CN114493400A - Intelligent purchase, sale and inventory analysis system - Google Patents
Intelligent purchase, sale and inventory analysis system Download PDFInfo
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- CN114493400A CN114493400A CN202111555436.3A CN202111555436A CN114493400A CN 114493400 A CN114493400 A CN 114493400A CN 202111555436 A CN202111555436 A CN 202111555436A CN 114493400 A CN114493400 A CN 114493400A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides an intelligent purchase, sale and inventory analysis system, which comprises: data management module, external system connection module and inventory change operation module still include: an intelligent analysis module; the intelligent analysis module includes: the analysis instruction acquisition unit is used for acquiring a data analysis instruction of a worker; the instruction analysis unit is used for analyzing the data analysis instruction and determining a data capture rule, a data analysis rule and an output rule; the data capturing unit is used for extracting data to be analyzed from the data management module based on the data capturing rule; the data analysis unit is used for analyzing the data to be analyzed based on the data analysis rule to obtain an analysis result; and the analysis output unit is used for outputting the analysis result based on the output rule. The intelligent purchase, sales and inventory analysis system provided by the invention analyzes according to the data records to generate various analysis reports, and presents the analysis reports according to the presentation mode set by the staff, so that the staff can better manage goods in the purchase, sales and inventory link of the enterprise.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent purchase, sale and inventory analysis system.
Background
Due to various goods, large business volume and complex inventory management in the processes of feeding, inventory and selling of enterprises, if manual management is adopted, the workload is large, and various errors based on subjective influences of workers can easily occur; the adoption of the purchase, sale and stock management system for management can improve the working efficiency of workers, not only converts the original manual operation into the automatic system management, but also avoids the occurrence of errors based on the subjective influence of the workers in the manual operation, and achieves clear purchase, sale and stock management;
however, the existing purchase-sale-stock management systems are only used for recording and calling data, and do not have a function of analyzing according to the data records.
Disclosure of Invention
One of the objectives of the present invention is to provide an intelligent purchase, sale, and stock analysis system, which analyzes according to data records to generate various analysis reports, and presents the analysis reports according to a presentation manner set by a worker, so as to achieve better management of goods in the purchase, sale, and stock links of an enterprise by the worker.
The embodiment of the invention provides an intelligent purchase, sale and inventory analysis system, which comprises: data management module, external system connection module and inventory change operation module still include: an intelligent analysis module;
the intelligent analysis module includes:
the analysis instruction acquisition unit is used for acquiring a data analysis instruction of a worker;
the instruction analysis unit is used for analyzing the data analysis instruction and determining a data capture rule, a data analysis rule and an output rule;
the data capturing unit is used for extracting data to be analyzed from the data management module based on a data capturing rule;
the data analysis unit is used for analyzing the data to be analyzed based on the data analysis rule to obtain an analysis result;
and the analysis output unit is used for outputting the analysis result based on the output rule.
Preferably, the data management module includes:
a supplier management unit for managing relevant data of suppliers;
a dealer management unit for managing relevant data of dealers;
the warehouse management unit is used for managing related data of a warehouse;
a brand management unit for managing data related to brands;
the goods management unit is used for managing related data of goods;
the owner management unit is used for managing relevant data of the owner;
the purchase price management unit is used for managing the related data of the purchase price;
a profit management unit for managing data related to profits;
and the authority management unit is used for managing the related data of the authority.
Preferably, the external system connection module includes:
the OMS system access module is used for connecting an external OMS system;
and the ERP system access module is used for connecting an external ERP system.
Preferably, the inventory change operation module includes:
the warehousing operation unit is used for receiving warehousing operation of a user;
the ex-warehouse operation unit is used for receiving ex-warehouse operation of a user;
an inventory operation unit for receiving an inventory operation of a user;
the garage moving operation unit is used for receiving the garage moving operation of a user;
and the destruction operation unit is used for receiving the destruction operation of the user.
Preferably, the purchase-sale-stock intelligent analysis system further includes: the account management module is used for managing a login account and account information of an enterprise;
the account management module comprises:
the authority configuration unit is used for newly building roles in the enterprise and configuring the authority of each role in the enterprise;
and the sub-account management unit is used for creating an employee account for the staff of the enterprise and associating the employee account with the role.
Preferably, the purchase-sale-stock intelligent analysis system further includes: an inventory analysis module;
the inventory analysis module performs the following operations:
acquiring current inventory information of each goods in the warehouse every other preset time or when receiving an inventory analysis command of a worker;
acquiring inventory threshold values of all goods;
analyzing the current inventory information and determining the current inventory of each goods;
when the current inventory is less than the inventory threshold, adding the corresponding goods into the list to be purchased;
and outputting the list to be purchased.
Preferably, the inventory threshold is determined by:
acquiring the goods input data and the historical sales data of the goods;
analyzing the goods feeding data to determine a goods feeding period;
performing feature extraction on the goods-feeding period and the historical sales data by adopting a preset first feature extraction rule to obtain a plurality of first feature values;
inputting a plurality of first characteristic values into a preset first neural network model, and determining a promotion and marketing factor;
inquiring a preset comparison table of the purchase and sale factors and the first stock quantity based on the purchase and sale factors, and determining the first stock quantity;
acquiring hot spot information related to goods and comment information corresponding to the hot spot information within preset time;
extracting features of the hotspot information and the comment information by adopting a preset second feature extraction rule to obtain a plurality of second feature values;
inputting the plurality of second characteristic values into a preset second neural network model, and determining hot spot factors;
inquiring a preset comparison table of the hot factors and the second stock quantity based on the hot factors, and determining the second stock quantity;
acquiring browsing information of a display interface associated with goods within preset time;
analyzing the browsing information, determining each browser and the browsing behavior of each browser, and adding the browsers into a to-be-analyzed behavior list;
determining whether the browser makes a purchase of the goods after browsing based on the historical sales data;
when the browser purchases goods, deleting the browser from the to-be-behavioral analysis list;
extracting the characteristics of the browsing behaviors of each browser in the behavior analysis list by adopting a preset third characteristic extraction rule, and acquiring a plurality of behavior characteristics corresponding to each browser;
inputting a plurality of behavior characteristics into a preset third neural network model, and determining a behavior factor;
determining the purchase probability of each browser in the behavior analysis list based on the behavior factors and a preset comparison table of the behavior factors and the purchase probability;
determining a third stock quantity based on the quantity that the purchase probability of the browser in the behavior analysis list is greater than a preset probability threshold;
an inventory threshold is determined based on the first quantity of stock, the second quantity of stock, and the third quantity of stock.
Preferably, the acquiring of the hot spot information related to the goods within the preset time includes:
performing feature extraction on the goods information of the goods by adopting a preset fourth feature extraction rule to obtain a plurality of fourth feature values;
constructing a second marking vector based on the plurality of fourth characteristic values;
acquiring all hotspot information within preset time;
performing feature extraction on each hot spot information by adopting a preset fourth feature extraction rule to obtain a plurality of third feature values;
constructing a first marking vector based on the plurality of third characteristic values;
calculating the similarity of the first marking vector and the second marking vector, wherein the similarity calculation formula is as follows:
wherein SP represents similarity; a isiAn ith parameter value representing the first marker vector; biAn ith parameter value representing a second index vector; n represents the total number of parameters of the first indicating vector or the total number of parameters of the second indicating vector;
and when the similarity is greater than a preset similarity threshold, determining that the hotspot information is related to the goods.
Preferably, the purchase-sale-stock intelligent analysis system further includes:
the joint decision module is used for establishing a joint analysis group among enterprises and sharing data in the group;
the joint decision module performs the following operations:
acquiring historical sales data of the same goods of other enterprises;
performing feature extraction on the same goods historical sales data of the goods of the shipment period and other enterprises by adopting a preset first feature extraction rule to obtain a plurality of first feature values;
inputting a plurality of first characteristic values into a preset first neural network model, and determining a promotion and marketing factor;
inquiring a preset comparison table of the sales factor and the first stock quantity based on the sales factor, and determining the first stock quantity determined based on historical sales data of each other enterprise;
correcting the first stock quantity of the enterprise based on the following formula:
wherein, B1Representing the corrected first stock quantity; x is the number ofjA first stock quantity determined for historical sales data for a jth other business; n is the total number of other enterprises; y is1The first stock quantity before correction; gamma ray1、γ2Is a preset weight; f (-) is a rounding function with integers up.
Preferably, the purchase-sale-stock intelligent analysis system further includes:
the scheduling module is used for acquiring goods scheduling requests of enterprises and acquiring scheduling responses aiming at the goods scheduling requests from other enterprises;
the scheduling module performs the following operations:
acquiring a goods scheduling request sent by a worker;
outputting a template of a preset goods dispatching request book;
receiving the filling of the worker on the template of the goods dispatching request book and generating the goods dispatching request book;
executing a preset auditing process corresponding to the goods scheduling request book;
when the audit is passed, acquiring schedulable goods lists of other enterprises in a preset goods scheduling matching group;
determining other enterprises which can be matched based on the goods scheduling request book and the schedulable goods list;
sending goods dispatching request books to other enterprises which can be matched;
receiving goods dispatching bargaining books of the goods dispatching request books corresponding to other enterprises which can be matched;
analyzing the goods scheduling bargaining book, and determining the scheduling price and the scheduling time of the goods;
obtaining credit evaluation values of other enterprises of the goods scheduling bargaining book;
determining a target enterprise and sending a scheduling protocol based on the credit evaluation value, the scheduling price and the scheduling time;
the reputation evaluation value is determined by the following steps:
configuring a credit evaluation value for the enterprise;
acquiring dispatching history records of the enterprises responding to other enterprises;
analyzing the scheduling history record, and determining the scheduling execution condition corresponding to the scheduling bargaining book scheduled each time;
after each scheduling execution, based on the scheduling execution condition and the credit evaluation value, the credit evaluation value is adjusted; the adjustment formula is as follows:
wherein X represents the adjusted reputation evaluation value; t is0The reputation evaluation value before adjustment is obtained; d represents the quantized value of the scheduling execution condition, when the scheduling execution condition accords with the scheduling bargaining book, the d is a positive value, otherwise, the d is a negative value; t represents an adjustment value, t0Indicating a trim value when d is positiveTime, t-dt0Is greater than 0; determining the value of | d | by querying a preset coefficient table based on the continuous number of the historical scheduling execution condition which is the same as the current scheduling execution condition; the larger the number of consecutive numbers in the coefficient table, the larger the value of | d |.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of an intelligent purchase, sale, and inventory analysis system according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an intelligent analysis module according to an embodiment of the invention;
FIG. 3 is a diagram illustrating an architecture of a purchase, sales, inventory intelligent analysis system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of external data importing according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides an intelligent purchase, sale and inventory analysis system, which comprises the following components as shown in figures 1 and 2: the data management module 1, the external system connection module 2 and the inventory change operation module 3 further include: an intelligent analysis module 4;
the intelligent analysis module 4 includes:
an analysis instruction acquisition unit 41 for acquiring a data analysis instruction of a worker; the data analysis instructions include: the method comprises the following steps of (1) carrying out purchase, sale and storage daily analysis, goods input daily analysis, sale daily analysis and the like;
the instruction analysis unit 42 is used for analyzing the data analysis instruction, and determining a data capture rule, a data analysis rule and an output rule;
a data capture unit 43, configured to extract data to be analyzed from the data management module based on a data capture rule; the data capture rule specifies the form of captured data, such as the date of the data analyzed by the sales diary, and the sales data corresponding to the date is acquired;
a data analysis unit 44, configured to analyze the data to be analyzed based on the data analysis rule, so as to obtain an analysis result; the analysis results of, for example, sales diary analysis include: increase sales, growth trend, etc.;
and an analysis output unit 45 for outputting the analysis result based on the output rule. The output rule specifies the output form of the analysis result, such as a pie chart, a graph and the like;
wherein, the data management module 1 includes:
a supplier management unit for managing relevant data of suppliers; the vendor information maintenance may import vendors and new vendors on a list of vendor information according to an import template. The supplier name is bound to be filled, and the supplier maintained in the system needs to be unique and can not be repeated; [ company name ]: a name of the partner dealer enterprise; [ company Address ]: company address, i.e., dealer enterprise address; [ collaboration time ]: start time-end time, whether the current time is within the collaboration period determines whether the provider is during collaboration; [ collaboration Attachments ]: the upper combination is equal; [ contact information ]: the contact information, the contact persons and the contact addresses of a plurality of suppliers can be maintained, and the maintained contact persons can be selected to select the addresses when the documents are input; [ Account information ]: the method can maintain a plurality of account information, the account information is used for settlement, and a settlement account number is determined according to a selected bank and an account during settlement; FIG. 3 is a detailed system architecture diagram;
a dealer management unit for managing relevant data of dealers; managing by a dealer; and filling in the information of the dealers or clicking the newly added dealers according to the imported template to create the information of the dealers. Dealer information: [ distributor name ]: unique in the system and non-repeatable; [ company name ]: a name of the partner dealer enterprise; [ company Address ]: company address, i.e., dealer enterprise address; [ dealer grade ]: temporarily, the grade maintenance is not carried out, and the version can be ignored firstly; [ collaboration time ]: start time-end time, whether the current time is within the collaboration period determines whether the dealer is during collaboration; [ collaboration Attachments ]: the upper combination is equal; [ contact information ]: the contact information, the contact persons and the contact addresses of a plurality of dealers can be maintained, and the maintained contact persons can be selected to select the addresses when the documents are input; [ Account information ]: the method can maintain a plurality of account information, the account information is used for settlement, and a settlement account number is determined according to a selected bank and an account during settlement; and (3) level management: the dealer needs to set a corresponding rank corresponding to the price (the added price margin) of the rank of the goods, and calculate the selling price of the goods when the sales order is built.
The warehouse management unit is used for managing related data of a warehouse; after the [ entity warehouse ] and the [ goods owner ] are configured for enterprise users, the enterprise users can establish new cloud warehouses; filling in details of the warehouse; the name of the warehouse, which will apply to the various inventory operations within the system; entity bin [ warehouse type ]: a repository having a physical repository and a cargo owner code; virtual bin: the warehouse without the entity warehouse and the goods main code is used for the warehouse in and out operation, namely, the automatic virtual return of the system is realized; the dimension of the warehouse can specify the express which is supported, if the default express is used, the express is used for pushing down when the delivery document is issued; the contact address is the default sender information of the warehouse, and the default contact address and the default contact person of the entity warehouse are generally automatically taken out; newly adding a warehouse configuration batch rule; [ rule 1 ] term management; the term of validity is preferably that the time of failure is taken as the standard, and the closer the term of validity is to the time of failure, the warehouse-out is preferably arranged; rule 2, first in, first out; the first-in first-out refers to that the warehouse entry time is taken as the standard, and the warehouse entry time is earlier, and the warehouse exit is preferentially arranged;
a brand management unit for managing data related to brands; maintaining the cooperative brand information;
the goods management unit is used for managing related data of goods; before goods are created, the brand needs to be maintained; the brand is not necessarily filled and the recommendation is preferably perfect. And adding new goods, and importing the goods list according to the import template. Newly building goods, and directly inputting the goods by the platform.
The owner management unit is used for managing relevant data of the owner;
the purchase price management unit is used for managing the related data of the purchase price; importing a purchase price; the purchase price maintenance is the supplier cooperation condition corresponding to the sku and the purchase price maintenance, can be imported in batches, one sku can be supplied by a plurality of suppliers, and the corresponding supply price is maintained; details of goods supply; the details of the goods supply are a supplier list of the goods supply, a popup window is checked, clicking and pulling down is carried out, and a newly added supplier can be selected; checking details of the purchase price; each supplier card can be added with a new price, the new added state is that (established) the audit needs to be submitted by clicking, and the card can take effect after the audit is completed.
A profit management unit for managing data related to profits; and (3) profit management: pulling all the goods sku by the profit list, wherein each goods has all grades of grade management; setting goods grade profits in batches; importing profits; the goods grade profits can be imported in batch; all the grade prices need to be audited after being processed, and after the profit data is updated, the state is updated to that (established) the profit can take effect only after the audit is passed;
and the authority management unit is used for managing the related data of the authority.
In addition, record management is also provided; importing filing data according to the goods filing import template; after the maintenance of the goods file is completed, the goods with the trade type of bonded tax need to be put on record and the operation such as goods stocking declaration and the like can be carried out; after the creation of the recorded data is completed, the data needs to be clicked and submitted, and the user can submit the data to the CCS operation (if the submission fails, the user needs to confirm whether the SKU is already recorded in the clearance system, if the old data is already recorded, the user needs to delete the updated data in connection with the customs, at this time, the recorded data must be ensured to be accurate, otherwise, the online C list submission result is influenced).
In one embodiment, the external system connection module 2 includes:
the OMS system access module is used for connecting an external OMS system; the OMS system is an order management system;
and the ERP system access module is used for connecting an external ERP system. ERP (Enterprise Resource Planning) is an Enterprise information management system mainly oriented to the manufacturing industry for integrated management of material resources, capital resources and information resources; fig. 4 is an external ERP data import diagram.
The inventory change operation module 3 includes:
the warehousing operation unit is used for receiving warehousing operation of a user; importing a warehousing bill; importing a warehousing bill according to the warehousing bill of the import page; newly adding a warehousing bill, selecting a warehousing type, associating documents, planning a warehousing date, and automatically filling goods details in a receiving warehouse;
the ex-warehouse operation unit is used for receiving ex-warehouse operation of a user; importing an ex-warehouse document; importing the delivery list according to the import template; newly adding a delivery list, [ delivery type ]: selling, transferring and delivering, and delivering other materials out of the warehouse and destroying the materials out of the warehouse; [ Association document ]: selecting associated documents, a delivery warehouse, logistics, receiver information and goods detail automatic filling;
an inventory operation unit for receiving an inventory operation of a user; carrying out inventory checking operation;
the garage moving operation unit is used for receiving the garage moving operation of a user; moving the goods in the whole warehouse to a new warehouse;
and the destruction operation unit is used for receiving the destruction operation of the user, namely destroying the goods and executing the warehouse sale operation.
In one embodiment, the purchase-sale-stock intelligent analysis system further comprises: the account management module is used for managing a login account and account information of an enterprise;
the account management module comprises:
the authority configuration unit is used for newly building roles in the enterprise and configuring the authority of each role in the enterprise; the authority configuration unit has the main functions of: and adding roles, namely roles required by newly-built enterprise management, wherein the roles comprise: procurement, finance, warehouse, etc.; and distributing the authorities of the operation menu, the functions and the like visible to the newly added role.
And the sub-account management unit is used for creating an employee account for the staff of the enterprise and associating the employee account with the role. The sub-account management main functions are as follows: adding sub-accounts, wherein the enterprise main account can add member sub-accounts, namely employee accounts; and binding the role, and opening the available authority for the sub-account.
In one embodiment, the purchase-sale-stock intelligent analysis system further comprises: an inventory analysis module;
the inventory analysis module performs the following operations:
acquiring current inventory information of each goods in the warehouse every other preset time or when receiving an inventory analysis command of a worker;
acquiring inventory threshold values of all goods;
analyzing the current inventory information and determining the current inventory of each goods;
when the current inventory is less than the inventory threshold, adding the corresponding goods into the list to be purchased;
and outputting the list to be purchased.
Through the inventory analysis module, whether the current inventory meets the sales demand or not is analyzed, so that the sales condition which possibly occurs is met, and the sales coping capability of an enterprise is improved.
Wherein the inventory threshold is determined by:
acquiring the goods input data and the historical sales data of the goods;
analyzing the goods feeding data to determine a goods feeding period;
performing feature extraction on the purchase cycle and historical sales data by adopting a preset first feature extraction rule to obtain a plurality of first feature values; the first characteristic value includes: a first sales volume per shipment period, an average of all first sales volumes, a maximum of all first sales volumes, a variance of all first sales volumes, a historical contemporaneous first sales volume, a maximum of historical contemporaneous first sales volume, etc.;
inputting a plurality of first characteristic values into a preset first neural network model, and determining a promotion and marketing factor; the first neural network model is converged based on a large amount of data training in advance, is used for analyzing historical sales data and predicting sales volume in a future stocking period to obtain a marketing factor;
inquiring a preset comparison table of the purchase and sale factors and the first stock quantity based on the purchase and sale factors, and determining the first stock quantity; converting a prediction result of the first neural network model, namely the sales factor into a specific first stock number by using a preset comparison table of the sales factor and the first stock number; the comparison table of the sales factor and the first stock quantity is also a statistical form obtained by analyzing a large amount of data in advance;
acquiring hot spot information related to goods and comment information corresponding to the hot spot information within preset time; the hotspot information comprises: hot broadcast of television shows, live news, and the like; for example: the sales volume of certain clothes is predictably increased due to the fact that certain movies or television shows bring fire, so that the influence of hot spot information is considered during inventory analysis, and inventory can be effectively guaranteed to deal with the sales condition in the future stocking period;
extracting features of the hotspot information and the comment information by adopting a preset second feature extraction rule to obtain a plurality of second feature values; the second characteristic value includes: a feature value indicating hot spot information indicating whether the direction is a forward direction, a feature value indicating hot spot information indicating whether the direction is a reverse direction, a feature value indicating the number of people who pay attention, and the like;
inputting the plurality of second characteristic values into a preset second neural network model, and determining hot spot factors; the second neural network model is converged based on influence data of a large amount of goods influenced by hot spot information;
inquiring a preset comparison table of the hot factors and the second stock quantity based on the hot factors, and determining the second stock quantity; the comparison table of the hot spot factors and the second stock quantity is a form which is analyzed based on a large amount of data and is used for quantifying the hot spot factors;
acquiring browsing information of a display interface associated with goods within preset time;
analyzing the browsing information, determining each browser and the browsing behavior of each browser, and adding the browsers into a to-be-analyzed behavior list;
determining whether the browser makes a purchase of the goods after browsing based on the historical sales data;
when the browser purchases goods, deleting the browser from the to-be-behavioral analysis list;
extracting the characteristics of the browsing behaviors of each browser in the behavior analysis list by adopting a preset third characteristic extraction rule, and acquiring a plurality of behavior characteristics corresponding to each browser;
inputting a plurality of behavior characteristics into a preset third neural network model, and determining a behavior factor;
determining the purchase probability of each browser in the behavior analysis list based on the behavior factors and a preset comparison table of the behavior factors and the purchase probability;
determining a third stock quantity based on the quantity that the purchase probability of the browser in the behavior analysis list is greater than a preset probability threshold;
an inventory threshold is determined based on the first quantity of stock, the second quantity of stock, and the third quantity of stock. The method and the system realize the comprehensive enterprise sales data, the generated hot spot information and the behavior of the browser, and reasonably determine the inventory threshold value so as to ensure that the inventory can deal with the sales of the next period, thereby realizing the reasonable management of the inventory.
Wherein, obtain the hot spot information relevant with the goods in the time of predetermineeing, include:
performing feature extraction on the goods information of the goods by adopting a preset fourth feature extraction rule to obtain a plurality of fourth feature values; the fourth characteristic value can extract keywords of description information in the goods information; taking clothes as an example, the method can extract the characteristics of pictures used for describing clothes in the goods information, and extract the parameter values representing the information such as clothes styles, colors, patterns and the like;
constructing a second marking vector based on the plurality of fourth characteristic values;
acquiring all hotspot information within preset time;
performing feature extraction on each hot spot information by adopting a preset fourth feature extraction rule to obtain a plurality of third feature values; taking a movie as an example, extracting parameter values such as styles, colors and patterns representing dresses of characters in the movie;
constructing a first marking vector based on the plurality of third characteristic values;
calculating the similarity of the first marking vector and the second marking vector, wherein the similarity calculation formula is as follows:
wherein SP represents similarity; a isiAn ith parameter value representing the first marker vector; biAn ith parameter value representing a second index vector; n represents the total number of parameters of the first indicating vector or the total number of parameters of the second indicating vector;
and when the similarity is larger than a preset similarity threshold (for example, 0.96), determining that the hotspot information is related to the goods.
Through feature extraction and similarity calculation, whether the hotspot information is related to the goods is determined, and a data basis is further provided for hotspot information influence analysis in the inventory threshold.
In one embodiment, the purchase-sale-stock intelligent analysis system further comprises:
the joint decision module is used for establishing a joint analysis group among enterprises and sharing data in the group; for example: historical sales data, minimum inventory data, etc.;
the joint decision module performs the following operations:
acquiring historical sales data of the same goods of other enterprises;
performing feature extraction on the same goods historical sales data of the goods of the shipment period and other enterprises by adopting a preset first feature extraction rule to obtain a plurality of first feature values; the first characteristic value includes: a first sales volume per shipment period, an average of all first sales volumes, a maximum of all first sales volumes, a variance of all first sales volumes, a historical contemporaneous first sales volume, a maximum of historical contemporaneous first sales volume, etc.;
inputting a plurality of first characteristic values into a preset first neural network model, and determining a promotion and marketing factor; the first neural network model is converged based on a large amount of data training in advance, is used for analyzing historical sales data and predicting sales volume in a future stocking period to obtain a marketing factor;
inquiring a preset comparison table of the sales factor and the first stock quantity based on the sales factor, and determining the first stock quantity determined based on historical sales data of each other enterprise; converting a prediction result of the first neural network model, namely the sales factor into a specific first stock number by using a preset comparison table of the sales factor and the first stock number; the comparison table of the sales factor and the first stock quantity is also a statistical table obtained in advance through mass data analysis.
Correcting the first stock quantity of the enterprise based on the following formula:
wherein, B1Representing the corrected first stock quantity; x is the number ofjA first stock quantity determined for historical sales data for a jth other business; n is the total number of other enterprises; y is1A first stock quantity before correction; gamma ray1、γ2Is a preset weight; f (-) is a rounding function with integers up.
The first stock quantity is adjusted by referring to the same goods sales condition of other enterprises, so that the influence of the sales environment can be reflected more accurately by the first stock quantity analyzed based on the sales data.
In one embodiment, the purchase-sale-stock intelligent analysis system further comprises:
the scheduling module is used for acquiring goods scheduling requests of enterprises and acquiring scheduling responses aiming at the goods scheduling requests from other enterprises;
the scheduling module performs the following operations:
acquiring a goods scheduling request sent by a worker;
outputting a template of a preset goods dispatching request book;
receiving the filling of the worker on the template of the goods dispatching request book and generating the goods dispatching request book; the goods dispatching request book comprises description information, demand time limit information and the like of demanded goods;
executing a preset auditing process corresponding to the goods scheduling request book; the auditing is mainly performed on the goods scheduling request book, for example: the staff is a salesman and needs to be sent to the higher supervisor, manager and the like of the staff for auditing; when the amount is larger, the total amount needs to be checked by the manager.
When the audit is passed, acquiring schedulable goods lists of other enterprises in a preset goods scheduling matching group; the schedulable goods list can be prestored on the server, and the scheduling module is connected to the server to obtain the schedulable goods list prestored by other enterprises;
determining other enterprises which can be matched based on the goods scheduling request book and the schedulable goods list; the other enterprises which can be matched are enterprises which have goods corresponding to the goods dispatching request books on the dispatchable goods list;
sending goods dispatching request books to other enterprises which can be matched;
receiving goods dispatching bargaining books of the goods dispatching request books corresponding to other enterprises which can be matched;
analyzing the goods scheduling bargaining book, and determining the scheduling price and the scheduling time of the goods;
obtaining credit evaluation values of other enterprises of the goods scheduling bargaining book;
determining a target enterprise and sending a scheduling protocol based on the credit evaluation value, the scheduling price and the scheduling time; when the credit evaluation value is greater than a preset credit evaluation threshold value, and other enterprises with the lowest scheduling price and the fastest scheduling time in the enterprises are target enterprises; and determining whether the target enterprise is reliable by adding a credit evaluation value, and ensuring the reliable execution of scheduling by signing a scheduling protocol. The reputation evaluation value is determined by the following steps:
configuring a credit evaluation value for the enterprise;
acquiring dispatching history records of the enterprises responding to other enterprises;
analyzing the scheduling history record, and determining the scheduling execution condition corresponding to the scheduling bargaining book scheduled each time;
after each scheduling execution, based on the scheduling execution condition and the credit evaluation value, the credit evaluation value is adjusted; the adjustment formula is as follows:
wherein X represents the adjusted reputation evaluation value; t is0The reputation evaluation value before adjustment is obtained; d represents the quantized value of the scheduling execution condition, when the scheduling execution condition accords with the scheduling bargaining book, the d is a positive value, otherwise, the d is a negative value; t represents an adjustment value, t0Represents a fine tuning value, t-dt when d is positive0Is greater than 0; determining the value of | d | by querying a preset coefficient table based on the continuous number of the historical scheduling execution condition which is the same as the current scheduling execution condition; the larger the number of consecutive numbers in the coefficient table, the larger the value of | d |; the d value is larger and larger when the enterprise scheduling execution condition is consistent with the scheduling bargaining book all the time through the coefficient table; when the enterprise scheduling execution condition is consistent and does not accord with the scheduling bargaining book, the larger the value of- | d |; the adjustment amplitude reward of the enterprise with good continuity is guaranteed, and the punishment strength is increased continuously relatively. And the enterprise scheduling is supervised to meet the scheduling bargaining book, and the competitive fairness is ensured.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A purchase-sale-stock intelligent analysis system comprising: data management module, external system connection module and inventory change operation module, its characterized in that still includes: an intelligent analysis module;
the intelligent analysis module comprises:
the analysis instruction acquisition unit is used for acquiring a data analysis instruction of a worker;
the instruction analysis unit is used for analyzing the data analysis instruction and determining a data capture rule, a data analysis rule and an output rule;
the data capturing unit is used for extracting data to be analyzed from the data management module based on the data capturing rule;
the data analysis unit is used for analyzing the data to be analyzed based on the data analysis rule to obtain an analysis result;
and the analysis output unit is used for outputting the analysis result based on the output rule.
2. The intelligent marketing inventory analysis system of claim 1, wherein the data management module comprises:
a supplier management unit for managing relevant data of suppliers;
a dealer management unit for managing relevant data of dealers;
the warehouse management unit is used for managing related data of a warehouse;
a brand management unit for managing data related to brands;
the goods management unit is used for managing related data of goods;
the owner management unit is used for managing relevant data of the owner;
the purchase price management unit is used for managing the related data of the purchase price;
a profit management unit for managing data related to profits;
and the authority management unit is used for managing the related data of the authority.
3. The intelligent marketing inventory analysis system of claim 1, wherein the external system connection module comprises:
the OMS system access module is used for connecting an external OMS system;
and the ERP system access module is used for connecting an external ERP system.
4. The intelligent inventory analysis system as recited in claim 1, wherein the inventory change operations module comprises:
the warehousing operation unit is used for receiving warehousing operation of a user;
the ex-warehouse operation unit is used for receiving ex-warehouse operation of a user;
an inventory operation unit for receiving an inventory operation of a user;
the garage moving operation unit is used for receiving the garage moving operation of a user;
and the destruction operation unit is used for receiving the destruction operation of the user.
5. The intelligent marketing inventory analysis system of claim 1, further comprising: the account management module is used for managing a login account and account information of an enterprise;
the account management module comprises:
the authority configuration unit is used for newly building roles in the enterprise and configuring the authority of each role in the enterprise;
and the sub-account management unit is used for creating an employee account for the staff of the enterprise and associating the employee account with the role.
6. The intelligent marketing inventory analysis system of claim 1, further comprising: an inventory analysis module;
the inventory analysis module performs the following operations:
acquiring current inventory information of each goods in the warehouse every other preset time or when receiving an inventory analysis command of a worker;
acquiring an inventory threshold value of each goods;
analyzing the current inventory information and determining the current inventory of each goods;
when the current inventory amount is smaller than the inventory threshold value, adding the corresponding goods into a list to be purchased;
and outputting the list to be purchased.
7. The intelligent purchase-sale-inventory analysis system as recited in claim 6, wherein the inventory threshold is determined by:
acquiring the goods input data and the historical sales data of the goods;
analyzing the goods feeding data to determine a goods feeding period;
performing feature extraction on the goods-feeding period and the historical sales data by adopting a preset first feature extraction rule to obtain a plurality of first feature values;
inputting a plurality of first characteristic values into a preset first neural network model, and determining an advance and sale factor;
inquiring a preset comparison table of the purchase and sale factors and the first stock quantity based on the purchase and sale factors, and determining the first stock quantity;
acquiring hot spot information related to the goods and comment information corresponding to the hot spot information within preset time;
extracting features of the hotspot information and the comment information by adopting a preset second feature extraction rule to obtain a plurality of second feature values;
inputting a plurality of second characteristic values into a preset second neural network model, and determining hot spot factors;
inquiring a preset comparison table of the hot factors and the second stock quantity based on the hot factors, and determining the second stock quantity;
acquiring browsing information of a display interface associated with the goods within preset time;
analyzing the browsing information, determining each browser and the browsing behavior of each browser, and adding the browser into a to-be-analyzed behavior list;
determining whether the browser made a purchase of the item after browsing based on historical sales data;
deleting the browser from the to-be-behavioral analysis list when the browser makes a purchase of the goods;
performing feature extraction on the browsing behavior of each browser in the behavior analysis list by adopting a preset third feature extraction rule to obtain a plurality of behavior features corresponding to each browser;
inputting a plurality of behavior characteristics into a preset third neural network model, and determining a behavior factor;
determining the purchase probability of each browser in the behavior analysis list based on the behavior factors and a preset comparison table of the behavior factors and the purchase probability;
determining a third stock quantity based on the quantity of the purchasing probability of the browser in the behavior analysis list, which is greater than a preset probability threshold value;
determining the inventory threshold based on the first quantity of stock, the second quantity of stock, and the third quantity of stock.
8. The intelligent marketing and inventory analysis system of claim 7, wherein the obtaining of hot spot information related to the goods within a preset time comprises:
performing feature extraction on the goods information of the goods by adopting a preset fourth feature extraction rule to obtain a plurality of fourth feature values;
constructing a second labeling vector based on a plurality of the fourth eigenvalues;
acquiring all hotspot information within preset time;
performing feature extraction on each hot spot information by adopting a preset fourth feature extraction rule to obtain a plurality of third feature values;
constructing a first marker vector based on a plurality of the third eigenvalues;
calculating the similarity of the first marker vector and the second marker vector, wherein the similarity calculation formula is as follows:
wherein SP represents the similarity; a isiAn ith parameter value representing the first marker vector; biAn ith parameter value representing the second marker vector; n represents the total number of parameters of the first marker vector or the total number of parameters of the second marker vector;
and when the similarity is greater than a preset similarity threshold, determining that the hotspot information is related to the goods.
9. The intelligent marketing inventory analysis system of claim 7, further comprising:
the joint decision module is used for establishing a joint analysis group among enterprises and sharing data in the group;
the joint decision module performs the following operations:
acquiring historical sales data of the same goods of other enterprises;
performing feature extraction on the same historical sales data of the goods of the shipment period and the other enterprises by adopting a preset first feature extraction rule to obtain a plurality of first feature values;
inputting a plurality of first characteristic values into a preset first neural network model, and determining an advance and sale factor;
inquiring a preset comparison table of the sales factor and the first stock quantity based on the sales factor, and determining the first stock quantity determined based on historical sales data of each other enterprise;
correcting the first stock quantity of the enterprise based on the following formula:
wherein, B1Representing the corrected first stock quantity; x is the number ofjThe first stock quantity determined for historical sales data for a jth other business; n is the total number of other enterprises; y is1The first stock quantity before correction; gamma ray1、γ2Is a preset weight; f (-) is a rounding function with integers up.
10. The intelligent marketing inventory analysis system of claim 1, further comprising:
the scheduling module is used for acquiring goods scheduling requests of enterprises and acquiring scheduling responses aiming at the goods scheduling requests from other enterprises;
the scheduling module performs the following operations:
acquiring a goods scheduling request sent by a worker;
outputting a template of a preset goods dispatching request book;
receiving the filling of the staff on the template of the goods dispatching request book and generating the goods dispatching request book;
executing a preset auditing process corresponding to the goods scheduling request book;
when the audit is passed, acquiring schedulable goods lists of other enterprises in a preset goods scheduling matching group;
determining other enterprises which can be matched based on the goods scheduling request book and the schedulable goods list;
sending the goods dispatching request book to other enterprises which can be matched;
receiving goods dispatching bargaining books corresponding to the goods dispatching request books of other enterprises which can be matched;
analyzing the goods scheduling bargaining book, and determining the scheduling price and the scheduling time of the goods;
obtaining credit evaluation values of the other enterprises of the goods scheduling bargaining book;
determining a target enterprise and sending a scheduling protocol based on the reputation evaluation value, the scheduling price and the scheduling time;
wherein the reputation evaluation value is determined by the steps of:
configuring a credit evaluation value for the enterprise;
acquiring dispatching history records of the enterprises responding to other enterprises;
analyzing the scheduling history record, and determining the scheduling execution condition corresponding to the scheduling bargaining book scheduled each time;
after each scheduling execution, based on the scheduling execution condition and the credit evaluation value, adjusting the credit evaluation value; the adjustment formula is as follows:
wherein X represents the adjusted reputation evaluation value; t is0The reputation evaluation value before adjustment is obtained; d represents the quantized value of the scheduling execution condition, when the scheduling execution condition accords with the scheduling bargaining book, the d is a positive value, otherwise, the d is a negative value; t represents an adjustment value, t0Represents a fine tuning value, t-dt when d is positive0Is greater than 0; determining the value of | d | by querying a preset coefficient table based on the continuous number of the historical scheduling execution condition which is the same as the current scheduling execution condition; the larger the number of consecutive numbers in the coefficient table, the larger the value of | d |.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115730892A (en) * | 2022-12-12 | 2023-03-03 | 武汉逸飞物流有限公司 | Intelligent logistics based cargo transportation method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115730892A (en) * | 2022-12-12 | 2023-03-03 | 武汉逸飞物流有限公司 | Intelligent logistics based cargo transportation method and device |
| CN115730892B (en) * | 2022-12-12 | 2023-07-25 | 武汉逸飞物流有限公司 | Cargo transportation method and device based on intelligent logistics |
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