CN108595448B - Information pushing method and device - Google Patents
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Abstract
The application discloses an information pushing method and device. One embodiment of the method comprises: acquiring a current attribute value and associated information of a target article, wherein the associated information comprises at least one of the following items: historical attribute values, orders including item information of the target item within a predetermined period of time; determining whether the current attribute value is abnormal or not based on the current attribute value and the association information; and responding to the determination that the current attribute value is abnormal, pushing prompt information for prompting that the current attribute value is abnormal. The implementation mode realizes information pushing with rich content.
Description
Technical Field
The application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to an information pushing method and device.
Background
Information push is a technology for reducing information overload by pushing information required by a user on the internet through a certain technical standard or protocol.
However, the conventional information push method has a problem that the types of information to be pushed are limited, and the pushed information is not abundant.
Disclosure of Invention
The present application aims to provide an improved information pushing method and apparatus to solve the technical problems mentioned in the above background section.
In a first aspect, an embodiment of the present application provides an information pushing method, where the method includes: acquiring a current attribute value and associated information of a target article, wherein the associated information comprises at least one of the following items: the historical attribute value and the order including the object information of the target object in a preset time period; determining whether the current attribute value is abnormal based on the current attribute value and the associated information; and responding to the determination that the current attribute value is abnormal, and pushing prompt information for prompting the abnormality of the current attribute value.
In some embodiments, the determining whether the current attribute value is abnormal based on the current attribute value and the association information includes: determining the order quantity of the obtained order and/or the total quantity of the target items in the obtained order; and determining whether the current attribute value is abnormal or not according to the order quantity and/or the total quantity of the articles.
In some embodiments, the determining whether the current attribute value is abnormal according to the order quantity and/or the total quantity of the items further includes: extracting contact information of a receiver in the acquired order, and determining the number of account numbers registered through the contact information indicated by the contact information for each piece of contact information in the extracted contact information; and determining whether the current attribute value is abnormal or not according to the determined number of the account numbers.
In some embodiments, the determining whether the current attribute value is abnormal according to the determined number of account numbers includes: determining abnormal values in the attribute value set comprising the acquired historical attribute values and the current attribute values by using an abnormal point detection algorithm; determining that the current attribute value is abnormal in response to determining that the determined abnormal value includes the current attribute value.
In some embodiments, the determining whether the current attribute value is abnormal based on the current attribute value and the association information includes: and importing the current attribute value and the associated information into a pre-trained identification model to obtain indication information for indicating whether the current attribute value is abnormal, wherein the identification model is used for representing the corresponding relation between the attribute value and the associated information and the indication information.
In some embodiments, the method further comprises the step of training a recognition model, wherein the step of training a recognition model comprises: adjusting an initial neural network model by using a genetic algorithm based on an elite retention strategy; determining a training sample, wherein the training sample comprises an attribute value, associated information and indicating information for indicating whether the attribute value is abnormal; and training the adjusted initial neural network model by using the determined training sample to obtain the recognition model.
In some embodiments, the importing the current attribute value and the association information into a pre-trained recognition model comprises: searching an identification model corresponding to the attribute value interval to which the historical attribute value belongs according to the historical attribute value of the target object; and importing the current attribute value and the associated information into the searched identification model.
In a second aspect, an embodiment of the present application provides an information pushing apparatus, where the apparatus includes: the device comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a current attribute value and associated information of a target article, and the associated information comprises at least one of the following items: the historical attribute value and the order including the object information of the target object in a preset time period; a determining unit configured to determine whether the current attribute value is abnormal based on the current attribute value and the association information; and the pushing unit is used for responding to the determination that the current attribute value is abnormal and pushing prompt information for prompting the abnormality of the current attribute value.
In some embodiments, the determining unit is further configured to: determining the order quantity of the obtained order and/or the total quantity of the target items in the obtained order; and determining whether the current attribute value is abnormal or not according to the order quantity and/or the total quantity of the articles.
In some embodiments, the determining unit is further configured to: extracting contact information of a receiver in the acquired order, and determining the number of account numbers registered through the contact information indicated by the contact information for each piece of contact information in the extracted contact information; and determining whether the current attribute value is abnormal or not according to the determined number of the account numbers.
In some embodiments, the determining unit is further configured to: determining abnormal values in the attribute value set comprising the acquired historical attribute values and the current attribute values by using an abnormal point detection algorithm; determining that the current attribute value is abnormal in response to determining that the determined abnormal value includes the current attribute value.
In some embodiments, the determining unit is further configured to: and importing the current attribute value and the associated information into a pre-trained identification model to obtain indication information for indicating whether the current attribute value is abnormal, wherein the identification model is used for representing the corresponding relation between the attribute value and the associated information and the indication information.
In some embodiments, the determining unit is further configured to: adjusting an initial neural network model by using a genetic algorithm based on an elite retention strategy; determining a training sample, wherein the training sample comprises an attribute value, associated information and indicating information for indicating whether the attribute value is abnormal; and training the adjusted initial neural network model by using the determined training sample to obtain the recognition model.
In some embodiments, the determining unit is further configured to: searching an identification model corresponding to the attribute value interval to which the historical attribute value belongs according to the historical attribute value of the target object; and importing the current attribute value and the associated information into the searched identification model.
In a third aspect, an embodiment of the present application provides a server, where the server includes: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the method according to the first aspect.
According to the information pushing method and the information pushing device, the current attribute value and the associated information of the target object are obtained firstly; then, determining whether the current attribute value is abnormal or not based on the current attribute value and the associated information; and finally, in response to the fact that the current attribute value is determined to be abnormal, prompting information for prompting the abnormality of the current attribute value is pushed, and information pushing with rich content is achieved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of an information push method according to the present application;
FIG. 3 is a schematic diagram of an application scenario of an information push method according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of an information push method according to the present application;
FIG. 5 is a schematic diagram of an embodiment of an information pushing device 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 present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
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 shows an exemplary system architecture 100 to which embodiments of the information pushing method or information pushing apparatus 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 user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as warehouse management applications, shopping applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a server that analyzes various information and derives information to be pushed. The server may analyze and otherwise process the received data, such as the attribute values of the articles, and feed back the processing results (e.g., the prompt information) to the terminal device.
It should be noted that the information pushing method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the information pushing apparatus is generally disposed in the server 105.
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.
With continued reference to FIG. 2, a flow 200 of one embodiment of an information push method according to the present application is shown. The information pushing method comprises the following steps:
In this embodiment, an electronic device (for example, a server shown in fig. 1) on which the information push method operates may obtain the current attribute value and the associated information of the target item from a local or other electronic device. Here, the related information includes, but is not limited to: historical attribute values and orders including the item information of the target items in a preset time.
In this embodiment, the target object may be an object having a physical entity, or may be a virtual object, for example, a virtual object indicating a physical entity or a virtual object indicating a service on an e-commerce website.
In this embodiment, the attribute value may be a value indicating an attribute of the item, the attribute of the item may include, but is not limited to, a size, a color, a price, and the like of the item, and the attribute value may include, but is not limited to, a size value, a price, a value indicating a color of the item, and the like. It should be noted that there may be differences in the types of attributes of different items, and that there may be some attributes of an item that are specific to that item. As an example, if the item is a camera, the attribute values of the camera may include price, maximum resolution, screen size, number of active pixels, and the like.
In this embodiment, the historical attribute value may be an attribute value of the target item at a historical time before the current time. The historical time may be one or more.
In this embodiment, the item information of the target item may be a name of the target item, and may be an item identifier indicating the target item. Here, the order including the item information may be an order in which the target item is ordered.
In some optional implementation manners of this embodiment, the association information may further include order related information obtained based on the order, and the order related information may include, but is not limited to: the order quantity of the obtained order, the total quantity of the target items in the obtained order and the account number. The number of the account numbers may be determined as follows: and extracting the contact information of the consignee in the acquired order, and determining the account number of the account registered by the contact indicated by the contact information for each piece of contact information in the extracted contact information. Here, the contact information may include, but is not limited to, a cell phone number, a landline number, a mailbox account number, and the like.
In this embodiment, the electronic device may determine whether the current attribute value is abnormal in various ways based on the current attribute value and the associated information.
In some optional implementations of this embodiment, step 202 may be implemented by: determining the order quantity of the obtained order; and determining whether the current attribute value is abnormal or not according to the order quantity. As an example, it may be determined whether the order quantity is within a preset order quantity numerical range, and in response to determining that the order quantity is not within the preset numerical range, it may be determined that the current attribute value is abnormal. As an example, the order quantity may be imported into a pre-trained recognition model to obtain indication information indicating whether the current feature value is abnormal, where the recognition model is used to represent a corresponding relationship between the current attribute value and the order quantity and the indication information.
In some optional implementations of this embodiment, step 202 may be implemented by: determining the total quantity of the items of the target item in the obtained order; and determining whether the current attribute value is abnormal or not according to the total quantity of the articles. As an example, it may be determined whether the total number of items is within a preset total number of items numerical range, and in response to determining that the total number of items is not within the preset total number of items numerical range, determining that the current attribute value is abnormal.
In some optional implementations of this embodiment, step 202 may be implemented by: extracting contact information of a receiver in the acquired order, and determining the number of account numbers registered through the contact information indicated by the contact information for each piece of contact information in the extracted contact information; and determining whether the current attribute value is abnormal or not according to the determined number of the account numbers. As an example, it may be determined whether the number of accounts is greater than a preset number of accounts threshold, and in response to determining that the total number of the items is greater than the preset number of accounts threshold, it is determined that the current attribute value is abnormal.
In some optional implementations of this embodiment, step 202 may be implemented by: determining the ratio of the acquired historical attribute value to the current attribute value; and determining that the current attribute value is abnormal in response to determining that the ratio is not within a preset ratio value range.
In some optional implementations of this embodiment, step 202 may be implemented by: determining abnormal values in the attribute value set comprising the acquired historical attribute values and the current attribute values by using an abnormal point detection algorithm; determining that the current attribute value is abnormal in response to determining that the determined abnormal value includes the current attribute value. The anomaly detection algorithm itself is well known to those skilled in the art and will not be described here in detail.
In some optional implementations of this embodiment, step 202 may be implemented by: and determining whether the current attribute value is abnormal according to one or more items of the order number, the total quantity of the items, the account number and the historical attribute value.
In some optional implementation manners of this embodiment, it may be determined that the current attribute value is abnormal when the order number, the total number of items, the account number, and the historical attribute value all satisfy respective abnormality determination conditions. Here, the abnormality determination condition of the current order may be that the order quantity is not within a preset order quantity numerical range. The abnormality determination condition for the total number of articles may be that the total number of articles is within a preset numerical range of the total number of articles. The abnormality determination condition of the number of the account numbers may be that the number of the account numbers is greater than a preset threshold of the number of the account numbers. The abnormality determination condition regarding the historical attribute value may be that a ratio of the acquired historical attribute value to the above-described current attribute value is not within a preset ratio numerical range. The abnormality determination condition on the historical attribute value may also be that the current attribute value determined from the historical attribute value and the abnormal point detection algorithm is an abnormal value.
In some optional implementation manners of this embodiment, an abnormality detection order related to each piece of association information may also be preset, and a process of determining whether the current attribute value is abnormal is performed. And when the current attribute value is determined to be abnormal by using the previous associated information, determining that the current attribute value is abnormal. And when the current attribute value is determined to be normal by using the previous associated information, determining whether the current attribute value is abnormal by using the subsequent associated information.
As an example, it may be first determined whether the order quantity is within a preset order quantity numerical range, and in response to determining that the order quantity is within the preset order quantity numerical range, determining that the current attribute value is normal; then, determining whether the total quantity of the articles is within a preset numerical range of the total quantity of the articles, and determining that the current attribute value is normal in response to determining that the total quantity of the articles is within the preset numerical range of the total quantity of the articles; and then, determining whether the current attribute value is abnormal according to the historical attribute value. It should be noted that the order of detecting the abnormality may be various, and should not be limited to the order in the example. The utilized related information may be all related information or part of related information referred to in the present application, and should not be limited to the related information referred to in the present example.
In some optional implementation manners of this embodiment, determining whether the current attribute value is abnormal according to one or more of the order quantity, the total quantity of items, the account quantity, and the historical attribute value may be to import the current attribute value and one or more of the order quantity, the total quantity of items, the account quantity, and the historical attribute value into a pre-trained recognition model, so as to obtain indication information for indicating whether the current attribute value is abnormal. Here, the identification model is used to characterize the correspondence of both the attribute values, the association information, and the indication information.
And step 203, responding to the determination that the current attribute value is abnormal, pushing prompt information for prompting that the current attribute value is abnormal.
In this embodiment, the electronic device may push prompt information for prompting that the current attribute value is abnormal in response to determining that the current attribute value is abnormal.
As an example, in the case where the target item is a model a computer and the current attribute value is the current price of this computer, the prompt information may be information "model a computer-price-abnormal" indicating that the current price of this computer is abnormal.
As an example, the prompt information may be pushed to at least one terminal device, and the user of the pushed terminal device may be a user who uploads the attribute value to the server, or a user who browses a web page including the item information of the target item.
With continuing reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the information push method according to the present embodiment. In the application scenario of fig. 3, first, the server may obtain, in the background, a current attribute value and associated information of a target item, where the target item may be, for example, a model a computer, the current attribute value price is 300 dollars, and the associated information may be a historical price of 3000 dollars and/or an order including item information "a model computer"; then, the above-mentioned server can determine whether the current attribute value (price 300 meta) is abnormal based on the current attribute value (price 300 meta) and the associated information (the historical price 3000 meta and/or the order including the item information of "a model computer"); finally, the server may push a prompt to the user ("model a computer-300 meta-exception") in response to determining that the current attribute value (price 300 meta) is anomalous.
According to the method provided by the embodiment of the application, the current attribute value and the associated information of the target object are firstly obtained; then, determining whether the current attribute value is abnormal or not based on the current attribute value and the associated information; and finally, in response to the fact that the current attribute value is determined to be abnormal, prompting information for prompting the abnormality of the current attribute value is pushed, and information pushing with rich content is achieved.
With further reference to fig. 4, a flow 400 of yet another embodiment of an information push method is shown. The process 400 of the information pushing method includes the following steps:
In this embodiment, an electronic device (for example, a server shown in fig. 1) on which the information push method operates may obtain the current attribute value and the associated information of the target item.
In this embodiment, the association information may include, but is not limited to: historical attribute values, and orders including item information of the target items in a preset time period.
In some optional implementation manners of this embodiment, the association information may further include order related information obtained based on the order, and the order related information may include, but is not limited to: the order quantity of the obtained order, the total quantity of the target items in the obtained order, and the account number, wherein the account number may be determined in the following manner: and extracting the contact information of the consignee in the acquired order, and determining the account number of the account registered by the contact indicated by the contact information for each piece of contact information in the extracted contact information.
In this embodiment, the electronic device may import the current attribute value and the association information into a recognition model trained in advance, and obtain indication information indicating whether the current attribute value is abnormal. Here, the indication information may be indication information for indicating that the current attribute value is abnormal. The indication information may also be indication information for indicating that the current attribute value is normal.
In some optional implementations of this embodiment, before step 402, the method provided by this embodiment may further include a step of training the recognition model.
In some optional implementation manners of this embodiment, the electronic device may further train a recognition model, and the recognition model may be trained by performing the following steps: acquiring an initial neural network model; determining a training sample, wherein the training sample comprises an attribute value, associated information and indicating information for indicating whether the attribute value is abnormal; and training the initial neural network model by using the determined training sample to obtain the recognition model.
It should be noted that how to obtain the initial neural network model is well known to those skilled in the art, and will not be described herein. The determined training samples may be of a large number. The initial neural network model may be a convolutional neural network model, a Back Propagation (BP) neural network model.
In some optional implementation manners of this embodiment, the electronic device may further adjust the initial neural network model by using a genetic algorithm based on an elite retention policy; and then, training the adjusted initial neural network model by using the determined training sample to obtain the recognition model. How to adjust the initial neural network model by using genetic algorithm is well known to those skilled in the art and will not be described herein. However, it should be noted that, by applying the elite reservation policy to the identification model for determining whether the attribute value of the article is abnormal, the identification model suitable for determining whether the attribute value of the article is abnormal can be quickly optimized.
In some optional implementations of this embodiment, step 402 may also be implemented by: and searching an identification model corresponding to the attribute value interval to which the historical attribute value belongs according to the historical attribute value of the target object, and importing the current attribute value and the associated information into the searched identification model. It should be noted that a plurality of recognition models may be trained according to the attribute value interval. As an example, the attribute value may be a price, and a plurality of recognition models may be trained according to price intervals (e.g., 0-50,50-200,200-1000,1000-3000, 3000 or more).
In some optional implementations of this embodiment, step 402 may also be implemented by: and searching an identification model corresponding to the item type according to the item type of the target item, and importing the current attribute value and the associated information into the searched identification model. A plurality of recognition models, for example, an electronic product recognition model, a personal care recognition model, and the like, may be trained in advance according to the article type.
And step 403, in response to obtaining the indication information for indicating that the current attribute value is abnormal, pushing prompt information for prompting that the current attribute value is abnormal.
In this embodiment, the electronic device may push prompt information for prompting that the current attribute value is abnormal in response to determining that the current attribute value is abnormal.
The implementation details of step 401 and step 403 in this embodiment may refer to the descriptions in step 201 and step 203, and are not described herein again.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the information pushing method in this embodiment highlights a step of determining whether the current attribute value is abnormal by using a pre-established recognition model. Therefore, the scheme described in the embodiment can determine whether the current attribute value is abnormal more conveniently and accurately, so that more effective information push is realized.
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 information pushing apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 5, the information pushing apparatus 500 provided in the present embodiment includes: an acquisition unit 501, a determination unit 502 and a push unit 503. The obtaining unit 501 is configured to obtain a current attribute value and associated information of a target item, where the associated information includes at least one of the following: the historical attribute value and the order including the object information of the target object in a preset time period; a determining unit 502, configured to determine whether the current attribute value is abnormal based on the current attribute value and the association information; a pushing unit 503, configured to, in response to determining that the current attribute value is abnormal, push a prompt message for prompting that the current attribute value is abnormal.
In this embodiment, the specific processing of the obtaining unit 501, the determining unit 502 and the pushing unit 503 of the apparatus 500 may refer to step 201, step 202 and step 203 in the corresponding embodiment of fig. 2, which is not described again.
In some optional implementation manners of this embodiment, the determining unit is further configured to: determining the order quantity of the obtained order and/or the total quantity of the target items in the obtained order; and determining whether the current attribute value is abnormal or not according to the order quantity and/or the total quantity of the articles.
In some optional implementation manners of this embodiment, the determining unit is further configured to: extracting contact information of a receiver in the acquired order, and determining the number of account numbers registered through the contact information indicated by the contact information for each piece of contact information in the extracted contact information; and determining whether the current attribute value is abnormal or not according to the determined number of the account numbers.
In some optional implementation manners of this embodiment, the determining unit is further configured to: determining abnormal values in the attribute value set comprising the acquired historical attribute values and the current attribute values by using an abnormal point detection algorithm; determining that the current attribute value is abnormal in response to determining that the determined abnormal value includes the current attribute value.
In some optional implementation manners of this embodiment, the determining unit is further configured to: and importing the current attribute value and the associated information into a pre-trained identification model to obtain indication information for indicating whether the current attribute value is abnormal, wherein the identification model is used for representing the corresponding relation between the attribute value and the associated information and the indication information.
In some optional implementation manners of this embodiment, the determining unit is further configured to: adjusting an initial neural network model by using a genetic algorithm based on an elite retention strategy; determining a training sample, wherein the training sample comprises an attribute value, associated information and indicating information for indicating whether the attribute value is abnormal; and training the adjusted initial neural network model by using the determined training sample to obtain the recognition model.
In some optional implementation manners of this embodiment, the determining unit is further configured to: searching an identification model corresponding to the attribute value interval to which the historical attribute value belongs according to the historical attribute value of the target object; and importing the current attribute value and the associated information into the searched identification model.
For details of implementation and technical effects of each unit of the apparatus provided in this embodiment, reference may be made to descriptions in other embodiments of the present application, and further description is omitted here.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a server according to embodiments of the present application. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a determination unit, and a push unit. The names of the units do not form a limitation to the units themselves in some cases, and for example, the acquiring unit may also be described as a "unit that acquires the current attribute value and the associated information of the target item".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring a current attribute value and associated information of a target article, wherein the associated information comprises at least one of the following items: the historical attribute value and the order including the object information of the target object in a preset time period; determining whether the current attribute value is abnormal based on the current attribute value and the associated information; and responding to the determination that the current attribute value is abnormal, and pushing prompt information for prompting the abnormality of the current attribute value.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (9)
1. An information pushing method, characterized in that the method comprises:
acquiring a current attribute value and associated information of a target article, wherein the associated information comprises at least one of the following items: an order including the item information of the target item within a predetermined period of time and a historical attribute value;
determining whether the current attribute value is abnormal based on the current attribute value and the association information, including: determining the order quantity of the obtained order and/or the total quantity of the target items in the obtained order; determining whether the current attribute value is abnormal or not according to the order quantity and/or the total quantity of the articles;
and responding to the fact that the current attribute value is abnormal, and pushing prompt information for prompting that the current attribute value is abnormal.
2. The method of claim 1, wherein said determining whether said current attribute value is abnormal based on said order quantity and/or said total quantity of items further comprises:
extracting contact information of a receiver in the acquired order, and determining the number of account numbers registered through the contact information indicated by the contact information for each piece of contact information in the extracted contact information;
and determining whether the current attribute value is abnormal or not according to the determined account number.
3. The method of claim 2, wherein determining whether the current attribute value is abnormal according to the determined number of account numbers comprises:
determining abnormal values in the attribute value set comprising the acquired historical attribute values and the current attribute values by using an abnormal point detection algorithm;
in response to determining that the determined outlier value includes the current attribute value, determining that the current attribute value is outlier.
4. The method of claim 1, wherein determining whether the current attribute value is abnormal based on the current attribute value and the association information comprises:
and importing the current attribute value and the associated information into a pre-trained identification model to obtain indication information for indicating whether the current attribute value is abnormal, wherein the identification model is used for representing the corresponding relation between the attribute value and the associated information and the indication information.
5. The method of claim 4, further comprising the step of training a recognition model, wherein the step of training a recognition model comprises:
adjusting an initial neural network model by using a genetic algorithm based on an elite retention strategy;
determining a training sample, wherein the training sample comprises an attribute value, association information and indication information for indicating whether the attribute value is abnormal;
and training the adjusted initial neural network model by using the determined training sample to obtain the recognition model.
6. The method of claim 5, wherein importing the current attribute values and the association information into a pre-trained recognition model:
searching an identification model corresponding to the attribute value interval to which the historical attribute value belongs according to the historical attribute value of the target object;
and importing the current attribute value and the associated information into the searched identification model.
7. An information pushing apparatus, characterized in that the apparatus comprises:
the device comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a current attribute value and associated information of a target article, and the associated information comprises at least one of the following items: an order including the item information of the target item within a predetermined period of time and a historical attribute value;
a determining unit, configured to determine whether the current attribute value is abnormal based on the current attribute value and the association information, including: determining the order quantity of the obtained order and/or the total quantity of the target items in the obtained order; determining whether the current attribute value is abnormal or not according to the order quantity and/or the total quantity of the articles;
and the pushing unit is used for responding to the determination that the current attribute value is abnormal and pushing prompt information for prompting that the current attribute value is abnormal.
8. A server, characterized in that the server comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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| CN109388548B (en) * | 2018-09-29 | 2020-12-22 | 京东数字科技控股有限公司 | Method and apparatus for generating information |
| CN111144430B (en) * | 2018-11-05 | 2023-08-01 | 中国移动通信集团广东有限公司 | Card-keeping number identification method and device based on genetic algorithm |
| CN111414566A (en) * | 2019-01-04 | 2020-07-14 | 北京京东尚科信息技术有限公司 | Method and device for pushing information |
| CN112633960B (en) * | 2019-09-24 | 2024-05-24 | 北京京东尚科信息技术有限公司 | Recommendation information pushing method and device and computer readable storage medium |
| CN113095756B (en) * | 2021-04-14 | 2024-01-12 | 北京京东振世信息技术有限公司 | Abnormal waybill feature determination method, device, equipment and computer readable medium |
| CN113781091A (en) * | 2021-04-14 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Method and device for monitoring article attribute value |
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