CN113743780A

CN113743780A - Method and device for determining fault shuttle

Info

Publication number: CN113743780A
Application number: CN202111029354.5A
Authority: CN
Inventors: 范超; 邵文
Original assignee: Beijing Jingdong Qianshi Technology Co Ltd
Current assignee: Beijing Jingdong Shangke Information Technology Co Ltd; Beijing Jingdong Qianshi Technology Co Ltd
Priority date: 2021-09-01
Filing date: 2021-09-01
Publication date: 2021-12-03

Abstract

The invention discloses a method and a device for determining a failed shuttle car, and relates to the technical field of warehouse logistics. One embodiment of the method comprises: acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle; determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods; and determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle, and generating a control instruction to control the failed shuttle vehicles to stop working. The shuttle vehicle evaluation method can evaluate the shuttle vehicle from multiple aspects and multiple dimensions, the characteristics of different emphasis and different advantages of each scoring method are fully utilized, the abnormal state of the shuttle vehicle is objectively and truly reflected, and finally the maintenance efficiency of the shuttle vehicle and the utilization rate of resources are improved.

Description

Method and device for determining fault shuttle

Technical Field

The invention relates to the field of warehouse logistics, in particular to a method and a device for determining a failed shuttle vehicle.

Background

The shuttle car in the existing warehouse logistics system mainly uses a single index of the failure frequency as an off-line maintenance evaluation standard, and the evaluation dimension is one-sided, so that the evaluation result is not objective and comprehensive. However, if the entropy method is adopted as the evaluation method, the problem that the index is zero cannot be overcome.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method and an apparatus for determining a faulty shuttle car, which can comprehensively evaluate the shuttle car from multiple dimensions, reduce information overlap ratio between variables of the multiple dimensions, overcome the problem that the index cannot be zero in the prior art, and solve the problems that the evaluation of one plane by a single method and the evaluation of multiple methods in the prior art are inconsistent by using the integrated comprehensive fault level as an evaluation index. Therefore, limited maintenance resources are fully utilized to process the confirmed fault shuttle, and the effectiveness and the efficiency of maintenance are improved.

To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of determining a faulty shuttle, including:

acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle;

determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data;

respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods;

and determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle, and generating a control instruction to control the failed shuttle vehicles to stop working.

Optionally, the acquiring the abnormal data corresponding to the multiple abnormal factors of each shuttle includes:

collecting data of various daily abnormal factors of each shuttle vehicle to be determined within a period of time;

respectively calculating sampling values of abnormal data corresponding to each abnormal factor of each shuttle vehicle in the period of time;

determining the range corresponding to each abnormal factor according to the sampling value, wherein the intermediate value corresponding to each abnormal factor of each shuttle vehicle is positively correlated with the range of the abnormal factor;

respectively aiming at each abnormal factor, sequencing the intermediate values of the shuttle vehicles under the abnormal factor according to the serial numbers of the shuttle vehicles to form a factor vector corresponding to the abnormal factor;

and taking the factor vector as abnormal data analyzed by the multiple fault level analysis methods.

Optionally, the sampling value is one of an average value, a median, a maximum value and a minimum value of the abnormal data corresponding to each abnormal factor of each shuttle vehicle in the period of time.

Optionally, one of the multiple fault level analysis methods is a correlation coefficient method scoring method, including:

calculating the correlation coefficient between each factor vector and all the factor vectors respectively aiming at each factor vector, and taking the corresponding correlation coefficient as the corresponding element in the correlation coefficient matrix according to the calculation sequence;

calculating the average value of the correlation coefficient corresponding to each abnormal factor according to the correlation coefficient matrix, wherein the difference value between the summation result of the j-th column element in the correlation coefficient matrix and 1 is determined, and then the difference value is divided by (m-1) to obtain the average value of the correlation coefficient corresponding to the j-th abnormal factor;

calculating the intermediate weight of each abnormal factor, wherein the intermediate weight of each abnormal factor is inversely correlated with the average value of the correlation coefficient corresponding to the abnormal factor;

calculating a weight value of each abnormal factor, wherein the weight value of the jth abnormal factor is equal to the intermediate weight of the abnormal factor divided by the sum of the intermediate weights of all the abnormal factors;

and calculating the fault grade of each shuttle vehicle under the correlation coefficient method scoring method, wherein for each shuttle vehicle, the product of the intermediate value corresponding to each abnormal factor and the weighted value of the corresponding abnormal factor is calculated in sequence, then all the products are summed to obtain the score of the shuttle vehicle under the correlation coefficient method scoring method, and then the scores of the shuttle vehicles under the correlation coefficient method scoring method are sorted to obtain the fault grade of the corresponding shuttle vehicle.

Optionally, one of the multiple fault level analysis methods is a variance method scoring method, including:

calculating the variance of the intermediate values corresponding to the abnormal factors of all the shuttles aiming at each abnormal factor;

calculating a weight value of each abnormality factor, wherein the weight value of the jth abnormality factor is equal to the variance of the abnormality factor divided by the sum of the variances of all the abnormality factors;

and calculating the fault grade of each shuttle vehicle under the variance method scoring method, wherein for each shuttle vehicle, the product of the intermediate value corresponding to each abnormal factor and the weighted value of the corresponding abnormal factor is calculated in sequence, then all the products are summed to obtain the score of the shuttle vehicle under the variance method scoring method, and then the scores of the shuttle vehicles under the variance method scoring method are sorted to obtain the fault grade of the corresponding shuttle vehicle.

Optionally, one of the multiple fault level analysis methods is a expert method scoring method, and the method includes:

presetting a weight value corresponding to each abnormal factor;

and calculating the fault grade of each shuttle vehicle under the expert method grading method, wherein products of the middle value corresponding to each abnormal factor and the weight value of the corresponding abnormal factor are sequentially calculated for each shuttle vehicle, then the products are summed to obtain the grade of the shuttle vehicle under the expert method grading method, and then the grades of the shuttle vehicles under the expert method grading method are sorted to obtain the fault grade of the corresponding shuttle vehicle.

Optionally, the determining, for each shuttle vehicle, a comprehensive fault level of the shuttle vehicle under a plurality of fault level analysis methods includes:

presetting a weight value corresponding to each fault level analysis method;

and aiming at each shuttle vehicle, sequentially calculating the product of the fault level of the shuttle vehicle under each fault level analysis method and the weight value corresponding to the fault level analysis method, summing all the products to obtain the comprehensive score of the shuttle vehicle, and sequencing the comprehensive scores of the shuttle vehicles to obtain the comprehensive fault level corresponding to the shuttle vehicle.

Optionally, the preset weight values corresponding to the fault level analysis methods are equal.

Optionally, the sum of the preset weight values is 1.

Optionally, determining the failed shuttle vehicle according to the comprehensive failure level of each shuttle vehicle further includes:

and selecting the first alpha% shuttle cars or the first a shuttle cars of the descending sorting result as fault shuttle cars according to the comprehensive fault grade.

According to a second aspect of the embodiments of the present invention, there is provided an apparatus for determining a faulty shuttle, comprising:

the acquisition module is used for acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle;

the evaluation module is used for determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods;

and the determining module is used for determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle and generating control instructions to control the failed shuttle vehicles to stop working.

Optionally, the acquiring, by the acquisition module, the abnormal data corresponding to the multiple abnormal factors of each shuttle includes:

Optionally, the sampling value in the acquisition module is one of an average value, a median, a maximum value and a minimum value of the abnormal data corresponding to each abnormal factor of each shuttle vehicle in the period of time.

Optionally, one of the multiple failure level analysis methods in the evaluation module is a correlation coefficient method scoring method, including:

Optionally, one of the multiple fault level analysis methods in the evaluation module is a variance method scoring method, which includes:

Optionally, one of the multiple failure level analysis methods in the evaluation module is a expert method scoring method, including:

presetting a weight value corresponding to each abnormal factor;

Optionally, the determining, by the evaluation module, a comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods includes:

presetting a weight value corresponding to each fault level analysis method;

Optionally, the weight values corresponding to the fault level analysis methods preset in the evaluation module are equal.

Optionally, the sum of the weight values preset in the evaluation module is 1.

Optionally, the determining, by the determining module, the faulty shuttle according to the comprehensive fault level of each shuttle further includes:

According to a third aspect of embodiments of the present invention, there is provided an electronic device for determining a malfunctioning shuttle, comprising:

one or more processors;

a storage device for storing one or more programs,

when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method provided by the first aspect of the embodiments of the present invention.

According to a fourth aspect of embodiments of the present invention, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method provided by the first aspect of embodiments of the present invention.

One embodiment of the above invention has the following advantages or benefits: determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods; determining a fault shuttle vehicle according to the comprehensive fault grade of each shuttle vehicle, and generating a control instruction to control the fault shuttle vehicle to stop working; the characteristics of different emphasis and different advantages of each analysis method can be fully utilized, and the abnormal state of the equipment can be objectively and truly reflected; the results of a plurality of scoring methods are comprehensively considered, so that the deviation caused by a single scoring method is effectively avoided; the method has the advantages that the fault shuttle car (namely the shuttle car stopping operation) after confirmation is processed and confirmed by fully utilizing limited maintenance resources, and the effectiveness and the efficiency of maintenance are improved; the problem of maintenance resource waste caused by misjudging of faulty equipment by an analysis method in the prior art is solved. The mutual repeated information among abnormal factors is effectively reduced or reduced by adopting a correlation coefficient method scoring method; that is, if a certain abnormal factor already contains information of other abnormal factors, the weight value of the abnormal factor should be small, otherwise, the weight value should be large; thereby reducing or minimizing the amount of information that is duplicated between the factors. The method overcomes the defect that the index cannot be zero in the entropy method by adopting a variance method scoring method, and directly measures the discrete degree of each factor by using variance, thereby realizing the same purpose as the entropy method.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic illustration of a main flow of a method of determining a faulty shuttle in accordance with an embodiment of the present invention;

fig. 2 is a schematic view of a main flow of a method for determining a faulty shuttle including a correlation coefficient method scoring method according to an embodiment of the present invention;

FIG. 3 is a schematic illustration of a major flow of a method of determining a faulty shuttle including a variance scoring method according to an embodiment of the present invention;

FIG. 4 is a schematic illustration of a major flow of a method of determining a failed shuttle vehicle including a expert scoring method in accordance with an embodiment of the present invention;

FIG. 5 is a schematic illustration of a primary flow of yet another method of determining a malfunctioning shuttle in accordance with an embodiment of the present invention;

FIG. 6 is a schematic diagram of the major modules of an apparatus for determining a faulty shuttle in accordance with an embodiment of the present invention;

FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;

fig. 8 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

According to one aspect of an embodiment of the present invention, a method of determining a malfunctioning shuttle is provided.

Fig. 1 is a schematic view of the main flow of a method of determining a faulty shuttle according to an embodiment of the present invention. As shown in fig. 1, a method of determining a faulty shuttle according to an embodiment of the present invention includes: step S101-step S104.

And S101, acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle.

Taking the field of storage logistics as an example, various abnormalities can occur in the use process of the shuttle car, the operation efficiency is influenced, and even partial abnormalities cause that the equipment needs to be offline for maintenance. The abnormity of the shuttle car comprises a left shifting fork falling fault, a right shifting fork falling fault, an Elmo double-stroke driver fault, a double-stroke power-on fault, a gear train planning speed out-of-tolerance and the like. In order to evaluate various abnormal factors in an all-around and multi-angle manner to obtain an accurate equipment fault evaluation result, abnormal data corresponding to the various abnormal factors of each shuttle vehicle needs to be acquired. Of course, depending on the type of data, the exception data may be appropriately processed to prepare for subsequent failure evaluation.

In practical application, in the prior art, only a single index of the failure times is used as an evaluation standard, evaluation dimensions are more comprehensive, and other important factors are not considered; or a method for comprehensively evaluating a plurality of abnormal factors is adopted, but the adopted method has the problem of repeated evaluation of abnormal information and cannot truly reflect objective faults. Therefore, the invention provides a method for comprehensively evaluating the abnormal factors by adopting a multi-dimension and multi-method, thereby objectively and really determining the specific fault shuttle. Firstly, acquiring data of m abnormal factors of n shuttles to be determined every day within a period of time; wherein n is more than or equal to 2 and is a positive integer; m is more than or equal to 2 and is a positive integer. An exemplary embodiment of the invention selects a shuttle as the device to be determined and collects daily data for a period of time: abnormal times, abnormal type number, abnormal rate and fault interval hours. That is, 4 kinds of abnormality factors, i.e., the number of abnormalities, the number of kinds of abnormalities, the abnormality rate, and the interval between failures are exemplarily selected here. The period of time may be selected from days, a week, a month, two months, a half year, a year, years, etc. that have been continuously near recently, and may be extended or shortened as appropriate according to the properties of the shuttle vehicle actually used. In order to facilitate processing of different abnormal data types and facilitate later integration processing of various fault level analysis methods to obtain a final comprehensive fault level, an embodiment of the present invention exemplarily calculates, for each shuttle vehicle, sampling values of abnormal data corresponding to each abnormal factor in the period of time, respectively. As described above, the sampling value x of the abnormal number is₁Indicating the number of anomalies occurring per day for each shuttle, i.e. the number of anomalies for each shuttle over a selected period of timeThe sum is divided by the period of time to obtain an average value; sampling value x of abnormal number₂The method comprises the following steps of (1) representing how many faults occur to each shuttle vehicle every day, namely the average value obtained by dividing the sum of the fault types of each shuttle vehicle every day in a selected period of time by the period of time; sampling value x of abnormal rate₃The ratio of the average daily abnormal times of each shuttle vehicle to the average daily bin number is obtained; hourly sample value x between failures₄The ratio of the average daily work time of each shuttle vehicle to the average daily abnormal times is obtained. As can be appreciated, x₁,x₂And x₃Larger means that the shuttle is more serious, but x₄Smaller means more serious. Therefore, in order to facilitate subsequent comprehensive analysis of each index, an exemplary choice of one embodiment of the present invention is x₄The reciprocal result is taken as an evaluation index, namely, the number of faults occurring in each hour is represented, and in this case, the larger the index is, the more serious the shuttle problem is. The sampling value may be the median of daily abnormal data corresponding to various abnormal factors of each shuttle car in a selected period of time, in addition to the average value described above. For example, said x₁Representing the median of each shuttle vehicle according to the abnormal times of each day in the selected time; x is the number of₂Representing the median of each shuttle vehicle according to how many faults occur each day in a selected time; x is the number of₃Determining a median for each shuttle vehicle within a selected time according to a ratio result of the number of daily anomalies to the number of daily carry bins; x is the number of₄The median determined for each shuttle vehicle at a selected time based on the ratio of the length of the day's operation to the number of anomalies per day, although x will be used here as well₄The reciprocal result is taken as an evaluation index. Further, the method further comprises the step of calculating a middle value corresponding to each abnormal factor of each shuttle vehicle according to the sampling value. Specifically, the range corresponding to each abnormal factor can be determined according to the sampling value, and the intermediate value corresponding to each abnormal factor of each shuttle vehicle is positively correlated with the range of the abnormal factor. Aiming at a certain abnormal factor, the range refers to the maximum sampling value of the abnormal factor of each shuttle car and each shuttle carOf the type of anomaly factor. The positive correlation includes, but is not limited to, being proportional. In one embodiment, the intermediate value corresponding to each anomaly factor for each shuttle is determined using the following method: firstly, determining the difference value between the sampling value of the jth abnormal factor of the ith shuttle vehicle and the minimum value of the sampling value of the jth abnormal factor of each shuttle vehicle, and then determining the difference value between the maximum value of the sampling value of the jth abnormal factor of each shuttle vehicle and the minimum value of the sampling value of the jth abnormal factor of each shuttle vehicle, wherein the middle value corresponding to the jth abnormal factor of the ith shuttle vehicle is equal to the first difference value divided by the second difference value; where i belongs to n and j belongs to m. Specifically, the flow of calculating the intermediate value is shown by the following formula:

wherein, y_ijRepresenting the intermediate value, x, corresponding to the jth abnormality factor of the ith shuttle_ijThe sampled value, Min { x, representing the jth abnormality factor for the ith shuttle_ijI 1, 2.. n } represents the minimum value of the sampled values of the j-th abnormality factor for each shuttle, Max { x }_ijI 1, 2.. n } represents the maximum value of the sampling value of the j-th abnormal factor of each shuttle vehicle, wherein i belongs to n and j belongs to m in the formula.

On the basis of all the obtained intermediate values, the intermediate values of the shuttle vehicles under the abnormal factors are further required to be sorted according to the serial numbers of the shuttle vehicles respectively aiming at each abnormal factor to form a factor vector y corresponding to the abnormal factor_jWherein

y_j＝(y_1j,y_2j,...,y_nj)

Wherein j belongs to m. And, according to the above description, after selecting m kinds of abnormal factors, m factor vectors can be obtained in total.

Finally, the factor vectors are used as abnormal data analyzed by the multiple fault level analysis methods, that is, each element in each factor vector is respectively analyzed by the multiple fault level analysis methods.

In addition, the four indexes of the shuttle car exemplarily selected by the embodiment are very representative. x is the number of₁And x₂Can be regarded as a measure of the absolute magnitude of a shuttle anomaly, or fault, x₃And x₄It can be regarded as a quantity measurement of the abnormity (or called fault) after the workload is considered, and each index has a definite practical meaning.

Optionally, the sampled value is one of an average value, a median value, a maximum value or a minimum value of the abnormal data corresponding to each abnormal factor of each shuttle vehicle in the period of time.

Because the different abnormal factors have different reaction fault types and severity degrees, different data processing methods or the same data processing method can be selected for the data corresponding to the different abnormal factors. Therefore, in the actual use process, according to the different types of the abnormalities represented by the specifically selected abnormality factors, the required data processing method can be properly selected, so that the specific fault condition of the equipment can be truly and effectively reflected. In practice, any one of the average, median, maximum or minimum values of the anomaly data over the period of time corresponding to each anomaly factor, such as each shuttle vehicle, may be selected. When the result of the confirmation of the fault shuttle vehicle is not in accordance with the actual result, the processing mode of the data corresponding to the relevant abnormal factor can be properly adjusted to obtain the required evaluation result.

And S102, determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data.

In order to avoid the problem that the scoring result is too complete due to the adoption of a single fault level analysis method, an embodiment of the invention exemplarily selects multiple fault level analysis methods according to different abnormal factors to perform abnormal data evaluation so as to solve the problem. An exemplary selection correlation coefficient method scoring method, a variance method scoring method, and an expert method scoring method according to an embodiment of the present invention. Of course, depending on the specific use case, the method can be usedTo adjust or increase or decrease the specific failure level analysis method appropriately. Because the emphasis of each fault grade analysis method is different, the advantages are different, and the deviation caused by a single scoring method can be effectively avoided after the multiple fault grade analysis methods are adopted. In the actual use process, it is possible to directly utilize multiple fault level analysis methods to evaluate the unprocessed abnormal data, or evaluate the factor vectors obtained through the processing described above, that is, replace the original abnormal data with the processed factor vectors. In order to avoid the problem of inconsistent evaluation of multiple fault level analysis methods, the results obtained by the multiple fault level analysis methods are convenient to integrate. In one embodiment of the invention, the analysis results obtained by each fault level analysis method are sorted exemplarily to obtain the abnormal situation sorting result of the shuttle car under the corresponding fault level analysis method, and the sorting result is used as the fault level. For example, by adopting a correlation coefficient method scoring method, a variance method scoring method, a expert method scoring method and the like, the sorting results of the shuttle cars under the corresponding fault level analysis method can be respectively obtained. Specifically, the corresponding rank value of each shuttle vehicle is calculated according to the score result of the score method of the correlation coefficient method from small to large, the rank value is called as the rank, and the rank of the ith shuttle vehicle is marked as

Namely the fault grade of the ith shuttle vehicle under the correlation coefficient method scoring method; calculating the corresponding ranking value of each shuttle vehicle according to the ranking result of the variance method ranking method from small to large, wherein the ranking value is called the rank, and the rank of the ith shuttle vehicle is marked as

Namely the fault grade of the ith shuttle vehicle under the variance method scoring method; calculating the corresponding ranking value of each shuttle vehicle according to the ranking value result of the expert method scoring method from small to large, wherein the ranking value is called the rank, and the rank of the ith shuttle vehicle is marked as

Namely the fault grade of the ith shuttle vehicle under the expert method scoring method.

Optionally, one of the multiple failure level analysis methods is a correlation coefficient method scoring method, including:

One embodiment as shown in fig. 2 includes a correlation coefficient method scoring method. In the actual use process, the correlation coefficient method scoring method firstly solves a correlation coefficient matrix R among m factor vectors. The correlation coefficient between the factor vectors can be solved by adopting various correlation coefficient methods, and an exemplary choice of an embodiment of the present invention is to calculate the pearson correlation coefficient between m factor vectors by adopting a pearson correlation coefficient method, so as to obtain a correlation coefficient matrix R, as shown below:

wherein r is_ijAnd representing the correlation coefficient between the ith factor vector and the jth factor vector, wherein i belongs to m, and j belongs to m.

Calculating the average value of the correlation coefficient corresponding to each abnormal factor according to the correlation coefficient matrix

Firstly, determining the difference value between the summation result of the j-th column element in the correlation coefficient matrix and 1, and then dividing the difference value by (m-1) to obtain the average value of the correlation coefficient corresponding to the j-th abnormal factor. I.e. as shown in the following equation:

wherein j belongs to m.

Further, the intermediate weight of each abnormal factor is calculated, and the intermediate weight of each abnormal factor is inversely correlated with the average value of the correlation coefficient corresponding to the abnormal factor. Specifically, for a certain abnormal factor, the negative correlation means that the intermediate weight of the abnormal factor and the average value of the correlation coefficient corresponding to the abnormal factor have a negative correlation. The negative correlation includes, but is not limited to, the negative correlation of the average of the correlation coefficients corresponding to the anomaly. In one embodiment, the intermediate weight d of the jth exception factor_jEqual to 1 minus the average value of the correlation coefficient corresponding to the abnormal factor; i.e. as shown in the following equation:

wherein j belongs to m.

Further, calculating the weight value of each abnormal factor, wherein the weight value w of the jth abnormal factor_jIs equal toThe intermediate weight of the exception factor is divided by the sum of the intermediate weights of all exception factors. I.e. as shown in the following equation:

where j belongs to m.

And further, calculating the fault grade of each shuttle vehicle under a correlation coefficient method scoring method, wherein for each shuttle vehicle, products of a middle value corresponding to each abnormal factor and a weight value of the corresponding abnormal factor are sequentially calculated, all the products are summed to obtain the score of the shuttle vehicle under the correlation coefficient method scoring method, and the scores of the shuttle vehicles under the correlation coefficient method scoring method are sorted to obtain the fault grade of the corresponding shuttle vehicle. I.e. as shown in the following equation:

wherein f is_i ^rAnd (3) representing the grade of the ith shuttle vehicle under the grading method of the correlation coefficient method, wherein i belongs to n, and j belongs to m.

As mentioned above, the score result is calculated according to the correlation coefficient method, the corresponding rank value of each shuttle vehicle is calculated from small to large, the rank is called here, the rank of the ith shuttle vehicle is marked as

Which represents the fault level of the ith shuttle vehicle under the correlation coefficient method scoring method. Optionally, the score of each shuttle vehicle calculated above may be used as its corresponding fault level.

The method for scoring by adopting the correlation coefficient method is used in one embodiment of the invention, and the aim is to effectively reduce or reduce the mutual repeated information among abnormal factors. If a certain abnormal factor already contains information of other abnormal factors, the weight value of the abnormal factor should be small, otherwise, the weight value should be large. Measuring information quantity between abnormal factors by the size of correlation coefficientThe degree of coincidence of (c). If the average value of the correlation coefficient between a certain abnormal factor and other abnormal factors is low, it indicates that the coincidence degree of the abnormal factor and other factors is low, and a larger weight should be given. Average value of the above

Calculation formula and intermediate weight d_jThe calculation formula embodies this step. Weight value w_jThe formula of (2) indicates that the weights are normalized.

Optionally, one of the multiple failure level analysis methods is a variance method scoring method, which includes:

One embodiment as shown in fig. 3 includes a variance scoring method. In the actual use process, the variance of the intermediate values corresponding to the abnormal factors of all the shuttle vehicles is calculated aiming at each abnormal factor; specifically as shown in the following formula:

wherein, var_jRepresents the variance of the intermediate value corresponding to the j-th abnormal factor,

to representAnd the average value of the intermediate values corresponding to the j-th abnormal factor, wherein i belongs to n, and j belongs to m.

Further, calculating the weight value of each abnormal factor, wherein the weight value w of the jth abnormal factor_jEqual to the variance of that anomaly factor divided by the sum of the variances of all anomaly factors; specifically as shown in the following formula:

wherein j belongs to m.

And calculating the fault grade of each shuttle vehicle under the variance method scoring method, wherein for each shuttle vehicle, the product of the intermediate value corresponding to each abnormal factor and the weighted value of the corresponding abnormal factor is calculated in sequence, then all the products are summed to obtain the score of the shuttle vehicle under the variance method scoring method, and then the scores of the shuttle vehicles under the variance method scoring method are sorted to obtain the fault grade of the corresponding shuttle vehicle. I.e. as shown in the following equation:

wherein f is_i ^vAnd (3) representing the score of the ith shuttle vehicle under the variance method scoring method, wherein i belongs to n, and j belongs to m.

As mentioned above, the scoring result according to the variance scoring method is used for calculating the corresponding ranking value of each shuttle vehicle from small to large, which is called the rank here, and the rank of the ith shuttle vehicle is marked as

Which represents the fault level of the ith shuttle under the variance method scoring method. Optionally, the score of each shuttle vehicle can be used as the corresponding fault level.

A primary objective of one embodiment of the present invention employing a variance scoring scheme is to improve entropy drawbacks. The essence of the entropy method is that if the variability of some index is large, a large weight is given. In the formula of the entropy method, a value which is zero cannot exist, otherwise, no definition exists. After conversion of the anomaly data by the median calculation formula, at least 1 component of each factor vector is zero. Therefore, entropy method is not suitable. In one embodiment of the invention, the variance is directly used for measuring the discrete degree of each factor by adopting a variance method scoring method, so that the same purpose as that of an entropy method is achieved, but the defect that the factor cannot have zero is not required.

Optionally, one of the multiple fault level analysis methods is a expert method scoring method, including:

presetting a weight value corresponding to each abnormal factor;

One embodiment, as shown in fig. 4, includes a expert scoring method. In the actual use process, firstly, according to the expert opinion, the weight value w corresponding to each abnormal factor is preset_j(ii) a Wherein, w_jA weight value representing the jth exception factor.

And calculating the fault grade of each shuttle vehicle under the expert method grading method, wherein products of the middle value corresponding to each abnormal factor and the weight value of the corresponding abnormal factor are sequentially calculated for each shuttle vehicle, all the products are summed to obtain the grade of the shuttle vehicle under the expert method grading method, and the grades of the shuttle vehicles under the expert method grading method are sorted to obtain the fault grade of the corresponding shuttle vehicle. I.e. as shown in the following equation:

wherein f is_i ^eAnd (3) representing the grade of the ith shuttle vehicle under the expert method grade method, wherein i belongs to n, and j belongs to m.

Calculating the corresponding ranking value of each shuttle vehicle according to the ranking value result of the expert method scoring method from small to large, wherein the ranking value is called the rank, and the rank of the ith shuttle vehicle is marked as

Which represents the fault level of the ith shuttle under the expert law scoring method. Optionally, the score of each shuttle vehicle can be used as the corresponding fault level.

As described above, for the 4 kinds of abnormal factors of the shuttle car shown in the embodiment of the present invention, i.e. the abnormal times, the abnormal kinds, the abnormal rate, and the fault interval are small, the following weight values are respectively selected in turn here for example: 0.5, 0.3, 0.1 and 0.1.

And 103, respectively determining the comprehensive fault level of each shuttle vehicle under the multiple fault level analysis methods.

Optionally, the determining, for each shuttle vehicle, a comprehensive fault level thereof under a plurality of fault level analysis methods includes:

presetting a weight value corresponding to each fault level analysis method;

As described above, the failure levels under the corresponding methods of each shuttle vehicle can be obtained for different failure level analysis methods. In order to comprehensively evaluate the above-mentioned respective failure levels, it is necessary to integrate the numerical values of the respective failure levels. In actual operation, the fault grades of the shuttle cars under different fault grade analysis methods can be directly added to obtain a final comprehensive fault grade. Of course, different weight values can be set according to different emphasis points of each fault level analysis method, products of the fault level of the shuttle vehicle under each fault level analysis method and the weight value corresponding to the fault level analysis method are sequentially calculated for each shuttle vehicle, all the products are summed to obtain a comprehensive score of the shuttle vehicle, and the comprehensive scores of the shuttle vehicles are sorted to obtain the comprehensive fault level corresponding to the shuttle vehicle. In practice, the comprehensive scores of all the shuttle vehicles are sorted in an ascending order, and the sorted values are used as the comprehensive fault grades corresponding to all the shuttle vehicles. Optionally, the composite score of each shuttle vehicle may also be used as its corresponding composite fault level. In one embodiment, as shown in fig. 5, the composite score is calculated using the following formula:

wherein f is_iRepresents the ith shuttle vehicle composite score, w_rWeight value, w, for correlation coefficient method scoring method_vWeight value, w, for variance method scoring method_eWeight values for expert scoring methods; and i belongs to n.

According to an embodiment of the invention, the weight values corresponding to the preset fault level analysis methods are exemplarily selected to be equal. Such as the ith shuttle vehicle composite score f for the three scoring methods shown in fig. 5_iIs shown in the following formula:

optionally, the sum of the preset weight values is 1.

In an exemplary embodiment of the invention, the sum of the weighted values corresponding to the preset abnormal factors in the expert method scoring method is 1, and/or the sum of the weighted values corresponding to the preset fault level analysis methods when the comprehensive fault level of each shuttle vehicle under multiple fault level analysis methods is calculated is 1, and/or the weighted values corresponding to the preset fault level analysis methods when the comprehensive fault level of each shuttle vehicle under multiple fault level analysis methods is calculated are equal and the sum is 1.

And step S104, determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle, and generating a control instruction to control the failed shuttle vehicles to stop working.

Optionally, determining the failed shuttle vehicle according to the composite failure level of each shuttle vehicle further comprises:

In the actual use process, the number of the fault shuttle vehicles needing offline maintenance can be comprehensively selected according to factors such as the number of actual maintenance personnel, the number of maintenance equipment and the maintenance proficiency of the maintenance personnel. Specifically, as described above, the comprehensive scores of each shuttle vehicle are sorted in an ascending order, and the sorted value is used as the comprehensive fault level corresponding to each shuttle vehicle. And sorting the shuttle vehicles in a descending order according to the comprehensive fault grade to select the shuttle vehicles with serious faults, namely selecting the first alpha percent of shuttle vehicles or the first a shuttle vehicles of the descending order result as the fault shuttle vehicles and generating a control instruction to control the fault shuttle vehicles to stop working. When the faulty shuttle vehicle is determined according to the percentage, the calculation result needs to be rounded, and specifically, the calculation result can be rounded upwards or downwards. Under the condition of sufficient maintenance resources, the numerical value can be properly increased, so that more equipment is offline for maintenance; and in the case of insufficient maintenance resources, the above value can be appropriately reduced. In summary, the method provided by the embodiment of the invention determines that the abnormality of the top-ranked device is serious. And further controlling the determined fault shuttle vehicle to stop working and then off-line maintenance, thereby pertinently utilizing limited maintenance resources, improving the utilization rate of the resources and finally achieving the purpose of optimizing the maintenance efficiency.

According to a second aspect of embodiments of the present invention, there is provided an apparatus for determining a malfunctioning shuttle.

Fig. 6 is a schematic diagram of the main modules of the apparatus for determining a faulty shuttle according to the embodiment of the present invention, and as shown in fig. 6, the apparatus 600 for determining a faulty shuttle includes:

the acquisition module 601 is used for acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle;

the evaluation module 602 determines the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods;

and the determining module 603 is used for determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle and generating control instructions to control the failed shuttle vehicles to stop working.

presetting a weight value corresponding to each abnormal factor;

presetting a weight value corresponding to each fault level analysis method;

Optionally, the sum of the weight values preset in the evaluation module is 1.

Fig. 7 illustrates an exemplary system architecture 700 for a method of determining a faulty shuttle or an apparatus for determining a faulty shuttle to which embodiments of the present invention may be applied.

As shown in fig. 7, the system architecture 700 may include

terminal devices

701, 702, 703, a network 704, and a server 705. The network 704 serves to provide a medium for communication links between the

terminal devices

701, 702, 703 and the server 705. Network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

701, 702, 703 to interact with a server 705 over a network 704, to receive or send messages or the like. The

terminal devices

701, 702, 703 may have installed thereon various communication client applications, such as a shopping-like application, a web browser application, a search-like application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only).

The

terminal devices

701, 702, 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 705 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the

terminal devices

701, 702, 703. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.

It should be noted that the method for determining a faulty shuttle provided by the embodiment of the present invention is generally executed by the server 705, and accordingly, the device for determining a faulty shuttle is generally disposed in the server 305.

It should be noted that the method for determining a faulty shuttle provided in the embodiment of the present invention is generally executed by a terminal device, and accordingly, a device for determining a faulty shuttle is generally disposed in the terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 8, shown is a block diagram of a computer system 800 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 801.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, an evaluation module, and a determination module. The names of the modules do not limit the modules themselves in some cases, for example, the collection module may also be described as a module for collecting abnormal data corresponding to various abnormal factors of each shuttle car.

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring abnormal data corresponding to various abnormal factors of each shuttle vehicle; determining the fault level of each shuttle car under the corresponding fault level analysis method according to the multiple fault level analysis methods and the abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods; and determining the failed shuttle vehicles according to the comprehensive failure grade of each shuttle vehicle, and generating a control instruction to control the failed shuttle vehicles to stop working.

According to the technical scheme of the embodiment of the invention, the fault grade of each shuttle car under the corresponding fault grade analysis method is determined according to a plurality of fault grade analysis methods and abnormal data; respectively determining the comprehensive fault level of each shuttle vehicle under a plurality of fault level analysis methods; determining a fault shuttle vehicle according to the comprehensive fault grade of each shuttle vehicle, and generating a control instruction to control the fault shuttle vehicle to stop working; the characteristics of different emphasis and different advantages of each analysis method can be fully utilized, and the abnormal state of the equipment can be objectively and truly reflected; the results of a plurality of scoring methods are comprehensively considered, so that the deviation caused by a single scoring method is effectively avoided; the method has the advantages that the fault shuttle car (namely the shuttle car stopping operation) after confirmation is processed and confirmed by fully utilizing limited maintenance resources, and the effectiveness and the efficiency of maintenance are improved; the problem of maintenance resource waste caused by misjudging of faulty equipment by an analysis method in the prior art is solved. The mutual repeated information among abnormal factors is effectively reduced or reduced by adopting a correlation coefficient method scoring method; that is, if a certain abnormal factor already contains information of other abnormal factors, the weight value of the abnormal factor should be small, otherwise, the weight value should be large; thereby reducing or minimizing the amount of information that is duplicated between the factors. The method overcomes the defect that the index cannot be zero in the entropy method by adopting a variance method scoring method, and directly measures the discrete degree of each factor by using variance, thereby realizing the same purpose as the entropy method.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method of determining a faulty shuttle, comprising:

2. The method of claim 1, wherein collecting anomaly data corresponding to a plurality of anomaly factors for each shuttle comprises:

3. The method of claim 2, wherein the sampled value is one of an average value, a median value, a maximum value or a minimum value of the anomaly data corresponding to each anomaly factor of each shuttle vehicle over the period of time.

4. The method of claim 2, wherein one of the plurality of fault rating analysis methods is a correlation coefficient method scoring method comprising:

5. The method of claim 2, wherein one of the plurality of fault-level analysis methods is a variance scoring method comprising:

6. The method of claim 2, wherein one of the plurality of fault rating analysis methods is a expert scoring method comprising:

presetting a weight value corresponding to each abnormal factor;

7. The method of claim 1, wherein the determining, for each shuttle vehicle individually, its composite fault level under a plurality of fault level analysis methods comprises:

presetting a weight value corresponding to each fault level analysis method;

8. The method according to claim 7, wherein the preset weight values corresponding to the failure level analysis methods are equal.

9. The method according to any one of claims 6 to 8, wherein the sum of the preset weight values is 1.

10. The method of claim 1, wherein determining the failed shuttle vehicle based on the composite fault rating for each shuttle vehicle further comprises:

11. An apparatus for determining a faulty shuttle, comprising:

12. An electronic device for determining a malfunctioning shuttle, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.

13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.