CN105139282A - Power grid index data processing method, device and calculation device - Google Patents
Power grid index data processing method, device and calculation device Download PDFInfo
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Abstract
The invention discloses a power grid index data processing method which is executed in a calculation device and is applicable to grading processing of index data, wherein the index data is power grid index data. The power grid index data processing method comprises steps of obtaining an index data sequence, wherein the index data sequence comprises multiple index data, calculating the average value and the standard deviation of the index data sequence and calculating the ratio of the standard deviation and the average value to obtain a variable coefficient of the index data sequence, determining the distribution type of the sequence data according to the variation coefficient of the index data sequence, wherein the distribution type comprises an intensive type, a middle type and a discrete type, determining a grading processing algorithm according to the distribution type of the index data, and performing grading processing on the index data according to the determined grading processing algorithm. The invention discloses a corresponding power grid data processing device and a calculation device comprising the power grid index data processing device.
Description
Technical field
The present invention relates to data processing field, particularly a kind of electrical network achievement data disposal route, device and computing equipment.
Background technology
In benchmark, a kind of method adopted at present chooses the unique a series of achievement datas of the numerical value of a certain mark post enterprise, and carry out bidding assessment as mark post, do not relate to or seldom relate to the more situation of each mark post data in this case.Another method considers each index situation of each enterprise, by certain method, for each index draws mark post data and with this to carry out benchmark.
In the process of carrying out benchmark, when selected mark post quantity is more, for ease of understanding self situation thus effectively carrying out benchmark, need to carry out certain classification process to a large amount of mark post achievement datas.The classification Processing Algorithm of carrying out achievement data is varied, how to select suitable classification Processing Algorithm just to become technical matters urgently to be resolved hurrily.
Summary of the invention
For this reason, the invention provides a kind of new electrical network achievement data disposal route, device and computing equipment, to try hard to solve or at least alleviate Problems existing above.
According to an aspect of the present invention, a kind of electrical network achievement data disposal route is provided, performs in computing equipment, be suitable for carrying out classification process to achievement data, achievement data is electrical network achievement data, and the method comprises: obtain achievement data sequence, achievement data sequence comprises multiple achievement data; The mean value of parameter data sequence and standard deviation, and the ratio calculating standard deviation and mean value, obtain the coefficient of variation of achievement data sequence; According to the distribution pattern of the coefficient of variation agriculture products data of achievement data sequence, distribution pattern comprises intensity, osculant and discrete type; And according to the distribution pattern determination classification Processing Algorithm of achievement data, and according to determined classification Processing Algorithm, classification process is carried out to achievement data sequence.
Alternatively, in electrical network achievement data disposal route according to the present invention, according to the distribution pattern of the coefficient of variation agriculture products data of achievement data sequence, comprising: when the coefficient of variation is less than or equal to first threshold, the distribution pattern of agriculture products data is intensive; When the coefficient of variation is greater than first threshold, and when being less than or equal to Second Threshold, the distribution pattern of agriculture products data is osculant; When the coefficient of variation is greater than Second Threshold, the distribution pattern of agriculture products data is discrete type.
Alternatively, in electrical network achievement data disposal route according to the present invention, first threshold is 1, and Second Threshold is 1.4.
Alternatively, in electrical network achievement data disposal route according to the present invention, according to the distribution pattern determination classification Processing Algorithm of achievement data, comprising: when the distribution pattern of achievement data is discrete type, determined classification Processing Algorithm is five period in arithmetrics; When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation; When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K means clustering algorithm.
According to a further aspect in the invention, a kind of electrical network achievement data treating apparatus is provided, reside in computing equipment, be suitable for carrying out classification process to achievement data, achievement data is electrical network achievement data, this device comprises: data capture unit, is suitable for obtaining achievement data sequence, and achievement data sequence comprises multiple achievement data; Computing unit, is suitable for mean value and the standard deviation of parameter data sequence, and calculates the ratio of standard deviation and mean value, obtain the coefficient of variation of achievement data sequence; Taxon, be suitable for the distribution pattern of the coefficient of variation agriculture products data according to achievement data sequence, distribution pattern comprises intensity, osculant and discrete type; And stage unit, be suitable for the distribution pattern determination classification Processing Algorithm according to achievement data, and according to determined classification Processing Algorithm, classification process carried out to achievement data sequence.
Alternatively, in electrical network achievement data treating apparatus according to the present invention, taxon is also suitable for: when the coefficient of variation is less than or equal to first threshold, and the distribution pattern of agriculture products data is intensive; When the coefficient of variation is greater than first threshold, and when being less than or equal to Second Threshold, the distribution pattern of agriculture products data is osculant; When the coefficient of variation is greater than Second Threshold, the distribution pattern of agriculture products data is discrete type.
Alternatively, in electrical network achievement data treating apparatus according to the present invention, first threshold is 1, and Second Threshold is 1.4.
Alternatively, in electrical network achievement data treating apparatus according to the present invention, stage unit is also suitable for: when the distribution pattern of achievement data is discrete type, and determined classification Processing Algorithm is five period in arithmetrics; When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation; When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K means clustering algorithm.
According to an aspect of the present invention, provide a kind of computing equipment, comprise any one electrical network achievement data treating apparatus as above.
According to technical scheme of the present invention, the coefficient of variation is adopted to classify to achievement data sequence, unit and (or) the different impact that two or more degree of variation is compared of average can be eliminated, thus according to the suitable computing method of the type selecting of each class achievement data, make result of calculation closing to reality more, in practical application, effect is more obvious.
Accompanying drawing explanation
In order to realize above-mentioned and relevant object; combine description below and accompanying drawing herein to describe some illustrative aspect; these aspects indicate the various modes can putting into practice principle disclosed herein, and all aspects and equivalent aspect thereof are intended to fall in the scope of theme required for protection.Read detailed description below in conjunction with the drawings, above-mentioned and other object of the present disclosure, Characteristics and advantages will become more obvious.Throughout the disclosure, identical Reference numeral is often referred to for identical parts or element.
Fig. 1 shows the block diagram of the Example Computing Device 100 according to electrical network achievement data treating apparatus of the present invention;
Fig. 2 shows the schematic diagram of the electrical network achievement data disposal route 200 according to the present invention's embodiment;
Fig. 3 shows five period in arithmetric schematic diagram of the normal distribution according to the present invention's embodiment;
Fig. 4 shows the process flow diagram of the optimal cut part method 400 according to the present invention's embodiment; And
Fig. 5 shows electrical network achievement data treating apparatus 500 schematic diagram according to the present invention's embodiment;
Embodiment
Below with reference to accompanying drawings exemplary embodiment of the present disclosure is described in more detail.Although show exemplary embodiment of the present disclosure in accompanying drawing, however should be appreciated that can realize the disclosure in a variety of manners and not should limit by the embodiment set forth here.On the contrary, provide these embodiments to be in order to more thoroughly the disclosure can be understood, and complete for the scope of the present disclosure can be conveyed to those skilled in the art.
Electrical network achievement data treating apparatus of the present invention resides in computing equipment, and Fig. 1 is arranged as the block diagram realized according to the Example Computing Device 100 of electrical network achievement data treating apparatus of the present invention.In basic configuration 102, computing equipment 100 typically comprises system storage 106 and one or more processor 104.Memory bus 108 may be used for the communication between processor 104 and system storage 106.
Depend on the configuration of expectation, processor 104 can be the process of any type, includes but not limited to: microprocessor ((μ P), microcontroller (μ C), digital information processor (DSP) or their any combination.Processor 104 can comprise the high-speed cache of one or more rank of such as on-chip cache 110 and second level cache 112 and so on, processor core 114 and register 116.The processor core 114 of example can comprise arithmetic and logical unit (ALU), floating-point unit (FPU), digital signal processing core (DSP core) or their any combination.The Memory Controller 118 of example can use together with processor 104, or in some implementations, Memory Controller 118 can be an interior section of processor 104.
Depend on the configuration of expectation, system storage 106 can be the storer of any type, includes but not limited to: volatile memory (such as RAM), nonvolatile memory (such as ROM, flash memory etc.) or their any combination.System storage 106 can comprise operating system 120, one or more application 122 and routine data 124.Application 122 can comprise and is configured to electrical network achievement data treating apparatus 500.In some embodiments, application 122 can be arranged as and utilize routine data 124 to operate on an operating system.
Computing equipment 100 can also comprise the interface bus 140 communicated contributed to from various interfacing equipment (such as, output device 142, Peripheral Interface 144 and communication facilities 146) to basic configuration 102 via bus/interface controller 130.The output device 142 of example comprises Graphics Processing Unit 148 and audio treatment unit 150.They can be configured to contribute to communicating with the various external units of such as display or loudspeaker and so on via one or more A/V port one 52.Example Peripheral Interface 144 can comprise serial interface controller 154 and parallel interface controller 156, they can be configured to the external unit contributed to via one or more I/O port one 58 and such as input equipment (such as, keyboard, mouse, pen, voice-input device, touch input device) or other peripheral hardwares (such as printer, scanner etc.) and so on and communicate.The communication facilities 146 of example can comprise network controller 160, and it can be arranged to is convenient to via one or more communication port 164 and the communication of one or more other computing equipments 162 by network communication link.
Network communication link can be an example of communication media.Communication media can be presented as computer-readable instruction, data structure, program module in the modulated data signal of such as carrier wave or other transmission mechanisms and so on usually, and can comprise any information delivery media." modulated data signal " can be such signal, the change of one or more or it of its data centralization can the mode of coded message in the signal be carried out.As nonrestrictive example, communication media can comprise the wire medium of such as cable network or private line network and so on, and such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless medium are at interior various wireless mediums.Term computer-readable medium used herein can comprise both storage medium and communication media.
Computing equipment 100 can be implemented as a part for small size portable (or mobile) electronic equipment, and these electronic equipments can be such as cell phone, personal digital assistant (PDA), personal media player equipment, wireless network browsing apparatus, individual helmet, application specific equipment or the mixing apparatus that can comprise any function above.Computing equipment 100 can also be embodied as the personal computer comprising desktop computer and notebook computer configuration.
Fig. 2 shows the schematic diagram of electrical network achievement data disposal route 200 according to an illustrative embodiment of the invention.The method resides in computing equipment 100 and performs.
Every field all can have a large amount of indexs assessed this area parameters, and such as international grid key index comprises the average power off time of system, clean energy resource accounting and power backup rate etc.In numerous achievement datas, forward type, flyback type and moderate type can be divided into by its characteristic, the index that wherein the larger evaluation of desired value is better, be called forward index (also claim profit evaluation model index or hope large-scale index), the index that the less evaluation of desired value is better, be called reverse index (also claim cost type index or hope small-sized index), desired value, more close to the index that certain value is better, is called moderate type index (also referred to as appropriate index).In prior art, attempt to select suitable Index grading algorithm according to pointer type, but implementation result is unsatisfactory.
Present inventor, through large quantifier elimination and experiment, finds that the distribution character (dispersion degrees of data) according to achievement data selects suitable Index grading algorithm, and more accurately and closing to reality, in practical application, effect is more obvious for its result.Particularly, achievement data can be categorized as discrete type, intensity and osculant by the dispersion degree type according to achievement data, when the distribution pattern of achievement data is discrete type, the classification Processing Algorithm being suitable for adopting is five period in arithmetrics, when the distribution pattern of achievement data is intensive, the classification Processing Algorithm being suitable for adopting is optimal segmentation, and when the distribution pattern of achievement data is osculant, the classification Processing Algorithm being suitable for adopting is K means clustering algorithm.
It should be noted that, five period in arithmetrics, optimal segmentation and K means clustering algorithm are this area and classify to data sequence or the algorithms most in use of classification (being divided into multiple data interval by a data sequence), the particular content of algorithm with reference to related art, can be described in detail below in conjunction with technical scheme of the present invention.
As shown in Figure 2, the following stated achievement data is all described for electrical network achievement data, in step S210, obtain achievement data sequence, achievement data sequence comprises multiple achievement data, such as, the average power off time of system, clean energy resource accounting and power backup rate in various countries' electrical network key index etc. (reference table 2).
Subsequently, in step S220, according to the achievement data sequence obtained, the mean value of parameter data sequence and standard deviation, and calculate the ratio of standard deviation and mean value, obtain the coefficient of variation of achievement data sequence, so that the distribution pattern of parameter data sequence.
The distribution pattern of data sequence can be divided by data discrete degree, and data discrete degree is analyzed mainly through standard deviation, and standard deviation is less, and the dispersion degree of data is less, otherwise then larger.Such as, but for the different data of two groups of levels height, unit and standard deviation are all different, and application standard difference carries out discreteness division, cannot eliminate the impact of data sequence level height.Therefore, in order to comparative analysis varying level variable data sequence between degree of variation, introduce the coefficient of variation.
The coefficient of variation (CoefficientofVariance, CV), also known as " standard rate ", is a kind of tolerance of relative variability, equals standard deviation divided by average, is the statistic weighing each observed reading degree of variation.Because it is a dimensionless number, so can be used for comparing the significantly different overall discreteness of average.Such as, when carrying out the comparison of two or more data variance degree, if linear module is identical with average, can directly utilize standard deviation to compare.If when unit and (or) average difference, relatively its degree of variation just can not adopt standard deviation, and the coefficient of variation need be adopted compare, thus unit and (or) the different impact that two or more data variance degree is compared of average can be eliminated.
Subsequently, in step S230, according to the coefficient of variation of the achievement data sequence calculated in step S220, the distribution pattern of agriculture products data, distribution pattern comprises intensity, osculant and discrete type.
According to a kind of embodiment, when the coefficient of variation is less than or equal to first threshold, such as first threshold is 1, and the distribution pattern of agriculture products data is intensive; When the coefficient of variation is greater than first threshold, and when being less than or equal to Second Threshold, such as first threshold is 1, Second Threshold is 1.4, and the distribution pattern of agriculture products data is osculant; When the coefficient of variation is greater than Second Threshold, such as Second Threshold is 1.4, and the distribution pattern of agriculture products data is discrete type.
Subsequently, in step S240, according to the distribution pattern determination classification Processing Algorithm of agriculture products data in step S230, and according to the classification Processing Algorithm determined, classification process is carried out to achievement data sequence.Such as, when the distribution pattern of achievement data is discrete type, determined classification Processing Algorithm is five period in arithmetrics; When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation; When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K average (K-means) clustering algorithm.
Hereafter be described in detail for the technical scheme of various countries' electrical network key index to invention.
First, obtain achievement data sequence, such as, obtain all data comprising the average power off time of system, clean energy resource accounting and power backup rate three indexs of various countries, detailed data is see hereinafter table 2.So that following, achievement data is classified.
Calculate the mean value of indices data sequence
with standard deviation s, and according to the mean value calculated and standard deviation, calculate coefficient of variation cv, i.e. the standard deviation s of achievement data sequence and mean value
ratio, formula is as follows:
Wherein, when coefficient of variation cv≤1, achievement data belongs to Method on Dense Type of Data Using; During coefficient of variation 1<cv≤1.4, achievement data belongs to osculant data; During coefficient of variation cv>1.4, achievement data belongs to discrete data.
Suitable algorithm is selected to carry out index evaluation according to the dispersion degree of achievement data, five period in arithmetrics are generally selected to carry out index evaluation for discrete data, generally select optimum segmentation algorithm to carry out index evaluation for Method on Dense Type of Data Using, generally select K mean cluster (K-means) algorithm to carry out index evaluation for osculant data.
Fig. 3 shows five points of position schematic diagram of normal distribution according to an embodiment of the invention.
With the data instance (referring to table 2) of the average power off time of system in various countries' electrical network key index, such as, when achievement data is discrete data, carry out index evaluation with five period in arithmetrics.Calculation procedure is as follows:
The mean value of parameter data sequence, formula is as follows:
Wherein, n is the number of achievement data, i.e. the national number of correspondence system index averaging time;
X
ibe i-th achievement data, i.e. i-th national achievement data;
The standard deviation of parameter data sequence, formula is as follows:
Achievement data is met to the data sequence of normal distribution, achievement data sequence can be divided into five sections, as shown in Figure 1, the interval separation wherein divided is respectively x+s, x+0.33*s, x-0.33*s, x-s, corresponding interval separation divides to be calculated between Index areas, as follows:
A：
B：x+0.33*s≤X<x+s；
C：
D：
F：
After dividing five segments; according to the normal demarcation interval level of the normal distribution such as 16%, 37%, 63%, 84%; namely each segment scope is that 16%, 21%, 26%, 21% and 16% pair of data sequence is tested, and normal distribution-test table is as shown in table 1:
Table 1 normal distribution-test table
Wherein, H is the check number sum of each segment, and formula is as follows:
When H≤6, represent that achievement data sequence meets normal distribution, carry out interval division according to above-mentioned method.When H>6, represent that achievement data sequence does not meet normal distribution, 5 segments can not be divided according to the method described above, need to arrange four fractiles respectively according to the sample fractiles ratio 16%, 37%, 63%, 84% of standardized normal distribution, divide five intervals.According to actual conditions, classifying rationally is carried out to data, draw first-class level index.
Fig. 4 shows the process flow diagram of the optimal cut part method 400 according to the present invention's embodiment.
With clean energy resource data instance (referring to table 2) in various countries' electrical network key index, such as, when achievement data is Method on Dense Type of Data Using, carries out index evaluation with optimal segmentation and with reference to figure 2, be hereafter described in conjunction with the embodiments.Calculation procedure is as follows:
Before calculating data, need to carry out pre-service to achievement data sequence, such as, carry out sort (detailed reference table 5) according to size of data, and be numbered accordingly.
In step S410, input pointer data sequence, such as clean energy resource data.
Subsequently, in the step s 420, the level diameter in parameter data sequence, formula is as follows:
D
1,k＝a
max-a
min
Wherein, k is point progression of achievement data sequence, a
max, a
minbe respectively the maximal value in each graded index data and minimum value.
Subsequently, in step S430, parameter data sequence each classification internal loss sum, recursion formula is as follows:
Wherein, n is data count in achievement data sequence;
Loss D (j, n) to represent in level from a jth index to the n-th index;
L [p (n, k)] represents the minimum value of level internal loss sum n data being divided into k level;
Subsequently, in step S440, choose the minimum value of L [p (n, k)] as achievement data sequence optimum solution, namely optimum point progression k, is divided into k level by achievement data sequence, selects first-class level achievement data further.
In actual conditions, point progression can choose a numerical value, such as 5.So after selected point progression k, only need error of calculation function,
Wherein, n is achievement data sequence indicator sum, and k is point progression of index, i
trepresent first index in t classification, i
t+1-1 represents the index before first index in t+1 interval, i.e. last index in t interval.D (i
t, i
t+1-1) error in the level representing t index.After n and k determines, make L [b (n, k)] minimum, namely between level index, error sum is minimum, and the classification of achievement data sequence is the most reasonable, divides reasonably interval, carries out horizontal division to achievement data.
With power backup rate data instance (referring to table 2) in various countries' electrical network key index, such as, when achievement data is osculant data, carries out index evaluation with K – means algorithm and with reference to figure 3, be hereafter described in conjunction with the embodiments.Calculation procedure is as follows:
Before calculating, carrying out pre-service to achievement data sequence, such as, is the difference of each index in parameter data sequence and achievement data serial mean, and by descending order sequence (detailed reference table 7) of data.
First, point progression of setting achievement data sequence, and determine interior barycenter at different levels, described barycenter is suitable for the Arbitrary Digit in each classification, such as, arbitrarily choose 5 barycenter;
Subsequently, calculate the distance of each achievement data and barycenter in each classification, the distance of each achievement data and barycenter is the absolute value of each index and barycenter difference, such as, the distance of each achievement data and barycenter in power backup rate in various countries' electrical network key index data, computing formula is as follows:
D(i，j)＝|x
i-x
j|，i＝1,2,……,N，j＝1,2,……K
Wherein, j is a jth barycenter, x
ifor the achievement data of i-th in achievement data sequence, index number in N achievement data sequence, K is the number of barycenter.
Subsequently, described index is also divided in the classification at this barycenter place by the index that selected distance barycenter is nearest.
Subsequently, calculate the barycenter in each new classification, described barycenter is the mean value of index in each classification.
Further, calculate the distance of each achievement data and barycenter in each classification, divide new classification, and calculate the barycenter of each classification.If the barycenter of double calculating is same numerical value, then export the achievement data in each classification, according to the level of point progression evaluation index number chosen.
According to an embodiment, be described for various countries' electrical network key index, as shown in table 2:
Table 2 various countries electrical network key index
Calculate the coefficient of variation of all kinds of achievement data sequence, according to the type of coefficient of variation Classification Index data sequence, sorted table is as shown in table 3:
Table 3 pointer type sorted table
| Index | The coefficient of variation | Pointer type |
| The average power off time of system | 1.73 | Decentralized index |
| Clean energy resource accounting | 0.91 | Intensive index |
| Power backup rate | 1.27 | Osculant index |
The average power off time of system is applicable to five period in arithmetrics, and five period in arithmetric division results are as shown in table 4:
The average power off time of table 4 system five points of positions divide table
Reference table 4 Data Placement table, the country be in first-class interval is Singapore, Luxembourg, Korea S, Denmark, Germany and Switzerland.
Clean energy resource accounting is that intensive index is applicable to optimal segmentation, and this method directly chooses k=5, before use optimal segmentation calculates, first carries out pre-service to data, as shown in table 5:
Table 5 clean energy resource accounting and numbering
The result using optimal segmentation calculating clean energy resource to account for is as shown in table 6:
Table 6 clean energy resource optimum segmentation algorithm result of calculation
| Interval | Country's numbering | |
| A section | 99.04％≤X | 1 |
| B section | 58.77％≤X<99.04％ | 2 to 7 |
| C section | 37.45％≤X<58.77％ | 8 to 11 |
| D section | 15.78％≤X<37.45％ | 12 to 18 |
| E section | X<15.78％ | 29 to 36 |
Reference table 6 Data Placement table, the country be in first-class level interval is Iceland.
Power backup rate is that osculant data are applicable to K-means algorithm, before the computation, needs to carry out pre-service to data, as shown in table 7:
Table 7 power backup rate data prediction
Utilize the result of calculation of K-means algorithm as shown in table 8:
Table 8 power backup rate K-means algorithm result of calculation
| Data interval | Unit number | |
| A section | [28.40,28.95] | 1 |
| B section | [19.55,37.80] | 5 |
| C section | [7.25,50.10] | 5 |
| D section | [-11.87,69.22] | 2 |
| E section | [-82.34,139.69] | 3 |
Reference table 8 Data Placement table, the country be in first-class level interval is: Britain, Israel and the U.S..
According to technical scheme of the present invention, the coefficient of variation is adopted to classify to achievement data sequence, unit and (or) the different impact that two or more degree of variation is compared of average can be eliminated, thus according to the suitable computing method of the type selecting of each class achievement data, make result of calculation closing to reality more, in practical application, effect is more obvious.
Fig. 5 shows electrical network achievement data treating apparatus 500 schematic diagram according to the present invention's embodiment.As shown in Figure 5, the following stated achievement data is electrical network achievement data, and electrical network achievement data treating apparatus 500 comprises data capture unit 510, computing unit 520 and taxon 530 and stage unit 540.
Data capture unit 510 is for obtaining achievement data sequence, achievement data sequence comprises multiple achievement data, such as, the average power off time of system, clean energy resource accounting and power backup rate in various countries' electrical network key index, each class index series comprises multiple achievement data.
Computing unit 520 according to the mean value of the achievement data sequence parameter data obtained and standard deviation, and calculates the ratio of standard deviation and mean value, obtains the coefficient of variation of achievement data sequence, and the above results is sent to taxon 530.
The distribution pattern of the coefficient of variation agriculture products data that taxon 530 calculates according to computing unit 520, distribution pattern comprises intensity, osculant and discrete type.According to a kind of embodiment, when the coefficient of variation is less than or equal to first threshold, such as, the coefficient of variation is less than or equal to 1, and the distribution pattern of agriculture products data is intensive.When described coefficient is greater than first threshold, and when being less than or equal to Second Threshold, such as, the coefficient of variation is greater than 1 and is less than or equal to 1.4, the distribution pattern of agriculture products data is osculant.When the coefficient of variation is greater than Second Threshold, such as, the coefficient of variation is greater than 1.4, and the distribution pattern of agriculture products data is discrete type.
Stage unit 540 according to the distribution pattern determination classification Processing Algorithm of the achievement data in taxon 530, and carries out classification process according to determined classification Processing Algorithm to achievement data sequence.According to a kind of embodiment, when the distribution pattern of achievement data is discrete type, determined classification Processing Algorithm is five period in arithmetrics.When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation.When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K means clustering algorithm.
Select suitable algorithm according to the distribution pattern of achievement data, specifically please refer to the embodiment of each algorithm above and each algorithm corresponding, do not do too much explanation herein.
In instructions provided herein, describe a large amount of detail.But can understand, embodiments of the invention can be put into practice when not having these details.In some instances, be not shown specifically known method, structure and technology, so that not fuzzy understanding of this description.
Similarly, be to be understood that, in order to simplify the disclosure and to help to understand in each inventive aspect one or more, in the description above to exemplary embodiment of the present invention, each feature of the present invention is grouped together in single embodiment, figure or the description to it sometimes.But, the method for the disclosure should be construed to the following intention of reflection: namely the present invention for required protection requires than the feature more multiple features clearly recorded in each claim.Or rather, as claims below reflect, all features of disclosed single embodiment before inventive aspect is to be less than.Therefore, the claims following embodiment are incorporated to this embodiment thus clearly, and wherein each claim itself is as independent embodiment of the present invention.
Those skilled in the art are to be understood that the module of the equipment in example disclosed herein or unit or assembly can be arranged in equipment as depicted in this embodiment, or alternatively can be positioned in one or more equipment different from the equipment in this example.Module in aforementioned exemplary can be combined as a module or can be divided into multiple submodule in addition.
Those skilled in the art are appreciated that and adaptively can change the module in the equipment in embodiment and they are arranged in one or more equipment different from this embodiment.Module in embodiment or unit or assembly can be combined into a module or unit or assembly, and multiple submodule or subelement or sub-component can be put them in addition.Except at least some in such feature and/or process or unit be mutually repel except, any combination can be adopted to combine all processes of all features disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) and so disclosed any method or equipment or unit.Unless expressly stated otherwise, each feature disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) can by providing identical, alternative features that is equivalent or similar object replaces.
In addition, those skilled in the art can understand, although embodiments more described herein to comprise in other embodiment some included feature instead of further feature, the combination of the feature of different embodiment means and to be within scope of the present invention and to form different embodiments.Such as, in the following claims, the one of any of embodiment required for protection can use with arbitrary array mode.
In addition, some in described embodiment are described as at this can by the processor of computer system or the method implemented by other device performing described function or the combination of method element.Therefore, there is the device of processor formation for implementing the method or method element of the necessary instruction for implementing described method or method element.In addition, the element described herein of device embodiment is the example as lower device: this device is for implementing the function performed by the element of the object in order to implement this invention.
As used in this, unless specifically stated so, use ordinal number " first ", " second ", " the 3rd " etc. to describe plain objects and only represent the different instances relating to similar object, and be not intended to imply the object be described like this must have the time upper, spatially, sequence aspect or in any other manner to definite sequence.
Although the embodiment according to limited quantity describes the present invention, benefit from description above, those skilled in the art understand, in the scope of the present invention described thus, it is contemplated that other embodiment.In addition, it should be noted that the language used in this instructions is mainly in order to object that is readable and instruction is selected, instead of select to explain or limiting theme of the present invention.Therefore, when not departing from the scope and spirit of appended claims, many modifications and changes are all apparent for those skilled in the art.For scope of the present invention, be illustrative to disclosing of doing of the present invention, and nonrestrictive, and scope of the present invention is defined by the appended claims.
Claims (9)
1. an electrical network achievement data disposal route, performs in computing equipment, is suitable for carrying out classification process to achievement data, and described achievement data is electrical network achievement data, and described method comprises:
Obtain achievement data sequence, described achievement data sequence comprises multiple achievement data;
The mean value of parameter data sequence and standard deviation, and the ratio calculating standard deviation and mean value, obtain the coefficient of variation of achievement data sequence;
According to the distribution pattern of the coefficient of variation agriculture products data of achievement data sequence, described distribution pattern comprises intensity, osculant and discrete type; And
According to the distribution pattern determination classification Processing Algorithm of achievement data, and according to determined classification Processing Algorithm, classification process is carried out to achievement data sequence.
2. method according to claim 1, wherein, the distribution pattern of the described coefficient of variation agriculture products data according to achievement data sequence, comprising:
When the described coefficient of variation is less than or equal to first threshold, the distribution pattern of agriculture products data is intensive;
When the described coefficient of variation is greater than first threshold, and when being less than or equal to Second Threshold, the distribution pattern of agriculture products data is osculant;
When the described coefficient of variation is greater than Second Threshold, the distribution pattern of agriculture products data is discrete type.
3. method according to claim 2, wherein, described first threshold is 1, and described Second Threshold is 1.4.
4. the method according to any one of claim 1-3, wherein, the described distribution pattern determination classification Processing Algorithm according to achievement data, comprising:
When the distribution pattern of achievement data is discrete type, determined classification Processing Algorithm is five period in arithmetrics;
When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation;
When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K means clustering algorithm.
5. an electrical network achievement data treating apparatus, resides in computing equipment, is suitable for carrying out classification process to achievement data, and described achievement data is electrical network achievement data, and described device comprises:
Data capture unit, be suitable for obtaining achievement data sequence, described achievement data sequence comprises multiple achievement data;
Computing unit, is suitable for mean value and the standard deviation of parameter data sequence, and calculates the ratio of standard deviation and mean value, obtain the coefficient of variation of achievement data sequence;
Taxon, be suitable for the distribution pattern of the coefficient of variation agriculture products data according to achievement data sequence, described distribution pattern comprises intensity, osculant and discrete type; And
Stage unit, is suitable for the distribution pattern determination classification Processing Algorithm according to achievement data, and carries out classification process according to determined classification Processing Algorithm to achievement data sequence.
6. device according to claim 5, wherein, described taxon is also suitable for:
When the described coefficient of variation is less than or equal to first threshold, the distribution pattern of agriculture products data is intensive;
When the described coefficient of variation is greater than first threshold, and when being less than or equal to Second Threshold, the distribution pattern of agriculture products data is osculant;
When the described coefficient of variation is greater than Second Threshold, the distribution pattern of agriculture products data is discrete type.
7. device according to claim 6, wherein, described first threshold is 1, and described Second Threshold is 1.4.
8. the method according to any one of claim 5-7, wherein, described stage unit is also suitable for:
When the distribution pattern of achievement data is discrete type, determined classification Processing Algorithm is five period in arithmetrics;
When the distribution pattern of achievement data is intensive, determined classification Processing Algorithm is optimal segmentation;
When the distribution pattern of achievement data is osculant, determined classification Processing Algorithm is K means clustering algorithm.
9. a computing equipment, resident just like the electrical network achievement data treating apparatus according to any one of claim 5-8 in described computing equipment.
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| CN105404979A (en) * | 2015-12-15 | 2016-03-16 | 国家电网公司 | Multi-source information-based power grid equipment quality rating method |
| CN106972501A (en) * | 2016-01-13 | 2017-07-21 | 清华大学 | A kind of method of power distribution network control |
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