CN114493726A - Order monitoring method and monitoring platform - Google Patents
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Abstract
The invention relates to the technical field of order management, and particularly discloses a monitoring method and a monitoring platform for an order, wherein the monitoring method comprises the steps of regularly updating related vocabularies of product information, determining the order popularity based on the related vocabularies, and determining the predicted order quantity based on the order popularity and basic order data; acquiring warehousing data, and determining distribution proportion based on the warehousing data and the predicted amount of orders; receiving an order generation request sent by a user, and generating order information based on the distribution proportion; and determining detection nodes based on the order information, receiving detection data fed back by each detection node in real time, and updating the order information in real time. According to the invention, the predicted order number and the warehousing data are obtained, products are provided for the customers according to the predicted order number and the warehousing data, and the limited service is provided for a plurality of customers, so that the requirements of the customers are met as much as possible, the stability of the customers is greatly improved, and the loss probability of the customers is reduced.
Description
Technical Field
The invention relates to the technical field of order management, in particular to an order monitoring method and a monitoring platform.
Background
The order monitoring aims at realizing the comprehensive, accurate and timely reflection of the running condition of the end-to-end service delivery flow aiming at the order line service flow; potential problems in the service and system operation process are discovered early through analysis of service operation indexes; the method helps operation and maintenance personnel to quickly locate application program bugs or system faults, control service errors and guarantee service quality; and further provides a basis for optimizing the business process.
Most of existing order management systems start to count after orders are generated and finish counting after orders are delivered to users, the process is premised on the fact that supply quantity is sufficient, and actually, the method is high in efficiency for retail orders, but for wholesale orders, the method has some inconveniences, for example, wholesale order users are mostly stable customers, and under the condition that supply quantity is not sufficient, a first-come first-serve method is adopted, and customer loss is likely to occur.
Disclosure of Invention
The invention aims to provide an order monitoring method and a monitoring platform to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of monitoring an order, the method comprising:
updating related words of product information at regular time, determining order popularity based on the related words, and determining predicted order number based on the order popularity and basic order data;
acquiring warehousing data, and determining distribution proportion based on the warehousing data and the predicted amount of orders;
receiving an order generation request containing user information sent by a user, determining a user level based on the user information, and generating order information based on the user level and a distribution proportion; the order information comprises logistics information;
and determining detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time, and updating the order information in real time.
As a further scheme of the invention: the step of updating relevant words of the product information at regular time, determining the order popularity based on the relevant words, and determining the predicted order number based on the order popularity and the basic order data comprises the following steps:
acquiring vacation information, and generating different time periods according to the vacation information;
inputting different time intervals into the trained order analysis model in sequence to determine basic order data;
inputting the product information into each mainstream App, acquiring related vocabularies, and determining keywords based on the related vocabularies; the main stream App is an App with a daily access volume reaching a preset access threshold and containing an information inquiry component;
inputting the keywords into each mainstream App again to obtain the heat value of the keywords, and inputting the heat value into a trained heat analysis model to obtain a correction rate;
and correcting the order data based on the correction rate to obtain a first predicted order number.
As a further scheme of the invention: the step of acquiring the vacation information and generating different time periods according to the vacation information comprises the following steps:
acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
determining light and busy season information of the product, and determining influence radius of each holiday based on the light and busy season information;
generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table.
As a further scheme of the invention: the step of updating relevant words of the product information at regular time, determining the order popularity based on the relevant words, and determining the predicted order number based on the order popularity and the basic order data further comprises the following steps:
acquiring the access quantity and the operation quantity in the promotion file, and determining the effective value of the promotion file according to the access quantity and the operation quantity;
reading the calculated promotion return rate, and correcting the return rate based on the effective value;
determining the number of the intended users according to the corrected return rate, and comparing the number of the intended users with the corresponding historical number of the intended users;
determining a floating proportion according to a comparison result, and generating a second predicted amount of orders based on the floating proportion and order data;
the predicted amount of orders is determined based on the first predicted amount of orders and the second predicted amount of orders.
As a further scheme of the invention: the step of obtaining the access quantity and the operation quantity in the promotion file and determining the effective value of the promotion file according to the access quantity and the operation quantity comprises the following steps:
acquiring the operation amount in the promotion file, and determining the weight values of different operations in the operation amount;
calculating interest values of corresponding operations according to the weight values;
and accumulating the interest values of different operations, and determining the effective value of the promotion file based on the accumulated interest values and the access amount of the promotion file.
As a further scheme of the invention: the step of receiving an order generation request containing user information sent by a user, determining a user level based on the user information, and generating order information based on the user level and an allocation proportion comprises the following steps:
receiving an order generation request sent by a user, acquiring user information, inputting the user information into a preset user record table, and acquiring the order frequency and the order average of the user;
determining the user level according to the order frequency and the order average;
modifying the allocation ratio based on the user level;
and generating order information according to the corrected distribution proportion.
As a further scheme of the invention: the step of determining detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time, and updating the order information in real time comprises the following steps:
acquiring logistics information in order information, and determining a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
sending a detection task containing order information to each detection node, and receiving detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
updating order information based on the time information and the integrity.
The technical scheme of the invention also provides an order monitoring platform, which comprises:
the order number predicting module is used for updating related vocabularies of product information at regular time, determining order popularity based on the related vocabularies and determining predicted order number based on the order popularity and basic order data;
the distribution proportion determining module is used for acquiring warehousing data and determining the distribution proportion based on the warehousing data and the predicted amount of orders;
the order information generation module is used for receiving an order generation request containing user information sent by a user, determining the user level based on the user information and generating the order information based on the user level and the distribution proportion; the order information comprises logistics information;
and the order information updating module is used for determining the detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time and updating the order information in real time.
As a further scheme of the invention: the order information generation module comprises:
the data acquisition unit is used for receiving an order generation request sent by a user, acquiring user information, inputting the user information into a preset user record table, and acquiring the order frequency and the order average of the user;
the level determining unit is used for determining the user level according to the order frequency and the order average;
a correction unit configured to correct the allocation ratio based on the user level;
and the first processing execution unit is used for generating order information according to the modified distribution proportion.
As a further scheme of the invention: the order information updating module comprises:
the node determining unit is used for acquiring logistics information in the order information and determining a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
the feedback receiving unit is used for sending a detection task containing order information to each detection node and receiving detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
and the second processing execution unit is used for updating the order information based on the time information and the integrity.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the predicted order number and the warehousing data are obtained, products are provided for the customers according to the predicted order number and the warehousing data, and the limited service is provided for a plurality of customers, so that the requirements of the customers are met as much as possible, the stability of the customers is greatly improved, and the loss probability of the customers is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a block flow diagram of a method for monitoring orders.
Fig. 2 is a first sub-flow block diagram of a method for monitoring orders.
FIG. 3 is a second sub-flow block diagram of a method for monitoring orders.
FIG. 4 is a third sub-flow block diagram of a method for monitoring orders.
FIG. 5 is a fourth sub-flow block diagram of a method for monitoring orders.
Fig. 6 is a block diagram of the structure of the order monitoring platform.
Fig. 7 is a block diagram of a structure of an order information generating module in the order monitoring platform.
Fig. 8 is a block diagram of a structure of an order information updating module in the order monitoring platform.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 is a flow chart of a monitoring method for an order, and in an embodiment of the present invention, the monitoring method includes:
step S100: updating related words of product information at regular time, determining order popularity based on the related words, and determining predicted order number based on the order popularity and basic order data;
step S200: acquiring warehousing data, and determining distribution proportion based on the warehousing data and the predicted amount of orders;
the existing order management systems mostly start to count after orders are generated, obtain logistics process of orders in real time, and finish counting after being delivered to users, the premise of the process is that supply quantity is sufficient, and actually, the method is very efficient for retail orders, but for wholesale orders, the method has some inconveniences, for example, wholesale order users are mostly stable customers, and under the condition that supply quantity is not sufficient, a first-come first-obtained mode is adopted, which possibly causes customer loss, so that the order quantity of each user needs to be controlled, and further, the customers are stabilized; the order amount of each user is controlled by simply predicting the order number, and the functions of steps S100 to S200 are to predict the order number and then determine the distribution ratio according to the predicted order number.
For example, if the predicted amount of orders is 100 pieces and the warehouse data is 50 pieces, the distribution ratio is 50%, when a certain user needs 100 pieces, only 50 pieces are provided to the user according to the distribution ratio, and the rest of warehouses can be distributed to other users, so that the user can be stabilized; and subsequently, replenishing goods to the user in a mode of increasing the energy production.
Step S300: receiving an order generation request containing user information sent by a user, determining a user level based on the user information, and generating order information based on the user level and a distribution proportion; the order information comprises logistics information;
step S300 is a specific allocation step, and is to receive an order generation request of a user, wherein the order generation request contains a demand amount and user information, and an allocation amount can be determined according to the demand amount and an allocation proportion, and on the basis, a user level is determined according to the user information, and then the allocation amount is finely adjusted to finally generate order information; the order information is the same as the conventional order information, and the content of the order information comprises information of both trading parties and the like; note that the logistics information is also included in the order information.
Step S400: determining detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time, and updating the order information in real time;
after order information containing logistics information is generated, detecting nodes are determined based on the logistics information, detecting tasks related to the order information are sent to the detecting nodes, when the detecting nodes detect the order, detecting data are fed back, and then the system updates the order information according to the detecting data; the updated order information is sent to the user, or may be hidden from the user, as the case may be.
Fig. 2 is a first sub-flowchart of the order monitoring method, wherein the related vocabulary of the product information is updated regularly, the order popularity is determined based on the related vocabulary, and the step of determining the predicted order number based on the order popularity and the basic order data comprises steps S101 to S105:
step S101: acquiring vacation information, and generating different time periods according to the vacation information;
step S102: inputting different time intervals into the trained order analysis model in sequence to determine basic order data;
step S103: inputting the product information into each mainstream App, acquiring related vocabularies, and determining keywords based on the related vocabularies; the main stream App is an App with a daily access volume reaching a preset access threshold and containing an information inquiry assembly;
step S104: inputting the keywords into each mainstream App again to obtain the heat value of the keywords, and inputting the heat value into a trained heat analysis model to obtain a correction rate;
step S105: and correcting the order data based on the correction rate to obtain a first predicted order number.
Step S101 to step S105 provide a specific order number prediction scheme, first, a basic order data is determined according to the time information, for a producer, different sales are provided for each quarter or each month, and correspondingly, the order number is a value which changes with time; therefore, basic order data is determined according to time; then, a correction rate can be generated based on the existing hot spot data, and the correction rate generally has the effect of positive correction, and the predicted order number has a certain fluctuation once the hot spot data related to the manufacturer product appears.
Fig. 3 is a second sub-flowchart of the order monitoring method, where the step of acquiring vacation information and generating different time periods according to the vacation information includes steps S1011 to S1013:
step S1011: acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
step S1012: determining light and busy season information of the product, and determining influence radius of each holiday based on the light and busy season information;
step S1013: generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table.
The content is that the determination process of the time slot is specifically limited, firstly, the vacation information is converted into nodes to generate each time slot, and then the information of the weak and busy seasons is added in each time slot, for example, if the product is ice cream, the order number in winter is less, therefore, the order number in the cold and vacation period is less, the specific influence mode is the influence radius, and if the radius is zero, the influence factor of the vacation period is actually reduced to zero; however, if the product is ice cream, the summer holiday period is important, and the holiday period is two months or one month, and depending on the region, in fact, for an enterprise whose product is ice cream, from 6 months to 10 months, the summer holiday period can be regarded as the "summer holiday period", and reflected in the above flow, the influence radius is increased. It is worth mentioning that the radius of influence is typically in days.
The reason for segmenting a year by holidays is that, except for some fixed holidays, the dates of most holidays for different years are different, and segmenting a year by holidays is more universal.
As a preferred embodiment of the technical solution of the present invention, the step of updating relevant vocabulary of the product information at regular time, determining the order popularity based on the relevant vocabulary, and determining the predicted order number based on the order popularity and the basic order data further includes:
acquiring the access quantity and the operation quantity in the promotion file, and determining the effective value of the promotion file according to the access quantity and the operation quantity;
reading the calculated promotion return rate, and correcting the return rate based on the effective value;
determining the number of the intended users according to the corrected return rate, and comparing the number of the intended users with the corresponding historical number of the intended users;
determining a floating proportion according to a comparison result, and generating a second predicted amount of orders based on the floating proportion and order data;
the predicted amount of orders is determined based on the first predicted amount of orders and the second predicted amount of orders.
The method is characterized in that another order prediction scheme is added to the existing order prediction scheme and is used for jointly determining the predicted order number, and specifically, the predicted order number is determined according to the access amount and the operation amount of the promotion file; the calculated return rate is a preset value, and the promotion return rates of different apps are counted by special workers.
The quantity of the intended users can be determined according to the return rate, the quantity of the intended users is compared with the corresponding historical quantity of the intended users, a floating proportion can be determined, and a second predicted amount of orders can be calculated through the floating proportion; wherein the calculation processes of the first predicted amount of orders and the second predicted amount of orders are independent, and they belong to two irrelevant quantities, and the accuracy of prediction can be improved by determining the predicted amount of orders by the two irrelevant quantities.
Further, the step of obtaining the access amount and the operation amount in the promotion file and determining the effective value of the promotion file according to the access amount and the operation amount includes:
acquiring the operation amount in the promotion file, and determining the weight values of different operations in the operation amount;
calculating interest values of corresponding operations according to the weight values;
and accumulating the interest values of different operations, and determining the effective value of the promotion file based on the accumulated interest values and the access amount of the promotion file.
The above details specifically limit the calculation process of the effective value, and for convenience of explanation, the present invention is described by specific examples: for example, a promotion document is an article, the system reads the access amount and the operation amount of the article, and determines a valid value of the article, where the valid value is a value reflecting the promotion effect, where interest values corresponding to the collection amount and the like are different, and the setting of the interest value is a statistic for different operations. It is worth mentioning that an access quantity may be considered as an interest value.
Fig. 4 is a third sub-flow block diagram of the order monitoring method, where the step of receiving an order generation request containing user information sent by a user, determining a user level based on the user information, and generating order information based on the user level and an allocation ratio includes steps S301 to S304:
step S301: receiving an order generation request sent by a user, acquiring user information, inputting the user information into a preset user record table, and acquiring the order frequency and the order average of the user;
step S302: determining the user level according to the order frequency and the order average;
step S303: modifying the allocation ratio based on the user level;
step S304: and generating order information according to the corrected distribution proportion.
The steps S301 to S304 specifically define the generation process of the order information, and the process is relatively simple, and first, if the order frequency and the order amount of a user are relatively high, it can be said that the user is an old customer, and correspondingly, on the premise of insufficient supply, the allocation proportion of the user can be slightly increased. Then, order information is determined according to the allocation ratio.
Fig. 5 is a fourth sub-flow block diagram of the order monitoring method, where the step of determining detection nodes based on the order information, sending a preset detection task to each detection node, receiving detection data fed back by each detection node in real time, and updating the order information in real time includes steps S401 to S403:
step S401: acquiring logistics information in order information, and determining a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
step S402: sending a detection task containing order information to each detection node, and receiving detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
step S403: updating order information based on the time information and the integrity.
The updating steps of the order form are specifically limited in steps S401 to S403, the core of the above is to determine detection nodes based on the logistics information, which is not difficult, the detection nodes are all preset detection stations, such as some important transfer stations, and in addition, if the corresponding capability is provided, the detection nodes can be arranged on some road sections, so that the real-time performance of the order form information can be improved.
Example 2
Fig. 6 is a block diagram of a structure of a monitoring platform for an order, in an embodiment of the present invention, the monitoring platform 10 includes:
the order number prediction module 11 is used for updating related vocabularies of the product information at regular time, determining order popularity based on the related vocabularies, and determining predicted order numbers based on the order popularity and basic order data;
the distribution proportion determining module 12 is used for acquiring warehousing data and determining the distribution proportion based on the warehousing data and the predicted amount of orders;
the order information generating module 13 is configured to receive an order generating request containing user information sent by a user, determine a user level based on the user information, and generate order information based on the user level and an allocation proportion; the order information comprises logistics information;
and the order information updating module 14 is configured to determine detection nodes based on the order information, send a preset detection task to each detection node, receive detection data fed back by each detection node in real time, and update the order information in real time.
Fig. 7 is a block diagram of a structure of an order information generating module 13 in an order monitoring platform, where the order information generating module 13 includes:
the data acquiring unit 131 is configured to receive an order generation request sent by a user, acquire user information, input the user information into a preset user record table, and acquire an order frequency and an order average of the user;
a level determining unit 132, configured to determine a user level according to the order frequency and the order average;
a correcting unit 133 configured to correct the allocation ratio based on the user level;
and the first processing execution unit 134 is configured to generate order information according to the modified distribution ratio.
Fig. 8 is a block diagram illustrating a structure of an order information updating module 14 in an order monitoring platform, where the order information updating module 14 includes:
a node determining unit 141, configured to obtain logistics information in the order information, and determine a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
the feedback receiving unit 142 is configured to send a detection task containing order information to each detection node, and receive detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
a second processing execution unit 143, configured to update the order information based on the time information and the integrity.
The functions that can be implemented by the order monitoring method are all performed by a computer device comprising one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories and loaded and executed by the one or more processors to implement the functions of the order monitoring method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (10)
1. A method for monitoring an order, the method comprising:
updating related words of product information at regular time, determining order popularity based on the related words, and determining predicted order number based on the order popularity and basic order data;
acquiring warehousing data, and determining distribution proportion based on the warehousing data and the predicted amount of orders;
receiving an order generation request containing user information sent by a user, determining a user level based on the user information, and generating order information based on the user level and a distribution proportion; the order information comprises logistics information;
and determining detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time, and updating the order information in real time.
2. The method for monitoring orders according to claim 1, wherein said step of updating relevant vocabulary of product information periodically, determining order popularity based on said relevant vocabulary, and determining predicted order quantity based on said order popularity and base order data comprises:
acquiring vacation information, and generating different time periods according to the vacation information;
inputting different time intervals into the trained order analysis model in sequence to determine basic order data;
inputting the product information into each mainstream App, acquiring related vocabularies, and determining keywords based on the related vocabularies; the main stream App is an App with a daily access volume reaching a preset access threshold and containing an information inquiry component;
inputting the keywords into each mainstream App again to obtain the heat value of the keywords, and inputting the heat value into a trained heat analysis model to obtain a correction rate;
and correcting the order data based on the correction rate to obtain a first predicted order number.
3. The method for monitoring an order according to claim 2, wherein the step of obtaining vacation information and generating different periods of time according to the vacation information comprises:
acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
determining light and busy season information of the product, and determining influence radius of each holiday based on the light and busy season information;
generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table.
4. The method for monitoring orders according to claim 2, wherein said step of updating relevant vocabulary of product information periodically, determining order popularity based on said relevant vocabulary, determining predicted order quantity based on said order popularity and base order data further comprises:
obtaining the access quantity and the operation quantity in the promotion file, and determining an effective value of the promotion file according to the access quantity and the operation quantity;
reading the calculated promotion return rate, and correcting the return rate based on the effective value;
determining the number of the intended users according to the corrected return rate, and comparing the number of the intended users with the corresponding historical number of the intended users;
determining a floating proportion according to a comparison result, and generating a second predicted amount of orders based on the floating proportion and the order data;
the predicted amount of orders is determined based on the first predicted amount of orders and the second predicted amount of orders.
5. The order monitoring method according to claim 4, wherein the step of obtaining the access quantity and the operation quantity in the promotion file, and determining the effective value of the promotion file according to the access quantity and the operation quantity comprises:
acquiring the operation amount in the promotion file, and determining the weight values of different operations in the operation amount;
calculating interest values of corresponding operations according to the weight values;
and accumulating the interest values of different operations, and determining the effective value of the promotion file based on the accumulated interest values and the access amount of the promotion file.
6. The method for monitoring the order according to claim 1, wherein the step of receiving the order generation request containing the user information sent by the user, determining the user level based on the user information, and generating the order information based on the user level and the allocation ratio comprises:
receiving an order generation request sent by a user, acquiring user information, inputting the user information into a preset user record table, and acquiring the order frequency and the order average of the user;
determining the user level according to the order frequency and the order average;
modifying the allocation ratio based on the user level;
and generating order information according to the corrected distribution proportion.
7. The order monitoring method according to claim 1, wherein the step of determining the detection nodes based on the order information, sending a preset detection task to each detection node, receiving detection data fed back by each detection node in real time, and updating the order information in real time comprises:
acquiring logistics information in order information, and determining a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
sending a detection task containing order information to each detection node, and receiving detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
updating order information based on the time information and the integrity.
8. A monitoring platform for an order, the monitoring platform comprising:
the order quantity predicting module is used for updating related vocabularies of the product information at regular time, determining order popularity based on the related vocabularies and determining predicted order quantity based on the order popularity and basic order data;
the distribution proportion determining module is used for acquiring warehousing data and determining the distribution proportion based on the warehousing data and the predicted amount of orders;
the order information generation module is used for receiving an order generation request containing user information sent by a user, determining the user level based on the user information and generating the order information based on the user level and the distribution proportion; the order information comprises logistics information;
and the order information updating module is used for determining the detection nodes based on the order information, sending preset detection tasks to the detection nodes, receiving detection data fed back by the detection nodes in real time and updating the order information in real time.
9. The order monitoring platform of claim 8, wherein the order information generating module comprises:
the data acquisition unit is used for receiving an order generation request sent by a user, acquiring user information, inputting the user information into a preset user record table, and acquiring the order frequency and the order average of the user;
the level determining unit is used for determining the user level according to the order frequency and the order average;
a correction unit configured to correct the allocation ratio based on the user level;
and the first processing execution unit is used for generating order information according to the modified distribution proportion.
10. The order monitoring platform of claim 8, wherein the order information update module comprises:
the node determining unit is used for acquiring logistics information in the order information and determining a detection node based on the logistics information; the detection nodes comprise a transfer station detection node and a road section detection node;
the feedback receiving unit is used for sending a detection task containing order information to each detection node and receiving detection data fed back by each detection node in real time; the detection data comprises time information and integrity;
and the second processing execution unit is used for updating the order information based on the time information and the integrity.
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