CN114897575A - Intelligent sales platform based on big data and method thereof - Google Patents
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Abstract
The invention discloses an intelligent sales platform based on big data and a method thereof, relating to the technical field of network sales; the system comprises an inventory monitoring module, an inventory analysis module, a controller, a database, an inventory management module, an evaluation module and a commodity display module; the inventory analysis module is used for receiving the inventory data of various commodities transmitted by the inventory monitoring module and analyzing and processing the inventory data; the inventory management module is used for receiving the inventory shortage signal, then supplementing the corresponding commodity and recording the supplement quantity BL; the controller is used for carrying out grade judgment on the replenishment quantity BL to obtain an evaluation signal; the evaluation module is used for comprehensively evaluating the evaluation signals with the time stamps stored in the database; the commodity display module is used for displaying commodities on the sales platform; the invention can adjust the display sequence of the commodity information according to the commodity selling condition, is simple and convenient, and effectively improves the commodity selling speed.
Description
Technical Field
The invention relates to the technical field of network sales, in particular to an intelligent sales platform based on big data and a method thereof.
Background
Modern social economy develops rapidly, rely on the progress of science and technology and social development, rely on the circulation of a great variety of commodities at the same time even more, so to speak the economy of modern society is the commodity economy, the network sales platform is the sales approach of carrying on the network transaction based on the Internet, and on the analysis control to the big data, carry on the analysis control of the data to the commodity, carry on the show in time to the commodity information at the same time, facilitate the sale of the commodity; under the rapid development of commodity circulation, inventory and stock of commodities are a big problem;
at present, the network sales platform has the problems that the commodity management is disordered, the goods selling condition needs to be paid attention to manually at any time, and once the goods selling condition is neglected, the goods are out of stock or the goods are overdue due to the goods being sold in a stagnation way; meanwhile, commodities cannot be reasonably arranged for display according to the selling conditions of the commodities, so that the commodity selling rate is increased; therefore, the invention aims to provide an intelligent sales platform based on big data and a method thereof, which can not only pay attention to the selling condition of commodities at any time, but also can timely replenish the goods in short supply at any time, can adjust the display sequence of commodity information according to the selling condition of the commodities, is simple and convenient, and effectively improves the selling rate of the commodities.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent sales platform based on big data and a method thereof.
The purpose of the invention can be realized by the following technical scheme: an intelligent sales platform based on big data comprises an inventory monitoring module, an inventory analysis module, a controller, a database, an inventory management module, an evaluation module and a commodity display module;
the inventory monitoring module is used for monitoring inventory data of various commodities in real time and transmitting the inventory data to the inventory analysis module; the inventory data includes the inventory amount and unit price of the commodity;
the inventory analysis module is used for receiving the inventory data of various commodities transmitted by the inventory monitoring module and analyzing and processing the inventory data, and the specific analysis steps are as follows:
the method comprises the following steps: when the commodity starts to be sold, the inventory monitoring module acquires inventory data of the commodity according to the monitoring interval duration corresponding to the commodity;
step two: setting inventory parameters, including: the method comprises the steps of firstly, presetting time T1, presetting time T2 and presetting inventory change rate value K; wherein T1, T2 and K are preset values;
step three: marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart;
step four: when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state;
step five: when the commodity is in a hot-selling state, acquiring a reference speed CK of the commodity;
step six: when the commodity is in a hot-selling state, calculating the stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a1), wherein a1 and a2 are coefficient factors;
comparing the out-of-stock coefficient BH with an out-of-stock coefficient threshold;
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
step seven: when the real-time inventory Li of the commodity is less than the inventory threshold value, marking the real-time inventory change rate value of the commodity as Kz;
calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a4), wherein a3 and a4 are coefficient factors;
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient stock signal;
the inventory analysis module is used for transmitting the inventory shortage signal and the corresponding real-time inventory Li, reference speed CK and real-time inventory change speed value Kz to the controller, and the controller is used for transmitting the inventory shortage signal and the corresponding real-time inventory Li, reference speed CK and real-time inventory change speed value Kz to the inventory management module;
the inventory management module is used for receiving the inventory shortage signal and then supplementing corresponding commodities;
the commodity display module is used for displaying commodities on the sales platform, and the specific steps are as follows:
v1: acquiring a current inventory change rate value Kz of a commodity;
v2: automatically acquiring a replenishment evaluation value TK of a commodity from a database;
v3: calculating a good development value YZ of a commercial product by using a formula YZ-Kz × g1+ TK × g2, wherein g1 and g2 are coefficient factors;
the commodity display module is used for displaying commodities in sequence according to the magnitude of the excellent and spread value YZ.
Further, when the commodity is in a hot-selling state, acquiring a reference speed CK of the commodity; the method specifically comprises the following steps:
acquiring a real-time inventory change rate value of the commodities within a first preset time T1, and marking the real-time inventory change rate value as Di to obtain a change rate information set;
obtaining the standard deviation alpha of the change rate information group according to a standard deviation calculation formula;
if alpha is less than or equal to a preset standard deviation threshold value; obtaining the average value of the change rate information group according to an average value calculation formula and marking the average value as Ks; simultaneously, making the reference rate CK equal to Ks;
if α > the preset standard deviation threshold, let the reference rate CK be (Ks × b1+ α × b2) 0.45 (ii) a Wherein b1 and b2 are coefficient factors.
Further, the inventory management module specifically comprises the following working steps:
s1: when the inventory management module receives the inventory shortage signal, the inventory management module acquires the real-time inventory Li at the moment and marks the real-time inventory Li as the inventory B before replenishment Front part ；
S2: replenishing the corresponding commodities, and stopping replenishing when the stock shortage coefficient BH is less than the stock shortage coefficient threshold multiplied by 1/2; and obtaining the real-time inventory at the moment and marking as B Rear end (ii) a Wherein when B Front side If the current value is less than the inventory threshold value, BH is 1/(Li multiplied by a3-Kz multiplied by a 4); when B is present Front side When the inventory value is larger than or equal to the inventory value threshold, BH (CK multiplied by a2)/(Li multiplied by a 1);
s3: using the formula BL ═ B Rear end -B Front side Calculating to obtain the replenishment quantity BL;
the inventory management module is used for transmitting the replenishment quantity BL to the controller.
Further, the controller is configured to perform level evaluation on the replenishment quantity BL to obtain an evaluation signal, and specifically includes:
EE 1: comparing the replenishment quantity BL with a replenishment threshold; the replenishment threshold comprises X2, X3; wherein X2, X3 are both fixed values and X2> X3;
EE 2: when BL is larger than or equal to X2, the evaluation signal is a large replenishment signal;
EE 3: when X3< BL < X2, the evaluation signal is a medium replenishment signal;
EE 4: when BL is less than or equal to X3, the evaluation signal is a small replenishment signal;
the controller is used for stamping the evaluation signals and transmitting the evaluation signals to the database for real-time storage.
Further, the evaluation module is used for comprehensively evaluating the evaluation signals with the time stamps stored in the database, and the specific evaluation method is as follows:
g1: acquiring an evaluation signal within ten days before the current time of the system according to the timestamp; the evaluation signals comprise large replenishment signals, medium replenishment signals and small replenishment signals;
g2: the times of the large replenishment signals are marked as Zb1, the times of the medium replenishment signals are marked as Zb2, and the times of the small replenishment signals are marked as Zb 3;
g3: calculating a replenishment evaluation value TK of the commodity by using a formula TK ═ Zb1 multiplied by 0.3+ Zb2 multiplied by 0.2+ Zb3 multiplied by 0.1;
the evaluation module is used for transmitting the replenishment evaluation value TK of the commodity to the database for storage.
Further, the specific analysis step of the inventory analysis module further includes:
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within the second preset time T2, the commodities are in a lost sales state; generating a hysteresis signal;
the inventory analysis module is used for transmitting a sale delay signal to the inventory management module; the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal.
Further, the working method of the intelligent sales platform based on the big data comprises the following steps:
w1: when a commodity is sold, acquiring inventory data of the commodity according to the monitoring interval duration corresponding to the commodity; analyzing and processing the inventory data; the method specifically comprises the following steps:
w11: marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart;
w12: when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state;
w13: when the commodity is in a hot sale state, acquiring a real-time inventory change rate value of the commodity within a first preset time T1, and marking the value as Di to obtain a change rate information set;
obtaining the standard deviation alpha of the change rate information group according to a standard deviation calculation formula;
if alpha is less than or equal to a preset standard deviation threshold value; obtaining the average value of the change rate information group according to an average value calculation formula and marking the average value as Ks; simultaneously, making the reference rate CK equal to Ks;
if α > the preset standard deviation threshold, let the reference rate CK be (Ks × b1+ α × b2) 0.45 ；
Calculating the stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a 1); if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
w13: when the real-time inventory Li of the commodity is less than the inventory threshold value, marking the real-time inventory change rate value of the commodity as Kz;
calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a 4);
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
w2: after receiving the inventory shortage signal, the inventory management module replenishes the corresponding commodity; recording the replenishment quantity BL; the controller is used for carrying out grade judgment on the replenishment quantity BL to obtain an evaluation signal; comprehensively evaluating the evaluation signals with the time stamps stored in the database through an evaluation module to obtain a replenishment evaluation value TK of the commodity;
w3: displaying the commodities on the sales platform according to the selling conditions of the commodities; the method comprises the following specific steps:
w31: acquiring a current inventory change rate value Kz of a commodity;
w32: automatically acquiring a replenishment evaluation value TK of a commodity from a database;
w33: calculating a good development value YZ of the commodity by using a formula YZ-Kz × g1+ TK × g 2; and displaying the commodities in sequence according to the magnitude of the excellent exhibition value YZ.
Further, the analyzing and processing the inventory data in step W1 further includes:
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within the second preset time T2, the commodities are in a lost sales state; generating a hysteresis signal;
the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal.
The invention has the beneficial effects that:
1. the inventory analysis module is used for analyzing and processing the inventory data; marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart; when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state; when the commodity is in a hot sales state, the reference speed CK is obtained through relevant processing; calculating a stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a1), and marking the real-time stock change rate value of the commodity as Kz when the real-time stock Li of the commodity is less than the stock threshold; calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a 4); if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal; the inventory management module is used for receiving the inventory shortage signal and then supplementing corresponding commodities; the invention can pay attention to the selling condition of the commodities at any time, and can timely replenish the goods in short of stock at any time, thereby effectively improving the selling rate of the commodities;
2. the commodity display module is used for displaying commodities on a sales platform and acquiring the current inventory change rate value Kz of the commodities; automatically acquiring a replenishment evaluation value TK of a commodity from a database; the excellent spread value YZ of the commodity is obtained by calculation according to a formula Kz multiplied by g1 and TK multiplied by g2, and the commodity display module is used for displaying the commodity in sequence according to the excellent spread value YZ;
3. when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within the second preset time T2, the commodities are in a lost sales state; generating a hysteresis signal; the inventory analysis module is used for transmitting the lost sales signal to the inventory management module; the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal; the problems of commodity overdue and the like caused by commodity delay are avoided, and the commodity sales rate is improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an intelligent sales platform based on big data includes an inventory monitoring module, an inventory analysis module, a controller, a database, an inventory management module, an evaluation module, and a merchandise display module;
the inventory monitoring module is used for monitoring inventory data of various commodities in real time and transmitting the inventory data to the inventory analysis module; the inventory data includes the inventory amount of the commodities and the unit price of the commodities;
the inventory analysis module is used for receiving the inventory data of various commodities transmitted by the inventory monitoring module and analyzing and processing the inventory data, and the specific analysis steps are as follows:
the method comprises the following steps: when the commodity starts to be sold, the inventory monitoring module acquires inventory data of the commodity according to the monitoring interval duration corresponding to the commodity;
step two: setting inventory parameters, including: the method comprises the steps of firstly, presetting time T1, presetting time T2 and presetting inventory change rate value K; wherein T1, T2 and K are preset values;
step three: marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart;
step four: when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state;
step five: when the commodity is in a hot sale state, acquiring a real-time inventory change rate value of the commodity within a first preset time T1, and marking the value as Di to obtain a change rate information group;
obtaining the standard deviation alpha of the change rate information group according to a standard deviation calculation formula;
if alpha is less than or equal to a preset standard deviation threshold value; obtaining the average value of the change rate information group according to an average value calculation formula and marking the average value as Ks; simultaneously, making the reference rate CK equal to Ks;
if α > the preset standard deviation threshold, let the reference rate CK be (Ks × b1+ α × b2) 0.45 (ii) a Wherein b1 and b2 are coefficient factors;
step six: when the commodity is in a hot-selling state, calculating the stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a1), wherein a1 and a2 are coefficient factors;
comparing the out-of-stock coefficient BH with an out-of-stock coefficient threshold;
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
step seven: when the real-time inventory Li of the commodity is less than the inventory threshold value, marking the real-time inventory change rate value of the commodity as Kz;
calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a4), wherein a3 and a4 are coefficient factors;
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
the inventory analysis module is used for transmitting the inventory shortage signal and the corresponding real-time inventory Li, reference speed CK and real-time inventory change speed value Kz to the controller, and the controller is used for transmitting the inventory shortage signal and the corresponding real-time inventory Li, reference speed CK and real-time inventory change speed value Kz to the inventory management module;
the inventory management module is used for receiving the inventory shortage signal and then supplementing corresponding commodities; the method comprises the following specific steps:
s1: when the inventory management module receives the inventory shortage signal, the inventory management module acquires the real-time inventory Li at the moment and marks the real-time inventory Li as the pre-supplement inventory B Front side ；
S2: replenishing the corresponding commodities, and stopping replenishing when the stock shortage coefficient BH is less than the stock shortage coefficient threshold multiplied by 1/2; and obtaining the real-time inventory at the moment and marking as B Rear end (ii) a Wherein when B Front part If the current value is less than the inventory threshold value, BH is 1/(Li multiplied by a3-Kz multiplied by a 4); when B is present Front side When the inventory value is larger than or equal to the inventory value threshold, BH (CK multiplied by a2)/(Li multiplied by a 1);
s3: using the formula BL ═ B Rear end -B Front side Calculating to obtain the replenishment quantity BL;
the inventory management module is used for transmitting the replenishment quantity BL to the controller, and the controller is used for carrying out grade judgment on the replenishment quantity BL to obtain an evaluation signal, and specifically comprises the following steps:
EE 1: comparing the replenishment quantity BL with a replenishment threshold; replenishment thresholds include X2, X3; wherein X2, X3 are both fixed values and X2> X3;
EE 2: when BL is larger than or equal to X2, the evaluation signal is a large replenishment signal;
EE 3: when X3< BL < X2, the evaluation signal is a medium replenishment signal;
EE 4: when BL is less than or equal to X3, the evaluation signal is a small replenishment signal;
the controller is used for stamping a time stamp on the evaluation signal and transmitting the evaluation signal to the database for real-time storage;
the evaluation module is used for comprehensively evaluating the evaluation signals with the time stamps stored in the database, and the specific evaluation method comprises the following steps:
g1: acquiring an evaluation signal within ten days before the current time of the system according to the timestamp; the evaluation signals comprise large replenishment signals, medium replenishment signals and small replenishment signals;
g2: the times of the large replenishment signals are marked as Zb1, the times of the medium replenishment signals are marked as Zb2, and the times of the small replenishment signals are marked as Zb 3;
g3: calculating a replenishment evaluation value TK of the commodity by using a formula TK-Zb 1 × 0.3+ Zb2 × 0.2+ Zb3 × 0.1;
the evaluation module is used for transmitting the replenishment evaluation value TK of the commodity to the database for storage;
the commodity display module is used for displaying commodities on the sales platform, and the specific steps are as follows:
v1: acquiring a current inventory change rate value Kz of a commodity;
v2: automatically acquiring a replenishment evaluation value TK of a commodity from a database;
v3: calculating a good development value YZ of a commercial product by using a formula YZ-Kz × g1+ TK × g2, wherein g1 and g2 are coefficient factors;
the commodity display module is used for sequentially displaying commodities according to the excellent and developed values YZ, the display sequence of commodity information can be adjusted according to the selling conditions of the commodities, the commodity display module is simple and convenient, and the commodity selling speed is effectively improved;
the specific analysis steps of the inventory analysis module further comprise:
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within the second preset time T2, the commodities are in a lost sales state; generating a hysteresis signal;
the inventory analysis module is used for transmitting the lost sales signal to the inventory management module; the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal;
a working method of an intelligent sales platform based on big data comprises the following steps:
w1: when a commodity is sold, acquiring inventory data of the commodity according to the monitoring interval duration corresponding to the commodity; analyzing and processing the inventory data; the method specifically comprises the following steps:
w11: marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart;
w12: when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state;
w13: when the commodity is in a hot sale state, acquiring a real-time inventory change rate value of the commodity within a first preset time T1, and marking the value as Di to obtain a change rate information group;
obtaining the standard deviation alpha of the change rate information group according to a standard deviation calculation formula;
if alpha is less than or equal to a preset standard deviation threshold value; obtaining the average value of the change rate information group according to an average value calculation formula and marking the average value as Ks; simultaneously, making the reference rate CK equal to Ks;
if α > the preset standard deviation threshold, let the reference rate CK be (Ks × b1+ α × b2) 0.45 ；
Calculating the stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a 1); if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
w13: when the real-time inventory Li of the commodity is less than the inventory threshold value, marking the real-time inventory change rate value of the commodity as Kz;
calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a 4);
if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
w2: after receiving the inventory shortage signal, the inventory management module replenishes the corresponding commodity; recording the replenishment quantity BL; the controller is used for carrying out grade judgment on the replenishment quantity BL to obtain an evaluation signal; comprehensively evaluating the evaluation signals with the time stamps stored in the database through an evaluation module to obtain a replenishment evaluation value TK of the commodity;
w3: displaying the commodities on the sales platform according to the selling conditions of the commodities; the method comprises the following specific steps:
w31: acquiring a current inventory change rate value Kz of a commodity;
w32: automatically acquiring a replenishment evaluation value TK of a commodity from a database;
w33: calculating a good development value YZ of the commodity by using a formula YZ-Kz × g1+ TK × g 2;
the commodities are displayed in sequence according to the magnitude of the excellent value YZ, so that the commodity selling rate is effectively improved;
the analyzing and processing of the inventory data in step W1 further includes:
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within a second preset time T2, the commodities are in a sale-delayed state; generating a hysteresis signal;
the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal.
The working principle of the invention is as follows:
when the intelligent sales platform based on big data works, when a commodity starts to be sold, an inventory monitoring module acquires inventory data of the commodity according to monitoring interval duration corresponding to the commodity; the inventory analysis module is used for analyzing and processing the inventory data; marking the real-time inventory of the commodity as Li; establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart; when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state; when the commodity is in a hot sale state, acquiring a real-time inventory change rate value of the commodity within a first preset time T1, and marking the value as Di to obtain a change rate information group; obtaining a reference rate CK through correlation processing; calculating a stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a1), and marking a real-time stock change rate value of the commodity as Kz when the real-time stock Li of the commodity is less than a stock threshold; calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a 4); if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
the inventory management module is used for receiving the inventory shortage signal, then supplementing the corresponding commodity and recording the supplement quantity BL; the controller is used for carrying out grade evaluation on the replenishment quantity BL to obtain an evaluation signal, the evaluation module is used for carrying out comprehensive evaluation on the evaluation signal with the timestamp stored in the database and calculating to obtain a replenishment evaluation value TK of the commodity; the commodity display module is used for displaying commodities on the sales platform and acquiring the current inventory change rate value Kz of the commodities; automatically acquiring a replenishment evaluation TK of a commodity from a database; the commodity display module is used for sequentially displaying commodities according to the excellent value YZ, and can adjust the display sequence of commodity information according to the commodity selling conditions, so that the commodity display module is simple and convenient, and the commodity selling rate is effectively improved;
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are smaller than the change rate lower limit threshold within the second preset time T2, the commodities are in a lost sales state; generating a hysteresis signal; the inventory analysis module is used for transmitting the lost sales signal to the inventory management module; the inventory management module is used for distributing sales personnel to carry out online and offline marketing on corresponding commodities after receiving the sale delay signal; the problems of commodity expiration and the like caused by commodity delay are avoided, and the commodity sales rate is improved.
The formula and the coefficient factor are both obtained by acquiring a large amount of data to perform software simulation and performing parameter setting processing by corresponding experts, and the formula and the coefficient factor which are consistent with a real result are obtained.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. An intelligent sales platform based on big data is characterized by comprising an inventory monitoring module, an inventory analysis module, a controller, a database, an inventory management module, an evaluation module and a commodity display module;
the inventory monitoring module is used for monitoring inventory data of various commodities in real time and transmitting the inventory data to the inventory analysis module; the inventory analysis module is used for analyzing and processing inventory data, and the specific analysis steps are as follows:
when the commodity starts to be sold, establishing a curve graph of the real-time inventory quantity changing along with time, and marking the curve graph as a commodity inventory quantity curve graph; the commodity inventory quantity curve chart is subjected to derivation to obtain a commodity inventory change rate curve chart;
when the real-time inventory Li of the commodities is larger than or equal to the inventory threshold, if the real-time inventory change rate values of the commodities are larger than a preset inventory change rate value K within a first preset time T1; indicating that the commodity is in a hot-selling state; when the commodity is in a hot-selling state, acquiring a reference speed CK of the commodity; calculating the stock shortage coefficient BH by using a formula BH (CK multiplied by a2)/(Li multiplied by a 1);
when the real-time inventory Li of the commodity is less than the inventory threshold value, marking the real-time inventory change rate value of the commodity as Kz; calculating the stock shortage coefficient BH by using a formula BH 1/(Li multiplied by a3-Kz multiplied by a 4); if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
the inventory management module is used for receiving the inventory shortage signal, then supplementing the corresponding commodity and recording the supplement amount; the commodity display module is used for displaying commodities on the sales platform, and specifically comprises the following steps:
acquiring a current inventory change rate value Kz of a commodity; automatically acquiring a replenishment evaluation TK of a commodity from a database; calculating a good development value YZ of the commodity by using a formula YZ-Kz × g1+ TK × g 2; and displaying the commodities in sequence according to the magnitude of the excellent exhibition value YZ.
2. The intelligent sales platform based on big data of claim 1, wherein the controller is configured to perform a grade evaluation on the replenishment quantity BL to obtain an evaluation signal, and specifically comprises:
comparing the replenishment quantity BL with a replenishment threshold; when BL is larger than or equal to X2, the evaluation signal is a large replenishment signal; when X3< BL < X2, the evaluation signal is a medium replenishment signal; when BL is less than or equal to X3, the evaluation signal is a small replenishment signal; the controller is used for stamping the evaluation signal and transmitting the time stamp to the database for real-time storage.
3. The intelligent sales platform based on big data of claim 1, wherein the evaluation module is configured to perform comprehensive evaluation on the evaluation signals with timestamps stored in the database, and the specific evaluation method is as follows:
acquiring an evaluation signal within ten days before the current time of the system according to the timestamp; the times of the large replenishment signal, the medium replenishment signal and the small replenishment signal are marked as Zb1, Zb2 and Zb3 in sequence; calculating a replenishment evaluation value TK of the commodity by using a formula TK-Zb 1 × 0.3+ Zb2 × 0.2+ Zb3 × 0.1; the evaluation module is used for transmitting the replenishment evaluation value TK of the commodity to the database for storage.
4. A working method of an intelligent sales platform based on big data is characterized by comprising the following steps:
w1: when a commodity is sold, acquiring inventory data of the commodity according to the monitoring interval duration corresponding to the commodity; analyzing and processing the inventory data; calculating to obtain a stock shortage coefficient BH; if the out-of-stock coefficient BH is larger than or equal to the out-of-stock coefficient threshold, judging that the stock of the commodity is insufficient at the moment, and generating an insufficient-stock signal;
w2: after receiving the inventory shortage signal, the inventory management module replenishes the corresponding commodity; recording the replenishment quantity BL; the controller is used for carrying out grade judgment on the replenishment quantity BL to obtain an evaluation signal; comprehensively evaluating the evaluation signals with the time stamps stored in the database through an evaluation module to obtain a replenishment evaluation value TK of the commodity;
w3: the method comprises the following steps of displaying commodities on a sales platform according to the selling conditions of the commodities:
acquiring a current inventory change rate value Kz of a commodity; automatically acquiring a replenishment evaluation value TK of a commodity from a database; calculating a good development value YZ of the commodity by using a formula YZ-Kz × g1+ TK × g 2; and displaying the commodities in sequence according to the magnitude of the excellent spread value YZ.
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Application publication date: 20220812 |