CN116228374A - Logistics industry market single data early warning method, device, equipment and storage medium - Google Patents

Logistics industry market single data early warning method, device, equipment and storage medium Download PDF

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CN116228374A
CN116228374A CN202310309297.9A CN202310309297A CN116228374A CN 116228374 A CN116228374 A CN 116228374A CN 202310309297 A CN202310309297 A CN 202310309297A CN 116228374 A CN116228374 A CN 116228374A
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orders
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华健
彭黎君
薛松
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Shanghai Shen Xue Supply Chain Management Co ltd
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Abstract

The application relates to a logistics industry market single data early warning method, a device, equipment and a storage medium, which are applied to the technical field of data processing, wherein the method comprises the following steps: acquiring basic order data; generating a market sector industry configuration table based on the base order data; filtering out business orders belonging to the market based on the market ecological industry configuration table; calculating an order drop ratio based on the business order; and generating early warning information based on the order drop ratio. The method and the device have the effect of fast and accurately carrying out single-quantity early warning.

Description

Logistics industry market single data early warning method, device, equipment and storage medium
Technical Field
The application relates to the technical field of data processing, in particular to a method, a device, equipment and a storage medium for pre-warning market single data in the logistics industry.
Background
With the popularization and development of the shopping mode of online shopping, the logistics and express industries are rapidly developed, but due to the large number of various logistics and express companies, the order quantity of the enterprise market is inevitably fluctuated, and due to the fact that the order quantity of the home city market is one of the most important business indexes of the headquarter market, early warning is needed according to the fluctuation of the order quantity.
At present, statistics of single data in a market department depends on a plurality of third party systems, and the single data is summarized and generated according to ecology, industry and project in the third party systems, but the definitions of different ecology, industry and project on index caliber are not uniform and cannot be summarized, so that statistics of the single data depends on manual arrangement, and further problems exist in accuracy and timeliness of single data statistics, and single early warning is difficult to rapidly and accurately perform.
Disclosure of Invention
In order to quickly and accurately perform single-volume early warning, the application provides a method, a device, equipment and a storage medium for single-volume data early warning in the market of logistics industry.
In a first aspect, the present application provides a method for pre-warning market single data in the logistics industry, which adopts the following technical scheme:
a market single data early warning method for logistics industry comprises the following steps:
acquiring basic order data;
generating a market sector industry configuration table based on the base order data;
filtering out business orders belonging to the market based on the market ecological industry configuration table;
calculating an order drop ratio based on the business order;
and generating early warning information based on the order drop ratio.
By adopting the technical scheme, the market sector industry configuration table is generated according to the basic order data, store information cooperated with the market sector is recorded in the market sector industry configuration table, business orders belonging to the market sector are filtered according to the store information cooperated with the market sector, the filtered business order data are more accurate, the order drop ratio of the market sector orders is calculated according to the business orders, early warning information is generated according to the order drop ratio, the filtering of the business orders and the calculation of the order drop ratio are automatically calculated by a system according to the store information cooperated with the market sector, the calculated numerical value is more accurate, and therefore single quantity early warning is rapidly and accurately carried out.
Optionally, the base order data includes a store information table, and generating the market sector industry configuration table based on the base order data includes:
acquiring a shop cooperation identifier of the market part;
extracting store information with the store cooperation mark in the store information table;
and generating a market department industry configuration table based on the store information with the store cooperation mark.
Optionally, the filtering the service order of the home market segment based on the market segment ecological industry configuration table includes:
acquiring all order information and store information in the order information;
judging whether the same store information exists between the store information in the order information and the store information in the market department industry configuration table;
if the same store information exists between the store information in the order information and the store information in the market department industry configuration table, taking the order corresponding to the same store information as a business order of the market department.
Optionally, the calculating an order drop ratio based on the service order includes:
acquiring dimension information of the service order, wherein the dimension information of the service order comprises ecological information, industry information and project information;
generating a project order form based on the ecological information, the industry information and the project information;
summarizing the project order form according to shops to generate a shop mild summary form;
querying the order quantity of a target dimension based on the shop mild summary table;
an order drop ratio is calculated based on the order quantity.
Optionally, the calculating the order drop ratio based on the order quantity includes:
acquiring the number of orders in a current preset calculation period in a current dimension and the number of orders in a preset calculation period in the current dimension;
judging whether the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period or not;
if the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period, calculating a difference value between the number of orders in the current preset calculation period and the number of orders in the previous preset calculation period;
and calculating the ratio of the difference value to the order quantity in the last preset calculation period, and taking the ratio as an order drop ratio.
Optionally, the generating the early warning information based on the order drop ratio includes:
acquiring an early warning threshold value;
judging whether the order drop ratio is not larger than the early warning threshold value;
if the order drop ratio is not greater than the early warning threshold, matching the order drop ratio with a preset early warning interval;
determining an early warning grade corresponding to the order drop ratio based on a preset early warning interval matched with the order drop ratio;
and generating early warning information based on the order drop ratio and the early warning grade.
Optionally, after the generating based on the order drop ratio, the method further includes:
and sending the early warning information to the terminal of each team personnel in the market department.
In a second aspect, the application provides a logistics industry market single data early warning device, which adopts the following technical scheme:
a market single data early warning device for logistics industry comprises:
the data acquisition module is used for acquiring basic order data;
the form generation module is used for generating a market department industry configuration form based on the basic order data;
the order filtering module is used for filtering out business orders belonging to the market part based on the market part ecological industry configuration table;
a drop calculation module for calculating an order drop ratio based on the business order;
and the early warning generation module is used for generating early warning information based on the order drop ratio.
By adopting the technical scheme, the market sector industry configuration table is generated according to the basic order data, store information cooperated with the market sector is recorded in the market sector industry configuration table, business orders belonging to the market sector are filtered according to the store information cooperated with the market sector, the filtered business order data are more accurate, the order drop ratio of the market sector orders is calculated according to the business orders, early warning information is generated according to the order drop ratio, the filtering of the business orders and the calculation of the order drop ratio are automatically calculated by a system according to the store information cooperated with the market sector, the calculated numerical value is more accurate, and therefore single quantity early warning is rapidly and accurately carried out.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
an electronic device comprising a processor coupled to a memory;
the processor is configured to execute a computer program stored in the memory, so that the electronic device executes the computer program of the logistics industry market volume data early warning method according to any one of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium storing a computer program loadable by a processor and performing the logistics industry market volume data pre-warning method of any one of the first aspects.
Drawings
Fig. 1 is a schematic flow chart of a method for pre-warning market single data in the logistics industry according to an embodiment of the present application.
Fig. 2 is a block diagram of a market single data early warning device in the logistics industry according to an embodiment of the present application.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a logistics industry market single data early warning method, which can be executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a desktop computer, etc.
Fig. 1 is a schematic flow chart of a method for pre-warning market single data in the logistics industry according to an embodiment of the present application.
As shown in fig. 1, the main flow of the method is described as follows (steps S101 to S105):
step S101, basic order data is acquired.
In this embodiment, the basic order data is the current order data of the logistics company, which is not subjected to subdivision, and since a large number of orders are generated by the logistics company every day and the sources of the orders are various, it is difficult to directly divide the orders belonging to the market, so that all the order data needs to be obtained, and the detailed classification is performed according to all the order data, i.e. the basic order data.
Step S102, generating a market department industry configuration table based on the basic order data.
Aiming at step S102, acquiring a shop cooperation mark of a market department; extracting store information with a store cooperation mark in a store information table; a market sector industry configuration table is generated based on store information having store cooperation identifiers.
In this embodiment, the store cooperating with the market portion has a special identifier, which indicates that the store cooperates with the market portion, all orders from the store belong to the market portion, the store cooperation identifier is used for distinguishing orders of the market portion from orders of other sources, the basic order data includes a store information table, in which order information of the store and dimension information of the store are recorded, and the industry configuration table of the market portion stores the order information of the store and the dimension information of the store with the store cooperation identifier together, so that the query of related information of the store related to the market portion is facilitated.
The market department industry configuration table comprises a main company market department industry configuration table which is generated by summarizing order information of all the cooperative shops of all the commercial departments of the logistics company and dimension information of the cooperative shops, a branch company market department industry configuration table which is generated by summarizing order information of all the cooperative shops of a single branch office business department and dimension information of the cooperative shops, and further a market team market department industry configuration table which is generated by summarizing order information of all the cooperative shops of each market team of the single branch office business department and dimension information of the cooperative shops of the logistics company.
In order to facilitate the distinction, the shop cooperation mark of each market team, the shop cooperation mark of each branch market portion and the shop cooperation mark of all market portions of the logistics company can be accumulated, so that the hierarchical relationship of the market portion cooperation shops can be more intuitively represented. For example, if store a is signed by the E market team of the C-province D branch of the B-logistics company, the store cooperation identifier of store a may be composed of the identifier of the E market team of the C-province D branch of the B-logistics company, if the identifier of the market part of the B-logistics company is 1, the identifier of the C-province D branch of the B-logistics company is 2, and the identifier of the E market team is 3, the store cooperation identifier of store a is 123, and when store a with store cooperation identifier 123 appears in the basic order data, the order of store a may be directly assigned to the E market team of the C-province D branch of the B-logistics company.
It should be noted that, the shop cooperation mark may be set to a number, a character string, a text, an english letter, etc., and the specific shop cooperation mark needs to be set according to actual requirements, which is not limited herein.
And step S103, filtering out the business order of the home market based on the ecological industry configuration table of the market.
Step S103, acquiring all order information and store information in the order information; judging whether the same shop information exists between the shop information in the order information and the shop information in the market department industry configuration table; if the same store information exists between the store information in the order information and the store information in the market department industry configuration table, the order corresponding to the same store information is taken as the business order of the market department.
In this embodiment, all the order information is the order information belonging to all the orders in the logistics company, where the order information includes store information, order creation time, order number, place to which the orders belong, and the like, store information in the order information is associated and matched with store information in the market industry configuration table, that is, it is determined whether the same store information exists between the store information in the order information and the store information in the market industry configuration table, and when the store information in the order information is the same as the store information in the market industry configuration table, all the orders placed by corresponding stores in the order information will belong to all the stores in the market, that is, the orders of the store are taken as service orders of the market.
Because the sources of the orders are various, some orders originate from post houses, and some orders are selected by the preference of the user, so that the orders contain a plurality of orders except business orders of the market part, store information in the order information and store information in the market part industry configuration table are required to be matched, and the business orders belonging to the market part are filtered from huge order information, so that the next calculation and early warning are convenient to perform.
Step S104, calculating the order drop ratio based on the business order.
Aiming at step S104, acquiring dimension information of a service order, wherein the dimension information of the service order comprises ecological information, industry information and project information; generating a project order form based on the ecological information, the industry information and the project information; summarizing the project order form according to shops to generate a shop mild summarization form; inquiring the order quantity of the target dimension based on a shop mild summary table; an order drop ratio is calculated based on the order quantity.
Further, the number of orders in a current preset calculation period in the current dimension and the number of orders in a previous preset calculation period in the current dimension are obtained; judging whether the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period or not; if the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period, calculating the difference value between the number of orders in the current preset calculation period and the number of orders in the previous preset calculation period; and calculating the ratio of the difference value to the number of orders in the previous preset calculation period, and taking the ratio as an order drop ratio.
In this embodiment, the light summary table is used to collect business orders from the store level, and first collect the single data of the store level, so that report query is convenient, if query is directly performed from the bottom layer, query efficiency is low, and operation pressure of the database is high.
The dimension information comprises ecological information, industry information and project information, the level of the ecological information is larger than that of the industry information, the level of the industry information is larger than that of the project information, a large ecological environment comprises at least one industry, one industry comprises at least one project, the project information table records the ecological information, the industry information and the project information of each store in detail, and the light summary table records the ecological information, the industry information and the project information of one store. When inquiring, the number of individual orders of one store can be inquired, the number of orders of all stores under the same item information can be inquired, the order data of all stores under the same industry information can be inquired, and the order data of all stores under the same ecology can be inquired.
When inquiring, the market information can be further selected to be inquired, the market information and the dimension information are overlapped, and the more the amount of overlapped information is, the more accurate the data is inquired. The method has the advantages that the limiting conditions of the query are different, the obtained query results are different, when the query is carried out, the order conditions of different market parts in the same preset calculation period in the same dimension can be queried, and the order conditions of the same market parts in different dimensions in the same preset calculation period can also be queried, so that when the number of orders fluctuates, the market parts causing the fluctuation of the orders can be queried rapidly, and the fluctuation sources can be analyzed rapidly.
In this embodiment, when calculating the order drop ratio, the order quantity in the same dimension needs to be calculated, and the market parts need to be the same during calculation, so when inquiring the order quantity, the market parts are invariant to be inquired, for example, the order quantity in the current preset calculation period and the order quantity in the preset calculation period in the current dimension of all market parts of the B logistics company can be inquired, the order drop ratio of all market parts of the B logistics company can be calculated, and the order quantity in the current calculation period and the preset calculation period in the current dimension of all market parts of the B logistics company can be inquired, and the order quantity in the current dimension of all market parts of the B logistics company can be calculated according to the order quantity in the current dimension of all market parts of the B logistics company can be calculated.
When the number of orders in the current preset calculation period in the current dimension is smaller than the number of orders in the previous preset calculation period in the current dimension, the number of orders is indicated to fall, an order fall ratio is required to be calculated, and the order fall ratio= (number of orders in the current preset calculation period in the current dimension-number of orders in the previous preset calculation period in the current dimension)/number of orders in the previous preset calculation period in the current dimension.
Step S105, generating early warning information based on the order drop ratio.
Aiming at step S105, an early warning threshold value is obtained; judging whether the drop ratio of the order is not more than an early warning threshold value; if the order drop ratio is not greater than the early warning threshold, matching the order drop ratio with a preset early warning interval; determining an early warning grade corresponding to the order drop ratio based on a preset early warning interval matched with the order drop ratio; and generating early warning information based on the order drop ratio and the early warning grade.
In this embodiment, since the number of orders in the current preset calculation period in the current dimension is smaller than the number of orders in the previous preset calculation period in the current dimension, the calculated order drop ratio is a negative number, and when the number of the negative number is larger, the number indicated by the negative number is smaller, so that when the order drop ratio is smaller than or equal to the early warning threshold value, an early warning interval in which the order drop ratio is located needs to be determined, an early warning level is determined according to the early warning interval, and early warning information is generated according to the early warning level and the order drop ratio.
The early warning interval, the early warning level and the early warning threshold value need to be set according to actual requirements, and are not particularly limited herein. When the numerical value of the order drop ratio is smaller, the corresponding early warning grade is higher, which indicates that the order quantity drops more seriously.
In this embodiment, the pre-warning information is sent to the terminals of each team person in the market segment.
Further, the number of staff in the market department to which the early warning information is to be sent needs to be set according to the early warning level, and the larger the early warning level is, the more the number of people to be sent is, so that more people can participate in the problem solving.
Fig. 2 is a block diagram of a market single data early warning device 200 in the logistics industry according to an embodiment of the present disclosure.
As shown in fig. 2, the logistics industry market single data pre-warning device 200 mainly includes:
a data acquisition module 201, configured to acquire basic order data;
a form generation module 202 for generating a market sector industry configuration form based on the base order data;
an order filtering module 203, configured to filter out a service order belonging to the market segment based on the market segment ecological industry configuration table;
a drop calculation module 204 for calculating an order drop ratio based on the business order;
the early warning generation module 205 is configured to generate early warning information based on the order drop ratio.
As an optional implementation manner of this embodiment, the form generation module 202 is specifically configured to obtain a store cooperation identifier of the market department; extracting store information with a store cooperation mark in a store information table; a market sector industry configuration table is generated based on store information having store cooperation identifiers.
As an optional implementation manner of this embodiment, the order filtering module 203 is specifically configured to obtain all order information and store information in the order information; judging whether the same shop information exists between the shop information in the order information and the shop information in the market department industry configuration table; if the same store information exists between the store information in the order information and the store information in the market department industry configuration table, the order corresponding to the same store information is taken as the business order of the market department.
As an alternative implementation of the present embodiment, the drop calculation module 204 includes:
the dimension information acquisition module is used for acquiring dimension information of the service order, wherein the dimension information of the service order comprises ecological information, industry information and project information;
the project order generation module is used for generating a project order form based on the ecological information, the industry information and the project information;
the light summary generation module is used for summarizing the project order form according to shops to generate a shop light summary form;
the order quantity inquiry module is used for inquiring the order quantity of the target dimension based on the shop mild summary table;
and the single-quantity drop calculation module is used for calculating the order drop ratio based on the order quantity.
In this optional embodiment, the single-volume drop calculation module is specifically configured to obtain the number of orders in a current preset calculation period in a current dimension and the number of orders in a previous preset calculation period in the current dimension; judging whether the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period or not; if the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period, calculating the difference value between the number of orders in the current preset calculation period and the number of orders in the previous preset calculation period; and calculating the ratio of the difference value to the number of orders in the previous preset calculation period, and taking the ratio as an order drop ratio.
As an optional implementation manner of this embodiment, the early warning generation module 205 is specifically configured to obtain an early warning threshold; judging whether the drop ratio of the order is not more than an early warning threshold value; if the order drop ratio is not greater than the early warning threshold, matching the order drop ratio with a preset early warning interval; determining an early warning grade corresponding to the order drop ratio based on a preset early warning interval matched with the order drop ratio; and generating early warning information based on the order drop ratio and the early warning grade.
As an optional implementation manner of this embodiment, the logistics industry market single volume data early warning device 200 further includes:
and the information sending module is used for sending the early warning information to the terminal of each team personnel in the market department.
In one example, a module in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when a module in an apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and modules described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Fig. 3 is a block diagram of an electronic device 300 according to an embodiment of the present application.
As shown in FIG. 3, electronic device 300 includes a processor 301 and memory 302, and may further include an information input/information output (I/O) interface 303, one or more of a communication component 304, and a communication bus 305.
The processor 301 is configured to control the overall operation of the electronic device 300, so as to complete all or part of the steps of the foregoing method for pre-warning market single data in the logistics industry; the memory 302 is used to store various types of data to support operation at the electronic device 300, which may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The Memory 302 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as one or more of static random access Memory (Static Random Access Memory, SRAM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The I/O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the corresponding communication component 104 may thus comprise: wi-Fi part, bluetooth part, NFC part.
The electronic device 300 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (Digital Signal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the logistics market single data pre-warning method as set forth in the above embodiments.
Communication bus 305 may include a pathway to transfer information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.
The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like, and may also be a server, and the like.
The application also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the steps of the logistics industry market single data early warning method are realized when the computer program is executed by a processor.
The computer readable storage medium may include: a U-disk, a removable hard disk, a read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
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.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.

Claims (10)

1. The utility model provides a logistics industry market single data early warning method which is characterized by comprising the following steps:
acquiring basic order data;
generating a market sector industry configuration table based on the base order data;
filtering out business orders belonging to the market based on the market ecological industry configuration table;
calculating an order drop ratio based on the business order;
and generating early warning information based on the order drop ratio.
2. The method of claim 1, wherein the base order data comprises a store information table, and wherein generating a market sector industry configuration table based on the base order data comprises:
acquiring a shop cooperation identifier of the market part;
extracting store information with the store cooperation mark in the store information table;
and generating a market department industry configuration table based on the store information with the store cooperation mark.
3. The method of claim 1, wherein filtering out the home market segment business orders based on the market segment ecological industry configuration table comprises:
acquiring all order information and store information in the order information;
judging whether the same store information exists between the store information in the order information and the store information in the market department industry configuration table;
if the same store information exists between the store information in the order information and the store information in the market department industry configuration table, taking the order corresponding to the same store information as a business order of the market department.
4. The method of claim 1, wherein said calculating an order drop ratio based on said business order comprises:
acquiring dimension information of the service order, wherein the dimension information of the service order comprises ecological information, industry information and project information;
generating a project order form based on the ecological information, the industry information and the project information;
summarizing the project order form according to shops to generate a shop mild summary form;
querying the order quantity of a target dimension based on the shop mild summary table;
an order drop ratio is calculated based on the order quantity.
5. The method of claim 4, wherein said calculating an order drop ratio based on said order quantity comprises:
acquiring the number of orders in a current preset calculation period in a current dimension and the number of orders in a preset calculation period in the current dimension;
judging whether the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period or not;
if the number of orders in the current preset calculation period is smaller than the number of orders in the previous preset calculation period, calculating a difference value between the number of orders in the current preset calculation period and the number of orders in the previous preset calculation period;
and calculating the ratio of the difference value to the order quantity in the last preset calculation period, and taking the ratio as an order drop ratio.
6. The method of claim 1, wherein the generating pre-warning information based on the order drop ratio comprises:
acquiring an early warning threshold value;
judging whether the order drop ratio is not larger than the early warning threshold value;
if the order drop ratio is not greater than the early warning threshold, matching the order drop ratio with a preset early warning interval;
determining an early warning grade corresponding to the order drop ratio based on a preset early warning interval matched with the order drop ratio;
and generating early warning information based on the order drop ratio and the early warning grade.
7. The method of claim 1, further comprising, after the generating based on the order drop ratio:
and sending the early warning information to the terminal of each team personnel in the market department.
8. The utility model provides a commodity circulation trade market single data early warning device which characterized in that includes:
the data acquisition module is used for acquiring basic order data;
the form generation module is used for generating a market department industry configuration form based on the basic order data;
the order filtering module is used for filtering out business orders belonging to the market part based on the market part ecological industry configuration table;
a drop calculation module for calculating an order drop ratio based on the business order;
and the early warning generation module is used for generating early warning information based on the order drop ratio.
9. An electronic device comprising a processor coupled to a memory;
the processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium comprising a computer program or instructions which, when run on a computer, cause the computer to perform the method of any of claims 1 to 7.
CN202310309297.9A 2023-03-27 2023-03-27 Logistics industry market single data early warning method, device, equipment and storage medium Pending CN116228374A (en)

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Application Number Priority Date Filing Date Title
CN202310309297.9A CN116228374A (en) 2023-03-27 2023-03-27 Logistics industry market single data early warning method, device, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310309297.9A CN116228374A (en) 2023-03-27 2023-03-27 Logistics industry market single data early warning method, device, equipment and storage medium

Publications (1)

Publication Number Publication Date
CN116228374A true CN116228374A (en) 2023-06-06

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Country Link
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117436709A (en) * 2023-12-20 2024-01-23 四川宽窄智慧物流有限责任公司 Cross-region order data overall warning method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117436709A (en) * 2023-12-20 2024-01-23 四川宽窄智慧物流有限责任公司 Cross-region order data overall warning method
CN117436709B (en) * 2023-12-20 2024-03-19 四川宽窄智慧物流有限责任公司 Cross-region order data overall warning method

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