CN111292000A - Method and device for determining performance score of supplier, storage medium and processor - Google Patents

Method and device for determining performance score of supplier, storage medium and processor Download PDF

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CN111292000A
CN111292000A CN202010108669.8A CN202010108669A CN111292000A CN 111292000 A CN111292000 A CN 111292000A CN 202010108669 A CN202010108669 A CN 202010108669A CN 111292000 A CN111292000 A CN 111292000A
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赵雪骞
钱梦迪
滕景竹
孙致远
门业堃
于钊
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a method and a device for determining a performance score of a supplier, a storage medium and a processor. The invention comprises the following steps: calculating an initial score of a target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; and evaluating the target suppliers to be evaluated according to the target scores. The invention solves the technical problem of low control efficiency of the network access equipment caused by the lack of a perfect equipment quality evaluation system in the evaluation method of the quality management aspect of the network access equipment in the related technology.

Description

Method and device for determining performance score of supplier, storage medium and processor
Technical Field
The invention relates to the field of power supply equipment, in particular to a method and a device for determining a performance score of a supplier, a storage medium and a processor.
Background
In the related art, with the development of society and the progress of science, technology and culture, countries and society put higher demands on the quality management of power enterprises, and the quality management of the power enterprises faces higher-standard challenges. The system is a necessary way for ensuring the quality of the network-accessing equipment and the safety of the power grid, and is also a necessary way for meeting central requirements and promoting the sustainable development of power enterprises. In recent years, companies increase the investment of power grid construction year by year, the quantity of network access equipment materials is huge every year, and if the quality of the equipment is not strictly closed, once unqualified equipment materials provided by a bad supplier enter a power grid to operate, hidden dangers are brought to the safe and stable operation of the power grid.
Meanwhile, in recent years, some equipment manufacturers have low requirement on network access qualification, and have mixed fishes and dragons, so that the quality problem of the power grid equipment materials for production and supply is increased in a blowout manner. The quality of power grid equipment is remarkably improved by analyzing the internal environment and the external environment, the production quality of equipment manufacturers is not strictly controlled, the punishment on poor suppliers is insufficient, and a relatively perfect equipment quality evaluation system is lacked. The quality information of the power grid equipment is not well fused, the evaluation of the quality of the power grid equipment does not form a system, and the running condition of the equipment cannot be effectively fed back to the equipment bidding and bidding in the network management. However, the performance evaluation of the current suppliers mostly depends on an organization expert to perform evaluation on manpower, the automation of a system is not realized, and a large amount of manpower and material resources are wasted. Although the existing ranking algorithm exists at present, an algorithm suitable for performance evaluation of suppliers is not available, the existing ranking algorithm mostly depends on the equipment goodness and the number of equipment to rank, however, if the existing ranking algorithm mainly depends on the equipment goodness, the existing ranking algorithm is not beneficial to large manufacturers with large equipment quantity, faults are increased inevitably, if the existing ranking algorithm mainly depends on the number of equipment goodness, the existing ranking algorithm does not provide opportunities for small manufacturers to win, however, the weighting bottleneck of the existing ranking algorithm and the number of equipment to win is still an unsolved problem, and an objective, accurate and comprehensive evaluation and selection method is lacked.
Although the power grid company has more systems and evaluation methods in the quality management aspect of the network access equipment at present, reasonable unified coordination is not carried out on the good evaluation rate and the equipment number, the evaluation is mainly carried out by depending on expert experience and manual operation, a relatively complete equipment quality evaluation system is lacked, and particularly, the performance evaluation work field of a supplier is blank, so that the equipment operation condition cannot be effectively fed back to the equipment bidding management.
Meanwhile, the prior art has the following two obvious disadvantages:
(1) the quality supervision work of the power grid equipment mainly depends on all levels of unit material departments, so that a large amount of equipment quality information of operation and inspection links is not fed back to the bidding and purchasing process, the credit consciousness of a supplier is weak, and the loss behavior after bidding is frequent, so that the resource allocation and the use efficiency of a company are greatly reduced.
(2) The performance evaluation of a supplier in the operation and inspection link of a power grid enterprise mainly depends on the fault information of equipment to perform good evaluation rate statistics and score, but the evaluation is only suitable for the condition that the equipment quantity is large enough, and for the condition that the supplier with small equipment installation quantity cannot completely reflect the supplier only depends on the good evaluation rate, the evaluation link is relatively fair and insufficient.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The invention mainly aims to provide a method and a device for determining a performance score of a supplier, a storage medium and a processor, so as to solve the technical problem that the control efficiency of network access equipment is low due to the fact that a relatively complete equipment quality evaluation system is not available in an evaluation method in the quality management aspect of the network access equipment in the related art.
To achieve the above object, according to one aspect of the present invention, a method of determining a performance score of a supplier is provided. The invention comprises the following steps: calculating an initial score of a target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; and evaluating the target suppliers to be evaluated according to the target scores.
Further, before calculating the initial score of the target supplier to be evaluated, the method comprises: determining an evaluation period of target equipment, wherein the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by a target supplier to be evaluated; judging whether the target equipment has defects in the evaluation period; if the target equipment is not defective in the evaluation period, calculating a first operation score of the target equipment according to the operation initial score and the preset step length of the target equipment; and if the target equipment has defects in the evaluation period, calculating a first operation score of the target equipment according to the age of the defects of the target equipment, the operation initial score and the preset step length of the target equipment.
Further, calculating the initial score of the target supplier to be evaluated comprises: determining a first total deduction value of the target equipment, wherein the first total deduction value is the total deduction value of the unplanned shutdown of the target equipment in the evaluation period; determining a second total deduction value of the target equipment, wherein the second total deduction value is the total deduction value of the target equipment which fails in the evaluation period; determining a third total deduction value of the target equipment, wherein the third total deduction value is the total deduction value reported by the provincial company of the target equipment in the evaluation period; determining a fourth total deduction value of the target equipment, wherein the fourth total deduction value is the total deduction value of the target equipment when the familial defect occurs in the evaluation period; determining a deduction coefficient of the target equipment; and calculating an initial score according to the first operation score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
Further, before the initial score is corrected according to the posterior probability of the Bayesian average algorithm model, the method further comprises the following steps: obtaining a plurality of posterior probabilities of a plurality of candidate models, wherein the candidate models are Bayesian average algorithm models corresponding to a plurality of candidate samples; obtaining prior probability corresponding to a Bayesian average algorithm model; and carrying out weighted average on the plurality of posterior probabilities according to the prior probability to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
Further, before the initial score is corrected according to the posterior probability of the Bayesian average algorithm model, the method further comprises the following steps: selecting m variables from k regression elements, wherein m is less than or equal to k; establishing a sub-regression model of the Bayesian average algorithm model according to the m variables, wherein the sub-regression model comprises a regression coefficient formed by m-dimensional column vectors; and establishing a conditional probability density function of the regression coefficient according to the evaluation period, wherein the conditional probability density function comprises the posterior probability of the sub-regression model under the condition given by the evaluation period and the posterior probability of the regression coefficient under the condition given by the evaluation period and the sub-regression model.
Further, before the initial score is corrected according to the posterior probability of the Bayesian average algorithm model, the method comprises the following steps: solving the posterior probability of the sub-regression model; solving the posterior probability of the regression coefficient; and obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probabilities of the sub-regression models and the posterior probability of the regression coefficients.
Further, calculating the target score of the target device provided by the target supplier to be evaluated according to the corrected score comprises: acquiring a first device number, wherein the first device number is the total number of target devices provided by a plurality of suppliers to be evaluated; acquiring a first initial score, wherein the first initial score is an arithmetic average of a plurality of initial scores corresponding to a plurality of suppliers to be evaluated; acquiring a correction score of a target supplier to be evaluated; acquiring a second equipment quantity, wherein the second equipment quantity is the quantity of target equipment provided by a target supplier to be evaluated; and calculating a target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number.
In order to achieve the above object, according to another aspect of the present application, there is provided a storage medium including a stored program, wherein the program performs a method of determining a performance score of a supplier of any one of the above.
To achieve the above object, according to another aspect of the present application, there is provided a processor, a storage medium including a stored program, wherein the program performs a method of determining a performance score of a supplier of any one of the above.
To achieve the above object, according to another aspect of the present invention, there is provided a supplier performance score determining apparatus. The device includes: the first calculating unit is used for calculating the initial score of the target supplier to be evaluated; the system comprises a first establishing unit, a second establishing unit and a third establishing unit, wherein the first establishing unit is used for establishing a Bayesian average algorithm model corresponding to a sample of target equipment, and a supplier to be evaluated is used for providing the target equipment; the correction unit is used for correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; the second calculating unit is used for calculating a target score of target equipment provided by a target supplier to be evaluated according to the correction score, wherein the target score is a performance score of the supplier to be evaluated; and the evaluation unit is used for evaluating the target supplier to be evaluated according to the target score.
The invention adopts the following steps: calculating an initial score of a target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; the method and the device have the advantages that the target to-be-evaluated supplier is evaluated according to the target scores, the technical problem that the control efficiency of the network access device is low due to the fact that a relatively complete device quality evaluation system does not exist in the evaluation method of the network access device in the related technology in the quality management aspect is solved, and the technical effect of objectively and accurately reflecting the quality of the device provided by the supplier through the scores is achieved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a method for determining a performance score of a supplier, provided in accordance with an embodiment of the present invention; and
fig. 2 is a schematic diagram of a supplier performance score determining apparatus provided in accordance with an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, some terms or expressions referring to the embodiments of the present invention are explained below:
familial defects: the same type of defects occur in the operation of different types, specifications, series and even varieties of power equipment produced by the same manufacturer. The defects may be caused by the same process, the same material, the same design concept and idea, and the like.
According to an embodiment of the present invention, a method of determining a performance score for a supplier is provided.
Fig. 1 is a flowchart of a method for determining a performance score of a supplier according to an embodiment of the present invention. As shown in fig. 1, the present invention comprises the steps of:
and step S101, calculating the initial score of the target supplier to be evaluated.
Step S102, establishing a Bayesian average algorithm model corresponding to the sample of the target equipment, wherein the supplier to be evaluated is used for providing the target equipment.
And S103, correcting the initial score according to the posterior probability of the Bayes average algorithm model to obtain a corrected score of the target supplier to be evaluated.
And step S104, calculating a target score of the target equipment provided by the target supplier to be evaluated according to the correction score, wherein the target score is the performance score of the supplier to be evaluated.
And step S105, evaluating the target supplier to be evaluated according to the target score.
In the application, based on the guidance of the construction of the national credit system and the requirement of the quality management of the equipment of the power grid enterprise, the performance evaluation algorithm system of the supplier in the operation and inspection link is comprehensively constructed from three aspects of basic information, an index system and evaluation ranking of the performance evaluation of the supplier in the operation and inspection link on the basis of the method of 'big data analysis' and 'Bayesian average'. Based on the power grid equipment suppliers, the evaluation accuracy and fairness of an evaluation system are emphasized, and scientific and credible optimal selection basis is provided for power grid bidding purchase.
Because of different sample capacities (the number of years of the provided equipment) of different suppliers, the number of years of some equipment suppliers is small, the defect records are few, the score of the supplier is high, and the quality of the supplier cannot be objectively reflected directly through the score. The method takes a Bayesian Model Averaging (Bayesian Model Averaging) ranking algorithm as a core, and calculates the performance score of the supplier by combining with the evaluation index of the supplier equipment, thereby realizing that the power grid company provides scientific and credible optimal basis for bidding purchase.
Specifically, in the embodiment of the present application, a power grid device is provided by different suppliers, where the method includes the following steps: calculating initial scores of suppliers, reducing the difference between different sample capacities based on a Bayesian average algorithm model, correcting the initial scores of the suppliers to obtain corrected scores, and calculating target scores of the suppliers according to the corrected scores of the suppliers.
The method for determining the performance score of the supplier, provided by the embodiment of the invention, comprises the steps of calculating the initial score of the target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; the method comprises the steps of evaluating a target supplier to be evaluated according to a target score, wherein a relatively complete equipment quality evaluation system does not exist in an evaluation method in the quality management aspect of network access equipment in the related technology, so that the technical problem that the network access equipment has low control efficiency is caused, and the technical effect of objectively and accurately reflecting the quality of equipment provided by the supplier through the score is further achieved.
Optionally, before calculating the initial score of the target supplier to be evaluated, the method includes: determining an evaluation period of target equipment, wherein the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by a target supplier to be evaluated; judging whether the target equipment has defects in the evaluation period; if the target equipment is not defective in the evaluation period, calculating a first operation score of the target equipment according to the operation initial score and the preset step length of the target equipment; and if the target equipment has defects in the evaluation period, calculating a first operation score of the target equipment according to the age of the defects of the target equipment, the operation initial score and the preset step length of the target equipment.
In the above, the initial year Yl and the final year Yu in the period in which the evaluation by the supplier is carried out are assumed, and therefore, the evaluation period is Yl-Yu. Then, in the supplier a with the number of shipped devices n, the number ti of device stations with a certain shipped year yi is:
Figure BDA0002389219110000061
total years of supplier A TAComprises the following steps:
Figure BDA0002389219110000062
further, from the start of the device commissioning, the device operation score is given an initial value of s0The sum step length f is increased in an arithmetic progression, and the score s of the j yearjComprises the following steps:
sj＝s0+f×(j-1) (3)
if the device has defects in the jth year, the year is divided into:
sj＝sd-k·d (4)
wherein s isdThe initial value of the defect occurrence year is calculated (different from the initial value of the defect-free year), d is the corrected deduction value (obtained according to the corresponding equipment evaluation guide), and k is the deduction coefficient (the specific value is shown in formula 14).
The next year of equipment defect occurrence is regarded as equipment re-operation, and the year is scored from s0Recalculating the running score S of the equipment y years after the equipment is put into operationiComprises the following steps:
Figure BDA0002389219110000071
in summary, the supplier device operation score SΣComprises the following steps:
Figure BDA0002389219110000072
if a device is not defective within the evaluation time, its operation score is SpComprises the following steps:
Figure BDA0002389219110000073
optionally, the calculating an initial score of the target supplier to be evaluated includes: determining a first total deduction value of the target equipment, wherein the first total deduction value is the total deduction value of the unplanned shutdown of the target equipment in the evaluation period; determining a second total deduction value of the target equipment, wherein the second total deduction value is the total deduction value of the target equipment which fails in the evaluation period; determining a third total deduction value of the target equipment, wherein the third total deduction value is the total deduction value reported by the provincial company of the target equipment in the evaluation period; determining a fourth total deduction value of the target equipment, wherein the fourth total deduction value is the total deduction value of the target equipment when the familial defect occurs in the evaluation period; determining a deduction coefficient of the target equipment; and calculating an initial score according to the first operation score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
In the above-described manner, after the supplier operation score is obtained, the supplier operation score is quantified in consideration of the presence of a failure, non-stop, quality event notification, and the like of the supplier.
The supplier happens nsThe calculated deduction value of the next non-stop is:
Ds＝ns×ds(8)
the supplier happens nfThe calculated deduction value of the secondary fault is as follows:
Df＝nf×df(9)
the supplier happens nbpThe calculated deduction value reported by the secondary province company is as follows:
Dbp＝nbp×dbp(10)
the supplier happens nbsThe calculated deduction value reported by the secondary network company is as follows:
Dbs＝nbs×dbs(11)
the supplier happens ndThe calculated deduction value of the familial defect of the company of the province is as follows:
Dd＝nd×dd(12)
in formulae (8) to (12), dsFor a single deduction of the value of non-stop of the plant, dfFor a single deduction value of the fault, dbpA single deduction value for company notice of occurrence province, dbsSingle deduction value for notification of network company of country of origin, ddThe single deduction value for the occurrence of provincial corporate family defects.
For a certain supplier with equipment years T, its initial score p is:
Figure BDA0002389219110000081
wherein the calculation of the deduction coefficient comprises the following steps: aiming at different defect (fault) rates of various devices, a dynamic adjustment factor is introduced, so that the problem that the operation condition or the fault condition of the devices affects the evaluation result in a unilateral way is solved, and the evaluation is ensured to comprehensively reflect the quality condition of the devices. Balancing the weight of the equipment operation score and the defect deduction value, and setting a deduction coefficient k:
Figure BDA0002389219110000082
in the formula (14), r is the ratio of the total score of some evaluation fineness (Dd + Df + Ds + Dbp + Dbs + Dd) to the defect-free running score Sp, and rmax and rmin are the maximum value and the minimum value of all evaluation fineness respectively.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: obtaining a plurality of posterior probabilities of a plurality of candidate models, wherein the candidate models are Bayesian average algorithm models corresponding to a plurality of candidate samples; obtaining prior probability corresponding to a Bayesian average algorithm model; and carrying out weighted average on the plurality of posterior probabilities according to the prior probability to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: selecting m variables from k regression elements, wherein m is less than or equal to k; establishing a sub-regression model of the Bayesian average algorithm model according to the m variables, wherein the sub-regression model comprises a regression coefficient formed by m-dimensional column vectors; and establishing a conditional probability density function of the regression coefficient according to the evaluation period, wherein the conditional probability density function comprises the posterior probability of the sub-regression model under the condition given by the evaluation period and the posterior probability of the regression coefficient under the condition given by the evaluation period and the sub-regression model.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method includes: solving the posterior probability of the sub-regression model; solving the posterior probability of the regression coefficient; and obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probabilities of the sub-regression models and the posterior probability of the regression coefficients.
As described above, in an alternative embodiment, the modifying the initial score of the supplier by the bayesian model averaging method includes:
first, a performance averaging algorithm model based on 'Bayesian averaging' is constructed, wherein the performance averaging algorithm model provides target equipment sample capacity by a supplier. Obtaining an average predicted value according to the posterior probability of a Bayes average model, wherein p (y | Mr) is the posterior probability of the model, the posterior estimated probability of candidate model parameters corresponding to different sample capacities is weighted and averaged, and the weighted average is calculated by using a Bayes formula according to the prior probability p (Mr) of the model:
(1) establishing a classical multiple linear regression model:
yi＝α+β1xi1+β2xi2+···+βkxik+εi(i＝1,···,n) (15)
wherein, α1,β2,···,βkIs the regression coefficient of the model, εi～N(0,σ2) I is 1, n. Due to the complexity of the evaluation index system, x is calculated at k regression elementsi1,···,xikThe uncertainty in selecting the significant variable is often encountered, i.e., the problem of uncertainty in the model (uncertainty in the model refers to uncertainty in the size of the model). From xi1,···,xikM (0 is more than or equal to M and more than or equal to k) variables are selected to form a model Mj(is model) determined sub-regression model:
Figure BDA0002389219110000091
wherein lnIs an n-dimensional column vector of 1, XjIs that any m regression elements form n x m matrix, βjIs a regression coefficient formed by a corresponding m-dimensional column vector; obviously, M (is the candidate model)jThe number of possible candidate models is J-2kAnd (4) respectively. How to select the optimal model among the plurality of models, or how to select the optimal set of variables among the k explanatory variablesAnd (6) mixing.
(2) Bayesian model averaging method
The basic idea of the Bayesian model averaging method is to perform weighted averaging on the posterior estimation probability of each candidate model parameter and determine the selected variable combination according to the probability. When sample y is equal to (y)1,···,yn)TWhen the commissioning cycle is given by y, the parameter vector β in the calculation model equation (16) is equal to (β)1,···,βn)TThe conditional probability density function p (β/y) of (1) is:
Figure BDA0002389219110000101
wherein, p (M)jY) represents M in model formula (16) given yj(j＝1,···,J＝2k) A posteriori probability of p (β)j/y,Mj) Is represented at y and MjParameter vector (regression coefficient) β under given conditionsjP (β) in the following equation (17) is calculated separately according to the idea of Koop et alj/y,Mj) And p (M)j/y)。
In the first step, p is calculated (β)j/y,Mj) Assume that the parameters α and σ are no information priors, i.e., α ℃. sup.1, α ℃. sup.σ-1Then, M is obtained by assuming the formula (17)jThe corresponding likelihood function is:
Figure BDA0002389219110000102
further assume that parameter βjThe prior distribution of (a) is:
βj/σ,Mj～N(0,σ2(gX'jXj)-1) (19)
wherein g is a parameter to be determined. By using the formulas (18) and (19), the following can be obtained:
Figure BDA0002389219110000103
from the assumption α ∈ σ-1Will beEquation (20) integrates σ and can prove βj/y,MjObeying a multidimensional t distribution with one degree of freedom n, namely:
βj/y,Mj～t(μj,∑j) (21)
let Vj＝[(1+g)X'jXj]-1，Pj＝In-Xj(X'jXj)-1X'jMu in formula (21)j＝E(βj/y,Mj)＝VjX'jy，
Figure BDA0002389219110000104
Figure BDA0002389219110000105
Second, calculate p (M)jY) using the expression (18), the expression (19) and the hypothesis α ℃. sigma-1About y, βj,σ/MjAnd then pair β with the likelihood function of (2)jThe sum sigma is integrated to obtain the sum of y/MjDistribution of (a):
Figure BDA0002389219110000106
the calculation then yields:
p(Mj/y)＝cp(y/Mj)p(Mj) (22)
wherein c is represented by
Figure BDA0002389219110000111
Determination of p (M)j) Finally, p (β/y) in equation (17) can be determined from the results of equations (21) and (22) to obtain a bayesian estimate of the corrected expectation score (the corrected score for the initial score of the supplier) as:
Figure BDA0002389219110000112
wherein C represents the number of devices installed by the supplier to be evaluated in the prior distribution, is a constant and is proportional to the size of the data set, m is the arithmetic mean of the data set, n is the total number of the data set, and the pagebass mean is calculated for each power supplier and is used as the basis for sorting.
Optionally, calculating the target score of the target device provided by the target supplier to be evaluated according to the revised score includes: acquiring a first device number, wherein the first device number is the total number of target devices provided by a plurality of suppliers to be evaluated; acquiring a first initial score, wherein the first initial score is an arithmetic average of a plurality of initial scores corresponding to a plurality of suppliers to be evaluated; acquiring a correction score of a target supplier to be evaluated; acquiring a second equipment quantity, wherein the second equipment quantity is the quantity of target equipment provided by a target supplier to be evaluated; and calculating a target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number.
Specifically, the following formula is obtained for different equipment of a certain supplier:
Figure BDA0002389219110000113
wherein AEN represents the number of equipment installed by all suppliers to be evaluated,
Figure BDA0002389219110000114
the correction score of the supplier to be evaluated is calculated, TOTAL represents the initial score of the supplier to be evaluated, and AJ represents the number of the equipment to be evaluated of the supplier to be evaluated. The ranking based on Bayesian average can avoid the influence of incomplete equipment scoring and the like caused by few equipment of suppliers, and can reflect the real situation better, the equipment suppliers with more evaluated equipment and higher performance scoring can get closer to the front, the equipment suppliers with more evaluated equipment and lower performance scoring can get closer to the back, and although some equipment has higher performance scoring, the equipment suppliers with lower performance scoring can get closer to the back correspondingly because of too few installed equipment, compared with the ranking based on the good scoring rate of each equipment of suppliers, the result is more meaningful and has better fairness.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment of the present invention further provides a device for determining a performance score of a supplier, and it should be noted that the device for determining a performance score of a supplier according to the embodiment of the present invention may be used to execute the method for determining a performance score of a supplier according to the embodiment of the present invention. The following describes a determination device for a performance score of a supplier according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a supplier performance score determining apparatus provided in accordance with an embodiment of the present invention. As shown in fig. 2, the apparatus includes: a first calculating unit 201, configured to calculate an initial score of a target supplier to be evaluated; the first establishing unit 202 is configured to establish a bayesian average algorithm model corresponding to a sample of the target device, where a provider to be evaluated is used to provide the target device; the correcting unit 203 is used for correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; the second calculating unit 204 is configured to calculate a target score of the target device provided by the target supplier to be evaluated according to the revised score, where the target score is a performance score of the supplier to be evaluated; and the evaluation unit 205 is configured to evaluate the target supplier to be evaluated according to the target score.
The device for determining the performance score of the supplier, provided by the embodiment of the invention, is used for calculating the initial score of the target supplier to be evaluated through the first calculating unit 201; the first establishing unit 202 is configured to establish a bayesian average algorithm model corresponding to a sample of the target device, where a provider to be evaluated is used to provide the target device; the correcting unit 203 is used for correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; the second calculating unit 204 is configured to calculate a target score of the target device provided by the target supplier to be evaluated according to the revised score, where the target score is a performance score of the supplier to be evaluated; the evaluation unit 205 is configured to evaluate the target provider to be evaluated according to the target score, so as to solve a technical problem that a control efficiency of the network access device is low due to the fact that a relatively complete device quality evaluation system is not available in an evaluation method in the quality management aspect of the network access device in the related art, and further achieve a technical effect of objectively and accurately reflecting the quality of the device provided by the provider through the score.
Optionally, the apparatus comprises: the system comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining the evaluation period of target equipment before calculating the initial score of a target supplier to be evaluated, the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by the target supplier to be evaluated; a judging unit for judging whether the target device has a defect within the evaluation period; the third calculating unit is used for calculating a first operation score of the target equipment according to the operation initial score and the preset step length of the target equipment under the condition that the target equipment has no defects in the evaluation period; and the fourth calculating unit is used for calculating the first operation score of the target equipment according to the age of the target equipment with the defect, the operation initial score of the target equipment and the preset step length under the condition that the target equipment has the defect in the evaluation period.
Optionally, the first computing unit 201 includes: the first determining subunit is configured to determine a first total deduction value of the target device, where the first total deduction value is a total deduction value of an unplanned outage occurring in the evaluation period of the target device; the second determining subunit is configured to determine a second total deduction value of the target device, where the second total deduction value is a total deduction value of the target device that fails in the evaluation period; the third determining subunit is configured to determine a third total deduction value of the target device, where the third total deduction value is a total deduction value notified by the provincial company of the target device in the evaluation period; the fourth determining subunit is configured to determine a fourth total deduction value of the target device, where the fourth total deduction value is the total deduction value of the target device for the familial defect occurring in the evaluation period; the fifth determining subunit is used for determining the deduction coefficient of the target device; and the first calculating subunit is used for calculating the initial score according to the first running score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
Optionally, the apparatus further comprises: the first obtaining unit is used for obtaining a plurality of posterior probabilities of a plurality of candidate models before correcting the initial scores according to the posterior probabilities of the Bayes average algorithm models, wherein the candidate models are the Bayes average algorithm models corresponding to a plurality of candidate samples; the second acquisition unit is used for acquiring the prior probability corresponding to the Bayesian average algorithm model; and the fifth calculating unit is used for carrying out weighted average on the plurality of posterior probabilities according to the prior probability so as to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
Optionally, the apparatus further comprises: the selection unit is used for selecting m variables from k regression elements before correcting the initial score according to the posterior probability of the Bayesian average algorithm model, wherein m is smaller than or equal to k; the second establishing unit 202 is configured to establish a sub-regression model of the bayesian average algorithm model according to the m variables, where the sub-regression model includes a regression coefficient formed by m-dimensional column vectors; a third establishing unit 202, configured to establish a conditional probability density function of the regression coefficient according to the evaluation period, where the conditional probability density function includes a posterior probability of the sub-regression model under the condition given by the evaluation period, and a posterior probability of the regression coefficient under the condition given by the evaluation period and the sub-regression model.
Optionally, before the initial score is corrected according to the posterior probability of the bayesian average algorithm model, the first solving unit is configured to solve the posterior probability of the sub-regression model; the second solving unit is used for solving the posterior probability of the regression coefficient; and the third acquisition unit is used for obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probability of the sub-regression model and the posterior probability of the regression coefficient.
Optionally, the second computing unit 204 includes: the system comprises a first obtaining subunit, a second obtaining subunit, a third obtaining subunit and a fourth obtaining subunit, wherein the first obtaining subunit is used for obtaining a first equipment number, and the first equipment number is the total number of target equipment provided by a plurality of suppliers to be evaluated; the second obtaining subunit is configured to obtain a first initial score, where the first initial score is an arithmetic average of multiple initial scores corresponding to multiple providers to be evaluated; the third acquisition subunit is used for acquiring the correction score of the target supplier to be evaluated; the fourth acquiring subunit is configured to acquire a second device number, where the second device number is the number of target devices provided by the target provider to be evaluated; and the second calculating subunit is used for calculating the target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number.
The device for determining the performance score of the supplier comprises a processor and a memory, wherein the first calculating unit 201 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the technical problem of low control efficiency of the network access equipment caused by no perfect equipment quality evaluation system in the evaluation method of the network access equipment in the aspect of quality management in the related technology is solved by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a storage medium having a program stored thereon, which when executed by a processor, implements a method of determining a performance score for a supplier.
The embodiment of the invention provides a processor, which is used for running a program, wherein the program executes a method for determining a performance score of a supplier during running.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps: calculating an initial score of a target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; and evaluating the target suppliers to be evaluated according to the target scores.
Optionally, before calculating the initial score of the target supplier to be evaluated, the method includes: determining an evaluation period of target equipment, wherein the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by a target supplier to be evaluated; judging whether the target equipment has defects in the evaluation period; if the target equipment is not defective in the evaluation period, calculating a first operation score of the target equipment according to the operation initial score and the preset step length of the target equipment; and if the target equipment has defects in the evaluation period, calculating a first operation score of the target equipment according to the age of the defects of the target equipment, the operation initial score and the preset step length of the target equipment.
Optionally, the calculating an initial score of the target supplier to be evaluated includes: determining a first total deduction value of the target equipment, wherein the first total deduction value is the total deduction value of the unplanned shutdown of the target equipment in the evaluation period; determining a second total deduction value of the target equipment, wherein the second total deduction value is the total deduction value of the target equipment which fails in the evaluation period; determining a third total deduction value of the target equipment, wherein the third total deduction value is the total deduction value reported by the provincial company of the target equipment in the evaluation period; determining a fourth total deduction value of the target equipment, wherein the fourth total deduction value is the total deduction value of the target equipment when the familial defect occurs in the evaluation period; determining a deduction coefficient of the target equipment; and calculating an initial score according to the first operation score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: obtaining a plurality of posterior probabilities of a plurality of candidate models, wherein the candidate models are Bayesian average algorithm models corresponding to a plurality of candidate samples; obtaining prior probability corresponding to a Bayesian average algorithm model; and carrying out weighted average on the plurality of posterior probabilities according to the prior probability to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: selecting m variables from k regression elements, wherein m is less than or equal to k; establishing a sub-regression model of the Bayesian average algorithm model according to the m variables, wherein the sub-regression model comprises a regression coefficient formed by m-dimensional column vectors; and establishing a conditional probability density function of the regression coefficient according to the evaluation period, wherein the conditional probability density function comprises the posterior probability of the sub-regression model under the condition given by the evaluation period and the posterior probability of the regression coefficient under the condition given by the evaluation period and the sub-regression model.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method includes: solving the posterior probability of the sub-regression model; solving the posterior probability of the regression coefficient; and obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probabilities of the sub-regression models and the posterior probability of the regression coefficients.
Optionally, calculating the target score of the target device provided by the target supplier to be evaluated according to the revised score includes: acquiring a first device number, wherein the first device number is the total number of target devices provided by a plurality of suppliers to be evaluated; acquiring a first initial score, wherein the first initial score is an arithmetic average of a plurality of initial scores corresponding to a plurality of suppliers to be evaluated; acquiring a correction score of a target supplier to be evaluated; acquiring a second equipment quantity, wherein the second equipment quantity is the quantity of target equipment provided by a target supplier to be evaluated; and calculating a target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The invention also provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: calculating an initial score of a target supplier to be evaluated; establishing a Bayesian average algorithm model corresponding to a sample of the target equipment, wherein a supplier to be evaluated is used for providing the target equipment; correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated; calculating a target score of target equipment provided by a target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated; and evaluating the target suppliers to be evaluated according to the target scores.
Optionally, before calculating the initial score of the target supplier to be evaluated, the method includes: determining an evaluation period of target equipment, wherein the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by a target supplier to be evaluated; judging whether the target equipment has defects in the evaluation period; if the target equipment is not defective in the evaluation period, calculating a first operation score of the target equipment according to the operation initial score and the preset step length of the target equipment; and if the target equipment has defects in the evaluation period, calculating a first operation score of the target equipment according to the age of the defects of the target equipment, the operation initial score and the preset step length of the target equipment.
Optionally, the calculating an initial score of the target supplier to be evaluated includes: determining a first total deduction value of the target equipment, wherein the first total deduction value is the total deduction value of the unplanned shutdown of the target equipment in the evaluation period; determining a second total deduction value of the target equipment, wherein the second total deduction value is the total deduction value of the target equipment which fails in the evaluation period; determining a third total deduction value of the target equipment, wherein the third total deduction value is the total deduction value reported by the provincial company of the target equipment in the evaluation period; determining a fourth total deduction value of the target equipment, wherein the fourth total deduction value is the total deduction value of the target equipment when the familial defect occurs in the evaluation period; determining a deduction coefficient of the target equipment; and calculating an initial score according to the first operation score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: obtaining a plurality of posterior probabilities of a plurality of candidate models, wherein the candidate models are Bayesian average algorithm models corresponding to a plurality of candidate samples; obtaining prior probability corresponding to a Bayesian average algorithm model; and carrying out weighted average on the plurality of posterior probabilities according to the prior probability to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method further comprises: selecting m variables from k regression elements, wherein m is less than or equal to k; establishing a sub-regression model of the Bayesian average algorithm model according to the m variables, wherein the sub-regression model comprises a regression coefficient formed by m-dimensional column vectors; and establishing a conditional probability density function of the regression coefficient according to the evaluation period, wherein the conditional probability density function comprises the posterior probability of the sub-regression model under the condition given by the evaluation period and the posterior probability of the regression coefficient under the condition given by the evaluation period and the sub-regression model.
Optionally, before the initial score is modified according to the posterior probability of the bayesian average algorithm model, the method includes: solving the posterior probability of the sub-regression model; solving the posterior probability of the regression coefficient; and obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probabilities of the sub-regression models and the posterior probability of the regression coefficients.
Optionally, calculating the target score of the target device provided by the target supplier to be evaluated according to the revised score includes: acquiring a first device number, wherein the first device number is the total number of target devices provided by a plurality of suppliers to be evaluated; acquiring a first initial score, wherein the first initial score is an arithmetic average of a plurality of initial scores corresponding to a plurality of suppliers to be evaluated; acquiring a correction score of a target supplier to be evaluated; acquiring a second equipment quantity, wherein the second equipment quantity is the quantity of target equipment provided by a target supplier to be evaluated; and calculating a target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present invention, and are not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (10)

1. A method of determining a performance score of a supplier, comprising:
calculating an initial score of a target supplier to be evaluated;
establishing a Bayesian average algorithm model corresponding to a sample of target equipment, wherein the supplier to be evaluated is used for providing the target equipment;
correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated;
calculating a target score of the target equipment provided by the target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated;
and evaluating the target supplier to be evaluated according to the target score.
2. The method of claim 1, wherein prior to calculating an initial score for a target supplier to be assessed, the method comprises:
determining an evaluation period of the target equipment, wherein the evaluation period is a preset number of years, and the preset number is the number of the target equipment provided by a target supplier to be evaluated;
judging whether the target equipment has defects in the evaluation period;
if the target equipment does not have the defects in the evaluation period, calculating a first operation score of the target equipment according to the operation initial score and a preset step length of the target equipment;
if the target device has the defect within the evaluation period, calculating the first operation score of the target device according to the age of the defect of the target device, the operation initial score of the target device and the preset step length.
3. The method of claim 2, wherein calculating an initial score for a target supplier to be evaluated comprises:
determining a first total deduction value of the target device, wherein the first total deduction value is the total deduction value of the unplanned outage of the target device in the evaluation period;
determining a second total deduction value of the target equipment, wherein the second total deduction value is the total deduction value of the target equipment which fails in the evaluation period;
determining a third total deduction value of the target device, wherein the third total deduction value is the total deduction value notified by a provincial company of the target device in the evaluation period;
determining a fourth total deduction value of the target device, wherein the fourth total deduction value is the total deduction value of the target device for the familial defect in the evaluation period;
determining a deduction coefficient of the target equipment;
and calculating the initial score according to the first operation score, the deduction coefficient, the first deduction total value, the second deduction total value, the third deduction total value and the fourth deduction total value.
4. The method of claim 3, wherein prior to correcting the initial score according to the posterior probability of the Bayesian average algorithm model, the method further comprises:
obtaining a plurality of posterior probabilities of a plurality of candidate models, wherein the candidate models are Bayesian average algorithm models corresponding to a plurality of candidate samples;
obtaining the prior probability corresponding to the Bayesian average algorithm model;
and carrying out weighted average on the plurality of posterior probabilities according to the prior probability to construct a multiple linear regression model, wherein the multiple linear regression model comprises k regression coefficients and k regression elements, and k is a positive integer greater than or equal to 1.
5. The method of claim 4, wherein prior to correcting the initial score according to the posterior probability of the Bayesian average algorithm model, the method further comprises:
selecting m variables from the k regression elements, wherein m is less than or equal to k;
establishing a sub-regression model of the Bayesian average algorithm model according to the m variables, wherein the sub-regression model comprises a regression coefficient formed by m-dimensional column vectors;
and establishing a conditional probability density function of the regression coefficient according to the evaluation period, wherein the conditional probability density function comprises the posterior probability of the sub-regression model under the given condition of the evaluation period and the posterior probability of the regression coefficient under the given condition of the evaluation period and the sub-regression model.
6. The method of claim 5, wherein prior to modifying the initial score according to the posterior probability of the Bayesian average algorithm model, the method comprises:
solving the posterior probability of the sub regression model;
solving the posterior probability of the regression coefficient;
and obtaining the posterior probability of the Bayes average algorithm model according to the conditional probability density function, the posterior probability of the sub-regression model and the posterior probability of the regression coefficient.
7. The method of claim 1, wherein calculating the target score for the target equipment provided by the target supplier to be evaluated according to the revised score comprises:
acquiring a first device number, wherein the first device number is the total number of the target devices provided by a plurality of suppliers to be evaluated;
acquiring a first initial score, wherein the first initial score is an arithmetic average of a plurality of initial scores corresponding to the plurality of suppliers to be evaluated;
acquiring a correction score of the target supplier to be evaluated;
acquiring a second device quantity, wherein the second device quantity is the quantity of the target devices provided by the target provider to be evaluated;
and calculating the target score according to the first equipment number, the first initial score, the correction score of the target supplier to be evaluated and the second equipment number.
8. An apparatus for determining a performance score of a supplier, comprising:
the first calculating unit is used for calculating the initial score of the target supplier to be evaluated;
the system comprises a first establishing unit, a second establishing unit and a third establishing unit, wherein the first establishing unit is used for establishing a Bayesian average algorithm model corresponding to a sample of target equipment, and the supplier to be evaluated is used for providing the target equipment;
the correction unit is used for correcting the initial score according to the posterior probability of the Bayesian average algorithm model to obtain a corrected score of the target supplier to be evaluated;
the second calculating unit is used for calculating a target score of the target equipment provided by the target supplier to be evaluated according to the corrected score, wherein the target score is a performance score of the supplier to be evaluated;
and the evaluation unit is used for evaluating the target supplier to be evaluated according to the target score.
9. A storage medium comprising a stored program, wherein the program performs a supplier performance score determination method as claimed in any one of claims 1 to 7.
10. A processor, characterized in that the processor is configured to run a program, wherein the program when run performs a method of determining a performance score of a supplier as claimed in any one of claims 1 to 7.
CN202010108669.8A 2020-02-21 2020-02-21 Method and device for determining performance score of supplier, storage medium and processor Pending CN111292000A (en)

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