CN115564342A - Catering commodity distribution analysis method and system based on deep learning and storage medium - Google Patents
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Abstract
The invention discloses a catering commodity distribution analysis method and system based on deep learning and a storage medium. The catering commodity distribution analysis method based on deep learning comprises the steps of obtaining current corresponding basic information, catering sales information and historical sales information of a designated train; analyzing replenishment demands corresponding to various supply food varieties and confirming food replenishment information; analyzing a target distribution warehouse, a target distribution position and target distribution time corresponding to the designated train; according to the analyzed food replenishment information, the target distribution warehouse, the target distribution position and the target distribution time, distributing; the invention effectively solves the problem of certain limitation of the current technology, avoids the defect that the food distribution analysis is only carried out according to the number of train passengers, improves the rationality of the food distribution number and the distribution type, and effectively reduces the subsequent abandonment rate of train food.
Description
Technical Field
The invention belongs to the technical field of commodity distribution analysis, and relates to a catering commodity distribution analysis method and system based on deep learning and a storage medium.
Background
As a vehicle with a plurality of characteristics of large passenger capacity, high safety, strong conveying capacity and the like, a train is widely applied to travel activities of people, particularly long-distance travel activities, and train catering service is one of main guarantee services in long-distance travel of people, and the importance of distribution analysis of the train is self-evident.
At present, the distribution analysis of train catering mainly focuses on the distribution of the number of food items, and mainly depends on the number of passengers corresponding to the train, but the number of passengers cannot completely determine the demand condition of the food items, so that the problems of the following several layers exist in the prior art:
1. the purchase condition of food in the train mainly depends on the purchase demand of passengers, the train passenger number only can reflect the purchase probability problem of the food, and the rationality of the food distribution number cannot be improved only by analyzing the number of the train passengers at present, so that the food distribution number is insufficient in accuracy, the subsequent abandonment rate of the food cannot be reduced, and the operation cost of the train catering service cannot be reduced;
2. the train meal generally comprises a plurality of types, the meal types with different personnel tendencies are different, the trend crowd conditions corresponding to the meal types are not analyzed at present, the fitting property of the distribution of the meal types cannot be guaranteed through a fixed distribution mode, the supply and demand states of train passengers for the meal items cannot be balanced, and the riding experience of the passengers cannot be improved;
3. the current train meal goods is joined in marriage for fixed time and fixed position, does not carry out the adjustment of joining in marriage according to the sales conditions of meal goods, and the mode of joining in marriage is too general for train meal goods joins in marriage has certain limitation, can't realize the individualized goods of joining in marriage of train meal goods, still can't effectual guarantee meal goods's new freshness simultaneously, also can't improve passenger's purchase desire in another aspect, makes food and beverage service effect and service quality not good.
Disclosure of Invention
In view of the above, in order to solve the problems in the background art, a method, a system and a storage medium for catering commodity distribution analysis based on deep learning are provided;
the purpose of the invention can be realized by the following technical scheme:
the invention provides a catering commodity distribution analysis method based on deep learning in a first aspect,
the method comprises the following steps:
step 1, obtaining basic information of a train: acquiring current corresponding basic information of a designated train, wherein the current corresponding basic information specifically comprises the number of staff members, the number of passengers, food inventory information, driving path information, associated warehouse information and ticket selling information;
step 2, obtaining train food information: acquiring a food supply time period and various food supply types corresponding to the appointed train, and numbering the food supply types 1,2, a.
Step 3, obtaining train meal sales information: obtaining sales information corresponding to various types of current supply meals of a specified train;
step 4, obtaining historical train sales information: the method comprises the steps that the number of passengers corresponding to each meal supply time period and sales information corresponding to each meal supply type at each train number in the history of the appointed train are extracted from a train meal management information base;
step 5, analyzing the replenishment demand of the train food: analyzing the replenishment demands corresponding to the supply food varieties to obtain replenishment demand evaluation indexes corresponding to the supply food varieties, and confirming the replenishment supply varieties and the replenishment quantity corresponding to the replenishment food varieties;
step 6, matching and analyzing a food distribution warehouse: extracting unit production duration corresponding to each supply meal type and single production quantity corresponding to each supply meal type from a meal management information base, and analyzing to obtain a target distribution warehouse, a target distribution position and target distribution time corresponding to the specified train;
and 7, feeding back the train food delivery information: and sending the replenishment quantity, the target delivery position and the target delivery time corresponding to the replenishment meal types, the replenishment meal types to be replenished to a target distribution warehouse and distributing the replenishment.
In a preferred embodiment of the present invention, the meal inventory information is an inventory amount corresponding to each variety of the served meal; the driving path information comprises positions corresponding to all the path stations and path time points corresponding to all the path stations; the associated warehouse information comprises the number of associated ground distribution warehouses and the corresponding positions of the associated ground distribution warehouses; the ticket selling information comprises the number of the tickets sold, the position of a starting station, the position of a destination station and basic information of associated ticket purchasing personnel, wherein the information of the associated ticket purchasing personnel is the sex and the age.
In a preferred embodiment of the present invention, the sales information includes sales volume, age and gender corresponding to each sales subject.
In a preferred embodiment of the present invention, the analyzing of the replenishment demand corresponding to each variety of the supply food includes the following steps:
s1, obtaining the next food supply time period corresponding to the appointed train according to the current time point of the appointed train and the food supply time period corresponding to the appointed train, and recording the next food supply time period as a target supply time period;
s2, positioning the route time point and the position corresponding to each route station from the current corresponding running path information of the designated train, and further matching to obtain the number of the route stations and the position corresponding to each route station of the designated train in the target supply time period according to the target supply time period corresponding to the designated train;
s3, positioning the number of the passengers getting in and out of the train corresponding to each path station in the target supply time period of the designated train from the ticket information corresponding to the designated train, counting to obtain the number of the comprehensive passengers carrying the designated train in the target supply time period, and positioning the sex and age corresponding to each comprehensive passenger carrying the designated train in the target supply time period from the ticket information corresponding to the designated train;
s4, extracting the inventory corresponding to each supply food type from the food inventory information corresponding to the designated train, and extracting the sales volume from the sales information corresponding to each current supply food type of the designated train, so as to obtain the residual inventory corresponding to each supply food type by difference;
s5, analyzing and obtaining reference sales information corresponding to each food supply type in the target supply time period of the appointed train according to the number of passengers corresponding to each food supply time period in each train number of the appointed train history and the sales information corresponding to each food supply type;
s6, analyzing and obtaining the expected sales volume of each supply food type in the target supply time period according to the sales information corresponding to each supply food type of the designated train, the comprehensive carried passenger number of the designated train in the target supply time period and the reference sales information;
s7, recording the residual inventory corresponding to each supply food type and the predicted sales corresponding to each supply food type in the target supply time period as C i And G i I represents a number corresponding to each food item, i =1,2
Analyzing to obtain a replenishment demand evaluation index lambda corresponding to each supply meal variety i And e represents a natural number, and mu is a set replenishment evaluation correction weight factor.
In a preferred embodiment of the present invention, the analyzing step obtains the reference sales information corresponding to each type of the supply food of the designated train in the target supply time period, and the specific analyzing step includes:
recording the corresponding comprehensive passenger carrying number of the appointed train in a target supply time period as K;
recording the number of passengers corresponding to the target supply time period in each train number in the designated train history as K j J represents a number corresponding to each historical train, and j =1,2.. To.m;
by analytical formulae
Analyzing to obtain the passenger matching degree beta corresponding to each train number in the history of the appointed train j Δ K is the set allowable passenger number difference, σ is the set match correction factor;
comparing passenger matching degrees corresponding to all historical train numbers of the appointed train, taking the train number with the highest passenger matching degree as a reference train number, extracting sales information corresponding to various supply meal types of the reference train number in a target supply time period, and taking the sales information as the reference sales information.
In a preferred embodiment of the present invention, the specific analysis process of the expected sales volume corresponding to each food type in the target supply time period includes the following steps:
analyzing and obtaining a main body sales age group and a main body sales gender corresponding to each supply meal variety class of the designated train in the target supply time period according to the reference sales information corresponding to each supply meal variety class of the designated train in the target supply time period;
comparing the sex corresponding to each comprehensive embarkation passenger of the designated train in the target supply time period, counting the number of the comprehensive embarkation passengers corresponding to each sex, taking the sex with the maximum number of the comprehensive embarkation passengers as the corresponding concentrated sex of the designated train in the target supply time period, and analyzing according to the concentrated sex analysis mode to obtain the corresponding concentrated age period of the designated train in the target supply time period;
respectively matching and comparing the concentrated sex and the concentrated age group corresponding to the designated train in the target supply time period with the main selling sex and the main selling age group corresponding to each food variety to obtain the selling sex matching degree and the selling age matching degree corresponding to each food variety of the designated train in the target supply time period, and respectively recording as phi i And
by analytical formulae
Analyzing to obtain the comprehensive matching degree delta corresponding to each variety of the supply meals of the designated train in the target supply time period i A1 and a2 are weight factors corresponding to the set gender matching and age matching;
comparing the comprehensive matching degree corresponding to each supply meal type of the designated train in the target supply time period with the set matching degree range corresponding to each sales influence factor to obtain the sales influence factor corresponding to each supply meal type of the designated train in the target supply time period, and recording the sales influence factor as the sales influence factor
Extracting sales volume from reference sales information corresponding to each supply meal type in a target supply time period of the specified train, and recording the sales volume as H i By means of a calculation formula
Calculating to obtain the expected sales volume G corresponding to each type of the supplied food in the target supply time period i ，H 0 i And (4) allowing error sales volume for the set ith supply meal variety.
In a preferred embodiment of the present invention, the determining the replenishment supply food type and the replenishment quantity corresponding to each food type to be replenished comprises:
comparing the replenishment demand evaluation index corresponding to each supply food variety with a set standard replenishment demand evaluation index, and if the replenishment demand evaluation index corresponding to a certain supply food variety is greater than the standard replenishment demand evaluation index, judging that the supply food variety is a replenishment supply food variety;
and extracting the predicted sales volume and the residual inventory corresponding to each replenishment supply food variety, and subtracting the predicted sales volume and the residual inventory to obtain the replenishment volume corresponding to each replenishment supply food variety.
In a preferred embodiment of the present invention, the analyzing step of analyzing the target distribution warehouse, the target distribution location, and the target distribution time corresponding to the designated train includes:
accumulating to obtain the comprehensive quantity to be manufactured corresponding to each supply food type in the target supply time period according to the residual inventory corresponding to each supply food type and the replenishment quantity corresponding to each replenishment supply food type, and recording as Z i ；
The unit production time length and the unit production quantity corresponding to each meal type are respectively recorded as t i And D i By means of a calculation formula
Calculating to obtain the corresponding meal making duration T of the designated train in the target supply time period 0 ，t 0 Compensating time for the set manufacturing;
according to the current time point, acquiring the interval duration between the current time period and the target supply time period, and recording as T 1 By analysis of the formula T' = T 1 -T 0 Analyzing to obtain the residual distribution time length T' corresponding to the supplied food;
accumulating the current time point and the residual distribution time length to obtain target distribution time corresponding to the supplied meal, and matching to obtain a target distribution station corresponding to the designated train as a target distribution position according to the path time point corresponding to each path station of the designated train;
and comparing the position corresponding to each associated ground distribution warehouse with the target distribution position corresponding to the designated train, and recording the associated ground distribution warehouse closest to the target distribution position corresponding to the designated train as the target distribution warehouse corresponding to the designated train.
The invention provides a commodity distribution analysis system based on deep learning in a second aspect, which comprises:
the train basic information acquisition module is used for acquiring the number of staff, the number of passengers, meal inventory information, driving path information, associated warehouse information and ticket selling information which correspond to the designated train at present;
the train meal associated information acquisition module is used for acquiring meal supply time periods and various meal supply varieties corresponding to the appointed trains and acquiring sales information corresponding to the various meal supply varieties of the appointed trains;
the train food replenishment demand analysis module is used for analyzing replenishment demands corresponding to various supply food varieties to obtain replenishment demand evaluation indexes corresponding to the various supply food varieties, so that the replenishment supply food varieties and the replenishment quantity corresponding to the various replenishment food varieties to be replenished are confirmed;
the food distribution warehouse matching analysis module is used for performing matching analysis on a target distribution warehouse, a target distribution position and a target distribution time period corresponding to the designated train;
the food management information base is used for storing the number of passengers corresponding to each food supply time period in each train number in the appointed train history and the sales information corresponding to each food supply variety, and storing the unit production duration corresponding to each food supply variety and the single production amount corresponding to each food supply variety;
and the train goods allocation information feedback terminal is used for sending the goods allocation information to the target goods allocation warehouse according to the types of the goods replenishment supply food, the goods replenishment quantity corresponding to each type of the food to be replenished, the target delivery position and the target delivery time, and performing goods allocation.
The invention provides a deep learning-based commodity distribution analysis storage medium, wherein a computer program is burnt in the deep learning-based commodity distribution analysis storage medium, and when the computer program runs in a memory of a server, the deep learning-based catering commodity distribution analysis method is realized.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the catering commodity distribution analysis method based on deep learning, the basic information, the catering product sales information and the historical sales information corresponding to the appointed train are obtained, so that the replenishment demand analysis is performed on each supply catering product type of the appointed train, and the target distribution warehouse, the target distribution position and the target distribution time period corresponding to the appointed train are analyzed at the same time, so that the distribution information corresponding to each distribution catering product of the appointed train is obtained.
2. According to the method and the device, the types of the replenishment supply food and the replenishment quantity corresponding to each type of the replenishment food to be supplied of the train are confirmed according to the basic information, the commodity sales information and the historical sales information corresponding to the designated train, the pertinence analysis of different types of the supply food is realized, the fitting property of the distribution of each type of the supply food is guaranteed, the supply and demand states of train passengers on the food are effectively balanced, the dining demands of passengers with different tendency types are met, and the riding experience of the passengers is effectively improved.
3. According to the invention, the train distribution time is carefully analyzed, so that the distribution warehouse is analyzed according to the distribution time, the limitation existing in the current general distribution mode is broken, the personalized distribution of the train food is realized, the freshness of the food is ensured to the maximum extent, the purchase desire of passengers is effectively promoted, and the service quality and the service effect of the train food and drink are greatly improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of the steps of the method of the present invention;
FIG. 2 is a schematic diagram of the connection of the modules of the system of the present invention.
Detailed Description
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Referring to fig. 1, the present invention provides a catering goods distribution analysis method based on deep learning, which includes the following steps:
step 1, obtaining basic information of a train: acquiring current corresponding basic information of a designated train, wherein the current corresponding basic information specifically comprises the number of staff, the number of passengers, meal inventory information, driving path information, associated warehouse information and ticket selling information;
specifically, the meal inventory information is the inventory corresponding to each variety of the supplied meal; the driving path information comprises positions corresponding to all the path stations and path time points corresponding to all the path stations; the associated warehouse information comprises the number of associated ground distribution warehouses and the corresponding positions of the associated ground distribution warehouses; the ticket selling information comprises the number of the tickets sold, the position of a starting station, the position of a destination station and basic information of associated ticket purchasing personnel, wherein the information of the associated ticket purchasing personnel is the sex and the age;
step 2, obtaining train food information: acquiring a meal supply time period and various meal supply variety corresponding to the appointed train, and numbering the meal supply variety types as 1,2,. I,. N in sequence according to a set sequence;
step 3, obtaining train meal sales information: obtaining sales information corresponding to various types of current supply meals of a specified train, wherein the sales information comprises sales volume, age and gender corresponding to various sales objects;
step 4, obtaining historical train sales information: the method comprises the steps that the number of passengers corresponding to each meal supply time period and sales information corresponding to each meal supply type at each train number in the history of the appointed train are extracted from a train meal management information base;
step 5, analyzing the replenishment demand of the train food: analyzing the replenishment demands corresponding to the supply food varieties to obtain replenishment demand evaluation indexes corresponding to the supply food varieties, and confirming the replenishment supply varieties and the replenishment quantity corresponding to the replenishment food varieties;
illustratively, the replenishment needs corresponding to the variety classes of the supplied meals are analyzed, and the specific analysis process comprises the following steps:
s1, obtaining the next food supply time period corresponding to the appointed train according to the current time point of the appointed train and the food supply time period corresponding to the appointed train, and recording as a target supply time period;
s2, positioning the route time point and the position corresponding to each route station from the current corresponding running path information of the designated train, and further matching to obtain the number of the route stations and the position corresponding to each route station of the designated train in the target supply time period according to the target supply time period corresponding to the designated train;
s3, positioning the number of inbound passengers and the number of outbound passengers corresponding to each path station of the designated train in the target supply time period from the ticketing information corresponding to the designated train, counting to obtain the number of comprehensive embarkation passengers of the designated train in the target supply time period, and positioning the gender and age corresponding to each comprehensive embarkation passenger of the designated train in the target supply time period from the ticketing information corresponding to the designated train;
s4, extracting the inventory corresponding to each supply food type from the food inventory information corresponding to the designated train, and extracting the sales volume from the sales information corresponding to each current supply food type of the designated train, so as to obtain the residual inventory corresponding to each supply food type by difference;
s5, analyzing and obtaining reference sales information corresponding to each food supply type in the target supply time period of the appointed train according to the number of passengers corresponding to each food supply time period in each train number of the appointed train history and the sales information corresponding to each food supply type;
further, analyzing and obtaining reference sales information corresponding to each supply meal type of the designated train in the target supply time period, wherein the specific analysis process is as follows:
recording the corresponding comprehensive passenger carrying number of the appointed train in a target supply time period as K;
recording the number of passengers corresponding to the target supply time period in each train number in the appointed train history as K j J represents a number corresponding to each historical train, and j =1,2.. To.m;
by analysis of formulas
Analyzing to obtain the passenger matching degree beta corresponding to each train number in the history of the appointed train j Δ K is the set difference in the number of allowed passengers, σ is the set matching correction factor;
comparing passenger matching degrees corresponding to all historical train numbers of the appointed train with each other, taking the train number with the highest passenger matching degree as a reference train number, extracting sales information corresponding to various types of supplied meals of the reference train number in a target supply time period, and taking the sales information as reference sales information;
s6, analyzing and obtaining the expected sales volume of each supply food type in the target supply time period according to the sales information corresponding to each supply food type of the designated train, the comprehensive carrying passenger number of the designated train in the target supply time period and the reference sales information;
further, the specific analysis process of the expected sales volume corresponding to each type of the supplied food in the target supply time period comprises the following steps:
f1, analyzing and obtaining a main body sales age group and a main body sales gender corresponding to each supply meal variety class of the designated train in the target supply time period according to the reference sales information corresponding to each supply meal variety class of the designated train in the target supply time period;
it should be noted that the specific analysis process of the subject sales age group and the subject sales gender corresponding to each supply meal category in the target supply time period for the designated train includes the following steps:
extracting the age and the sex corresponding to each sale object from the reference sale information corresponding to each supply meal variety in the target supply time period of the designated train;
matching and comparing the age corresponding to each sales object in each supply food variety with each set age range, and screening to obtain the comprehensive sales object number corresponding to each age range in each supply food variety, so as to count the sales ratio corresponding to each age range in each supply food variety, and taking the age range with the largest sales ratio as the main sales age range corresponding to each supply food variety;
comparing the sexes corresponding to the sales objects in the various types of supply meals, and counting the number of male sales objects and the number of female sales objects in the various types of supply meals, so as to count the male sales ratio and the female sales ratio in the various types of supply meals, and taking the sex of the sales object with the large sales ratio as the main sales sex;
understandably, each supplyThe calculation formula of the sales ratio corresponding to each age group in the meal variety is
K f i Showing the sales ratio corresponding to the f-th age group in the ith meal category, B i f Expressed as the number of sales objects, R, corresponding to the f-th age group in the ith meal category i Expressed as the number of sales objects corresponding to the ith meal item class, f denotes each sales age group number, f =1,2.... U, i denotes the number corresponding to each meal item class, i =1,2.. N,
the male sales proportion calculation formula in each variety of the food supply is
K 0 i Respectively expressed as the sales ratio of the ith meal supply variety male to the male, N i Representing the number of male sales objects in the ith meal category;
the calculation formula of the female sales ratio in each variety of the food supply is
K 1 i Respectively expressed as the sales ratio of the ith meal supply variety male to the male, W i Representing the number of male sales objects in the ith meal category;
it should be added that, when the current gender sales percentage difference is 0, the current gender sales percentage difference is evaluated according to the number corresponding to each gender sales target, if the number of a certain gender sales target is greater than the set reference sales target number, the gender is taken as the main sales gender, when the gender sales percentage difference is 0, the current gender sales percentage difference is evaluated according to the number corresponding to each age sales target, and if the number of a certain age sales target is greater than the set reference sales target number, the age is taken as the main sales age;
f2, comparing the sex corresponding to each comprehensive embarkation passenger of the designated train in the target supply time period, counting the number of the comprehensive embarkation passengers corresponding to each sex, taking the sex with the maximum number of the comprehensive embarkation passengers as the concentrated sex corresponding to the designated train in the target supply time period, and analyzing according to the concentrated sex analysis mode to obtain the concentrated age period corresponding to the designated train in the target supply time period;
f3, respectively matching and comparing the concentrated sex and the concentrated age group corresponding to the designated train in the target supply time period with the main selling sex and the main selling age group corresponding to each supply meal variety to obtain the selling sex matching degree and the selling age matching degree corresponding to each supply meal variety of the designated train in the target supply time period, and respectively recording as phi i And
understandably, the specific matching process of the sales gender matching degree corresponding to each supply meal variety in the target supply time period of the designated train is as follows: if the corresponding centralized gender of the designated train in the target supply time period is matched with the main body sales gender corresponding to a certain supply meal variety, the sales gender matching degree corresponding to the supply meal variety is recorded as eta 1, otherwise, the sales gender matching degree corresponding to the supply meal variety is recorded as eta 2, and therefore the sales gender matching degree phi corresponding to each supply meal variety of the designated train in the target supply time period is obtained i ，φ i With values of η 1 or η 2, η 1>η2；
It can also be understood that the specific matching process of the sales age matching degree corresponding to each supply meal variety class in the target supply time period of the designated train is as follows: if the concentrated age group corresponding to the designated train in the target supply time period is matched with the main sales age group corresponding to a certain supply variety, the sales age group matching degree corresponding to the supply variety is recorded as tau 1, otherwise, the sales age group matching degree corresponding to the supply variety is recorded as tau 2, and therefore the sales age group matching degree corresponding to each supply variety of the designated train in the target supply time period is obtained
Taking the value of tau 1 or tau 2, tau 1>τ2；
F4, passing analysis formula
Analyzing to obtain the comprehensive matching degree delta corresponding to each variety of the supply meals of the designated train in the target supply time period i A1 and a2 are weight factors corresponding to the set gender matching and age matching;
f5, comparing the comprehensive matching degree corresponding to each supply food variety class of the designated train in the target supply time period with the set matching degree range corresponding to each sales influence factor to obtain the sales influence factor corresponding to each supply food variety class of the designated train in the target supply time period, and recording the sales influence factor as the sales influence factor
F6, extracting sales volume from reference sales information corresponding to each supply meal type in the target supply time period of the designated train and recording the sales volume as H i By means of a calculation formula
Calculating to obtain the expected sales volume G corresponding to each type of the supplied food in the target supply time period i ，H 0 i And (4) allowing error sales volume for the set ith supply meal variety.
S7, recording the residual inventory corresponding to each supply food type and the predicted sales corresponding to each supply food type in the target supply time period as C i And G i And then by analytical formulae
Analyzing to obtain a replenishment demand evaluation index lambda corresponding to each supply meal variety i E represents a natural number, and mu is a set replenishment evaluation correction weight factor;
according to the method and the device, the types of the replenishment supply food and the replenishment quantity corresponding to each type of the food to be replenished of the train are confirmed according to the basic information, the commodity sales information and the historical sales information corresponding to the designated train, the pertinence analysis of different types of the supply food is realized, the fitting property of the distribution of each type of the supply food is guaranteed, the supply and demand states of the food by train passengers are effectively balanced, the dining demands of the passengers with different tendency types are met, and the riding experience of the passengers is effectively improved.
Step 6, matching and analyzing a food distribution warehouse: extracting unit production duration corresponding to each supply meal type and single production quantity corresponding to each supply meal type from a meal management information base, and analyzing to obtain a target distribution warehouse, a target distribution position and target distribution time corresponding to the specified train;
illustratively, analyzing a target distribution warehouse, a target delivery position and a target delivery time corresponding to a specified train, wherein the specific analysis process is as follows:
m1, accumulating to obtain the comprehensive quantity to be manufactured corresponding to each supply food type in the target supply time period according to the residual inventory corresponding to each supply food type and the replenishment quantity corresponding to each replenishment supply food type, and recording as Z i ；
M2, recording unit production time length and unit production quantity corresponding to each meal supply variety as t i And D i By means of a calculation formula
Calculating to obtain the corresponding meal making duration T of the designated train in the target supply time period 0 ，t 0 Compensating time for the set manufacturing;
m3, acquiring the interval duration between the current time period and the target supply time period according to the current time point, and recording the interval duration as T 1 By analysis of the formula T' = T 1 -T 0 Analyzing to obtain the residual distribution time length T' corresponding to the supplied food;
m4, accumulating the current time point and the residual distribution time length to obtain target distribution time corresponding to the supplied meal, and further matching according to the path time points of the designated train corresponding to the path stations to obtain target distribution stations corresponding to the designated train as target distribution positions;
it should be noted that the matching method corresponding to the target distribution station corresponding to the designated train is as follows: and according to the target delivery time corresponding to the supply food, locating each path site after the target delivery time corresponding to the supply food is obtained from the information of the current driving path of the appointed train, filtering, comparing the path time point corresponding to each path site left after filtering with the target delivery time corresponding to the supply food, obtaining the interval duration between the path time of each path site and the target delivery time corresponding to the supply food, and taking the path site with the shortest interval duration as the target delivery site corresponding to the appointed train.
M5, comparing the corresponding position of each associated ground distribution warehouse with the corresponding target distribution position of the designated train, and marking the associated ground distribution warehouse closest to the corresponding target distribution position of the designated train as the target distribution warehouse corresponding to the designated train;
according to the embodiment of the invention, the train goods distribution time is carefully analyzed, so that the goods distribution warehouse is analyzed according to the goods distribution time, the limitation existing in the current general goods distribution mode is broken, the personalized goods distribution of the train food is realized, the freshness of the food is guaranteed to the maximum extent, the purchasing desire of passengers is effectively promoted, and the service quality and the service effect of the train food and beverage are greatly improved.
And 7, feeding back the train cargo allocation information: and sending the replenishment quantity, the target delivery position and the target delivery time corresponding to the replenishment meal types, the replenishment meal types to be replenished to a target distribution warehouse and distributing the replenishment.
According to the embodiment of the invention, by acquiring the basic information, the meal sales information and the historical sales information corresponding to the appointed train, the replenishment demand analysis is carried out on each supply meal type of the appointed train, and the target distribution warehouse, the target distribution position and the target distribution time period corresponding to the appointed train are simultaneously analyzed, so that the distribution information corresponding to each distribution meal type of the appointed train is further obtained, the problem that the prior art has certain limitation is effectively solved, the defect that the meal distribution analysis is carried out only according to the number of train passengers at present is avoided, the rationality of the number of the meal distribution and the distribution type is improved, the number of the meal distribution and the distribution accuracy of the distribution type are further improved, the subsequent abandonment rate of the train meal is effectively reduced, the operation cost of the train catering service is effectively reduced, and the train meal distribution analysis result is more persuasive.
Referring to fig. 2, the invention provides a deep learning-based commodity distribution analysis system, which includes a train basic information acquisition module, a train meal associated information acquisition module, a train meal replenishment demand analysis module, a meal distribution warehouse matching analysis module, a meal management information base and train distribution information feedback; the train food replenishment requirement analysis module is connected with the food replenishment requirement analysis module;
the train food related information acquisition module is used for acquiring a food supply time period and each food supply type corresponding to the appointed train and acquiring sales information corresponding to each current food supply type of the appointed train;
the train food replenishment demand analysis module is used for analyzing replenishment demands corresponding to various types of supply food to obtain replenishment demand evaluation indexes corresponding to various types of supply food, so that the types of replenishment supply food and the replenishment quantity corresponding to various types of replenishment food to be replenished are confirmed;
the food distribution warehouse matching analysis module is used for performing matching analysis on a target distribution warehouse, a target distribution position and a target distribution time period corresponding to the designated train;
the meal management information base is used for storing the number of passengers corresponding to each meal supply time period in each train number in the appointed train history and the sales information corresponding to each meal supply variety, and storing the unit production duration corresponding to each meal supply variety and the single production amount corresponding to each meal supply variety;
and the train goods allocation information feedback terminal is used for sending the goods allocation information to the target goods allocation warehouse according to the types of the goods supplement supply food, the goods supplement amount corresponding to each type of the food to be supplemented, the target delivery position and the target delivery time, and allocating the goods.
The invention also provides a commodity distribution analysis storage medium based on deep learning, wherein a computer program is burnt in the commodity distribution analysis storage medium based on deep learning, and when the computer program runs in a memory of a server, the catering commodity distribution analysis method based on deep learning is realized.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (10)
1. A catering commodity distribution analysis method based on deep learning is characterized by comprising the following steps: the method comprises the following steps:
step 1, obtaining basic information of a train: acquiring current corresponding basic information of a designated train, wherein the current corresponding basic information specifically comprises the number of staff, the number of passengers, meal inventory information, driving path information, associated warehouse information and ticket selling information;
step 2, obtaining train food information: acquiring a food supply time period and various food supply types corresponding to the appointed train, and numbering the food supply types 1,2, a.
Step 3, obtaining train meal sales information: acquiring sales information corresponding to various types of current supply meals of a specified train;
step 4, obtaining historical train sales information: the method comprises the steps that the number of passengers corresponding to each meal supply time period and sales information corresponding to each meal supply type at each train number in the history of the appointed train are extracted from a train meal management information base;
step 5, analyzing the demand of replenishment of the train food: analyzing the replenishment demands corresponding to the supply food varieties to obtain replenishment demand evaluation indexes corresponding to the supply food varieties, and confirming the replenishment supply varieties and the replenishment quantity corresponding to the replenishment food varieties;
step 6, matching and analyzing a food distribution warehouse: extracting unit production duration corresponding to each supply meal type and single production quantity corresponding to each supply meal type from a meal management information base, and analyzing to obtain a target distribution warehouse, a target distribution position and target distribution time corresponding to the specified train;
and 7, feeding back the train food delivery information: and sending the replenishment quantity, the target delivery position and the target delivery time corresponding to the replenishment meal types, the replenishment meal types to be replenished to a target distribution warehouse and distributing the replenishment.
2. The catering commodity distribution analysis method based on deep learning as claimed in claim 1, wherein: the food inventory information is the inventory corresponding to each variety of the supplied food; the driving path information comprises positions corresponding to all the path stations and path time points corresponding to all the path stations; the associated warehouse information comprises the number of associated ground distribution warehouses and the corresponding positions of the associated ground distribution warehouses; the ticket selling information comprises the number of the tickets sold, the position of a starting station, the position of an ending station and basic information of related ticket purchasing personnel, wherein the position of the starting station and the position of the ending station correspond to each ticket sold, and the information of the related ticket purchasing personnel is sex and age.
3. The catering commodity distribution analysis method based on deep learning as claimed in claim 2, wherein: the sales information includes sales volume, age and sex corresponding to each sales object.
4. The catering commercial distribution analysis method based on deep learning as claimed in claim 3, wherein: the method comprises the following steps of analyzing replenishment demands corresponding to various supply food varieties, wherein the specific analysis process comprises the following steps:
s1, obtaining the next food supply time period corresponding to the appointed train according to the current time point of the appointed train and the food supply time period corresponding to the appointed train, and recording as a target supply time period;
s2, positioning the route time point and the position corresponding to each route station from the current corresponding running path information of the designated train, and further matching to obtain the number of the route stations and the position corresponding to each route station of the designated train in the target supply time period according to the target supply time period corresponding to the designated train;
s3, positioning the number of the passengers getting in and out of the train corresponding to each path station in the target supply time period of the designated train from the ticket information corresponding to the designated train, counting to obtain the number of the comprehensive passengers carrying the designated train in the target supply time period, and positioning the sex and age corresponding to each comprehensive passenger carrying the designated train in the target supply time period from the ticket information corresponding to the designated train;
s4, extracting the inventory corresponding to each supply food type from the food inventory information corresponding to the designated train, and extracting the sales volume from the sales information corresponding to each current supply food type of the designated train, so as to obtain the residual inventory corresponding to each supply food type by difference;
s5, analyzing and obtaining reference sales information corresponding to each food supply type in the target supply time period of the appointed train according to the number of passengers corresponding to each food supply time period in each train number of the appointed train history and the sales information corresponding to each food supply type;
s6, analyzing and obtaining the expected sales volume of each supply food type in the target supply time period according to the sales information corresponding to each supply food type of the designated train, the comprehensive carried passenger number of the designated train in the target supply time period and the reference sales information;
s7, recording the residual inventory corresponding to each supply food type and the expected sales corresponding to each supply food type in the target supply time period asC i And G i I represents a number corresponding to each food item, i =1,2
Analyzing to obtain a replenishment demand evaluation index lambda corresponding to each supply meal variety i And e represents a natural number, and mu is a set replenishment evaluation correction weight factor.
5. The catering commercial distribution analysis method based on deep learning as claimed in claim 4, wherein: the analysis obtains reference sales information corresponding to each supply meal variety class of the designated train in the target supply time period, and the specific analysis process is as follows:
recording the corresponding comprehensive passenger carrying number of the appointed train in the target supply time period as K;
recording the number of passengers corresponding to the target supply time period in each train number in the appointed train history as K j J represents a number corresponding to each historical train, and j =1,2.. To.m;
by analysis of formulas
Analyzing to obtain the passenger matching degree beta corresponding to each train number in the history of the appointed train j Δ K is the set allowable passenger number difference, σ is the set match correction factor;
comparing passenger matching degrees corresponding to all historical train numbers of the appointed train, taking the train number with the highest passenger matching degree as a reference train number, extracting sales information corresponding to various supply meal types of the reference train number in a target supply time period, and taking the sales information as the reference sales information.
6. The catering commodity distribution analysis method based on deep learning as claimed in claim 4, wherein: the specific analysis process of the predicted sales volume corresponding to each supply food category in the target supply time period comprises the following steps:
analyzing and obtaining a main body selling age group and a main body selling sex corresponding to each food supply variety of the designated train in the target supply time period according to the reference selling information corresponding to each food supply variety of the designated train in the target supply time period;
comparing the sex corresponding to each comprehensive embarkation passenger of the designated train in the target supply time period, counting the number of the comprehensive embarkation passengers corresponding to each sex, taking the sex with the maximum number of the comprehensive embarkation passengers as the corresponding concentrated sex of the designated train in the target supply time period, and analyzing according to the concentrated sex analysis mode to obtain the corresponding concentrated age period of the designated train in the target supply time period;
respectively matching and comparing the concentrated sex and the concentrated age group corresponding to the designated train in the target supply time period with the main selling sex and the main selling age group corresponding to each food variety to obtain the selling sex matching degree and the selling age matching degree corresponding to each food variety of the designated train in the target supply time period, and respectively recording as phi i And
by analytical formulae
Analyzing to obtain the comprehensive matching degree delta corresponding to each variety of the supply meals of the designated train in the target supply time period i A1 and a2 are weight factors corresponding to the set gender matching and age matching;
comparing the comprehensive matching degree corresponding to each supply meal type of the designated train in the target supply time period with the set matching degree range corresponding to each sales influence factor to obtain the sales influence factor corresponding to each supply meal type of the designated train in the target supply time period, and recording the sales influence factor as the sales influence factor
Reference sales credits corresponding to each meal supply type in target supply time period from designated trainThe sales volume of the extract is recorded as H i By means of a calculation formula
Calculating to obtain the expected sales volume G corresponding to each type of the supplied food in the target supply time period i ，H 0 i And (4) allowing error sales volume for the set ith supply meal variety.
7. The catering commodity distribution analysis method based on deep learning as claimed in claim 6, wherein: the method comprises the following steps of confirming the replenishment quantity corresponding to the replenishment supply food variety and each replenishment food variety to be replenished, wherein the specific confirmation process comprises the following steps:
comparing the replenishment demand evaluation index corresponding to each supply food variety with a set standard replenishment demand evaluation index, and if the replenishment demand evaluation index corresponding to a certain supply food variety is greater than the standard replenishment demand evaluation index, judging that the supply food variety is a replenishment supply food variety;
and extracting the predicted sales volume and the residual inventory corresponding to each replenishment supply meal type, and differencing the predicted sales volume and the residual inventory to obtain the replenishment volume corresponding to each replenishment supply meal type.
8. The catering commercial distribution analysis method based on deep learning as claimed in claim 7, wherein: the analysis specifies a target distribution warehouse, a target distribution position and a target distribution time corresponding to the train, and the specific analysis process is as follows:
according to the remaining inventory corresponding to each supply food variety and the replenishment quantity corresponding to each replenishment supply food variety, accumulating to obtain the comprehensive quantity to be made corresponding to each supply food variety in the target supply time period, and recording as Z i ；
The unit production time length and the unit production quantity corresponding to each meal type are respectively recorded as t i And D i By means of a calculation formula
Calculating to obtain the corresponding meal making duration T of the designated train in the target supply time period 0 ，t 0 Compensating time for the set manufacturing;
according to the current time point, acquiring the interval duration between the current time period and the target supply time period, and recording as T 1 By analysis of the formula T' = T 1 -T 0 Analyzing to obtain the residual distribution time length T' corresponding to the supplied food;
accumulating the current time point and the residual distribution time length to obtain target distribution time corresponding to the supplied meal, and matching to obtain a target distribution station corresponding to the designated train as a target distribution position according to the path time point corresponding to each path station of the designated train;
and comparing the position corresponding to each associated ground distribution warehouse with the target distribution position corresponding to the designated train, and recording the associated ground distribution warehouse closest to the target distribution position corresponding to the designated train as the target distribution warehouse corresponding to the designated train.
9. A commodity distribution analysis system based on deep learning, comprising:
the train basic information acquisition module is used for acquiring the number of staff, the number of passengers, meal inventory information, driving path information, associated warehouse information and ticket selling information which correspond to the designated train at present;
the train meal associated information acquisition module is used for acquiring meal supply time periods and various meal supply varieties corresponding to the appointed trains and acquiring sales information corresponding to the various meal supply varieties of the appointed trains;
the train food replenishment demand analysis module is used for analyzing replenishment demands corresponding to various supply food varieties to obtain replenishment demand evaluation indexes corresponding to the various supply food varieties, so that the replenishment supply food varieties and the replenishment quantity corresponding to the various replenishment food varieties to be replenished are confirmed;
the food distribution warehouse matching analysis module is used for performing matching analysis on a target distribution warehouse, a target distribution position and a target distribution time period corresponding to the designated train;
the food management information base is used for storing the number of passengers corresponding to each food supply time period in each train number in the appointed train history and the sales information corresponding to each food supply variety, and storing the unit production duration corresponding to each food supply variety and the single production amount corresponding to each food supply variety;
and the train goods allocation information feedback terminal is used for sending the goods allocation information to the target goods allocation warehouse according to the types of the goods replenishment supply food, the goods replenishment quantity corresponding to each type of the food to be replenished, the target delivery position and the target delivery time, and performing goods allocation.
10. A commodity distribution analysis storage medium based on deep learning, characterized in that: the deep learning-based commodity distribution analysis storage medium is burned with a computer program, and the computer program realizes the method of any one of the above claims 1-8 when running in the memory of the server.
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