CN113469461A - Method and device for generating information - Google Patents
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Abstract
The application discloses a method and a device for generating information, and relates to the technical field of warehousing management. One embodiment of the method comprises: acquiring target time sequence data of the associated information of the target object; generating seasonal strong and weak change information and a first seasonal strong and weak identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; generating a second seasonal strong and weak identification based on the text description information corresponding to the target object; in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality, generating prediction information for the target item based on the target timing data and seasonal intensity variation information between months. The implementation method effectively improves the accuracy and the interpretability of the generated information, and further improves the effectiveness of inventory management.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for generating information.
Background
According to the distribution and the characteristics of the time sequence, the time sequence can be divided into a plurality of scenes, corresponding processing flows and methods are provided for the prediction of each scene, and the prediction accuracy is different. The identification and prediction of the time sequence with 'seasonality' has great difficulty, and the difficulty mainly lies in the ways of 'seasonality' strong and weak judgment, period identification, data processing flow, model coupling and the like.
In the prior art, for 'seasonal' time series prediction, there are generally only stages of data processing and prediction processes, and there are roughly three operations as follows: 1. based on the statistical thought, the components of the time sequence are split; 2. based on a machine learning idea, constructing a contemporaneous and recent feature aiming at historical data; 3. baseline prediction was not considered 'seasonal', post-processed in a homocyclic manner. The operation has the defects of being easily influenced by a sensitive value, weak interpretability, high dependence on experimental effect, data mining and the like, and the prediction result is easily subjected to larger deviation, so that the effectiveness of warehousing management is influenced.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for generating information.
According to a first aspect, an embodiment of the present application provides a method for generating information, the method including: acquiring target time sequence data of the associated information of the target object; generating seasonal strong and weak change information and a first seasonal strong and weak identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; generating a second seasonal strong and weak identification based on the text description information corresponding to the target object; in response to determining that both the first seasonal intensity indicator and the second seasonal intensity indicator indicate strong seasonality, generating prediction information for the target item based on target timing data and seasonal intensity variation information between months.
According to a second aspect, an embodiment of the present application provides an apparatus for generating information, the apparatus including: an acquisition data module configured to acquire target time series data of the associated information of the target item; the first generation module is configured to generate seasonal intensity change information and a first seasonal intensity identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; the second generation module is configured to generate a second seasonal strong and weak identification based on the text description information corresponding to the target item; a generate information module configured to generate prediction information for the target item based on the target timing data and seasonal intensity change information between months in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality.
According to a third aspect, embodiments of the present application provide an electronic device, which includes one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement a method of generating information as in any embodiment of the first aspect.
According to a fourth aspect, embodiments of the present application provide a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the method of generating information as in any of the embodiments of the first aspect.
The method comprises the steps of obtaining target time sequence data of associated information of a target object; generating seasonal strong and weak change information and a first seasonal strong and weak identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; generating a second seasonal strong and weak identification based on the text description information corresponding to the target object; and in response to the fact that the first seasonal strong and weak identifier and the second seasonal strong and weak identifier both indicate strong seasonality, generating prediction information of the target object based on target time sequence data and seasonal strong and weak change information among months, and facilitating accurate prediction based on a partial time sequence, wherein the accuracy is particularly reflected in prediction of magnitude and prediction of time nodes with magnitude changes, and meanwhile, the prediction has certain interpretability. Further, the forecast information may also be used in inventory management to increase the effectiveness of inventory management.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram for one embodiment of a method of generating information according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a method of generating information according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method of generating information according to the present application;
FIG. 5 is a schematic diagram of one embodiment of an apparatus to generate information, according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
FIG. 1 illustrates an exemplary system architecture 100 to which embodiments of the methods of generating information of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 101, 102, 103 interact with a server 105 via a network 104 to receive or send messages or the like. Various communication client applications, such as shopping applications, communication applications, etc., may be installed on the terminal devices 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen, including but not limited to a mobile phone and a notebook computer. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of software or software modules (for example to provide a service for generating information) or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, for example, target time series data that acquires associated information of a target item; generating seasonal strong and weak change information and a first seasonal strong and weak identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; generating a second seasonal strong and weak identification based on the text description information corresponding to the target object; in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality, generating prediction information for the target item based on the target timing data and seasonal intensity variation information between months.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (for example, to provide a service for generating information), or as a single software or software module. And is not particularly limited herein.
It should be noted that the method for generating information provided by the embodiment of the present disclosure may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, or may be executed by the server 105 and the terminal devices 101, 102, and 103 in cooperation with each other. Accordingly, each part (for example, each unit, sub-unit, module, sub-module) included in the information generating apparatus may be provided entirely in the server 105, entirely in the terminal devices 101, 102, and 103, or may be provided in the server 105 and the terminal devices 101, 102, and 103, respectively.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 shows a schematic flow diagram 200 of an embodiment of a method of generating information that can be applied to the present application. In this embodiment, the method for generating information includes the following steps:
In the present embodiment, the execution principal (such as the server 105 or the terminal devices 101, 102, 103 shown in fig. 1) may acquire target time series data of the associated information of the target item by a wired or wireless manner.
Here, the target item may be any item to be subjected to information prediction. The associated information may be various information related to the above-described target item, for example, price, sales amount, approval amount, stock amount, and the like.
The target time series data is usually time series type numerical data, and may also include external information data for explaining the change of the time series numerical data, text description information, and the like.
Specifically, taking the e-commerce industry as an example, the target object is a mosquito net, the related information is a sales volume, the target time sequence data may be a sales volume of the mosquito net in a certain area and a period of date, and in addition, the target time sequence data may further include temperature data, inventory data and the like in the same time period of the area, and text descriptions of the mosquito net, such as a brand, a color, applicable people and the like.
Here, the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other now known or later developed wireless connection means.
In this embodiment, the execution subject may directly generate seasonal intensity change information and a first seasonal intensity mark between months of the target item according to the time series data of the association information of the category to which the target item belongs and a preset data threshold; at least one item of index data can be generated according to the time sequence data of the associated information of the category to which the target article belongs, and seasonal intensity change information and a first seasonal intensity mark between months of the target article are generated based on the index data.
The seasonal strong and weak change information between the months of the target item is used for indicating the change information of the association degree of the association information of the months of the target item and the season, for example, the association degree of the association information of 2-5 months and the season is higher than the association degree of the association information of the rest of the whole year and the season, and the first seasonal strong and weak identifier is used for indicating whether the historical time series data of the association information of the category to which the target item belongs is strong seasonality, namely whether the historical time series data is strongly associated with the season.
Here, the first seasonal strong and weak indicator may be represented by a number, a letter, a character, or the like, which is not limited in the present application. For example, the strong seasonality is "1" and the weak seasonality is "0".
In this embodiment, the execution subject may obtain the text description information corresponding to the target item, and extract the keyword information associated with the time sequence, for example, the name: spring, summer, autumn and winter, attribute thickness, special holidays: and generating a second seasonal strong and weak identification according to the extracted keyword information in the lunar calendar, solar calendar festival and the like.
If the keyword information associated with the time sequence is not extracted from the text description information, attribute similarity recognition (such as metric learning), browsing similarity association (such as item embedding) and the like can be performed on the text description information by means of an existing time sequence image pool to generate a second seasonal strong and weak identifier.
Here, the second seasonal strong and weak flag indicates whether or not the historical time-series data of the association information indicating the category to which the target item belongs is strongly seasonal, i.e., strongly associated with the season. The second seasonal strong and weak identifier may be represented by numbers, characters, and the like, which is not limited in this application. For example, the strong seasonality is "1" and the weak seasonality is "0".
Further, it is to be noted that, if the second seasonal strong and weak flag is still not sufficiently generated by the keyword extraction and the above similarity recognition, it may be assumed that the target time-series data is not sufficient to support seasonal determination and may be ignored.
In this embodiment, after acquiring the first seasonal strong and weak identifier and the second seasonal strong and weak identifier, the execution main body determines the first seasonal strong and weak identifier and the second seasonal strong and weak identifier, and if both indicate strong seasonality and strong seasonality, may generate time series data of associated information of the target item in a future preset time period according to the target time series data and seasonal strong and weak change information between the months.
Specifically, the target object is a mosquito net, the correlation information of the target object is sales volume, the time sequence data of the correlation information of the target object is daily sales volume of the mosquito net in 2020-5-1 to 2020-5-5, and if the first seasonal strong and weak identifier and the second seasonal strong and weak identifier both indicate that the target time sequence data is strong and seasonal, sales volume data of the target object mosquito net in the next 1 year can be generated according to the target time sequence data and seasonal strong and weak change information between months.
In addition, after the execution body generates the prediction information, the prediction information can be kept on a distributed data storage and pushed to a database through the plomber data, and the prediction information is displayed by a downstream system and a front end in a Hive table and a Mysql mode.
In some optional modes, generating prediction information of the target item based on the target time sequence data and seasonal intensity change information between months comprises: and in response to determining that the time sequence length of the target time sequence data is smaller than a preset length threshold, generating prediction information of the target object based on the target time sequence data, historical time sequence data of the association information of the class to which the target object belongs and seasonal strong and weak change information among months.
In the implementation manner, the execution main body judges the time sequence length of the target time sequence data, if the time sequence length of the target time sequence data is smaller than a preset length threshold, the execution main body can firstly perform baseline prediction based on the target time sequence data to obtain a baseline prediction result, then superimposes the same-ring ratio information of the historical time sequence data of the relevant information of the category to which the target article belongs on the baseline prediction result to obtain a superimposed prediction result, and further generates the prediction information of the target article by combining seasonal intensity change information (such as inflection points of magnitude rise and fall) among months on the basis of the superimposed prediction result.
The baseline prediction may include various types, such as a statistical learning-based baseline prediction, a machine learning-based baseline prediction, an ensemble mechanism-based baseline prediction, and the like.
Here, the preset length threshold may be set according to experience and practical requirements, for example, one year, half a year, etc., and the present application is not limited thereto.
According to the implementation mode, the time sequence length of the target time sequence data is determined to be smaller than the preset length threshold, and the prediction information of the target object is generated based on the target time sequence data, historical time sequence data of the association information of the object class of the target object and seasonal strong and weak change information among months, so that the seasonal prediction of the short time sequence is facilitated, and meanwhile, the inflection points of ascending and descending magnitude are hidden.
In some optional modes, generating prediction information of the target item based on the target time sequence data and seasonal intensity change information between months comprises: performing factor disassembly on the target time sequence data in response to the fact that the time sequence length of the target time sequence data is larger than or equal to a preset length threshold value, and obtaining sub-target time sequence data corresponding to seasonal factors; and generating the prediction information of the target object based on the sub-target time sequence data and seasonal strong and weak change information among months.
In this implementation, the execution main body may determine the time sequence length of the target time sequence data, and if the time sequence length of the target time sequence data is greater than or equal to a preset length threshold, the execution main body may perform factor decomposition on the target time sequence data to obtain sub-target time sequence data corresponding to seasonal factors, and further combine seasonal strong and weak change information between months on the basis of the sub-target time sequence data to generate prediction information of the target item. Here, the execution subject may perform factor decomposition, that is, Seasonal Time Series decomposition, on the target Time Series data by using a decomposition method in the prior art or a future development technology, for example, an X11 decomposition method, a SEATS (Seasonal Extraction in ARIMA Time Series ), or the like, that is, the target Time Series data is divided into a plurality of factors by assuming that the target Time Series data is an additive model, so as to obtain sub-target Time Series data corresponding to the Seasonal factors.
It should be noted that the execution subject may further combine the seasonal related factors, such as weather, holidays, etc., on the basis of the sub-target time series data and the seasonal strong and weak change information between months to generate the prediction information of the target item.
The implementation mode is characterized in that in response to the fact that the time sequence length of the target time sequence data is larger than or equal to a preset length threshold, factor decomposition is carried out on the target time sequence data to obtain sub-target time sequence data corresponding to seasonal factors; based on the sub-target time sequence data and seasonal strong and weak change information among months, the prediction information of the target object is generated, seasonal prediction of long time sequence is facilitated, and meanwhile, inflection points of magnitude rise and magnitude fall are hidden.
In some optional modes, generating prediction information of the target item based on the target time sequence data and seasonal intensity change information between months comprises: smoothing the target time sequence data to obtain smoothed target time sequence data; and generating the prediction information of the target object based on the target time sequence data after the smoothing processing and the seasonal intensity change information among the months.
In this implementation, the execution subject needs to perform smoothing on the target time series data according to the auxiliary data before performing information prediction to obtain smoothed target time series data, where the smoothing may include multiple manners, such as enhancing or weakening the amplitude of the target time series data, and further generate prediction information of the target item according to the smoothed target time series data and seasonal intensity change information between months.
Specifically, the time interval 1 and the time interval 2 in the target time series data are the same time interval in two consecutive years and months, wherein the magnitude of the associated information value of the time interval 1 is very low, for example, 0, but the magnitude is low and does not meet the actual situation in combination with the auxiliary data, for example, due to the fact that the target article cannot be sold and the like, the data of the time interval 1 needs to be further combined with the seasonal strong and weak information, the browsing amount and other auxiliary data of each month of the target article, so that the processed target time series data better meets the objective fact, and in the subsequent prediction, the reason that the time interval 1 is abnormal cannot be used as a reference factor.
The implementation mode obtains target time sequence data after smoothing treatment by smoothing the target time sequence data; and generating the prediction information of the target object based on the target time sequence data after the smoothing processing and the seasonal intensity change information among the months, thereby further improving the reliability and the rationality of the generated prediction information.
In some optional ways, the method further comprises: in response to determining that there are factors that affect the associated information for the time period corresponding to the predicted information, the predicted information is adjusted based on the factors.
In this implementation manner, after the execution main body acquires the prediction information, if it is determined that there are known factors or potential factors affecting the association information of the time period corresponding to the prediction information, the execution main body adjusts the prediction information according to the factors.
Specifically, the prediction information is the monthly sales volume of the mosquito net in the next year, and the executive body can adjust the prediction information according to the factors which have recently occurred and influence the subsequent mosquito net sales volume or the factors which are known to occur in a certain time period in the next year to obtain the adjusted prediction information.
According to the implementation mode, factors influencing the associated information of the time period corresponding to the prediction information are determined, and the prediction information is adjusted based on the factors, so that the accuracy and the reliability of the prediction information are further improved.
In some optional ways, the method further comprises: the inventory information of the target item is adjusted based on the forecast information of the target item.
In this implementation, after acquiring the prediction information of the target item, the execution main body may further increase or decrease the inventory of the target item according to the prediction information.
According to the implementation mode, the inventory information of the target object is adjusted based on the prediction information of the target object, namely, the inventory adjustment is carried out according to the magnitude of the target object in the seasonal period indicated by the prediction information to optimize the inventory management, namely, the prejudgment is carried out according to the magnitude trend of the target object given in advance within the preset time length, and the pre-preparation is carried out to save a large amount of cost and improve the efficiency.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method of generating information according to the present embodiment.
In the application scenario of fig. 3, an execution subject 301 obtains target time series data 302 of relevant information, such as sales volume, of a target item, such as a mosquito net, for example, sales volume of the mosquito net per day during 2020-5-1 ~ 2020-5-5; generating seasonal intensity variation information 303 and a first seasonal intensity identification 304 between months of the target item based on historical time series data of the associated information of the category to which the target item belongs, such as home textiles; generating a second seasonal strong and weak identifier 305 based on the text description information corresponding to the target item; in response to determining that the first seasonal intensity indicator 304 and the second seasonal intensity indicator 305 both indicate strong seasonality 306, predictive information for the target item, for example, sales data for a mosquito net in the next 1 year, is generated based on the target timing data and seasonal intensity variation information between months 307. Further, the executive agent 301 may optimize inventory management information based on the forecast information.
The method for generating the information comprises the steps of obtaining target time sequence data of the associated information of a target article; generating seasonal strong and weak change information and a first seasonal strong and weak identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs; generating a second seasonal strong and weak identification based on the text description information corresponding to the target object; in response to the fact that the first seasonal strong and weak identification and the second seasonal strong and weak identification both indicate strong seasonality, prediction information of the target object is generated on the basis of target time sequence data and seasonal strong and weak change information among months, and accuracy and interpretability of the prediction information are effectively improved.
With further reference to fig. 4, a flow 400 of yet another embodiment of the method of generating information shown in fig. 2 is illustrated. In this embodiment, the process 400 of the method for generating information may include the following steps:
In this embodiment, details of implementation and technical effects of step 401 may refer to the description of step 201, and are not described herein again.
In this embodiment, after acquiring the historical time series data of the associated information of the category to which the target item belongs, the execution main body may divide the time series data into month dimensions to obtain month dimension time series data.
Specifically, the target object is a mosquito net, the class of the target object is home textiles, the related information of the class of the target object is the sales volume of the home textiles, the historical time series data is the sales volume of the home textiles in the past year every day, and the execution main body can collect and count the historical time series data to obtain month dimension time series data, namely the sales volume data of the home textiles in the past year every month.
And 403, generating at least one intensity index based on the ranking of the associated information value of each month of the target object in the month dimension time sequence data.
In this embodiment, the executing subject may generate at least one strength indicator according to the ranking of the associated information value of each month in the month dimension time series data, where the strength indicator is used to indicate seasonal strength information of the category to which the target item belongs, that is, the degree of association between the associated information of the category to which the target item belongs and the season.
The strength index may include a basic index and an extension index.
Here, the base index is generally generated based on month dimension time series data of a time series length of one year or less, and the base index may include at least one of: the first basic index is used for sorting the associated information values corresponding to each month in the month dimension time sequence data from large to small, and determining months corresponding to a first preset number of associated information values which are in the top of the sorting; the second basic index is used for sorting the associated information values corresponding to each month in the month dimension time sequence data from large to small, and determining months corresponding to a second preset number of associated information values which are sorted later; a third basic index, which is a ratio of the sum of the first preset number of associated information values in the top ranking to the sum of the associated information values in the month dimension time sequence data; and the fourth basic index is the ratio of the sum of the second preset number of the associated information values which are sequenced later to the sum of the associated information values in the month dimension time sequence data.
The first preset number and the second preset number may be set according to experience and actual requirements, for example, three, four, and the like, which is not limited in the present application. Here, the first preset number and the second preset number may be the same or different, and the application does not limit this.
The extension index can be obtained from the base index. The extension indicator may include at least one of: a first extension index, a second extension index, and a third extension index. Wherein the first and second spread indicators are typically generated based on monthly dimensional time series data having a time series length greater than one year. The first extension index: if the month dimension time series data is multi-year data, dividing the month dimension time series data into sub-month dimension time series data, wherein each sub-month dimension time series data corresponds to a year, and calculating the intersection of a first preset number of months (first basic indexes) with the highest ranking of the associated information values in each sub-month dimension time series data (for example, the months with the highest ranking in 2018 are 3 months, 4 months and 5 months, the months with the highest ranking in 2019 are 4 months, 5 months and 6 months, and the intersection is 4 months and 5 months) or calculating the intersection of a second preset number of months (second basic indexes) with the lowest ranking of the associated information values in each sub-month dimension time series data; the second extension index: if the month dimension time series data is multi-year data, the month dimension time series data is divided into sub-month dimension time series data, each sub-month dimension time series data corresponds to a year, a union of a first preset number of months (first basic indexes) with the highest ranking of the associated information values in each sub-month dimension time series data is calculated (for example, the months with the highest ranking in 2018 are 3 months, 4 months and 5 months, the months with the highest ranking in 2019 are 4 months, 5 months and 6 months, and the union is 3 months, 4 months, 5 months and 6 months), or a union of a second preset number of months (second basic indexes) with the highest ranking of the associated information values in each sub-month dimension time series data is calculated. The third extension index: and the fourth basic index is the ratio of the sum of the second preset number of associated information values in the month dimension time sequence data after the associated information values are sorted to the sum of the associated information values in the month dimension time sequence data.
Wherein the first base indicator/the second base indicator is used for depicting the amplitude or seasonal absolute intensity of each month; a third/fourth base index for delineating the amplitude or seasonal relative strength of each month; the first extension index is used for depicting the strength of strong and weak overlapping of months of years; the second extension index is used for depicting the strength of the chaos degree of the strength of each month of the years; the third stretch index is used to characterize the intensity of high and low amplitude or seasonal differences over the year.
And step 404, generating seasonal intensity change information and a first seasonal intensity identifier between months based on at least one intensity index and at least one preset intensity threshold.
In this embodiment, the execution subject may generate seasonal variation information between months and a first seasonal variation identifier according to at least one intensity indicator, such as one or more of the base indicators and/or one or more of the extension indicators, and at least one preset intensity threshold.
Specifically, the target object is a mosquito net, the class to which the target object belongs is home textiles, the associated information of the class to which the target object belongs is sales volume, and the month dimension time series data is sales volume of the home textiles in the past year and month. The execution main body obtains a first basic index, namely the sales amount top4 is month 4, month 5, month 6 and month 7, a second basic index, namely the sales amount bottom4 is month 9, month 10, month 11 and month 12, a third basic index, namely the sales amount of top4 is 92.1% of sales amount in the whole year, a fourth basic index, namely the sales amount bottom4 is month 6.4%, a third extension index, namely a difference value of 85.7% between 92.1% and 6.4% is generated according to the third basic index and the fourth basic index, if a preset strength threshold value is 75%, a first seasonal strong and weak identifier can be output as an identifier indicating strong seasonality, such as '1', and seasonal strong and weak change information between months can be output, such as the month as strong and seasonal: month 4, month 5, month 6, month 7.
In this embodiment, details of implementation and technical effects of step 405 may refer to the description of step 203, and are not described herein again.
In this embodiment, details of implementation and technical effects of step 406 may refer to the description of step 204, and are not described herein again.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the process 400 of the method for generating information in this embodiment includes dividing historical time series data of the association information of the category to which the target item belongs into month dimensions to obtain month dimension time series data, generating at least one intensity index based on the ranking of the association information value of each month of the target item in the month dimension time series data, generating seasonal strong and weak variation information between the months and a first seasonal strong and weak identifier based on the at least one intensity index and at least one preset intensity threshold, further generating prediction information of the target item in response to determining that both the first seasonal strong and weak identifier and the second seasonal strong and weak identifier indicate strong seasonality, and generating a set of more general and accurate identifier system based on the target time series data and the seasonal strong and weak variation information between the months, that is realized by designing a reasonable intensity index, the method avoids excessively relying on parameter optimization experience and constructing complex feature pools and feature projects, improves the efficiency and the reasonability of the generated seasonal strong and weak change information and the first seasonal strong and weak identification, further improves the efficiency and the reasonability of the generated prediction information, and improves the efficiency and the reasonability of inventory management.
With further reference to fig. 5, as an implementation of the method shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for generating information, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the information generating apparatus 500 of the present embodiment includes: an acquisition data module 501, a first generation module 502, a second generation module 503, and a generation information module 504.
The data obtaining module 501 may be configured to obtain target time series data of the associated information of the target item.
The first generating module 502 may be configured to generate seasonal variation information between months of the target item and a first seasonal intensity identification based on historical time series data of the association information of the category to which the target item belongs.
The second generating module 503 may be configured to generate a second seasonal strong and weak identifier based on the text description information corresponding to the target item.
A generate information module 504 may be configured to generate prediction information for the target item based on the target timing data and seasonal intensity change information between months in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality.
In some optional manners of this embodiment, the first generating module further includes: the data dividing unit is configured to divide historical time sequence data of the associated information of the categories to which the target articles belong into month dimensions to obtain month dimension time sequence data; a generation index unit configured to generate at least one intensity index based on a ranking of associated information values of the target item in the month dimension time series data for each month; a production identification unit configured to generate seasonal intensity variation information and a first seasonal intensity identification between months based on at least one intensity index and at least one preset intensity threshold.
In some alternatives of this embodiment, the information generating module is further configured to: in response to determining that the time sequence length of the target time sequence data is smaller than a preset length threshold, generating prediction information of the target item based on the target time sequence data, historical time sequence data of association information of the category to which the target item belongs and seasonal strong and weak change information among months.
In some alternatives of this embodiment, the information generating module is further configured to: performing factor disassembly on the target time sequence data in response to the fact that the time sequence length of the target time sequence data is larger than or equal to a preset length threshold value, and obtaining sub-target time sequence data corresponding to seasonal factors; and generating the prediction information of the target object based on the sub-target time sequence data and the seasonal strong and weak change information among the months.
In some alternatives of this embodiment, the information generating module is further configured to: smoothing the target time sequence data to obtain smoothed target time sequence data; and generating the prediction information of the target object based on the target time sequence data after the smoothing processing and the seasonal intensity change information among the months.
In some optional manners of this embodiment, the apparatus further includes: and an adjustment information module configured to adjust the prediction information based on factors in response to determining that the factors have influence on the associated information of the time period corresponding to the prediction information.
In some optional manners of this embodiment, the apparatus further includes: an adjusting inventory module configured to adjust inventory information of the target item based on the forecast information of the target item.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 6, the electronic device is a block diagram of an electronic device according to an embodiment of the present application.
600 is a block diagram of an electronic device that generates information in accordance with an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the method of generating information provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the method of generating information provided herein.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of generating information in the embodiments of the present application (for example, the data acquiring module 501, the first generating module 502, the second generating module 503, and the information generating module 504 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing, i.e., a method of generating information in the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 602.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of the electronic device that generates the information, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory located remotely from the processor 601, which may be connected to an electronic device generating the information over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method of generating information may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information, such as an input device like a touch screen, keypad, mouse, track pad, touch pad, pointer, one or more mouse buttons, track ball, joystick, etc. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the accuracy and the interpretability of the generated information are effectively improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A method of generating information, the method comprising:
acquiring target time sequence data of the associated information of the target object;
generating seasonal intensity change information and a first seasonal intensity identification between months of the target object based on historical time sequence data of the associated information of the category to which the target object belongs;
generating a second seasonal strong and weak identification based on the text description information corresponding to the target object;
in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality, generating prediction information for the target item based on the target timing data and seasonal intensity variation information between months.
2. The method of claim 1, wherein the generating seasonal variation of intensity information between months and a first seasonal intensity identification of the target item based on historical time series data of the associated information of the category to which the target item belongs comprises:
dividing historical time sequence data of the associated information of the category to which the target object belongs into month dimensions to obtain month dimension time sequence data;
generating at least one intensity index based on the ranking of the associated information value of each month of the target item in the month dimension time sequence data, wherein the intensity index is used for indicating seasonal intensity information of the category to which the target item belongs;
and generating seasonal strong and weak change information and a first seasonal strong and weak identification among the months based on the at least one intensity index and the at least one preset intensity threshold.
3. The method of claim 1, wherein generating the forecast information for the target item based on the target time series data and seasonal intensity change information between months comprises:
in response to the fact that the time sequence length of the target time sequence data is smaller than a preset length threshold value, generating prediction information of the target object based on the target time sequence data, historical time sequence data of association information of the class to which the target object belongs and seasonal strong and weak change information among months.
4. The method of claim 1, wherein generating the forecast information for the target item based on the target time series data and seasonal intensity change information between months comprises:
performing factor disassembly on the target time sequence data in response to the fact that the time sequence length of the target time sequence data is larger than or equal to a preset length threshold value, and obtaining sub-target time sequence data corresponding to seasonal factors; and generating the prediction information of the target item based on the sub-target time sequence data and the seasonal strong and weak change information between the months.
5. The method of any of claims 1-4, wherein generating the forecast information for the target item based on the target time series data and seasonal intensity change information between months comprises:
smoothing the target time sequence data to obtain smoothed target time sequence data;
and generating the prediction information of the target object based on the target time sequence data after the smoothing processing and seasonal intensity change information among months.
6. The method of any of claims 1-4, further comprising:
in response to determining that there are factors that affect the associated information for the time period corresponding to the predicted information, the predicted information is adjusted based on the factors.
7. The method of any of claims 1-4, further comprising:
adjusting inventory information for the target item based on the forecast information for the target item.
8. An apparatus to generate information, the apparatus comprising:
an acquisition data module configured to acquire target time series data of the associated information of the target item;
the first generation module is configured to generate seasonal intensity change information and a first seasonal intensity identification between months of the target item based on historical time sequence data of the associated information of the category to which the target item belongs;
the second generation module is configured to generate a second seasonal strong and weak identification based on the text description information corresponding to the target item;
a generate information module configured to generate prediction information for the target item based on the target timing data and seasonal intensity change information between months in response to determining that the first seasonal intensity indicator and the second seasonal intensity indicator both indicate strong seasonality.
9. The apparatus of claim 8, wherein the first generation module further comprises:
the dividing data unit is configured to divide historical time sequence data of the associated information of the category to which the target object belongs into month dimensions to obtain month dimension time sequence data;
a generation index unit configured to generate at least one intensity index for indicating seasonal intensity information of a category to which a target item belongs, based on ranking of associated information values of the target item for each month in the month dimension time series data;
a production identification unit configured to generate seasonal intensity variation information and a first seasonal intensity identification between months based on the at least one intensity index and at least one preset intensity threshold.
10. The apparatus of claim 8, wherein the generate information module is further configured to:
in response to the fact that the time sequence length of the target time sequence data is smaller than a preset length threshold value, generating prediction information of the target object based on the target time sequence data, historical time sequence data of association information of the class to which the target object belongs and seasonal strong and weak change information among months.
11. The apparatus of claim 8, wherein the generate information module is further configured to:
performing factor disassembly on the target time sequence data in response to the fact that the time sequence length of the target time sequence data is larger than or equal to a preset length threshold value, and obtaining sub-target time sequence data corresponding to seasonal factors; and generating the prediction information of the target item based on the sub-target time sequence data and the seasonal strong and weak change information between the months.
12. The apparatus of any of claims 8-11, wherein the generate information module is further configured to:
smoothing the target time sequence data to obtain smoothed target time sequence data;
and generating the prediction information of the target object based on the target time sequence data after the smoothing processing and seasonal intensity change information among months.
13. The apparatus of any of claims 8-11, further comprising:
and an adjustment information module configured to adjust the prediction information based on factors in response to determining that the factors have influence on the associated information of the time period corresponding to the prediction information.
14. The apparatus of any of claims 8-11, further comprising:
an adjustment inventory module configured to adjust inventory information for the target item based on the forecast information for the target item.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory is stored with instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
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| WO2023005635A1 (en) | 2023-02-02 |
| CN113469461B (en) | 2024-07-19 |
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