CN115062875A

CN115062875A - Stock quantity prediction method, stock quantity prediction device, storage medium and electronic equipment

Info

Publication number: CN115062875A
Application number: CN202210983790.4A
Authority: CN
Inventors: 王志超
Original assignee: Shenzhen Meiyunji Network Technology Co ltd
Current assignee: Shenzhen Meiyunji Network Technology Co ltd
Priority date: 2022-08-17
Filing date: 2022-08-17
Publication date: 2022-09-16

Abstract

The application relates to the technical field of data molecules, and particularly discloses an inventory prediction method, an inventory prediction device, a storage medium and electronic equipment, wherein the method comprises the following steps: inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic; selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model; selecting a pre-estimation model for the historical data by combining the second characteristics under the model category; optimizing the pre-estimation model by using the historical data and the second characteristics; and obtaining estimated data by using the historical data and the optimized estimated model. The prediction method takes historical data as a base point, and different prediction models are selected according to the characteristics of the historical data, so that the prediction result is closer to the real data.

Description

Stock quantity prediction method, stock quantity prediction device, storage medium and electronic equipment

Technical Field

The embodiment of the application relates to the technical field of data analysis, in particular to an inventory prediction method, an inventory prediction device, a storage medium and electronic equipment.

Background

In order to maintain normal sales volume, under the premise of ensuring the production and operation requirements of enterprises, the inventory is always kept at a reasonable level, the enterprises need to flexibly master the dynamic state of the inventory, put forward orders in due time and in a proper amount, avoid over-storage or shortage of goods, reduce the occupied inventory space, reduce the inventory cost, control the occupied inventory fund, and accelerate fund turnover. The current mainstream prediction methods are based on historical sales estimates, for example: the daily sales volume = 20% for 3 days, 20% for 7 days, 20% for 14 days, 30% for 60 days, 20% for 60 days and 10% for 90 days, the estimation deviation of the inventory prediction method for the commodities with large variation of sales volume is large, and the same prediction model is used for predicting all the commodities, so that the prediction deviation degree is large.

Disclosure of Invention

The embodiment of the application provides an inventory prediction method, an inventory prediction device, a storage medium and electronic equipment.

In a first aspect, an embodiment of the present application provides an inventory prediction method, including:

inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic;

selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model;

selecting a pre-estimation model for the historical data by combining the second characteristics under the model category;

optimizing the predictive model using the historical data and the second features;

and obtaining estimated data by using the historical data and the optimized estimated model.

In the inventory prediction method provided in the embodiment of the present application, the inputting of the plurality of historical data into the data analysis model includes:

acquiring the historical data;

standardizing the historical data;

and inputting the normalized historical data into the data analysis model.

In the inventory prediction method provided in the embodiment of the present application, the selecting a model category for the historical data according to the first characteristic includes:

the first feature comprises a first sub-feature, a second sub-feature and a third sub-feature;

when the first characteristic is a first sub-characteristic, selecting the first category model for the historical data;

when the first characteristic is a second sub-characteristic, selecting the second category model for the historical data;

and when the first characteristic is a third sub-characteristic, selecting the third category model for the historical data.

In the inventory prediction method provided in the embodiment of the present application, when the first feature is a first sub-feature, selecting the first class model for the historical data includes:

the first sub-characteristic is high sales and steady sales, and the first category model is as follows:

wherein the content of the first and second substances,

is the factor of the smoothing factor that is,

is composed of

The time of day history data is stored in a memory,

is composed of

Time of day history data.

In the inventory prediction method provided in the embodiment of the present application, when the first characteristic is a second sub-characteristic, selecting the second category model for the historical data includes:

the second sub-feature is a sales height variation, and the second category model is a time series model.

In the inventory prediction method provided in the embodiment of the present application, when the first feature is a third sub-feature, selecting the third category model for the historical data includes:

the third sub-feature is slow sales, and the third class model is:

wherein the content of the first and second substances,

are respectively historical data

The corresponding weight.

In a second aspect, an embodiment of the present application provides an inventory amount prediction device, including:

the analysis unit is used for inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic;

the first model unit is used for selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model;

the second model unit is used for selecting a pre-estimated model for the historical data by combining the second characteristics under the model category;

an optimization unit, configured to optimize the pre-estimation model using the historical data and the second feature;

and the output unit is used for obtaining the estimated data by utilizing the historical data and the optimized estimated model.

In the inventory prediction device according to the embodiment of the present application, the parsing unit is further configured to:

acquiring the historical data;

standardizing the historical data;

and inputting the normalized historical data into the data analysis model.

In a third aspect, embodiments of the present application provide a storage medium, where a plurality of instructions are stored, where the instructions are suitable for a processor to load and execute the steps in the inventory quantity prediction method according to any one of the embodiments of the present application.

In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the inventory quantity prediction method according to any one of the embodiments of the present application.

To sum up, the embodiment of the present application provides an inventory prediction method, including: inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic; selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model; selecting a pre-estimation model for the historical data by combining the second characteristics under the model category; optimizing the pre-estimation model by using the historical data and the second characteristics; and obtaining estimated data by using the historical data and the optimized estimated model. The prediction method takes historical data as a base point, and different prediction models are selected according to the characteristics of the historical data, so that the prediction result is closer to the real data.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of an inventory quantity prediction method according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of an inventory quantity prediction device according to an embodiment of the present application.

Fig. 3 is a schematic structural diagram of a server according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first" and "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The embodiment of the application provides an inventory prediction method and device, a storage medium and electronic equipment.

Referring to fig. 1, fig. 1 is a schematic flow chart of an inventory quantity prediction method provided in an embodiment of the present application, and a specific flow of the inventory quantity prediction method may be as follows:

101. and inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic.

In some embodiments, a number of historical data are input into the data parsing model, including:

acquiring historical data;

standardizing historical data;

and inputting the normalized historical data into a data analysis model.

Specifically, a rough trend of the historical sales of the product can be seen from the historical data of the product, in a plane coordinate system, the historical data is generally composed of discrete points (time and sales), the time can be day, week, month, quarter or year, the obtained historical data is standardized into a uniform format, the trend of the sales over time can be generally obtained by curve fitting or difference averaging, such as high sales and stable sales, high sales or slow sales, for the product with a highly-changed message, it is possible that the product is closely related to the recent sales (such as sales promotion), that is, the recent sales of the product occupies a great weight in the whole sales, or the product with a highly-changed sales is not related to the recent sales, and for the two types of products with highly-changed sales, a second feature needs to be further added for predicting the sales data.

102. And selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model.

103. And selecting a pre-estimation model for the historical data by combining the second characteristics under the model category.

In some embodiments, step 102 further comprises:

when the first characteristic is a first sub-characteristic, selecting a first category model for the historical data;

when the first characteristic is a second sub-characteristic, selecting a second category model for the historical data;

and when the first characteristic is a third sub-characteristic, selecting a third category model for the historical data.

Specifically, the first sub-feature is high and steady sales, and the first class model is:

wherein the content of the first and second substances,

is the factor of the smoothing factor that is,

is composed of

The time of day history data is stored in a memory,

is composed of

Time of day history data. According to the first class model, when

When the weight of the model given to past history data is smaller near 1, the estimated data obtained finally is more likely to change according to the current history data, and when the weight is smaller

As the model approaches 0, the weight given to the past historical data gradually increases, and the predicted data is less affected by the current historical data. For the products, the relevance between the estimated data and the recent time is the second characteristic which determines

The value of (a). In the specific implementation process, the weight of the historical data of different stages of the product is determined

Value, bookThe application is not specifically limited thereto.

Specifically, the second sub-feature is the variation in the height of the sales volume, and the second class model is a time series model.

The sales height variation comprises a close correlation with the recent sales amount and a less correlation with the recent sales amount, wherein when the sales height variation is closely related with the recent sales amount, the recent historical data has a larger weight in the time series model, such as the sales height variation of the product may be closely related with the recent sales amount when the product is promoted in a special festival in the recent period, when the sales height variation is less related with the recent sales amount, the order period can be considered for periodic prediction, the time series model with the order period as a time node is selected, for the product with the sales height variation, a second characteristic is whether the sales variation is related with the recent period, and the time series model is further determined according to the second characteristic.

Specifically, the third sub-feature is slow sales, and the third category model is:

wherein the content of the first and second substances,

are respectively historical data

The corresponding weight. For slow selling products, a certain amount of safety stock may be maintained every quarter of the year to ensure proper supply of the product, taking into account safety stock. For the product, the weight of each quarter is determined as the second characteristic according to the product sales characteristics, and in the specific implementation process, the third class model can select historical data of four quarters at most, such as

，

Respectively the historical data of four quarters of the time,

the weight corresponding to the historical data may be specifically determined according to the second characteristic of the product, and this is not specifically limited in this application.

104. And optimizing the pre-estimation model by using the historical data and the second characteristic.

105. And obtaining estimated data by using the historical data and the optimized estimated model.

Specifically, based on historical data, the weights or fitted curves may be adjusted so that the estimated data is closer to the true inventory.

All the above technical solutions can be combined arbitrarily to form the optional embodiments of the present application, and are not described herein again.

The embodiment of the present application further provides an inventory amount prediction device 200, as shown in fig. 2, fig. 2 is a schematic structural diagram of the inventory amount prediction device 200 provided in the embodiment of the present application. The inventory amount prediction device 200 includes an analysis unit 201, a first model unit 202, a second model unit 203, an optimization unit 204, and an output unit 205. Wherein the content of the first and second substances,

the analysis unit 201 is used for inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic;

the first model unit 202 is configured to select a model category for the historical data according to the first characteristic, where the model category includes a first category model, a second category model, and a third category model;

the second model unit 203 is used for selecting a pre-estimation model for the historical data by combining the second characteristics under the model category;

an optimizing unit 204, configured to optimize the pre-estimation model using the historical data and the second feature;

and the output unit 205 is configured to obtain the estimated data by using the historical data and the optimized estimated model.

In some embodiments, the parsing unit 201 may specifically be configured to:

acquiring historical data;

standardizing historical data;

and inputting the normalized historical data into a data analysis model.

The meaning of the term is the same as that in the above stock quantity prediction method, and specific implementation details can refer to the description in the method embodiment.

The inventory prediction device 200 provided by the embodiment of the application inputs a plurality of historical data into a data analysis model through the analysis unit 201 to obtain data characteristics of the historical data, wherein the data characteristics include a first characteristic and a second characteristic; the first model unit 202 is configured to select a model category for the historical data according to the first feature, where the model category includes a first category model, a second category model, and a third category model; the second model unit 203 is used for selecting a pre-estimation model for the historical data by combining the second characteristics under the model category; the optimization unit 204 is configured to optimize the pre-estimation model by using the historical data and the second feature; the output unit 205 is configured to obtain the estimated data by using the historical data and the optimized estimated model. The inventory prediction device 200 selects different estimation models according to the characteristics of historical data by taking the historical data as a base point, so that the prediction result is closer to the real data.

The embodiment of the present application further provides a server, as shown in fig. 3, which shows a schematic structural diagram of the server according to the embodiment of the present application, specifically:

the server may include components such as a processor 301 of one or more processing cores, memory 302 of one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that the server architecture shown in FIG. 3 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:

the processor 301 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302, thereby performing overall monitoring of the server. Optionally, processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301.

The memory 302 may be used to store software programs and modules, and the processor 301 executes various functional applications and data processing by operating the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

The server further includes a power supply 303 for supplying power to the various components, and preferably, the power supply 303 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 303 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.

The server may also include an input unit 304, the input unit 304 being operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

Although not shown, the server may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor 301 in the server loads the executable file corresponding to the process of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302, thereby implementing various functions as follows:

inputting a plurality of historical data into a data analysis model to obtain data characteristics of the historical data, wherein the data characteristics comprise a first characteristic and a second characteristic; selecting model categories for the historical data according to the first characteristics, wherein the model categories comprise a first category model, a second category model and a third category model; selecting a pre-estimation model for the historical data by combining the second characteristics under the model category; optimizing the pre-estimation model by using the historical data and the second characteristics; and obtaining estimated data by using the historical data and the optimized estimated model.

The above operations can be specifically referred to the previous embodiments, and are not described herein.

Accordingly, an electronic device according to an embodiment of the present disclosure may include, as shown in fig. 4, a Radio Frequency (RF) circuit 401, a memory 402 including one or more computer-readable storage media, an input unit 403, a display unit 404, a sensor 405, an audio circuit 406, a Wireless Fidelity (WiFi) module 407, a processor 408 including one or more processing cores, and a power supply 409. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 4 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:

the RF circuit 401 may be used for receiving and transmitting signals during a message transmission or communication process, and in particular, for receiving downlink information of a base station and then sending the received downlink information to the one or more processors 408 for processing; in addition, data relating to uplink is transmitted to the base station. In general, the RF circuitry 401 includes, but is not limited to, an antenna, at least one Amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 401 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.

The memory 402 may be used to store software programs and modules, and the processor 408 executes various functional applications and data processing by operating the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic device, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 408 and the input unit 403 access to the memory 402.

The input unit 403 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, in a particular embodiment, the input unit 403 may include a touch-sensitive surface as well as other input devices. The touch-sensitive surface, also referred to as a touch display screen or a touch pad, may collect touch operations by a user (e.g., operations by a user on or near the touch-sensitive surface using a finger, a stylus, or any other suitable object or attachment) thereon or nearby, and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 408, and can receive and execute commands from the processor 408. In addition, touch sensitive surfaces may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. The input unit 403 may include other input devices in addition to the touch-sensitive surface. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

The display unit 404 may be used to display information input by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 404 may include a Display panel, and optionally, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch-sensitive surface may overlay the display panel, and when a touch operation is detected on or near the touch-sensitive surface, the touch operation is transmitted to the processor 408 to determine the type of touch event, and then the processor 408 provides a corresponding visual output on the display panel according to the type of touch event. Although in FIG. 4 the touch-sensitive surface and the display panel are shown as two separate components to implement input and output functions, in some embodiments the touch-sensitive surface may be integrated with the display panel to implement input and output functions.

The electronic device may also include at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or the backlight when the electronic device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, can be used for applications for recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor and the like which can be configured for the electronic device, and are not described herein again.

Audio circuitry 406, a speaker, and a microphone may provide an audio interface between the user and the electronic device. The audio circuit 406 may transmit the electrical signal converted from the received audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 406 and converted into audio data, which is then processed by the audio data output processor 408, and then passed through the RF circuit 401 to be sent to, for example, another electronic device, or output to the memory 402 for further processing. The audio circuitry 406 may also include an earbud jack to provide communication of a peripheral headset with the electronic device.

WiFi belongs to short distance wireless transmission technology, and the electronic device can help the user send and receive e-mail, browse web page and access streaming media, etc. through the WiFi module 407, which provides wireless broadband internet access for the user. Although fig. 4 shows the WiFi module 407, it is understood that it does not belong to the essential constitution of the electronic device, and may be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 408 is a control center of the electronic device, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby monitoring the mobile phone as a whole. Optionally, processor 408 may include one or more processing cores; preferably, the processor 408 may integrate an application processor, which handles primarily the operating system, user interface, applications, etc., and a modem processor, which handles primarily the wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 408.

The electronic device also includes a power source 409 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 408 via a power management system to manage charging, discharging, and power consumption via the power management system. The power supply 409 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.

Although not shown, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 408 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 402 according to the following instructions, and the processor 408 runs the application programs stored in the memory 402, thereby implementing various functions:

It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, embodiments of the present application provide a storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the inventory prediction methods provided by the embodiments of the present application. For example, the instructions may perform the steps of:

The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.

Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

Since the instructions stored in the storage medium can execute the steps in any inventory prediction method provided in the embodiment of the present application, the beneficial effects that can be achieved by any inventory prediction method provided in the embodiment of the present application can be achieved, and detailed descriptions are omitted herein for the foregoing embodiment.

The inventory prediction method, the inventory prediction device, the storage medium and the electronic device provided by the embodiment of the application are introduced in detail, a specific example is applied in the description to explain the principle and the implementation of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An inventory prediction method, comprising:

2. The inventory prediction method of claim 1, wherein said inputting a plurality of historical data into a data parsing model comprises:

acquiring the historical data;

standardizing the historical data;

and inputting the normalized historical data into the data analysis model.

3. The inventory prediction method of claim 1 in which said selecting a model category for said historical data based on said first characteristic comprises:

4. The inventory prediction method of claim 3, wherein said selecting said first class model for said historical data when said first characteristic is a first sub-characteristic comprises:

the first sub-characteristic is high and steady sales, and the first class model is:

wherein the content of the first and second substances,

is the factor of the smoothing factor that is,

is composed of

The time of day history data is stored in a memory,

is composed of

Time of day history data.

5. The inventory prediction method of claim 3, wherein said selecting said second category model for said historical data when said first characteristic is a second sub-characteristic comprises:

6. The inventory prediction method of claim 3, wherein said selecting said third category model for said historical data when said first characteristic is a third sub-characteristic comprises:

the third sub-feature is slow sales, and the third class model is:

wherein, in the step (A),

are respectively historical data

The corresponding weight.

7. An inventory prediction device, comprising:

8. The inventory prediction device of claim 7, wherein the parsing unit is further configured to:

acquiring the historical data;

standardizing the historical data;

and inputting the normalized historical data into the data analysis model.

9. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the inventory prediction method of any of claims 1-6.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the inventory prediction method according to any one of claims 1-6 when executing the computer program.