Content generation method and device
Through the two-stage matching process, the identification information screening is used for the first time and the characteristic information matching is used for the second time, which solves the problems of low efficiency and high computing power consumption in the existing technology, and realizes efficient content generation strategy identification and positioning.
Patent Information
- Application Number
- CN202510526787.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-24
- Publication Date
- 2025-08-05
AI Technical Summary
The prior art has low efficiency and high computing power consumption when generating targeted content, making it difficult to efficiently identify and locate content generation strategies for target files.
A two-stage matching process is adopted, firstly, a quick filter is performed based on the identification information of the target file. When the first match fails, more detailed feature information is used to match to reduce computing power consumption.
Through the two-stage matching process, the computing power consumption during the content generation process is significantly reduced, and the efficiency of identifying and positioning target content generation strategies is improved.
Smart Images
Figure CN120429274A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing, in particular to the field of artificial intelligence, and specifically to a content generation method and device thereof. Background Art
[0002] With the rapid development of big data and artificial intelligence (AI) technologies, more and more platforms have begun to mine data value for users through knowledge organization and creation, and then generate targeted content for users to consult and refer to. For example, after a user opens a document, a summary of the key content of the document can be automatically generated for the user's reference. Summary of the Invention
[0003] The present disclosure provides a content generation method, apparatus, device, and storage medium.
[0004] According to one aspect of the present disclosure, a content generation method is provided, including: determining a target file, and determining a first matching rule corresponding to the target file; determining a first matching parameter based on identification information of the target file, and performing a first matching process on the first matching parameter and the first matching rule; in response to a matching failure of the first matching process, obtaining a second matching rule corresponding to the target file; determining a second matching parameter based on feature information corresponding to the target file, and performing a second matching process on the second matching parameter and the second matching rule; determining a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generating auxiliary content for the target file based on the target content generation strategy.
[0005] Through a two-stage process of first matching and second matching, this application can more efficiently identify and locate the target content generation strategy corresponding to the target file. The first matching realizes rapid screening based on the identification information of the target file, and in the event of failure of the first matching, more detailed feature information is used for second matching. Considering that the calculation of feature information consumes relatively large computing power, this application only calculates feature information when the first matching fails. Therefore, this solution can also significantly reduce the computing power consumed in the matching process.
[0006] According to another aspect of the present disclosure, a content generation device is provided, including: a first determination module for determining a target file and a first matching rule corresponding to the target file; a first matching module for determining a first matching parameter based on identification information of the target file, and performing a first matching process on the first matching parameter and the first matching rule; a second determination module for obtaining a second matching rule corresponding to the target file in response to a matching failure of the first matching process; a second matching module for determining a second matching parameter based on feature information corresponding to the target file, and performing a second matching process on the second matching parameter and the second matching rule; a content generation module for determining a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generating auxiliary content for the target file based on the target content generation strategy.
[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned content generation method.
[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above-mentioned content generation method.
[0009] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the above content generation method when executed by a processor.
[0010] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.
[0012] Figure 1 is a schematic diagram of an exemplary implementation of a content generation method according to an exemplary embodiment of the present disclosure.
[0013] Figure 2 is a schematic diagram of an exemplary implementation of a content generation method according to an exemplary embodiment of the present disclosure.
[0014] Figure 3 is a schematic diagram of an exemplary implementation of a content generation method according to an exemplary embodiment of the present disclosure.
[0015] Figure 4 is an exemplary schematic diagram of a content generating device according to an exemplary embodiment of the present disclosure.
[0016] Figure 5 is a schematic diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION
[0017] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0018] Artificial Intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). This discipline encompasses both hardware and software technologies. AI hardware technologies generally include computer vision, speech recognition, natural language processing, as well as deep learning / learning, big data processing, and knowledge graphs.
[0019] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0020] Figure 1 is a schematic diagram of an exemplary embodiment of a content generation method shown in this application, such as Figure 1 As shown, the content generation method includes the following steps:
[0021] S101: Determine a target file, and determine a first matching rule corresponding to the target file.
[0022] Exemplarily, the target file may be a multimodal data type file such as a video file, a document file, or an audio file.
[0023] The first matching rule refers to a matching rule corresponding to the first round of matching when determining the target content generation strategy. Exemplarily, the first matching rule may include a low-traffic matching rule and a whitelist matching rule.
[0024] The first matching rule may be one or more.
[0025] S102: Determine a first matching parameter based on identification information of the target file, and perform a first matching process on the first matching parameter and a first matching rule.
[0026] If there are multiple first matching rules, the first matching parameters corresponding to each first matching rule are determined based on the identification information of the target file, that is, the first matching rules correspond to the first matching parameters one-to-one.
[0027] The identification information of the target file may be a file identifier of the target file, and the file identifier is unique.
[0028] The identification information of the target file may be a user identification corresponding to the target file, and the file is unique to the user.
[0029] When performing the first matching process on the first matching parameter and the first matching rule, the matching process of the first matching rule and its corresponding first matching parameter is sequentially executed according to the correspondence between the first matching rule and the first matching parameter to obtain the matching result of the first matching process.
[0030] S103: In response to the first matching process failing to match, a second matching rule corresponding to the target file is obtained.
[0031] If there is only one first matching rule, if the first matching rule fails to match its corresponding first matching parameter, it means that the first matching process fails.
[0032] If there are multiple first matching rules, if each first matching rule fails to match its corresponding first matching parameter, it means that the first matching process fails.
[0033] If the first matching process fails, a second matching rule corresponding to the target file is obtained. The second matching rule refers to the matching rule corresponding to the second round of matching when determining the target content generation strategy.
[0034] The second matching rule may be one or more.
[0035] S104: Determine a second matching parameter based on the characteristic information corresponding to the target file, and perform a second matching process on the second matching parameter and the second matching rule.
[0036] The characteristic information is information used to represent the characteristics of the target file. For example, the characteristic information corresponding to the target file may include characteristic information such as resource classification, file label, and user level of the target file.
[0037] In this application, the second matching rule is related to the characteristic information corresponding to the target file. For example, the second matching rule may be: if the resource classification of the target file includes a specific classification and the file tag includes a specific tag and the user level is within a specific level range, then it is determined that content generation strategy 1 is hit; if the above conditions are not met at the same time, content generation strategy 2 is hit.
[0038] In the present application, a second matching parameter is determined based on the feature information corresponding to the target file, and a second matching process is performed on the second matching parameter and the second matching rule.
[0039] S105 : Determine a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generate auxiliary content for the target file based on the target content generation strategy.
[0040] The content generation strategy found in the second matching process is obtained as the target content generation strategy, and the target content generation strategy is executed on the target file to obtain auxiliary content for the target file. The auxiliary content can be understood as content that helps users understand the content of the target file. For example, the auxiliary content can be a summary of the target file, key knowledge points of the target file, etc.
[0041] The embodiment of the present application proposes a content generation method, comprising: determining a target file, and determining a first matching rule corresponding to the target file; determining a first matching parameter based on identification information of the target file, and performing a first matching process on the first matching parameter and the first matching rule; in response to a matching failure of the first matching process, obtaining a second matching rule corresponding to the target file; determining a second matching parameter based on feature information corresponding to the target file, and performing a second matching process on the second matching parameter and the second matching rule; determining a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generating auxiliary content for the target file based on the target content generation strategy. The present application can more efficiently identify and locate the target content generation strategy corresponding to the target file through a two-stage process of first matching and second matching. The first matching realizes rapid screening based on the identification information of the target file, and in the event of a first matching failure, a second matching is performed using more detailed feature information. Considering that the calculation of feature information consumes relatively large computing power, the present application only calculates feature information in the event of a first matching failure, so the present solution can also significantly reduce the computing power consumed in the matching process.
[0042] Figure 2 is a schematic diagram of an exemplary embodiment of a content generation method shown in this application, such as Figure 2 As shown, the content generation method includes the following steps:
[0043] S201, determining a target file and determining auxiliary content generation requirements for the target file.
[0044] Exemplarily, the target file may be a multimodal data type file such as a video file, a document file, or an audio file.
[0045] For example, the auxiliary content generation requirement may be a summary generation requirement, a key content extraction requirement, or a segment summary requirement.
[0046] Among them, one target file may correspond to one or more auxiliary content generation requirements. If one target file corresponds to multiple auxiliary content generation requirements, the content generation method proposed in this application can be executed sequentially based on the multiple auxiliary content generation requirements to sequentially generate auxiliary content corresponding to the multiple auxiliary content generation requirements.
[0047] S202: Obtain a first matching rule corresponding to the auxiliary content generation requirement.
[0048] Each auxiliary content generation requirement has its own corresponding first matching rule and second matching rule. If the generation of summary content corresponding to the summary generation requirement is currently being executed, the first matching rule corresponding to the summary generation requirement is obtained.
[0049] The first matching rule may be one or more.
[0050] In some embodiments, the first matching rule may include a low-traffic matching rule.
[0051] In some embodiments, the first matching rule may include a whitelist matching rule.
[0052] In some embodiments, the first matching rule may include a low-traffic matching rule and a whitelist matching rule.
[0053] S203: Determine a first matching parameter based on the identification information of the target file, and perform a first matching process on the first matching parameter and the first matching rule.
[0054] In some embodiments, if the first matching rule includes a low-volume matching rule, a method for obtaining a first matching parameter corresponding to the low-volume matching rule includes: obtaining a target identifier corresponding to the target file; performing a hash operation on the target identifier to obtain a hash value of the target identifier; normalizing the hash value to obtain a normalized value of the hash value; and using the normalized value as the first matching parameter corresponding to the low-volume matching rule. In this way, by converting the target identifier into a hash value and performing normalization through a hash operation, the complexity of the original data can be reduced, and the matching efficiency corresponding to the low-volume matching rule can be improved.
[0055] In some embodiments, the target identifier corresponding to the target file is the file identifier corresponding to the target file, and the file identifier is a unique identifier. Using a unique file identifier can effectively prevent the occurrence of duplicate data and avoid different files having the same normalized value when matching small traffic using file identifiers.
[0056] In some embodiments, the target identifier corresponding to the target file is the user identifier corresponding to the target file, and the user identifier is a unique identifier. Using a unique user identifier can effectively prevent the occurrence of duplicate data and avoid the occurrence of the same normalized value for different users when matching small traffic using the user identifier.
[0057] After the first matching parameter corresponding to the small flow matching rule is determined as described above, it is necessary to perform a matching process between the first matching parameter and the small flow matching rule. Exemplarily, the small flow matching rule can be designed as a matching logic unit that determines whether the first matching parameter corresponding to it is within a preset normalized value interval. For example, if the preset normalized value interval is set to [0, 0.1], that is, when performing the matching of the small flow matching rule, it is determined whether the first matching parameter corresponding to the small flow matching rule is between [0, 0.1]. If it is between [0, 0.1], it means that the matching result of the small flow matching rule is a successful match. If it is not between [0, 0.1], it means that the matching result of the small flow matching rule is a failed match.
[0058] In some embodiments, if the first matching rule includes a whitelist matching rule, the method for obtaining the first matching parameter corresponding to the whitelist matching rule includes: obtaining a user identifier corresponding to the target file; and using the user identifier as the first matching parameter corresponding to the whitelist matching rule. Thus, by using the user identifier as the first matching parameter corresponding to the whitelist matching rule, the system can flexibly define and manage multiple whitelists, thereby increasing the flexibility of the whitelist matching rule.
[0059] After the first matching parameter corresponding to the whitelist matching rule is determined as described above, it is necessary to perform a matching process between the first matching parameter and the whitelist matching rule. Exemplarily, the whitelist matching rule can be designed as a matching logic unit that determines whether the first matching parameter corresponding to it contains a preset numerical segment. For example, if the preset numerical segment is [123,124], that is, when performing the matching of the whitelist matching rule, it is determined whether the first matching parameter corresponding to it contains the numerical segment "123" or the numerical segment "124". If the first matching parameter corresponding to it contains the numerical segment "123" or the numerical segment "124", then the matching result of the whitelist matching rule is a successful match. If the first matching parameter corresponding to it contains neither the numerical segment "123" nor the numerical segment "124", then the matching result of the whitelist matching rule is a failed match.
[0060] In this application, if there are multiple first matching rules, the matching is performed in the order of the first matching rules. For example, if the first matching rules include a low-traffic matching rule and a whitelist matching rule, the low-traffic matching rule is executed first. If the low-traffic matching rule fails to match, the whitelist matching rule is further executed. If the low-traffic matching rule is matched successfully, it means that the first matching process is successful, and the content generation strategy that successfully matches the low-traffic matching rule is used as the target content generation strategy.
[0061] S204 : In response to a matching failure in the first matching process, obtaining a second matching rule corresponding to the auxiliary content generation requirement, where the second matching rule includes a feature value matching rule.
[0062] The second matching rule may be one or more and at least include a feature value matching rule.
[0063] If the generation of summary content corresponding to the summary generation requirement is currently being executed, the second matching rule corresponding to the summary generation requirement is obtained.
[0064] S205: Determine matching features corresponding to the auxiliary content generation requirements.
[0065] For example, if the auxiliary content generation requirement is a summary generation requirement, the corresponding matching features may include three matching features of the target file: "resource classification", "file tag", and "user level".
[0066] Among them, different auxiliary content generation requirements can correspond to different matching features.
[0067] S206: Determine the feature value operation logic corresponding to each matching feature.
[0068] In this application, the characteristic value operation logic can be abstracted into a configurable logic unit, and judgment logic such as contain (whether it is included), exclude (whether it is not included), equal (whether it is equal to), and between (whether it is between two values) can be applied for judgment.
[0069] For example, if the matching feature is "resource classification", its feature value operation logic can be configured as follows: if the "resource classification" of the target file contains "education category" or "training category", then the feature value corresponding to the matching feature "resource classification" is "TRUE"; if the "resource classification" of the target file contains neither "education category" nor "training category", then the feature value corresponding to the matching feature "resource classification" is "FALSE".
[0070] For example, if the matching feature is "user level", its feature value operation logic can be configured as follows: if the "user level" corresponding to the target file is between [1,4], then the feature value corresponding to the matching feature "user level" is "TRUE"; if the "user level" corresponding to the target file is not between [1,4], then the feature value corresponding to the matching feature "user level" is "FALSE".
[0071] S207 , performing an operation on any matching feature based on the feature value operation logic corresponding to the matching feature to obtain a feature value corresponding to the matching feature.
[0072] Among them, the matching features correspond to the feature value operation logic one by one.
[0073] In some embodiments, the feature value corresponding to each matching feature can be represented by "TRUE" or "FALSE".
[0074] S208: Use the feature value as a second matching parameter corresponding to the feature value matching rule.
[0075] S209: Perform a second matching process on the second matching parameter and the second matching rule.
[0076] Exemplarily, the feature value matching rule constructs a logical expression of the feature value through multiple logical operators, and is stored in the form of a mapping relationship between the logical expression and its corresponding policy identifier. That is, it means that the feature value matching rule can be designed as a matching logic unit that performs policy matching through logical operators such as AND / OR / NOT. For example, if one of the matching logics of the feature value matching rule is: STRATEGY S1:=(A∧B)∨(C∧!D), it means that if (A∧B) is true, or (C∧!D) is true, the policy identifier of the matched target content generation strategy is S1. Among them, A, B, C, and D in the matching logic represent the feature values of their corresponding matching features. For example, the value of A can correspond to the feature value of the above-mentioned matching feature being "resource classification", and the value of B can correspond to the feature value of the above-mentioned matching feature being "user level". Assuming that the feature value corresponding to the matching feature "resource classification" is "TRUE" and the feature value corresponding to the matching feature "user level" is "TRUE", then (A∧B) is true, that is, it is considered that the content generation policy with the policy identifier S1 is hit.
[0077] S210 , determining a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generating auxiliary content for the target file based on the target content generation strategy.
[0078] The content generation strategy hit by the second matching process is obtained as the target content generation strategy, and the target content generation strategy is executed on the target file to obtain the auxiliary content of the target file.
[0079] The embodiment of the present application introduces in detail the process of obtaining the feature values used in the secondary matching, and abstracts the selection conditions of each strategy into a combination of multiple logical judgments or ANDs to achieve rapid iteration, rapid changes, and efficient and concise strategy application management; the present application helps to achieve dynamic traffic allocation by introducing whitelist matching rules and small traffic matching rules, and distributes specific strategies to different traffic; the present application uses a two-stage process of first matching and second matching to more efficiently identify and locate the target content generation strategy corresponding to the target file. The first match achieves rapid screening based on the identification information of the target file, and in the event of a failure in the first match, more detailed feature information is used for secondary matching. Considering that the calculation of feature information consumes relatively large computing power, the present application only calculates the feature information when the first match fails, so this solution can also significantly reduce the computing power consumed in the matching process.
[0080] The above describes the matching logic when the second matching rule is a feature value matching rule. In actual application, the second matching rule may also include other matching rules. For example, if the first matching rule does not include a low-flow matching rule, then in addition to the feature value matching rule, the low-flow matching rule can also be configured as the second matching rule. For example, if the first matching rule does not include a whitelist matching rule, then in addition to the feature value matching rule, the whitelist matching rule can also be configured as the second matching rule. The specific matching process can be referred to above and will not be repeated here.
[0081] Figure 3 is a schematic diagram of an exemplary embodiment of a content generation method shown in this application, such as Figure 3 As shown, the content generation method includes the following steps:
[0082] S301, determining a target file and determining auxiliary content generation requirements for the target file.
[0083] S302: Determine a first matching parameter based on identification information of the target file.
[0084] S303: Input the first matching parameter into the policy matching engine to execute a first matching process of a first matching rule corresponding to the auxiliary content generation requirement.
[0085] S304: Obtain the matching result of the first matching process returned by the policy matching engine.
[0086] S305: If the first matching process is successful, obtain the target content generation strategy identification information carried by the matching result of the first matching process.
[0087] S306: If the first matching process fails, the matching features corresponding to the auxiliary content generation requirements are determined from the feature configuration center.
[0088] The feature configuration center is pre-configured with matching features related to each auxiliary content generation requirement.
[0089] S307, determining the feature value operation logic corresponding to each matching feature, performing operation on any matching feature based on the feature value operation logic corresponding to the matching feature, obtaining the feature value corresponding to the matching feature, and using the feature value as the second matching parameter corresponding to the feature value matching rule.
[0090] S308: Input the second matching parameter into the policy matching engine to execute a second matching process of a second matching rule corresponding to the auxiliary content generation requirement, where the second matching rule includes a feature value matching rule.
[0091] S309: Obtain the matching result of the second matching process returned by the policy matching engine, and obtain the target content generation policy identification information carried by the matching result of the second matching process.
[0092] S310: Obtain a target content generation policy corresponding to a target file from a policy configuration center based on the target content generation policy identification information.
[0093] The policy configuration center pre-sets the content generation strategy corresponding to each policy identifier. The content generation strategy may include prompt words or content generation models that need to be called corresponding to the auxiliary content generation requirements.
[0094] S311 , generating auxiliary content of a target file based on a target content generation strategy.
[0095] The embodiment of the present application decouples feature calculation from policy matching, allowing the two to be developed and maintained independently, enabling hot policy updates of the policy matching engine and reducing development costs. After obtaining the matched policy identification information, the present application obtains detailed configuration information of the target content generation policy from the policy configuration center based on the policy identification information, thereby decoupling policy matching from policy configuration and improving development flexibility.
[0096] Furthermore, after the auxiliary content of the target file is generated based on the target content generation strategy, a target area corresponding to the auxiliary content on the current interface is determined. The auxiliary content is displayed in the target area. For example, if the auxiliary content is a generated summary, the auxiliary content is displayed in the target area corresponding to the summary on the current interface.
[0097] Figure 4 This is an exemplary schematic diagram of a content generation device shown in this application. Figure 4 As shown, the content generation device 400 includes a first determination module 401, a first matching module 402, a second determination module 403, a second matching module 404 and a content generation module 405, wherein:
[0098] A first determining module 401 is configured to determine a target file and a first matching rule corresponding to the target file;
[0099] A first matching module 402 is configured to determine a first matching parameter based on identification information of a target file, and perform a first matching process on the first matching parameter and a first matching rule;
[0100] A second determining module 403 is configured to obtain a second matching rule corresponding to the target file in response to a matching failure in the first matching process;
[0101] A second matching module 404 is configured to determine a second matching parameter based on the characteristic information corresponding to the target file, and perform a second matching process on the second matching parameter and the second matching rule;
[0102] The content generation module 405 is configured to determine a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generate auxiliary content for the target file based on the target content generation strategy.
[0103] Through a two-stage process of first matching and second matching, this device can more efficiently identify and locate the target content generation strategy corresponding to the target file. The first matching realizes rapid screening based on the identification information of the target file, and in the event of failure of the first matching, more detailed feature information is used for second matching. Considering that the calculation of feature information consumes relatively large computing power, this application only calculates the feature information when the first matching fails. Therefore, this solution can also significantly reduce the computing power consumed in the matching process.
[0104] Furthermore, the first determination module 401 is further configured to: determine the auxiliary content generation requirement of the target file; and obtain a first matching rule corresponding to the auxiliary content generation requirement; and the second determination module 403 is further configured to: obtain a second matching rule corresponding to the auxiliary content generation requirement.
[0105] Furthermore, the second matching rule includes a feature value matching rule, and the second matching module 404 is also used to: determine the matching features corresponding to the auxiliary content generation requirements; determine the feature value operation logic corresponding to each matching feature; for any matching feature, perform an operation based on the feature value operation logic corresponding to the matching feature to obtain the feature value corresponding to the matching feature; and use the feature value as the second matching parameter corresponding to the feature value matching rule.
[0106] Furthermore, in response to the first matching rule including a low-traffic matching rule, the first matching module 402 is also used to: obtain a target identifier corresponding to the target file, wherein the target identifier is a file identifier or a user identifier; perform a hash value operation on the target identifier to obtain a hash value of the target identifier; normalize the hash value to obtain a normalized value of the hash value; and use the normalized value as the first matching parameter corresponding to the low-traffic matching rule.
[0107] Furthermore, the file identifier is a unique identifier, and the user identifier is a unique identifier.
[0108] Furthermore, in response to the first matching rule including the whitelist matching rule, the first matching module 402 is further configured to: obtain a user identifier corresponding to the target file; and use the user identifier as a first matching parameter corresponding to the whitelist matching rule.
[0109] Furthermore, the first matching module 402 is further configured to: in response to a successful matching in the first matching process, determine a target content generation strategy corresponding to the target file based on the matching result of the first matching process.
[0110] Furthermore, the first matching module 402 is further configured to: generate target content strategy identification information carried by the matching result of the first matching process; and obtain a target content generation strategy corresponding to the target file from a strategy configuration center based on the target content generation strategy identification information.
[0111] Furthermore, the content generation module 405 is further configured to: generate target content strategy identification information carried by the matching result of the second matching process; and obtain a target content generation strategy corresponding to the target file from a strategy configuration center based on the target content generation strategy identification information.
[0112] Furthermore, the content generating device 400 further includes: a display module, configured to determine a target area corresponding to the auxiliary content on the current interface; and display the auxiliary content in the target area.
[0113] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.
[0114] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0115] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the RAM 503. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.
[0116] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.
[0117] The computing unit 501 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the content generation method. For example, in some embodiments, the content generation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the content generation method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the content generation method by any other suitable means (e.g., by means of firmware).
[0118] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0119] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.
[0120] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0121] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 can be received in any form (including acoustic input, voice input, or tactile input).
[0122] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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 a local area network (LAN), a wide area network (WAN), and the Internet.
[0123] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.
[0124] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.
[0125] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.
Claims
1. A content generation method, comprising: Determining a target file, and determining a first matching rule corresponding to the target file; Determining a first matching parameter based on the identification information of the target file, and performing a first matching process on the first matching parameter and the first matching rule; In response to a matching failure in the first matching process, obtaining a second matching rule corresponding to the target file; Determining a second matching parameter based on the characteristic information corresponding to the target file, and performing a second matching process on the second matching parameter and the second matching rule; A target content generation strategy corresponding to the target file is determined based on the matching result of the second matching process, and auxiliary content of the target file is generated based on the target content generation strategy.
2. The method according to claim 1, wherein Determining the first matching rule corresponding to the target file includes: Determine the auxiliary content generation requirements for the target file: Obtaining a first matching rule corresponding to the auxiliary content generation requirement; The obtaining of the second matching rule corresponding to the target file includes: A second matching rule corresponding to the auxiliary content generation requirement is obtained.
3. The method according to claim 2, wherein: The second matching rule includes a feature value matching rule, and determining the second matching parameter based on the feature information corresponding to the target file includes: Determining matching features corresponding to the auxiliary content generation requirements; Determine the feature value operation logic corresponding to each matching feature; For any of the matching features, performing an operation based on the feature value operation logic corresponding to the matching feature to obtain the feature value corresponding to the matching feature; The characteristic value is used as a second matching parameter corresponding to the characteristic value matching rule.
4. The method according to claim 2 or 3, wherein: In response to the first matching rule including a low-traffic matching rule, determining the first matching parameter based on the identification information of the target file includes: Obtaining a target identifier corresponding to the target file, wherein the target identifier is a file identifier or a user identifier; Performing a hash operation on the target identifier to obtain a hash value of the target identifier; Normalizing the hash value to obtain a normalized value of the hash value; The normalized value is used as the first matching parameter corresponding to the low-flow matching rule.
5. The method according to claim 4, wherein The file identifier is a unique identifier, and the user identifier is a unique identifier.
6. The method according to claim 2 or 3, wherein: In response to the first matching rule including a whitelist matching rule, determining the first matching parameter based on the identification information of the target file includes: Obtaining a user ID corresponding to the target file; The user identifier is used as the first matching parameter corresponding to the whitelist matching rule.
7. The method according to claim 1, wherein The method further comprises: In response to a successful matching in the first matching process, a target content generation strategy corresponding to the target file is determined based on the matching result of the first matching process.
8. The method according to claim 7, wherein: The determining the target content generation strategy corresponding to the target file based on the matching result of the first matching process, or the determining the target content generation strategy corresponding to the target file based on the matching result of the second matching process, includes: For any matching result of the first matching process and the matching result of the second matching process, obtaining target content generation strategy identification information carried by any matching result; The target content generation policy corresponding to the target file is obtained from a policy configuration center based on the target content generation policy identification information.
9. The method according to claim 8, wherein After the auxiliary content of the target file is generated based on the target content generation strategy, the method further includes: Determining a target area corresponding to the auxiliary content on the current interface; The auxiliary content is displayed in the target area.
10. A content generation device, comprising: A first determining module is configured to determine a target file and a first matching rule corresponding to the target file; a first matching module, configured to determine a first matching parameter based on the identification information of the target file, and perform a first matching process on the first matching parameter and the first matching rule; a second determining module, configured to obtain a second matching rule corresponding to the target file in response to a matching failure in the first matching process; a second matching module, configured to determine a second matching parameter based on the characteristic information corresponding to the target file, and perform a second matching process on the second matching parameter and the second matching rule; The content generation module is configured to determine a target content generation strategy corresponding to the target file based on the matching result of the second matching process, and generate auxiliary content for the target file based on the target content generation strategy.
11. An electronic device comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.
12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 9.
13. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.