CN109543918A - Data predication method, device, computer installation and computer readable storage medium - Google Patents
Data predication method, device, computer installation and computer readable storage medium Download PDFInfo
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Abstract
The history operation data including obtaining the history operation data of enterprise, and is scaled a time series data by a kind of data predication method；Based on the processing to the time series data, data prediction model corresponding with the time series data is obtained；The estimates of parameters of the data prediction model is calculated, and model testing is carried out to the data prediction model according to the estimates of parameters；Judge whether the data prediction model is effective according to the result of the model testing；And if so, enterprise operation data are inputted into the data prediction model to obtain prediction result.The present invention also provides a kind of data prediction meanss, computer installation and computer readable storage mediums.The present invention relates to data analysis fields, are predicted by the operation data to enterprise, realize the purpose for saving human cost.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of data predication method, device, computer installation and
Computer readable storage medium.
Background technique
Performance analysis platform displaying billboard is a kind of form of expression of managing visual, the i.e. shape to data, information etc.
Condition shows at a glance, mainly includes the transparent management activity carried out to the management project, particularly information of company.It is logical
Cross various forms such as poster, present situation plate, chart, electrical screen on file, the information that in brains or scene etc. is hiding discloses
Come, so that anyone can grasp management status and necessary information in time, so as to rapid development and implement reply arrange
It applies.Therefore, performance analysis platform displaying billboard is the highly effective and intuitive means found the problem, solved the problems, such as, is excellent
Elegant one of the essential tool of company management.However, displaying billboard on the market, can not only based on showing at present
Data analysis and prediction is done, cost is taken a substantial amount of time.
Summary of the invention
In view of the foregoing, it is necessary to propose a kind of data predication method and device, calculating that can save time cost
Machine device and computer readable storage medium.
The first aspect of the application provides a kind of data predication method, which comprises
The history operation data of enterprise is obtained, and the history operation data is scaled a time series data；
Based on the processing to the time series data, data prediction mould corresponding with the time series data is obtained
Type；
The estimates of parameters of the data prediction model is calculated, and mould is predicted to the data according to the estimates of parameters
Type carries out model testing；
Judge whether the data prediction model is effective according to the result of the model testing；And
If so, enterprise operation data are inputted the data prediction model to obtain prediction result.
The history operation data for obtaining enterprise in one of the embodiments, and according to the history operation data
Construct time series data the step of include:
One predetermined period is set, according to history operation data described in the predetermined period timing acquisition；
Judge whether the timing of the history operation data has stationarity；And
If it is not, then to the history operation data carry out tranquilization processing, using the history operation data after tranquilization as
The time series data.
It is described in one of the embodiments, that tranquilization processing is carried out to the history operation data, after tranquilization
History operation data includes: as the step of time series data
The history management data is in chronological sequence sequentially arranged, and calculates the mean value of the history management data；
The mean value of each history management data and the history management data is subjected to subtraction, obtains tranquilization
History operation data afterwards；And
Using the history operation data after the tranquilization as the time series data.
It is described in one of the embodiments, that the time series data is handled, it obtains and the time series
The step of data corresponding data prediction model includes:
Obtain one group of preset model；
First time sequence data value in the time series data is input in this group of preset model, obtains one group
Output valve；And
Each output valve and the second time series data value in the time series data are compared, and root
Data prediction model corresponding with the time series data is selected from this group of preset model according to comparing result.
In one of the embodiments, the estimates of parameters include: moments estimation value, least-squares estimation value and greatly seemingly
Right estimated value.
The estimates of parameters for calculating the data prediction model in one of the embodiments, and according to the ginseng
Counting the step of estimated value carries out model testing to the data prediction model includes:
Obtain the model parameter of the data prediction model, and the data prediction model according to the model parameter calculation
Moments estimation value, least-squares estimation value and maximum likelihood estimation；And
Judge whether the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into default threshold
It is worth in region, to carry out model testing to the data prediction model.
Whether the result according to the model testing judges the data prediction model in one of the embodiments,
Effective step includes:
When the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into the preset threshold
When in region, determine that the data prediction model is effective；When the moments estimation value, the least-squares estimation value and it is described greatly
When likelihood estimator is not in the preset threshold region, determine that the data prediction model is invalid.
The second aspect of the application provides a kind of data prediction meanss, and described device includes:
Module is obtained, is scaled the time for obtaining the history operation data of enterprise, and by the history operation data
Sequence data；
Identification module, it is corresponding with the time series data for obtaining based on the processing to the time series data
Data prediction model；
Inspection module, for calculating the estimates of parameters of the data prediction model, and according to the estimates of parameters pair
The data prediction model carries out model testing；
Judgment module, for judging whether the data prediction model is effective according to the result of the model testing；And
Prediction module, for enterprise operation data to be inputted the data prediction model to obtain prediction result.
The third aspect of the application provides a kind of computer installation, and the computer installation includes processor, the processing
Device is for realizing the data predication method when executing the computer program stored in memory.
The fourth aspect of the application provides a kind of computer readable storage medium, is stored thereon with computer program, described
The data predication method is realized when computer program is executed by processor.
The history operation data is scaled by above-mentioned data predication method by obtaining the history operation data of enterprise
One time series data；Based on the processing to the time series data, data corresponding with the time series data are obtained
Prediction model；The estimates of parameters of the data prediction model is calculated, and the data are predicted according to the estimates of parameters
Model carries out model testing；Judge whether the data prediction model is effective according to the result of the model testing；If so, will
Enterprise operation data input the data prediction model to obtain prediction result.To, using enterprise history operation data into
Line number is it was predicted that save time cost.
Detailed description of the invention
Fig. 1 is the flow chart of data predication method provided in an embodiment of the present invention.
Fig. 2 is the structure chart of data prediction meanss provided in an embodiment of the present invention.
Fig. 3 is the schematic diagram of computer installation provided in an embodiment of the present invention.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying example, the present invention will be described in detail.It should be noted that in the absence of conflict, embodiments herein and embodiment
In feature can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, described embodiment is only
It is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Term as used herein in the specification of the present invention is intended merely to description tool
The purpose of the embodiment of body, it is not intended that in the limitation present invention.
Preferably, data predication method of the invention is applied in one or more computer installation.The computer
Device is that one kind can be according to the instruction for being previously set or storing, the automatic equipment for carrying out numerical value calculating and/or information processing,
Hardware includes but is not limited to microprocessor, specific integrated circuit (Application Specific Integrated
Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processing unit
(Digital Signal Processor, DSP), embedded device etc..
The computer installation can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The computer installation can carry out people by modes such as keyboard, mouse, remote controler, touch tablet or voice-operated devices with user
Machine interaction.
Embodiment one
Fig. 1 is the flow chart for the data predication method that the embodiment of the present invention one provides.The data predication method is applied to
Computer installation.The data predication method carries out number it was predicted that saving time cost using the history operation data of enterprise.
As shown in Figure 1, the data predication method specifically includes the following steps:
Step 101, the history operation data of enterprise is obtained, and the history operation data is scaled a time sequence number
According to.
Wherein, the history operation data may include history manpower data, history financial data, history research and development data,
Historic market sales data etc..Time series data refers to that some statistical indicator is each on different time by certain phenomenon
Numerical value, in chronological sequence sequence arrange and formed sequence.
History manpower data may include the application of the actual hours, standard work force, designated period of time (such as monthly) of employee
Number employs number, registration number, leaving office number, initial number, end of term number etc..
History financial data may include the net profit of designated period of time (such as monthly), income from sales, operating profit, business
Income, total profit, cost total value etc..
History research and development data may include the research and development demand of designated period of time (such as monthly), demand performance etc..
Historic market sales data may include the customer name expanded, the customer name in expansion, each client
Consumption sum etc..
It can be docked with the operation system of enterprise, obtain the history operation data from the operation system.For example, from enterprise
The day reporting system of industry obtains actual hours, the standard work force of each department employee.
Alternatively, the history operation data can be obtained from specified database.User can regular going through each department
History operation data imports the specified database.
Alternatively, can receive the history operation data of user's upload.
In one embodiment, before the history operation data is scaled a time series data, the step
Further include being pre-processed to the history operation data, obtains the achievement data in pre-set business field.
The pre-set business field may include manpower, finance, research and development, market sale etc..
Carrying out processing to the history operation data may include being handled to obtain manpower field to history manpower data
Achievement data.
For example, can according to application number, employ number calculating employ rate.It can be calculated according to following formula: employ rate
=(employing number ÷ application number) × 100%.
For another example, separation rate is calculated according to leaving office number, number on the regular payroll.It can be calculated according to following formula: separation rate=(from
Duty number ÷ (registration number+beginning number)) × 100%.
It for another example, can be with computing staff's utilization rate.It can be calculated according to following formula: personnel's utilization rate=(actual hours ÷
Standard work force) × 100%.The average actual hours and average working hour that each department, enterprise can be calculated, according to each portion
The average actual hours of door and average personnel's utilization rate of each department of average man-hour calculation.For example, some department is flat
Equal actual hours is 7.5, and average working hour is 8, then averagely personnel's utilization rate is 7.5/8 × 100%=93.75%.
Handling the history operation data can also include handling history financial data, obtain financial neck
The achievement data in domain.
For example, calculating net profit on sales rate, net profit on sales rate=(net profit ÷ income from sales) according to net profit, income from sales
× 100%.The ratio is bigger, and the profitability of enterprise is stronger.
For another example, it is calculated according to operating profit, operating income, operating profit ratio operating profit ratio=(operating profit ÷ business
Income) × 100%.The ratio is bigger, and the profitability of enterprise is stronger.
For another example, cost profit margin, cost profit margin=(benefit are calculated according to total profit, cost total value
Moisten total value ÷ cost total value) × 100%.The ratio is bigger, and the effectiveness of operation of enterprise is higher.
Handling the history operation data can also include handling history research and development data, obtain research and development neck
The achievement data in domain.
For example, calculating requirement fulfillment rate according to research and development demand, demand performance.It can calculate: need according to following formula
Seek fulfilling rate=(research and development demand ÷ demand performance) × 100%.
Handling the history operation data can also include handling historic market sales data, obtain city
The achievement data of field realm of sale.
For example, statistics has expanded customer quantity, customer quantity, big customer's quantity etc. in expansion.
In one embodiment, the history operation data for obtaining enterprise, and constructed according to the history operation data
The step of time series data includes: one predetermined period of setting, runs number according to history described in the predetermined period timing acquisition
According to；Judge whether the timing of the history operation data has stationarity；If it is not, carrying out tranquilization to the history operation data
Processing, and time series data is obtained according to the history operation data after tranquilization.Wherein, a time series data,
If mean value does not change, variance does not change, and strictly eliminates cyclically-varying, just it is referred to as to be stable, i.e. this time
Sequence data has stationarity.
For example, the predetermined period is January, monthly No. 1 history that last month enterprise is obtained from operation system is transported
Seek data.The data that will acquire are arranged in chronological order and calculate the mean value of the history management data.To each institute
State the mean value that history management data subtracts the history management data, the history operation data after obtaining tranquilization.By institute
The data of history operation data composition after stating tranquilization are as the time series data.
Step 102, based on the processing to the time series data, data corresponding with the time series data are obtained
Prediction model.
Wherein it is possible to be handled by one group of preset model the time series data, the preset model is normal
The image recognition model seen, such as autocorrelogram identification model and partial correlation figure identification model.
In one embodiment, one group of preset model is obtained；By the first time sequence number in the time series data
It is input in the preset model according to value, obtains one group of output valve；By the in the output valve and the time series data
Two time series data values compare, and data prediction corresponding with the time series data is selected from the preset model
Model.
In one embodiment, firstly, calculating the auto-correlation coefficient in autocorrelogram identification model and the identification of partial correlation figure
Partial correlation coefficient in model.Secondly, observing the auto-correlation system and the partial correlation coefficient.Finally, by described in observation
Auto-correlation system and the partial correlation coefficient are fitted to obtain the data prediction model.
Step 103, the estimates of parameters of the data prediction model is calculated, and according to the estimates of parameters to the number
It is predicted that model carries out model testing.
Wherein, the estimates of parameters is to be obtained according to the model parameter calculation of the data prediction model: moments estimation
Value, least-squares estimation value and maximum likelihood estimation.The model testing includes the T method of inspection to the data prediction model
(Student's test, student examine), F method of inspection (joint hypotheses test, joint hypothesis method of inspection) Ji Kafang
It examines.
The moments estimation value is calculated by moments estimation method.Corresponding parameter in totality is estimated using sample moment.Tool
For body, the equation of the population moment containing the moments estimation value is derived first, and population moment is the power of considered stochastic variable
Desired value.It then takes out a sample and estimates population moment from the sample.Finally replace population moment using sample moment, solves institute
State moments estimation value.To obtain the moments estimation value of the data prediction model.
The least-squares estimation value is calculated by least squares estimate.Least square method is also known as least square
Method is a kind of mathematical optimization techniques.Unknown data can be easily acquired using least squares estimate, and these are asked
The quadratic sum of error is minimum between the data and real data obtained, and these unknown data, that is, data prediction model
Least-squares estimation value.
The maximum likelihood estimation passes through Maximum Likelihood Estimation (Maximum Likelihood Estimate, letter
Claim MLE) also referred to as most probably it is calculated like estimation or maximal possibility estimation.The probability that some known parameter can be such that sample occurs
Maximum, can be using the parameter as the true value of estimation, and the parameter is the Maximum-likelihood estimation of the data prediction model
Value.
Step 104, judge whether the data prediction model is effective according to the result of the model testing.
In one embodiment, the model parameter of the data prediction model is obtained, and according to the model parameter calculation
Moments estimation value, least-squares estimation value and the maximum likelihood estimation of the data prediction model；By judging the moments estimation
Whether value, the least-squares estimation value and the maximum likelihood estimation fall into preset region, predict the data
Model carries out model testing.
In one embodiment, when the moments estimation value, the least-squares estimation value and the maximum likelihood estimation
When falling into preset region, determine that the data prediction model is effective, otherwise, when the moments estimation value, the least square
When estimated value and the maximum likelihood estimation be not in preset region, determine that the data prediction model is invalid.
In one embodiment, the Model Checking may be selected to be F method of inspection, by examining two normal states to become at random
Whether the population variance of amount is equal to be tested.Assuming that the sample of time series data X, Y described in two groups is respectively X1,
X2 ... ..., Xn and Y1, Y2 ... ..., Yn, sample variance are respectively S12 and S22.Now the population variance DX's and Y of inspection X is total
Whether body variance DY is equal.Assuming that H0:DX=DY=σ 2.According to statistical theory, if X, Y are normal distribution, when assuming that setting up
When, statistic obeys F-distribution that the first freedom degree is n1, the second freedom degree n2.Previously given reliability α.F-distribution table is looked into,
Obtain α/2 F.If the F value calculated is less than α/2 F, it assumes that set up, otherwise assume unreasonable.Wherein, described to be assumed to be the equivalence
Mould model is effective.
Step 105, if so, enterprise operation data are inputted the data prediction model to obtain prediction result.
Wherein it is possible to generate data prediction interface, the achievement data is shown in the data prediction interface.For example,
Different data prediction interfaces can be generated according to the permission level of user.Permission level can be carried out to user in advance to set
It is fixed, different data prediction interfaces is generated to the user of different rights level.For example, senior enterprise leader is generated management level and is seen
Plate；For department head, department's billboard is generated, is checked by department head；For domain expert, generates domain expert and see
Plate.Data prediction interface corresponding with its permission level can be shown according to the log-on message (such as user name) of user.
In one embodiment, the operation data for obtaining enterprise's last month, the operation data of the last month is scaled
One time series data.And then tranquilization processing is carried out to the time series data, with tranquilization treated time series
Input of the data as the data prediction model, the output valve of the data prediction model are enterprise's operation data next month
Prediction result.
In one embodiment, the data prediction interface may include three regions:
First viewing area can also show message informing, help and exit for showing user name, user right level
Function button, the first viewing area can be in the upsides (can be upper right side) of the data prediction interface；
The index in different business field is checked for entering different business scopes in second viewing area, that is, navigation bar
Data.The navigation bar may be displayed on the left side of the data prediction interface.
Third viewing area, that is, show area, for showing the achievement data.
It can be layered and show the achievement data.For example, the page of two levels is generated for each business scope, including
Homepage and the second level page.The homepage shows the key index data of the business scope, and the second level page shows the industry
Other achievement datas (achievement data except key index data) in business field.Alternatively, each business scope can be directed to
Generate the page of three levels or three or more level.
The field feedback can be sent to default processing side, and receive the default processing side to the user
The reply of feedback information determines whether the achievement data is abnormal according to the reply.The default processing side is to the user
Feedback information is confirmed, if the achievement data is abnormal, the reply of returned data exception.The default processing side can be with
Return to modified achievement data.The default processing side can be the provider of history operation data.
Alternatively, the achievement data can be compared with preset metrics-thresholds, if the achievement data is more than just
Constant value range then determines that the achievement data is abnormal.
In one embodiment, information feedback entrance or information feedback area domain can be shown in data prediction interface, connect
Receive the field feedback that entrance or the input of information feedback area domain are fed back by the information.
The information feedback entrance can be information feedback button, after receiving the clicking operation to the feedback button,
Show information feedback interface.
The field feedback may include business scope, problem types, problem explanation, attachment etc..Described problem class
Type may include data age, data accuracy, data area etc..
In one embodiment, if the achievement data is abnormal, the achievement data is adjusted, shows index number adjusted
According to.
In another embodiment, if the data predication method can also include: that the achievement data is abnormal, data are different
Normal open, which is known, is sent to default contact person (such as responsible person of the affiliated business scope of the achievement data).It can be by mail, short
Data exception notice is sent to default contact person by the modes such as letter, wechat.
In another embodiment, the history operation data for obtaining enterprise, and according to the history operation data structure
The step of building time series data includes: one predetermined period of setting, according to history operation described in the predetermined period timing acquisition
Data；Judge whether the timing of the history operation data has stationarity；And if it is not, then the history operation data is carried out
Tranquilization processing, using the history operation data after tranquilization as the time series data.
In another embodiment, described that tranquilization processing is carried out to the history operation data, by going through after tranquilization
History operation data include: as the step of time series data the history management data is in chronological sequence sequentially arranged, and
Calculate the mean value of the history management data；The mean value of each history management data and the history management data is carried out
Subtraction, the history operation data after obtaining tranquilization；And using the history operation data after the tranquilization as it is described when
Between sequence data.
In another embodiment, described that the time series data is handled, it obtains and the time series number
It include: to obtain one group of preset model according to the step of corresponding data prediction model；By in the time series data first when
Between sequence data value be input in this group of preset model, obtain one group of output valve；And by each output valve and the time
The second time series data value in sequence data compares, and is selected from this group of preset model according to comparing result and institute
State the corresponding data prediction model of time series data.
In another embodiment, the estimates of parameters of the data prediction model is calculated, and according to the parameter Estimation
Value to the data prediction model carry out model testing the step of include: obtain the model parameter of the data prediction model, and
According to the moments estimation value of data prediction model described in the model parameter calculation, least-squares estimation value and Maximum-likelihood estimation
Value；And judge whether the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into preset threshold
In region, to carry out model testing to the data prediction model.
In another embodiment, the result according to the model testing judges whether the data prediction model has
The step of effect include: when the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into it is described pre-
If when in threshold region, determining that the data prediction model is effective；When the moments estimation value, the least-squares estimation value and institute
When stating maximum likelihood estimation not in the preset threshold region, determine that the data prediction model is invalid.
The history operation data is scaled by above-mentioned data predication method by obtaining the history operation data of enterprise
One time series data；The time series data is handled, it is pre- to obtain data corresponding with the time series data
Survey model；The estimates of parameters of the data prediction model is calculated, and mould is predicted to the data according to the estimates of parameters
Type carries out model testing；Judge whether the data prediction model is effective according to the result of the model testing；If so, using
The prediction result of the data prediction model output enterprise operation data.To be counted using the history operation data of enterprise
It was predicted that saving time cost.
Embodiment two
Fig. 2 is the structure chart of data prediction meanss provided by Embodiment 2 of the present invention.As shown in Fig. 2, the data prediction
Device 10 may include: to obtain module 201, identification module 202, inspection module 203, judgment module 204 and prediction module 205.
The history operation data for obtaining module 201 and being used to obtain enterprise, and the history operation data is scaled
One time series data.
Wherein, the history operation data may include history manpower data, history financial data, history research and development data,
Historic market sales data etc..Time series data refers to that some statistical indicator is each on different time by certain phenomenon
Numerical value, in chronological sequence sequence arrange and formed sequence.
History manpower data may include the application of the actual hours, standard work force, designated period of time (such as monthly) of employee
Number employs number, registration number, leaving office number, initial number, end of term number etc..
History financial data may include the net profit of designated period of time (such as monthly), income from sales, operating profit, business
Income, total profit, cost total value etc..
History research and development data may include the research and development demand of designated period of time (such as monthly), demand performance etc..
Historic market sales data may include the customer name expanded, the customer name in expansion, each client
Consumption sum etc..
The acquisition module 201 can be docked with the operation system of enterprise, obtain the history fortune from the operation system
Seek data.For example, obtaining actual hours, the standard work force of each department employee from the day reporting system of enterprise.
Alternatively, the acquisition module 201 can obtain the history operation data from specified database.User can be regular
The history operation data of each department is imported into the specified database.
Alternatively, can receive the history operation data of user's upload.
In one embodiment, before the history operation data is scaled a time series data, the step
Further include being pre-processed to the history operation data, obtains the achievement data in pre-set business field.
The pre-set business field may include manpower, finance, research and development, market sale etc..
It may include at history manpower data that the acquisition module 201, which carries out processing to the history operation data,
Reason obtains the achievement data in manpower field.
For example, the acquisition module 201 can according to application number, employ number calculating employ rate.It can be according to following
Formula calculates: employing rate=(employing number ÷ application number) × 100%.
For another example, the acquisition module 201 calculates separation rate according to leaving office number, number on the regular payroll.It can be according to following formula
It calculates: separation rate=(leaving office number ÷ (registration number+beginning number)) × 100%.
For another example, the acquisition module 201 can be with computing staff's utilization rate.Can calculate according to following formula: personnel utilize
Rate=(standard work force actual hours ÷) × 100%.Average actual hours and the average of each department, enterprise can be calculated
Working hour, according to average personnel's utilization rate of the average actual hours of each department and each department of average man-hour calculation.Example
Such as, the average actual hours of some department is 7.5, and average working hour is 8, then averagely personnel's utilization rate is 7.5/8 × 100%
=93.75%.
It can also include carrying out to history financial data that the acquisition module 201, which handles the history operation data,
Processing, obtains the achievement data in financial field.
For example, the acquisition module 201 calculates net profit on sales rate, net profit on sales rate=(net according to net profit, income from sales
Profit ÷ income from sales) × 100%.The ratio is bigger, and the profitability of enterprise is stronger.
For another example, the acquisition module 201 is calculated according to operating profit, operating income, and operating profit ratio operating profit ratio=
(operating profit ÷ operating income) × 100%.The ratio is bigger, and the profitability of enterprise is stronger.
For another example, the acquisition module 201 calculates cost profit margin, cost according to total profit, cost total value
Expense profit margin=(total profit ÷ cost total value) × 100%.The ratio is bigger, and the effectiveness of operation of enterprise is higher.
It can also include carrying out to history research and development data that the acquisition module 201, which handles the history operation data,
Processing, obtains the achievement data in research and development field.
For example, the acquisition module 201 calculates requirement fulfillment rate according to research and development demand, demand performance.It can be according to
Following formula calculates: requirement fulfillment rate=(research and development demand ÷ demand performance) × 100%.
It can also include to historic market sales data that the acquisition module 201, which handles the history operation data,
It is handled, obtains the achievement data in market sale field.
For example, the acquisition module 201 statistics has expanded customer quantity, customer quantity, big customer's quantity etc. in expansion.
In one embodiment, the history operation data for obtaining enterprise, and constructed according to the history operation data
The step of time series data includes: that a predetermined period is arranged in the acquisition module 201, and the acquisition module 201 is according to described
History operation data described in predetermined period timing acquisition；The acquisition module 201 judges that the timing of the history operation data is
It is no that there is stationarity；The acquisition module 201 if not carries out tranquilization processing to the history operation data, and according to steady
The history operation data after change obtains time series data.Wherein, a time series data, if mean value does not become
Change, variance do not change, and strictly eliminate cyclically-varying, are just referred to as to be that smoothly, i.e., this time sequence data has flat
Stability.
For example, the predetermined period is January, monthly No. 1 history that last month enterprise is obtained from operation system is transported
Seek data.The data that the acquisition module 201 will acquire are arranged in chronological order and calculate the history management data
Mean value.The mean value that the history management data is subtracted to each history management data, described after obtaining tranquilization are gone through
History operation data.Using the data of the history operation data composition after the tranquilization as the time series data.
The identification module 202 is used to obtain and the time series number based on the processing to the time series data
According to corresponding data prediction model.
Wherein, the identification module 202 can be handled the time series data by one group of preset model, institute
Stating preset model is common image recognition model, such as autocorrelogram identification model and partial correlation figure identification model.
In one embodiment, the identification module 202 obtains one group of preset model；The identification module 202 will be described
First time sequence data value in time series data is input in the preset model, obtains one group of output valve；The knowledge
Other module 202 compares the output valve and the second time series data value in the time series data, from described
Data prediction model corresponding with the time series data is selected in preset model.
In one embodiment, firstly, the identification module 202 calculates the auto-correlation coefficient in autocorrelogram identification model
And the partial correlation coefficient in partial correlation figure identification model.Secondly, observing the auto-correlation system and the partial correlation coefficient.Most
Afterwards, it is fitted to obtain the data prediction model by observing the auto-correlation system and the partial correlation coefficient.
The inspection module 203 is used to calculate the estimates of parameters of the data prediction model, and is estimated according to the parameter
Evaluation carries out model testing to the data prediction model.
Wherein, the estimates of parameters is to be obtained according to the model parameter calculation of the data prediction model: moments estimation
Value, least-squares estimation value and maximum likelihood estimation.The model testing includes the T method of inspection to the data prediction model
(Student's test, student examine), F method of inspection (joint hypotheses test, joint hypothesis method of inspection) Ji Kafang
It examines.
The moments estimation value is calculated by moments estimation method.Corresponding parameter in totality is estimated using sample moment.Tool
For body, the equation of the population moment containing the moments estimation value is derived first, and population moment is the power of considered stochastic variable
Desired value.It then takes out a sample and estimates population moment from the sample.Finally replace population moment using sample moment, solves institute
State moments estimation value.To obtain the moments estimation value of the data prediction model.
The least-squares estimation value is calculated by least squares estimate.Least square method is also known as least square
Method is a kind of mathematical optimization techniques.Unknown data can be easily acquired using least squares estimate, and these are asked
The quadratic sum of error is minimum between the data and real data obtained, and these unknown data, that is, data prediction model
Least-squares estimation value.
The maximum likelihood estimation passes through Maximum Likelihood Estimation (Maximum Likelihood Estimate, letter
Claim MLE) also referred to as most probably it is calculated like estimation or maximal possibility estimation.The probability that some known parameter can be such that sample occurs
Maximum, can be using the parameter as the true value of estimation, and the parameter is the Maximum-likelihood estimation of the data prediction model
Value.
The judgment module 204 is used to judge whether the data prediction model has according to the result of the model testing
Effect.
In one embodiment, model parameter of the inspection module 203 according to the data prediction model, the inspection
Module 203 calculates the moments estimation value, least-squares estimation value and maximum likelihood estimation of the data prediction model；Pass through judgement
Whether the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into preset region, to institute
It states data prediction model and carries out model testing.
In one embodiment, when the moments estimation value, the least-squares estimation value and the maximum likelihood estimation
When falling into preset region, the inspection module 203 determines that the data prediction model is effective, otherwise, when the moments estimation
When value, the least-squares estimation value and the maximum likelihood estimation be not in preset region, the inspection module 203 is sentenced
The fixed data prediction model is invalid.
In one embodiment, the Model Checking may be selected to be F method of inspection, by examining two normal states to become at random
Whether the population variance of amount is equal to be tested.Assuming that the sample of time series data X, Y described in two groups is respectively X1,
X2 ... ..., Xn and Y1, Y2 ... ..., Yn, sample variance are respectively S12 and S22.Now the population variance DX's and Y of inspection X is total
Whether body variance DY is equal.Assuming that H0:DX=DY=σ 2.According to statistical theory, if X, Y are normal distribution, when assuming that setting up
When, statistic obeys F-distribution that the first freedom degree is n1, the second freedom degree n2.Previously given reliability α.F-distribution table is looked into,
Obtain α/2 F.If the F value calculated is less than α/2 F, it assumes that set up, otherwise assume unreasonable.Wherein, described to be assumed to be the equivalence
Mould model is effective.
The prediction module 205 is used to enterprise operation data inputting the data prediction model to obtain prediction result.
Wherein, data prediction interface can be generated in the prediction module 205, in the data prediction interface described in displaying
Achievement data.For example, the prediction module 205 can generate different data prediction interfaces according to the permission level of user.Institute
The setting of permission level can be carried out to user in advance by stating prediction module 205, be generated to the user of different rights level different
Data prediction interface.For example, generating management level billboard for senior enterprise leader；For department head, department's billboard is generated, by
Department head checks；For domain expert, domain expert's billboard is generated.It can be according to the log-on message of user (such as user
Name) display data prediction interface corresponding with its permission level.
In one embodiment, the data prediction interface may include three regions:
First viewing area can also show message informing, help and exit for showing user name, user right level
Function button, the first viewing area can be in the upsides (can be upper right side) of the data prediction interface；
The index in different business field is checked for entering different business scopes in second viewing area, that is, navigation bar
Data.The navigation bar may be displayed on the left side of the data prediction interface.
Third viewing area, that is, show area, for showing the achievement data.
The prediction module 205, which can be layered, shows the achievement data.For example, generating two for each business scope
The page of level, including homepage and the second level page.The homepage shows the key index data of the business scope, described two
The grade page shows other achievement datas (achievement data except key index data) of the business scope.Alternatively, can be with needle
The page of three levels or three or more level is generated to each business scope.
The field feedback can be sent to default processing side by the prediction module 205, and be received described default
Reply of the processing side to the field feedback determines whether the achievement data is abnormal according to the reply.It is described default
Processing side confirms the field feedback, if the achievement data is abnormal, the reply of returned data exception.It is described
Default processing side can also return to modified achievement data.The default processing side can be the offer of history operation data
Side.
Alternatively, the achievement data can be compared by the prediction module 205 with preset metrics-thresholds, if described
Achievement data is more than range of normal value, then determines that the achievement data is abnormal.
In one embodiment, the prediction module 205 can be shown in data prediction interface information feedback entrance or
Information feedback area domain receives the field feedback that entrance or the input of information feedback area domain are fed back by the information.
The information feedback entrance can be information feedback button, after receiving the clicking operation to the feedback button,
Show information feedback interface.
The field feedback may include business scope, problem types, problem explanation, attachment etc..Described problem class
Type may include data age, data accuracy, data area etc..
In one embodiment, if the achievement data is abnormal, the prediction module 205 adjusts the achievement data, shows
Show achievement data adjusted.
In another embodiment, if the data predication method can also include: that the achievement data is abnormal, data are different
Normal open, which is known, is sent to default contact person (such as responsible person of the affiliated business scope of the achievement data).It can be by mail, short
Data exception notice is sent to default contact person by the modes such as letter, wechat.
Embodiment three
The present embodiment provides a kind of computer readable storage medium, computer is stored on the computer readable storage medium
Program, the computer program realize the step in above-mentioned data predication method embodiment when being executed by processor, such as shown in Fig. 1
Step 101-105:
Step 101, the history operation data of enterprise is obtained, and the history operation data is scaled a time sequence number
According to；
Step 102, based on the processing to the time series data, data corresponding with the time series data are obtained
Prediction model；
Step 103, the estimates of parameters of the data prediction model is calculated, and according to the estimates of parameters to the number
It is predicted that model carries out model testing；
Step 104, judge whether the data prediction model is effective according to the result of the model testing；And
Step 105, if so, enterprise operation data are inputted the data prediction model output to obtain prediction result.
Alternatively, the function of each module/unit in above-mentioned apparatus embodiment is realized when the computer program is executed by processor,
Such as the unit 201-205 in Fig. 2:
Module 201 is obtained, is scaled for the moment for obtaining the history operation data of enterprise, and by the history operation data
Between sequence data；
Identification module 202, for based on the processing to the time series data, obtaining and the time series data pair
The data prediction model answered；
Inspection module 203, for calculating the estimates of parameters of the data prediction model, and according to the estimates of parameters
Model testing is carried out to the data prediction model；
Judgment module 204, for judging whether the data prediction model is effective according to the result of the model testing；And
Prediction module 205, for enterprise operation data to be inputted the data prediction model to obtain prediction result.
Example IV
Fig. 3 is the schematic diagram for the computer installation that the embodiment of the present invention four provides.The computer installation 1 includes memory
20, processor 30 and the computer program 40 that can be run in the memory 20 and on the processor 30, example are stored in
In full it is predicted that program.The processor 30 is realized when executing the computer program 40 in above-mentioned data predication method embodiment
The step of, such as step 101-105 shown in FIG. 1:
Step 101, the history operation data of enterprise is obtained, and the history operation data is scaled a time sequence number
According to；
Step 102, the time series data is handled, it is pre- obtains data corresponding with the time series data
Survey model；
Step 103, the estimates of parameters of the data prediction model is calculated, and according to the estimates of parameters to the number
It is predicted that model carries out model testing；
Step 104, judge whether the data prediction model is effective according to the result of the model testing；And
Step 105, if so, exporting the prediction result of enterprise operation data using the data prediction model.
Alternatively, realizing each module in above-mentioned apparatus embodiment/mono- when the processor 30 executes the computer program 40
The function of member, such as the unit 201-205 in Fig. 2:
Module 201 is obtained, is scaled for the moment for obtaining the history operation data of enterprise, and by the history operation data
Between sequence data；
Identification module 202 obtains corresponding with the time series data for handling the time series data
Data prediction model；
Inspection module 203, for calculating the estimates of parameters of the data prediction model, and according to the estimates of parameters
Model testing is carried out to the data prediction model；
Judgment module 204, for judging whether the data prediction model is effective according to the result of the model testing；And
Prediction module 205, the prediction result for application data prediction model output enterprise operation data.
Illustratively, the computer program 40 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 20, and are executed by the processor 30, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 40 in the computer installation 1 is described.For example, the computer program 40 can be by
Acquisition module 201, identification module 202, inspection module 203, judgment module 204 and the prediction module 205 being divided into Fig. 2, respectively
Module concrete function is referring to embodiment two.
The computer installation 1 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.It will be understood by those skilled in the art that the schematic diagram 3 is only the example of computer installation 1, do not constitute to computer
The restriction of device 1 may include perhaps combining certain components or different components, example than illustrating more or fewer components
Such as described computer installation 1 can also include input-output equipment, network access equipment, bus.
Alleged processor 30 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor 30 is also possible to any conventional processor
Deng the processor 30 is the control centre of the computer installation 1, utilizes various interfaces and connection entire computer dress
Set 1 various pieces.
The memory 20 can be used for storing the computer program 40 and/or module/unit, and the processor 30 passes through
Operation executes the computer program and/or module/unit being stored in the memory 20, and calls and be stored in memory
Data in 20 realize the various functions of the computer installation 1.The memory 20 can mainly include storing program area and deposit
Store up data field, wherein storing program area can application program needed for storage program area, at least one function (for example sound is broadcast
Playing function, image player function etc.) etc.；Storage data area, which can be stored, uses created data (ratio according to computer installation 1
Such as audio data, phone directory) etc..In addition, memory 20 may include high-speed random access memory, it can also include non-easy
The property lost memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or other
Volatile solid-state part.
If the integrated module/unit of the computer installation 1 is realized in the form of SFU software functional unit and as independence
Product when selling or using, can store in a computer readable storage medium.Based on this understanding, of the invention
It realizes all or part of the process in above-described embodiment method, can also instruct relevant hardware come complete by computer program
At the computer program can be stored in a computer readable storage medium, which is being executed by processor
When, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code, described
Computer program code can be source code form, object identification code form, executable file or certain intermediate forms etc..The meter
Calculation machine readable medium may include: can carry the computer program code any entity or device, recording medium, USB flash disk,
Mobile hard disk, magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory
Device (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs to illustrate
It is that the content that the computer-readable medium includes can be fitted according to the requirement made laws in jurisdiction with patent practice
When increase and decrease, such as in certain jurisdictions, according to legislation and patent practice, computer-readable medium does not include electric carrier wave letter
Number and telecommunication signal.
In several embodiments provided by the present invention, it should be understood that disclosed computer installation and method, it can be with
It realizes by another way.For example, computer installation embodiment described above is only schematical, for example, described
The division of unit, only a kind of logical function partition, there may be another division manner in actual implementation.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in same treatment unit
It is that each unit physically exists alone, can also be integrated in same unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds software function module.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included in the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.This
Outside, it is clear that one word of " comprising " does not exclude other units or steps, and odd number is not excluded for plural number.It is stated in computer installation claim
Multiple units or computer installation can also be implemented through software or hardware by the same unit or computer installation.The
One, the second equal words are used to indicate names, and are not indicated any particular order.
Finally it should be noted that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although reference
Preferred embodiment describes the invention in detail, those skilled in the art should understand that, it can be to of the invention
Technical solution is modified or equivalent replacement, without departing from the spirit of the technical scheme of the invention.
Claims (10)
1. a kind of data predication method, which is characterized in that the described method includes:
The history operation data of enterprise is obtained, and the history operation data is scaled a time series data；
Based on the processing to the time series data, data prediction model corresponding with the time series data is obtained；
Calculate the estimates of parameters of the data prediction model, and according to the estimates of parameters to the data prediction model into
Row model testing；
Judge whether the data prediction model is effective according to the result of the model testing；And
If so, enterprise operation data are inputted the data prediction model to obtain prediction result.
2. data predication method as described in claim 1, which is characterized in that the history operation data for obtaining enterprise, and
The step of history operation data is scaled a time series data include:
One predetermined period is set, according to history operation data described in the predetermined period timing acquisition；
Judge whether the timing of the history operation data has stationarity；And
If it is not, then tranquilization processing is carried out to the history operation data, using the history operation data after tranquilization as described in
Time series data.
3. data predication method as claimed in claim 2, which is characterized in that described to be carried out steadily to the history operation data
Change is handled, and includes: as the step of time series data using the history operation data after tranquilization
The history management data is in chronological sequence sequentially arranged, and calculates the mean value of the history management data；
The mean value of each history management data and the history management data is subjected to subtraction, after obtaining tranquilization
History operation data；And
Using the history operation data after the tranquilization as the time series data.
4. data predication method as described in claim 1, which is characterized in that it is described to the time series data at
Reason, the step of obtaining data prediction model corresponding with the time series data include:
Obtain one group of preset model；
First time sequence data value in the time series data is input in this group of preset model, one group of output is obtained
Value；And
Each output valve and the second time series data value in the time series data are compared, and according to right
Data prediction model corresponding with the time series data is selected from this group of preset model than result.
5. data predication method as described in claim 1, which is characterized in that the estimates of parameters includes: moments estimation value, most
Small two multiply estimated value and maximum likelihood estimation.
6. data predication method as claimed in claim 5, which is characterized in that the parameter for calculating the data prediction model
Estimated value, and the step of carrying out model testing to the data prediction model according to the estimates of parameters includes:
Obtain the model parameter of the data prediction model, and the square of the data prediction model according to the model parameter calculation
Estimated value, least-squares estimation value and maximum likelihood estimation；And
Judge whether the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into preset threshold area
In domain, to carry out model testing to the data prediction model.
7. data predication method as claimed in claim 6, which is characterized in that described to be judged according to the result of the model testing
The whether effective step of the data prediction model includes:
When the moments estimation value, the least-squares estimation value and the maximum likelihood estimation fall into the preset threshold region
When interior, determine that the data prediction model is effective；When the moments estimation value, the least-squares estimation value and the maximum likelihood
When estimated value is not in the preset threshold region, determine that the data prediction model is invalid.
8. a kind of data prediction meanss, which is characterized in that described device includes:
Module is obtained, is scaled a time series for obtaining the history operation data of enterprise, and by the history operation data
Data；
Identification module, for based on the processing to the time series data, obtaining number corresponding with the time series data
It is predicted that model；
Inspection module, for calculating the estimates of parameters of the data prediction model, and according to the estimates of parameters to described
Data prediction model carries out model testing；
Judgment module, for judging whether the data prediction model is effective according to the result of the model testing；And
Prediction module, for enterprise operation data to be inputted the data prediction model to obtain prediction result.
9. a kind of computer installation, it is characterised in that: the computer installation includes processor, and the processor is deposited for executing
The computer program stored in reservoir is to realize the data predication method as described in any one of claim 1-7.
10. a kind of computer readable storage medium, computer program, feature are stored on the computer readable storage medium
It is: realizes the data predication method as described in any one of claim 1-7 when the computer program is executed by processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260085A (en) * | 2020-01-09 | 2020-06-09 | 杭州中恒电气股份有限公司 | Device replacement man-hour evaluation method, device, equipment and medium |
| CN112508414A (en) * | 2020-12-08 | 2021-03-16 | 北京无线电测量研究所 | Working hour accounting method, system and computer equipment |
| CN113762621A (en) * | 2021-09-09 | 2021-12-07 | 南京领行科技股份有限公司 | Network taxi appointment driver departure prediction method and system |
| CN115952427A (en) * | 2023-03-14 | 2023-04-11 | 山东美航天天能源技术有限公司 | Industrial park digital operation management method and system |
-
2018
- 2018-11-30 CN CN201811457626.XA patent/CN109543918A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260085A (en) * | 2020-01-09 | 2020-06-09 | 杭州中恒电气股份有限公司 | Device replacement man-hour evaluation method, device, equipment and medium |
| CN111260085B (en) * | 2020-01-09 | 2023-12-12 | 杭州中恒电气股份有限公司 | Device replacement man-hour assessment method, device, equipment and medium |
| CN112508414A (en) * | 2020-12-08 | 2021-03-16 | 北京无线电测量研究所 | Working hour accounting method, system and computer equipment |
| CN113762621A (en) * | 2021-09-09 | 2021-12-07 | 南京领行科技股份有限公司 | Network taxi appointment driver departure prediction method and system |
| CN115952427A (en) * | 2023-03-14 | 2023-04-11 | 山东美航天天能源技术有限公司 | Industrial park digital operation management method and system |
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