CN111401975A - Big data-based supply and demand early warning method - Google Patents
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Abstract
The invention provides a big data-based supply and demand early warning method, which comprises the following steps: crawling historical purchase records of a user on a purchase platform, and establishing a historical purchase database; when a user prepares to place an order on a purchasing platform, whether historical similar information exists or not is judged based on a historical purchasing database, if not, a questionnaire survey is pushed to a client of the user, and a questionnaire submission result is dynamically compensated; intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on a first supply and demand function, and when the current ordering requirement is greater than a preset requirement, ordering successfully; meanwhile, ordering information of the similar articles is crawled, whether the commercial tenant needs to load the similar articles is intelligently judged according to a second supply and demand function, and relevant information is pushed to the commercial tenant end. Through first supply and demand function, confirm user's demand, and then restrict the user placing an order, through second supply and demand function, the merchant demand of the intelligent determination is convenient for improve the selling efficiency of merchant.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data-based supply and demand early warning method.
Background
With the rapid development of e-commerce, more and more commercial platforms are commonly used, and online shopping becomes an important consumption mode at present, but due to the consumption psychology of users, when purchasing commodities, the commodities are generally purchased according to subjective ideas, and then impulse consumption is performed, but the existing e-commerce platform does not have the setting of purchasing reminding for the consumption of the users, and only is used for the users to perform ordering, payment and other operations to complete the purchase, and corresponding merchants generally need to check the stock residual amount and the like continuously to perform replenishment, so that the supply and demand early warning method based on big data is provided.
Disclosure of Invention
The invention provides a big data-based supply and demand early warning method which is used for determining the demands of users through a first supply and demand function so as to limit ordering of the users, thereby being beneficial to limiting impulse consumption of the users.
The invention provides a big data-based supply and demand early warning method, which comprises the following steps:
crawling historical purchase records of a user on a purchase platform, and establishing a historical purchase database;
when a user prepares to place an order on the purchasing platform, whether historical similar information exists or not is judged based on a historical purchasing database, if not, a questionnaire survey is pushed to a client of the user, and a questionnaire submission result is dynamically compensated;
intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on a first supply and demand function, and when the current ordering requirement is greater than a preset requirement, ordering successfully;
meanwhile, order placing information of the similar articles is crawled, whether a merchant needs to load the similar articles is intelligently judged according to a second supply and demand function, and relevant information is pushed to a merchant terminal;
wherein, when the user prepares to place an order in the purchasing platform, the method further comprises the following steps:
acquiring a configuration factor change value of a lower single port at regular time, and screening the configuration factor change value to obtain a configuration change curve;
configuring a label to be traded related to the order placing information to an order placing port of the client according to the configuration change curve, and if the user is detected to be in an order placing failure state in a preset time period, reconfiguring the label to be traded of the order placing information according to the configuration change curve;
judging whether to jump from the lower single port to a standby port or not based on the configured to-be-traded label, if so, jumping to the standby port, and reserving the to-be-traded label configured to the lower single port;
otherwise, adding a list code in the label to be traded based on the historical purchasing database and according to a list classification result to form a special label of the standby port, and checking a communication channel between the standby port and the purchasing platform if the user is detected to be in a purchase order failure state in a preset time period;
and when the activity value of the communication channel is lower than a preset value, performing third early warning on the client.
In one possible implementation, crawling a historical purchase record of a user on a purchase platform, the step of building a historical purchase database includes:
crawling historical purchase records of a user on a purchase platform, and performing inventory classification on the historical purchase records according to a cargo classification table;
establishing the goods incidence relation of all goods which are placed on the same order;
establishing order issuing incidence relations among all order issuing times within a preset time period and between orders issued each time;
optimizing the list classification according to the goods incidence relation and the ordering incidence relation, and performing time sequencing on the goods in the optimized list classification according to the time sequence so as to establish a historical purchase database;
wherein each good in the inventory classification has a unique identity for the good.
In one possible implementation, when the user prepares to place an order at the purchasing platform, the step of determining whether there is history similar information based on the history purchasing database includes:
acquiring order waiting information, wherein the order waiting information comprises a cargo model, a cargo type and a cargo color;
calling a list of goods to be compared from a historical purchase database according to the information of the order to be placed, sequentially comparing the information of the order to be placed with each goods in the list of the goods to be compared, and judging whether historical goods with similarity greater than preset degree exist;
if yes, judging that historical goods exist, and acquiring the purchase quantity of the historical goods and the purchase time of each time;
otherwise, judging that no historical goods exist;
and the information related to the historical cargos is historical similar information.
In one possible implementation manner, the step of pushing a questionnaire survey to a client of a user and dynamically compensating the questionnaire submission result includes:
the purchasing platform screens and forms a set of problems to be investigated based on the historical purchasing records of the user;
determining a weight value of each to-be-investigated question in the to-be-investigated question set according to a preset evaluation parameter, wherein the preset evaluation parameter comprises: the priority of the problem to be investigated, the ratio of the problem to be investigated and the quality of the problem to be investigated;
selecting a preset number of questions to be investigated from the question set to be investigated according to the weight values, pushing the questions to be investigated as questionnaires to the client, and investigating the user, wherein the selected questions to be investigated are obtained from large to small according to the weight values;
after the questionnaire survey of the user is finished, collecting the question answers of all questions to be surveyed in the questionnaire submitting results submitted by the user, and acquiring answer error data;
decomposing the answer error data to obtain a plurality of error components and obtaining an error function of each error component;
according to the error function, carrying out dynamic error compensation processing on the corresponding error component;
and according to the dynamic error compensation processing, performing answer error compensation on the corresponding question to be investigated, and transmitting the answer error compensation to the purchasing platform.
In a possible implementation manner, the process of intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on the first supply and demand function includes:
determining a questionnaire question and questionnaire answer set of the dynamically compensated questionnaire submission results
；
Wherein,
respectively representing the number of questionnaire questions and the number of questionnaire answers in the dynamic compensation questionnaire submission result, wherein the number of the questions is the same as the number of the answers;
is shown as
A question of individual questionnaires;
is shown as
Individual questionnaire answers; the questionnaire questions correspond to the questionnaire answers one by one, wherein when the user does not answer the questionnaire questions, information to be answered is pushed to the client;
establishing a first objective function of the information to be placed and the questionnaire question and questionnaire answer set
And a second objective function
；
wherein,
represents the information of the order to be placed, and
wherein
the goods index quantity representing the order placing information;
is shown as
Index values of the individual cargo indexes;
a target question function representing a questionnaire question;
an objective answer function representing a questionnaire answer;
a target ordering function representing ordering information;
is shown as
The index value of the individual goods index is based on the error factor
A corrected correction value;
an objective function representing questionnaire questions and questionnaire answers;
based on a first supply and demand function
For the first objective function
And a second objective function
Performing matching processing and obtaining matching value
；
When the matching value is
When the current ordering requirement of the user belongs to reasonable consumption, judging that the current ordering requirement of the user belongs to reasonable consumption;
otherwise, judging that the current ordering requirement of the user belongs to excessive consumption, and simultaneously extracting excessive consumption information and transmitting the excessive consumption information to the client side for displaying.
In a possible implementation manner, the step of crawling ordering information of the similar articles and intelligently judging whether the merchant needs to load the similar articles according to the second supply and demand function comprises the following steps:
determining uniformityOrder collection of order information for an item
；
Wherein,
representing the number of orders of the same kind of articles;
is shown as
Ordering information of ordering;
establishing inventory information for the merchant
Third objective function with lower order set
;
wherein,
a ordering function representing ordering information;
an inventory function representing inventory information;
based on a second supply-demand function
For the third objective function
Performing fusion processing, and acquiring supply and demand values;
when the supply and demand value is within a preset supply and demand range, judging that the merchant does not need to supplement the inventory;
otherwise, when the supply and demand value is smaller than the minimum value of the preset supply and demand range, judging that the merchant needs to supplement the inventory, and performing first early warning;
and when the supply and demand value is larger than the maximum value of the preset supply and demand range, judging that the inventory goods are lost, and carrying out second early warning.
In a possible implementation manner, when the current ordering requirement is greater than the preset requirement, the ordering success process further includes:
when the current ordering requirement is larger than a preset requirement, receiving a payment instruction input by a user based on a client;
judging whether the user successfully pays according to the payment instruction, and if the client of the user displays successful payment and payment success information related to the payment instruction can be taken out on the purchase platform, indicating that ordering is successful;
and if the client of the user displays that the payment is successful and the payment success information related to the payment instruction cannot be called out on the purchase platform, refreshing the network and re-acquiring.
In one possible way of realisation,
the first supply and demand function is related to a user;
the second supply and demand function is associated with a merchant.
The invention has the beneficial effects that:
1. through first supply and demand function, confirm user's demand, and then place an order to the user and restrict, be favorable to restricting the user and rush the consumption, through second supply and demand function, the goods selling efficiency of merchant is convenient for improve to the definite merchant demand of intelligence, avoids the merchant because of the pressure goods or the lack of goods, leads to the loss.
2. The method comprises the steps of determining the weight value of each to-be-surveyed question through preset evaluation parameters, facilitating obtaining of effective questionnaire survey, obtaining a plurality of error components through obtaining answer error data and decomposition processing, obtaining an error function, feeding back the error function to perform dynamic error compensation processing on the error components, and finally performing answer error compensation, so that the efficiency of the current ordering requirement of an intelligent judgment user is facilitated to be improved.
3. The target function of the information of the order to be placed and the answer set of the problem is established and combined with the first supply and demand function, whether the current order placing requirement of the user belongs to excessive consumption or not is convenient to determine, effectiveness and efficiency of judgment are further improved, the target function of the order placing set and the inventory credit is established by determining the order placing set, and meanwhile, the second supply and demand function is combined, so that whether the merchant needs to replenish the inventory or not is convenient to judge.
4. Determining a configuration factor of a next single port, constructing a configuration change curve, reconfiguring a label to be traded when the first order placing fails, judging whether to jump to a standby port according to the label to be traded, constructing a special label, and checking an activity value of a communication channel if the second order placing fails after the standby port is jumped to, so that the success of the order placing can be effectively ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a big data-based supply and demand early warning method in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention provides a big data-based supply and demand early warning method, as shown in figure 1, comprising the following steps:
step 1: crawling historical purchase records of a user on a purchase platform, and establishing a historical purchase database;
step 2: when a user prepares to place an order on the purchasing platform, whether historical similar information exists or not is judged based on a historical purchasing database, if not, a questionnaire survey is pushed to a client of the user, and a questionnaire submission result is dynamically compensated;
and step 3: intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on a first supply and demand function, and when the current ordering requirement is greater than a preset requirement, ordering successfully;
and 4, step 4: meanwhile, ordering information of the similar articles is crawled, whether the commercial tenant needs to load the similar articles is intelligently judged according to a second supply and demand function, and relevant information is pushed to the commercial tenant end.
Preferably, the first supply and demand function is associated with a user, and the second supply and demand function is associated with a merchant.
The beneficial effects of the embodiment are as follows: crawling historical purchase records (purchase information of each order purchase made on the platform) of a user on a purchase platform (daily UXIAN, Taobao or Jingdong), and establishing a historical purchase database; when a user prepares to place an order on a purchase platform, whether historical similar information exists is judged based on a historical purchase database (for example, the user purchases a water dispenser at 2020.05.01 days, the prepared order information is the water dispenser, and it can be considered that the water dispenser is purchased as the historical similar information at 2020.05.01 days), if the historical similar information does not exist, a questionnaire (questionnaire formed by various selectable questions) is pushed to a client (such as a mobile phone, a computer and the like) of the user, and a questionnaire submission result is dynamically compensated (for example, answer correction is performed on the submission result of the questionnaire, for example, the user does not fill in the answer of the question 1, at the moment, after the questionnaire is submitted again, the question 1 is independently modified, and then the questionnaire is perfected); intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on a first supply and demand function, and when the current ordering requirement is larger than a preset requirement (the preset requirement can be set manually and is to avoid impulse consumption of the user), the ordering is successful; order placing information of the same kind of articles (such as the same kind of articles related to the water dispenser) is crawled, whether the commercial tenant needs to load the same kind of articles is intelligently judged according to the second supply and demand function, and related information (such as information of loading, goods sale delay and the like) is pushed to the commercial tenant end.
The beneficial effects of the above technical scheme are: through first supply and demand function, confirm user's demand, and then place an order to the user and restrict, be favorable to restricting the user and rush the consumption, through second supply and demand function, the goods selling efficiency of merchant is convenient for improve to the definite merchant demand of intelligence, avoids the merchant because of the pressure goods or the lack of goods, leads to the loss.
The invention provides a big data-based supply and demand early warning method, which comprises the following steps of crawling historical purchase records of users on a purchase platform and establishing a historical purchase database:
crawling historical purchase records of a user on a purchase platform, and performing inventory classification on the historical purchase records according to a cargo classification table;
establishing the goods incidence relation of all goods which are placed on the same order;
establishing order issuing incidence relations among all order issuing times within a preset time period and between orders issued each time;
optimizing the list classification according to the goods incidence relation and the ordering incidence relation, and performing time sequencing on the goods in the optimized list classification according to the time sequence so as to establish a historical purchase database;
wherein each good in the inventory classification has a unique identity for the good.
In this embodiment, the goods category table, such as the category table for food, is: fruits, vegetables, flavors, etc., and further, for example, for the classification of clothes, such as: short sleeves, cotton suits, shorts, etc.;
in this embodiment, in the ordering process, a user may purchase a plurality of foods at one time, such as mango, apple and eggplant, and establish a relationship between the mango, apple and eggplant, for example, the relationship between mango and apple is better than the relationship between mango and eggplant or between apple and eggplant;
in this embodiment, an association relationship between each bill within a preset time period, for example, within 7 days, is established;
the goods incidence relation can be a local variable, and the incidence relation between the sheets can be a global variable, so that the list classification can be optimized conveniently.
In this embodiment, the unique identification code of the goods is a specific identification of the goods, such as formed by a combination of numbers, letters, and the like.
The beneficial effects of the above technical scheme are: the list classification is convenient for improving the classification of the information of the order to be placed, the list classification is convenient for optimizing by determining the incidence relation, the orderliness is convenient for improving by sequencing according to time, and a data basis is provided for limiting the order placement of the user subsequently.
The invention provides a big data-based supply and demand early warning method, which comprises the following steps of judging whether historical similar information exists or not based on a historical purchase database when a user prepares to place an order on a purchase platform:
acquiring order waiting information, wherein the order waiting information comprises a cargo model, a cargo type and a cargo color;
calling a list of goods to be compared from a historical purchase database according to the information of the order to be placed, sequentially comparing the information of the order to be placed with each goods in the list of the goods to be compared, and judging whether historical goods with similarity greater than preset degree exist;
if yes, judging that historical goods exist, and acquiring the purchase quantity of the historical goods and the purchase time of each time;
otherwise, judging that no historical goods exist;
and the information related to the historical cargos is historical similar information.
In this embodiment, the historical shipment means a purchased shipment or the like;
in the embodiment, the quantity of the existing goods in the list of the goods to be compared is not fixed, and the goods can be effectively screened by comparing one by one, so that the judgment efficiency is improved.
The beneficial effects of the above technical scheme are: through comparison one by one, effective screening can be achieved, judgment efficiency is improved, and a data basis is provided for limiting subsequent ordering of users.
The invention provides a big data-based supply and demand early warning method, which comprises the following steps of pushing questionnaire surveys to a client of a user and dynamically compensating questionnaire submission results:
the purchasing platform screens and forms a set of problems to be investigated based on the historical purchasing records of the user;
determining a weight value of each to-be-investigated question in the to-be-investigated question set according to a preset evaluation parameter, wherein the preset evaluation parameter comprises: the priority of the problem to be investigated, the ratio of the problem to be investigated and the quality of the problem to be investigated;
selecting a preset number of questions to be investigated from the question set to be investigated according to the weight values, pushing the questions to be investigated as questionnaires to the client, and investigating the user, wherein the selected questions to be investigated are obtained from large to small according to the weight values;
after the questionnaire survey of the user is finished, collecting the question answers of all questions to be surveyed in the questionnaire submitting results submitted by the user, and acquiring answer error data;
decomposing the answer error data to obtain a plurality of error components and obtaining an error function of each error component;
according to the error function, carrying out dynamic error compensation processing on the corresponding error component;
and according to the dynamic error compensation processing, performing answer error compensation on the corresponding question to be investigated, and transmitting the answer error compensation to the purchasing platform.
In this embodiment, the to-be-investigated question set is formed by screening, and may be screened from a questionnaire survey database, a preset number of to-be-investigated questions are selected from the to-be-investigated question set according to the weight values, where the preset number may be a number of the selected first questions, such as 10, and 20, which are sorted from large to small.
In this embodiment, the question answers of each question to be investigated in the questionnaire submission results submitted by the user are collected, answer error data (for example, errors are filled unintentionally due to subjective thoughts of the user, or the user intentionally fills the errors, and the like, and needs to be corrected) is obtained, the answer error data (for example, the wrong answers with 3 questions) is decomposed (into a plurality of parameter factors, for example, the wrong answers with 3 questions are decomposed one by one, and corresponding questions are decomposed one by one), a plurality of error components (for example, error components between the decomposed wrong answers and the intelligently determined user answers) are obtained, and an error function (an error function of each error component, which may be obtained by calling from an error database based on the error components) is obtained; and according to the error function, performing dynamic error compensation processing on the corresponding error component (namely, correcting the error component until the error component is consistent with the answer of the intelligently determined user answer), and further realizing answer error compensation.
The beneficial effects of the above technical scheme are: the method comprises the steps of determining the weight value of each to-be-surveyed question through preset evaluation parameters, facilitating obtaining of effective questionnaire survey, obtaining a plurality of error components through obtaining answer error data and decomposition processing, obtaining an error function, feeding back the error function to perform dynamic error compensation processing on the error components, and finally performing answer error compensation, so that the efficiency of the current ordering requirement of an intelligent judgment user is facilitated to be improved.
The invention provides a big data-based supply and demand early warning method, wherein the process of intelligently judging the current ordering requirement of a user according to the submission result of a dynamic compensation questionnaire and based on a first supply and demand function comprises the following steps:
determining a questionnaire question and questionnaire answer set of the dynamically compensated questionnaire submission results
；
Wherein,
respectively representing the number of questionnaire questions and the number of questionnaire answers in the dynamic compensation questionnaire submission result, wherein the number of the questions is the same as the number of the answers;
is shown as
A question of individual questionnaires;
is shown as
Individual questionnaire answers; the questionnaire questions correspond to the questionnaire answers one by one, wherein when the user does not answer the questionnaire questions, information to be answered is pushed to the client;
establishing a first objective function of the to-be-issued order information and the questionnaire question and questionnaire answer set
And a second objective function
；
wherein,
represents the information of the order to be placed, and
wherein
the goods index quantity representing the order placing information;
is shown as
Index values of the individual cargo indexes;
a target question function representing a questionnaire question;
an objective answer function representing a questionnaire answer;
a target ordering function representing ordering information;
is shown as
The index value of the individual goods index is based on the error factor
A corrected correction value;
an objective function representing questionnaire questions and questionnaire answers;
based on a first supply and demand function
For the first objective function
And a second objective function
Performing matching processing and obtaining matching value
；
When the matching value is
When the current ordering requirement of the user belongs to reasonable consumption, judging that the current ordering requirement of the user belongs to reasonable consumption;
otherwise, judging that the current ordering requirement of the user belongs to excessive consumption, and simultaneously extracting excessive consumption information and transmitting the excessive consumption information to the client side for displaying.
The beneficial effects of the above technical scheme are: the target function of the information of the order to be placed and the question answer set is established, the target function is matched based on the first supply and demand function, a matching value is obtained, whether the current order placing requirement of the user belongs to excessive consumption or not is determined according to the matching value, and effectiveness and efficiency of judgment are improved.
The invention provides a big data-based supply and demand early warning method, which comprises the following steps of crawling ordering information of similar articles, and intelligently judging whether a merchant needs to load the similar articles according to a second supply and demand function:
ordering set for determining ordering information of similar articles
；
Wherein,
representing the number of orders of the same kind of articles;
order information indicating the 1 i-th order;
establishing inventory information for the merchant
Third objective function with lower order set
;
wherein,
a ordering function representing ordering information;
an inventory function representing inventory information;
based on a second supply-demand function
For the third objective function
Performing fusion processing, and acquiring supply and demand values;
when the supply and demand value is within a preset supply and demand range, judging that the merchant does not need to supplement the inventory;
otherwise, when the supply and demand value is smaller than the minimum value of the preset supply and demand range, judging that the merchant needs to supplement the inventory, and performing first early warning;
and when the supply and demand value is larger than the maximum value of the preset supply and demand range, judging that the inventory goods are lost, and carrying out second early warning.
In this embodiment, the inventory information includes, for example: the remaining amount of goods;
in this embodiment, the first warning, for example a light warning, such as a green light warning, and the second warning, for example a light warning, such as a red light warning.
In this embodiment, the preset supply and demand range is set manually or is default by the system.
The beneficial effects of the above technical scheme are: and determining a supply and demand value by determining a supply and demand set and establishing an objective function of the supply and demand set and inventory information, and performing fusion processing on the second supply and demand function and the objective function, and judging whether the merchant needs to replenish the inventory or not by performing comparative analysis on the supply and demand value.
The invention provides a big data-based supply and demand early warning method, which further comprises the following steps in the process that a user prepares to place an order on a purchasing platform:
acquiring a configuration factor change value of a lower single port at regular time, and screening the configuration factor change value to obtain a configuration change curve;
configuring a label to be traded related to the order placing information to an order placing port of the client according to the configuration change curve, and if the user is detected to be in an order placing failure state in a preset time period, reconfiguring the label to be traded of the order placing information according to the configuration change curve;
judging whether to jump from the lower single port to a standby port or not based on the configured to-be-traded label, if so, jumping to the standby port, and reserving the to-be-traded label configured to the lower single port;
otherwise, adding a list code in the label to be traded based on the historical purchasing database and according to a list classification result to form a special label of the standby port, and checking a communication channel between the standby port and the purchasing platform if the user is detected to be in a purchase order failure state in a preset time period;
and when the activity value of the communication channel is lower than a preset value, performing third early warning on the client.
In this embodiment, the ordering port is, for example, an ordering interface in the panning, and the configuration factor change value of the ordering interface may be a change value of a network information variable on the interface, a configuration factor to be acquired by ordering in the interface, such as cargo information, and the like, and the configuration factor change value is subjected to a screening process to obtain a configuration change curve (a representative value is extracted from the configuration change curve to form a representative curve);
in this embodiment, the acquired tag to be transacted may include the goods information, the payment information, and the network information, and the preset time period is, for example, 15 minutes;
the spare port is provided to ensure normal operation by performing relevant operations by the spare port when the single port cannot be normally executed, and for example, when the network of the single port (the traffic of the adopted card 1) is not running, the spare port (the traffic of the adopted card 2) is switched to, and the single port can be a communication connection port or the like,
adding a list code in the tag to be traded, wherein the added list code comprises list information to form a unique tag;
if the user is detected to be still in the order placing failure state within the preset time period, checking a communication channel between the standby port and the purchase platform, and suspending all services when the activity value of the communication channel is lower than a preset value (for example, a connection channel established between the client and the platform, the connection communication channel fails, and for example, when the system is in an update state).
In this embodiment, the third warning, for example, is a vibration alarm.
In this embodiment, the order issuing port may also be a selected payment method, such as WeChat or Paibao, if WeChat, and the corresponding tag to be transacted may be payment related.
The beneficial effects of the above technical scheme are: determining a configuration factor of a next single port, constructing a configuration change curve, reconfiguring a label to be traded when the first order placing fails, judging whether to jump to a standby port according to the label to be traded, constructing a special label, and checking an activity value of a communication channel if the second order placing fails after the standby port is jumped to, so that the success of the order placing can be effectively ensured.
The invention provides a big data-based supply and demand early warning method, when the current ordering requirement is greater than the preset requirement, the ordering success process further comprises the following steps:
when the current ordering requirement is larger than a preset requirement, receiving a payment instruction input by a user based on a client;
judging whether the user successfully pays according to the payment instruction, and if the client of the user displays successful payment and payment success information related to the payment instruction can be taken out on the purchase platform, indicating that ordering is successful;
and if the client of the user displays that the payment is successful and the payment success information related to the payment instruction cannot be called out on the purchase platform, refreshing the network and re-acquiring.
The beneficial effects of the above technical scheme are: and determining whether the order placing is successful or not by acquiring the payment information of the user side and the purchasing platform.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A big data-based supply and demand early warning method is characterized by comprising the following steps:
crawling historical purchase records of a user on a purchase platform, and establishing a historical purchase database;
when a user prepares to place an order on the purchasing platform, whether historical similar information exists or not is judged based on a historical purchasing database, if not, a questionnaire survey is pushed to a client of the user, and a questionnaire submission result is dynamically compensated;
intelligently judging the current ordering requirement of the user according to the dynamic compensation questionnaire submission result and based on a first supply and demand function, and when the current ordering requirement is greater than a preset requirement, ordering successfully;
meanwhile, order placing information of the similar articles is crawled, whether a merchant needs to load the similar articles is intelligently judged according to a second supply and demand function, and relevant information is pushed to a merchant terminal;
wherein, when the user prepares to place an order in the purchasing platform, the method further comprises the following steps:
acquiring a configuration factor change value of a lower single port at regular time, and screening the configuration factor change value to obtain a configuration change curve;
according to the configuration change curve, configuring a label to be traded related to the order placing information to an order placing port of a client, and if the user is detected to be in an order placing failure state in a preset time period, reconfiguring the label to be traded of the order placing information according to the configuration change curve;
judging whether to jump from the lower single port to a standby port or not based on the configured to-be-traded label, if so, jumping to the standby port, and reserving the to-be-traded label configured to the lower single port;
otherwise, adding a list code in the label to be traded based on the historical purchasing database and according to a list classification result to form a special label of the standby port, and checking a communication channel between the standby port and the purchasing platform if the user is detected to be in a purchase order failure state in a preset time period;
and when the activity value of the communication channel is lower than a preset value, performing third early warning on the client.
2. The supply and demand warning method of claim 1 wherein the step of crawling historical purchase records of users on a purchase platform and building a historical purchase database comprises:
crawling historical purchase records of a user on a purchase platform, and performing inventory classification on the historical purchase records according to a cargo classification table;
establishing the goods incidence relation of all goods which are placed on the same order;
establishing order issuing incidence relations among all order issuing times within a preset time period and between orders issued each time;
optimizing the list classification according to the goods incidence relation and the ordering incidence relation, and performing time sequencing on the goods in the optimized list classification according to the time sequence so as to establish a historical purchase database;
wherein each good in the inventory classification has a unique identity for the good.
3. The supply and demand warning method according to claim 1, wherein the step of determining whether there is history similar information based on the history purchase database when the user prepares to place an order at the purchase platform comprises:
acquiring order waiting information, wherein the order waiting information comprises a cargo model, a cargo type and a cargo color;
calling a list of goods to be compared from a historical purchase database according to the information of the order to be placed, sequentially comparing the information of the order to be placed with each goods in the list of the goods to be compared, and judging whether historical goods with similarity greater than preset degree exist;
if yes, judging that historical goods exist, and acquiring the purchase quantity of the historical goods and the purchase time of each time;
otherwise, judging that no historical goods exist;
and the information related to the historical cargos is historical similar information.
4. The supply and demand warning method according to claim 1, wherein the step of pushing questionnaire surveys to the client of the user and dynamically compensating the questionnaire submission results comprises:
the purchasing platform screens and forms a set of problems to be investigated based on the historical purchasing records of the user;
determining a weight value of each to-be-investigated question in the to-be-investigated question set according to a preset evaluation parameter, wherein the preset evaluation parameter comprises: the priority of the problem to be investigated, the ratio of the problem to be investigated and the quality of the problem to be investigated;
selecting a preset number of questions to be investigated from the question set to be investigated according to the weight values, pushing the questions to be investigated as questionnaires to the client, and investigating the user, wherein the selected questions to be investigated are obtained from large to small according to the weight values;
after the questionnaire survey of the user is finished, collecting the question answers of all questions to be surveyed in the questionnaire submitting results submitted by the user, and acquiring answer error data;
decomposing the answer error data to obtain a plurality of error components and obtaining an error function of each error component;
according to the error function, carrying out dynamic error compensation processing on the corresponding error component;
and according to the dynamic error compensation processing, performing answer error compensation on the corresponding question to be investigated, and transmitting the answer error compensation to the purchasing platform.
5. The supply and demand early warning method according to claim 1, wherein the process of intelligently judging the current ordering requirement of the user based on a first supply and demand function according to the dynamic compensation questionnaire submission result comprises:
determining a questionnaire question and questionnaire answer set of the dynamically compensated questionnaire submission results
；
Wherein,
respectively representing the number of questionnaire questions and the number of questionnaire answers in the dynamic compensation questionnaire submission result, wherein the number of the questions is the same as the number of the answers;
is shown as
A question of individual questionnaires;
is shown as
Individual questionnaire answers; the questionnaire questions correspond to the questionnaire answers one by one, wherein when the user does not answer the questionnaire questions, information to be answered is pushed to the client;
establishing a first objective function of the to-be-issued order information and the questionnaire question and questionnaire answer set
And a second objective function
；
wherein,
represents the information of the order to be placed, and
wherein
the goods index quantity representing the order placing information;
is shown as
Index values of the individual cargo indexes;
a target question function representing a questionnaire question;
an objective answer function representing a questionnaire answer;
a target ordering function representing ordering information;
is shown as
The index value of the individual goods index is based on the error factor
A corrected correction value;
an objective function representing questionnaire questions and questionnaire answers;
based on a first supply and demand function
For the first objective function
And a second objective function
Performing matching processing and obtaining matching value
；
When the matching value is
When the current ordering requirement of the user belongs to reasonable consumption, judging that the current ordering requirement of the user belongs to reasonable consumption;
otherwise, judging that the current ordering requirement of the user belongs to excessive consumption, and simultaneously extracting excessive consumption information and transmitting the excessive consumption information to the client side for displaying.
6. The supply and demand early warning method according to claim 1, wherein the step of crawling ordering information of the similar items and intelligently judging whether a merchant needs to load the similar items according to a second supply and demand function comprises the following steps:
ordering set for determining ordering information of similar articles
；
Wherein,
representing the number of orders of the same kind of articles;
is shown as
Ordering information of ordering;
establishing inventory information for the merchant
Third objective function with lower order set
;
wherein,
a ordering function representing ordering information;
an inventory function representing inventory information;
based on a second supply-demand function
For the third objective function
Performing fusion processing, and acquiring supply and demand values;
when the supply and demand value is within a preset supply and demand range, judging that the merchant does not need to supplement the inventory;
otherwise, when the supply and demand value is smaller than the minimum value of the preset supply and demand range, judging that the merchant needs to supplement the inventory, and performing first early warning;
and when the supply and demand value is larger than the maximum value of the preset supply and demand range, judging that the inventory goods are lost, and carrying out second early warning.
7. The supply and demand early warning method as set forth in claim 1, wherein when the current ordering requirement is greater than the preset requirement, the ordering success process further comprises:
when the current ordering requirement is larger than a preset requirement, receiving a payment instruction input by a user based on a client;
judging whether the user successfully pays according to the payment instruction, and if the client of the user displays successful payment and payment success information related to the payment instruction can be taken out on the purchase platform, indicating that ordering is successful;
and if the client of the user displays that the payment is successful and the payment success information related to the payment instruction cannot be called out on the purchase platform, refreshing the network and re-acquiring.
8. The supply and demand warning method of claim 1,
the first supply and demand function is related to a user;
the second supply and demand function is associated with a merchant.
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