CN111178692A - Resource usage amount estimation method and device and electronic equipment - Google Patents

Resource usage amount estimation method and device and electronic equipment Download PDF

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CN111178692A
CN111178692A CN201911271298.9A CN201911271298A CN111178692A CN 111178692 A CN111178692 A CN 111178692A CN 201911271298 A CN201911271298 A CN 201911271298A CN 111178692 A CN111178692 A CN 111178692A
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陈博
黎文杰
郑盛麟
刘禹彤
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Shanghai Qiyue Information Technology Co Ltd
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Abstract

The disclosure relates to a resource usage amount estimation method, a resource usage amount estimation device, an electronic device and a computer readable medium. The method comprises the following steps: acquiring the use data of the target resource in the previous time period; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit. The resource usage amount estimation method, the resource usage amount estimation device, the electronic equipment and the computer readable medium can predict the future borrowing cash flow, so that reference is provided for fund preparation, the fund demand of borrowing of a user can be met, excessive fund redundancy can be avoided, and the fund cost is effectively saved.

Description

Resource usage amount estimation method and device and electronic equipment
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a method and an apparatus for estimating a resource usage amount, an electronic device, and a computer-readable medium.
Background
The financial resources refer to the sum or aggregate of a series of objects related to the structure, quantity, scale, distribution, effect and interaction relationship of financial service subjects and objects in the financial field, and in production and life, only the financial resources are allocated efficiently, so that the financial and economic sustainable development can be realized. For companies that provide internet financial services, the financial resource may be the total amount of funds, or the amount of assets equivalent to funds, or the like. For companies that provide internet financial services, some of the financial assets may be used to provide financial services to individual users, some may be used to provide financial services to other enterprise users, and others may be used to invest in the development of the company or perform other financial-related transactions.
For companies with internet financial services, it is important to allocate financial resources reasonably among different businesses because the total financial resources are limited in a relatively fixed time. For enterprise users or other financial related businesses served by the internet financial service company, the time and period for occupying financial resources can be generally approved through plan approval in advance, which is favorable for overall arrangement of allocation of the financial resources. For individual users, due to individual differences of the individual users, an internet financial service company can hardly predict plans and time of financial resource demands of the individual users in advance, how to better predict the financial service demands of the individual users, and how to more reasonably distribute the financial resources of the individual users are difficult problems faced by the internet financial service company at present.
Therefore, a new method, apparatus, electronic device and computer readable medium for estimating resource usage amount are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a resource usage amount estimation method, device, electronic device and computer readable medium, which can predict a future cash flow for borrowing, so as to provide a reference for fund preparation, and can meet the fund demand of borrowing of a user, and at the same time, avoid excessive fund redundancy, thereby effectively saving the fund cost.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a method for estimating a resource usage amount is provided, where the method includes: acquiring the use data of the target resource in the previous time period; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit.
Optionally, the method further comprises: determining the resource reserve amount based on the usage data of the target resource in the period.
Optionally, the method further comprises: acquiring actual use data of the target resource in the period; and updating the calculation parameters based on the estimated use data and the actual use data of the target resource in the period.
Optionally, the obtaining of the usage data of the target resource in the previous time period includes: and acquiring the use data of the target resource on the previous natural day.
Optionally, determining the usage rate of the target resource in the time period according to the difference-integrated moving average autoregressive model includes: acquiring historical use data of a historical user on a target resource; acquiring historical use authority data of a target resource of a historical user; and generating a predicted utilization rate by differentially integrating a moving average autoregressive model with the historical utilization data and the historical utilization right data.
Optionally, determining the usage rate of the target resource in the time period according to the difference integrated moving average autoregressive model, further includes: determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and determining the utilization rate of the target resource in the time period according to the time period parameter, the wind control adjusting parameter, the operation adjusting parameter and the predicted utilization rate.
Optionally, determining the user usage amount of the target resource in the time period includes: acquiring an estimated use limit; determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and determining the user usage amount of the target resource in the time period according to the estimated usage amount, the time period parameter, the wind control adjustment parameter and the operation adjustment parameter.
Optionally, determining the estimated usage data of the target resource in the current period based on the usage data, the user usage rate, and the user usage amount includes: generating user usage data for a cost cycle based on the user usage rate and the user usage amount; and determining the estimated use data of the target resource in the period according to the user use data of the period and the user use data of the previous time period.
Optionally, updating the calculation parameter based on the estimated usage data and the actual usage data of the target resource in the present period includes: and updating time period parameters, and/or wind control adjusting parameters and/or operation adjusting parameters based on the estimated use data and the actual use data of the target resource in the period.
Optionally, updating the calculation parameter based on the estimated usage data and the actual usage data of the target resource in the present period includes: determining a difference limit based on the estimated use data and the actual use data of the target resource in the period; and updating the calculation parameters when the difference limit is larger than a threshold value.
According to an aspect of the present disclosure, an apparatus for estimating a resource usage amount is provided, the apparatus including: the usage data module is used for acquiring the usage data of the target resource in the previous time period; the utilization rate module is used for determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; the usage amount module is used for determining the usage amount of the target resource in the user in the time period; and the estimated data module is used for determining estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit.
Optionally, the method further comprises: and the resource reservation module is used for determining the resource reservation quantity based on the use data of the target resource in the period.
Optionally, the method further comprises: the parameter updating module is used for acquiring actual use data of the target resource in the period; and updating the calculation parameters based on the estimated use data and the actual use data of the target resource in the period.
Optionally, the usage data module is further configured to obtain usage data of the target resource on a previous natural day.
Optionally, the usage module includes: the historical data unit is used for acquiring historical use data of a historical user on the target resource; acquiring historical use authority data of a target resource of a historical user; and the predicted utilization rate unit is used for generating a predicted utilization rate by differentially integrating a moving average autoregressive model, the historical utilization data and the historical utilization right data.
Optionally, the usage rate module further includes: a first parameter unit for determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and the utilization rate module is used for determining the utilization rate of the target resource in the time period according to the time period parameter, the wind control adjusting parameter, the operation adjusting parameter and the predicted utilization rate.
Optionally, the usage amount module includes: the second parameter unit is used for acquiring the estimated using amount; determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and the use limit unit is used for determining the user use limit of the target resource in the time period through the estimated use limit, the time period parameter, the wind control adjusting parameter and the operation adjusting parameter.
Optionally, the forecast data module is further configured to generate user usage data of a cost cycle based on the user usage rate and the user usage amount; and determining the estimated use data of the target resource in the period according to the user use data of the period and the user use data of the previous time period.
Optionally, the parameter updating module is further configured to update a time period parameter, and/or a wind control adjustment parameter and/or an operation adjustment parameter based on the estimated usage data and the actual usage data of the target resource in the current period.
Optionally, the parameter updating module includes: the comparison unit is used for determining a difference limit based on the estimated use data and the actual use data of the target resource in the period; and the threshold unit is used for updating the calculation parameters when the difference limit is larger than a threshold value.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the resource usage amount estimation method, device, electronic equipment and computer readable medium disclosed by the disclosure, the usage data of the target resource in the previous time period is acquired; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user utilization rate and the user use amount, and predicting the future borrowing cash flow so as to provide reference for fund preparation, meet the fund demand of borrowing of the user, avoid excessive fund redundancy and effectively save the fund cost.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow diagram illustrating a method for resource usage amount estimation in accordance with an exemplary embodiment.
FIG. 2 is a flow diagram illustrating a method for resource usage amount estimation in accordance with another exemplary embodiment.
FIG. 3 is a flow diagram illustrating a method for resource usage amount estimation in accordance with another exemplary embodiment.
Fig. 4 is a block diagram illustrating a resource usage amount estimation apparatus according to an example embodiment.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 6 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
FIG. 1 is a flow diagram illustrating a method for resource usage amount estimation in accordance with an exemplary embodiment. The method 10 for estimating the resource usage amount at least includes steps S102 to S108.
As shown in fig. 1, in S102, usage data of the target resource in a previous time period is acquired.
The target resource may be a cash resource, more specifically, a cash resource calculated by rmb, and the target resource may also be an article resource, such as a shared article resource available for users to borrow; the target resource may also be a natural resource, such as a water resource for use by a user, and the like, which the disclosure is not limited to.
The time period may be any time period, such as a time period calculated in months, and the previous time period is the usage data of the previous month. It is also possible, for example, to calculate the time period of the week, the previous time period then being the last week's usage data. In this disclosure, the time period may be a time period calculated by day, and specifically may include: and acquiring the use data of the target resource on the previous natural day.
In S104, the user usage rate of the target resource in the time period is determined according to the difference-integrated moving average autoregressive model. Can include the following steps: acquiring historical use data of a historical user on a target resource; acquiring historical use authority data of a target resource of a historical user; and generating a predicted utilization rate by differentially integrating a moving average autoregressive model with the historical utilization data and the historical utilization right data.
In one embodiment, determining the usage rate of the target resource in the time period according to a differential integrated moving average autoregressive model further includes: determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and determining the utilization rate of the target resource in the time period according to the time period parameter, the wind control adjusting parameter, the operation adjusting parameter and the predicted utilization rate.
the daily occurrence of the cash flow for borrowing can be considered as a function of the credit line granted to all the users and is a simple linear function, let the cash flow for borrowing be Y, the credit line be X, β be the ratio of credit to debit (abbreviated as borrowing rate), then:
Y＝f(X)＝βX
the users who borrow on the same day can be classified according to the credit date of the users. Setting:
ya，bthe credit users are granted on b days, the borrowing amount on a day (a is more than or equal to b),
xbthe credit amount of the credit user is granted on the b day,
βa，bnamely the loan rate of the credit user on the b day on the a day,
then there are: y isa，b＝βa，bxb
The amount of money to be borrowed Y of day TTCan be expressed as:
Figure BDA0002314277610000081
therefore, when the T +1 day debit amount is predicted, the historical credit amount is known, and the T +1 credit amount needs to be estimated
Figure BDA0002314277610000082
And the borrowing rate of the historical daily credit user in T +1 day
Figure BDA0002314277610000083
I.e. YT+1Can be expressed as:
Figure BDA0002314277610000084
wherein
Figure BDA0002314277610000085
(b belongs to [0, T +1 ]]) And
Figure BDA0002314277610000086
to be predicted, xb(b is of [0, T ]]) Is a known value.
To predict
Figure BDA0002314277610000087
Needs to input historical borrowing and credit data of usersAnd time sequence prediction is carried out through an ARIMA model by considering time period factors, operation factors and wind control regulation factors.
If the time period factor variable is w, the operation factor is o, and the wind control adjustment factor is z, the borrowing rate can be expressed as:
Figure BDA0002314277610000088
wherein f isT+1(βT，b) The ARIMA (differential integrated moving average autoregressive model) model prediction value for the T +1 borrowing rate is shown.
In S106, the user usage amount of the target resource in the time period is determined. The method comprises the following steps: acquiring an estimated use limit; determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and determining the user usage amount of the target resource in the time period according to the estimated usage amount, the time period parameter, the wind control adjustment parameter and the operation adjustment parameter.
To predict
Figure BDA0002314277610000091
The number of credit providers is determined according to the actual service condition and the target of the number of credit providers set in the current month. Because the final number of credits per month is strongly correlated with the target number set at the beginning of the month, the final number of credits may be influenced by time period, operational factors, and wind control.
Assuming that the daily credit granting target set in the current month is p, the predicted value of credit granting number (amount) can be expressed as:
Figure BDA0002314277610000092
in S108, estimated usage data of the target resource in the period is determined based on the usage data, the user usage rate, and the user usage amount. The method comprises the following steps: generating user usage data for a cost cycle based on the user usage rate and the user usage amount; and determining the estimated use data of the target resource in the period according to the user use data of the period and the user use data of the previous time period.
Will be provided with
Figure BDA0002314277610000093
And fT+1(βT，b) Into YT+1In the formula, the predicted value of the borrowing amount in the T +1 day can be obtained, and the borrowing amount in the future 30 days can be continuously calculated and iteratively predicted step by step.
According to the resource usage amount estimation method disclosed by the invention, the usage data of the target resource in the previous time period is acquired; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user utilization rate and the user use amount, and predicting the future borrowing cash flow so as to provide reference for fund preparation, meet the fund demand of borrowing of the user, avoid excessive fund redundancy and effectively save the fund cost.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
In one embodiment, further comprising: determining the resource reserve amount based on the usage data of the target resource in the period. And forecasting daily borrowing cash flow of a future month by inputting historical data and inputting regulating factors of all parts, comparing the forecasting value with an actual value every day, and self-checking and adjusting a forecasting coefficient of the future cash flow according to the condition of deviation degree. The method can accurately predict the future borrowing cash flow, can provide reference for fund preparation, can meet the fund demand of borrowing of a user, can avoid excessive fund redundancy, and effectively saves the fund cost.
FIG. 2 is a flow diagram illustrating a method for resource usage amount estimation in accordance with another exemplary embodiment. The flow shown in fig. 2 is a detailed description of "updating the calculation parameters based on the estimated usage data and the actual usage data of the target resource in the present period".
As shown in fig. 2, in S202, a difference quota is determined based on the estimated usage data and the actual usage data of the target resource in the present period.
the actual borrowing rate β of the day can be calculatedT，bAnd predicting the borrowing rate
Figure BDA0002314277610000101
And carrying out comparison and judgment. When the average value and the dispersion degree of the difference between the actual value and the predicted value reach a certain threshold value within a period of time, the time period factor in the prediction model is corrected, so that the actual value and the predicted value can keep the difference within a certain range, and the fluctuation trend is basically consistent.
Let the actual and expected difference degree of T day be dT，bThen, then
Figure BDA0002314277610000102
Selecting a sequence of degrees of difference { d over N daysT，b，dT-1，b，dT-2，b，……dT-N，bCalculate the mean of the sequence (denoted as e)T，b) And calculating its standard deviation (denoted as σ)T，b). And manually setting a threshold and a change condition, once the mean value and the standard deviation of the difference exceed the threshold, adjusting the prediction coefficient, and finally ensuring that the prediction is basically consistent with the actual prediction.
In S204, it is determined whether the difference amount is greater than a threshold.
In S206, when the difference amount is greater than the threshold, the calculation parameter is updated. The time period parameters, and/or the wind control regulation parameters and/or the operational regulation parameters may be updated, for example, based on the estimated usage data and the actual usage data of the target resource during the present period.
Setting a threshold for the mean of the differenceIs E0Then new time factor variable w'T，bCan be expressed as:
Figure BDA0002314277610000111
setting the threshold value of the standard deviation of the difference as D0Then new time factor variable w'T，bCan be expressed as:
Figure BDA0002314277610000112
finally adjusted new time period coefficient w'T，bThe next time period is included for prediction.
FIG. 3 is a flow diagram illustrating a method for resource usage amount estimation in accordance with another exemplary embodiment. The flow shown in fig. 3 is a detailed description of the whole process of the resource usage amount estimation method.
As shown in fig. 3, in S302, user loan and credit history data is input.
In S304, adjustment factors are input, which may include time factors, operation factors, and wind control adjustment factors.
In S306, a future cash flow for borrowing is output.
In S308, the actual cash flow for borrowing is recorded.
In S310, a self-check is performed to adjust future cash flow forecasts.
And forecasting daily borrowing cash flow of a future month by inputting historical data and inputting regulating factors of all parts, comparing the forecasting value with an actual value every day, and self-checking and adjusting a forecasting coefficient of the future cash flow according to the condition of deviation degree. The method can accurately predict the future borrowing cash flow, can provide reference for fund preparation, can meet the fund demand of borrowing of a user, can avoid excessive fund redundancy, and effectively saves the fund cost.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 4 is a block diagram illustrating a resource usage amount estimation apparatus according to an example embodiment. The resource usage amount estimation device 40 shown in fig. 4 may include: a usage data module 402, a usage rate module 404, a usage amount module 406, a forecast data module 408, a resource reservation module 410, and a parameter update module 412.
The usage data module 402 is used to obtain usage data of the target resource in a previous time period; the usage data module 402 is further configured to obtain usage data of the target resource on a previous natural day.
The utilization rate module 404 is configured to determine a user utilization rate of the target resource in the current time period according to a differential-integration moving-average autoregressive model; the usage module 404 includes: the historical data unit is used for acquiring historical use data of a historical user on the target resource; acquiring historical use authority data of a target resource of a historical user; and the predicted utilization rate unit is used for generating a predicted utilization rate by differentially integrating a moving average autoregressive model, the historical utilization data and the historical utilization right data. The usage module 404 further includes: a first parameter unit for determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and the utilization rate module is used for determining the utilization rate of the target resource in the time period according to the time period parameter, the wind control adjusting parameter, the operation adjusting parameter and the predicted utilization rate.
The usage amount module 406 is configured to determine a usage amount of the target resource for the user in the time period; the credit module 406 includes: the second parameter unit is used for acquiring the estimated using amount; determining a time period parameter; determining a wind control adjusting parameter; determining operation adjustment parameters; and the use limit unit is used for determining the user use limit of the target resource in the time period through the estimated use limit, the time period parameter, the wind control adjusting parameter and the operation adjusting parameter.
The estimated data module 408 is configured to determine estimated usage data of the target resource in the period based on the usage data, the user usage rate, and the user usage amount. The forecast data module 408 is further configured to generate user usage data of the cost cycle based on the user usage rate and the user usage amount; and determining the estimated use data of the target resource in the period according to the user use data of the period and the user use data of the previous time period.
The resource reservation module 410 is configured to determine a resource reservation amount based on the usage data of the target resource in the current period.
The parameter updating module 412 is configured to obtain actual usage data of the target resource in the current period; and updating the calculation parameters based on the estimated use data and the actual use data of the target resource in the period. The parameter updating module 412 is further configured to update the time period parameter, and/or the wind control adjustment parameter and/or the operation adjustment parameter based on the estimated usage data and the actual usage data of the target resource in the current period. The parameter update module 412 includes: the comparison unit is used for determining a difference limit based on the estimated use data and the actual use data of the target resource in the period; and the threshold unit is used for updating the calculation parameters when the difference limit is larger than a threshold value.
According to the resource usage amount estimation device disclosed by the invention, the usage data of the target resource in the previous time period is acquired; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user utilization rate and the user use amount, and predicting the future borrowing cash flow so as to provide reference for fund preparation, meet the fund demand of borrowing of the user, avoid excessive fund redundancy and effectively save the fund cost.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 500 according to this embodiment of the disclosure is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 that couples various system components including the memory unit 520 and the processing unit 510, a display unit 540, and the like.
Wherein the storage unit stores program code executable by the processing unit 510 to cause the processing unit 510 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 510 may perform the steps as shown in fig. 1, 2, 3.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The memory unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 530 may be one or more of any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 500 may also communicate with one or more external devices 500' (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 560. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 6, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring the use data of the target resource in the previous time period; determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model; determining the user usage limit of the target resource in the time period; and determining the estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (10)

1. A method for estimating the resource usage amount is characterized by comprising the following steps:
acquiring the use data of the target resource in the previous time period;
determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model;
determining the user usage limit of the target resource in the time period; and
and determining the estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit.
2. The method of claim 1, further comprising:
determining the resource reserve amount based on the usage data of the target resource in the period.
3. The method of claims 1-2, further comprising:
acquiring actual use data of the target resource in the period; and
and updating the calculation parameters based on the estimated use data and the actual use data of the target resource in the period.
4. The method of claims 1-3, wherein obtaining usage data for the target resource for a previous time period comprises:
and acquiring the use data of the target resource on the previous natural day.
5. The method of claims 1-4, wherein determining the target resource usage rate for the time period based on a differential integrated moving average autoregressive model comprises:
acquiring historical use data of a historical user on a target resource;
acquiring historical use authority data of a target resource of a historical user;
and generating a predicted utilization rate by differentially integrating a moving average autoregressive model with the historical utilization data and the historical utilization right data.
6. The method of claims 1-5, wherein determining the target resource usage rate for the time period based on a differential integrated moving average autoregressive model, further comprises:
determining a time period parameter;
determining a wind control adjusting parameter;
determining operation adjustment parameters;
and determining the utilization rate of the target resource in the time period according to the time period parameter, the wind control adjusting parameter, the operation adjusting parameter and the predicted utilization rate.
7. The method of claims 1-6, wherein determining the user's usage amount of the target resource during the time period comprises:
acquiring an estimated use limit;
determining a time period parameter;
determining a wind control adjusting parameter;
determining operation adjustment parameters;
and determining the user usage amount of the target resource in the time period according to the estimated usage amount, the time period parameter, the wind control adjustment parameter and the operation adjustment parameter.
8. An apparatus for estimating the amount of resource usage, comprising:
the usage data module is used for acquiring the usage data of the target resource in the previous time period;
the utilization rate module is used for determining the user utilization rate of the target resource in the time period according to the difference integration moving average autoregressive model;
the usage amount module is used for determining the usage amount of the target resource in the user in the time period; and
and the estimated data module is used for determining estimated use data of the target resource in the period based on the use data, the user use rate and the user use limit.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
CN201911271298.9A 2019-12-12 2019-12-12 Resource usage amount estimation method and device and electronic equipment Pending CN111178692A (en)

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