CN115034523A - Enterprise ERP integrated management system and method based on big data - Google Patents
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Abstract
The invention discloses an enterprise ERP integrated management system and method based on big data, comprising the following steps: a data acquisition module, a data management center, a data analysis module, a warehouse management module and a transportation data management module, the data acquisition module is used for acquiring the historical data of the commodity orders, the warehouse inventory corresponding to the merchant, the warehouse position data and the user receiving position data, all the collected data are stored and managed through the data management center, the order quantity change data of the commodities are analyzed through the data analysis module, the time of uploading the data at the commodity transportation transfer station is predicted, when more than one warehouse which meets the commodity supply requirement and is closest to the receiving position of the user is screened out through the warehouse management module, the best warehouse is selected for shipment, the optimal transportation transfer station is selected to upload data when the data uploading is abnormal through the transportation data management module, so that the current and future warehouse delivery and supply capacity is synchronously improved, and the synchronous management of ERP information is ensured.
Description
Technical Field
The invention relates to the technical field of ERP management, in particular to an enterprise ERP comprehensive management system and method based on big data.
Background
ERP refers to enterprise resource planning, and represents an enterprise information management system which is mainly oriented to the manufacturing industry and performs integrated management on material resources, capital resources and information resources, electronic commerce is a mainstream business mode for optimizing enterprise operation, the ERP system in the era of electronic commerce fully utilizes network and information integration technology, comprehensively integrates and optimizes functions such as supply chain management, customer relationship management, enterprise office automation and the like, and reasonably performs ERP management to meet the requirements of resource optimization and inter-enterprise collaborative development in the era of electronic commerce;
however, the conventional management method has the following problems: first, in terms of management of the supply chain of an enterprise, namely management of market, demand, order, raw material procurement, production, inventory, supply, distribution and shipment, a merchant often faces a problem of how to select a proper warehouse for shipment after receiving an order, and in terms of warehouse selection, the supply demand and shipment cost are often considered as priority, in the prior art, such as chinese patent CN 114240302A: the publication time is as follows: 2022.03.25, it discloses that the order is forwarded to the warehouse meeting the supply requirement and the lowest cost warehouse is selected for placing order, only the supply requirement and delivery cost of the current warehouse are considered, but whether the warehouse can meet the requirement of the user order in the future for long-term delivery is not considered, and the delivery capacity of the current and future warehouses cannot be improved at the same time; secondly, in the process of transporting commodities, data needs to be uploaded to an ERP integrated management system to ensure synchronous information management, abnormal data uploading occurs, the existing management mode cannot find and process abnormal uploading problems in time, and synchronous management of ERP information is not facilitated.
Therefore, a system and a method for enterprise ERP integrated management based on big data are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an enterprise ERP integrated management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an enterprise ERP integrated management system based on big data, the system comprises: the system comprises a data acquisition module, a data management center, a data analysis module, a warehouse management module and a transportation data management module;
the data acquisition module is used for acquiring commodity order historical data, warehouse inventory data and warehouse position data corresponding to merchants and user receiving position data;
storing and managing all the collected data through the data management center;
the data analysis module is used for analyzing the order quantity of the commodity in different time periods every year in the past, predicting the order quantity of the commodity in different time periods this year according to the order quantity of the commodity in different time periods every year in the past, and predicting the time of the commodity transportation transfer station needing to upload data;
comparing the commodity order quantity with the commodity quantity stored in the warehouse through the warehouse management module, screening out the warehouse of which the stored commodity quantity is greater than or equal to the commodity order quantity, and in the screened warehouse: comparing the linear distances from the warehouse positions to the goods receiving positions, screening out the warehouse corresponding to the shortest linear distance, and selecting the warehouse corresponding to the shortest linear distance for goods delivery if only one warehouse is screened out; if more than one screened warehouse is available, selecting the best warehouse from the screened warehouses for shipment;
and when the commodity transportation transfer station uploads data at the predicted time, the transportation data management module selects the best transportation transfer station to upload data from the remaining transportation transfer stations which store the data to be uploaded by the corresponding transportation transfer stations.
Further, the data acquisition module comprises an order data acquisition unit, a warehouse data acquisition unit and a user information acquisition unit, wherein the order data acquisition unit is used for averagely dividing one year into n sections and acquiring historical data of commodity orders: the order quantity of the same commodity in different time periods; the warehouse data acquisition unit is used for acquiring different warehouse data corresponding to merchants: the number of commodities stored in the warehouse and the position data of the warehouse are stored; the user information acquisition unit is used for acquiring user order information received by a merchant, confirming a receiving position according to logistics information remarked on an order, and transmitting all acquired data to the data management center.
Further, the data analysis module comprises an order quantity prediction unit and a data updating prediction unit, wherein the order quantity prediction unit is used for analyzing the order quantity of the commodity in different time periods every year in the past according to the historical data of the commodity order and predicting the order quantity of the commodity in different time periods in the present year; the data updating prediction unit is used for predicting the time for the commodity transportation transfer station to transmit data to the ERP integrated management system.
Further, the warehouse management module comprises a shipment analysis unit and a warehouse ERP screening unit, wherein the shipment analysis unit is used for acquiring the current order quantity of the commodities and the quantity of the commodities stored in the warehouse, and comparing the current order quantity of the commodities with the quantity of the commodities stored in the warehouse: if the quantity of the stored commodities is more than or equal to the commodity order quantity, judging that the corresponding warehouse meets the commodity supply requirement; if the number of the stored commodities is less than the commodity order quantity, judging that the corresponding warehouse does not meet the commodity supply requirement, screening the warehouse of which the number of the stored commodities is more than or equal to the commodity order quantity, comparing the linear distance from the position of the screened warehouse to the receiving position of the user, and screening the warehouse corresponding to the shortest linear distance; the warehouse ERP screening unit is used for counting the number of screened warehouses: if only one screened warehouse is available, selecting the warehouse corresponding to the shortest straight-line distance for shipment; when more than one warehouse which meets the goods supply requirement and is closest to the receiving position of the user is screened out: and after a random warehouse is selected to deliver the commodities, the number of warehouses meeting the supply requirements of the commodities in different remaining time periods in the current year is counted, the adaptability of the commodity delivered by the random warehouse is analyzed and selected, and the warehouse with the highest adaptability is selected as the best warehouse for delivery.
Further, the transportation data management module comprises an update abnormity early warning unit and a data transmission management unit, wherein the update abnormity early warning unit is used for sending a data update abnormity warning signal to the data transmission management unit when the transportation transfer station does not upload data to the ERP integrated management system at the predicted time; and the data transmission management unit is used for selecting the optimal transportation transfer station from the transportation transfer stations which store the abnormal transportation transfer stations and need to upload data, and uploading the data to the ERP comprehensive management system through the optimal transportation transfer station.
An enterprise ERP integrated management method based on big data comprises the following steps:
s01: equally dividing the annual time into n sections, and collecting the order number of the same commodity in different time periods, warehouse data corresponding to merchants and receiving position data when a user purchases the corresponding commodity;
s02: predicting the order quantity of commodities in different time periods in the year;
s03: analyzing commodity order quantity, commodity quantity stored in the warehouse and distance data from the warehouse position to the receiving position, screening out the warehouse which meets commodity supply requirements and is closest to the receiving position of the user, and when screening out more than one warehouse which meets the commodity supply requirements and is closest to the receiving position of the user: selecting the best warehouse from the screened warehouses for shipment;
s04: analyzing the commodity transportation route, and predicting the time of the commodity transportation transfer station needing to upload data;
s05: and selecting the best transportation transfer station which stores the data to be uploaded by the abnormal transportation transfer station to upload the data when the data is not uploaded at the forecast time at the commodity transportation transfer station.
Further, in steps S01-S02: the annual time is divided into n sections on average: the order quantity collection of the same commodity in the same time period of the past m years is collected to be A = { A = { A } 1 ，A 2 ，…，A m Set a smoothing initial value to
：
Wherein A is i Representing the order quantity of the corresponding goods in the corresponding time period of the ith year, and setting a smoothing parameter as
，
Predicting the order quantity B of the corresponding commodity in the corresponding time period of the year according to the following formula j ：
Wherein the content of the first and second substances,
expressing the first exponential smooth predicted value of the order quantity of the corresponding commodity in the corresponding time period of the mth year according to a formula
Obtaining a first exponential smoothing predicted value of the order quantity in the time period corresponding to the first year, and sequentially obtaining the first exponential smoothing predicted values of the order quantity in the time periods corresponding to the second year to the m-1 th year according to the same calculation mode, wherein the first exponential smoothing predicted value of the order quantity in the time period corresponding to the m-1 th year is
According to the formula
Obtaining a first exponential smoothing predicted value of the order quantity in the corresponding time period of the mth year
Predicting the order quantity set of the commodity in different time periods of the year by the same calculation mode to be B = { B = { (B) 1 ，B 2 ，…，B j ，…，B n And predicting the order quantity of the commodity in the year by acquiring and analyzing the historical order quantity of the same commodity, aiming at synchronously analyzing the order quantity of the commodity in the current year with the commodity quantity stored in the warehouse to select the best warehouse for shipment, and predicting the future order quantity by using an index smoothing method, so that all historical data are compatible, prediction errors are reduced, and the method is smooth and smoothThe self-definition of the parameters is beneficial to improving the sensitivity of prediction.
Further, in step S03: acquiring that the user order quantity currently received by a merchant is M, and acquiring that the number set of corresponding commodities stored in a warehouse corresponding to the merchant is N = { N1, N2, …, Nk }, wherein k represents the number of warehouses, and comparing Ni with M: if Ni<M, the corresponding warehouse does not meet the goods supply requirement; if Ni is larger than or equal to M, the corresponding warehouse meets the commodity supply requirement, wherein Ni represents the quantity of the corresponding commodities stored in one warehouse at random, and the warehouse meeting the commodity supply requirement is screened out: sending the user order to the screened warehouse, acquiring a receiving position of the user according to logistics remark information in the user order, and counting the linear distance set from the screened warehouse position to the receiving position as d = { d1, d2, …, dp }, wherein p represents the number of warehouses meeting goods supply requirements, and comparing the linear distances: if only one warehouse meeting the shortest linear distance is available, selecting the warehouse corresponding to the dmin for shipment; if more than one warehouse meeting the shortest straight-line distance is available, selecting the best warehouse from the warehouses meeting the shortest straight-line distance for shipment: obtaining a total of q warehouses meeting the shortest linear distance, and collecting the number set of the commodities currently stored in the warehouses as N ’ ={N1 ’ ，N2 ’ ，…，Nq ’ Collecting the quantity of orders sold in the corresponding commodities in the current year as B ’ ={ B1 ’ ， B2 ’ ，…， Bv ’ And (4) selecting a random warehouse for shipment when the current time belongs to the (v + 1) th time period of the year: the number of the commodities which are obtained from the residual storage of the warehouse is N ’ ={N1 ’ ，N2 ’ ，…，Ni ’ -M，…，Nq ’ And predicting that the order quantity set of the commodity in the time periods from v +2 th to nth is B ’’ ={B v+2 ，B v+3 ，…，B n And (c) the step of (c) in which,
comparing the order quantity of the commodities in the v +2 th to the n-th time periods with the quantity of the commodities stored in the warehouse: the number of the warehouses which meet the supply demand of the commodities in each remaining time period in the year is counted as E = { E = } v+2 ，E v+3 ，…，E n Calculating and selecting a random warehouse for shipment fitness Wi according to the following formula:
wherein E is j The method comprises the steps of representing the quantity of warehouses meeting the goods supply requirement of a random time period left in the year, obtaining a fitness set W = { W1, W2, …, Wi, … and Wq } by selecting q warehouses for shipment in the same calculation mode, comparing the fitness, selecting the warehouse with the highest fitness for shipment, in the prior art, when selecting the warehouse for shipment, preferably considering whether the warehouse meets the goods supply requirement of the goods and the cost of shipment, considering whether the warehouse can meet the goods supply requirement of the goods after shipment of the currently selected warehouse lacks the consideration, not guaranteeing the future order requirement of a user to the maximum extent on the premise of saving the cost, when more than one warehouse meeting the goods supply requirement and closest to the goods receiving position of the user is screened, further selecting the best warehouse for shipment, considering whether the remaining warehouses meet the future goods supply requirement after shipment of the current goods in the selection process, the method is characterized in that the fitness of selecting different warehouses for shipment is calculated in a mode that the total warehouse quantity of the warehouse quantity required by commodity supply in the future time period is calculated after selecting one warehouse to shipment the current commodity: the warehouse delivery system has the advantages that the current warehouse and the future warehouse have certain capacity for delivery before replenishment, the delivery and supply capacity of the current warehouse and the future warehouse is favorably and synchronously improved, and the probability that the warehouse cannot meet the supply demand in the future is reduced.
Further, in step S04: analyzing the commodity transportation route: the number of the transfer stations needed to pass through in the current transportation process of the commodity is u, and the distance set between the adjacent transfer stations according to the transportation sequence is D = { D = 1 ，D 2 ，…，D u-1 And acquiring adjacent transfer stations in the previous random commodity transportation processThe interval duration set of uploading data to the ERP integrated management system is t = { t = { t = } 1 ，t 2 ，…，t u-1 H, using least squares to data points { (D) 1 ，t 1 ），（D 2 ，t 2 ），…，（D u-1 ，t u-1 ) Performing straight line fitting, and setting a fitting function as follows: y = ax + b, wherein a and b represent fitting coefficients, the transfer station which acquires the current data to be uploaded is the ith transfer station, and the distance between the ith-1 transfer station and the ith transfer station is D i-1 I is not less than 2, and i-1 substituting the fitting function, and predicting the interval duration t after the data is uploaded by the ith transfer station at the (i-1) th transfer station i-1 When data is required to be uploaded, t i-1 =a* D i-1 And + b, predicting the normal time of the data uploaded by the transfer station by using a least square method, so that the transfer station with abnormal data uploading can be found in time and an alarm signal can be sent.
Further, in step S05: when the interval duration exceeds t after the ith transfer station uploads the data in the (i-1) th transfer station i-1 When data is not uploaded later, sending a data updating abnormal alarm signal to acquire the number f of transfer stations storing the data to be uploaded of the ith transfer station, wherein f is the number of the transfer stations<And u, acquiring the data uploading speed of f transfer stations, selecting the transfer station with the highest speed as the optimal transportation transfer station, uploading the data to be uploaded by the ith transfer station stored in the optimal transportation transfer station to the ERP integrated management system, and selecting the transfer station with the highest data transmission speed to upload the data to be uploaded by the abnormal transfer station to the ERP integrated management system after finding the abnormal transfer station due to data sharing among the transfer stations, so that the abnormal problem of updating the transportation data is solved quickly, and the synchronous management of the ERP information is ensured.
Compared with the prior art, the invention has the following beneficial effects:
the invention predicts the order quantity of the commodity in the present year by collecting and analyzing the historical order quantity of the same commodity, considers the seasonal change problem of the order quantity of the commodity, the predicted order quantity is the order quantity in different time periods in the year, improves the accuracy of the prediction result, in addition, predicts the future order quantity by using an index smoothing method, is compatible with all historical data, reduces the prediction error and improves the prediction sensitivity, and when more than one warehouse which meets the commodity supply requirement and is nearest to the receiving position of a user is screened out, the best warehouse is further selected for delivery: the predicted order quantity is compared with the inventory, the fitness of the shipment of different warehouses is calculated in a mode that after one warehouse is randomly selected to ship the current commodity, the total quantity of the warehouses capable of meeting the commodity supply requirement in the future time period is calculated, and the warehouse with the highest fitness is selected to ship the commodity, so that the delivery and supply capabilities of the current and future warehouses are synchronously improved, the probability that the warehouse cannot meet the supply requirement in the future is reduced, and the future order requirement of a user is guaranteed to the greatest extent on the premise of saving cost;
the method has the advantages that the normal time of data uploading of the commodity transportation transfer station is predicted, the transfer station with abnormal data uploading can be found in time conveniently, an alarm signal is sent, the transfer station with the highest data transmission speed and storing the data needing to be uploaded by the abnormal transfer station is selected to upload the data to the ERP integrated management system after the abnormality is found, the abnormal problem is rapidly processed, and the synchronous management of ERP information is guaranteed.
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 block diagram of an enterprise ERP integrated management system based on big data according to the present invention;
FIG. 2 is a flowchart of an enterprise ERP integrated management method based on big data according to 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.
Referring to fig. 1-2, the present invention provides a technical solution: an enterprise ERP integrated management system based on big data comprises: the system comprises a data acquisition module, a data management center, a data analysis module, a warehouse management module and a transportation data management module;
the method comprises the steps that commodity order historical data, warehouse inventory data and warehouse position data corresponding to merchants and user receiving position data are collected through a data collection module;
storing and managing all the acquired data through a data management center;
analyzing the order quantity of the commodity in different time periods every year in the past through a data analysis module, predicting the order quantity of the commodity in different time periods in the present year according to the order quantity of the commodity in different time periods every year in the past, and predicting the time of uploading data required by a commodity transportation transfer station;
comparing the commodity order quantity with the commodity quantity stored in the warehouse through a warehouse management module, screening out the warehouse of which the stored commodity quantity is more than or equal to the commodity order quantity, and in the screened warehouse: comparing the linear distances from the warehouse positions to the goods receiving positions, screening out the warehouse corresponding to the shortest linear distance, and selecting the warehouse corresponding to the shortest linear distance for goods delivery if only one warehouse is screened out; if more than one screened warehouse is available, selecting the best warehouse from the screened warehouses for shipment;
and when the data are not uploaded at the forecast time by the commodity transportation transfer station through the transportation data management module, selecting the best transportation transfer station to upload the data from the transportation transfer stations which are stored with the data to be uploaded by the corresponding transportation transfer stations.
The data acquisition module comprises an order data acquisition unit, a warehouse data acquisition unit and a user information acquisition unit, wherein the order data acquisition unit is used for averagely dividing one year into n sections and acquiring commodity order historical data: the order quantity of the same commodity in different time periods; the warehouse data acquisition unit is used for acquiring different warehouse data corresponding to merchants: the number of goods stored in the warehouse and the position data of the warehouse are stored; the user information acquisition unit is used for acquiring user order information received by a merchant, confirming a receiving position according to logistics information remarked on an order, and transmitting all acquired data to the data management center.
The data analysis module comprises an order quantity prediction unit and a data updating prediction unit, wherein the order quantity prediction unit is used for analyzing the order quantity of the commodity in different time periods every year in the past according to the historical data of the commodity order and predicting the order quantity of the commodity in different time periods in the present year; the data updating prediction unit is used for predicting the time for the commodity transportation transfer station to transmit data to the ERP integrated management system.
The warehouse management module comprises a shipment analysis unit and a warehouse ERP screening unit, wherein the shipment analysis unit is used for acquiring the current order quantity of the commodities and the quantity of the commodities stored in the warehouse, and comparing the current order quantity of the commodities with the quantity of the commodities stored in the warehouse: if the quantity of the stored commodities is more than or equal to the commodity order quantity, judging that the corresponding warehouse meets the commodity supply requirement; if the stored quantity of the commodities is less than the commodity order quantity, judging that the corresponding warehouse does not meet the commodity supply requirement, screening out the warehouse of which the stored quantity of the commodities is more than or equal to the commodity order quantity, comparing the linear distance from the position of the screened warehouse to the receiving position of the user, and screening out the warehouse corresponding to the shortest linear distance; the warehouse ERP screening unit is used for counting the screened warehouse quantity: if only one screened warehouse is available, selecting the warehouse corresponding to the shortest straight-line distance for shipment; if more than one warehouse which meets the goods supply requirement and is closest to the receiving position of the user is screened out: and after a random warehouse is selected to deliver the commodities, the number of warehouses meeting the supply requirements of the commodities in different remaining time periods in the current year is counted, the adaptability of the commodity delivered by the random warehouse is analyzed and selected, and the warehouse with the highest adaptability is selected as the best warehouse for delivery.
The transportation data management module comprises an abnormal updating early warning unit and a data transmission management unit, wherein the abnormal updating early warning unit is used for sending a data updating abnormal alarm signal to the data transmission management unit when the transportation transfer station does not upload data to the ERP comprehensive management system at the predicted time; and the data transmission management unit is used for selecting the optimal transportation transfer station from the transportation transfer stations which store the abnormal transportation transfer stations and need to upload data, and uploading the data to the ERP comprehensive management system through the optimal transportation transfer station.
An enterprise ERP integrated management method based on big data comprises the following steps:
s01: equally dividing the annual time into n sections, and collecting the order number of the same commodity in different time periods, warehouse data corresponding to merchants and receiving position data when a user purchases the corresponding commodity;
s02: predicting the order quantity of commodities in different time periods in the year;
s03: analyzing commodity order quantity, commodity quantity stored in the warehouse and distance data from the warehouse position to the receiving position, screening out the warehouse which meets commodity supply requirements and is closest to the receiving position of the user, and when screening out more than one warehouse which meets the commodity supply requirements and is closest to the receiving position of the user: selecting the best warehouse from the screened warehouses for shipment;
s04: analyzing the commodity transportation route, and predicting the time of the commodity transportation transfer station needing to upload data;
s05: and selecting the best transportation transfer station which stores the data to be uploaded by the abnormal transportation transfer station to upload the data when the data is not uploaded at the forecast time at the commodity transportation transfer station.
In steps S01-S02: the annual time is divided into n sections on average: the collection of the order quantity of the same commodity in the same time period of the past m years is A = { A = { (A) } 1 ，A 2 ，…，A m Set a smoothing initial value to
：
Wherein A is i Representing the order quantity of the corresponding commodity in the corresponding time period of the ith year, and setting a smoothing parameter as
，
According to the followingPredicting the order quantity B of the corresponding commodity in the corresponding time period of the year j ：
Wherein the content of the first and second substances,
expressing the first exponential smooth predicted value of the order quantity of the corresponding commodity in the corresponding time period of the mth year according to a formula
Obtaining a first exponential smoothing predicted value of the order quantity in the time period corresponding to the first year, and sequentially obtaining the first exponential smoothing predicted value of the order quantity in the time period corresponding to the second to m-1 years according to the same calculation mode, wherein the first exponential smoothing predicted value of the order quantity in the time period corresponding to the m-1 year is
According to the formula
Obtaining a first exponential smoothing predicted value of the order quantity in the corresponding time period of the mth year
The order quantity set of the commodity in different time periods in the current year is predicted to be B = { B by the same calculation mode 1 ，B 2 ，…，B j ，…，B n And predicting the future order quantity by using an index smoothing method, so that all historical data are compatible, the prediction sensitivity is improved, and the prediction error is reduced.
In step S03: acquiring that the user order quantity currently received by a merchant is M, and acquiring that the number set of corresponding commodities stored in a warehouse corresponding to the merchant is N = { N1, N2, …, Nk }, wherein k represents the number of warehouses, and comparing Ni with M: if Ni<M, explaining that the corresponding warehouse does not meet the goods supply requirement(ii) a If Ni is larger than or equal to M, it is stated that the corresponding warehouse meets the commodity supply requirement, wherein Ni represents the number of corresponding commodities stored in one warehouse at random, and the warehouse meeting the commodity supply requirement is screened out: sending the user order to the screened warehouse, acquiring a receiving position of the user according to logistics remark information in the user order, and counting the linear distance set from the screened warehouse position to the receiving position as d = { d1, d2, …, dp }, wherein p represents the number of warehouses meeting goods supply requirements, and comparing the linear distances: obtaining the shortest straight line distance dmin, and if only one warehouse meeting the shortest straight line distance is available, selecting the warehouse corresponding to dmin for shipment; if more than one warehouse meeting the shortest straight-line distance is available, selecting the best warehouse from the warehouses meeting the shortest straight-line distance for shipment: the total number of the obtained warehouses meeting the shortest straight line distance is q, and the number of the commodities collected to be currently stored in the warehouses is N ’ ={N1 ’ ，N2 ’ ，…，Nq ’ Collecting the quantity of orders sold in the corresponding commodities in the current year as B ’ ={ B1 ’ ， B2 ’ ，…， Bv ’ And (4) selecting a random warehouse for shipment when the current time belongs to the (v + 1) th time period of the year: the number of the commodities which are obtained from the residual storage of the warehouse is N ’ ={N1 ’ ，N2 ’ ，…，Ni ’ -M，…，Nq ’ Predicting to obtain an order quantity set of the commodity from the v +2 th time period to the n-th time period as B ’’ ={B v+2 ，B v+3 ，…，B n And (c) the step of (c) in which,
comparing the order amount of the commodities in the v +2 th to the n-th time periods with the quantity of the commodities stored in the warehouse: the number of the warehouses which meet the supply demand of the commodities in each remaining time period in the year is counted as E = { E = } v+2 ，E v+3 ，…，E n Calculating and selecting a random warehouse for shipment fitness Wi according to the following formula:
wherein E is j The quantity of the warehouses meeting the supply requirement of the commodity in a random time period left this year is represented, the fitness set of selecting q warehouses for shipment is obtained in the same calculation mode and is W = { W1, W2, …, Wi, … and Wq }, the fitness is compared, the warehouse with the highest fitness is selected for shipment, and the delivery capacity of the current warehouse and the future warehouse is synchronously improved.
In step S04: analyzing a commodity transportation route: the number of the transfer stations needed to pass through in the current transportation process of the commodity is u, and the distance set between the adjacent transfer stations according to the transportation sequence is D = { D = 1 ，D 2 ，…，D u-1 And acquiring a set of interval time from data uploaded by adjacent transfer stations to an ERP comprehensive management system in the previous random commodity transportation process as t = { t = } 1 ，t 2 ，…，t u-1 Using least square method to data points { (D) 1 ，t 1 ），（D 2 ，t 2 ），…，（D u-1 ，t u-1 ) Performing straight line fitting, and setting a fitting function as follows: y = ax + b, wherein a and b represent fitting coefficients, the transfer station which acquires the current data to be uploaded is the ith transfer station, and the distance between the (i-1) th transfer station and the ith transfer station is D i-1 I is not less than 2, and i-1 substituting the fitting function, and predicting the interval duration t after the data is uploaded by the ith transfer station at the (i-1) th transfer station i-1 When data is required to be uploaded, t i-1 =a* D i-1 And + b, the transfer station with abnormal data uploading can be found in time conveniently and an alarm signal can be sent.
In step S05: when the interval duration exceeds t after the ith transfer station uploads the data at the (i-1) th transfer station i-1 When data is not uploaded later, sending a data updating abnormal alarm signal to acquire the number f of transfer stations storing the data to be uploaded of the ith transfer station, wherein f is the number of the transfer stations<u, acquiring the data uploading speed of f transfer stations, selecting the transfer station with the highest speed as the optimal transportation transfer station, uploading the data to be uploaded of the ith transfer station stored in the optimal transportation transfer station to an ERP integrated management system, and facilitating quick processing of abnormityThe problem is solved and the synchronous management of the ERP information is ensured.
The first embodiment is as follows: divide the annual time equally into n =4 segments: the order quantity collection of the same commodity in the first time period of previous m =5 years is collected as A = { A = { (A) 1 ，A 2 ，A 3 ，A 4 ，A 5 } = {200, 100, 300, 260, 180}, and the smoothing initial value is set to be
：
Setting a smoothing parameter to
According to the formula
Predicting the order quantity B of the corresponding commodity in the first time period of the year 1 =202, the order quantity set of the commodity in different time periods of this year is predicted to be B = { B } by the same calculation method 1 ，B 2 ，B 3 ，B 4 The user order quantity currently received by the merchant is M =110, the corresponding quantity set of the commodities stored in the warehouse corresponding to the merchant is collected to be N = { N1, N2, N3, N4, N5} = {50, 600, 180, 350, 200}, and Ni and M are compared: screening p =4 warehouses meeting the commodity supply requirement, sending the user order to the screened warehouses, acquiring the receiving position of the user according to the logistics remark information in the user order, and counting the linear distance set from the screened warehouse position to the receiving position as d = { d1, d2, d3, d4} = {10, 50, 30, 10}, wherein the unit is: km, comparison of linear distance: the shortest comprehensive distance dmin =10 is obtained, more than one warehouse meeting the shortest straight-line distance is obtained, and the best warehouse is selected from the warehouses meeting the shortest straight-line distance for shipment: the total number of the obtained warehouses meeting the shortest straight line distance is q =2, and the number set of the commodities collected and currently stored in the warehouse is N ’ ={N1 ’ ，N2 ’ } = {600, 200}, adoptThe order quantity collected to the corresponding goods sold in the current year is B ’ ={ B1 ’ ， B2 ’ } = {200, 280}, the current time belongs to the v +1=3 time slots this year, when the 2 nd warehouse is selected for shipment: the number of the commodities which are obtained from the residual storage of the warehouse is N ’ ={N1 ’ -M，N2 ’ } = {490, 200}, and the aggregate of the order quantity of the commodity in the v +2=4 time periods is predicted to be B ’’ ={B 4 =500, the number of warehouses meeting the supply demand of the commodity in each remaining time period of this year is counted as E = { E = } 4 =0, according to the formula
Calculating the fitness W1=0 of the 2 nd warehouse for shipment; when the 5 th warehouse is selected for shipment: the number of the commodities which are obtained from the residual storage of the warehouse is N ’ ={N1 ’ ，N2 ’ -M } = {600, 90}, and the number of warehouses meeting the supply demand of the commodity in each remaining time slot of this year is counted as E = { E } 4 =1, according to the formula
Calculating and selecting the 5 th warehouse for shipment, wherein the fitness W2=1>W1, selecting the 5 th warehouse for shipment;
example two: the number of the transfer stations required to pass through in the current transportation process of the commodity is u =3, and the distance set between the adjacent transfer stations according to the transportation sequence is D = { D = { (D) 1 ，D 2 The method is characterized in that = {9, 8}, and the time interval set of the data uploaded to the ERP integrated management system by the adjacent transfer stations in the past random commodity transportation process is acquired as t = { t = 1 ，t 2 } = {0.6, 0.5}, unit: day, data points { (D) using least squares 1 ，t 1 ），（D 2 ，t 2 ) Performing straight line fitting, and setting a fitting function as follows: y = ax + b, according to the formula
And
calculating fitting coefficients a and b respectively: a =0.1, b = -0.3, the transfer station which acquires the current data needing to be uploaded is the i =2 transfer stations, and the distance between the 1 st transfer station and the 2 nd transfer station is D 1 =9, will D 1 Substituting the fitting function, and predicting the interval duration t after the 2 nd transfer station uploads the data at the 1 st transfer station i-1 When data is required to be uploaded, t i-1 =a* D i-1 + b =0.6, and the interval duration exceeds t after the 2 nd transfer station uploads the data at the 1 st transfer station i-1 And when the data is not uploaded after the number of the transfer stations storing the data to be uploaded of the 2 nd transfer station is 1: and taking the 3 rd transfer station as the optimal transportation transfer station for the 3 rd transfer station, and uploading data to be uploaded by the 2 nd transfer station stored in the optimal transportation transfer station to the ERP integrated management system.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An enterprise ERP integrated management system based on big data is characterized in that: the system comprises: the system comprises a data acquisition module, a data management center, a data analysis module, a warehouse management module and a transportation data management module;
the data acquisition module is used for acquiring commodity order historical data, warehouse inventory data and warehouse position data corresponding to merchants and user receiving position data;
storing and managing all the collected data through the data management center;
the data analysis module is used for analyzing the order quantity of the commodity in different time periods every year in the past, predicting the order quantity of the commodity in different time periods this year according to the order quantity of the commodity in different time periods every year in the past, and predicting the time of the commodity transportation transfer station needing to upload data;
comparing the commodity order quantity with the commodity quantity stored in the warehouse through the warehouse management module, screening out the warehouse of which the stored commodity quantity is greater than or equal to the commodity order quantity, and in the screened warehouse: comparing the linear distances from the warehouse positions to the receiving positions, screening out the warehouse corresponding to the shortest linear distance, and selecting the warehouse corresponding to the shortest linear distance for shipment if only one warehouse is screened out; if more than one screened warehouse is available, selecting the best warehouse from the screened warehouses for shipment;
and selecting the best transportation transfer station to upload data from the transportation transfer stations which are stored with the data to be uploaded corresponding to the transportation transfer stations when the transportation data management module does not upload the data at the forecast time.
2. The enterprise ERP integrated management system based on big data as claimed in claim 1, wherein: the data acquisition module comprises an order data acquisition unit, a warehouse data acquisition unit and a user information acquisition unit, wherein the order data acquisition unit is used for averagely dividing the annual time into n sections and acquiring historical data of commodity orders: the order quantity of the same commodity in different time periods; the warehouse data acquisition unit is used for acquiring different warehouse data corresponding to merchants: the number of commodities stored in the warehouse and the position data of the warehouse are stored; the user information acquisition unit is used for acquiring user order information received by a merchant, confirming a receiving position according to logistics information remarked on an order, and transmitting all acquired data to the data management center.
3. The enterprise ERP integrated management system based on big data as claimed in claim 1, wherein: the data analysis module comprises an order quantity prediction unit and a data updating prediction unit, wherein the order quantity prediction unit is used for analyzing the order quantity of the commodity in different time periods every year in the past according to the historical data of the commodity order and predicting the order quantity of the commodity in different time periods in the present year; the data updating prediction unit is used for predicting the time that the data needs to be transmitted to the ERP integrated management system by the commodity transportation transfer station.
4. The enterprise ERP integrated management system based on big data as claimed in claim 1, wherein: the warehouse management module comprises a shipment analysis unit and a warehouse ERP screening unit, wherein the shipment analysis unit is used for acquiring the current order quantity of the commodities and the quantity of the commodities stored in the warehouse, and comparing the current order quantity of the commodities with the quantity of the commodities stored in the warehouse: if the quantity of the stored commodities is more than or equal to the commodity order quantity, judging that the corresponding warehouse meets the commodity supply requirement; if the number of the stored commodities is less than the commodity order quantity, judging that the corresponding warehouse does not meet the commodity supply requirement, screening the warehouse of which the number of the stored commodities is more than or equal to the commodity order quantity, comparing the linear distance from the position of the screened warehouse to the receiving position of the user, and screening the warehouse corresponding to the shortest linear distance; the warehouse ERP screening unit is used for counting the number of screened warehouses: if only one screened warehouse is available, selecting the warehouse corresponding to the shortest straight-line distance for shipment; if more than one warehouse which meets the goods supply requirement and is closest to the receiving position of the user is screened out: and after a random warehouse is selected to deliver the commodities, the number of warehouses meeting the supply requirements of the commodities in different remaining time periods in the current year is counted, the adaptability of the commodity delivered by the random warehouse is analyzed and selected, and the warehouse with the highest adaptability is selected as the best warehouse for delivery.
5. The enterprise ERP integrated management system based on big data as claimed in claim 1, wherein: the transportation data management module comprises an abnormal updating early warning unit and a data transmission management unit, wherein the abnormal updating early warning unit is used for sending a data updating abnormal alarm signal to the data transmission management unit when the transportation transfer station does not upload data to the ERP integrated management system at the predicted time; and the data transmission management unit is used for selecting the optimal transportation transfer station from the transportation transfer stations which store the abnormal transportation transfer stations and need to upload data, and uploading the data to the ERP comprehensive management system through the optimal transportation transfer station.
6. An enterprise ERP integrated management method based on big data is characterized in that: the method comprises the following steps:
s01: equally dividing the annual time into n sections, and collecting the order number of the same commodity in different time periods, warehouse data corresponding to merchants and receiving position data when a user purchases the corresponding commodity;
s02: predicting the order quantity of commodities in different time periods in the year;
s03: analyzing commodity order quantity, commodity quantity stored in the warehouse and distance data from the warehouse position to the receiving position, screening out the warehouse which meets commodity supply requirements and is closest to the receiving position of the user, and when screening out more than one warehouse which meets the commodity supply requirements and is closest to the receiving position of the user: selecting the best warehouse from the screened warehouses for shipment;
s04: analyzing the commodity transportation route, and predicting the time of the commodity transportation transfer station needing to upload data;
s05: and selecting the best transportation transfer station which stores the data to be uploaded by the abnormal transportation transfer station to upload the data when the data is not uploaded at the forecast time at the commodity transportation transfer station.
7. The enterprise ERP integrated management method based on big data as claimed in claim 6, wherein: in steps S01-S02: the annual time is divided into n sections on average: the order quantity collection of the same commodity in the same time period of the past m years is collected to be A = { A = { A } 1 ，A 2 ，…，A m Set a smoothing initial value to
：
Wherein A is i Representing the order quantity of the corresponding goods in the corresponding time period of the ith year, and setting a smoothing parameter as
，
Predicting the order quantity B of the corresponding commodity in the corresponding time period of the year according to the following formula j ：
Wherein the content of the first and second substances,
expressing the first exponential smooth predicted value of the order quantity of the corresponding commodity in the corresponding time period of the mth year according to a formula
Obtaining a first exponential smoothing predicted value of the order quantity in the time period corresponding to the first year, and sequentially obtaining the first exponential smoothing predicted values of the order quantity in the time periods corresponding to the second year to the m-1 th year according to the same calculation mode, wherein the first exponential smoothing predicted value of the order quantity in the time period corresponding to the m-1 th year is
According to the formula
Obtaining a first exponential smoothing predicted value of the order quantity in the corresponding time period of the mth year
The quotient is predicted by the same calculation methodThe collection of the order quantity of the product in different time periods of this year is B = { B = { (B) 1 ，B 2 ，…，B j ，…，B n }。
8. The enterprise ERP integrated management method based on big data as claimed in claim 7, wherein: in step S03: acquiring that the user order quantity currently received by a merchant is M, and acquiring that the number set of corresponding commodities stored in a warehouse corresponding to the merchant is N = { N1, N2, …, Nk }, wherein k represents the number of warehouses, and comparing Ni with M: if Ni<M, the corresponding warehouse does not meet the goods supply requirement; if Ni is larger than or equal to M, the corresponding warehouse meets the commodity supply requirement, wherein Ni represents the quantity of the corresponding commodities stored in one warehouse at random, and the warehouse meeting the commodity supply requirement is screened out: sending the user order to the screened warehouse, acquiring a receiving position of the user according to logistics remark information in the user order, and counting the linear distance set from the screened warehouse position to the receiving position as d = { d1, d2, …, dp }, wherein p represents the number of warehouses meeting goods supply requirements, and comparing the linear distances: obtaining the shortest straight line distance dmin, and if only one warehouse meeting the shortest straight line distance is available, selecting the warehouse corresponding to dmin for shipment; if more than one warehouse meeting the shortest straight-line distance is available, selecting the best warehouse from the warehouses meeting the shortest straight-line distance for shipment: obtaining a total of q warehouses meeting the shortest linear distance, and collecting the number set of the commodities currently stored in the warehouses as N ’ ={N1 ’ ，N2 ’ ，…，Nq ’ Collecting the quantity of orders sold in the corresponding commodities in the current year as B ’ ={ B1 ’ ， B2 ’ ，…， Bv ’ And (4) selecting a random warehouse for shipment when the current time belongs to the (v + 1) th time period of the year: the number of the commodities which are obtained from the residual storage of the warehouse is N ’ ={N1 ’ ，N2 ’ ，…，Ni ’ -M，…，Nq ’ Predicting to obtain an order quantity set of the commodity from the v +2 th time period to the n-th time period as B ’’ ={B v+2 ，B v+3 ，…，B n And (c) the step of (c) in which,
comparing the order amount of the commodities in the v +2 th to the n-th time periods with the quantity of the commodities stored in the warehouse: the number of the warehouses which meet the supply demand of the commodities in each remaining time period in the year is counted as E = { E = } v+2 ，E v+3 ，…，E n Calculating and selecting a random warehouse for shipment according to the following formula:
wherein, E j The quantity of the warehouses meeting the supply requirement of the commodity in a random time period left this year is represented, the fitness set of selecting q warehouses for shipment is obtained in the same calculation mode and is W = { W1, W2, …, Wi, … and Wq }, the fitness is compared, and the warehouse with the highest fitness is selected for shipment.
9. The enterprise ERP integrated management method based on big data as claimed in claim 6, wherein: in step S04: analyzing the commodity transportation route: the number of the transfer stations needed to pass through in the current transportation process of the commodity is u, and the distance set between the adjacent transfer stations according to the transportation sequence is D = { D = 1 ，D 2 ，…，D u-1 And acquiring a time interval set of the data uploaded to the ERP integrated management system by the adjacent transfer stations in the previous random commodity transportation process as t = { t = } 1 ，t 2 ，…，t u-1 Using least square method to data points { (D) 1 ，t 1 ），（D 2 ，t 2 ），…，（D u-1 ，t u-1 ) Performing straight line fitting, and setting a fitting function as follows: y = ax + b, wherein a and b represent fitting coefficients, the transfer station which acquires the current data to be uploaded is the ith transfer station, and the distance between the (i-1) th transfer station and the ith transfer station is D i-1 I is not less than 2, and D i-1 Substituting the fitting function to predict the ith transfer station in the (i-1) th transfer stationThe interval duration is t after the data is uploaded by the transfer station i-1 When data is required to be uploaded, t i-1 =a* D i-1 +b。
10. The enterprise ERP integrated management method based on big data as claimed in claim 9, wherein: in step S05: when the interval duration exceeds t after the ith transfer station uploads the data in the (i-1) th transfer station i-1 When data is not uploaded later, sending a data updating abnormal alarm signal to acquire the number f of transfer stations storing the data to be uploaded of the ith transfer station, wherein f is the number of the transfer stations<And u, acquiring the data uploading speed of the f transfer stations, selecting the transfer station with the highest speed as the optimal transportation transfer station, and uploading the data to be uploaded by the ith transfer station stored in the optimal transportation transfer station to the ERP integrated management system.
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