CN114936776A - Service data processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence and discloses a method, a device, equipment and a storage medium for processing service data. The method comprises the following steps: acquiring business data of business personnel participating in a preset activity; acquiring a historical service data set from a preset database, and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model; performing index analysis on the service data through an index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index; determining reward rules according to each reward index and the weight corresponding to each reward index; and performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing the reward corresponding to the service data to an account corresponding to a service person. The invention also relates to a block chain technology, and the service data can be stored in the block chain.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a device, and a storage medium for processing service data.
Background
Currently, rewarding business personnel is implemented by manually establishing reward rules of sales orders of the business personnel and then issuing rewards to the business personnel according to the reward rules. Although the mode can flexibly modify the reward rules according to various different sales orders, thereby coping with different business scenes.
In the existing manner, the individual reward rules are programmed with the activity rules for each activity. And determining whether the business personnel can obtain the activity reward by operating corresponding program logic so as to realize corresponding activity development. However, when the number of sales orders or business personnel increases and reaches a certain level, or when the way of manually establishing the reward rule is complicated, the efficiency of manual processing is low, calculation errors may occur, and the accuracy is low.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for processing service data, which are used for solving the technical problem of low efficiency in processing the service data.
A first aspect of the present invention provides a method for processing service data, including: acquiring business data of business personnel participating in a preset activity, wherein the business data comprises information of the business personnel corresponding to a business order and data of the business order; acquiring a historical service data set from a preset database, and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model; performing index analysis on the service data through the index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index; determining reward rules according to each reward index and the weight corresponding to each reward index; and performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing the reward corresponding to the service data to an account corresponding to the service personnel.
Optionally, in a first implementation manner of the first aspect of the present invention, the obtaining a historical service data set from a preset database, and constructing an index analysis model according to the historical service data set, where the obtaining of the corresponding index analysis model includes: accessing a preset database, and acquiring a corresponding historical service data set from the preset database; preprocessing historical business data in the historical business data set to generate standardized data; and constructing a corresponding index analysis model based on the business content, the business rule and the data requirement of the preset activity and the standardized data.
Optionally, in a second implementation manner of the first aspect of the present invention, the preprocessing the historical service data in the historical service data set, and generating standardized data includes: traversing historical service data in the historical service data set to obtain a target data set which meets a preset condition, and performing fusion expression on the target data set to obtain historical service data after data fusion; performing data cleaning on the historical service data subjected to data fusion to obtain historical service data subjected to data cleaning; performing data desensitization on the historical service data after data cleaning according to a preset data security rule to obtain the historical service data after data desensitization; and carrying out data encryption processing on the history service data after the data desensitization to obtain the standardized data.
Optionally, in a third implementation manner of the first aspect of the present invention, the constructing a corresponding index analysis model based on the service content, the service rule, the data requirement, and the standardized data of the preset activity includes: establishing an abstract expression model of the historical service data to obtain a corresponding neural network model; inputting the standard data serving as a training set into the neural network model for calculation to obtain a corresponding calculation result; adding an evaluation label to the calculation result, performing iterative training on the neural network model by taking the input standard data and the output evaluation label as a group of data, and finishing the training when the precision of the calculation result output by the neural network model reaches a preset threshold value to obtain a corresponding index analysis model.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the performing, by the index analysis model, index analysis on the service data, determining a reward index set of the service data, and performing weight value calculation on each reward index in the reward index set, where determining a weight value corresponding to each reward index includes: determining a reward index set of the business data and a relative weight between every two reward indexes in the reward index set according to the index analysis model; performing matrix construction according to the reward index set and the relative weight between every two reward indexes in the reward index set to obtain a corresponding target matrix; and carrying out normalization processing on the target matrix to obtain the weight corresponding to each reward index in the reward index set.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the performing standard-reaching analysis on the service data according to the incentive rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing an incentive corresponding to the service data to an account corresponding to the service person includes: carrying out format conversion on the service data to obtain service data in an operation format; calling a preset expression in the reward rule, and judging whether the current data format of the service data in the operation format meets the preset expression or not; if the current data format of the service data in the operation format is judged to meet the preset expression, the service personnel meet the reward rule, reward matching calculation is carried out on the service data through the reward rule, the reward corresponding to the service data is obtained, and the reward corresponding to the service data is issued to an account corresponding to the service personnel.
Optionally, in a sixth implementation manner of the first aspect of the present invention, after the performing standard-reaching analysis on the service data according to the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing a reward corresponding to the service data to an account corresponding to the service person, the method further includes: recording rewards corresponding to the service data according to a preset time dimension to obtain a corresponding target reward data set; and generating a corresponding statistical form through the target reward data set, and transmitting the statistical form to a preset terminal.
A second aspect of the present invention provides a device for processing service data, including: the acquisition module is used for acquiring the business data of business personnel participating in the preset activity, wherein the business data comprises the information of the business personnel corresponding to the business order and the data of the business order; the system comprises a construction module, a data acquisition module and a data analysis module, wherein the construction module is used for acquiring a historical service data set from a preset database and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model; the calculation module is used for performing index analysis on the service data through the index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index; the determining module is used for determining reward rules according to each reward index and the weight corresponding to each reward index; and the analysis module is used for performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, the reward corresponding to the service data is issued to an account corresponding to the service personnel.
Optionally, in a first implementation manner of the second aspect of the present invention, the building module specifically includes: the access unit is used for accessing a preset database and acquiring a corresponding historical service data set from the preset database; the processing unit is used for preprocessing historical service data in the historical service data set to generate standardized data; and the construction unit is used for constructing a corresponding index analysis model based on the business content, the business rule and the data requirement of the preset activity and the standardized data.
Optionally, in a second implementation manner of the second aspect of the present invention, the processing unit is specifically configured to: traversing historical service data in the historical service data set to obtain a target data set which meets preset conditions, and performing fusion expression on the target data set to obtain historical service data after data fusion; performing data cleaning on the historical service data subjected to data fusion to obtain historical service data subjected to data cleaning; performing data desensitization on the historical service data after data cleaning according to a preset data security rule to obtain the historical service data after data desensitization; and carrying out data encryption processing on the history service data after the data desensitization to obtain the standardized data.
Optionally, in a third implementation manner of the second aspect of the present invention, the building unit is specifically configured to: establishing an abstract expression model of the historical service data to obtain a corresponding neural network model; inputting the standard data serving as a training set into the neural network model for calculation to obtain a corresponding calculation result; adding an evaluation label to the calculation result, performing iterative training on the neural network model by taking the input standard data and the output evaluation label as a group of data, and finishing the training when the precision of the calculation result output by the neural network model reaches a preset threshold value to obtain a corresponding index analysis model.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the calculation module is specifically configured to: determining a reward index set of the business data and a relative weight between every two reward indexes in the reward index set according to the index analysis model; performing matrix construction according to the reward index set and the relative weight between every two reward indexes in the reward index set to obtain a corresponding target matrix; and carrying out normalization processing on the target matrix to obtain the weight corresponding to each reward index in the reward index set.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the analysis module is specifically configured to: carrying out format conversion on the service data to obtain service data in an operation format; calling a preset expression in the reward rule, and judging whether the current data format of the service data in the operation format meets the preset expression or not; and if the current data format of the service data in the operation format is judged to meet the preset expression, the service personnel meets the reward rule, reward matching calculation is carried out on the service data through the reward rule to obtain the reward corresponding to the service data, and the reward corresponding to the service data is issued to an account corresponding to the service personnel.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the device for processing service data further includes:
the recording module is used for recording the rewards corresponding to the service data according to a preset time dimension to obtain a corresponding target reward data set;
and the generating module is used for generating a corresponding statistical form through the target reward data set and transmitting the statistical form to a preset terminal.
A third aspect of the present invention provides a computer apparatus comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calls the instructions in the memory to enable the computer device to execute the business data processing method.
A fourth aspect of the present invention provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the above-mentioned service data processing method.
In the technical scheme provided by the invention, a server constructs an index analysis model according to information of service personnel and data of a sales order, the index analysis model is a classifier which trains and predicts samples by using a neural network model and can be used for solving the classification problem and the regression problem, the server acquires a historical service data set from a preset database and constructs an index analysis model according to the historical service data set to obtain a corresponding index analysis model, the server further determines a reward index set of the service order and relative weight between every two reward indexes in the reward index set according to the index analysis model, further constructs a target matrix according to the reward index set and the relative weight between every two reward indexes in the reward index set, and finally normalizes the target matrix, the weight corresponding to each reward index in the reward index set is obtained, the reward index weight can be effectively determined for the business data of business personnel, and therefore the business data processing efficiency is improved.
Drawings
Fig. 1 is a schematic diagram of an embodiment of a method for processing service data in an embodiment of the present invention;
fig. 2 is a schematic diagram of another embodiment of a method for processing service data according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment of a device for processing service data according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another embodiment of a device for processing service data according to an embodiment of the present invention;
FIG. 5 is a diagram of an embodiment of a computer device in an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a method, a device, equipment and a storage medium for processing service data, which are used for solving the technical problem of low accuracy in processing the service data.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. The artificial intelligence is a theory, a method, a technology and an application system which simulate, extend and expand human intelligence by using a digital computer or a machine controlled by the digital computer, sense the environment, acquire knowledge and obtain the best result by using the knowledge. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
For convenience of understanding, a specific flow of the embodiment of the present invention is described below, and referring to fig. 1, an embodiment of a method for processing service data in the embodiment of the present invention includes:
101. acquiring business data of business personnel participating in a preset activity, wherein the business data comprises information of the business personnel corresponding to a business order and data of the business order;
it is to be understood that the execution subject of the present invention may be a processing device of service data, and may also be a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
It should be noted that the preset activity refers to an initiated sales incentive activity with a reward, and when a corresponding service person enters and participates in the preset activity, a corresponding service data set and a trigger activity reward event are generated, where the service data set includes service data related to the service person and the preset activity, and specifically includes but is not limited to information carried by the service person, such as a unique identifier of the service person, an equipment identifier, an equipment type, an IP address, and the like, and data generated in an activity process. Specifically, a plurality of service data of the service staff may be obtained from the service data set of the service staff, or the whole service data set of the service staff may be obtained. It is emphasized that, to further ensure the privacy and security of the service data, the service data may also be stored in a node of a block chain.
102. Acquiring a historical service data set from a preset database, and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model;
specifically, the server can construct an index analysis model according to information of business personnel and data of a sales order, the index analysis model is a classifier which trains and predicts samples by using a neural network model and can be used for solving the classification problem and the regression problem, and specifically, the server obtains a historical business data set from a preset database and constructs the index analysis model according to the historical business data set to obtain a corresponding index analysis model.
103. Performing index analysis on the service data through an index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index;
specifically, the server may determine a reward index set of the service order and a relative weight between every two reward indexes in the reward index set according to an index analysis model, further may construct a target matrix according to the reward index set and the relative weight between every two reward indexes in the reward index set, and finally performs normalization processing on the target matrix to obtain a weight corresponding to each reward index in the reward index set. The reward indexes in the reward index set are service personnel information and service order data selected from the information of service personnel and the data of service orders, the selected service personnel information and the selected service order data are used as reward indexes, and the weight corresponding to each reward index in the reward index set is determined according to an index analysis model.
104. Determining reward rules according to each reward index and the weight corresponding to each reward index;
specifically, the reward rule is determined according to each reward index and the weight corresponding to each reward index, wherein the reward rule may be the sum of products of each reward index and the weight corresponding to each reward index. For example, taking the service staff information and the service order data selected from the service staff information and the service order data as examples, three reward indexes are selected, and the three reward indexes may be the sales amount of the service order, the follow-up times of the service staff, and the position level of the service staff respectively. The weights corresponding to the three reward indexes may be D corresponding to the sales amount of the business order, E corresponding to the number of times of follow-up of the business personnel, and F corresponding to the position level of the business personnel. The reward rule may be the reward data is D x the sales amount of the business order + E x the number of times the business person follows + F x the job level of the business person. The sum of the weights of the reward indicators is 1, i.e., D + E + F is 1. Furthermore, when the reward data of a certain service order needs to be calculated, the specific numerical values corresponding to the reward indexes in the service order can be obtained, namely the sales amount of the service order, the follow-up times of service personnel and the position level of the service personnel are obtained. And further multiplying the obtained data by the corresponding weights respectively to obtain reward data of the service order, and further sending the reward data of the service order to the sales terminal of the service personnel corresponding to the service order.
105. And performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing the reward corresponding to the service data to an account corresponding to a service person.
Specifically, the server performs standard-reaching analysis calculation on the service data according to the reward rule, judges whether the service data reaches the standard, calculates the reward corresponding to the service data when the service data reaches the standard, obtains the reward corresponding to the service data, and sends the reward corresponding to the service data to the account corresponding to the service personnel.
In the embodiment of the invention, a server constructs an index analysis model according to information of service personnel and data of a sales order, the index analysis model is a classifier which trains and predicts samples by using a neural network model and can be used for solving the classification problem and the regression problem, the server acquires a historical service data set from a preset database and constructs the index analysis model according to the historical service data set to obtain a corresponding index analysis model, the server further determines a reward index set of the service order and relative weight between every two reward indexes in the reward index set according to the index analysis model, further constructs a target matrix according to the reward index set and the relative weight between every two reward indexes in the reward index set, and finally normalizes the target matrix to obtain the weight corresponding to each reward index in the reward index set, the method and the device can effectively determine the reward index weight of the business data of the business personnel, thereby improving the efficiency of processing the business data.
Referring to fig. 2, another embodiment of the method for processing service data according to the embodiment of the present invention includes:
201. acquiring business data of business personnel participating in a preset activity, wherein the business data comprises information of the business personnel corresponding to a business order and data of the business order;
specifically, in this embodiment, the specific implementation of step 201 is similar to that of step 101, and is not described herein again.
202. Accessing a preset database, and acquiring a corresponding historical service data set from the preset database;
specifically, the server acquires the original data, collects data of historical service data sets of each preset database in an online or offline manner, and extracts the required original historical service data so as to perform subsequent preprocessing on the original historical service data.
203. Preprocessing historical service data in the historical service data set to generate standardized data;
specifically, the server traverses historical service data in the historical service data set to obtain a target data set meeting preset conditions, and performs fusion expression on the target data set to obtain historical service data after data fusion; the server performs data cleaning on the historical service data after data fusion to obtain historical service data after data cleaning; the server desensitizes the historical service data after the data cleaning according to a preset data safety rule to obtain the historical service data after the data desensitization; and the server carries out data encryption processing on the history service data after data desensitization to obtain standardized data.
The method comprises the steps that a server traverses all original historical service data, it needs to be noted that preset conditions can be set by user definition, after a target data set of the preset conditions is obtained by traversing the original historical service data, data which are inconsistent in expression but substantially the same in expression are fused, the server performs data cleaning on the historical service data subjected to data fusion, invalid data are cleaned, the server formulates a data safety rule, data desensitization is performed on the historical service data subjected to data cleaning according to the data safety rule, and the server performs data encryption processing on the historical service data subjected to desensitization processing to form standardized data.
204. Constructing a corresponding index analysis model based on the business content, the business rule, the data requirement and the standardized data of the preset activity;
specifically, the server establishes an abstract expression model of historical service data to obtain a corresponding neural network model; the server inputs the standard data serving as a training set into the neural network model for calculation to obtain a corresponding calculation result; and adding an evaluation label to the calculation result by the server, performing iterative training on the neural network model by taking the input standard data and the output evaluation label as a group of data, and ending the training when the precision of the calculation result output by the neural network model reaches a preset threshold value to obtain a corresponding index analysis model.
The method comprises the steps of collecting all preprocessed standardized data to an effective data set, constructing an analysis and monitoring model, constructing a plurality of index analysis models based on business contents, business rules and data requirements, calculating historical business data through the effective data set by the index analysis models, and performing abstract expression on the business requirements, wherein the abstract expression models refer to a group of mathematical expressions which are established on the basis of analysis and high abstraction in actual problems and can reflect objective things and change trends. Specifically, the server builds a neural network model, builds an abstract expression model of historical business data, inputs an effective data set as a training set into the abstract expression model of the historical business data for calculation, and obtains a calculation result; adding an evaluation label to the calculation result, performing iterative training on the abstract expression model by taking the input effective data and the output evaluation label as a group of data, adjusting the index of the neural network model, and finishing the training when the precision of the calculation result output by the abstract expression model reaches a preset threshold value to obtain a corresponding index analysis model.
205. Performing index analysis on the service data through an index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index;
specifically, the server determines a reward index set of the service data and relative weight between every two reward indexes in the reward index set according to an index analysis model; the server carries out matrix construction according to the reward index set and the relative weight between every two reward indexes in the reward index set to obtain a corresponding target matrix; and the server performs normalization processing on the target matrix to obtain the weight corresponding to each reward index in the reward index set.
The monitoring model extracts at least one index analysis model for monitoring, and a monitoring threshold value is set for monitoring historical service data obtained by the index analysis model, wherein the larger the difference between the relative weight and the weight of the target matrix is, the more important the index is. So that the index with the most reduced gap can be selected as the reward index. Therefore, the importance of the index can be determined according to the gap value, so that the most important N indexes are determined as the reward indexes according to the importance. In a possible implementation manner, the relative weight between every two reward indexes in the reward index set is used for constructing the target matrix so as to determine the weight of the reward index more objectively. Therefore, the weight corresponding to each reward index can be obtained in a normalization processing mode after the target matrix is constructed.
206. Determining reward rules according to each reward index and the weight corresponding to each reward index;
specifically, in this embodiment, the specific implementation of step 206 is similar to step 104 described above, and is not described herein again.
207. And performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing the reward corresponding to the service data to an account corresponding to a service person.
Specifically, the server performs format conversion on the service data to obtain service data in an operation format; the server calls a preset expression in the reward rule and judges whether the current data format of the operation format service data meets the preset expression or not; and if the server judges that the current data format of the service data in the operation format meets the preset expression, the service personnel meet the reward rule, reward matching calculation is carried out on the service data through the reward rule to obtain the reward corresponding to the service data, and the reward corresponding to the service data is issued to the account corresponding to the service personnel.
The preset rule is configured in an expression form, so when the preset rule and the corresponding service data are compared correspondingly, the preset rule and the corresponding service data are equivalently calculated, and therefore the service data corresponding to the current rule to be processed needs to be converted into an operation format. For example, the string-type service data is converted into an integer. It should be noted that, in order to perform an operation on the service data and the expression, a plurality of operators may be predefined for the and operation, the server converts the service data into a format that can be processed by the corresponding operator, then performs a processing through the expression, and determines whether the format of the service data corresponding to the current rule to be processed after the conversion into the operation format satisfies the expression. If the business data format corresponding to the current rule to be processed converted into the operation format meets the expression, the business personnel meets the reward rule, reward matching calculation is carried out on the business data through the reward rule to obtain reward corresponding to the business data, and if the business data format corresponding to the current rule to be processed converted into the operation format does not meet the expression, the business personnel do not meet the reward rule.
Optionally, when the analysis result is up to standard, the method may include: the server records rewards corresponding to the service data according to a preset time dimension to obtain a corresponding target reward data set; and the server generates a corresponding statistical report form through the target reward data set and transmits the statistical report form to a preset terminal.
Specifically, after the activity is finished, the server automatically counts and collects activity-related data by taking days as a time dimension, and the related data mainly comprises: the system comprises a server and a server, wherein the server is used for recording business personnel daily standard reaching conditions and return records according to day dimensions, recording business personnel daily return conditions and activity overviews according to the day dimensions, and is a statistical report of different dimensions of each activity, wherein the report data of cost and limit reports such as activity effective participation number, activity standard reaching number, activity passenger obtaining number and the like, activity estimated cost, actual cashing cost, branch center estimated limit, branch center residual limit and the like of financial personnel through a return-to-inventory branch center scheme are corresponding statistical reports, and the server transmits the statistical reports to a preset terminal after generating the corresponding statistical reports.
In the embodiment of the invention, the server converts the business data into a format which can be processed by a corresponding operator, then processes the business data through the expression, and judges whether the business data format corresponding to the current rule to be processed after the business data format is converted into the operation format meets the expression. If the business data format corresponding to the current rule to be processed converted into the operation format meets the expression, the business personnel meet the reward rule, reward matching calculation is carried out on the business data through the reward rule to obtain reward corresponding to the business data, if the business data format corresponding to the current rule to be processed converted into the operation format does not meet the expression, the business personnel do not meet the reward rule, reward matching analysis can be carried out on the business data rapidly, corresponding reward data is determined, after the activity is finished, a server can automatically count and collect activity related data by taking days as time dimensions, and after a corresponding statistical report is generated, the statistical report is transmitted to a preset terminal, convenience can be brought to subsequent data analysis, and the processing efficiency of the business data is improved.
Referring to fig. 3, an embodiment of a device for processing service data according to an embodiment of the present invention includes:
an obtaining module 301, configured to obtain service data of a service person participating in a preset activity, where the service data includes information of the service person corresponding to a service order and data of the service order;
the building module 302 is configured to obtain a historical service data set from a preset database, and build an index analysis model according to the historical service data set to obtain a corresponding index analysis model;
a calculating module 303, configured to perform index analysis on the service data through the index analysis model, determine a reward index set of the service data, perform weight value calculation on each reward index in the reward index set, and determine a weight value corresponding to each reward index;
a determining module 304, configured to determine an incentive rule according to each incentive index and a weight corresponding to each incentive index;
the analysis module 305 is configured to perform standard-reaching analysis on the service data according to the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issue a reward corresponding to the service data to an account corresponding to the service person.
Referring to fig. 4, another embodiment of a device for processing service data according to an embodiment of the present invention includes:
an obtaining module 301, configured to obtain service data of a service person participating in a preset activity, where the service data includes information of the service person corresponding to a service order and data of the service order;
the building module 302 is configured to obtain a historical service data set from a preset database, and build an index analysis model according to the historical service data set to obtain a corresponding index analysis model;
a calculating module 303, configured to perform index analysis on the service data through the index analysis model, determine a reward index set of the service data, perform weight value calculation on each reward index in the reward index set, and determine a weight value corresponding to each reward index;
a determining module 304, configured to determine an incentive rule according to each incentive index and a weight corresponding to each incentive index;
the analysis module 305 is configured to perform standard-reaching analysis on the service data according to the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issue a reward corresponding to the service data to an account corresponding to the service person.
Optionally, the building module 302 specifically includes:
an access unit 3021, configured to access a preset database, and obtain a corresponding historical service data set from the preset database;
a processing unit 3022, configured to pre-process historical service data in the historical service data set to generate standardized data;
a constructing unit 3023, configured to construct a corresponding index analysis model based on the service content, the service rule, the data requirement, and the standardized data of the preset activity.
Optionally, the processing unit 3022 is specifically configured to: traversing historical service data in the historical service data set to obtain a target data set which meets preset conditions, and performing fusion expression on the target data set to obtain historical service data after data fusion; performing data cleaning on the historical service data subjected to data fusion to obtain historical service data subjected to data cleaning; performing data desensitization on the historical service data after data cleaning according to a preset data security rule to obtain the historical service data after data desensitization; and carrying out data encryption processing on the history service data after the data desensitization to obtain the standardized data.
Optionally, the building unit 3023 is specifically configured to: establishing an abstract expression model of the historical service data to obtain a corresponding neural network model; inputting the standard data serving as a training set into the neural network model for calculation to obtain a corresponding calculation result; and adding an evaluation label to the calculation result, performing iterative training on the neural network model by taking the input standard data and the output evaluation label as a group of data, and ending the training when the precision of the calculation result output by the neural network model reaches a preset threshold value to obtain a corresponding index analysis model.
Optionally, the calculating module 303 is specifically configured to: determining a reward index set of the business data and a relative weight between every two reward indexes in the reward index set according to the index analysis model; performing matrix construction according to the reward index set and the relative weight between every two reward indexes in the reward index set to obtain a corresponding target matrix; and carrying out normalization processing on the target matrix to obtain the weight corresponding to each reward index in the reward index set.
Optionally, the analysis module 305 is specifically configured to: carrying out format conversion on the service data to obtain service data in an operation format; calling a preset expression in the reward rule, and judging whether the current data format of the service data in the operation format meets the preset expression or not; and if the current data format of the service data in the operation format is judged to meet the preset expression, the service personnel meets the reward rule, reward matching calculation is carried out on the service data through the reward rule to obtain the reward corresponding to the service data, and the reward corresponding to the service data is issued to an account corresponding to the service personnel.
Optionally, the device for processing service data further includes:
the recording module 306 is configured to record rewards corresponding to the service data according to a preset time dimension to obtain a corresponding target reward data set;
and the generating module 307 is configured to generate a corresponding statistical form through the target reward data set, and transmit the statistical form to a preset terminal.
Fig. 5 is a schematic structural diagram of a computer device 500 according to an embodiment of the present invention, where the computer device 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a sequence of instructions operating on the computer device 500. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the computer device 500.
The computer device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 5 does not constitute a limitation of computer devices, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The present invention also provides a computer device, which includes a memory and a processor, where the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the method for processing service data in the foregoing embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and may also be a volatile computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the method for processing business data.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
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. The blockchain, which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, each data block contains information of a batch of network transactions for verifying the validity (anti-counterfeiting) of the information and generating a next block, and the blockchain may include a blockchain bottom platform, a platform product service layer, an application service layer, and the like.
Claims (10)
1. A method for processing service data, comprising:
acquiring business data of business personnel participating in a preset activity, wherein the business data comprises information of the business personnel corresponding to a business order and data of the business order;
acquiring a historical service data set from a preset database, and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model;
performing index analysis on the service data through the index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index;
determining an incentive rule according to each incentive index and the weight corresponding to each incentive index;
and performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing the reward corresponding to the service data to an account corresponding to the service personnel.
2. The method for processing business data according to claim 1, wherein the obtaining of the historical business data set from the preset database and the index analysis model construction according to the historical business data set, and obtaining the corresponding index analysis model comprises:
accessing a preset database, and acquiring a corresponding historical service data set from the preset database;
preprocessing historical service data in the historical service data set to generate standardized data;
and constructing a corresponding index analysis model based on the service content, the service rule and the data requirement of the preset activity and the standardized data.
3. The method for processing business data according to claim 2, wherein the preprocessing the historical business data in the historical business data set to generate normalized data comprises:
traversing historical service data in the historical service data set to obtain a target data set which meets preset conditions, and performing fusion expression on the target data set to obtain historical service data after data fusion;
performing data cleaning on the historical service data subjected to data fusion to obtain historical service data subjected to data cleaning;
performing data desensitization on the historical service data after data cleaning according to a preset data security rule to obtain the historical service data after data desensitization;
and carrying out data encryption processing on the history service data after the data desensitization to obtain the standardized data.
4. The method for processing business data according to claim 2, wherein the constructing a corresponding index analysis model based on the business content, the business rules and the data requirements of the preset activities and the standardized data comprises:
establishing an abstract expression model of the historical service data to obtain a corresponding neural network model;
inputting the standard data serving as a training set into the neural network model for calculation to obtain a corresponding calculation result;
adding an evaluation label to the calculation result, performing iterative training on the neural network model by taking the input standard data and the output evaluation label as a group of data, and finishing the training when the precision of the calculation result output by the neural network model reaches a preset threshold value to obtain a corresponding index analysis model.
5. The method according to claim 1, wherein the performing, by the index analysis model, index analysis on the service data determines a set of reward indexes of the service data, and performs weight value calculation on each reward index in the set of reward indexes, and determining a weight value corresponding to each reward index includes:
determining a reward index set of the business data and a relative weight between every two reward indexes in the reward index set according to the index analysis model;
performing matrix construction according to the reward index set and the relative weight between every two reward indexes in the reward index set to obtain a corresponding target matrix;
and carrying out normalization processing on the target matrix to obtain the weight corresponding to each reward index in the reward index set.
6. The method for processing business data according to claim 1, wherein the performing a standard analysis on the business data according to the reward rule to obtain a corresponding analysis result, and when the analysis result is a standard, issuing a reward corresponding to the business data to an account corresponding to the business person comprises:
carrying out format conversion on the service data to obtain service data in an operation format;
calling a preset expression in the reward rule, and judging whether the current data format of the service data in the operation format meets the preset expression or not;
and if the current data format of the service data in the operation format is judged to meet the preset expression, the service personnel meets the reward rule, reward matching calculation is carried out on the service data through the reward rule to obtain the reward corresponding to the service data, and the reward corresponding to the service data is issued to an account corresponding to the service personnel.
7. The method for processing service data according to any one of claims 1 to 6, wherein the performing standard-reaching analysis on the service data according to the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, issuing a reward corresponding to the service data to an account corresponding to the service staff, further comprises:
recording rewards corresponding to the service data according to a preset time dimension to obtain a corresponding target reward data set;
and generating a corresponding statistical form through the target reward data set, and transmitting the statistical form to a preset terminal.
8. A device for processing service data, wherein the device for processing service data comprises:
the acquisition module is used for acquiring the business data of business personnel participating in the preset activity, wherein the business data comprises the information of the business personnel corresponding to the business order and the data of the business order;
the system comprises a construction module, a data acquisition module and a data analysis module, wherein the construction module is used for acquiring a historical service data set from a preset database and constructing an index analysis model according to the historical service data set to obtain a corresponding index analysis model;
the calculation module is used for performing index analysis on the service data through the index analysis model, determining a reward index set of the service data, performing weight value calculation on each reward index in the reward index set, and determining a weight value corresponding to each reward index;
the determining module is used for determining reward rules according to each reward index and the weight corresponding to each reward index;
and the analysis module is used for performing standard-reaching analysis on the service data through the reward rule to obtain a corresponding analysis result, and when the analysis result is standard-reaching, the reward corresponding to the service data is issued to an account corresponding to the service personnel.
9. A computer device, characterized in that the computer device comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the computer device to perform the method of processing business data of any one of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement a method for processing service data according to any one of claims 1 to 7.
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