CN117273861B - Sales order management method and system - Google Patents
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Abstract
The invention provides a sales order management method and a sales order management system, and relates to the technical field of data processing. In the invention, a plurality of enterprise suppliers are subjected to supplier selection management and control to determine at least one target enterprise supplier; respectively extracting sales order data corresponding to each target enterprise provider, wherein the sales order data is formed by providing services for the target enterprises based on the target enterprise providers; for each target enterprise provider, based on sales order data corresponding to the target enterprise provider, and combining relevant order data of at least one dimension corresponding to the target enterprise provider, analyzing sales order abnormal identification data corresponding to the target enterprise provider; and respectively carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data. Based on the method, the reliability of abnormal management of the sales order can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a sales order management method and a sales order management system.
Background
In sales order management, abnormal management of sales orders is an important link. For example, after the sales order data is extracted, the sales order data may be subjected to order anomaly analysis to obtain corresponding sales order anomaly identification data, so that anomaly management may be performed based on the sales order anomaly identification data.
Disclosure of Invention
Accordingly, the present invention is directed to a sales order management method and system, which can improve the reliability of abnormal management of sales orders.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a sales order management method, comprising:
Performing supplier selection management on a plurality of enterprise suppliers to determine at least one target enterprise supplier, wherein the target enterprise supplier is used for providing service supply for a target enterprise;
Respectively extracting sales order data corresponding to each target enterprise provider, wherein the sales order data is formed by providing services for the target enterprises based on the target enterprise providers;
For each target enterprise provider, based on sales order data corresponding to the target enterprise provider, analyzing sales order abnormal identification data corresponding to the target enterprise provider by combining relevant order data of at least one dimension corresponding to the target enterprise provider;
and respectively carrying out sales order abnormal management operation on each target enterprise provider based on the corresponding sales order abnormal identification data.
In some preferred embodiments, in the sales order management method, the step of, for each of the target enterprise suppliers, analyzing sales order anomaly identification data corresponding to the target enterprise supplier based on sales order data corresponding to the target enterprise supplier and in combination with related order data of at least one dimension corresponding to the target enterprise supplier includes:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider.
In some preferred embodiments, in the sales order management method, the step of analyzing sales order anomaly identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set includes:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set.
In some preferred embodiments, in the sales order management method, the step of analyzing sales order anomaly identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set includes:
Performing feature mining operation on sales order data corresponding to the target enterprise provider to form a first information feature vector corresponding to the target enterprise provider;
Performing feature mining operation on the third historical sales order data set to form a second information feature vector corresponding to the target enterprise provider;
performing feature mining operation on the second historical sales order data set to form a third information feature vector corresponding to the target enterprise provider;
And vector aggregation operation is carried out on the first information feature vector, the second information feature vector and the third information feature vector so as to output an aggregate information feature vector corresponding to the target enterprise provider, and sales order abnormal identification data corresponding to the target enterprise provider is analyzed based on the aggregate information feature vector.
In some preferred embodiments, in the sales order management method, the step of performing a vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector to output an aggregate information feature vector corresponding to the target enterprise provider, and analyzing sales order anomaly identification data corresponding to the target enterprise provider based on the aggregate information feature vector includes:
Loading the first information feature vector, the second information feature vector and the third information feature vector into a target neural network, wherein the target neural network is formed by network optimization based on sample data and actual sales order abnormal identification data corresponding to the sample data;
Performing vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector by using a vector aggregation unit included in the target neural network so as to output corresponding aggregated information feature vectors;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the aggregate information feature vector corresponding to the target enterprise provider by using an order abnormal analysis unit included in the target neural network.
In some preferred embodiments, in the sales order management method, the step of performing a vector aggregation operation on the first information feature vector, the second information feature vector, and the third information feature vector by using a vector aggregation unit included in the target neural network to output corresponding aggregated information feature vectors includes:
loading the first information feature vector, the second information feature vector and the third information feature vector into a vector aggregation unit included in the target neural network;
Multiplying the second information feature vector and the third information feature vector to form a corresponding fourth information feature vector, and subtracting the second information feature vector and the third information feature vector to form a corresponding fifth information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fourth information feature vector to form a corresponding first focusing information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fifth information feature vector to form a corresponding second focusing information feature vector;
And performing splicing operation on the first focusing information feature vector and the second focusing information feature vector to form a corresponding aggregation information feature vector.
In some preferred embodiments, in the sales order management method, the step of performing vendor selection control on the plurality of enterprise vendors to determine at least one target enterprise vendor includes:
For each enterprise provider, extracting service supply characteristic information corresponding to the enterprise provider, wherein each enterprise provider belongs to a provider of a target enterprise, and the service supply characteristic information is used for reflecting the supply characteristics of the corresponding enterprise provider to each enterprise in history;
Determining provider identification data corresponding to each enterprise provider based on service provision characteristic information corresponding to each enterprise provider;
determining provider correlation information between every two enterprise providers based on provider identification data corresponding to each enterprise provider;
and based on the supplier correlation information between every two enterprise suppliers, carrying out supplier selection management and control on a plurality of enterprise suppliers so as to determine at least one target enterprise supplier.
The embodiment of the invention also provides a sales order management system, which comprises:
the system comprises a supplier determining module, a server and a server, wherein the supplier determining module is used for carrying out supplier selection management and control on a plurality of enterprise suppliers so as to determine at least one target enterprise supplier, and the target enterprise supplier is used for providing service supply for a target enterprise;
The order data extraction module is used for respectively extracting sales order data corresponding to each target enterprise provider, and the sales order data is formed by providing services for the target enterprises based on the target enterprise providers;
the order anomaly analysis module is used for analyzing sales order anomaly identification data corresponding to each target enterprise provider based on sales order data corresponding to the target enterprise provider and combining relevant order data of at least one dimension corresponding to the target enterprise provider;
And the order anomaly management module is used for carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data.
In some preferred embodiments, in the sales order management system described above, the order anomaly analysis module is specifically configured to:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider.
In some preferred embodiments, in the sales order management system, the analyzing the sales order anomaly identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set includes:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set.
The sales order management method and the sales order management system provided by the embodiment of the invention can be used for selecting and controlling the suppliers of a plurality of enterprise suppliers to determine at least one target enterprise supplier; respectively extracting sales order data corresponding to each target enterprise provider, wherein the sales order data is formed by providing services for the target enterprises based on the target enterprise providers; for each target enterprise provider, based on sales order data corresponding to the target enterprise provider, and combining relevant order data of at least one dimension corresponding to the target enterprise provider, analyzing sales order abnormal identification data corresponding to the target enterprise provider; and respectively carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data. Based on the foregoing, in the process of analyzing the sales order anomaly identification data, not only sales order data corresponding to the target enterprise provider is used, but also related order data of at least one dimension corresponding to the target enterprise provider is combined, so that the basis is more sufficient, and the reliability of sales order anomaly management can be improved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a sales order management platform according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating steps involved in a sales order management method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the sales order management system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, an embodiment of the present invention provides a sales order management platform. Wherein the sales order management platform can include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute an executable computer program stored in the memory, thereby implementing a sales order management method provided by an embodiment of the present invention (as described below).
Alternatively, in some embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
Alternatively, in some embodiments, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Alternatively, in some embodiments, the sales order management platform may be a server with data processing capabilities.
With reference to fig. 2, the embodiment of the present invention further provides a sales order management method, which can be applied to the sales order management platform. Wherein, the method steps defined by the flow related to the sales order management method can be realized by the sales order management platform.
The specific flow shown in fig. 2 will be described in detail.
In step S100, a plurality of enterprise suppliers are subjected to supplier selection management to determine at least one target enterprise supplier.
In an embodiment of the present invention, the sales order management platform may perform vendor selection management on a plurality of enterprise vendors to determine at least one target enterprise vendor. The target enterprise provider is configured to provide service offerings for the target enterprise.
And step 200, respectively extracting sales order data corresponding to each target enterprise provider.
In the embodiment of the invention, the sales order management platform can respectively extract sales order data corresponding to each target enterprise provider. The sales order data is formed based on the target enterprise provider providing services to the target enterprise.
Step S300, for each target enterprise provider, based on the sales order data corresponding to the target enterprise provider, and in combination with the related order data of at least one dimension corresponding to the target enterprise provider, analyzing the sales order anomaly identification data corresponding to the target enterprise provider.
In the embodiment of the invention, the sales order management platform may analyze, for each target enterprise provider, sales order abnormal identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and in combination with related order data of at least one dimension corresponding to the target enterprise provider.
And step 400, performing sales order abnormal management operation on each target enterprise provider based on the corresponding sales order abnormal identification data.
In the embodiment of the present invention, the sales order management platform may perform sales order anomaly management operation on each of the target enterprise suppliers based on the corresponding sales order anomaly identification data, for example, perform anomaly monitoring on the target enterprise suppliers having anomalies.
Based on the foregoing (step S100-step S400), in the process of analyzing the sales order anomaly identification data, not only sales order data corresponding to the target enterprise provider is used, but also related order data of at least one dimension corresponding to the target enterprise provider is combined, so that the basis is more sufficient, and thus, the reliability of sales order anomaly management can be improved.
Optionally, in some embodiments, the step of performing vendor selection control on the multiple enterprise vendors to determine the at least one target enterprise vendor may further include the following steps, such as step S110, step S120, step S130, and step S140.
In step S110, for each enterprise provider, service provision feature information corresponding to the enterprise provider is extracted.
In the embodiment of the invention, the sales order management platform can extract the service supply characteristic information corresponding to each enterprise provider for each enterprise provider. Each of the enterprise suppliers belongs to a supplier of a target enterprise, and the service supply characteristic information is used for reflecting supply characteristics of the corresponding enterprise supplier on each enterprise in history, such as historically supplied service content, supply time, supply duration, supply evaluation and the like.
Step S120, determining the vendor identification data corresponding to each of the enterprise suppliers based on the service provisioning feature information corresponding to each of the enterprise suppliers.
In the embodiment of the present invention, the sales order management platform may determine the vendor identification data corresponding to each of the enterprise suppliers based on the service provision feature information corresponding to each of the enterprise suppliers. The vendor identification data may or may not have a specific meaning, such as a symbol for distinguishing, etc., identifying the type of vendor.
Step S130, determining vendor correlation information between every two enterprise vendors based on vendor identification data corresponding to each enterprise vendor.
In the embodiment of the present invention, the sales order management platform may determine, based on vendor identification data corresponding to each of the enterprise vendors, vendor correlation information between each two of the enterprise vendors.
Step S140, performing vendor selection control on the multiple enterprise vendors based on vendor correlation information between each two enterprise vendors, so as to determine at least one target enterprise vendor.
In an embodiment of the present invention, the sales order management platform may perform vendor selection management on a plurality of the enterprise vendors based on vendor correlation information between each two of the enterprise vendors, so as to determine at least one target enterprise vendor, where the target enterprise vendor is configured to provide service provision for the target enterprise.
Based on the above, in the process of performing vendor selection control, vendor related relationship information between every two enterprise vendors is not arbitrarily selected but fully considered, so that the reliability of vendor management can be improved.
Optionally, in some embodiments, the step of determining the provider identification data corresponding to each of the enterprise providers based on the service provision feature information corresponding to each of the enterprise providers may further include the following:
Performing feature mining operation on service supply feature information corresponding to each enterprise provider to output a supply information feature vector corresponding to each enterprise provider, for example, the service supply feature information may be mapped to a feature space to obtain a supply information feature vector;
For each enterprise provider, determining a relevant enterprise provider corresponding to the enterprise provider from other enterprise providers except the enterprise provider;
And determining the provider identification data corresponding to each enterprise provider based on the provider information feature vector corresponding to the enterprise provider and the provider information feature vector corresponding to the relevant enterprise provider corresponding to the enterprise provider.
Optionally, in some embodiments, the step of determining, for each of the enterprise suppliers, a relevant enterprise supplier corresponding to the enterprise supplier among other enterprise suppliers besides the enterprise supplier may further include the following contents:
determining historical supply information corresponding to the target enterprise;
based on the historical supply information, respectively determining supply co-occurrence frequency information between every two enterprise suppliers, wherein the supply co-occurrence frequency information is used for reflecting the number of times that the corresponding two enterprise suppliers are simultaneously selected as historical target enterprise suppliers in history;
for each enterprise provider, determining the supply co-occurrence frequency information with the maximum value from the supply co-occurrence frequency information between the enterprise provider and each other enterprise provider, and taking the supply co-occurrence frequency information as target supply co-occurrence frequency information corresponding to the enterprise provider;
And using other enterprise suppliers corresponding to the target supply co-occurrence frequency information as related enterprise suppliers corresponding to the enterprise suppliers.
Optionally, in some embodiments, the step of determining the provider identification data corresponding to each of the enterprise suppliers based on the supply information feature vector corresponding to the enterprise supplier and the supply information feature vector corresponding to the relevant enterprise supplier corresponding to the enterprise supplier may further include the following contents:
The method comprises the steps of carrying out segmentation operation on supply information feature vectors corresponding to enterprise suppliers to form a plurality of corresponding first local supply information feature vectors, and carrying out segmentation operation on supply information feature vectors corresponding to relevant enterprise suppliers corresponding to the enterprise suppliers to form a plurality of corresponding second local supply information feature vectors, wherein the number of the plurality of first local supply information feature vectors and the number of the plurality of second local supply information feature vectors are consistent, and the segmentation modes are consistent;
Based on the distribution positions in the supply information feature vectors, the first local supply information feature vectors and the second local supply information feature vectors are associated one by one, so that each first local supply information feature vector is associated with a second local supply information feature vector;
For each first local supply information feature vector, performing focusing feature analysis operation on the first local supply information feature vector according to the associated second local supply information feature vector so as to form a focusing feature vector corresponding to the first local supply information feature vector;
Combining focusing feature vectors corresponding to each first local supply information feature vector based on the distribution position of each first local supply information feature vector in the supply information feature vector so as to form a depth supply information feature vector corresponding to the enterprise provider;
And mapping and outputting the depth supply information feature vector corresponding to the enterprise provider, for example, by an incentive function, so as to obtain the provider identification data corresponding to the enterprise provider.
Optionally, in some embodiments, the step of determining the vendor correlation information between each two enterprise vendors based on vendor identification data corresponding to each enterprise vendor may further include the following:
For each two enterprise suppliers, calculating data similarity of supplier identification data corresponding to the two enterprise suppliers to output corresponding data similarity, which can be semantic similarity;
And determining the provider correlation information between every two enterprise providers based on the corresponding data similarity, wherein the correlation degree represented by the provider correlation information and the data similarity have a positive correlation corresponding relationship.
Optionally, in some embodiments, the step of performing vendor selection control on the multiple enterprise vendors based on vendor correlation information between each two enterprise vendors to determine at least one target enterprise vendor may further include the following:
constructing a first set of suppliers based on each of the enterprise suppliers, the first set of suppliers including each of the enterprise suppliers in an initial state;
constructing a second set of suppliers that is initially empty;
determining any one enterprise provider in the current first provider set, marking the enterprise provider as a selected enterprise provider, screening the selected enterprise provider from the current first provider set, and placing the selected enterprise provider into the second provider set;
Determining, in a current first set of suppliers, suppliers of the enterprise that are not related to the current selected supplier of the enterprise, marking the suppliers of the enterprise as new selected suppliers of the enterprise, screening out the selected suppliers of the enterprise from the current first set of suppliers, and placing the selected suppliers of the enterprise into the second set of suppliers, wherein the suppliers of the enterprise that are not related to the current selected supplier of the enterprise are characterized by the supplier related information between the suppliers of the enterprise and the selected suppliers of the enterprise that is lower than a preset degree;
At least once executing the steps of determining, in a current first set of suppliers, suppliers of the enterprise that are not related to the current selected supplier of the enterprise, marking the suppliers of the enterprise as new selected suppliers of the enterprise, screening out from the current first set of suppliers, and placing the selected suppliers of the enterprise into the second set of suppliers until no new selected suppliers of the enterprise can be determined, marking the current second set of suppliers as candidate sets of suppliers;
Executing the step of constructing a second supplier set which is an initial empty set in a revolving way;
After the step of constructing the second provider set which is an initial empty set is executed, for each candidate provider set in the obtained plurality of candidate provider sets, performing mean value calculation on the correlation degree of the provider correlation information characterization between every two enterprise providers in the candidate provider set so as to output a mean value correlation degree corresponding to the candidate provider set;
Determining one candidate supplier set from the candidate supplier sets based on the average value correlation degree corresponding to each candidate supplier set and the number of set elements included in each candidate supplier set, and taking the candidate supplier set as a target supplier set;
and taking each enterprise provider included in the target provider set as a target enterprise provider to obtain at least one target enterprise provider.
Optionally, in some embodiments, the step of determining, from among the plurality of candidate provider sets, one candidate provider set as the target provider set based on the average correlation degree corresponding to each candidate provider set and the number of set elements included in each candidate provider set may further include the following:
based on the mean correlation degree corresponding to each candidate provider set, respectively determining a first priority coefficient corresponding to each candidate provider set, wherein a corresponding relation with negative correlation is formed between the first priority coefficient and the mean correlation degree;
Based on the number of set elements corresponding to each candidate provider set, respectively determining a second priority coefficient corresponding to each candidate provider set, wherein a corresponding relation with positive correlation exists between the second priority coefficient and the number of set elements;
Performing a fusion operation, such as mean calculation or weighted summation, on the first priority coefficient and the corresponding second priority coefficient to form a fused priority coefficient of the corresponding candidate provider set;
Based on the fusion priority coefficient of each candidate supplier set, one candidate supplier set is determined from the candidate supplier sets, and the fusion priority coefficient can have the maximum value as a target supplier set.
Optionally, in some embodiments, the step of, for each of the target enterprise suppliers, analyzing sales order abnormal identification data corresponding to the target enterprise supplier based on sales order data corresponding to the target enterprise supplier and in combination with relevant order data of at least one dimension corresponding to the target enterprise supplier may further include the following contents:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider.
Optionally, in some embodiments, the step of analyzing the sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set may further include the following contents:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set.
Optionally, in some embodiments, the step of analyzing the sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set may further include the following contents:
Performing feature mining operation on sales order data corresponding to the target enterprise provider, and mapping the sales order data to a feature space to form a first information feature vector corresponding to the target enterprise provider;
performing feature mining operation on the third historical sales order data set, and mapping the third historical sales order data set to a feature space to form a second information feature vector corresponding to the target enterprise provider;
performing feature mining operation on the second historical sales order data set, and mapping the feature space to form a third information feature vector corresponding to the target enterprise provider;
And vector aggregation operation is carried out on the first information feature vector, the second information feature vector and the third information feature vector so as to output an aggregate information feature vector corresponding to the target enterprise provider, and sales order abnormal identification data corresponding to the target enterprise provider is analyzed based on the aggregate information feature vector.
Optionally, in some embodiments, the step of performing a vector aggregation operation on the first information feature vector, the second information feature vector, and the third information feature vector to output an aggregate information feature vector corresponding to the target enterprise provider, and analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the aggregate information feature vector may further include the following contents:
Loading the first information feature vector, the second information feature vector and the third information feature vector into a target neural network, wherein the target neural network is formed by network optimization based on sample data and actual sales order abnormal identification data corresponding to the sample data;
Performing vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector by using a vector aggregation unit included in the target neural network so as to output corresponding aggregated information feature vectors;
And the order anomaly analysis unit comprises an activation function and is used for analyzing sales order anomaly identification data corresponding to the target enterprise provider based on the aggregate information feature vector corresponding to the target enterprise provider.
Optionally, in some embodiments, the step of performing a vector aggregation operation on the first information feature vector, the second information feature vector, and the third information feature vector by using a vector aggregation unit included in the target neural network to output corresponding aggregated information feature vectors may further include the following contents:
loading the first information feature vector, the second information feature vector and the third information feature vector into a vector aggregation unit included in the target neural network;
Multiplying the second information feature vector and the third information feature vector to form a corresponding fourth information feature vector, and subtracting the second information feature vector and the third information feature vector to form a corresponding fifth information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fourth information feature vector to form a corresponding first focusing information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fifth information feature vector to form a corresponding second focusing information feature vector;
And performing splicing operation on the first focusing information feature vector and the second focusing information feature vector to form a corresponding aggregation information feature vector.
With reference to fig. 3, an embodiment of the present invention further provides a sales order management system, which is applicable to the sales order management platform. Wherein the sales order management system may include:
the system comprises a supplier determining module, a server and a server, wherein the supplier determining module is used for carrying out supplier selection management and control on a plurality of enterprise suppliers so as to determine at least one target enterprise supplier, and the target enterprise supplier is used for providing service supply for a target enterprise;
The order data extraction module is used for respectively extracting sales order data corresponding to each target enterprise provider, and the sales order data is formed by providing services for the target enterprises based on the target enterprise providers;
the order anomaly analysis module is used for analyzing sales order anomaly identification data corresponding to each target enterprise provider based on sales order data corresponding to the target enterprise provider and combining relevant order data of at least one dimension corresponding to the target enterprise provider;
And the order anomaly management module is used for carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data.
Optionally, in some embodiments, the order anomaly analysis module is specifically configured to:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider.
Optionally, in some embodiments, the analyzing sales order anomaly identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set includes:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set.
In summary, the sales order management method and system provided by the invention can perform vendor selection management and control on multiple enterprise vendors to determine at least one target enterprise vendor; respectively extracting sales order data corresponding to each target enterprise provider, wherein the sales order data is formed by providing services for the target enterprises based on the target enterprise providers; for each target enterprise provider, based on sales order data corresponding to the target enterprise provider, and combining relevant order data of at least one dimension corresponding to the target enterprise provider, analyzing sales order abnormal identification data corresponding to the target enterprise provider; and respectively carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data. Based on the foregoing, in the process of analyzing the sales order anomaly identification data, not only sales order data corresponding to the target enterprise provider is used, but also related order data of at least one dimension corresponding to the target enterprise provider is combined, so that the basis is more sufficient, and the reliability of sales order anomaly management can be improved.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A sales order management method, comprising:
Performing supplier selection management on a plurality of enterprise suppliers to determine at least one target enterprise supplier, wherein the target enterprise supplier is used for providing service supply for a target enterprise;
Respectively extracting sales order data corresponding to each target enterprise provider, wherein the sales order data is formed by providing services for the target enterprises based on the target enterprise providers;
For each target enterprise provider, based on sales order data corresponding to the target enterprise provider, analyzing sales order abnormal identification data corresponding to the target enterprise provider by combining relevant order data of at least one dimension corresponding to the target enterprise provider;
Respectively carrying out sales order abnormal management operation on each target enterprise provider based on the corresponding sales order abnormal identification data;
The step of analyzing sales order abnormal identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining related order data of at least one dimension corresponding to the target enterprise provider for each target enterprise provider includes:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
Based on sales order data corresponding to the target enterprise provider, and combining the first historical sales order data set and the second historical sales order data set, analyzing sales order abnormal identification data corresponding to the target enterprise provider, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider;
the step of analyzing sales order abnormal identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set includes:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
Based on sales order data corresponding to the target enterprise provider, and combining the third historical sales order data set and the second historical sales order data set, analyzing sales order abnormal identification data corresponding to the target enterprise provider;
The step of analyzing sales order abnormal identification data corresponding to the target enterprise provider based on sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set includes:
Performing feature mining operation on sales order data corresponding to the target enterprise provider to form a first information feature vector corresponding to the target enterprise provider;
Performing feature mining operation on the third historical sales order data set to form a second information feature vector corresponding to the target enterprise provider;
performing feature mining operation on the second historical sales order data set to form a third information feature vector corresponding to the target enterprise provider;
Vector aggregation operation is carried out on the first information feature vector, the second information feature vector and the third information feature vector so as to output an aggregate information feature vector corresponding to the target enterprise provider, and sales order abnormal identification data corresponding to the target enterprise provider is analyzed based on the aggregate information feature vector;
The step of performing a vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector to output an aggregate information feature vector corresponding to the target enterprise provider, and analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the aggregate information feature vector includes:
Loading the first information feature vector, the second information feature vector and the third information feature vector into a target neural network, wherein the target neural network is formed by network optimization based on sample data and actual sales order abnormal identification data corresponding to the sample data;
Performing vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector by using a vector aggregation unit included in the target neural network so as to output corresponding aggregated information feature vectors;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the aggregate information feature vector corresponding to the target enterprise provider by using an order abnormal analysis unit included in the target neural network.
2. The sales order management method according to claim 1, wherein the step of performing a vector aggregation operation on the first information feature vector, the second information feature vector, and the third information feature vector using a vector aggregation unit included in the target neural network to output corresponding aggregated information feature vectors includes:
loading the first information feature vector, the second information feature vector and the third information feature vector into a vector aggregation unit included in the target neural network;
Multiplying the second information feature vector and the third information feature vector to form a corresponding fourth information feature vector, and subtracting the second information feature vector and the third information feature vector to form a corresponding fifth information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fourth information feature vector to form a corresponding first focusing information feature vector;
Performing focusing feature analysis operation on the first information feature vector based on the fifth information feature vector to form a corresponding second focusing information feature vector;
And performing splicing operation on the first focusing information feature vector and the second focusing information feature vector to form a corresponding aggregation information feature vector.
3. The sales order management method according to claim 1, wherein the step of performing vendor selection management on the plurality of enterprise vendors to determine the at least one target enterprise vendor comprises:
For each enterprise provider, extracting service supply characteristic information corresponding to the enterprise provider, wherein each enterprise provider belongs to a provider of a target enterprise, and the service supply characteristic information is used for reflecting the supply characteristics of the corresponding enterprise provider to each enterprise in history;
Determining provider identification data corresponding to each enterprise provider based on service provision characteristic information corresponding to each enterprise provider;
determining provider correlation information between every two enterprise providers based on provider identification data corresponding to each enterprise provider;
and based on the supplier correlation information between every two enterprise suppliers, carrying out supplier selection management and control on a plurality of enterprise suppliers so as to determine at least one target enterprise supplier.
4. A sales order management system, comprising:
the system comprises a supplier determining module, a server and a server, wherein the supplier determining module is used for carrying out supplier selection management and control on a plurality of enterprise suppliers so as to determine at least one target enterprise supplier, and the target enterprise supplier is used for providing service supply for a target enterprise;
The order data extraction module is used for respectively extracting sales order data corresponding to each target enterprise provider, and the sales order data is formed by providing services for the target enterprises based on the target enterprise providers;
the order anomaly analysis module is used for analyzing sales order anomaly identification data corresponding to each target enterprise provider based on sales order data corresponding to the target enterprise provider and combining relevant order data of at least one dimension corresponding to the target enterprise provider;
the order anomaly management module is used for carrying out sales order anomaly management operation on each target enterprise provider based on the corresponding sales order anomaly identification data;
Wherein for each target enterprise provider, based on sales order data corresponding to the target enterprise provider, and combined with related order data of at least one dimension corresponding to the target enterprise provider, the analysis of sales order anomaly identification data corresponding to the target enterprise provider includes:
Extracting a first historical sales order data set corresponding to the target enterprise provider, wherein each first historical sales order data set included in the first historical sales order data set is used for representing the service sold by the target enterprise provider for one enterprise in the history;
Extracting a second historical sales order data set corresponding to the target enterprise, wherein each second historical sales order data set comprises second historical sales order data used for representing the service of one enterprise sales received by the target enterprise in a historical manner;
Based on sales order data corresponding to the target enterprise provider, and combining the first historical sales order data set and the second historical sales order data set, analyzing sales order abnormal identification data corresponding to the target enterprise provider, wherein the first historical sales order data set and the second historical sales order data set are used as related order data of at least one dimension corresponding to the target enterprise provider;
wherein the analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the first historical sales order data set and the second historical sales order data set includes:
Determining a complement of the second historical sales order data set in the first historical sales order data set to obtain a third historical sales order data set;
Based on sales order data corresponding to the target enterprise provider, and combining the third historical sales order data set and the second historical sales order data set, analyzing sales order abnormal identification data corresponding to the target enterprise provider;
wherein the analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the sales order data corresponding to the target enterprise provider and combining the third historical sales order data set and the second historical sales order data set includes:
Performing feature mining operation on sales order data corresponding to the target enterprise provider to form a first information feature vector corresponding to the target enterprise provider;
Performing feature mining operation on the third historical sales order data set to form a second information feature vector corresponding to the target enterprise provider;
performing feature mining operation on the second historical sales order data set to form a third information feature vector corresponding to the target enterprise provider;
Vector aggregation operation is carried out on the first information feature vector, the second information feature vector and the third information feature vector so as to output an aggregate information feature vector corresponding to the target enterprise provider, and sales order abnormal identification data corresponding to the target enterprise provider is analyzed based on the aggregate information feature vector;
The vector aggregation operation is performed on the first information feature vector, the second information feature vector and the third information feature vector, so as to output an aggregate information feature vector corresponding to the target enterprise provider, and based on the aggregate information feature vector, sales order abnormal identification data corresponding to the target enterprise provider is analyzed, including:
Loading the first information feature vector, the second information feature vector and the third information feature vector into a target neural network, wherein the target neural network is formed by network optimization based on sample data and actual sales order abnormal identification data corresponding to the sample data;
Performing vector aggregation operation on the first information feature vector, the second information feature vector and the third information feature vector by using a vector aggregation unit included in the target neural network so as to output corresponding aggregated information feature vectors;
And analyzing sales order abnormal identification data corresponding to the target enterprise provider based on the aggregate information feature vector corresponding to the target enterprise provider by using an order abnormal analysis unit included in the target neural network.
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