CN113706019A - Service capability analysis method, device, equipment and medium based on multidimensional data - Google Patents
Service capability analysis method, device, equipment and medium based on multidimensional data Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a service capability analysis method based on multidimensional data, which comprises the following steps: acquiring multi-dimensional service data of a salesman, and performing outlier cleaning and dimension normalization to obtain a normalized data set; performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector; extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector; and respectively calculating the distance value between the vector characteristic of each data vector and the labels of a plurality of preset services, and calculating the capability value of the operator for each preset service according to the distance value. In addition, the invention also relates to a block chain technology, and the multidimensional service data can be stored in the block chain nodes. The invention also provides a device, equipment and a medium for analyzing the service capability based on the multidimensional data. The invention can improve the accuracy of the service capability analysis of the service personnel.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a service capability analysis method and device based on multidimensional data, electronic equipment and a computer readable storage medium.
Background
With the gradual increase of user demands, the occupational literacy requirements of companies and enterprises on the business staff are increased day by day, but facing various different businesses, the business staff cannot realize proficiency and excellence on all the businesses, so that the companies and the enterprises need to analyze data of the business staff in order to improve the overall occupational literacy of the business staff, so as to analyze business capacity of the business staff and further improve targeted skills.
The existing business capability analysis methods for the business personnel mostly are performance-based analysis, that is, when the performance of the business personnel in a certain business is high, the business personnel is considered to have strong processing capability on the business. However, in the method, only the performance of the salesman is considered, so that the result of the business capability analysis of the salesman is relatively one-sided, and the business capability of the salesman cannot be accurately evaluated and analyzed.
Disclosure of Invention
The invention provides a method and a device for analyzing business capability based on multidimensional data and a computer readable storage medium, and mainly aims to solve the problem of low accuracy of analyzing the business capability of a salesman.
In order to achieve the above object, the present invention provides a service capability analysis method based on multidimensional data, which includes:
acquiring multidimensional service data of a waiter, and cleaning abnormal values of the multidimensional service data to obtain a standard service data set;
carrying out dimension normalization processing on the standard service data set to obtain a normalized data set;
performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector;
extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector;
and respectively calculating a distance value between the vector characteristic of each data vector and a label of a preset service, and calculating the capability value of each preset service of the salesman according to the distance value.
Optionally, the performing outlier cleaning on the multidimensional service data to obtain a standard service data set includes:
selecting one data from the multidimensional service data one by one as target data;
calculating a local reachable density ratio of the target data and the adjacent data of the target data;
judging whether the local reachable density ratio is less than or equal to a preset value;
if the local reachable density ratio is larger than the preset value, returning to the step of selecting one data from the multidimensional service data one by one as target data;
and if the local reachable density ratio is smaller than or equal to the preset value, replacing the target data by using the mean value of the adjacent data of the target data to obtain a standard service data set.
Optionally, the calculating a local reachable density ratio of the target data to neighboring data of the target data includes:
calculating a local reachable density ratio LF of the target data and neighboring target data of the target data by using a comparison algorithmk(q)：
Wherein N isk(q) is a set of neighboring target data to the target data, p is the target data, q is Nk(q) any of the neighboring target data, ldk(q) is Nk(q) a data density, ld (p) is a self density of the target data, and k is Nk(q) the number of adjacent target data, and reach-disk (p, q) is an operation for calculating the distance between p, q.
Optionally, the performing dimensional normalization processing on the standard service data set to obtain a normalized data set includes:
mapping the numerical data in the standard service data set to a preset coordinate space by using a preset mapping function to obtain a space coordinate of each numerical data;
encoding non-numerical data in the standard service data set by using a preset encoding mode to obtain data encoding of each non-numerical data;
carrying out coordinate conversion on the data codes according to the dimensionality of the coordinate space, and mapping the coordinates obtained by the coordinate conversion to the coordinate space to obtain the space coordinates of each non-numerical data;
and collecting the space coordinates of all numerical data and the space coordinates of all non-numerical data into the normalized data set.
Optionally, the performing coordinate transformation on the data codes according to the dimension of the coordinate space includes:
counting the coding length of each code in the data codes, and calculating the minimum common multiple between the maximum length in the coding lengths and the dimension of the coordinate space as a target length;
extending the coding length of all data codes in the coded data to the target length by using preset parameters to obtain extended codes;
and uniformly splitting each extension code according to the dimension of the coordinate space to obtain the coordinate corresponding to each extension code.
Optionally, the performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector includes:
selecting one of the data from the normalized data set one by one, and coding the selected data according to the position information of the selected data in the normalized data set to obtain a position code;
and converting the selected data into an initial vector, and writing the position code into the initial vector to obtain a data vector corresponding to the selected data.
Optionally, the performing feature extraction on the data vectors by using a preset deep learning model to obtain a vector feature of each data vector includes:
acquiring a preset deep neural network, wherein the deep neural network comprises an input layer, a hidden layer and an output layer;
selecting one vector from the data vectors one by one as a target vector, and performing feature description on the target vector by using an input layer of the deep neural network to obtain input features;
performing feature screening on the input features by using a hidden layer of the deep neural network to obtain screened features;
and calculating the feature grade of the screened feature by using the output layer of the deep neural network, and selecting the feature with the maximum feature grade as the vector feature of the target vector.
In order to solve the above problem, the present invention further provides a device for analyzing business capability based on multidimensional data, the device comprising:
the data cleaning module is used for acquiring multi-dimensional service data of a salesman and cleaning abnormal values of the multi-dimensional service data to obtain a standard service data set;
the normalization module is used for carrying out dimension normalization processing on the standard business data set to obtain a normalized data set;
the vector conversion module is used for carrying out vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector;
the characteristic extraction module is used for extracting the characteristics of the data vectors by using a preset deep learning model to obtain the vector characteristics of each data vector;
and the capability evaluation module is used for respectively calculating the distance values between the vector characteristics of each data vector and the labels of the plurality of preset services and calculating the capability value of the salesman for each preset service according to the distance values.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the multidimensional data based business capability analysis method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the multidimensional data based business capability analysis method described above.
The embodiment of the invention can acquire the multidimensional service data of the salesman, perform operations such as cleaning and normalization on the multidimensional service data to unify the data dimensions, further perform characteristic analysis on the normalized data set to realize the evaluation on the service capability of the salesman, realize the comprehensive analysis and evaluation on the service capability of the salesman by using the data with various dimensions, and improve the accuracy of service capability analysis. Therefore, the multidimensional data-based business capability analysis method, the multidimensional data-based business capability analysis device, the electronic equipment and the computer-readable storage medium can solve the problem of low precision in product recommendation.
Drawings
Fig. 1 is a schematic flowchart of a multidimensional data-based business capability analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of outlier cleaning according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of coordinate transformation according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a multidimensional data based business capability analysis apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the multidimensional data based service capability analysis method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a service capability analysis method based on multi-dimensional data. The execution subject of the multidimensional data based business capability analysis method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the multidimensional data-based business capability analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a multidimensional data-based business capability analysis method according to an embodiment of the present invention. In this embodiment, the method for analyzing service capability based on multidimensional data includes:
s1, obtaining the multidimensional service data of the service staff, and cleaning the abnormal values of the multidimensional service data to obtain a standard service data set.
In the embodiment of the present invention, the multidimensional service data refers to service data of the service staff in multiple aspects, for example, service dimensional data (service age, service processing qualification, gender, age, etc.) of the service staff, performance dimensional data (service coverage, product sales range, service operation capacity, etc.), customer group dimensional data (customer group category, customer group age composition, customer group regionality, etc.), and customer evaluation dimensional data (customer satisfaction, customer evaluation data, etc.) of the customer are obtained, and by acquiring the multidimensional service data of the service staff, comprehensive analysis of the service capacity can be realized according to the multidimensional data, so as to improve accuracy of an analysis result.
In detail, the multidimensional service data of the operator can be crawled from a pre-constructed storage area for storing the multidimensional service data by using a computer sentence (java sentence, python sentence, etc.) with a data crawling function, wherein the storage area includes but is not limited to a database, a block chain node and a network cache.
In one practical application scenario of the present invention, because some error information is contained in the acquired multidimensional service data, if the multidimensional service data is directly analyzed, the error information therein may affect the analysis result, and further, the accuracy of the result obtained by the analysis may be reduced, so that the embodiment of the present invention may correct the abnormal value in the multidimensional service data to implement abnormal value cleaning on the multidimensional service data, thereby improving the accuracy of the multidimensional service data, and further improving the accuracy of the service capability of the service personnel obtained by subsequently analyzing the multidimensional service data.
In an embodiment of the present invention, referring to fig. 2, the performing outlier cleaning on the multidimensional service data to obtain a standard service data set includes:
s21, selecting one data from the multidimensional service data one by one as target data;
s22, calculating a local reachable density ratio of the target data and the adjacent data of the target data;
s23, judging whether the local reachable density ratio is less than or equal to a preset value;
if the local reachable density ratio is greater than the preset value, returning to S21;
if the local reachable density ratio is less than or equal to the preset value, S24 is executed to replace the target data with the mean value of the neighboring data of the target data, so as to obtain a standard service data set.
In detail, the calculating a local reachable density ratio of the target data to neighboring data of the target data includes:
calculating a local reachable density ratio LF of the target data and neighboring target data of the target data by using a comparison algorithmk(q)：
Wherein N isk(q) is a set of neighboring target data to the target data, p is the target data, q is Nk(q) any of the neighboring target data, ldk(q) is Nk(q) a data density, ld (p) is a self density of the target data, and k is Nk(q) the number of adjacent target data, and reach-disk (p, q) is an operation for calculating the distance between p, q.
In the embodiment of the invention, the abnormal value cleaning is carried out on the multidimensional service data, so that the accuracy of the multidimensional service data is improved, and the accuracy of the service capability of the service personnel obtained by the subsequent analysis of the multidimensional service data is further improved.
And S2, carrying out dimension normalization processing on the standard service data set to obtain a normalized data set.
In the embodiment of the invention, because the standard service data set obtained after the abnormal value cleaning of the multidimensional service data comprises the service data with multiple dimensions, but the data with different dimensions are inconsistent in numerical range and data format, if the marked service data is directly analyzed, a large amount of computing resources are occupied due to the difference of the data dimensions in the standard service data set, and the analysis efficiency is low, therefore, the dimension normalization can be performed on the standard service data, so that the data in the standard data set is in a uniform dimension, a normalized data set is obtained, and the efficiency of analyzing the service capability of a salesman is improved.
In the embodiment of the present invention, the performing dimension normalization processing on the standard service data set to obtain a normalized data set includes:
mapping the numerical data in the standard service data set to a preset coordinate space by using a preset mapping function to obtain a space coordinate of each numerical data;
encoding non-numerical data in the standard service data set by using a preset encoding mode to obtain data encoding of each non-numerical data;
carrying out coordinate conversion on the data codes according to the dimensionality of the coordinate space, and mapping the coordinates obtained by the coordinate conversion to the coordinate space to obtain the space coordinates of each non-numerical data;
and collecting the space coordinates of all numerical data and the space coordinates of all non-numerical data into the normalized data set.
In detail, the mapping function may be any function having a coordinate mapping relationship, for example, a gaussian function, a map function, and the like.
Illustratively, when the coordinate space is a two-dimensional coordinate space, the numerical data in the standard service data set may be mapped to a preset coordinate space by using the following mapping function:
yi＝(xi)
wherein f (x) is the mapping function, xiFor the ith numerical data (abscissa), y in the standard service data setiAnd the corresponding ordinate of the ith numerical data in the coordinate space is obtained.
Specifically, since the standard service data set includes some non-numeric data of types such as characters and symbols in addition to numeric data, in order to perform dimension normalization on the non-numeric data, the non-numeric data may be encoded, the non-numeric data is converted into a numeric form, and then the encoded data is converted into coordinates in the coordinate space, thereby implementing the dimension normalization on all data in the standard service data set.
In the embodiment of the present invention, the non-numerical data in the standard service data set may be encoded by using encoding modes such as GB212 encoding and ASCII encoding, so as to obtain a data code for each non-numerical data.
In the embodiment of the present invention, referring to fig. 3, the performing coordinate transformation on the data codes according to the dimension of the coordinate space includes:
s21, counting the coding length of each code in the data coding, and calculating the minimum common multiple between the maximum length in the coding lengths and the dimension of the coordinate space as a target length;
s22, extending the coding length of all data codes in the coded data to the target length by using preset parameters to obtain extended codes;
and S23, uniformly dividing each extension code according to the dimension of the coordinate space to obtain the coordinate corresponding to each extension code.
In one practical application scenario of the invention, because the coding lengths of different data codes may have differences, it is not beneficial to map the data codes with different lengths into the same coordinate space
For example, the data encoding includes two encodings: 12 and 3, knowing that the maximum coding length is 2, and the dimension of the coordinate space is 3 dimensions, so that the target length is 6, and then extending the coding length of each data code to 6 by using a preset parameter (such as 0) to obtain an extended code: 120000 and 300000, to achieve the unification of the lengths of all data codes.
Furthermore, the extension codes can be uniformly divided according to the dimension of the space coordinate, so as to obtain the coordinate of each extension code in the coordinate space.
For example, the extension code 120000 is divided by dimension 3 to obtain coordinates (12,00,00) of the extension code.
In the embodiment of the invention, the dimension normalization of the standard data is carried out, so that the dimension normalization of the data in the standard data set can be realized, and the efficiency of carrying out the service capability analysis on the service staff subsequently is improved.
And S3, performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector.
In the embodiment of the invention, the position information of each piece of data in the data may have an important influence on the meaning of the data.
For example, in the customer evaluation dimension data acquired according to the time sequence, the good data of the customer is first, and the bad data is later, it can be shown that the business level of the salesman is reduced to a certain extent; alternatively, when the customer's good data is behind and the bad data is in front, it can be shown that the business level of the salesperson is improved to some extent.
Therefore, in order to improve the accuracy of analyzing the service capability of the salesperson by using the normalized data set obtained by using the multidimensional service data, vector conversion can be performed on each data in the normalized data set according to the position information of each data in the normalized data set, so as to realize embedding of the position information of the data, obtain a data vector containing the position information, and further be beneficial to improving the accuracy of analyzing the service capability of the salesperson according to the data vector.
In this embodiment of the present invention, the performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector includes:
selecting one of the data from the normalized data set one by one, and coding the selected data according to the position information of the selected data in the normalized data set to obtain a position code;
and converting the selected data into an initial vector, and writing the position code into the initial vector to obtain a data vector corresponding to the selected data.
In detail, the position information refers to information of a sequence of each data in the normalized data set, and each data in the normalized data set can be encoded according to the position information by using a preset Positional Encoding technology to obtain a position code.
Specifically, the selected data may be converted into an initial vector by using a preset vector conversion model having a vector conversion function, where the vector conversion model includes but is not limited to a bert model and a word2vec model, and the position code is directly written into the initial vector, so that a data vector including position information may be obtained.
And S4, extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector.
In the embodiment of the present invention, the deep learning model may be any artificial intelligence model with a vector feature extraction function, where the artificial intelligence model includes, but is not limited to: NLP (Natural Language Processing) Model, HMM (Hidden Markov Model).
In the embodiment of the present invention, the extracting features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector includes:
acquiring a preset deep neural network, wherein the deep neural network comprises an input layer, a hidden layer and an output layer;
selecting one vector from the data vectors one by one as a target vector, and performing feature description on the target vector by using an input layer of the deep neural network to obtain input features;
performing feature screening on the input features by using a hidden layer of the deep neural network to obtain screened features;
and calculating the feature grade of the screened feature by using the output layer of the deep neural network, and selecting the feature with the maximum feature grade as the vector feature of the target vector.
In detail, the pre-trained deep neural network may include the following network hierarchies:
an input layer: the system is used for carrying out feature description on input data so as to facilitate the network layer behind the input layer to process the described input features;
hiding the layer: the system comprises a hidden layer, a mapping layer, a selection discarding layer and the like, wherein the hidden layer is used for performing operations such as dimension reduction, mapping and selective discarding on input features transmitted from an input layer so as to realize screening of the features, and the hidden layer can comprise a plurality of network layers (such as a batch normalization layer, a discarding layer, a full connection layer and the like);
an output layer: and the method is used for performing final classification output on the data obtained by the hidden layer processing.
Specifically, the input layer may be utilized to perform a matrix operation on the data vector to implement the feature description of the target vector, for example, perform a dot multiplication, a cross multiplication, or the like on the target vector and a preset matrix to implement the feature description of the target vector.
Further, the hidden layer of the deep neural network can be used for carrying out batch normalization on the input features, feature discarding and other processing according to preset weights, so that the input features are screened to obtain screened features, and then the feature grades of the screened features are calculated by using activation functions in the output layer of the deep neural network, wherein the activation functions include but are not limited to sigmoid activation functions and relu activation functions.
In the embodiment of the invention, the feature with the largest feature level can be selected as the vector feature of the target vector.
And S5, respectively calculating the distance value between the vector feature of each data vector and the labels of a plurality of preset services, and calculating the ability value of the salesman for each preset service according to the distance value.
In the embodiment of the present invention, the labels of the multiple preset services refer to labels that are generated in advance and used for respectively marking different preset services, and the labels can be used for identifying characteristics of different services, so that a distance value between each vector characteristic and the labels of the multiple preset services can be respectively calculated, and then a capability value of the operator for each preset service is determined according to the distance value.
In the embodiment of the present invention, the calculating the distance value between each vector feature and the tags of the multiple preset services respectively includes:
respectively calculating the distance value between each vector feature and the labels of a plurality of preset services by using the following distance value algorithm:
wherein, anAs a vector feature of the nth data vector, bmFor the m-th label of the preset service, Dn,mAnd the distance value between the vector characteristic of the nth data vector and the label of the mth preset service is obtained.
In the embodiment of the present invention, since the farther the distance value is, the worse the ability of the operator to the preset service corresponding to the tag is identified, the ability value of the operator to each service can be calculated according to the distance value by using the following formula:
wherein, PwemCapacity value for the business person for the mth preset business, Dn,mAnd the distance value between the vector characteristic of the nth data vector and the label of the mth preset service is obtained, and N is the number of the data vectors.
The embodiment of the invention can acquire the multidimensional service data of the salesman, perform operations such as cleaning and normalization on the multidimensional service data to unify the data dimensions, further perform characteristic analysis on the normalized data set to realize the evaluation on the service capability of the salesman, realize the comprehensive analysis and evaluation on the service capability of the salesman by using the data with various dimensions, and improve the accuracy of service capability analysis. Therefore, the multidimensional data-based business capability analysis method can solve the problem of low precision in product recommendation.
Fig. 4 is a functional block diagram of a service capability analysis apparatus based on multidimensional data according to an embodiment of the present invention.
The service capability analysis apparatus 100 based on multidimensional data according to the present invention can be installed in an electronic device. According to the realized functions, the multidimensional data based business capability analysis device 100 can comprise a data cleaning module 101, a normalization module 102, a vector conversion module 103, a feature extraction module 104 and a capability evaluation module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data cleaning module 101 is configured to acquire multidimensional service data of a service staff, and perform outlier cleaning on the multidimensional service data to obtain a standard service data set;
the normalization module 102 is configured to perform dimension normalization processing on the standard service data set to obtain a normalized data set;
the vector conversion module 103 is configured to perform vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set, so as to obtain a data vector;
the feature extraction module 104 is configured to perform feature extraction on the data vectors by using a preset deep learning model to obtain a vector feature of each data vector;
the capability evaluation module 105 is configured to calculate distance values between the vector feature of each data vector and the tags of the multiple preset services, and calculate a capability value of the salesman for each preset service according to the distance values.
In detail, when the modules in the multidimensional data based service capability analysis apparatus 100 according to the embodiment of the present invention are used, the same technical means as the multidimensional data based service capability analysis method described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device implementing a multidimensional data-based business capability analysis method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a business capability analysis program based on multidimensional data, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a service capability analysis program based on multidimensional data, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a business capability analysis program based on multidimensional data, but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The multidimensional data based business capability analysis program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize:
acquiring multidimensional service data of a waiter, and cleaning abnormal values of the multidimensional service data to obtain a standard service data set;
carrying out dimension normalization processing on the standard service data set to obtain a normalized data set;
performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector;
extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector;
and respectively calculating the distance value between the vector characteristic of each data vector and the labels of a plurality of preset services, and calculating the capability value of the salesman to each preset service according to the distance value.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring multidimensional service data of a waiter, and cleaning abnormal values of the multidimensional service data to obtain a standard service data set;
carrying out dimension normalization processing on the standard service data set to obtain a normalized data set;
performing vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector;
extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector;
and respectively calculating the distance value between the vector characteristic of each data vector and the labels of a plurality of preset services, and calculating the capability value of the salesman to each preset service according to the distance value.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A service capability analysis method based on multidimensional data is characterized by comprising the following steps:
acquiring multidimensional service data of a waiter, and cleaning abnormal values of the multidimensional service data to obtain a standard service data set;
carrying out dimension normalization processing on the standard service data set to obtain a normalized data set;
performing vector conversion on each normalized data set in the normalized data sets according to the position information of each normalized data set in the normalized data sets to obtain data vectors;
extracting the features of the data vectors by using a preset deep learning model to obtain the vector features of each data vector;
and respectively calculating a distance value between the vector characteristic of each data vector and a label of a preset service, and calculating the capability value of each preset service of the salesman according to the distance value.
2. The method for analyzing business capability based on multidimensional data as claimed in claim 1, wherein the performing outlier cleaning on the multidimensional data to obtain a standard business data set comprises:
selecting one data from the multidimensional service data one by one as target data;
calculating a local reachable density ratio of the target data and the adjacent data of the target data;
judging whether the local reachable density ratio is less than or equal to a preset value;
if the local reachable density ratio is larger than the preset value, returning to the step of selecting one data from the multidimensional service data one by one as target data;
and if the local reachable density ratio is smaller than or equal to the preset value, replacing the target data by using the mean value of the adjacent data of the target data to obtain a standard service data set.
3. The method for analyzing business capability based on multidimensional data as claimed in claim 2, wherein the calculating the local reachable density ratio of the target data and the data adjacent to the target data comprises:
calculating a local reachable density ratio LF of the target data and neighboring target data of the target data by using a comparison algorithmk(q)：
Wherein N isk(q) is a set of neighboring target data to the target data, p is the target data, q is Nk(q) any of the neighboring target data, ldk(q) is Nk(q) a data density, ld (p) is a self density of the target data, and k is Nk(q) the number of adjacent target data, and reach-disk (p, q) is an operation for calculating the distance between p, q.
4. The method for analyzing business capability based on multidimensional data according to claim 1, wherein the performing dimension normalization processing on the standard business data set to obtain a normalized data set comprises:
mapping the numerical data in the standard service data set to a preset coordinate space by using a preset mapping function to obtain a space coordinate of each numerical data;
encoding non-numerical data in the standard service data set by using a preset encoding mode to obtain data encoding of each non-numerical data;
carrying out coordinate conversion on the data codes according to the dimensionality of the coordinate space, and mapping the coordinates obtained by the coordinate conversion to the coordinate space to obtain the space coordinates of each non-numerical data;
and collecting the space coordinates of all numerical data and the space coordinates of all non-numerical data into the normalized data set.
5. The method for analyzing business capability based on multidimensional data as claimed in claim 4, wherein the coordinate transformation of the data codes according to the dimension of the coordinate space comprises:
counting the coding length of each code in the data codes, and calculating the minimum common multiple between the maximum length in the coding lengths and the dimension of the coordinate space as a target length;
extending the coding length of all data codes in the coded data to the target length by using preset parameters to obtain extended codes;
and uniformly splitting each extension code according to the dimension of the coordinate space to obtain the coordinate corresponding to each extension code.
6. The method for analyzing business capability based on multidimensional data according to claim 1, wherein the vector conversion of each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector comprises:
selecting one of the data from the normalized data set one by one, and coding the selected data according to the position information of the selected data in the normalized data set to obtain a position code;
and converting the selected data into an initial vector, and writing the position code into the initial vector to obtain a data vector corresponding to the selected data.
7. The method for analyzing business capability based on multidimensional data according to any of claims 1 to 6, wherein the performing feature extraction on the data vectors by using a preset deep learning model to obtain the vector feature of each data vector comprises:
acquiring a preset deep neural network, wherein the deep neural network comprises an input layer, a hidden layer and an output layer;
selecting one vector from the data vectors one by one as a target vector, and performing feature description on the target vector by using an input layer of the deep neural network to obtain input features;
performing feature screening on the input features by using a hidden layer of the deep neural network to obtain screened features;
and calculating the feature grade of the screened feature by using the output layer of the deep neural network, and selecting the feature with the maximum feature grade as the vector feature of the target vector.
8. A business capability analysis apparatus based on multidimensional data, the apparatus comprising:
the data cleaning module is used for acquiring multi-dimensional service data of a salesman and cleaning abnormal values of the multi-dimensional service data to obtain a standard service data set;
the normalization module is used for carrying out dimension normalization processing on the standard business data set to obtain a normalized data set;
the vector conversion module is used for carrying out vector conversion on each data in the normalized data set according to the position information of each data in the normalized data set to obtain a data vector;
the characteristic extraction module is used for extracting the characteristics of the data vectors by using a preset deep learning model to obtain the vector characteristics of each data vector;
and the capability evaluation module is used for respectively calculating the distance values between the vector characteristics of each data vector and the labels of the plurality of preset services and calculating the capability value of the salesman for each preset service according to the distance values.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the multidimensional data based business capability analysis method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the multidimensional data based business capability analysis method according to any one of claims 1 to 7.
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