CN117829897A

CN117829897A - Data prediction method, device, computer equipment and storage medium

Info

Publication number: CN117829897A
Application number: CN202410021093.XA
Authority: CN
Inventors: 柴松举
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2024-01-05
Filing date: 2024-01-05
Publication date: 2024-04-05

Abstract

The application belongs to the field of big data and the field of financial science and technology, and relates to a data prediction method, which comprises the following steps: acquiring a target date to be predicted; acquiring a date type of a target date; invoking a target prediction rule corresponding to the date type; acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on a target prediction rule, and generating a performance prediction result corresponding to the target enterprise; acquiring a performance threshold corresponding to the date type; and adjusting the performance prediction result based on the performance threshold value to obtain a target performance prediction result corresponding to the target enterprise. The application also provides a data prediction device, computer equipment and a storage medium. Further, performance prediction results of the present application may be stored in a blockchain. The method and the device can be applied to enterprise performance prediction scenes in the financial field, and the accuracy of the generated performance prediction result is effectively improved based on the use of the target prediction rule performance threshold corresponding to the date type of the target date.

Description

Data prediction method, device, computer equipment and storage medium

Technical Field

The present disclosure relates to the field of big data technologies and financial technologies, and in particular, to a data prediction method, a data prediction device, a computer device, and a storage medium.

Background

For financial science and technology enterprises, such as insurance enterprises, banks and the like, performance achievement prediction is always an important point of daily attention of the financial science and technology enterprises, and on one hand, marketing strategies can be quickly adjusted according to the difference between the predicted performance achievement and target achievement; on the other hand, the daily overall movement of the market can be judged according to the predicted performance.

The prior performance prediction processing mode of the financial and scientific enterprises generally carries out the current performance prediction based on the historical performance achievement, if sudden events are encountered, the predicted performance data can have larger fluctuation, and the deviation of the performance data is extremely large, so that the accuracy of the performance prediction is lower.

Disclosure of Invention

An objective of the present embodiments is to provide a data prediction method, apparatus, computer device and storage medium, so as to solve the technical problem that the accuracy of performance prediction is low because the current performance prediction is usually performed only based on historical performance achievement in the existing performance prediction processing manner of a financial and scientific enterprise, so that the predicted performance data will have larger fluctuation, and the deviation of the performance data is extremely large.

In order to solve the above technical problems, the embodiments of the present application provide a data prediction method, which adopts the following technical schemes:

acquiring a target date to be predicted;

acquiring a date type of the target date;

invoking a target prediction rule corresponding to the date type;

acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date;

acquiring a performance threshold corresponding to the date type; wherein the performance threshold includes a performance upper limit and a performance lower limit;

and adjusting the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date.

Further, the step of obtaining performance data of the target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date specifically includes:

if the date type of the target date is the working day from Monday to friday, acquiring the accumulated achievement of the first day of the target date expiration statistical time point;

Acquiring a first historical date which is the same as the target date in a first preset time period, and removing a non-working day in the first historical date to obtain a second historical date;

acquiring a first expiration statistical time point accumulated performance of the second historical date;

acquiring a first full-day achievement performance of the second historical date;

and calling a preset first calculation formula to calculate the first day accumulated achievement, the first expiration statistics time accumulated achievement and the first all day achieved achievement, so as to obtain a performance prediction result of the target enterprise corresponding to the target date.

if the target date is a non-working day from monday to friday, acquiring a cumulative achievement of the second day of the target date expiration statistical time point;

acquiring a first preset number of appointed historical non-working days adjacent to the target date, and acquiring a first whole point accumulated achievement performance of the appointed historical non-working days;

Obtaining the achievement of the second full day of the specified historical non-working day;

and calling a preset second calculation formula to calculate the second day accumulated achievement, the first whole point accumulated achievement and the second whole day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date.

if the target date is a non-working day of Saturday or sunday, acquiring a third day accumulated achievement of the target date expiration statistical time point;

acquiring a second historical date which is the same as the target date in a second preset time period, and removing the working day in the second historical date to obtain a third historical date;

acquiring a second whole point accumulated achievement performance of the third historical date;

obtaining a third full-day achievement performance of the third historical date;

and calling a preset third calculation formula to calculate the third day accumulated achievement, the second whole point accumulated achievement and the third whole day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date.

if the target date is the working day of Saturday or sunday, acquiring a fourth day accumulated achievement of the target date expiration statistical time point;

acquiring a second preset number of appointed historical workdays adjacent to the target date, and acquiring a second deadline statistical time point accumulated achievement of the appointed historical workdays;

obtaining a third full-day achievement performance of the specified historical workday;

and calling a preset fourth calculation formula to calculate the fourth day accumulated achievement, the second expiration statistics time accumulated achievement and the third full day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date.

Further, the step of adjusting the performance prediction result based on the performance threshold to obtain a target performance prediction result of the target enterprise corresponding to the target date specifically includes:

If the performance prediction result is smaller than the performance lower limit value, the performance lower limit value is used as a target performance prediction result of the target enterprise corresponding to the target date;

if the performance prediction result is greater than the performance upper limit value, the performance upper limit value is used as a target performance prediction result of the target enterprise corresponding to the target date;

and if the performance prediction result is greater than the performance lower limit value and less than the performance upper limit value, the performance prediction result is used as a target performance prediction result of the target enterprise corresponding to the target date.

Further, the step of obtaining the performance threshold corresponding to the date type specifically includes:

if the date type is a workday, a first threshold generating mode corresponding to the workday is called to generate a performance threshold corresponding to the date type;

and if the date type is a non-working day, generating a performance threshold corresponding to the date type by adopting a second threshold generation mode corresponding to the non-working day.

In order to solve the above technical problems, the embodiments of the present application further provide a data prediction apparatus, which adopts the following technical scheme:

The first acquisition module is used for acquiring a target date to be predicted;

the second acquisition module is used for acquiring the date type of the target date;

the calling module is used for calling the target prediction rule corresponding to the date type;

the first processing module is used for acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date;

a third obtaining module, configured to obtain a performance threshold corresponding to the date type; wherein the performance threshold includes a performance upper limit and a performance lower limit;

and the second processing module is used for adjusting the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date.

In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:

acquiring a target date to be predicted;

acquiring a date type of the target date;

invoking a target prediction rule corresponding to the date type;

In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:

acquiring a target date to be predicted;

acquiring a date type of the target date;

invoking a target prediction rule corresponding to the date type;

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

firstly, acquiring a target date to be predicted; then acquiring the date type of the target date; then, invoking a target prediction rule corresponding to the date type; obtaining performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date; further acquiring a performance threshold corresponding to the date type; and finally, adjusting the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date. According to the method and the device for predicting the performance of the target enterprises, after the target date to be predicted is determined, the date type of the target date is acquired, the target prediction rule corresponding to the date type is called, then the performance data of the target enterprises are acquired based on the date type, the performance data are analyzed and processed based on the target prediction rule, the performance prediction result of the target enterprises corresponding to the target date is generated, and therefore the performance prediction result of the target enterprises corresponding to the target date is generated rapidly. In addition, the performance prediction result is further subjected to adjustment processing by using the performance threshold corresponding to the date type, so that a target performance prediction result of the target enterprise corresponding to the target date is obtained, the initially obtained performance prediction result is correspondingly adjusted based on the performance threshold, and the accuracy of the generated target performance prediction result is further improved.

Drawings

For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a data prediction method according to the present application;

FIG. 3 is a schematic diagram of a structure of one embodiment of a data prediction device according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the data prediction method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the data prediction apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a data prediction method according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The data prediction method provided by the embodiment of the application can be applied to any scene needing to be subjected to enterprise performance prediction, and the data prediction method can be applied to products of the scenes, such as performance prediction of financial enterprises in the field of financial insurance. The data prediction method comprises the following steps:

Step S201, a target date to be predicted is acquired.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data prediction method operates may acquire the target date to be predicted through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The target date to be predicted refers to a prediction time corresponding to the need of predicting the achievement of the performance of the enterprise. In the business scenario of performance prediction of a financial business of a financial insurance, the foregoing may refer to a financial science and technology business, and may include, for example, an insurance business, a bank, and the like.

Step S202, obtaining the date type of the target date.

In the present embodiment, the date type of the target date may include 4 types corresponding to the weekdays of monday through friday, the non-weekdays of wednesday or sunday, and the weekdays of wednesday or sunday.

Step S203, invoking a target prediction rule corresponding to the date type.

In the present embodiment, for the foregoing various date types, prediction rules for performing performance prediction for an enterprise are set in advance according to actual processing requirements, corresponding to one-to-one correspondence with the various date types.

Step S204, obtaining performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date.

In this embodiment, the above-mentioned performance data of the target enterprise is obtained based on the date type, and the performance data is analyzed and processed based on the target prediction rule, so as to generate a specific implementation process of the performance prediction result of the target enterprise corresponding to the target date, which will be described in further detail in the following specific embodiments, which will not be described herein.

Step S205, obtaining a performance threshold corresponding to the date type; wherein the performance threshold includes a performance upper limit and a performance lower limit.

In this embodiment, the above implementation process of obtaining the performance threshold corresponding to the date type will be described in further detail in the following embodiments, which will not be described herein.

And step S206, performing adjustment processing on the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date.

In this embodiment, the above-mentioned adjustment processing is performed on the performance prediction result based on the performance threshold value, so as to obtain a specific implementation process of the target performance prediction result of the target enterprise corresponding to the target date, which will be described in further detail in the following specific embodiments, which are not described herein.

Firstly, acquiring a target date to be predicted; then acquiring the date type of the target date; then, invoking a target prediction rule corresponding to the date type; obtaining performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date; further acquiring a performance threshold corresponding to the date type; and finally, adjusting the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date. According to the method and the device, after the target date to be predicted is determined, the date type of the target date is acquired, the target prediction rule corresponding to the date type is called, the performance data of the target enterprise is acquired based on the date type, the performance data are analyzed and processed based on the target prediction rule, the performance prediction result of the target enterprise corresponding to the target date is generated, and therefore the performance prediction result of the target enterprise corresponding to the target date is generated rapidly. In addition, the performance prediction result is further subjected to adjustment processing by using the performance threshold corresponding to the date type, so that a target performance prediction result of the target enterprise corresponding to the target date is obtained, the initially obtained performance prediction result is correspondingly adjusted based on the performance threshold, and the accuracy of the generated target performance prediction result is further improved.

In some alternative implementations, step S204 includes the steps of:

and if the date type of the target date is the working date from Monday to friday, acquiring the accumulated achievement of the first day of the target date expiration statistical time point.

In this embodiment, the cutoff statistical time point may be a performance statistical time node preset according to an actual business requirement. The first day accumulated achievement performance corresponding to the target date expiration statistical time point can be queried from the performance statistical data by acquiring the performance statistical data corresponding to the target date.

And acquiring a first historical date which is the same as the target date in a first preset time period, and eliminating a non-working day in the first historical date to obtain a second historical date.

In this embodiment, the value of the first preset time period is not specifically limited, and may be set according to actual use requirements, for example, may be set to a time period of ten weeks adjacent to the current time. For example, if the target date is tuesday, a first history date identical to the target date in a first preset time period is tuesday in each week in the preset time period. The date type of any date can be obtained to determine whether the date belongs to a working day or a non-working day.

And acquiring a first expiration statistical time point accumulated performance of the second historical date.

In this embodiment, the first deadline statistical time point accumulated performance of the second history date may be queried from the historical statistical performance data by acquiring the historical statistical performance data corresponding to the second history date.

And obtaining the achievement of the first whole day of the second historical date.

In this embodiment, the achievement of the first full day of the second historical date may be achieved by acquiring historical statistical performance data corresponding to the second historical date, and querying the historical statistical performance data for the first full day of the second historical date.

In this embodiment, the first calculation formula specifically includes: s1= (A1/B1) ×c1, where S1 is a performance prediction result of the target enterprise corresponding to the target date, A1 is a cumulative achievement on the first day of the expiration statistics time point of the target date, B1 is a cumulative achievement on the first expiration statistics time point of the second history date, and C1 is a cumulative achievement on the first full day of the second history date.

If the date type of the target date is detected to be the working day from Monday to friday, acquiring the accumulated achievement of the first day of the target date expiration statistical time point; then, a first historical date which is the same as the target date in a first preset time period is obtained, and a non-working day in the first historical date is removed to obtain a second historical date; then, acquiring a first expiration statistical time point accumulated performance of the second historical date; obtaining the achievement of the first whole day of the second historical date; and finally, a preset first calculation formula is called to calculate the first day accumulated achievement, the first expiration statistics time accumulated achievement and the first all day achieved achievement, so that a performance prediction result of the target enterprise corresponding to the target date is obtained. After the date type of the detected target date is the working date of Monday to Friday, the second historical date is obtained by obtaining the first historical date which is the same as the target date in the first preset time period and rejecting the non-working date in the first historical date, and then the preset first calculation formula is intelligently called, and the obtained first day accumulated achievement, the first expiration statistics time accumulated achievement and the first day achieved achievement are calculated, so that the achievement prediction result of the target enterprise corresponding to the target date can be quickly generated, and the obtained achievement prediction result of the target enterprise is the all-day achieved achievement result obtained by carrying out real-time prediction based on the two dimensions corresponding to the historical achievement of the target enterprise and the real-time achievement of the same day, and the accuracy and the reliability of the achievement prediction result of the produced target enterprise are effectively improved by only relying on the historical achievement data of the target enterprise like the prior art.

In some alternative implementations of the present embodiment, step S204 includes the steps of:

and if the target date is a non-working day from Monday to friday, acquiring the accumulated achievement of the second day of the target date expiration statistical time point.

In this embodiment, the achievement of the second day corresponding to the expiration statistical time point of the target date may be further queried from the performance statistical data by obtaining the performance statistical data corresponding to the target date.

And acquiring a first preset number of appointed historical non-working days adjacent to the target date, and acquiring a first whole point accumulated achievement of the appointed historical non-working days.

In this embodiment, the value of the first preset number is not specifically limited, and may be set according to actual use requirements, for example, may be set to 10. The achievement performance can be accumulated by acquiring the historical statistical performance data corresponding to the appointed historical non-working day and inquiring the first whole point of the appointed historical non-working day from the historical statistical performance data.

And obtaining the achievement of the second whole day of the specified historical non-working day.

In this embodiment, the achievement of the second full day of the specified historical non-working day is achieved by acquiring the historical statistical performance data corresponding to the specified historical non-working day and querying the historical statistical performance data.

In this embodiment, the second calculation formula specifically includes: s2= (A2/B2) ×c2, where S2 is a result of predicting performance of the target enterprise corresponding to the target date, A2 is a cumulative achievement on the second day of the expiration statistical time point of the target date, B2 is a cumulative achievement on the first whole point of the specified historical non-working day, and C2 is a achievement on the second whole day of the specified historical non-working day.

If the target date is detected to be a non-working day from Monday to friday, acquiring a cumulative achievement of the second day of the target date expiration statistical time point; then, acquiring a first preset number of appointed historical non-working days close to the target date, and acquiring a first whole point accumulated achievement performance of the appointed historical non-working days; obtaining the achievement of the second whole day of the appointed historical non-working day; and subsequently calling a preset second calculation formula to calculate the accumulated achievement on the second day, the accumulated achievement on the first whole point and the achievement on the second whole day, so as to obtain a performance prediction result of the target enterprise corresponding to the target date. According to the method and the device, after the date type of the target date is the non-working days of Monday to Friday, the appointed historical non-working days of the first preset number close to the target date are determined, and then the preset second calculation formula is intelligently called, and calculation processing is carried out on the acquired accumulated achievement of the second day, the accumulated achievement of the first whole point and the achievement of the second whole day, so that the achievement prediction result of the target enterprise corresponding to the target date can be quickly generated, and the achievement prediction result of the target enterprise is obtained by carrying out real-time prediction based on the historical achievement of the target enterprise and the two dimensions corresponding to the real-time achievement of the current day, instead of only relying on the historical achievement data of the target enterprise to predict like the prior art, and the accuracy and the reliability of the achievement prediction result of the target enterprise are effectively improved.

In some alternative implementations, step S204 includes the steps of:

and if the target date is a non-working day of Saturday or sunday, acquiring a third day accumulated achievement of the target date expiration statistical time point.

In this embodiment, the cutoff statistical time point may be a performance statistical time node preset according to an actual business requirement. The third day accumulated achievement performance corresponding to the target date expiration statistical time point can be queried from the performance statistical data by acquiring the performance statistical data corresponding to the target date.

And obtaining a second historical date which is the same as the target date in a second preset time period, and removing the working day in the second historical date to obtain a third historical date.

In this embodiment, the value of the second preset time period is not specifically limited, and may be set according to actual use requirements, for example, may be set to a time period of ten weeks adjacent to the current time. The date type of any date can be obtained to determine whether the date belongs to a working day or a non-working day.

And acquiring a second integral point accumulated achievement performance of the third historical date.

In this embodiment, the achievement of the second whole point accumulation of the third historical date may be obtained by obtaining the historical statistical performance data corresponding to the third historical date, and inquiring the second whole point accumulation of the third historical date from the historical statistical performance data.

And obtaining the achievement of the third whole day of the third historical date.

In this embodiment, the performance may be achieved by obtaining the historical statistical performance data corresponding to the third historical date, and querying the historical statistical performance data for the third full day of the third historical date.

In this embodiment, the third calculation formula specifically includes: s3= (A3/B3) ×c3, where S3 is a performance prediction result of the target enterprise corresponding to the target date, A3 is a third day accumulated achievement of the target date expiration statistics time point, B3 is a second whole point accumulated achievement of a third history date, and C3 is a third whole day achievement of the third history date.

If the target date is detected to be a non-working day of Saturday or a sunday, acquiring a third day accumulated achievement of the target date expiration statistical time point; then, a second historical date which is the same as the target date in a second preset time period is obtained, and the working day in the second historical date is removed to obtain a third historical date; then obtaining a second whole point accumulated achievement of the third historical date; obtaining a third full-day achievement of the third historical date subsequently; and finally, calling a preset third calculation formula to calculate and process the third day accumulated achievement, the second whole point accumulated achievement and the third whole day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date. After detecting that the target date is a non-working date of Saturday or a weekday, the method and the device can obtain a third historical date by obtaining a second historical date which is the same as the target date in a second preset time period and rejecting the working date in the second historical date, and then intelligently calling a preset third calculation formula to calculate and process the obtained third day accumulated achievement, the second whole point accumulated achievement and the third whole day achievement, so that the method and the device can quickly generate the achievement prediction result of the target enterprise corresponding to the target date, and the obtained achievement prediction result of the target enterprise is the whole day achievement result obtained by carrying out real-time prediction based on two dimensions corresponding to the historical achievement and the real-time achievement of the target enterprise, and not only the historical achievement data of the target enterprise is relied on for prediction like the prior art, thereby effectively improving the accuracy and the reliability of the achievement prediction result of the produced target enterprise.

In some alternative implementations, step S204 includes the steps of:

and if the target date is the working day of Saturday or sunday, acquiring a fourth day accumulated achievement of the target date expiration statistical time point.

In this embodiment, the cutoff statistical time point may be a performance statistical time node preset according to an actual business requirement. The fourth day accumulated achievement performance corresponding to the target date expiration statistical time point can be queried from the performance statistical data by acquiring the performance statistical data corresponding to the target date.

And acquiring a second preset number of appointed historical workdays adjacent to the target date, and acquiring a second deadline statistical time point accumulated achievement of the appointed historical workdays.

In this embodiment, the value of the second preset number is not specifically limited, and may be set according to the actual use requirement, for example, may be set to 10. The achievement performance can be accumulated by acquiring the historical statistical performance data corresponding to the appointed historical working days and inquiring the second expiration statistical time point of the appointed historical working days from the historical statistical performance data.

And obtaining the achievement of the third full day of the appointed historical working day.

In this embodiment, the achievement of the third full day of the specified historical workday may be achieved by acquiring the historical statistical performance data corresponding to the specified historical workday and querying the historical statistical performance data.

In this embodiment, the third calculation formula specifically includes: s4= (A4/B4) ×c4, where S4 is a performance prediction result of the target enterprise corresponding to the target date, A4 is a fourth day accumulated achievement of the target date expiration statistics time point, B4 is a second expiration statistics time point accumulated achievement of the specified historical workday, and C4 is a third day achieved of the specified historical workday.

If the target date is detected to be the working day of Saturday or sunday, acquiring a fourth day accumulated achievement performance of a target date expiration statistical time point; then, a second preset number of appointed historical workdays close to the target date are obtained, and a second deadline statistical time point accumulated achievement performance of the appointed historical workdays is obtained; obtaining the achievement of the third full day of the appointed historical working day; and subsequently calling a preset fourth calculation formula to calculate the fourth day accumulated achievement, the second expiration statistics time accumulated achievement and the third day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date. After the working days of Saturday or Sunday are detected, a preset fourth calculation formula is intelligently called by determining a second preset number of appointed historical working days close to the target date, and the acquired accumulated achievement performance of the fourth day, accumulated achievement performance of a second expiration statistics time point and the third achievement performance are calculated, so that the performance prediction result of the target enterprise corresponding to the target date can be quickly generated, and the obtained performance prediction result of the target enterprise is an all-day achievement performance result obtained by carrying out real-time prediction based on two dimensions corresponding to the historical achievement performance of the target enterprise and the real-time achievement performance of the day, and the accuracy and reliability of the performance prediction result of the target enterprise are effectively improved as the prior art only relies on the historical achievement performance data of the target enterprise to predict.

In some alternative implementations of the present embodiment, step S206 includes the steps of:

and if the performance prediction result is smaller than the performance lower limit value, the performance lower limit value is used as a target performance prediction result of the target enterprise corresponding to the target date.

In this embodiment, if the performance prediction result is detected to be smaller than the performance lower limit value, the performance lower limit value is intelligently regarded as the target performance prediction result of the target enterprise corresponding to the target date, so as to ensure the accuracy and rationality of the generated target performance prediction result of the target enterprise.

And if the performance prediction result is greater than the performance upper limit value, using the performance upper limit value as a target performance prediction result of the target enterprise corresponding to the target date.

In this embodiment, if the performance prediction result is detected to be greater than the performance upper limit value, the performance upper limit value is intelligently regarded as the target performance prediction result of the target enterprise corresponding to the target date, so as to ensure the accuracy and rationality of the generated target performance prediction result of the target enterprise.

When the performance prediction result is detected to be smaller than the performance lower limit value, the performance lower limit value is used as a target performance prediction result of the target enterprise corresponding to the target date; and if the performance prediction result is greater than the performance upper limit value, using the performance upper limit value as a target performance prediction result of the target enterprise corresponding to the target date; and if the performance prediction result is greater than the performance lower limit value and less than the performance upper limit value, the performance prediction result is used as a target performance prediction result of the target enterprise corresponding to the target date. According to the method and the device, the performance predicted result and the performance threshold value are subjected to numerical analysis, and the performance predicted result is adjusted according to the obtained numerical analysis result, so that the target performance predicted result of the target enterprise corresponding to the target date is obtained, and the accuracy and the rationality of the generated target performance predicted result of the target enterprise can be effectively ensured.

In some alternative implementations of the present embodiment, step S205 includes the steps of:

And if the date type is a workday, calling a first threshold generation mode corresponding to the workday to generate a performance threshold corresponding to the date type.

In this embodiment, if the date type of the target date is a working date, the content of the corresponding first threshold generating manner specifically includes: taking 1.5 times of the average value of the performances of the latest 30 workday signboards as the upper limit value of the achievement of the actual performances of the same day; and taking 0.5 times of the average value of the performances of the latest 30 workday signboards as the lower limit value of the performances achieved in the current day, thereby obtaining the performance threshold value corresponding to the date type, comprising the upper limit value and the lower limit value of the performances.

In this embodiment, if the date type of the target date is a non-working day, the content of the corresponding second threshold generating manner specifically includes: taking 25 times of the average value of the performance of the non-workday ticket in the last ten weeks as the upper limit value of the performance achieved in the current day; taking 0.5 times of the latest ten-week non-workday ticket performance average value as the performance lower limit value of the actual performance achievement of the current day, thereby obtaining a performance threshold value corresponding to the date type, comprising a performance upper limit value and a performance lower limit value.

If the date type is detected to be a workday, a first threshold generating mode corresponding to the workday is called to generate a performance threshold corresponding to the date type; and if the date type is detected to be a non-working day, generating a performance threshold corresponding to the date type by adopting a second threshold generation mode corresponding to the non-working day. According to the method and the device, the date type of the target date to be predicted is obtained, and then the threshold generation mode corresponding to the date type is called to realize the generation of the corresponding performance threshold, so that the accuracy of the generated performance threshold is ensured.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It is emphasized that the target performance prediction results may also be stored in nodes of a blockchain in order to further ensure privacy and security of the target performance prediction results.

The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a data prediction apparatus, where an embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 3, the data prediction apparatus 300 according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, a calling module 303, a first processing module 304, a third acquisition module 305, and a second processing module 306. Wherein:

A first obtaining module 301, configured to obtain a target date to be predicted;

a second obtaining module 302, configured to obtain a date type of the target date;

a calling module 303, configured to call a target prediction rule corresponding to the date type;

a first processing module 304, configured to obtain performance data of a target enterprise based on the date type, and perform analysis processing on the performance data based on the target prediction rule, so as to generate a performance prediction result of the target enterprise corresponding to the target date;

a third obtaining module 305, configured to obtain a performance threshold corresponding to the date type; wherein the performance threshold includes a performance upper limit and a performance lower limit;

and a second processing module 306, configured to perform adjustment processing on the performance prediction result based on the performance threshold, so as to obtain a target performance prediction result of the target enterprise corresponding to the target date.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method in the foregoing embodiment one by one, and are not described herein again.

In some alternative implementations of the present embodiment, the first processing module 304 includes:

The first acquisition sub-module is used for acquiring the accumulated achievement of the first day of the target date expiration statistical time point if the date type of the target date is the working day of monday to friday;

the first processing sub-module is used for acquiring a first historical date which is the same as the target date in a first preset time period, and eliminating non-working days in the first historical date to obtain a second historical date;

the second obtaining sub-module is used for obtaining a first expiration statistical time point accumulated performance of the second historical date;

a third obtaining sub-module, configured to obtain a first achievement on all days of the second history date;

and the first calculation sub-module is used for calling a preset first calculation formula to calculate and process the first day accumulated achievement, the first expiration statistics time accumulated achievement and the first all day achieved achievement to obtain a achievement prediction result of the target enterprise corresponding to the target date.

A fourth obtaining sub-module, configured to obtain a cumulative achievement on a second day of the target date deadline statistical time point if the target date is a non-working day from monday to friday;

a fifth obtaining sub-module, configured to obtain a first preset number of specified historical non-working days adjacent to the target date, and obtain a first whole point accumulated achievement performance of the specified historical non-working days;

a sixth obtaining sub-module, configured to obtain a performance achieved on a second full day of the specified historical non-working day;

and the second calculation sub-module is used for calling a preset second calculation formula to calculate the second day accumulated achievement, the first whole point accumulated achievement and the second whole day achieved achievement so as to obtain a performance prediction result of the target enterprise corresponding to the target date.

a seventh obtaining sub-module, configured to obtain a third day accumulated achievement performance of the target date deadline statistics time point if the target date is a non-working day of Saturday or a sunday;

An eighth obtaining sub-module, configured to obtain a second history date that is the same as the target date in a second preset time period, and reject a working day in the second history date to obtain a third history date;

a ninth obtaining sub-module, configured to obtain a second full-point accumulated achievement of the third history date;

a tenth obtaining sub-module, configured to obtain a third full-day achievement performance of the third history date;

and the third calculation sub-module is used for calling a preset third calculation formula to calculate the third day accumulated achievement, the second whole point accumulated achievement and the third whole day achieved achievement so as to obtain a performance prediction result of the target enterprise corresponding to the target date.

an eleventh obtaining sub-module, configured to obtain a fourth day accumulated achievement of the target date expiration statistical time point if the target date is a working day of Saturday or a sunday;

A twelfth obtaining sub-module, configured to obtain a second preset number of specified historical workdays adjacent to the target date, and obtain a second deadline statistics time point accumulated achievement performance of the specified historical workdays;

a thirteenth obtaining sub-module, configured to obtain a third full-day achievement of the specified historical workday;

and the fourth calculation sub-module is used for calling a preset fourth calculation formula to calculate and process the fourth day accumulated achievement, the second expiration statistics time accumulated achievement and the third full day achieved achievement to obtain a performance prediction result of the target enterprise corresponding to the target date.

In some alternative implementations of the present embodiment, the second processing module 306 includes:

a first processing sub-module, configured to, if the performance prediction result is less than the performance lower limit value, take the performance lower limit value as a target performance prediction result of the target enterprise corresponding to the target date;

a second processing sub-module, configured to, if the performance prediction result is greater than the performance upper limit value, take the performance upper limit value as a target performance prediction result of the target enterprise corresponding to the target date;

And a third processing sub-module, configured to, if the performance prediction result is greater than the performance lower limit value and less than the performance upper limit value, take the performance prediction result as a target performance prediction result of the target enterprise corresponding to the target date.

In some optional implementations of this embodiment, the third obtaining module 305 includes:

the first generation module is used for calling a first threshold generation mode corresponding to the working day to generate a performance threshold corresponding to the date type if the date type is the working day;

and the second generation module is used for generating a performance threshold corresponding to the date type by adopting a second threshold generation mode corresponding to the non-working day if the date type is the non-working day.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a data prediction method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the data prediction method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, after the target date to be predicted is determined, the date type of the target date is acquired first, the target prediction rule corresponding to the date type is called, then the performance data of the target enterprise is acquired based on the date type, the performance data is analyzed and processed based on the target prediction rule, and the performance prediction result of the target enterprise corresponding to the target date is generated, so that the performance prediction result of the target enterprise corresponding to the target date is generated rapidly, and the accuracy of the generated performance prediction result is ensured because the obtained performance prediction result of the target enterprise is generated after the prediction processing is performed based on the target prediction rule corresponding to the date type of the target date. In addition, the performance prediction result is further subjected to adjustment processing by using the performance threshold corresponding to the date type, so that a target performance prediction result of the target enterprise corresponding to the target date is obtained, the initially obtained performance prediction result is correspondingly adjusted based on the performance threshold, and the accuracy of the generated target performance prediction result is further improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the data prediction method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.

Claims

1. A method of data prediction comprising the steps of:

acquiring a target date to be predicted;

acquiring a date type of the target date;

invoking a target prediction rule corresponding to the date type;

2. The data prediction method according to claim 1, wherein the step of acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date specifically comprises:

3. The data prediction method according to claim 1, wherein the step of acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date specifically comprises:

4. The data prediction method according to claim 1, wherein the step of acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date specifically comprises:

5. The data prediction method according to claim 1, wherein the step of acquiring performance data of a target enterprise based on the date type, analyzing and processing the performance data based on the target prediction rule, and generating a performance prediction result of the target enterprise corresponding to the target date specifically comprises:

6. The data prediction method according to claim 1, wherein the step of performing adjustment processing on the performance prediction result based on the performance threshold value to obtain a target performance prediction result of the target enterprise corresponding to the target date specifically comprises:

7. The method of claim 1, wherein the step of obtaining a performance threshold corresponding to the date type comprises:

8. A data prediction apparatus, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the data prediction method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the data prediction method according to any of claims 1 to 7.