments, determining a score corresponding to the index value based on the probability density integral value includes: when the index is a positive correlation index, determining the score corresponding to the index value according to the following mode: l + (H-L) c; wherein H denotes a second score, L denotes a first score, c denotes a probability density integral value of the index value, and the second score is larger than the first score.
In some embodiments, the quantitative scoring method for the index further includes: acquiring historical data of the index, wherein the historical data comprises a plurality of historical values of the index; determining a bandwidth parameter for performing kernel density estimation according to historical data; determining a set of samples for kernel density estimation based on the historical data and the bandwidth parameter; and performing kernel density estimation according to the sample set and the bandwidth parameters to obtain a probability density function of the index.
In some embodiments, the historical data is represented as X: { x1，x2，…，xi，…，xnN, representing the number of the historical values as n, representing the standard deviation of the historical values as hi, and representing the quartile distance of the historical values as IQR; let lo be min (hi, IQR/1.34), if lo is 0, when hi is not 0, lo equals hi; when hi is 0, if x1Not 0, lo equals x1If x is1Is 0, lo equals 1; the bandwidth parameter was determined to be 0.9 lo n-0.2。
In some embodiments, determining a set of samples for kernel density estimation based on historical data and bandwidth parameters comprises: estimating an upper limit value and a lower limit value of the index according to the bandwidth parameter, wherein the lower limit value is determined as min (X) -k h, the upper limit value is determined as max (X) + k h, X is historical data, h is the bandwidth parameter, and k is a preset super-parameter value; and reconstructing the historical data according to the upper limit value, the lower limit value and the preset sample number to obtain a sample set with the preset sample number evenly distributed in the range of the upper limit value and the lower limit value.
In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor; the computer program is executed by a processor to realize the steps of the quantitative index scoring method of any one of the above embodiments.
In a third aspect, the present application provides a computer-readable storage medium, on which an index quantitative scoring program is stored, and when the index quantitative scoring program is executed by a processor, the steps of the index quantitative scoring method in any one of the above embodiments are implemented.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method provided by the embodiment of the application, the probability density distribution of the index value is obtained by utilizing kernel density estimation based on the historical data of the index, then the probability density of the index value distribution is integrated to obtain the probability density integral value of the index value, and the score corresponding to the index value is determined and obtained based on the probability density integral value. The method realizes more detailed evaluation criteria to quantitatively evaluate the performance of the system or the user experience, and can give a score to each index value.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of an embodiment of a quantitative index scoring method according to an embodiment of the present disclosure.
Fig. 2 is a flowchart of another implementation of the quantitative index scoring method according to the embodiment of the present disclosure.
Fig. 3 is a flowchart of an implementation manner of a probability density function determination method according to an embodiment of the present application.
Fig. 4 is a block diagram of a structure of an embodiment of an index quantitative rating system provided in an embodiment of the present application.
Fig. 5 is a hardware schematic diagram of an implementation manner of a computer device according to an embodiment of the present application.
Fig. 6 is a schematic diagram illustrating an example of a probability density distribution of index values according to an embodiment of the present application.
Fig. 7 is a schematic diagram illustrating an example of a probability density integral value of an index value according to an embodiment of the present application.
FIG. 8 is a diagram illustrating an example of a score curve of a negative relevance indicator according to an embodiment of the present application.
Fig. 9 is a schematic diagram of an example of a score curve of a positive correlation indicator provided in the embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The embodiment of the application provides an index quantitative scoring method, which is used for quantitatively scoring the index value of an index and obtaining the score for representing system performance or user experience and the like according to the index value of the index. In the embodiment of the application, the index represents the performance or user experience of the system. The quantitative scoring of the indexes obtains the scores of the indexes, and the scores can be used for comprehensively evaluating multiple indexes, but are not limited to. The system in the embodiment of the present application may include various software, hardware and service systems combined with the software and hardware, and by way of example, the system in the embodiment of the present application may include a communication network (e.g., a mobile cellular network, an IPv6 network), an application (e.g., but not limited to, an application running on an operating system such as Windows, iOS and MacOS of microsoft corporation, and Android of google corporation), a distributed architecture application, a micro service architecture application, and the like. And monitoring through system operation indexes to obtain various indexes representing system performance or user experience in system operation. Whether the system is abnormal or not, the availability of the system and the like is determined through the analysis of the indexes, and the system maintenance personnel can maintain the system conveniently. Taking page browsing as an example, the indexes may include: the method comprises the steps of total downloading time, total downloading byte number, downloading speed, basic page downloading byte number, first screen object tree, first screen downloading byte number, DNS analysis times, DNS analysis total time, connection establishment times, connection establishment total time and the like.
Fig. 1 is a flowchart of an embodiment of a quantitative index scoring method according to an embodiment of the present disclosure, and as shown in fig. 1, the quantitative index scoring method includes steps S102 to S106.
Step S102, obtaining an index value of the index, wherein the index represents the performance of the system or the user experience.
In some embodiments, in step S102, the index value of the index is obtained from the index collecting system in real time, so as to perform real-time quantitative scoring on the index. As an example, the index acquisition system includes probes (for example, probes configured to be used for monitoring indexes such as response time of a calling chain node at each calling chain node in a micro service architecture application), a data processing server, and a database, which are arranged in one or more parts of the monitored system.
In other embodiments, in step S102, periodically or according to a user selection, an index value of the index satisfying a predetermined condition (e.g., a time period, etc.) is obtained, so as to implement offline quantitative scoring of the index.
And step S104, judging the size relation between the index value and a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value.
And step S106, when the index value is between the first threshold value and the second threshold value, determining a probability density integral value of the index value according to a probability density function of the index, and determining a score corresponding to the index value according to the probability density integral value, wherein the probability density function is obtained by performing kernel density estimation according to historical data of the index.
In the embodiment of the present application, the index value is mapped to the probability density integral value of the index value, and in the step S106, the larger the index value is, the lower the score determined to be obtained is; for a positive correlation index, the greater the index value, the lower the score determined. In some embodiments, in the step S106, the score corresponding to the index value and the probability density integral value are in a linear relationship, but the embodiment of the present application is not limited thereto.
In the embodiment of the application, as shown in fig. 6, there is an interval in which the index value exists, the performance of the system or the user experience is sensitive, and the score changes nonlinearly from high score to low score or from low score to high score. Therefore, in the embodiment of the present application, for an index value between a first threshold value and a second threshold value, a probability density integrated value of the index value is determined from a probability density function of the index (as shown in fig. 7), and a score corresponding to the index value is determined from the probability density integrated value.
In some embodiments, in step S106, for the negatively correlated index, the score corresponding to the index value is determined as follows: h- (H-L) c; where H is a preset value near the full score, L is a preset value near the zero score, c represents a probability density integral value of the index value, and the score curve is shown with reference to fig. 8. However, the embodiment of the present application is not limited thereto, and a manner in which the score corresponding to other index values and the probability density integral value are in a linear relationship is also conceivable, and details thereof are not repeated in the embodiment of the present application.
In some embodiments, in step S106, for the positive correlation index, the score corresponding to the index value is determined as follows: l + (H-L) c; where H is a preset value near the full score, L is a preset value near the zero score, c represents a probability density integral value of the index value, and the score curve is shown with reference to fig. 9. However, the embodiment of the present application is not limited thereto, and a manner in which the score corresponding to other index values and the probability density integral value are in a linear relationship is also conceivable, and details thereof are not repeated in the embodiment of the present application.
In some embodiments, in step S102, a real-time value of the index is obtained from the index collection system, so as to score the index in real time. In other embodiments, in step 102, index values are obtained from the stored index data, and the index is scored.
In the embodiment of the present application, the term "index value" herein may be an original value of the index, or may be a processed value of the original value, which is not limited in the embodiment of the present application.
The first threshold value and the second threshold value may be set as needed. In some embodiments, the first threshold and the second threshold are determined according to a distribution of index values, such that the distribution of index values between the first threshold and the second threshold is relatively denser, but the embodiments of the present application are not limited thereto.
Fig. 2 is a flowchart of another implementation of the quantitative index scoring method according to the embodiment of the present disclosure, and as shown in fig. 2, the quantitative index scoring method according to the embodiment includes steps S202 to S210.
In step S202, an index value of the index is acquired.
Step S204, judging the size relationship between the index value and a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value, and when the index value is smaller than or equal to the first threshold value, entering step S206; when the index value is between the first threshold and the second threshold, go to step S208; when the index value is greater than or equal to the second threshold value, the process proceeds to step S210.
In step S206, the score corresponding to the index value is determined as the first score.
In step S208, a probability density integral value of the index value is determined based on a probability density function of the index, which is obtained by performing kernel density estimation based on historical data of the index, and a score corresponding to the index value is determined based on the probability density integral value.
Step S210, determining the score corresponding to the index value as a second score.
In the embodiment of the present application, the index value is mapped to the probability density integral value of the index value, and in the step S208, the larger the index value is, the lower the score is determined to be; for a positive correlation index, the greater the index value, the lower the score determined. In some embodiments, in the step S208, the score corresponding to the index value and the probability density integral value are in a linear relationship, but the embodiment of the present application is not limited thereto.
In some embodiments, a linear relationship between the score corresponding to the index value and the probability density integral value is determined based on the first score and the second score.
In some embodiments, in step S208, for the negatively correlated index, the score corresponding to the index value is determined as follows: h- (H-L) c; where H denotes a first score, L denotes a second score, and c denotes a probability density integral value of the index value, and the first score is larger than the second score. However, the embodiment of the present application is not limited thereto, and a manner in which the score corresponding to other index values and the probability density integral value are in a linear relationship is also conceivable, and details thereof are not repeated in the embodiment of the present application.
In some embodiments, in step S208, for the positive correlation index, the score corresponding to the index value is determined as follows: l + (H-L) c; wherein H denotes a second score, L denotes a first score, c denotes a probability density integral value of the index value, and the second score is larger than the first score. However, the embodiment of the present application is not limited thereto, and a manner in which the score corresponding to other index values and the probability density integral value are in a linear relationship is also conceivable, and details thereof are not repeated in the embodiment of the present application.
The present application further provides a method for determining a probability density function, and as shown in fig. 3, one embodiment of the method for determining a probability density function includes steps S302 to S308.
Step S302, obtaining historical data of the index, wherein the historical data comprises a plurality of historical values of the index.
Step S304, determining a bandwidth parameter for performing kernel density estimation according to the historical data.
In some embodiments, the historical data is represented as X: { x1，x2，…，xi，…，xnN, representing the number of the historical values as n, representing the standard deviation of the historical values as hi, and representing the quartile distance of the historical values as IQR; let lo be min (hi,IQR/1.34), if lo is 0, when hi is not 0, lo is equal to hi; when hi is 0, if x1Not 0, lo equals x1If x is1Is 0, lo equals 1; the bandwidth parameter was determined to be 0.9 lo n-0.2。
Step S306, a sample set for kernel density estimation is determined based on the historical data and the bandwidth parameter.
In some embodiments, the step S306 includes: estimating an upper limit value and a lower limit value of the index according to the bandwidth parameter, wherein the lower limit value is determined as min (X) -k h, the upper limit value is determined as max (X) + k h, X is historical data, h is the bandwidth parameter, and k is a preset super-parameter value; and reconstructing the historical data according to the upper limit value, the lower limit value and the preset sample number to obtain a sample set with the preset sample number evenly distributed in the range of the upper limit value and the lower limit value.
And step S308, performing kernel density estimation according to the bandwidth parameters according to the sample set to obtain a probability density function of the index.
In the embodiment of the present application, kernel density estimation may be performed by using a kernel function in the prior art to establish a probability density function of the index, for example, a uniform kernel function, a triangular kernel function, a gamma kernel function, a gaussian kernel function, and the like.
The embodiment of the present application further provides an index quantitative scoring system, as shown in fig. 4, the index quantitative scoring apparatus 400 includes: a scoring model determining device 410, an index quantitative scoring device 420 and a database 430.
And a scoring model determining device 410 for determining a probability density function of the index according to the historical data of the index. As shown in fig. 4, the scoring model determining means 410 includes: a historical data obtaining module 412, configured to obtain historical data of the index from a database 430, where the historical data includes a plurality of historical values of the index; a bandwidth parameter determining module 414, connected to the historical data obtaining module 412, for determining a bandwidth parameter for performing kernel density estimation according to the historical data; a sample set determination module 416, connected to the historical data acquisition module 412 and the bandwidth parameter determination module 414, for determining a sample set for kernel density estimation based on the historical data and the bandwidth parameter; and the kernel density estimation module 418 is connected to the sample set determination module 416 and the bandwidth parameter determination module 414, and is configured to perform kernel density estimation according to the sample set and the bandwidth parameter to obtain a probability density function of the index.
In some embodiments, the historical data is represented as X: { x1，x2，…，xi，…，xnN, a bandwidth parameter determining module 414, configured to determine a standard deviation hi of the historical values, a quartile distance IQR of the historical values, and determine lo as min (hi, IQR/1.34), where if lo is 0, if hi is not 0, lo is equal to hi; when hi is 0, if x1Not 0, lo equals x1If x is1Is 0, lo equals 1; the bandwidth parameter was determined to be 0.9 lo n-0.2。
In some embodiments, the sample set determining module 416 is configured to estimate an upper limit and a lower limit of the indicator according to the bandwidth parameter, where the lower limit is determined as min (X) -k × h, the upper limit is determined as max (X) + k × h, X is history data, h is the bandwidth parameter, and k is a preset super-parameter; and reconstructing the historical data according to the upper limit value, the lower limit value and the preset sample number to obtain a sample set with the preset sample number evenly distributed in the range of the upper limit value and the lower limit value.
And an index quantitative scoring device 420 for determining the score of the index. As shown in fig. 4, the index quantitative scoring means 420 includes: an obtaining module 422, configured to obtain an index value of the index; a determining module 424, connected to the obtaining module 422, configured to determine a size relationship between the index value and a first threshold and a second threshold, where the first threshold is smaller than the second threshold; the first scoring module 426 is connected to the determining module 424, and is configured to determine a probability density integral value of the index value according to a probability density function of the index when the index value is between the first threshold and the second threshold, and determine a score corresponding to the index value according to the probability density integral value. A second scoring module 428, connected to the determining module 424, configured to determine, when the index value is less than or equal to the first threshold, a score corresponding to the index value as a first score; and when the index value is greater than or equal to the second threshold value, determining the score corresponding to the index value as a second score.
In some embodiments, the score corresponding to the index value is linear with the probability density integral value.
In some embodiments, for a negatively correlated indicator, the first scoring module 426 is configured to determine a score corresponding to the indicator value by: h- (H-L) c; where H denotes a first score, L denotes a second score, and c denotes a probability density integral value of the index value, and the first score is larger than the second score.
In some embodiments, for a positive correlation indicator, the first scoring module 426 is configured to determine a score corresponding to the indicator value as follows: l + (H-L) c; wherein H denotes a second score, L denotes a first score, c denotes a probability density integral value of the index value, and the second score is larger than the first score.
The embodiment of the application also provides computer equipment. Fig. 5 is a schematic hardware structure diagram of an implementation manner of a computer device provided in an embodiment of the present application, and as shown in fig. 5, a computer device 10 according to an embodiment of the present application includes: including at least but not limited to: a memory 11 and a processor 12 communicatively coupled to each other via a system bus. It is noted that fig. 5 only shows a computer device 10 with components 11-12, but it is to be understood that not all shown components are required to be implemented, and that more or fewer components may be implemented instead.
In this embodiment, the memory 11 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the computer device 10, such as a hard disk or a memory of the computer device 10. In other embodiments, the memory 11 may also be an external storage device of the computer device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 10. Of course, the memory 11 may also include both internal and external storage devices of the computer device 10. In this embodiment, the memory 11 is generally used for storing an operating system and various types of software installed in the computer device 10. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally operative to control overall operation of the computer device 10. In this embodiment, the processor 12 is configured to execute the program code stored in the memory 11 or process data, for example, the program code implementing the quantitative score method for the index described above.
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer-readable storage medium of the present embodiment stores program codes of an index quantitative scoring method, and implements the steps of the index quantitative scoring method when executed by a processor.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the technical scheme provided by the embodiment of the application, the probability density distribution of the index value is obtained by utilizing kernel density estimation based on historical data of the index, then the probability density of the index value distribution is integrated to obtain the probability density integral value of the index value, and the score corresponding to the index value is obtained based on the probability density integral value and in a rated mode. The method realizes more detailed evaluation criteria to quantitatively evaluate the performance of the system or the user experience, and can give a score to each index value.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A quantitative index scoring method is characterized by comprising the following steps:
acquiring an index value of an index, wherein the index represents the performance or user experience of a system;
judging the magnitude relation between the index value and a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value;
when the index value is between the first threshold value and the second threshold value, determining a probability density integral value of the index value according to a probability density function of the index, and determining a score corresponding to the index value according to the probability density integral value, wherein the probability density function is obtained by performing kernel density estimation according to historical data of the index.
2. The quantitative scoring method for an index according to claim 1, wherein the score corresponding to the index value is linear with the probability density integral value.
3. The quantitative scoring method for an index according to claim 1 or 2, further comprising:
when the index value is smaller than or equal to the first threshold value, determining that the score corresponding to the index value is a first score;
when the index value is larger than or equal to the second threshold value, determining that the score corresponding to the index value is a second score.
4. The quantitative scoring method for an index according to claim 3, wherein determining a score corresponding to the index value based on the probability density integral value includes:
when the index is a negative correlation index, determining the score corresponding to the index value according to the following mode: h- (H-L) c;
wherein H denotes the first score, L denotes the second score, and c denotes a probability density integral value of the index value, and the first score is larger than the second score.
5. The quantitative scoring method for an index according to claim 3, wherein determining a score corresponding to the index value based on the probability density integral value includes:
when the index is a positive correlation index, determining the score corresponding to the index value according to the following mode: l + (H-L) c;
wherein H denotes the second score, L denotes the first score, and c denotes a probability density integral value of the index value, and the second score is larger than the first score.
6. The quantitative scoring method for an index according to claim 1, further comprising:
acquiring historical data of an index, wherein the historical data comprises a plurality of historical values of the index;
determining a bandwidth parameter for performing kernel density estimation according to the historical data;
determining a set of samples for kernel density estimation based on the historical data and the bandwidth parameter;
and performing kernel density estimation according to the sample set and the bandwidth parameters to obtain a probability density function of the index.
7. The quantitative rating method for an index according to claim 6,
the historical data is represented as X: { x1，x2，…，xi，…，xnN, representing the number of the historical values as n, representing the standard deviation of the historical values as hi, and representing the quartile distance of the historical values as IQR;
let lo be min (hi, IQR/1.34), if lo is 0, when hi is not 0, lo equals hi; when hi is 0, if x1Not 0, lo equals x1If x is1Is 0, lo equals 1;
the bandwidth parameter is determined to be 0.9 lo n-0.2。
8. The quantitative scoring method for indicators according to claim 6 or 7, wherein determining a set of samples for kernel density estimation based on the historical data and the bandwidth parameters comprises:
estimating an upper limit value and a lower limit value of the index according to the bandwidth parameter, wherein the lower limit value is determined as min (X) -k, the upper limit value is determined as max (X) + k h, X is the historical data, h is the bandwidth parameter, and k is a preset super-parameter value;
and reconstructing the historical data according to the upper limit value, the lower limit value and a preset sample number to obtain a sample set with the preset sample number evenly distributed in the range of the upper limit value and the lower limit value.
9. A computer device, characterized in that the computer device comprises:
a memory, a processor, and a computer program stored on the memory and executable on the processor;
the computer program when executed by the processor implements the steps of the quantitative scoring method for an index as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that an index quantitative scoring program is stored on the computer-readable storage medium, and when executed by a processor, the steps of the index quantitative scoring method according to any one of claims 1 to 8 are implemented.
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