Content security processing and differentiated pushing method and medium for new media of government affairs

Abstract

Embodiments of the present disclosure disclose a content security processing and differentiated pushing method and medium for government affairs new media. A specific embodiment of the method comprises: obtaining account state information corresponding to an account identifier from a pre-created account management database; generating a health score of a target account based on the account state information; generating desensitization level information of to-be-pushed content data; performing desensitization processing on the to-be-pushed content data to obtain desensitized to-be-pushed content data; generating information entropy difference value information based on the to-be-pushed content data and the desensitized to-be-pushed content data; generating pushing priority information and pushing range information; distributing the desensitized to-be-pushed content data to at least one sending queue corresponding to at least one target platform, and scheduling and sending the desensitized to-be-pushed content data according to the priority order of the sending queue through a network card hardware scheduler. The embodiment relates to the field of computer technology, and improves the speed of desensitization processing and pushing of high-quality active account pushing content.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, specifically to a method and medium for content security processing and differentiated push notifications for government new media. Background Technology

[0002] Content security processing and differentiated push for government new media is a technology used in government new media management platforms to achieve collaborative linkage of content security detection, hierarchical desensitization, and differentiated push.

[0003] Statistics show that the total number of government new media accounts at the prefecture-level city level and above in my country has exceeded 100,000, with an average daily content release volume of millions. A provincial-level government new media management platform needs to simultaneously connect to multiple target platforms such as WeChat official accounts, Weibo, Douyin, and Toutiao, managing hundreds to thousands of government accounts and processing thousands of pieces of content to be pushed daily. Especially in scenarios such as emergency response to public emergencies and centralized release of major policies, multiple accounts often need to submit push requests intensively within the same time window, causing the server (i.e., the publishing server) to face extremely high concurrent processing pressure during specific periods. The CPU core load rate rises sharply and exceeds the safety threshold, and the system enters a high-concurrency push period. The aforementioned platforms have extremely high requirements for the compliance, timeliness, and security of government content. The number of government new media matrix accounts is huge. If pushes are made to all accounts (especially "unreliable accounts" that have not been updated for a long time and have no service functions), it will cause a huge waste of computing resources (CPU) and network bandwidth (network card). Therefore, it is essential to constrain inefficient accounts through activity identification (e.g., by demoting them, limiting their reach, or shutting them down and consolidating them), and to concentrate computing and network resources on high-quality, highly active main accounts to ensure the timeliness of information disseminated by these accounts. Furthermore, government new media platforms are highly susceptible to including sensitive privacy information such as ID numbers, mobile phone numbers, and home addresses when publishing content related to public services and policy announcements. To prevent privacy leaks, the system must automatically scan and anonymize the content to be published. Therefore, sensitive information detection and anonymization must be completed before publication. In the actual operation of content security processing and push notifications, there is a lack of quantitative assessment methods for the degree of change in content before and after anonymization. This makes it impossible to translate the severity of content security modifications into a reference for push notification scheduling, resulting in high-risk content with significant security modifications receiving the same push priority as low-risk content with minor or no modifications. As the core carrier of the above processing flow, the server needs to achieve coordinated operation of content security detection, tiered anonymization, and differentiated push notifications while meeting high-concurrency processing performance requirements. Currently, the secure handling and distribution of government new media content mainly relies on the following methods: performing uniform sensitive information detection and de-identification on all accounts' content to be distributed; and distributing the same content indiscriminately to all platforms. However, when using the above methods for the secure handling and distribution of government new media content, the following technical problems often arise in the actual operation scenarios of large-scale government new media matrices: Applying the same level of sensitivity detection and de-identification to all accounts, without any processing, blocking, or interception of content pushed from accounts with extremely low health status, results in a significant amount of CPU cores being continuously consumed by de-identification tasks from dormant and inactive accounts. The de-identification process of accounts with extremely low health status chronically occupies CPU time slices. Furthermore, the lack of tiered de-identification intensity based on the health status of the source accounts means that content from high-quality, active accounts is easily slowed down due to excessive use of hardware encryption accelerator cards. Moreover, this content competes equally with content from extremely low-health accounts in the accelerator card request queue, further slowing down processing and resulting in slower secure processing and delivery of new media content from high-quality, active government accounts. Simultaneously, pushing the same content indiscriminately to all platforms can easily cause systemic congestion in the network card's sending queue.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose methods, apparatuses, electronic devices, and computer-readable media for content security processing and differentiated push for government new media, in order to solve one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a method for content security processing and differentiated push for government new media. The method includes: in response to determining that the CPU core load rate of the government new media's publishing server exceeds a preset threshold, after receiving a new media content push request from a government operation terminal device containing an account identifier and content data to be pushed, obtaining account status information corresponding to the aforementioned account identifier from a pre-created account management database; generating a health score for the target account represented by the aforementioned account identifier based on the aforementioned account status information; and in response to determining that the aforementioned health score is greater than or equal to a preset health threshold, generating a content security processing and differentiated push method for the target account based on the health score. The process involves: defining the anonymization level of the content data to be pushed; performing anonymization processing on the content data to be pushed based on the anonymized level information to obtain anonymized content data to be pushed; generating an information entropy difference information based on the content data to be pushed and the anonymized content data to be pushed; generating push priority information and push range information based on the health score and the information entropy difference information; allocating the anonymized content data to be pushed to at least one sending queue corresponding to at least one target platform based on the push priority information and the push range information, and scheduling and sending the data according to the priority order of the sending queues through the network card hardware scheduler of the publishing server.

[0008] Secondly, some embodiments of this disclosure provide a content security processing and differentiated push device for government new media. The device includes: an acquisition unit configured to, in response to determining that the CPU core load rate of the government new media's publishing server exceeds a preset threshold, after receiving a new media content push request sent by a government operation terminal device containing an account identifier and content data to be pushed, acquire account status information corresponding to the aforementioned account identifier from a pre-created account management database; a first generation unit configured to, based on the aforementioned account status information, generate a health score for the target account represented by the aforementioned account identifier; and a second generation unit configured to, in response to determining that the aforementioned health score is greater than or equal to a preset health threshold, generate a content security processing and differentiated push device corresponding to the aforementioned content data to be pushed. The data includes: a desensitization level information; a desensitization processing unit configured to desensitize the content data to be pushed based on the aforementioned desensitization level information to obtain desensitized content data to be pushed; a third generation unit configured to generate information entropy difference information based on the aforementioned content data to be pushed and the aforementioned desensitized content data to be pushed; a fourth generation unit configured to generate push priority information and push range information based on the aforementioned health score and the aforementioned information entropy difference information; and a scheduling and sending unit configured to allocate the aforementioned desensitized content data to be pushed to at least one sending queue corresponding to at least one target platform based on the aforementioned push priority information and the aforementioned push range information, and to schedule and send the data according to the priority order of the aforementioned sending queues through the network card hardware scheduler of the aforementioned publishing server.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above-described embodiments of this disclosure have the following beneficial effects: The content security processing and differentiated push method for government new media, as described in some embodiments of this disclosure, improves the speed of content security processing and push for high-quality, active government accounts and alleviates systemic congestion in the network card sending queue. Specifically, the reasons for the slow speed of content security processing and push for high-quality, active government accounts and the potential for systemic congestion in the network card sending queue are as follows: All accounts uniformly perform sensitive information detection and desensitization with the same intensity, without any form of processing, blocking, or push interception of content pushed by accounts with extremely low health. This results in a large number of CPU cores' computing resources being continuously occupied by the desensitization tasks of zombie accounts and dormant accounts. The desensitization process of accounts with extremely low health long-term occupies CPU time slices. Furthermore, the desensitization intensity is not graded according to the health status of the source account. Content from high-quality, active accounts is prone to having its processing speed slowed down due to excessive use of hardware encryption acceleration cards, and it competes equally with content with extremely low health in the acceleration card request queue, further slowing down the processing speed and resulting in a slow speed of content security processing and push for high-quality, active government accounts. Meanwhile, indiscriminately pushing the same content to all platforms can easily cause systemic congestion in the network card's transmission queue. Based on this, some embodiments of the present disclosure of a content security processing and differentiated push method for government new media firstly, in response to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, upon receiving a new media content push request from a government operation terminal device containing an account identifier and the content to be pushed, the method retrieves account status information corresponding to the aforementioned account identifier from a pre-created account management database. This allows for matching the account status information corresponding to the content to be pushed, which is used to generate the health score of the target account. Then, based on the aforementioned account status information, a health score representing the target account as indicated by the aforementioned account identifier is generated. Thus, the health score can be quantified based on the account status information to distinguish between high-quality active accounts and low-value inactive / dormant accounts. Next, in response to determining that the aforementioned health score is greater than or equal to a preset health threshold, an anonymization level information for the aforementioned content to be pushed is generated based on the health score. Then, based on the aforementioned anonymization level information, the aforementioned content to be pushed is anonymized to obtain anonymized content to be pushed data. Therefore, the anonymization strength (i.e., anonymization level) can be matched to the account status, and tiered anonymization processing can be implemented based on the anonymization level. This abandons the traditional uniform high-intensity anonymization model, avoids excessive use of hardware encryption acceleration cards for high-quality account content, and avoids the problem of slow processing speed for high-quality content caused by excessive anonymization. Next, based on the aforementioned content data to be pushed and the aforementioned anonymized content data to be pushed, information entropy difference information is generated. Then, based on the aforementioned health score and the aforementioned information entropy difference information, push priority information and push scope information are generated.Therefore, based on health scores and information entropy difference information, the priority of push notifications can be hierarchically classified, and the scope of target platforms for distribution can be limited, resulting in push priority information and push scope information. Then, based on the aforementioned push priority information and push scope information, the anonymized content data to be pushed is allocated to at least one sending queue corresponding to at least one target platform, and scheduled for transmission according to the priority order of the sending queues by the network interface card (NIC) hardware scheduler of the publishing server. This ensures that high-priority content is allocated to high-priority sending queues and sent preferentially by the NIC hardware scheduler, while low-priority content is allocated to low-priority sending queues and sent only after the high-priority queues have finished sending. This avoids indiscriminately pushing the same content to all platforms, achieving differentiated scheduling and staggered transmission of content with different priorities. It fundamentally avoids congestion caused by disorderly competition between content of different priorities in the sending queues, thus alleviating the systemic congestion problem of the NIC sending queues. Subsequently, in response to the determination that the aforementioned health score is less than a preset health threshold and greater than a preset blocking threshold, the aforementioned account identifier and the content data to be pushed are sent to a preset review terminal, and the de-identification process corresponding to the aforementioned target account is suspended to pause the de-identification processing and transmission of the aforementioned content data to be pushed. In response to the determination that the aforementioned health score is less than the aforementioned preset health threshold and less than the aforementioned preset blocking threshold, the memory pages storing the content data to be pushed in memory are marked as reclaimable, and the de-identification process corresponding to the aforementioned target account is removed from the run queue of the currently bound CPU core to block the de-identification processing and transmission of the aforementioned content data to be pushed. Thus, the content of accounts with low health can be transferred to the manual review channel, no longer occupying CPU resources in the automatic processing flow. In the case of extremely low health, the de-identification processing and transmission of the content data to be pushed can be blocked, freeing up CPU core computing resources and memory space occupied by zombie accounts and dormant accounts with extremely low health. Because it employs a tiered approach to accounts based on health scores (e.g., high-health accounts receive lightweight de-identification and are given high priority for delivery, medium- and low-health accounts have their de-identification process suspended and transferred to review, and extremely low-health accounts have their memory pages reclaimed and removed from the CPU execution queue), it reduces the CPU core resource consumption of de-identification tasks for dormant or inactive accounts, preventing accounts with extremely low health from monopolizing CPU time slices for extended periods. Furthermore, because de-identification levels are dynamically generated based on health scores, content from high-quality, active accounts can avoid excessive use of hardware encryption acceleration cards, thus accelerating the de-identification processing and delivery speed of content from these accounts. Finally, by allocating content to different delivery queues on different platforms based on delivery priority and scope, and scheduling delivery according to priority, it alleviates the systemic congestion in network card delivery queues caused by indiscriminately pushing the same content to all platforms. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the content security processing and differentiated push method for government new media according to this disclosure; Figure 2 This is a structural schematic diagram of some embodiments of the content security processing and differentiated push device for government new media based on this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure; Figure 4 This is a schematic diagram of the account status information management interface for a content security processing and differentiated push method for government new media based on some embodiments of this disclosure; Figure 5 This is a schematic diagram showing the test results of sensitive information detection and desensitization processing in a content security processing and differentiated push method for government new media based on some embodiments of this disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 A flowchart 100 is shown, illustrating some embodiments of a content security processing and differentiated push method for government new media according to this disclosure. This content security processing and differentiated push method for government new media includes the following steps: Step 101: In response to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, after receiving a new media content push request containing account identifier and content data to be pushed sent by the government operation terminal device, the account status information corresponding to the account identifier is obtained from the pre-created account management database.

[0021] In some embodiments, the execution entity (e.g., a server, i.e., a publishing server) of the content security processing and differentiated push method for government new media can, in response to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, obtain account status information corresponding to the aforementioned account identifier from a pre-created account management database after receiving a new media content push request containing an account identifier and content data to be pushed from a government operation terminal device. The aforementioned content data to be pushed includes one of text data, image data, and video data. The aforementioned publishing server refers to a server deployed in the government new media management platform, used to perform content security processing and differentiated push computational tasks, i.e., the execution entity of this method. The aforementioned publishing server is equipped with a multi-core central processing unit (CPU) and communicates with the government operation terminal device and various target platforms via a network card. The aforementioned government new media management platform refers to an information system deployed and operated by a government agency or a technology operation unit entrusted by the government, used for unified registration management, content security monitoring, content release review, and multi-platform distribution of government new media accounts within its jurisdiction. CPU core load rate refers to the ratio between the actual workload of the central processing unit (CPU) of a publishing server and its maximum processing capacity per unit time, usually expressed as a percentage. For example, a CPU core load rate of 85% means that 85% of the CPU core's processing capacity is currently being used, with only 15% of the remaining processing capacity available for new computing tasks. The aforementioned preset threshold is a pre-set critical value for CPU core load rate used to determine whether the server is currently in a high-concurrency push period. When the CPU core load rate exceeds this preset threshold, it indicates that the server's current concurrent processing pressure is approaching its processing capacity limit, and the system enters a high-concurrency push period, requiring the implementation of the differentiated processing and push processing proposed in this application to avoid system overload. The aforementioned government operation terminal equipment refers to terminal equipment used by government new media operators for editing and submitting content to be pushed, including but not limited to personal computers and laptops. Government new media refers to government accounts or applications opened by administrative organs at all levels, public institutions undertaking administrative functions, and their internal departments on third-party platforms such as Weibo, WeChat, Douyin, Toutiao, and Baijiahao, as well as self-developed mobile clients (APPs). The aforementioned account identifier refers to a string or code that identifies a government account. The aforementioned content data to be pushed refers to the content data that the government's new media account is preparing to push. The aforementioned new media content push request refers to a request sent by a government operation terminal device to the implementing entity to request the content data to be pushed. The aforementioned government account refers to an official account registered and operated by government agencies, public institutions, or relevant government departments on target platforms (including but not limited to WeChat, Weibo, Douyin, and Toutiao) for publishing government information, providing public services, and conducting government-citizen interaction.In practice, account identifiers are typically automatically generated by the system during government account registration and are stored in the account management database in conjunction with the account's filing information, operating entity information, etc. The aforementioned account management database refers to a database pre-created within the execution entity's storage system for storing and managing data related to government accounts. This database stores the correspondence between account identifiers and account status information for each government account according to a pre-defined data table structure. The aforementioned account status information refers to a data set characterizing the operational status of a government account across multiple dimensions, including filing status data, activity status data, compliance status data, interaction status data, and authentication status data. The aforementioned filing status data indicates whether a government account has completed filing approval and whether the filing information is within its validity period. In practice, filing status data is recorded in the account filing information table of the account management database, with field values ​​including "filing and valid," "filing about to expire (within 30 days)," and "not filed." The aforementioned activity status data indicates the time of the government account's most recent content posting and the frequency of posting within the most recent pre-defined time period. In practice, activity status data is recorded in the content posting history table of the account management database. The aforementioned implementing entities can obtain activity status data by querying the posting time and posting frequency fields stored in this table. The aforementioned recent preset time period is the last 30 days. The aforementioned compliance status data refers to data used to indicate whether the government account has historical violation records and whether it is in a rectification period. In practice, compliance status data is recorded in the violation record table of the account management database. The aforementioned implementing entities can obtain compliance status data by querying the violation type, violation time, and rectification status fields stored in this table. The aforementioned interaction status data refers to data used to indicate the government account's response rate to public comments and average response time. The aforementioned authentication status data refers to data used to indicate whether the government account has passed the official authentication of the target platform and whether the authentication has expired. For example, when the implementing entity receives a new media content push request from a government operation terminal device, it extracts the account identifier "GOV_WEIXIN_001" from the Authorization field of the request, and then queries the account status information corresponding to the account identifier in the account management database to obtain the following account status information: the filing status data is "filed and valid", the activity status data is "published in the last 7 days", the compliance status data is "no violation record", the interaction status data is "response rate 85%", and the authentication status data is "authenticated".

[0022] Step 102: Based on the account status information, generate a health score for the target account that represents the account identifier.

[0023] In some embodiments, the executing entity may generate a health score for the target account, represented by the account identifier, based on the account status information. The health score is a numerical value used to quantify the overall operational health of the target account across multiple dimensions. A higher score indicates a healthier operational status for the target account (represented as a high-quality, active account), while a lower score indicates a less healthy operational status (potentially a dormant or inactive account). The health score is a dimensionless numerical value.

[0024] In some optional implementations of certain embodiments, the execution entity may generate a health score for the target account represented by the account identifier based on the account status information through the following steps: The first step involves conducting health checks on the account status information, including filing status data, activity status data, compliance status data, interaction status data, and authentication status data, to obtain sub-health scores for each dimension. In practice, the implementing entity can map each dimension of status data to its corresponding sub-health score according to preset scoring rules. These preset scoring rules refer to predefined mapping rules used to convert the raw values ​​of each dimension of status data into sub-health scores. For example, a preset scoring rule could be that when the filing status data is "filing and valid," the corresponding filing sub-health score is 100 points; when the filing status data is "filing about to expire (within 30 days) or has expired," the corresponding filing sub-health score is 70 points; and when the filing status data is "not filed," the corresponding filing sub-health score is 0 points. When the activity status data is "Published in the last 7 days", the corresponding activity sub-health score is 100 points; when the activity status data is "Published in the last 30 days", the corresponding activity sub-health score is 50 points; when the activity status data is "No publications in more than 30 days", the corresponding activity sub-health score is 10 points. When the compliance status data is "No violations", the corresponding compliance sub-health score is 100 points; when the compliance status data is "Minor violations have been rectified", the corresponding compliance sub-health score is 60 points; when the compliance status data is "Serious violations are being rectified", the corresponding compliance sub-health score is 20 points. When the interaction status data response rate is greater than 80%, the corresponding interaction sub-health score is 100 points; when the interaction status data response rate is greater than or equal to 50% and less than or equal to 80%, the corresponding interaction sub-health score is 60 points; when the interaction status data response rate is less than 50%, the corresponding interaction sub-health score is 20 points. When the authentication status data is "authenticated", the corresponding authentication sub-health score is 100 points; when the authentication status data is "authentication expired", the corresponding authentication sub-health score is 50 points; when the authentication status data is "unauthenticated", the corresponding authentication sub-health score is 30 points.

[0025] The first step is to generate a health score for the target account, represented by an account identifier, based on the aforementioned sub-health scores. In practice, the implementing entity can determine the average of the various sub-health scores as the target account's health score.

[0026] Step 103: In response to determining that the health score is greater than or equal to the preset health threshold, generate the anonymization level information of the content data to be pushed based on the health score.

[0027] In some embodiments, the execution entity may, in response to determining that the health score is greater than or equal to a preset health threshold, generate anonymization level information for the content data to be pushed, based on the health score. The anonymization level information refers to a level identifier indicating the degree of anonymization to be performed on the content data to be pushed. The anonymization level information includes one of the following level identifiers: a first anonymization level identifier and a second anonymization level identifier. The preset health threshold is a pre-set threshold value for health scores used to distinguish whether a target account meets the automatic anonymization and push processing admission conditions. The preset health threshold can be configured and adjusted according to actual business scenarios. For example, in one application scenario, the preset health threshold can be set to 60 points. The first anonymization level identifier is a level identifier indicating the performance of mild anonymization processing. When the content data to be pushed is text content, the anonymization processing method corresponding to the first anonymization level identifier is mask replacement processing, which is executed by the CPU core and does not call the hardware encryption acceleration card. The first anonymization level identifier in the anonymization level information indicates that the target account represented by the account identifier has a high health score, and its content data to be pushed only needs to undergo mild anonymization processing. The aforementioned second desensitization level identifier refers to a level identifier used to indicate the execution of standard desensitization processing. When the content data to be pushed is text content, the desensitization processing method corresponding to the second desensitization level identifier is format-preserving encryption processing, which calls a hardware encryption acceleration card to perform format-preserving encryption based on the national cryptographic algorithm. In the desensitization level information, the second desensitization level identifier indicates that the health score of the target account represented by the account identifier is in the range between a preset health threshold and a preset blocking threshold, and its content data to be pushed needs to undergo standard desensitization processing. In practice, in response to determining that the health score is greater than or equal to the preset health threshold and less than or equal to the second preset health threshold, the second desensitization level identifier is determined as desensitization level information. Then, in response to determining that the above health score is greater than the second preset health threshold, the first desensitization level identifier is determined as desensitization level information. Here, the aforementioned second preset health threshold refers to a pre-set health score threshold value used to distinguish between the first desensitization level identifier and the second desensitization level identifier. For example, in one application scenario, the second preset health threshold can be set to 80 points.

[0028] Step 104: Based on the anonymization level information, the data to be pushed is anonymized to obtain anonymized data to be pushed.

[0029] In some embodiments, the aforementioned executing entity may perform desensitization processing on the aforementioned content data to be pushed based on the aforementioned desensitization level information to obtain desensitized content data to be pushed.

[0030] In some optional implementations of certain embodiments, the aforementioned execution entity may perform de-identification processing on the content data to be pushed based on the aforementioned de-identification level information through the following steps to obtain de-identified content data to be pushed: The first step involves performing content type identification processing on the content data to be pushed, obtaining a content type identifier. This content type identifier can be one of three: a text identifier, an image identifier, or a video identifier. In practice, when the file header information of the content data to be pushed indicates "text / plain" or "text / html" or the file extension is ".txt" or ".html", the executing entity determines the content type identifier to be a text identifier. When the file header information of the content data to be pushed indicates "image / jpeg" or "image / png" or the file extension is ".jpg" or ".png", the executing entity determines the content type identifier to be an image identifier. When the file header information of the content data to be pushed indicates "video / mp4" or the file extension is ".mp4", the executing entity determines the content type identifier to be a video identifier. The text identifier refers to the content type identifier used to indicate that the content data to be pushed is text data. The text data can be plain text. The image identifier refers to the content type identifier used to indicate that the content data to be pushed is image data. The image data can include at least one image. The aforementioned video identifier refers to a content type identifier used to indicate that the content data to be pushed is video data. Video data refers to dynamic visual information data stored in the form of a video frame sequence and its corresponding audio data.

[0031] The second step involves de-identifying the content data to be pushed based on the aforementioned content type identifier and de-identification level information, resulting in de-identified content data to be pushed. In practice, the corresponding de-identification process can be matched from a preset multimodal de-identification strategy mapping table according to the content type of the content data to be pushed; simultaneously, the corresponding de-identification strength parameter is determined from the de-identification process based on the de-identification level information, and then the de-identification process is executed on the content data to be pushed according to the matched de-identification process and de-identification strength parameter.

[0032] In some optional implementations of certain embodiments, the aforementioned execution entity may perform desensitization processing on the content data to be pushed based on the aforementioned content type identifier and the aforementioned desensitization level information through the following steps to obtain desensitized content data to be pushed: The first step, in response to determining that the above content type identifier is a text identifier, is to perform the following text desensitization processing: The first sub-step involves performing text-sensitive field identification processing on the aforementioned content data to be pushed, obtaining the identification location information for each sensitive field. In practice, the aforementioned execution entity can use regular expressions to perform pattern matching on the text content in the content data to be pushed. When a string that matches the preset sensitive field format rules is matched, the field type of the string (e.g., ID card number, mobile phone number, etc.), as well as the starting and ending character positions in the text content, are recorded as sensitive field identification location information. The aforementioned preset sensitive field format rules refer to a predefined set of rules used to describe the format patterns (i.e., regular expressions) of various sensitive fields. This set of rules is stored in the execution entity's storage system in the form of a configuration file or database table, containing the regular expressions corresponding to ID card number, mobile phone number, bank card number, home address, and email address respectively. The field type is used to identify which type of ID card number, mobile phone number, bank card number, home address, or email address the sensitive field belongs to. The starting character position refers to the character index of the first character of the sensitive field in the text content. The ending character position refers to the character index of the last character of the sensitive field in the text content. Specifically, the aforementioned executing entity can scan and match the text content in the text data, read the rule set from the storage system, and sequentially use regular expressions corresponding to various sensitive fields to match the text content of the data to be pushed. This identifies personal information such as ID card numbers, mobile phone numbers, bank card numbers, home addresses, and email addresses, and records the field type (e.g., ID card number, mobile phone number), start character position, and end character position as sensitive field identification location information. For example, if the mobile phone number "138××××5678" is matched and located between the 16th and 26th characters of the text content, a sensitive field identification location information is generated, with the field type being "mobile phone number," the start character position being 16, and the end character position being 26.

[0033] The second sub-step involves identifying the location information for each of the aforementioned sensitive fields, and then performing the following steps: Sub-step one involves identifying the text content corresponding to the sensitive field identification location information in the aforementioned content data to be pushed as the text content to be anonymized. Here, the text content to be anonymized refers to the text segment between the start and end character positions indicated by the sensitive field identification location information in the content data to be pushed. In practice, based on the start and end character positions in the sensitive field identification location information, a substring between these positions can be extracted from the complete text content of the content data to be pushed, and this substring can be used as the text content to be anonymized.

[0034] Sub-step two: In response to determining that the anonymization level information includes a first anonymization level identifier, the text content to be anonymized in the aforementioned content data to be pushed is subjected to mask replacement processing to update the content data to be pushed. In practice, at least some characters in the text content to be anonymized can be replaced with preset mask characters. Preset mask characters refer to predefined characters used to replace some characters in sensitive fields, usually the "*" character. Specifically, the aforementioned execution entity can determine the mask rules for mask replacement processing based on the field type in the sensitive field identification location information. Then, the mask replacement processing is performed according to the matched mask rules to generate mask-replaced text content, and then the text content to be anonymized in the content data to be pushed is replaced with the mask-replaced text content to update the content data to be pushed. For example, the execution entity receives a text content to be anonymized "138××××5678", whose field type is mobile phone number, and the anonymization level information includes a first anonymization level identifier. The executing entity matches the corresponding mask rule based on the field type "phone number" as "keep the first 3 characters and the last 4 characters, and replace the middle 4 characters with the preset mask characters". The executing entity replaces the middle 4 characters "××××" in "138××××5678" with "*" to obtain the masked text content "138****5678". Then, it replaces "138××××5678" in the data to be pushed with "138****5678", thus completing the update of the data to be pushed.

[0035] In sub-step three, in response to the determination that the desensitization level information includes a second desensitization level identifier, the text content to be desensitized is sent to the hardware encryption accelerator card. The hardware encryption accelerator card is then controlled to perform format-preserving encryption processing and receive the encrypted text content returned by the hardware encryption accelerator card. The text content to be desensitized in the content data to be pushed is then replaced with the encrypted text content to update the content data to be pushed. The hardware encryption accelerator card refers to a hardware device specifically designed for performing cryptographic operations, connected to the execution entity via a high-speed serial computer extended bus standard (Peripheral Component Interconnect Express, or PCIe) bus. It integrates an SM4 national cryptographic algorithm encryption engine. The hardware encryption accelerator card performs cryptographic operations independently of the execution entity's central processing unit (CPU core). The execution entity sends the text content to be desensitized to the hardware encryption accelerator card via the PCIe bus. After the hardware encryption accelerator card performs the SM4 national cryptographic algorithm encryption operation internally, it returns the encryption result to the execution entity via the PCIe bus. During encryption operations performed by the hardware encryption accelerator card, the CPU core of the executing entity does not need to participate in cryptographic operations, thus freeing up CPU core computing resources for processing other tasks. In practice, the executing entity can determine the corresponding format preservation encryption parameters (character set type and field length) based on the field type of the text content to be de-identified (such as mobile phone number or ID card number), assemble the text content to be de-identified and the format preservation encryption parameters into an encryption request, and send it to the hardware encryption accelerator card via the PCIe bus. The hardware encryption accelerator card reads the encryption key from its internal key store, performs encryption on the text content to be de-identified using the SM4 national cryptographic algorithm, and maps the ciphertext to data with the same format as the original text according to the format preservation encryption parameters, generating encrypted text content, which is returned via the PCIe bus. The executing entity receives the encrypted text content and replaces the text content to be de-identified in the content data to be pushed with the encrypted text content. As an example, the text to be anonymized is the mobile phone number "138××××5678". The aforementioned execution entity can determine that its field type is mobile phone number, the character set type is numeric, and the field length is 11 characters. It sends "138××××5678" and the aforementioned parameters to the hardware encryption acceleration card. The hardware encryption acceleration card returns "139××××4321" after format preservation encryption. The aforementioned execution entity can then replace "138××××5678" with "139××××4321" in the data to be pushed.

[0036] Sub-step four: Determine the updated content data to be pushed as de-identified content data to be pushed.

[0037] The second step, in response to determining that the above content type identifier is an image identifier, is to perform the following image desensitization processing: The first sub-step involves desensitizing each image in the image data included in the aforementioned push content data, based on the aforementioned desensitization level information, to obtain a desensitized image. In practice, the executing entity can perform sensitive target detection processing on the aforementioned image to obtain at least one sensitive target location information. Then, in response to determining that at least one sensitive target location information is not empty and that the aforementioned desensitization level information includes a first desensitization level identifier, for each of the aforementioned at least one sensitive target location information, the following processing is performed: First, content type recognition processing is performed on the image of the region where the sensitive target location information is located in the aforementioned image to obtain a content type identifier. Then, in response to determining that the aforementioned content type identifier is a non-text content identifier, mosaic processing or blurring processing is performed on the image of the region where the aforementioned sensitive target location information is located to update the image. Afterwards, in response to determining that the aforementioned content type identifier is a text content identifier, text recognition processing is performed on the image of the region where the aforementioned sensitive target location information is located to obtain recognized text content information. Next, sensitive word recognition desensitization processing is performed on the aforementioned recognized text content information to obtain replaced text content information. Then, scene text editing processing is performed on the image region indicated by the aforementioned sensitive target location information to replace the original text content corresponding to the aforementioned sensitive target location information with the aforementioned replacement text content information, thereby updating the image and obtaining a desensitized image. In response to determining that at least one sensitive target location information is not empty and that the aforementioned desensitization level information includes a second desensitization level identifier, mosaic or blurring processing is performed on the image region indicated by at least one sensitive target location information in the image to update the image. Finally, the updated image is determined as the desensitized image.

[0038] The second sub-step involves identifying at least one de-identified image as the de-identified content data to be pushed.

[0039] The third step involves, in response to the determination that the aforementioned content type identifier is a video identifier, performing audiovisual desensitization processing on the aforementioned content data to be pushed, based on the aforementioned desensitization level information, to obtain desensitized content data to be pushed. The video data included in the aforementioned content data to be pushed comprises image data and audio data, and the aforementioned image data includes video frame sequences. Image data refers to the visual information portion of the video data. Audio data refers to the sound information portion of the video data, including but not limited to pulse code modulation format, advanced audio coding format, and MP3 format.

[0040] In the process of using this technical solution to solve the technical problems mentioned above, for the application scenario: the process of desensitizing video data containing sensitive targets such as faces, license plates, screen text, and document images in the desensitization process of government new media content, the following technical problems often arise: the image content of the sensitive target area in the video frame contains two types of content: non-text content (such as the face, license plate, and document images themselves) and text content (such as screen text and text information in the document). Existing technology cannot distinguish whether the area contains text content. When uniform mosaic or blurring processing is applied to the image of the sensitive target area, if the area contains text content, the mosaic or blurring processing will destroy all text information in the entire area, including non-sensitive text. Non-sensitive text in the video frame is mistakenly desensitized, causing the originally readable non-sensitive text (such as the body of policy announcements, notice titles, etc.) in the desensitized video frame to become unrecognizable. The following requirements are necessary for this application scenario: before government new media video data is pushed, it needs to be desensitized within a short period of time, and the desensitized video frames need to maintain the clear readability of non-sensitive areas (including text areas) to ensure that the public can normally view non-sensitive text information such as policy information and notices in the video, while sensitive information (such as faces, license plates, document images, ID numbers, mobile phone numbers, etc. in the text displayed on the screen) is effectively masked.

[0041] In some optional implementations of certain embodiments, the aforementioned executing entity may perform audiovisual desensitization processing on the aforementioned content data to be pushed based on the aforementioned desensitization level information through the following steps to obtain desensitized content data to be pushed: The first step is to perform the following processing on each video frame in the video frame sequence of the content data to be pushed: The first sub-step involves performing sensitive target detection processing on the aforementioned video frames to obtain at least one sensitive target location information. In practice, the executing entity can use a deep learning-based target detection model (such as the YOLO series, SSD model, or Faster R-CNN model) to perform sensitive target detection processing on the video frame images and output at least one sensitive target location information. Here, the aforementioned sensitive target refers to a target object in the image that may contain sensitive personal information, including but not limited to faces, license plates, screen-displayed text, and ID document images. The aforementioned sensitive target location information refers to information used to indicate the position of the sensitive target in the video frame image, including the top-left corner pixel coordinates, width, and height of the sensitive target's bounding box. The top-left corner pixel coordinates, in pixels, represent the starting position of the sensitive target's bounding box in the image (horizontal x-coordinate and vertical y-coordinate), and the width and height, in pixels, represent the dimensions of the sensitive target's bounding box in the horizontal and vertical directions. The executing entity processes the video frame image using the target detection model and outputs at least one sensitive target location information. For example, when an execution entity performs sensitive target detection processing on a video frame and detects a face, the sensitive target location information is generated as follows: top-left corner pixel coordinates (120, 80), width 64 pixels, height 64 pixels. If a license plate is detected, the sensitive target location information is generated as follows: top-left corner pixel coordinates (320, 200), width 120 pixels, height 40 pixels. As an example, at least one sample video frame image and the sample sensitive target location information corresponding to each sample video frame image (i.e., the labeled location information of the sensitive target (such as a face, license plate, screen text, or document image) in that sample video frame image) can be obtained first. Then, each sample video frame image is used as input, and the sample sensitive target location information corresponding to each sample video frame image is used as the expected output to train the target detection model.

[0042] The second sub-step, in response to determining that at least one sensitive target location information is not empty and that the aforementioned desensitization level information includes a first desensitization level identifier, performs the following processing for each of the at least one sensitive target location information: Sub-step one involves performing content type recognition processing on the image of the area containing the sensitive target location information in the aforementioned video frame to obtain a content type identifier. The image of the area containing the sensitive target location information in the aforementioned video frame refers to a sub-image cropped from the video frame based on the sensitive target location information (top-left pixel coordinates, width, and height), containing only the area where the sensitive target is located. In practice, the aforementioned execution entity can process the image of the area containing the sensitive target location information using an optical character detection model to detect whether there is identifiable text information in the image. If text information is detected, the content type identifier is determined to be a text content identifier; if no text information is detected, the content type identifier is determined to be a non-text content identifier. Here, the aforementioned content type identifier refers to an identifier used to indicate whether the image of the area containing the sensitive target location information contains text content or non-text content. A text content identifier is a content type identifier used to indicate that the image contains identifiable text information. A non-text content identifier is a content type identifier used to indicate that the image does not contain identifiable text information.

[0043] Sub-step two: In response to determining that the above content type identifier is a non-text content identifier, perform mosaic or blur processing on the image of the area where the above sensitive target location information is located to update the video frame, and replace the updated video frame with the above video frame in the above video frame sequence to update the video frame sequence.

[0044] In sub-step three, in response to determining that the above content type identifier is a text content identifier, text recognition processing is performed on the image of the area where the above sensitive target location information is located to obtain the identified text content information. In practice, the executing entity can use deep learning-based optical character recognition technology (such as CRNN model, Attention OCR model, or TrOCR model) to process the image of the area where the sensitive target location information is located, identify the text content contained in the image, and convert it into text information as the identified text content information.

[0045] Sub-step four involves performing sensitive word identification and desensitization processing on the identified text content information to obtain replacement text content information. Sensitive word identification is performed on the text within the identified text content information to identify the sensitive words it contains. These sensitive words are then desensitized (including but not limited to mask replacement processing) to generate replacement text content information. Sensitive words refer to words or strings containing sensitive personal information, including but not limited to ID card numbers, mobile phone numbers, and bank card numbers. In practice, the executing entity uses regular expressions to perform pattern matching on the identified text content information, identifies strings that conform to the sensitive word format, and performs mask replacement processing on the identified sensitive words according to their field type to generate replacement text content information.

[0046] Sub-step five involves performing scene text editing on the image area indicated by the aforementioned sensitive target location information. This replaces the original text content corresponding to the sensitive target location information with the replacement text content, thereby updating the video frame. In practice, the executing entity can employ scene text editing technology to replace the original text content in the image area indicated by the aforementioned sensitive target location information with new text content, i.e., replacement text content information. Simultaneously, the replaced image area maintains a natural integration with the original image area in terms of font, font size, color, tilt angle, and background texture, thereby updating the video frame.

[0047] The third sub-step involves, in response to determining that at least one sensitive target location information is not empty and that the aforementioned desensitization level information includes a second desensitization level identifier, performing mosaic or blurring processing on the image region indicated by at least one sensitive target location information in the aforementioned video frame to update the video frame, and replacing the updated video frame with the aforementioned video frame in the aforementioned video frame sequence to update the video frame sequence.

[0048] The second step is to identify the updated video frame sequence as desensitized image data; The third step is to de-identify the audio data in the above-mentioned content data to be pushed, and obtain de-identified audio data.

[0049] The fourth step involves identifying the aforementioned desensitized image data and audio data as the desensitized content to be pushed. In practice, the executing entity can identify the human voice portion of the audio data, convert the voice into text, identify sensitive information within the text, and perform mute processing or replace the audio segments corresponding to sensitive information (e.g., ID card numbers, mobile phone numbers, etc.) with preset prompt sounds to generate desensitized audio data. The aforementioned mute processing refers to setting the sound amplitude of the audio segment to zero, so that the segment does not produce any sound. The aforementioned preset prompt sounds refer to short audio signals, including but not limited to "beep" or "drop" sounds, pre-stored in the executing entity for replacing sensitive audio segments.

[0050] Step 105: Generate information entropy difference information based on the content data to be pushed and the anonymized content data to be pushed.

[0051] In some embodiments, the executing entity can generate information entropy difference information based on the content data to be pushed and the anonymized content data to be pushed. The information entropy difference information refers to the difference between the information entropy of the content data to be pushed and the information entropy of the anonymized content data to be pushed. The information entropy is calculated using the Shannon entropy formula. The information entropy difference information is used to quantify the degree of damage to the content information caused by the anonymization process. When the information entropy difference information is large, it indicates that the anonymization process has led to a significant decrease in the content information and a large loss in content quality; when the information entropy difference information is small, it indicates that the anonymization process has a smaller impact on the content information and the content quality is well maintained. Specifically, for text data, the executing entity first performs word segmentation on the text content, dividing the text into independent words or character units. Then, it counts the number of times each unit appears in the text and calculates its frequency. The frequency of each unit is used as the probability value of that unit, and the summation is performed using the Shannon information entropy calculation formula to obtain the information entropy of the text data. For image data, the aforementioned execution entity first converts the image to a grayscale image, counts the frequency of each grayscale level (0 to 255, a total of 256 grayscale levels) in the image, and calculates its frequency. The frequency of each grayscale level is used as its probability value, which is then substituted into the Shannon information entropy calculation formula for summation to obtain the information entropy of the image data. More precisely, the execution entity can also perform two-dimensional entropy calculation on the image based on the grayscale co-occurrence matrix to simultaneously reflect the grayscale distribution information and the spatial relationship information between pixels. For video data, the execution entity decomposes the video into multiple video frames, calculates the information entropy of each frame according to the calculation method for image data, and then sums or averages the information entropies of all frames to obtain the information entropy of the video data. The execution entity performs the above calculations on the content data to be pushed and the anonymized content data to be pushed, respectively, to obtain the information entropy of the content data to be pushed and the information entropy of the anonymized content data to be pushed. Then, the difference between the two is calculated to generate information entropy difference information. It should be noted that when the information entropy difference is small, it means that there is less sensitive information in the original content, the desensitization process has only been slightly adjusted, the content security risk is low, and it can be prioritized for push to multiple platforms.

[0052] The above-mentioned technical solution and related content, as an inventive point of this disclosure, solve the technical problem of "non-sensitive text in video frames being mistakenly desensitized, causing the originally readable non-sensitive text (such as the body of policy announcements, notice titles, etc.) in the desensitized video frames to become unrecognizable." The factors that cause non-sensitive text in video frames to be mistakenly desensitized, resulting in the originally readable non-sensitive text (such as the body of policy announcements, notice titles, etc.) in the desensitized video frames becoming unrecognizable, are often as follows: The image content of the sensitive target area in the video frame contains two types of content: non-text content (such as faces, license plates, and the images of documents themselves) and text content (such as text displayed on the screen, text information in documents). Existing technologies cannot distinguish whether the area contains text content. When a uniform mosaic or blurring process is applied to the image of the sensitive target area, if the area contains text content, the mosaic or blurring process will destroy all text information in the entire area, including non-sensitive text. Therefore, non-sensitive text in the video frame is mistakenly desensitized, causing the originally readable non-sensitive text (such as the body of policy announcements, notice titles, etc.) in the desensitized video frames to become unrecognizable. If the above factors are addressed, it is possible to maintain the readability of non-sensitive text while masking sensitive information. To achieve this, firstly, sensitive target detection processing is performed on each video frame in the video frame sequence to obtain at least one sensitive target location information. This allows us to obtain the location information of the area where the sensitive target is located in the video frame. Then, in response to determining that at least one sensitive target location information is not empty and that the desensitization level information includes a first desensitization level identifier, for each of the at least one sensitive target location information, content type recognition processing is performed on the image of the area where the sensitive target location information is located in the video frame to obtain a content type identifier. This allows us to obtain the content type identifier of the image of the area where the sensitive target location information is located. Next, in response to determining that the content type identifier is a non-text content identifier, mosaic or blurring processing is performed on the image of the area where the sensitive target location information is located to update the video frame, and the updated video frame is replaced with a video frame in the video frame sequence to update the video frame sequence. Thus, mosaic or blurring processing can be performed on non-text content (such as faces, license plates, and document images themselves) to mask non-text sensitive targets. In response to the determination that the content type identifier is a text content identifier, text recognition processing is performed on the image of the area where the sensitive target location information is located to obtain the identified text content information. Thus, the text content information contained in the area where the sensitive target location information is located can be obtained. Then, sensitive word identification and desensitization processing is performed on the identified text content information to obtain the replacement text content information. Thus, the replacement text content after desensitizing the sensitive words in the identified text content information can be obtained.Next, scene text editing processing is performed on the image area indicated by the sensitive target location information to replace the original text content corresponding to the sensitive target location information with replacement text content information, thereby updating the video frame. This allows for precise replacement of sensitive words in the original text content of the image area with desensitized text, while keeping non-sensitive text unchanged. In response to determining that at least one sensitive target location information is not empty and the desensitization level information includes a second desensitization level identifier, mosaic or blurring processing is performed on the image area indicated by at least one sensitive target location information in the video frame to update the video frame, and the updated video frame is replaced with a video frame in the video frame sequence to update the video frame sequence. This allows for uniform mosaic or blurring processing of the sensitive target area when the desensitization level is the second desensitization level. Finally, the updated video frame sequence is determined as desensitized image data, and the audio data in the content to be pushed is desensitized to obtain desensitized audio data. The desensitized image data and desensitized audio data are determined as desensitized content to be pushed. This yields desensitized content to be pushed, where both the image and audio have been desensitized. Because it employs content type recognition processing, it distinguishes whether the image in the area containing sensitive targets is text content or non-text content, and adaptively selects the desensitization method according to the content type identifier. That is, non-text content is processed by mosaic or blurring, while text content is processed by text recognition, sensitive word recognition desensitization, and scene text editing to achieve precise targeted desensitization. Therefore, it can maintain the clear readability of non-sensitive text while masking sensitive information.

[0053] Step 106: Based on the health score and information entropy difference information, generate push priority information and push scope information.

[0054] In some embodiments, the aforementioned executing entity can generate push priority information and push scope information based on the aforementioned health score and information entropy difference information. The push priority information refers to information indicating the priority level that the anonymized content data to be pushed should be assigned in the subsequent network interface card (NIC) transmission queue. This information is represented by a push priority score; a higher push priority score indicates that the content should be sent first, and a lower push priority score indicates that the content should be sent later. The push priority score is a dimensionless numerical value. The push scope information refers to information indicating which target platforms the anonymized content data to be pushed to.

[0055] In some optional implementations of certain embodiments, the aforementioned executing entity may generate push priority information and push range information based on the aforementioned health score and the aforementioned information entropy difference information through the following steps: The first step is to generate initial push priority information based on the aforementioned health score. The initial push priority score is a dimensionless numerical value ranging from 0 to 100, directly proportional to the health score: the higher the health score, the higher the initial push priority score; the lower the health score, the lower the initial push priority score. In practice, the initial push priority score is mapped directly to the health score; that is, the initial push priority score equals the health score value. For example, a health score of 72 results in an initial push priority score of 72; a health score of 92 results in an initial push priority score of 92; and a health score of 62 results in an initial push priority score of 62.

[0056] The second step involves generating a content quality attenuation coefficient based on the aforementioned information entropy difference. This coefficient characterizes the proportion of reduction in push priority caused by the degree of damage to content information caused by the desensitization process. The content quality attenuation coefficient is a dimensionless value ranging from 0 to 1. It is directly proportional to the information entropy difference: a larger information entropy difference indicates more severe damage to content information caused by the desensitization process, resulting in a larger coefficient; conversely, a smaller information entropy difference indicates less damage and a smaller coefficient. In practice, the executing entity can divide the information entropy difference value by the original information entropy value (the information entropy of the content data to be pushed (i.e., the content data before desensitization)) to obtain the content quality attenuation coefficient. A larger coefficient indicates more severe content quality loss; a smaller coefficient indicates less severe loss. For example, if the information entropy difference is 2.6 bits and the original information entropy is 7.8 bits, then the content quality attenuation coefficient = 2.6 / 7.8.

[0057] The third step involves generating push priority information based on the initial push priority information and the content quality attenuation coefficient. In practice, the value represented by the initial push priority information can be multiplied by (1 minus the content quality attenuation coefficient) to obtain a push priority score. Then, in response to determining that the push priority score is less than or equal to a first preset priority score (e.g., 40), a third preset priority identifier (e.g., level three) is determined as the push priority information. In response to determining that the push priority score is greater than the first preset priority score and less than or equal to a second preset priority score (e.g., 80), a second preset priority identifier (e.g., level two) is determined as the push priority information. In response to determining that the push priority score is greater than the second preset priority score, a first preset priority identifier (e.g., level one) is determined as the push priority information. The priority level of the first preset priority identifier (e.g., level one) is greater than the priority level of the second preset priority identifier (e.g., level two), and the priority level of the second preset priority identifier (e.g., level two) is greater than the priority level of the third preset priority identifier (e.g., level three).

[0058] The fourth step involves generating push scope information based on the aforementioned health score. This push scope information includes one of two options: full-platform push scope information and partial-platform push scope information. The full-platform push scope information indicates which target platforms the anonymized content data can be pushed to, and includes the platform identifier of at least one target platform. The full-platform push scope information includes the platform identifiers of all preset target platforms (i.e., multiple preset target platforms). The partial-platform push scope information includes the platform identifiers of one or more specific target platforms among all preset target platforms. The number of these one or more specific target platforms is less than the total number of preset target platforms. The partial-platform push scope information indicates that the anonymized content data is only allowed to be pushed to these one or more specific target platforms, and not to other preset target platforms. In practice, in response to determining that the health score is less than or equal to a preset health score threshold, the executing entity can determine the preset partial-platform push scope information as the push scope information. The one or more specific target platforms corresponding to the partial-platform push scope information are the one or more target platforms with the highest review stringency. In response to a determination that the health score is greater than a preset health score threshold (e.g., 70), the aforementioned executing entity can determine the preset platform-wide push scope information as the push scope information. Here, the preset health score threshold refers to a pre-set critical value used to distinguish the range of health scores belonging to a target account in order to determine the push scope information. The preset health score threshold can be configured and adjusted according to actual business scenarios.

[0059] Step 107: Based on push priority information and push scope information, the de-identified content data to be pushed is allocated to at least one sending queue corresponding to at least one target platform, and the data is scheduled and sent according to the priority order of the sending queues through the network card hardware scheduler of the publishing server.

[0060] In some embodiments, the execution entity can allocate the anonymized content data to at least one sending queue corresponding to at least one target platform based on the push priority information and the push range information, and schedule the data to be sent according to the priority order of the sending queues through the network card hardware scheduler of the publishing server. The push priority information includes a priority identifier. The sending queue refers to a ring buffer allocated in the network card driver for each target platform's network interface, used to buffer data packets to be sent. Each sending queue has a corresponding priority identifier, which indicates the scheduling priority level of the sending queue in the network card hardware scheduler. Each target platform corresponds to at least one sending queue, and each sending queue has a corresponding priority identifier. The network card hardware scheduler refers to the hardware logic circuit integrated in the network card chip, used to manage the scheduling order of multiple sending queues. The network card hardware scheduler operates independently of the CPU core, and according to the priority identifier of each sending queue, it retrieves data packets from each sending queue in descending order of priority level and sends them out through the physical network interface.

[0061] In the process of adopting this technical solution to solve the technical problems mentioned above, for the application scenario: high-concurrency differentiated content push of government new media multi-target platforms (*WeChat, Douyin, Toutiao, etc.), it is necessary to prioritize pushing government content from high-quality and active accounts to multiple target platforms. This often leads to the following technical problems: when multiple platforms push de-identified government content in parallel, data of different priorities enter the network card sending queue mixed together. Traditional software-level scheduling mechanisms cannot accurately match the priority identifier of the network card hardware queue, nor can they dynamically adjust the data storage strategy based on real-time network status and content data volume. This can easily result in high-priority government content being blocked by low-priority content and push delays. The following requirements are necessary for this application scenario: the priority identifier in the push priority information must accurately match the priority identifier of the target platform's hardware sending queue, allocating content of different priorities to the corresponding hardware sending queue; simultaneously, the push process must be aware of real-time network status and data volume, adaptively deciding whether to compress the data before queuing and storing it; finally, the network card hardware scheduler traverses each sending queue from high to low priority, ensuring that high-priority government affairs content receives priority sending at the network card hardware level, guaranteeing that urgent government affairs content from high-quality, active accounts is not blocked by low-priority content, and ensuring the timeliness of pushes.

[0062] In some optional implementations of certain embodiments, the aforementioned execution entity may, based on the aforementioned push priority information and push range information, allocate the de-identified content data to at least one sending queue corresponding to at least one target platform, and schedule its transmission according to the priority order of the sending queues through the network card hardware scheduler of the aforementioned publishing server: The first step involves determining at least one target platform to be sent to from a plurality of preset target platforms, based on the aforementioned push scope information. Each target platform is associated with at least one sending queue, and each sending queue has a corresponding priority identifier. The plurality of preset target platforms refer to all target platforms pre-configured in the system configuration as the recipients of government new media content, including but not limited to WeChat, Weibo, Douyin, and Toutiao. The aforementioned priority identifier can be one of the following: a third preset priority identifier (e.g., level three), a second preset priority identifier (e.g., level two), or a third preset priority identifier (e.g., level three).

[0063] The second step, for each of the at least one target platform mentioned above, is to perform the following steps: The first sub-step involves determining the priority identifier corresponding to at least one transmission queue of the target platform as at least one priority identifier to be compared. Here, the at least one transmission queue corresponding to the target platform refers to multiple transmission queues allocated in the network interface card driver for the network interface of the target platform.

[0064] The second sub-step involves determining the priority identifier included in the aforementioned push priority information as the comparison priority identifier.

[0065] The third sub-step is to determine the target priority identifier as the one that is the same as the above-mentioned comparison priority identifier among the at least one comparison priority identifiers.

[0066] The fourth sub-step involves identifying the sending queue corresponding to the target priority identifier from at least one of the aforementioned sending queues as the sending queue to be written.

[0067] The fifth sub-step involves obtaining network status information for the communication link with the target platform. This network status information can be either network bandwidth utilization or network latency. Network bandwidth utilization refers to the percentage of total bandwidth currently used on the network link. Network latency refers to the round-trip time required for a data packet to travel from the executing entity to the target platform and receive acknowledgment. Packet loss rate refers to the percentage of data packets lost per unit time out of the total number of data packets sent.

[0068] The sixth sub-step involves determining the data volume of the anonymized content to be pushed. This data volume refers to the number of bytes occupied by the anonymized content in the storage medium.

[0069] The seventh sub-step involves adaptively storing the anonymized content data to be pushed into the write-to-send queue based on the aforementioned comparison priority identifier, network status information, and data volume. In practice, the executing entity can dynamically decide whether to compress the anonymized content data before storing it in the write-to-send queue based on the comparison priority identifier, network status information, and data volume.

[0070] The eighth sub-step involves using the network interface card (NIC) hardware scheduler to traverse the transmission queues corresponding to the at least one priority level represented by the at least one priority identifier to be compared, from highest to lowest. In practice, the executing entity can control the NIC hardware scheduler to traverse each transmission queue in descending order of the priority level represented by the priority identifier of each transmission queue. The NIC hardware scheduler first traverses the transmission queue with the highest priority level, then traverses the transmission queue with the next highest priority level, and so on, until all non-empty transmission queues have been traversed (e.g., first traverse the transmission queues corresponding to the first priority level, then traverse the transmission queues corresponding to the second priority level, and so on, until all non-empty transmission queues have been traversed (e.g., first traverse the transmission queues corresponding to the first priority level, then traverse the transmission queues corresponding to the second priority level, then traverse the transmission queues corresponding to the second priority level again, and finally traverse the transmission queues corresponding to the third priority level).

[0071] The ninth sub-step, during the traversal of each send queue, executes the following scheduling send steps: Sub-step one: Determine the current queue depth as the current sending queue depth. Here, the queue depth refers to the number of anonymized content data items waiting to be sent in the current sending queue.

[0072] Sub-step two: In response to determining that the current queue depth is not zero, the anonymized content data to be pushed is sequentially retrieved from the currently traversed sending queue and sent to the target platform via the target platform's network interface. In practice, the executing entity can control the network card hardware scheduler to sequentially retrieve data packets (i.e., the anonymized content data to be pushed) from the head of the sending queue and send them to the target platform via the target platform's network interface. When the current queue depth is zero, it indicates that there are no data packets (i.e., the anonymized content data to be pushed) to be sent in the sending queue. The network card hardware scheduler skips this queue and continues traversing the next priority level of the sending queue.

[0073] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem of "high-priority government content being blocked and delayed by low-priority content." Factors leading to the blocking and delay of high-priority government content by low-priority content often include: when multiple platforms push de-identified government content in parallel, data of different priorities enter the network card's sending queue in a mixed manner. Traditional software-level scheduling mechanisms cannot accurately match the priority identifiers of the network card's hardware queues, nor can they dynamically adjust data storage strategies based on real-time network status and content data volume, easily resulting in high-priority government content being blocked and delayed by low-priority content. Solving these factors can achieve the effect of prioritizing the push of high-priority government content and reducing push delays. To achieve this effect, firstly, based on the aforementioned push range information, at least one target platform to be sent is determined from multiple preset target platforms. Each target platform is associated with at least one sending queue, and each sending queue has a corresponding priority identifier. Thus, the platform range indicated by the push range information can be concretized into at least one target platform to be sent, achieving targeted distribution at the platform level. Then, for each of the at least one target platform mentioned above, the following steps are performed: First, the priority identifier corresponding to at least one sending queue of the target platform is determined as at least one priority identifier to be compared. Second, the priority identifier included in the push priority information is determined as the comparison priority identifier. Third, the comparison priority identifier that is the same as the comparison priority identifier among the at least one comparison priority identifier is determined as the target priority identifier. This allows for precise location of the hardware queue identifier that perfectly matches the current content priority. Fourth, the sending queue corresponding to the target priority identifier among the at least one sending queue is determined as the writing sending queue. This allows government content of the corresponding priority to directly enter the dedicated hardware sending queue, completely avoiding the mixing of data of different priorities. Fifth, the network status information of the communication link with the target platform is obtained. This allows for obtaining the network status information of the current link. Sixth, the data volume of the anonymized content to be pushed is determined. This allows for determining the size of the content to be pushed, i.e., the data volume. Seventh, based on the comparison priority identifier, the network status information, and the data volume, the anonymized content to be pushed is adaptively stored in the writing sending queue. Therefore, data storage strategies can be dynamically adjusted based on priority, real-time network status, and content data volume, ensuring that the data writing rhythm to the queue perfectly matches the current link conditions, thus avoiding queue overflow and unnecessary congestion. The eighth step involves using the network interface card hardware scheduler to traverse the transmission queues corresponding to the at least one priority level represented by the at least one priority identifier to be compared, from highest to lowest.Therefore, priority scheduling can be implemented directly at the network card hardware level, completely bypassing the performance bottleneck of traditional software scheduling and ensuring that high-priority queues are processed first. In the ninth step, during the traversal of each sending queue, the following scheduling and sending steps are executed: the queue depth of the currently traversed sending queue is determined as the current queue depth. In response to the determination that the current queue depth is not zero, the anonymized content data to be pushed is sequentially retrieved from the currently traversed sending queue and sent to the target platform through the network interface of the target platform. Thus, the network card hardware scheduler only retrieves packets from non-empty queues for sending, skipping empty queues, improving the traversal efficiency of the hardware scheduler and avoiding the scheduling overhead caused by invalid polling. Because of the precise matching between push priority identifiers and network card hardware transmission queue priority identifiers, high-priority content is allocated to the corresponding priority hardware transmission queue before being enqueued, avoiding equal competition with low-priority content entering the same queue. Furthermore, the use of adaptive compression storage based on real-time network status and data volume before enqueuing ensures that data packets are proactively compressed before enqueuing in cases of network congestion or large data volumes, reducing network bandwidth consumption and queue congestion risks. Finally, the network card hardware scheduler traverses each transmission queue sequentially from highest to lowest priority, ensuring that data packets in high-priority queues are preferentially retrieved at the network card hardware level and sent through the physical network interface. This guarantees priority transmission of high-priority government content at the network card hardware level, preventing low-priority content from blocking high-priority content and reducing the push latency of high-priority government content.

[0074] In some optional implementations of certain embodiments, the execution entity may adaptively store the anonymized content data to be pushed into the write-to-send queue based on the comparison priority identifier, the network status information, and the data volume through the following steps: First, in response to determining that the priority level represented by the above comparison priority identifier is equal to the first preset priority level, the following adaptive storage process is performed: The first sub-step involves, in response to determining that the data volume is less than or equal to a first preset data volume and that the network status information meets a preset network idle condition, storing the anonymized content data to be pushed without compression into the write-to-send queue. The first preset priority level can refer to a pre-set threshold value used to distinguish whether something belongs to the highest priority level; for example, the first preset priority level can be the priority level represented by a first preset priority identifier (e.g., level one). The preset network idle condition can be, but is not limited to, one of the following: network bandwidth utilization is lower than a preset bandwidth utilization threshold, network latency is lower than a preset latency threshold, and packet loss rate is lower than a preset packet loss rate threshold.

[0075] The second sub-step involves, in response to determining that the data volume exceeds the first preset data volume or that the network status information does not meet the preset network idle condition, compressing the anonymized content data to be pushed according to the first preset compression rate to obtain compressed anonymized content data to be pushed, and storing the compressed anonymized content data to be pushed in the write-to-send queue. The first preset compression rate refers to a pre-set compression rate used for the highest priority content. The compression algorithm corresponding to the first preset compression rate is the LZO compression algorithm. In practice, the executing entity can call the LZO compression algorithm, compress the anonymized content data to be pushed according to the first preset compression rate to obtain compressed anonymized content data to be pushed, and then store the compressed anonymized content data to be pushed in the write-to-send queue.

[0076] The second step involves, in response to the determination that the priority level indicated by the aforementioned comparison priority identifier is lower than the first preset priority level, compressing the anonymized content data to be pushed according to the second preset compression rate to obtain compressed anonymized content data to be pushed, and storing the compressed anonymized content data to be pushed in the aforementioned write-to-send queue. In practice, when the priority level indicated by the comparison priority identifier is lower than the first preset priority level, it indicates that the anonymized content data to be pushed has a lower priority level. The aforementioned second preset compression rate refers to a pre-set compression rate for low-priority content. A second preset compression rate greater than the first preset compression rate means that low-priority content uses a higher compression rate. The compression algorithm corresponding to the second preset compression rate is the zlib compression algorithm, which is characterized by a high compression rate. The executing entity can call the zlib compression algorithm to perform compression processing on the anonymized content data to be pushed, obtain compressed anonymized content data to be pushed, and then store the compressed anonymized content data to be pushed in the write-to-send queue.

[0077] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem that "when pushing de-identified content data to a target platform, the real-time network status and data volume are not perceived before the data packets are enqueued; when the network fluctuates, a large amount of content is sent at once, causing network card bandwidth contention and queue congestion, resulting in increased push latency and data packets being dropped at the physical layer." The factors leading to increased push latency and data packet drop at the physical layer are often as follows: the real-time network status and data volume are not perceived before the data packets are enqueued; when the network is congested, a large number of data packets still enter the sending queue at their original size, causing the queue depth to increase rapidly. When the data packet exceeds the network card hardware buffer capacity, the data packet is dropped at the physical layer, triggering upper-layer protocol retransmission, further exacerbating congestion and latency. Solving these factors can alleviate queue congestion, reduce data packet drop at the physical layer, and improve bandwidth utilization. To achieve this effect, firstly, in response to determining that the priority level represented by the comparison priority identifier is equal to the first preset priority level, the following adaptive storage processing is performed: First sub-step: In response to determining that the data volume is less than or equal to the first preset data volume and the network status information meets the preset network idle condition, the anonymized content data to be pushed is stored uncompressed in the write-to-send queue. Thus, when the content has the highest priority and the network is idle, the data packet is directly enqueued without compression, avoiding the CPU overhead and latency caused by compression processing, ensuring that the highest priority content is enqueued and sent with the lowest possible latency. Second sub-step: In response to determining that the data volume is greater than the first preset data volume or the network status information does not meet the preset network idle condition, the anonymized content data to be pushed is compressed according to the first preset compression ratio to obtain compressed anonymized content data to be pushed, and the compressed anonymized content data to be pushed is stored in the write-to-send queue. The compression algorithm corresponding to the first preset compression ratio is the LZO compression algorithm. Therefore, when the data volume of the highest priority content is large or the network is congested, lightweight compression is performed. This reduces the network bandwidth consumption of data packets while maintaining low CPU consumption and fast compression speed, allowing the highest priority content to still be enqueued and sent quickly. Then, in response to the determination that the priority level represented by the priority identifier is lower than the first preset priority level, compression processing is performed on the anonymized content data to be pushed according to the second preset compression rate. The compressed anonymized content data to be pushed is then stored in the queue to be written and sent. The second preset compression rate is greater than the first preset compression rate, and the corresponding compression algorithm is the zlib compression algorithm. Thus, for low-priority content, a higher compression rate is used to perform compression processing, significantly reducing the data packet size, thereby occupying less queue space during enqueuing and less bandwidth resources during network transmission.Because it employs adaptive compression decisions based on three factors—priority identifiers, network status information, and data volume—before data packets are enqueued, high-priority content is enqueued directly without compression when the network is idle to reduce latency. High-priority content undergoes lightweight compression when the network is congested or the data volume is large to balance latency and bandwidth usage. Low-priority content undergoes high-compression compression to maximize bandwidth savings. This achieves dynamic matching between compression strategies, content priorities, and real-time network status. This alleviates network interface card (NIC) queue congestion caused by the concentrated transmission of large amounts of content during network fluctuations and reduces the probability of data packets being dropped at the physical layer.

[0078] Optionally, the aforementioned implementing entity may also perform the following steps: The first sub-step involves, in response to determining that the aforementioned health score is less than a preset health threshold but greater than a preset blocking threshold, sending the account identifier and the content data to be pushed to a preset review terminal, and suspending the de-identification process corresponding to the target account to pause the de-identification processing and transmission of the content data to be pushed. The preset blocking threshold is a pre-set critical value used to distinguish whether a target account belongs to a health score requiring complete blocking; its value ranges from 0 to 100, and the preset blocking threshold is less than the preset health threshold. In practice, the preset blocking threshold can be configured and adjusted according to the actual business scenario. For example, in one application scenario, the preset blocking threshold can be set to 40. The preset review terminal refers to a pre-configured terminal device used by reviewers for manual review of the content data to be pushed, including but not limited to personal computers, laptops, or tablets. The preset review terminal is connected to the executing entity via a network; the executing entity can send data to the preset review terminal via the network, and reviewers can view the content to be reviewed and make review decisions through the preset review terminal. The de-identification process refers to the computational process within the executing entity used to perform the de-identification processing. When a process is suspended, the operating system removes it from the CPU's run queue. The process no longer participates in CPU time slice allocation, meaning it no longer occupies CPU computing resources. The suspended process's memory context (including the program counter, register contents, memory page tables, etc.) is preserved. When the resumption conditions are met (such as receiving a pass instruction from the auditing terminal), the operating system can restore the process to a ready state, allowing it to re-enter the run queue and participate in CPU scheduling. Suspension is a standard operation in operating system process scheduling. Its physical effect is to prevent the process from consuming CPU time slices while preserving its complete context for subsequent resumption. Specifically, the executing entity can assemble the account identifier and the content data to be pushed into an audit task data packet, which is then sent over the network to a preset auditing terminal for manual review by auditors. Simultaneously, the aforementioned execution entity can use the process control interface provided by the operating system (such as the signal mechanism or process state setting function in Linux) to switch the status of the de-identification process corresponding to the target account from the running or ready state to the blocked state (i.e., suspended). This removes the de-identification process from the CPU's run queue, preventing it from occupying CPU time slices, thereby pausing the de-identification processing and sending of the content data to be pushed. During the suspension period, the de-identification process does not consume CPU computing resources, but the process's memory context is retained in memory so that execution can resume after the review is completed.

[0079] The second sub-step, in response to determining that the above health score is less than the above preset health threshold and less than the above preset blocking threshold, marks the memory page storing the content data to be pushed in memory as recyclable, and removes the de-identification process corresponding to the above target account from the running queue of the currently bound CPU core, so as to block the de-identification processing and sending of the above content data to be pushed.

[0080] The above-described embodiments of this disclosure have the following beneficial effects: The content security processing and differentiated push method for government new media, as described in some embodiments of this disclosure, improves the speed of content security processing and push for high-quality, active government accounts and alleviates systemic congestion in the network card sending queue. Specifically, the reasons for the slow speed of content security processing and push for high-quality, active government accounts and the potential for systemic congestion in the network card sending queue are as follows: All accounts uniformly perform sensitive information detection and desensitization with the same intensity, without any form of processing, blocking, or push interception of content pushed by accounts with extremely low health. This results in a large number of CPU cores' computing resources being continuously occupied by the desensitization tasks of zombie accounts and dormant accounts. The desensitization process of accounts with extremely low health long-term occupies CPU time slices. Furthermore, the desensitization intensity is not graded according to the health status of the source account. Content from high-quality, active accounts is prone to having its processing speed slowed down due to excessive use of hardware encryption acceleration cards, and it competes equally with content with extremely low health in the acceleration card request queue, further slowing down the processing speed and resulting in a slow speed of content security processing and push for high-quality, active government accounts. Meanwhile, indiscriminately pushing the same content to all platforms can easily cause systemic congestion in the network card's transmission queue. Based on this, some embodiments of the present disclosure of a content security processing and differentiated push method for government new media firstly, in response to determining that the CPU core load rate exceeds a preset threshold, after receiving a new media content push request from a government operation terminal device containing an account identifier and the content to be pushed, the method retrieves account status information corresponding to the aforementioned account identifier from a pre-created account management database. This allows matching the account status information corresponding to the content to be pushed with the account status information used to generate the health score of the target account. Then, based on the aforementioned account status information, a health score representing the target account as indicated by the aforementioned account identifier is generated. Thus, the health score can be quantified based on the account status information to distinguish between high-quality active accounts and low-value inactive / dormant accounts. Next, in response to determining that the aforementioned health score is greater than or equal to a preset health threshold, an anonymization level information for the aforementioned content to be pushed is generated based on the health score. Then, based on the aforementioned anonymization level information, the aforementioned content to be pushed is anonymized to obtain anonymized content to be pushed data. Therefore, the anonymization strength (i.e., anonymization level) can be matched to the account status, and tiered anonymization processing can be implemented based on the anonymization level. This abandons the traditional uniform high-intensity anonymization model, avoids excessive use of hardware encryption acceleration cards for high-quality account content, and avoids the problem of slow processing speed for high-quality content caused by excessive anonymization. Next, based on the aforementioned content data to be pushed and the aforementioned anonymized content data to be pushed, information entropy difference information is generated. Then, based on the aforementioned health score and the aforementioned information entropy difference information, push priority information and push scope information are generated.Therefore, based on health scores and information entropy difference information, the priority of push notifications can be hierarchically classified, and the scope of target platforms for distribution can be limited, resulting in push priority information and push scope information. Then, based on the aforementioned push priority information and push scope information, the anonymized content data to be pushed is allocated to at least one sending queue corresponding to at least one target platform, and scheduled for transmission according to the priority order of the sending queues by the network interface card (NIC) hardware scheduler of the publishing server. This ensures that high-priority content is allocated to high-priority sending queues and sent preferentially by the NIC hardware scheduler, while low-priority content is allocated to low-priority sending queues and sent only after the high-priority queues have finished sending. This avoids indiscriminately pushing the same content to all platforms, achieving differentiated scheduling and staggered transmission of content with different priorities. It fundamentally avoids congestion caused by disorderly competition between content of different priorities in the sending queues, thus alleviating the systemic congestion problem of the NIC sending queues. Subsequently, in response to the determination that the aforementioned health score is less than a preset health threshold and greater than a preset blocking threshold, the aforementioned account identifier and the content data to be pushed are sent to a preset review terminal, and the de-identification process corresponding to the aforementioned target account is suspended to pause the de-identification processing and transmission of the aforementioned content data to be pushed. In response to the determination that the aforementioned health score is less than the aforementioned preset health threshold and less than the aforementioned preset blocking threshold, the memory pages storing the content data to be pushed in memory are marked as reclaimable, and the de-identification process corresponding to the aforementioned target account is removed from the run queue of the currently bound CPU core to block the de-identification processing and transmission of the aforementioned content data to be pushed. Thus, the content of accounts with low health can be transferred to the manual review channel, no longer occupying CPU resources in the automatic processing flow. In the case of extremely low health, the de-identification processing and transmission of the content data to be pushed can be blocked, freeing up CPU core computing resources and memory space occupied by zombie accounts and dormant accounts with extremely low health. Because it employs a tiered approach to accounts based on health scores (e.g., high-health accounts receive lightweight de-identification and are given high priority for delivery, medium- and low-health accounts have their de-identification process suspended and transferred to review, and extremely low-health accounts have their memory pages reclaimed and removed from the CPU execution queue), it reduces the CPU core resource consumption of de-identification tasks for dormant or inactive accounts, preventing accounts with extremely low health from monopolizing CPU time slices for extended periods. Furthermore, because de-identification levels are dynamically generated based on health scores, content from high-quality, active accounts can avoid excessive use of hardware encryption acceleration cards, thus accelerating the de-identification processing and delivery speed of content from these accounts. Finally, by allocating content to different delivery queues on different platforms based on delivery priority and scope, and scheduling delivery according to priority, it alleviates the systemic congestion in network card delivery queues caused by indiscriminately pushing the same content to all platforms.

[0081] Figure 4 This is a schematic diagram of an account status information management interface for a content security processing and differentiated push method for government new media based on some embodiments of this disclosure. It is an operation interface used to display and manage the status information of various government new media accounts.

[0082] Figure 5 This is a schematic diagram showing the test results of sensitive information detection and desensitization processing in a content security processing and differentiated push method for government new media based on some embodiments of this disclosure.

[0083] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of a content security processing and differentiated push device for government new media. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this content security processing and differentiated push device for government new media can be specifically applied to various electronic devices.

[0084] like Figure 2 As shown, a content security processing and differentiated push device 200 for government new media in some embodiments includes: an acquisition unit 201, a first generation unit 202, a second generation unit 203, a desensitization processing unit 204, a third generation unit 205, a fourth generation unit 206, and a scheduling and sending unit 207. The acquisition unit 201 is configured to, in response to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, after receiving a new media content push request containing an account identifier and content data to be pushed from a government operation terminal device, acquire account status information corresponding to the aforementioned account identifier from a pre-created account management database; the first generation unit 202 is configured to, based on the aforementioned account status information, generate a health score for the target account represented by the aforementioned account identifier; the second generation unit 203 is configured to, in response to determining that the aforementioned health score is greater than or equal to a preset health threshold, generate desensitization level information for the aforementioned content data to be pushed based on the health score; the desensitization processing unit 204 is configured to, based on... Based on the aforementioned anonymization level information, the aforementioned content data to be pushed is anonymized to obtain anonymized content data to be pushed; the third generation unit 205 is configured to generate information entropy difference information based on the aforementioned content data to be pushed and the aforementioned anonymized content data to be pushed; the fourth generation unit 206 is configured to generate push priority information and push range information based on the aforementioned health score and the aforementioned information entropy difference information; the scheduling and sending unit 207 is configured to allocate the aforementioned anonymized content data to be pushed to at least one sending queue corresponding to at least one target platform based on the aforementioned push priority information and the aforementioned push range information, and to schedule and send the data according to the priority order of the aforementioned sending queues through the network card hardware scheduler of the aforementioned publishing server.

[0085] It is understandable that the various units and references recorded in the content security processing and differentiated push device 200 for government new media are related to this. Figure 1 The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above are also applicable to the content security processing and differentiated push device 200 for government new media and the units contained therein, and will not be repeated here.

[0086] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0087] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory 302 or a program loaded from a storage device 308 into a random access memory 303. The random access memory 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, the read-only memory 302, and the random access memory 303 are interconnected via a bus 304. An input / output interface 305 is also connected to the bus 304.

[0088] Typically, the following devices can be connected to the input / output interface 305: input devices 306 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0089] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a read-only memory 302. When the computer program is executed by the processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0090] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0091] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0092] Computer-readable media may be contained within an electronic device or may exist independently, not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: respond to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, upon receiving a new media content push request from a government operation terminal device containing an account identifier and content data to be pushed, retrieve account status information corresponding to the aforementioned account identifier from a pre-created account management database; generate a health score for the target account represented by the aforementioned account identifier based on the aforementioned account status information; and, responding to determining that the aforementioned health score is greater than or equal to a preset health threshold, generate a health score corresponding to the aforementioned account identifier based on the health score. The anonymization level information of the content data to be pushed; based on the anonymization level information, the content data to be pushed is anonymized to obtain anonymized content data to be pushed; based on the content data to be pushed and the anonymized content data to be pushed, information entropy difference information is generated; based on the health score and the information entropy difference information, push priority information and push range information are generated; based on the push priority information and the push range information, the anonymized content data to be pushed is allocated to at least one sending queue corresponding to at least one target platform, and is scheduled and sent according to the priority order of the sending queues through the network card hardware scheduler of the publishing server.

[0093] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0095] The units described in some embodiments of this disclosure can be implemented in software or in hardware. The described units can also be located in a processor.

[0096] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0097] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for content security processing and differentiated push notifications for government new media, characterized in that, include: In response to the determination that the CPU core load rate of the government new media publishing server exceeds a preset threshold, after receiving a new media content push request containing account identifier and content data to be pushed sent by the government operation terminal device, the system retrieves the account status information corresponding to the account identifier from the pre-created account management database. Based on the account status information, a health score for the target account, represented by the account identifier, is generated; In response to determining that the health score is greater than or equal to a preset health threshold, an anonymization level information of the content data to be pushed is generated based on the health score; Based on the desensitization level information, the content data to be pushed is desensitized to obtain desensitized content data to be pushed. Based on the content data to be pushed and the de-identified content data to be pushed, information entropy difference information is generated; Based on the health score and the information entropy difference information, push priority information and push range information are generated; Based on the push priority information and the push range information, the de-identified content data to be pushed is allocated to at least one sending queue corresponding to at least one target platform, and is scheduled and sent according to the priority order of the sending queues by the network card hardware scheduler of the publishing server.

2. The method according to claim 1, characterized in that, The method further includes: In response to determining that the health score is less than a preset health threshold and greater than a preset blocking threshold, the account identifier and the data to be pushed are sent to a preset review terminal, and the de-identification process corresponding to the target account is suspended to pause the de-identification processing and sending of the data to be pushed. In response to determining that the health score is less than the preset health threshold and less than the preset blocking threshold, the memory page storing the content data to be pushed is marked as recyclable, and the de-identification process corresponding to the target account is removed from the running queue of the currently bound CPU core, so as to block the de-identification processing and sending of the content data to be pushed.

3. The method according to claim 1, characterized in that, The process of de-identifying the content data to be pushed based on the de-identification level information to obtain de-identified content data to be pushed includes: The content data to be pushed is processed for content type identification to obtain a content type identifier, wherein the content type identifier is one of text identifier, image identifier, and video identifier; Based on the content type identifier and the anonymization level information, the content data to be pushed is anonymized to obtain anonymized content data to be pushed.

4. The method according to claim 1, characterized in that, The account status information includes filing status data, activity status data, compliance status data, interaction status data, and authentication status data, as well as the generation of a health score for the target account based on the account status information, represented by the account identifier, including: Health checks are performed on the account status information, including filing status data, activity status data, compliance status data, interaction status data, and authentication status data, to obtain a sub-health score. Based on each of the sub-health scores, a health score for the target account, represented by an account identifier, is generated.

5. The method according to claim 1, characterized in that, The anonymization level information includes one of the following level identifiers: a first anonymization level identifier and a second anonymization level identifier; the content data to be pushed includes one of text data, image data, and video data; and the anonymization processing of the content data to be pushed based on the content type identifier and the anonymization level information to obtain anonymized content data to be pushed includes: In response to determining that the content type identifier is a text identifier, the following text desensitization processing is performed: The text-sensitive field recognition processing is performed on the content data to be pushed to obtain the recognition location information of each sensitive field; For each of the sensitive field identification location information, perform the following steps: The text content corresponding to the sensitive field identification location information in the content data to be pushed is determined as the text content to be desensitized; In response to determining that the desensitization level information includes a first desensitization level identifier, the text content to be desensitized in the content data to be pushed is masked and replaced to update the content data to be pushed; In response to determining that the desensitization level information includes a second desensitization level identifier, the text content to be desensitized is sent to the hardware encryption acceleration card, the hardware encryption acceleration card is controlled to perform format preservation encryption processing, and the encrypted text content returned by the hardware encryption acceleration card is received. The text content to be desensitized in the content data to be pushed is replaced with the encrypted text content to update the content data to be pushed. The updated content data to be pushed is identified as de-identified content data to be pushed. In response to determining that the content type identifier is an image identifier, the following image desensitization processing is performed: For each image in the image data included in the pushed content data, based on the desensitization level information, the image is desensitized to obtain a desensitized image; At least one de-identified image is identified as the de-identified content data to be pushed; In response to determining that the content type identifier is a video identifier, the audiovisual desensitization processing is performed on the content data to be pushed based on the desensitization level information to obtain desensitized content data to be pushed.

6. The method according to claim 1, characterized in that, The step of generating push priority information and push range information based on the health score and the information entropy difference information includes: Based on the health score, generate initial push priority information; Based on the information entropy difference information, a content quality degradation coefficient is generated; Based on the initial push priority information and the content quality attenuation coefficient, push priority information is generated; Based on the health score, push notification range information is generated.

7. A content security processing and differentiated push device for government new media, characterized in that, include: The acquisition unit is configured to, in response to determining that the CPU core load rate of the government new media publishing server exceeds a preset threshold, after receiving a new media content push request containing an account identifier and content data to be pushed sent by a government operation terminal device, retrieve account status information corresponding to the account identifier from a pre-created account management database. The first generation unit is configured to generate a health score of the target account represented by the account identifier based on the account status information. The second generation unit is configured to generate anonymization level information of the content data to be pushed based on the health score in response to determining that the health score is greater than or equal to a preset health threshold. The desensitization processing unit is configured to perform desensitization processing on the content data to be pushed based on the desensitization level information to obtain desensitized content data to be pushed; The third generation unit is configured to generate information entropy difference information based on the content data to be pushed and the de-identified content data to be pushed. The fourth generation unit is configured to generate push priority information and push range information based on the health score and the information entropy difference information; The scheduling and sending unit is configured to allocate the de-identified content data to be pushed to at least one sending queue corresponding to at least one target platform based on the push priority information and the push range information, and to schedule and send the data according to the priority order of the sending queues through the network card hardware scheduler of the publishing server.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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