CN112686707A

CN112686707A - Demand analysis method and device

Info

Publication number: CN112686707A
Application number: CN202011633488.3A
Authority: CN
Inventors: 戴震
Original assignee: Chezhubang Beijing Technology Co Ltd
Current assignee: Energy chain logistics technology Co.,Ltd.
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-04-20

Abstract

The invention discloses a demand analysis method and a device, comprising the following steps: acquiring historical transaction record data of an oil product order of a refinery; the historical transaction record data includes: order amount, required delivery time, actual delivery time, and transaction completion status; calculating supply credibility reference data and ordering trend analysis data of the refinery according to historical transaction record data; wherein the supply credibility reference data comprises: at least one of an order fulfillment rate, a delivery time fulfillment rate, an order volume fulfillment rate, and an order amount fulfillment rate, the order trend analysis data including: order quantity trend analysis data and/or order amount trend analysis data; and generating demand forecast data of the refinery according to the supply credibility reference data and the ordering quantity trend analysis data. The invention provides a scientific calculation method for demand prediction for an oil station, and provides effective data reference for demand analysis and capability of improving risk avoidance.

Description

Demand analysis method and device

Technical Field

The invention relates to the technical field of data processing, in particular to a demand analysis method and device.

Background

With the globalization of economy, the traditional petroleum and petrochemical industry has come up with the opportunity of industrial upgrading. The coming of economic globalization and knowledge economy expands the market of the petroleum and petrochemical industry and improves the technical level of industry, but simultaneously, the competition becomes more fierce, the operation and profit space becomes smaller gradually, and the service and brand new ordering means become necessary means of petroleum sales enterprises.

How to effectively utilize the order data to further improve the market competitiveness of the enterprise and the capability of avoiding risks is the problem discussed by the invention.

Disclosure of Invention

The invention aims to provide a demand analysis method and a demand analysis device, provides a scientific calculation method for demand prediction for an oil station, and provides effective data reference for demand analysis and capability of improving risk avoidance.

In view of this, an embodiment of the present invention provides a demand analysis method, where the method includes:

acquiring historical transaction record data of an oil product order of a refinery; the historical transaction record data includes: order amount, required delivery time, actual delivery time, and transaction completion status;

calculating supply reliability reference data and ordering trend analysis data of the refinery according to the historical transaction record data;

wherein the supply credibility reference data comprises: at least one of an order fulfillment rate, a delivery time fulfillment rate, an order volume fulfillment rate, and an order amount fulfillment rate, the order trend analysis data comprising: order quantity trend analysis data and/or order amount trend analysis data;

and generating demand forecast data of the refinery according to the supply credibility reference data and the ordering quantity trend analysis data. Preferably, the method further comprises:

and when the supply credibility reference data of the refinery is lower than a preset threshold, generating transaction risk prompt information.

Preferably, the first and second liquid crystal materials are,

the transaction completion status includes: an active close state, a timeout close state, an abnormal cancel state and a complete state;

the order achievement rate is equal to the order quantity in the completion state/(timeout closing state + abnormal cancellation state + completion state) multiplied by 100%;

the order quantity achievement rate is equal to the total order quantity of orders in the finished state/(the overtime closed state + the abnormal cancellation state + the finished state) multiplied by 100 percent;

the order amount achievement rate is 100% of the total amount of orders in the completed state/(timeout closed state + abnormal cancellation state + completed state).

Further preferably, when the actual delivery time exceeds the required delivery time preset time threshold, the transaction completion status of the oil product order is set to a time-out closing status.

Further preferably, the method further comprises:

and correcting the demand prediction data of the refinery according to the transaction risk prompt information.

A second aspect of an embodiment of the present invention provides a demand analysis apparatus, including:

a processing module for

and generating demand forecast data of the refinery according to the supply credibility reference data and the ordering quantity trend analysis data.

A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

the processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps of the first aspect;

the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.

A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.

The invention provides a demand analysis method, provides a scientific calculation method of demand prediction for an oil station, and provides effective data reference for demand analysis and capability of improving and avoiding risks.

Drawings

The technical solutions of the embodiments of the present invention are further described in detail with reference to the accompanying drawings and embodiments.

FIG. 1 is a flow chart of a demand analysis method according to an embodiment of the present invention;

FIG. 2 is a block diagram of a demand analysis processing apparatus according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a demand analysis method, which can predict demands and improve the capability of avoiding risks by utilizing historical ordering data analysis. Fig. 1 is a flowchart of a demand analysis method according to an embodiment of the present invention, and the following first describes main method steps of the demand analysis method according to the present invention with reference to fig. 1. As shown in fig. 1, the method of the present invention mainly comprises the following steps:

step 110, acquiring historical transaction record data of oil product ordering of a refinery;

the historical transaction record data may include: order amount, required delivery time, actual delivery time, and transaction completion status;

specifically, the acquisition of the historical transaction record data may be achieved from multiple aspects, for example, by inputting query information such as required delivery time, actual delivery time, transaction completion status, and the like by a user for querying, or by querying an order meeting the time interval according to a set query time interval.

The following is a specific example of obtaining historical order information from a server based on a query time interval:

receiving a query instruction and a query time interval of historical transaction record data input by a user; and inquiring historical transaction record data of oil product ordering within the inquiry time interval according to the inquiry instruction, wherein the required delivery time and/or the actual delivery time are/is. Of course, it may be preset or customized to inquire according to the required delivery time or the actual delivery time, or it may be specified that two times are output as valid return values within the inquiry time interval at the same time. In addition, the query can be performed according to the order creation time.

The transaction record for the oil order is generated by the order creation. When an order is created, an order record comprising information such as order creation time, oil station information, refinery information, oil product type, order quantity, required delivery time, payment mode type and the like is generated; and calculating according to the oil type, the order amount and the current oil price to obtain the order amount, and adding the order amount into the order record. The refinery information may not be input by the user, but automatically recommended by the system or selected by the user based on the recommendation.

And after the order is delivered, monitoring the actual delivery time, monitoring payment information according to different payment mode types and corresponding strategies, and determining that the payment completion state is completed when the payment completion is monitored.

After each order is created, the orders are stored according to the transaction records of the orders, and therefore historical transaction record data are obtained.

Step 120, calculating supply credibility reference data and ordering trend analysis data of the refinery according to historical transaction record data;

specifically, statistical analysis is carried out according to the ordering amount, the required delivery time, the actual delivery time and the transaction completion state in the historical transaction record data with the same refinery information to obtain supply reliability reference data for each refinery;

the transaction completion status includes: an active close state, a timeout close state, an abnormal cancel state and a complete state; when the actual delivery time exceeds the preset time threshold of the required delivery time, the transaction completion state of the oil product order is set to be an overtime closing state.

The provisioning credibility reference data specifically includes: at least one of an order achievement rate, a delivery time achievement rate, an order quantity achievement rate, and an order amount achievement rate, which can be calculated based on data of the historical transaction record, respectively, by the following method.

the ratio of the actual delivery time to the order within the required delivery time among the orders whose delivery time achievement rate is in the finished state;

the order quantity achievement rate is equal to the total order quantity of orders in the finished state/(the overtime closed state + the abnormal cancellation state + the finished state), and the total order quantity of the orders is multiplied by 100%;

Step 130, calculating supply credibility reference data and ordering trend analysis data of the refinery according to the historical transaction record data;

the provisioning confidence reference data includes: at least one of an order fulfillment rate, a delivery time fulfillment rate, an order volume fulfillment rate, and an order amount fulfillment rate, the order trend analysis data including: order quantity trend analysis data and/or order amount trend analysis data;

specifically, the invention adopts a trend analysis method, and the change trend of the ordering condition is explained by determining the direction, amount and amplitude of increase and decrease change of continuous same indexes, such as ordering amount, ordering amount and the like, in a transaction record queue generated by historical transaction record data according to actual delivery time. The independent variable of trend analysis is time, and the order trend analysis data that can be obtained includes, but is not limited to: order amount trend analysis data and order amount trend analysis data. The difference between the required delivery time and the actual delivery time can be counted to obtain trend analysis data of the delivery time achievement condition.

The above various analytical data were characterized for quantification. An objective function for each item of trend analysis data may be set in advance, and the deviation rate of the actual variation trend corresponding to each item from the objective function may be calculated as the item of analysis data.

For example, the order amount trend analysis data is taken as an example, the order amount is counted and counted in units of natural weeks, a graph of weekly order amount accumulated data in a range from 1 month to 10 months and 31 days in 2020 is generated according to the statistical data, and the slope of the trend line is obtained. Generating a curve graph of order quantity reference accumulated data in an interval from 1 month and 1 day to 10 months and 31 days in 2020 by using a preset week reference order quantity, wherein the order quantity reference accumulated data can be determined according to the average level of the same time period in the same industry; and a reference slope is obtained. And taking the slope of the actual trend line and the slope of the reference slope as order quantity trend analysis data. And calculating the change trend of the slope ratio in stages to serve as order quantity trend analysis data.

The above algorithm is merely an example of the practical application in one embodiment, and is not intended to limit the present invention to perform trend analysis only in this way. The specific calculation implementation of each item of analysis data can be carried out by those skilled in the art according to the actual requirements without creative labor.

And 140, generating demand forecast data of the refinery according to the supply reliability reference data and the ordering quantity trend analysis data.

Specifically, the weighted calculation can be performed according to the order achievement rate, the delivery time achievement rate, the order quantity achievement rate and the order amount achievement rate in the supply credibility reference data, the weighting coefficient can be set to be 1:1:1:1, namely, the weight ratio of each is 25%, and the coefficient is used for multiplying the order quantity trend analysis data to generate the demand prediction data of the refinery. Such as: when the order achievement rate is 0.9, the lead time achievement rate is 0.8, the order quantity achievement rate is 0.95, and the order amount achievement rate is 0.95, the obtained delivery reliability reference data is 25% × 0.9+ 25% × 0.8+ 25% × 0.95 is 0.9. The corresponding weighting coefficients may also be increased depending on the item or items of most practical interest to the oil station. And multiplying the result of the weighted calculation by the order quantity trend analysis data to generate demand forecast data of the refinery. For example, if a certain oil station is very concerned about the accuracy of order delivery time, the weighting coefficients for performing weighted calculation on the order achievement rate, the delivery time achievement rate, the ordering amount achievement rate and the ordering amount achievement rate can be set to be 1:2:1:1, namely, the ratio is respectively 20%, 40%, 20% and 20%, so as to obtain supply reliability reference data, and then the supply amount trend analysis data is multiplied to generate the demand prediction data of the refinery, namely the prediction amount of oil ordered to the refinery.

For example, for a supplier with both a and B refineries being a certain oil station, the orders from both a and B refineries will be x respectively in the future period of time calculated according to the normal trend, but the reference data of the supply reliability of both a and B refineries shows that the supply reliability of a is higher than that of B, and then the orders from both a and B refineries will be adjusted based on this data.

In addition, when the supply credibility reference data of one refinery is lower than a preset threshold, transaction risk prompt information is generated;

further, in order to avoid the risk, a preset threshold is set for the supply credibility reference data of the refinery, and after the supply credibility reference data of the refinery is obtained, if the supply credibility reference data is lower than the preset threshold, it is indicated that there is a large risk in ordering with the refinery, including a risk of failure in supply or delayed supply time, and in this case, transaction risk prompt information is generated. Refinery information of the refinery is also included in the transaction risk prompt message.

Therefore, the demand forecast data for each refinery in a future period can be corrected according to the transaction risk prompt information. For example, an appropriate risk coefficient is set and multiplied by the demand forecast data to obtain the revised demand forecast data.

The demand analysis method provided by the invention provides a scientific calculation method for demand prediction for an oil station, and provides effective data reference for demand analysis and capability of improving risk avoidance.

Fig. 2 is a block diagram of a demand analysis apparatus according to an embodiment of the present invention, where the apparatus may be an apparatus, such as a chip system, capable of implementing the method according to embodiment 1 of the present invention. As shown in fig. 2, the apparatus includes:

the processing module (201) is provided with a plurality of modules,

In a specific implementation manner provided in this embodiment, the processing module 201 is further configured to:

In another specific implementation manner provided by this embodiment, the transaction completion status includes: an active close state, a timeout close state, an abnormal cancel state and a complete state;

In another specific implementation manner provided in this embodiment, the processing module 201 is further configured to:

and when the actual delivery time exceeds the preset time threshold of the required delivery time, setting the transaction completion state of the oil product order as an overtime closing state.

The demand analysis apparatus provided in the embodiment of the present invention may execute the method steps in the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the determining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the determining module is called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when some of the above modules are implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can invoke the program code. As another example, these modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optics, Digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, bluetooth, microwave, etc.). DVD), or semiconductor media (e.g., Solid State Disk (SSD)), etc.

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic device 300 may include: a processor 31 (e.g., CPU), a memory 32, a transceiver 33; the transceiver 33 is coupled to the processor 31, and the processor 31 controls the transceiving operation of the transceiver 33. Various instructions may be stored in memory 32 for performing various processing functions and implementing method steps performed by the electronic device of embodiments of the present invention. Preferably, the electronic device according to an embodiment of the present invention may further include: a power supply 34, a system bus 35, and a communication port 36. The system bus 35 is used to implement communication connections between the elements. The communication port 36 is used for connection communication between the electronic device and other peripherals.

The system bus mentioned in fig. 3 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM) and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The Processor may be a general-purpose Processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.

It should be noted that the embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method and the processing procedure provided in the above-mentioned embodiment.

The embodiment of the invention also provides a chip for running the instructions, and the chip is used for executing the method and the processing process provided by the embodiment.

Embodiments of the present invention also provide a program product, which includes a computer program stored in a storage medium, from which the computer program can be read by at least one processor, and the at least one processor executes the methods and processes provided in the embodiments.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for demand analysis, the method comprising:

2. The demand analysis method of claim 1, further comprising:

3. The demand analysis method of claim 1, wherein the trade completion status of the order comprises: a timeout closed state, an exception cancellation state and a completion state;

4. The demand analysis method of claim 3, further comprising:

5. The demand analysis method of claim 2, further comprising:

6. A demand analysis apparatus, characterized in that the apparatus comprises:

the processing module is used for acquiring historical transaction record data of an oil product order of a refinery; the historical transaction record data includes: order amount, required delivery time, actual delivery time, and transaction completion status;

7. An electronic device, comprising: a memory, a processor, and a transceiver;

the processor is used for being coupled with the memory, reading and executing the instructions in the memory to realize the method steps of any one of the claims 1-5;

8. A computer-readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-5.