Data processing method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a data processing method and device, and relates to the technical field of computers, in particular to the technical fields of large models, deep learning, natural language processing and the like. The specific implementation scheme is as follows: acquiring to-be-processed data; classifying the content of the to-be-processed data, and determining the subject type of the to-be-processed data; in response to the fact that the subject type belongs to a scientific subject type, detecting whether the content has natural phenomenon information; in response to detecting that the content does not have the natural phenomenon information, extracting subject information of the content; on the basis of the subject information, subject content early warning is performed on the data to be processed.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, specifically to technical fields such as large models, deep learning, and natural language processing, and particularly relates to a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the development of the information age, current AI-generated videos and synthetic videos are becoming increasingly mature, and there is no effective means to judge the authenticity of content such as videos, pictures, and texts. Especially for the elderly, they are greatly affected by external data, and how to prevent them from being misled needs to be considered first. Summary of the Invention

[0003] The present disclosure provides a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

[0004] According to a first aspect, there is provided a data processing method, the method comprising: obtaining data to be processed; classifying the content of the data to be processed to determine the subject type of the data to be processed; in response to the subject type belonging to the scientific subject type, detecting whether the content belongs to natural law information; in response to detecting that the content does not belong to natural law information, extracting the main idea information of the content; and based on the main idea information, giving a main idea content warning for the data to be processed.

[0005] According to a second aspect, there is provided a data processing apparatus, the apparatus comprising: an obtaining unit configured to obtain data to be processed; a classifying unit configured to classify the content of the data to be processed to determine the subject type of the data to be processed; a detecting unit configured to, in response to the subject type belonging to the scientific subject type, detect whether the content belongs to natural law information; an extracting unit configured to, in response to detecting that the content does not belong to natural law information, extract the main idea information of the content; and a warning unit configured to, based on the main idea information, give a main idea content warning for the data to be processed.

[0006] According to a third aspect, there is provided an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method described in any implementation manner of the first aspect.

[0008] According to a fifth aspect, a computer program product is provided, including a computer program which, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0009] The data processing method and apparatus provided by the embodiments of the present disclosure first obtain data to be processed; secondly, classify the content of the data to be processed to determine the subject type of the data to be processed; then, in response to the subject type belonging to the scientific subject type, detect whether the content belongs to natural law information; again, in response to detecting that the content does not belong to natural law information, extract the main idea information of the content; finally, based on the main idea information, issue a warning about the main content of the data to be processed. Thus, on the basis of detecting the subject type of the data to be processed, by detecting natural law information and main idea information, the detection reliability of the data to be processed is improved.

[0010] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Description of the Drawings

[0011] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0012] Figure 1 is a flowchart of an embodiment of the data processing method according to the present disclosure;

[0013] Figure 2 is a timing diagram of data interaction among various modules during the data processing of the present disclosure;

[0014] Figure 3 is a schematic structural diagram of an embodiment of the data processing apparatus according to the present disclosure;

[0015] Figure 4 is a block diagram of an electronic device for implementing the data processing method of the embodiments of the present disclosure. Detailed Embodiments

[0016] The following makes an explanation of the exemplary embodiments of the present disclosure with reference to the drawings. Various details of the embodiments of the present disclosure are included to help understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described here without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted below.

[0017] In the traditional technology, AI-generated videos and synthetic videos are becoming increasingly mature. After people obtain the pushed data from the Internet, they generally make a manual judgment on whether the pushed data is naturally generated or synthetic. For the elderly and teenagers with poor judgment ability, they may suffer property and personal damages due to the falsity of the pushed data.

[0018] In view of the defects in the traditional technology, the present disclosure proposes a data processing method, which can realize the detection and content warning of the data to be processed, and improve the security of data detection. Figure 1 Fig. 100 shows a flow of an embodiment of the data processing method according to the present disclosure. The above data processing method includes the following steps:

[0019] Step 101, obtain the data to be processed.

[0020] In this embodiment, the data to be processed is the data that needs to be judged for content information. When the content of the data to be processed involves false or harmful information, the data processing method of the present disclosure can directly give a warning about the data to be processed.

[0021] In this embodiment, the data to be processed can be data widely collected from diversified channels. The data types of the data to be processed include, but are not limited to, data formats such as text, image, audio, and video.

[0022] In this embodiment, the above step 101 includes: obtaining the data to be processed, performing quality control on the data to be processed, removing the incorrect, missing, or abnormal data therein to obtain preliminary data; integrating the preliminary data to form structured data, storing the structured data in a database; and extracting the key features of the structured data. Among them, performing preliminary quality control on the data, removing the incorrect, missing, or abnormal data, ensures the accuracy and integrity of the data. Integrating the preliminary data to form a structured data and securely storing it in the database provides a solid foundation for subsequent data processing and analysis.

[0023] In the technical solution of the present disclosure, the processing of the data to be processed, such as collection, storage, use, processing, transmission, provision, and disclosure, is performed after authorization and complies with relevant laws and regulations. The information related to the user in the data to be processed is the information obtained after the user's permission, and the information related to the user in the data to be processed is processed under confidentiality conditions.

[0024] As Figure 2 shown, the data to be processed can be at least one modal data collected by the data acquisition module from an image processing system, a sound processing system, and a video processing system.

[0025] Step 102, classify the content of the data to be processed to determine the subject type of the data to be processed.

[0026] In this embodiment, the content of the data to be processed is the data obtained after extracting the core information of the data to be processed. When the modal data represented by the data to be processed is different, the extraction method of the content of the data to be processed is different. For example, when the data to be processed is text, and the data to be processed is: "Ah, ah, after the rain, the sun shows its face from behind the clouds", content analysis is performed on the data to be processed, and the content of the data to be processed is obtained as: "Sunrise after the rain". Another example is that when the data to be processed is a video or an image, key elements in the data to be processed image and the plot development in the video are extracted, and the content of the data to be processed is the multi-modal data after describing the plot development and elements in the video or image. The multi-modal data can be video or voice data.

[0027] In this embodiment, the above step 102 includes: determining the content expressed by the data to be processed; matching the content with the disciplinary elements of multiple disciplinary types, and in response to the matching degree between the content and the disciplinary elements of any one disciplinary type reaching a preset matching threshold, determining the disciplinary type of the data to be processed as this disciplinary type.

[0028] In this embodiment, the multiple disciplinary types include: scientific disciplines, historical disciplines, political disciplines, and cultural disciplines. Each disciplinary type among the multiple disciplinary types has corresponding disciplinary elements. Among them, the disciplinary elements of historical disciplines include: historical events, figures, and historical backgrounds, etc. By identifying historical events, figures, and historical backgrounds in the content of the data to be processed and classifying them into the corresponding historical disciplines, this helps to quickly understand the historical background of the content and thus make more accurate judgments.

[0029] The disciplinary elements of political disciplines include: political viewpoints, stances, and events. The execution entity on which the data processing method runs classifies them into the corresponding political disciplines by identifying political viewpoints, stances, and events in the content of the data to be processed, which is of great significance for monitoring political dynamics and analyzing public opinion trends.

[0030] The disciplinary elements of cultural disciplines include: cultural elements, customs, and symbols. By identifying cultural elements, customs, and symbols in the content of the data to be processed and classifying them into the corresponding cultural disciplines, this helps to understand the content differences under different cultural backgrounds and promote cultural exchange and understanding.

[0031] The scientific disciplines include: the science and technology category and the medical category. The disciplinary elements of the science and technology category include: scientific and technological elements, technological progress, and innovation points. By identifying the scientific and technological elements, technological progress, and innovation points in the content of the data to be processed, and classifying them into the corresponding science and technology category, it plays an important role in tracking the development of science and technology and grasping the innovation trend. The disciplinary elements of the medical category include: pathology, treatment plan. By identifying whether there are professional medical terms such as pathology and treatment plan in the content, it can be judged whether the relevant medical knowledge described in the video is correct.

[0032] Optionally, in order to quickly obtain the disciplinary type of the data to be processed, the key features of the data to be processed can be extracted, and the disciplinary type of the data to be processed can be determined through the key features. The key features are the features with substantial significance in the data to be processed. For example, for an image, the key features are the information such as the people, scenery, and animals described in the image. For a document, the key features are the entities described in the document and the relationships between the entities, etc. The key features can include keywords, the relationships between keywords, the descriptive text of keywords, etc.

[0033] In this embodiment, after obtaining the key features, the disciplinary features of multiple pre-selected disciplinary types are calculated for similarity with the key features. In response to the similarity between the disciplinary features of a disciplinary type and the key features being greater than the similarity threshold, it is determined that the content of the data to be processed belongs to this disciplinary type, and the disciplinary type of the data to be processed is this disciplinary type.

[0034] As Figure 2 shown, through the disciplinary classification module, the content of the data to be processed can be effectively classified into disciplines such as politics, history, and culture.

[0035] Step 103, in response to the disciplinary type belonging to the scientific disciplinary type, detect whether the content has natural phenomenon information.

[0036] In this embodiment, the scientific disciplinary type is a disciplinary type including any objective knowledge. Among them, the scientific disciplinary type includes natural science, social science, and applied science. Natural science is a branch of science that describes, understands, and predicts natural phenomena. Natural science includes physical science and life science. Life science is also called biology, while physical science is a discipline that studies natural phenomena. Physical science is subdivided into multiple branches: physics, chemistry, astronomy, earth science, and space science.

[0037] In this embodiment, the natural law information is related to natural science and describes natural phenomenon information, such as lightning, chemical elements, etc. By comparing the content of the data to be processed with the natural law information one by one, it is judged whether there is natural law information in the content of the data to be processed, and thus it can be further judged whether the data to be processed is natural science.

[0038] AsFigure 2 As shown, through the natural law analysis module, it is possible to effectively detect whether the content of the data to be processed has natural phenomenon information.

[0039] Step 104: In response to detecting that the content does not have natural phenomenon information, extract the main idea information of the content.

[0040] In this embodiment, when the content of the data to be processed does not have natural phenomenon information, at this time, it can be determined that the discipline of the data to be processed is non-natural science in the scientific discipline type. For example, the chip has broken through one nanometer. Among them, although the chip belongs to the scientific discipline category, it is not natural science.

[0041] In this embodiment, the main idea information of the content is the key information of the content. The above extraction of the main idea information of the content includes: using a natural language processing model to identify the key information of the content of the data to be processed, summarizing the main content of the data to be processed, and obtaining the main idea information. For example, if the data to be processed is a news report video, the main idea information is to identify the core event and main viewpoints in the news report. Another example is that if the data to be processed is text material, the main idea information is to identify the key information of the article or paragraph in the text material, summarize its main content, which helps to quickly understand the core points of a large amount of text material.

[0042] Such as Figure 2 As shown, through the feature analysis module, it is possible to effectively extract the main idea information of the content.

[0043] Step 105: Based on the main idea information, perform a warning for the main content of the data to be processed.

[0044] In this embodiment, the warning for the main content is a warning analysis based on the main idea information. When it is found that there is a problem with the main idea information, it is possible to make the recipient determine that there is a problem with the data to be processed by performing a warning for the main content.

[0045] The above step 105 includes: after obtaining the main idea information, identify the measured object in the main idea information, determine the relevant object related to the measured object, search for materials related to the measured object and the relevant object, determine whether the material has disclosed false problems, if the material has disclosed false problems, determine that there is a false phenomenon in the main content of the data to be processed, and issue a warning for the main content; if the material has not disclosed false problems, determine that there is no false phenomenon in the main content of the data to be processed.

[0046] Optionally, step 105 includes: identifying the object under test in the main idea information, determining the relevant objects related to the object under test, determining the components of the object under test and the relevant objects by retrieving in the Internet, detecting whether there are harmful substances in the components, issuing a warning for the main idea content in response to detecting harmful substances in the components; not issuing a warning for the main idea content in response to detecting no harmful substances in the components. For example: in a health product sales advertisement video, when the video main idea is identified as selling health products, the system will further analyze the relevant health products, retrieve relevant papers and components, and determine whether there are exaggerated components in the efficacy of the health products. If there are exaggerated components, a warning for the main idea content will be issued and relevant conclusions will be prompted to the user.

[0047] Optionally, the warning for the main idea content in step 105 can be implemented by Figure 2 the warning module shown as follows. Step 105 includes: based on the main idea information, determining the matching warning conditions from the alarm system, selecting the warning recipients from the warning recipient management system, and selecting the notification method from the multi-channel notification reach system; when the main idea information triggers the warning conditions, sending the warning information for the main idea content to the terminals corresponding to the warning recipients by using the selected notification method; the warning module can not only issue warnings for the main idea content but also issue warnings in other aspects. The warning module can set a series of precise warning rules and thresholds to monitor the analysis results in real time. Once the warning conditions are triggered, the warning mechanism will be immediately activated to ensure that the information recipients can quickly respond and cope with potential risks and challenges.

[0048] In Figure 2 it, the warning recipient management system is an important part of the warning module, which is responsible for managing and maintaining the list of recipients of warning information. This system not only supports adding, deleting and modifying the basic information of warning recipients, such as names, contact information, etc., but also can set different warning levels and notification methods for different warning recipients according to the actual needs of the enterprise. In this way, when warning information is generated, the system can quickly send the information to the designated warning recipients to ensure that they can receive it in time and take corresponding countermeasures.

[0049] In Figure 2Among them, the alarm system is one of the key functions in the early warning module. It is responsible for generating and sending early warning messages when early warning conditions (such as being triggered when the score is lower than the threshold; being triggered when the number of transmissions exceeds the threshold; being triggered in scenarios threatening personal safety, and being triggered in major natural disaster events) are triggered. This system can automatically judge the urgency and importance of the early warning, and generate corresponding early warning messages according to the set rules and thresholds. These messages not only contain the specific content of the early warning, but also are attached with the data and analysis results that triggered the early warning, so that the early warning recipients can quickly understand the background and reasons for the early warning. At the same time, the alarm system also supports multiple ways of generating early warning messages, such as text, voice, image, etc., to meet the needs in different scenarios.

[0050] In Figure 2 Among them, the refined threshold management system is another important function in the early warning module. It allows enterprises to set and adjust early warning rules and thresholds according to their own business needs and risk tolerance. This system supports the setting of multiple early warning indicators, such as keyword frequency, sentiment score, topic popularity, etc., and allows enterprises to set different early warning thresholds for different early warning indicators. In this way, when the analysis results reach or exceed the set thresholds, the system will trigger the early warning mechanism to ensure that enterprises can timely discover and respond to potential risks.

[0051] In Figure 2 Among them, the multi-channel notification reaching system is an important tool for realizing the transmission of early warning messages in the early warning module. It supports multiple notification methods, such as text messages, emails, mobile application notifications, etc., to ensure that early warning messages can be quickly and accurately conveyed to the early warning recipients. This system can not only achieve the instant push of early warning messages, but also supports functions such as scheduled sending and repeated sending of messages to ensure that early warning recipients will not miss any important early warning messages. At the same time, the multi-channel notification reaching system also supports the real-time monitoring and feedback of notification effects, so that enterprises can continuously optimize notification strategies and improve the transmission efficiency and accuracy of early warning messages.

[0052] In summary, through the comprehensive application of the early warning recipient management system, the alarm system, the refined threshold management system, and the multi-channel notification reaching system, the early warning module realizes the comprehensive management and efficient transmission of early warning messages. This design not only improves the timeliness and effectiveness of early warnings, but also provides strong support for the risk management and decision-making of enterprises.

[0053] The data processing method provided by the embodiments of the present disclosure includes: First, obtain the data to be processed; Second, classify the content of the data to be processed to determine the subject type of the data to be processed; Next, in response to the subject type belonging to the scientific subject type, detect whether the content has natural phenomenon information; Then, in response to detecting that the content does not have natural phenomenon information, extract the main idea information of the content; Finally, based on the main idea information, give a warning about the main content of the data to be processed. Thus, on the basis of detecting the subject type of the data to be processed, by detecting the natural law information and the main idea information, the detection reliability of the data to be processed is improved.

[0054] In some embodiments of the present disclosure, the above data processing method further includes: In response to detecting that the content has natural phenomenon information, detect whether the content meets the natural law conditions; In response to the content not meeting the natural law conditions, send a law warning message.

[0055] In this embodiment, the natural law conditions refer to the conditions involved in the natural principles and natural laws of multiple disciplines involved in the content. The multiple disciplines corresponding to the natural law conditions include: optics, mechanics, electricity, etc. The natural principles and natural laws of mechanics include: Newton's laws, the law of conservation of momentum, the law of conservation of mechanical energy, and the law of universal gravitation; The natural principles and natural laws of optics include: the law of reflection of light, the law of refraction of light, and the law of interference of light; The natural principles and natural laws of electricity include: Ohm's law, Kirchhoff's voltage law, and Kirchhoff's current law. The natural law conditions include: phenomenon formation conditions, phenomenon background conditions, phenomenon maintenance conditions, etc. Among them, the phenomenon formation conditions are the conditions required to form natural phenomenon information, the phenomenon background conditions are the conditions that suppress natural phenomenon information in the content of the data to be processed, and the phenomenon maintenance conditions are the conditions for the continuous occurrence of natural phenomenon information.

[0056] In this embodiment, the law warning message is used to warn that the current data to be processed does not meet the natural law conditions. When the content of the data to be processed does not meet the natural law conditions, it means that the content expressed by the data to be processed is false content, and a law warning message is sent.

[0057] In this embodiment, the above detection of whether the content meets the natural law conditions includes: determining the natural law principle involved in the content, and detecting whether the natural phenomenon information meets the natural law principle. In a specific example, the content of the data to be processed is: The phenomenon of an explosion is caused by hail hitting the ground. Among them, the natural phenomenon information is: Hail hitting the ground. Whether the content of the data to be processed meets the natural law conditions is analyzed by detecting whether hail hitting the ground satisfies the physical principle. The specific steps are as follows:

[0058] The first step: The physical principle involved in hail hitting the ground

[0059] Acceleration due to gravity: The acceleration due to gravity on the Earth's surface is approximately 9.8 m / s². When a hailstone falls from the sky, it is subject to the force of gravity during its fall, and its final velocity will be limited by air resistance and reach a terminal velocity.

[0060] Friction: A hailstone will experience friction with the air during its fall. The frictional force will impede the falling speed of the hailstone, causing it to eventually reach a stable speed (terminal velocity). The kinetic energy of the hailstone when it hits the ground mainly comes from the conversion of its gravitational potential energy during the fall.

[0061] Law of conservation of energy: The kinetic energy of the hailstone when it hits the ground will be converted into other forms of energy, such as sound energy, thermal energy, or deformation energy. An explosion is a process of rapid energy release, which usually requires a large amount of energy accumulation and specific conditions.

[0062] Law of conservation of momentum: If a hailstone collides when it hits the ground, its momentum will be transferred to the ground or other objects, but no additional energy will be created out of nothing.

[0063] Second step: Detect whether the natural phenomenon information satisfies the physical principles

[0064] Energy analysis of a hailstone hitting the ground: The energy of a hailstone when it hits the ground mainly comes from the conversion of its gravitational potential energy during the fall. Assuming a hailstone has a mass of m and falls from a height h, its kinetic energy when it hits the ground is Ek = mgh. Even if the hailstone hits the ground at a relatively high speed, its energy is far from sufficient to trigger an explosion.

[0065] Conditions for an explosion: An explosion usually needs to meet one of the following conditions: (1) Chemical reaction (such as the explosion of explosives); (2) Rapid release of high-pressure gas (such as the explosion of a boiler); (3) Rapid accumulation and release of energy (such as a nuclear explosion). The energy of a hailstone when it hits the ground cannot meet these conditions.

[0066] Effect of friction: A hailstone is impeded by air resistance during its fall, and its speed when it hits the ground is much lower than the free-fall speed. Therefore, its kinetic energy when it hits the ground is limited and not sufficient to trigger an explosion.

[0067] Calculation of the kinetic energy of a hailstone hitting the ground: Assume a hailstone has a mass of 50 g = 0.05 kg and falls from a height of 1000 m. Its kinetic energy when it hits the ground is: Ek = mgh = 0.05 kg × 9.8 m / s² × 1000 m = 490 J. This energy is equivalent to that of a small firecracker and is far from sufficient to trigger an explosion. Comparison with an explosion: The energy of a small chemical explosion (such as TNT) is approximately 10⁶ J. In comparison, the energy of a hailstone hitting the ground is negligible. Conclusion: The energy of a hailstone hitting the ground mainly comes from the conversion of gravitational potential energy during its fall. Its energy is limited and far from sufficient to trigger an explosion. Through the analysis of gravitational acceleration, friction, the law of conservation of energy, and the law of conservation of momentum, it can be determined that the energy of a hailstone hitting the ground is not sufficient to trigger an explosion. Therefore, any claim that a hailstone hitting the ground causes an explosion is unscientific and should be marked as misleading content.

[0068] The data processing method provided by this optional implementation detects that the content has natural phenomenon information, checks whether the content meets the conditions of natural laws, and in response to the content not meeting the conditions of natural laws, sends a law warning message, improving the naturality of the data to be processed.

[0069] Optionally, before checking whether the content meets the conditions of natural laws, the above data processing method further includes: detecting whether the content of the data to be processed is generated by an intelligent model; in response to the content of the data to be processed not being generated by an intelligent model, then checking whether the content meets the conditions of natural laws.

[0070] In some optional implementations of the present disclosure, the above-mentioned in response to detecting that the content has natural phenomenon information and checking whether the content meets the conditions of natural laws includes: in response to detecting that the content has natural phenomenon information, extracting the environmental characteristics of the object with natural phenomenon information; checking whether the environmental characteristics meet the phenomenon formation conditions; in response to detecting that the environmental characteristics meet the phenomenon formation conditions, determining that the content meets the conditions of natural laws; in response to detecting that the environmental characteristics do not meet the phenomenon formation conditions, determining that the content does not meet the conditions of natural laws.

[0071] In this optional implementation, the object of natural phenomenon information is the subject where the natural phenomenon information appears or is generated, and the environmental characteristics of the object refer to the characteristics of the surrounding environment of the subject, and the phenomenon formation conditions refer to the conditions involved in forming the natural phenomenon information.

[0072] Specifically, the content of a piece of data to be processed is: Hail on the moon. Among them, "the moon" is the object of the natural phenomenon, and "hail" is the natural phenomenon information. To determine the authenticity of this content, the specific steps implemented by the method of the present disclosure are as follows:

[0073] The first step is to determine the environmental characteristics of the moon.

[0074] Atmospheric absence: The moon has almost no atmosphere, and the surface pressure is close to a vacuum state. The atmosphere is one of the key conditions for the formation of precipitation (including hail), because precipitation requires the condensation, cooling, and aggregation of water vapor in the atmosphere.

[0075] Extreme temperatures: The temperature on the moon's surface is extreme, reaching about 127°C during the day and dropping to about -173°C at night. Such temperature conditions are not conducive to the existence of liquid water or ice, let alone supporting the formation of hail.

[0076] Scarcity of water resources: Although there is a small amount of water ice at the poles of the moon, these water ices mainly exist in a solid state in permanently shadowed areas and cannot form a water cycle system similar to that on Earth.

[0077] The second step is to determine the formation conditions of the hail phenomenon

[0078] Abundant water vapor: The formation of hail requires a large amount of water vapor in the atmosphere. These water vapors cool and condense in the updraft, forming ice crystals and gradually growing.

[0079] Strong updraft: Hail needs to form in strong convective weather. The updraft carries the ice crystals to high altitudes, causing them to grow continuously and eventually form hail.

[0080] Suitable temperature stratification: The formation of hail requires a certain temperature gradient in the atmosphere, enabling the ice crystals to collide, merge, and grow continuously during the upward movement.

[0081] The third step is to detect whether the environmental characteristics meet the phenomenon formation conditions

[0082] Lack of water vapor: The moon has no liquid water and no water vapor circulation in the atmosphere, so it cannot provide the material basis for the formation of hail.

[0083] Lack of convection: The moon has no atmosphere, so there is no convective movement and no updraft can be formed to support the formation and growth of ice crystals.

[0084] Unsuitable temperature conditions: The extreme temperature changes on the moon's surface cannot support the formation and stable existence of ice crystals.

[0085] The method for detecting the conditions of natural laws provided by this optional implementation extracts the environmental characteristics of the object with natural phenomenon information in response to detecting that the content has natural phenomenon information; detects whether the environmental characteristics meet the phenomenon formation conditions; determines that the content meets the conditions of natural laws in response to detecting that the environmental characteristics meet the phenomenon formation conditions; and determines that the content does not meet the conditions of natural laws in response to detecting that the environmental characteristics do not meet the phenomenon formation conditions, improving the comprehensiveness of the analysis of the conditions of natural laws.

[0086] Optionally, after detecting whether the content conforms to the laws of nature, the above data processing method further includes: analyzing the content rationality of the data to be processed, giving the analysis result of rationality, and adding the analysis result to the law warning information. Specifically, for the data to be processed of "hail on the moon" above, analyze the rationality of ecological and natural laws.

[0087] Universality of natural laws: Natural phenomena on Earth (such as hail) are formed based on specific environmental conditions (such as atmosphere, water vapor, temperature, and convection). These conditions are completely absent on the moon, so it is impossible for similar natural phenomena to occur on the moon.

[0088] Ecological balance law: The formation and existence of natural phenomena must conform to the ecological balance law of the ecosystem. The environmental conditions on the moon are very different from those on Earth, lacking the ecological basis to support precipitation phenomena.

[0089] The analysis result is: According to the above analysis, the environmental characteristics of the moon (such as the lack of atmosphere, water vapor, and convective motion) do not conform to the formation conditions of hail phenomena at all. Therefore, the statement of "hail on the moon" violates the laws of nature and is incorrect. This description belongs to false information and does not conform to the real situation of nature.

[0090] In some embodiments of the present disclosure, the above method further includes: in response to the subject type not belonging to the scientific subject type, detecting whether the subject type belongs to the historical subject; in response to the subject type belonging to the historical subject, extracting the main idea information of the content; and based on the main idea information, performing a main idea content warning on the data to be processed.

[0091] In this embodiment, the historical subject is identified by the subject elements of the historical subject. When the subject type of the data to be processed is detected as the historical subject, the main idea information of the data to be processed is extracted.

[0092] In this embodiment, the main idea information of the content is the key information of the content. The above extraction of the main idea information of the content includes: using a natural language processing model to identify the key information of the content of the data to be processed, summarizing the main content of the data to be processed, and obtaining the main idea information.

[0093] In this embodiment, the main idea content warning is a warning analysis based on the main idea information. When a problem is found in the main idea information, a main idea content alarm can be issued so that the recipient can determine that there is a problem with the data to be processed.

[0094] The data processing method provided by this alternative implementation, in response to the subject type not belonging to the scientific subject type, detects whether the subject type belongs to the historical subject; in response to the subject type belonging to the historical subject, extracts the main idea information of the content; based on the main idea information, issues a main idea content warning for the data to be processed, providing a reliable implementation method for the content detection of the data to be processed in the historical subject, and improving the security of the detection of the data to be processed.

[0095] In some alternative implementation manners of the present disclosure, the above-mentioned issuing a main idea content warning for the data to be processed according to the main idea information includes: detecting whether the main idea information meets the main idea warning condition; in response to detecting that the main idea information meets the main idea warning condition, sending a main idea warning message.

[0096] In this alternative implementation manner, the main idea warning condition is a condition for false judgment of the main idea information. When the main idea warning condition is met, it is determined that the data to be processed is false data. The main idea warning condition may be information describing the data to be processed obtained from the Internet. Since the data to be processed can belong to any one of multiple subject types, the main idea warning condition may have different contents based on different subject types.

[0097] In this alternative implementation manner, the main idea warning message is an alarm message issued due to a main idea problem. The main idea warning message may include: a main idea alarm identifier, the content of the data to be processed, the main idea warning condition, and the specific position where the content of the data to be processed does not meet the main idea warning condition. Among them, the main idea alarm identifier is used to identify that the current alarm message is of the main idea type. Through the main idea warning message, the user can know the current alarm status. For example, the main idea warning message is: In the introduction information of a certain health product, the ingredients of a certain health product are unqualified. Please do not believe this introduction information.

[0098] The method for issuing a main idea content warning for the data to be processed provided by this alternative implementation manner detects whether the main idea information meets the main idea warning condition; in response to detecting that the main idea information meets the main idea warning condition, issues a main idea warning message, and can directly determine the main idea problem through the main idea warning message, improving the efficiency of the warning of the data to be processed.

[0099] In some embodiments of the present disclosure, the above-mentioned data processing method further includes: in response to detecting that the main idea information does not meet the main idea warning condition, extracting the subjective and objective information of the content; based on the subjective and objective information, issuing a subjective and objective content warning for the data to be processed.

[0100] In this embodiment, the main idea warning condition is a condition for false judgment of the main idea information. When the main idea information does not meet the main idea warning condition, it is determined that further judgment of the data to be processed is required.

[0101] In this embodiment, the subjective and objective information refers to the subjective information and objective information reflected by the content of the data to be processed. The subjective and objective content warning is a warning analysis based on the subjective information and / or objective information. When it is found that there is a problem with the objective information and / or objective information expressed by the content of the data to be processed, the recipient can be made to determine that there is a problem with the data to be processed by issuing a subjective and objective content warning.

[0102] In this embodiment, performing subjective and objective content warning on the data to be processed based on the subjective and objective information includes: when the objective information in the content of the data to be processed is contrary to the actual objective facts or objective data, determining and issuing an objective information alert; and / or when the subjective information in the content of the data to be processed does not conform to the attitude or emotion of the actual object, determining and issuing a subjective information alert. Among them, the objective information alert includes: an objective alert identifier, the content of the data to be processed, the objective information, and the specific position where the content of the data to be processed deviates from the objective facts or objective data. The subjective information alert includes: a subjective alert identifier, the content of the data to be processed, the subjective information, and the specific position where the content of the data to be processed deviates from the subjective facts or subjective data.

[0103] The data processing method provided in this embodiment, after the main idea information does not meet the main idea warning condition, performs subjective and objective content warning on the data to be processed through the subjective and objective information, improving the reliability and comprehensiveness of the main idea content detection.

[0104] Optionally, when the main idea information does not meet the main idea warning condition, it can be directly determined that the data to be processed is not false data. Specifically, the above data processing method includes: in response to detecting that the main idea information does not meet the main idea warning condition, determining that the data to be processed is normal.

[0105] In some alternative implementation manners of the present disclosure, the above subjective and objective information includes: subjective information and objective information. In response to detecting that the main idea information does not meet the main idea warning condition, extracting the subjective and objective information of the content includes: in response to detecting that the main idea information does not meet the main idea warning condition, extracting the narrative elements in the content and using the narrative elements as the objective information; extracting the emotional tendency text in the content and using the emotional tendency text as the subjective information.

[0106] In this alternative implementation manner, the objective information is the facts and data in the content of the data to be processed, and the subjective information is the emotion and attitude information in the content of the data to be processed.

[0107] In this alternative implementation, the elements of a narrative include: time, place, characters, and events. The execution entity on which the data processing method runs can automatically identify the elements of a narrative in the content of the data to be processed, such as time, place, characters, events, etc., and regard the elements of the narrative as objective information. For example, if the content of the data to be processed is: Hua Tuo invented Compendium of Materia Medica, this obviously does not conform to the objective information: Li Shizhen compiled Compendium of Materia Medica. In this case, the system will mark it as not conforming to objective facts.

[0108] For subjective information, the execution entity on which the data processing method runs can analyze the emotional words, tone of voice, expressions, actions, etc. in the content of the data to be processed through deep learning algorithms, infer the user's emotional tendency information, and regard the inferred emotional tendency information as subjective information. For example, in some drug promotion videos, after a user takes a certain drug, their waist and legs no longer hurt and they walk more easily. By comparing before and after, it is indicated that the subjective information expressed in the video is continuously improving and getting better. It may be helpful to the user and analyze the authenticity of the verified subjective information.

[0109] The method for extracting subjective and objective information provided in this alternative implementation extracts the elements of a narrative in the content and regards the elements of the narrative as objective information in response to detecting that the main idea information does not meet the main idea warning condition; extracts the emotional tendency text in the content and regards the emotional tendency text as subjective information, improving the comprehensiveness of the extraction of subjective and objective information.

[0110] In some embodiments of the present disclosure, the above data processing method further includes: extracting the view bias information of the content in response to the subject type not belonging to the history subject; obtaining the user characteristics and behavior patterns of the data to be processed; determining the target group of the data to be processed based on the user characteristics and behavior patterns; and performing group bias warning on the data to be processed based on the view bias information and the target group.

[0111] In this embodiment, the view bias information is the view bias aspect shown in the content of the data to be processed. The view bias aspect can be positive, negative, neutral, whether it is an absolute view, etc.; the view bias information of the content of the data to be processed can be extracted through a deep learning model. Specifically, the deep learning model can be a classification model for classifying the view bias aspect in the content of the data to be processed.

[0112] In this embodiment, for a positive, negative, or neutral view bias aspect, the content of a piece of data to be processed is: Qin Shi Huang is completely a tyrant. The execution entity on which the data processing method runs will consult relevant history and mark the view in combination with known historical facts, mark it as negative, and remind the user to pay attention.

[0113] In this embodiment, for the view bias aspect of whether it is an absolute view, another example of the content of the data to be processed is: Drinking is absolutely harmful. Then mark the view as absolute and analyze whether the view is correct. If it is uncertain or incorrect, prompt that there may be an error.

[0114] In this embodiment, when the content of the data to be processed is video or image content, the view bias information can be determined by analyzing the expressions, actions, and backgrounds in the video or image through a deep learning model, as well as the speech content in the video.

[0115] In this embodiment, the execution entity on which the data processing method runs can understand and classify different user groups. Specifically, the execution entity mines the user characteristics and behavior patterns of users from a large amount of user data through deep learning algorithms. User characteristics include but are not limited to basic information such as the user's age, gender, professional background, and consumption habits, and behavior patterns include the user's interest preferences, online behaviors, etc.

[0116] In this embodiment, the execution entity divides users into different groups according to various user characteristics and behavior characteristics of the users, such as young groups, middle-aged and elderly groups, technology enthusiasts groups, fashion followers groups, etc. This helps the system analyze the target group corresponding to the video or picture and possible intentions.

[0117] The data processing method provided in this embodiment extracts the view bias information of the content in response to the subject type not belonging to the history subject; obtains the user characteristics and behavior patterns of the data to be processed; determines the target group of the data to be processed based on the user characteristics and behavior patterns; and performs group bias warning on the data to be processed based on the view bias information and the target group. Thus, when the subject type does not belong to the history subject, the content of the data to be processed is detected through the target group and the view bias information, improving the reliability of the detection of the data to be processed.

[0118] In some alternative implementation manners of the present disclosure, the above-mentioned performing group bias warning on the data to be processed based on the view bias information and the target group includes: sending a group view warning message in response to detecting that the view bias information is negative and the target group is a young group.

[0119] In this alternative implementation manner, the group view warning message refers to an alarm message issued due to the view problem of the target group. The group view warning message includes: a group alarm identifier, the content of the data to be processed, and the reason for alarming the data to be processed. Among them, the above reason is that the data to be processed is issued by a young group with a negative view and the view of the young group needs to be changed.

[0120] The method for group bias warning provided by this alternative implementation sends a group opinion warning message in response to detecting that the opinion bias information is negative and the target group is the young group, improving the reliability and accuracy of the warning for the data to be processed.

[0121] In some alternative implementations of the present disclosure, classifying the content of the data to be processed and determining the subject type of the data to be processed includes: preprocessing the data to be processed to obtain preprocessed data; detecting whether there is a subject element of any one of multiple subject types in the content of the preprocessed data; in response to the preprocessed data having the subject element of this subject type, determining the subject type of the data to be processed as this subject type.

[0122] In this alternative implementation, the operation of preprocessing the data to be processed described above is Figure 2 the preprocessing module shown in Figure 2 . The preprocessing module plays a crucial role before the data enters in-depth analysis. It will first perform in-depth cleaning on the data to be processed to remove duplicate data, that is Figure 2 perform data deduplication processing on the data in Figure 2 , and perform data formatting processing on the processed data for subsequent analysis. In addition, the preprocessing module will also perform standardization processing on the data, including adjusting the data format, data type, and data structure, etc., to ensure that the data can be compared and fused in the subsequent analysis process.

[0123] In this alternative implementation, the preprocessed data is the data obtained after preprocessing the data to be processed. Among them, the preprocessing may include one or more of data cleaning, formatting processing, and standardization processing.

[0124] In this alternative implementation, the multiple subject types include: science subjects, history subjects, political subjects, and cultural subjects. Each subject type among the multiple subject types has corresponding subject elements. Specifically, the subject elements are as described in the above embodiments.

[0125] The method for determining the subject type provided by this alternative implementation preprocesses the data to be processed to obtain preprocessed data; detects whether there is a subject element of any one of multiple subject types in the preprocessed data; in response to the content of the preprocessed data having the subject element of this subject type, determines the subject type of the data to be processed as this subject type, improving the comprehensiveness of obtaining the subject type.

[0126] In some alternative implementations of the present disclosure, the above-mentioned detecting whether the content has natural phenomenon information in response to the subject type belonging to the scientific subject type includes: in response to the subject type belonging to the scientific subject type, performing word segmentation on the data to be processed to obtain a word segmentation result; matching the word segmentation result with at least one piece of natural phenomenon information in the natural science subject type; and in response to the word segmentation result matching at least one piece of natural phenomenon information successfully, determining that the content has natural phenomenon information.

[0127] In this alternative implementation, the data to be processed includes text data, and the word segmentation result is at least one word. The above-mentioned matching the word segmentation result with at least one piece of natural phenomenon information in the natural science subject type includes: matching each word in the word segmentation result with at least one piece of natural phenomenon information in the natural science subject type to obtain a conclusion on whether each word matches the natural phenomenon information.

[0128] In this alternative implementation, the natural phenomenon information of the natural science subject type is existing natural phenomenon texts, such as heavy rain, lightning, magnetic field, etc. The above-mentioned matching each word in the word segmentation result with at least one piece of natural phenomenon information in the natural science subject type means: calculating the similarity between each word and each piece of natural phenomenon information in the natural science subject type, and when the similarity between the word and the natural phenomenon information is greater than the similarity threshold, determining that the word matches the natural phenomenon information.

[0129] The method for detecting whether there is natural phenomenon information provided by this alternative implementation, in response to the subject type belonging to the scientific subject type, performs word segmentation on the data to be processed, matches the word segmentation result with at least one piece of natural phenomenon information in the natural science subject type; and in response to the word segmentation result matching at least one piece of natural phenomenon information successfully, determines that the content has natural phenomenon information, improving the comprehensiveness of the analysis of natural phenomenon information.

[0130] In some alternative implementations of the present disclosure, the above-mentioned obtaining the data to be processed includes: obtaining acquisition data including at least one type of modal data; converting the acquisition data into text data; and using the text data as the data to be processed.

[0131] In this alternative implementation, the acquisition data is data directly obtained from the sender, and this data includes at least one type of modal data, and the modal data can be any one of image data, voice data, and text data.

[0132] In this alternative implementation manner, the above-mentioned conversion of the acquired data into text data includes: determining a text conversion tool based on the modality data in the acquired data; using the text conversion tool to convert the modality data in the acquired data into text data. The text conversion tool can be an image-to-text tool, or a voice data-to-text tool, or a video data-to-text tool. The specific principles and implementation manners of the text conversion tool are all mature technologies in the traditional technology and will not be elaborated here.

[0133] The method for extracting key features provided by this alternative implementation manner improves the reliability of the obtained data to be processed by converting the acquired data into text data when the acquired data includes multi-modal data.

[0134] Optionally, the data to be processed can also be a type of multi-modal data. The above-mentioned acquisition of the data to be processed includes: acquiring initial data including at least one type of modality data, and performing data preprocessing on the initial data to obtain the data to be processed.

[0135] For further reference Figure 3 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a data processing device. This device embodiment corresponds to Figure 1 the method embodiment shown, and this device can be specifically implemented in various electronic devices.

[0136] As Figure 3 shown, the data processing device 300 provided in this embodiment includes: an acquisition unit 301, a classification unit 302, a detection unit 303, an extraction unit 304, and an early warning unit 305. Among them, the above-mentioned acquisition unit 301 is configured to acquire the data to be processed. The above-mentioned classification unit 302 can be configured to classify the content of the data to be processed and determine the subject type of the data to be processed. The above-mentioned detection unit 303 can be configured to detect whether the content belongs to natural law information in response to the subject type belonging to the scientific subject type. The above-mentioned extraction unit 304 can be configured to extract the main information of the content in response to detecting that the content does not belong to natural law information. The above-mentioned early warning unit 305 can be configured to perform early warning on the main content of the data to be processed based on the main information.

[0137] In this embodiment, in the data processing device 500: the specific processing of the acquisition unit 301, the classification unit 302, the detection unit 303, the extraction unit 304, and the early warning unit 305 and the technical effects brought by them can respectively refer to Figure 1 the relevant descriptions of step 101, step 102, step 103, step 104, and step 105 in the corresponding embodiments, and will not be elaborated here.

[0138] In some alternative implementation manners of this embodiment, the above-mentioned apparatus 500 further includes: a regularity warning unit (not shown in the figure), and the regularity warning unit is configured to: in response to detecting that the content has natural phenomenon information, detect whether the content meets the natural law conditions; in response to the content not meeting the natural law conditions, send a regularity warning message.

[0139] In some alternative implementation manners of this embodiment, the above-mentioned regularity warning unit is further configured to: in response to detecting that the content has natural phenomenon information, extract the environmental features of the object with natural phenomenon information; detect whether the environmental features meet the phenomenon formation conditions; in response to detecting that the environmental features meet the phenomenon formation conditions, determine that the content meets the natural law conditions; in response to detecting that the environmental features do not meet the phenomenon formation conditions, determine that the content does not meet the natural law conditions.

[0140] In some alternative implementation manners of this embodiment, the above-mentioned apparatus 500 further includes: a historical warning unit (not shown in the figure), and the historical warning unit is configured to: in response to the subject type not belonging to the scientific subject type, detect whether the subject type belongs to the historical subject; in response to the subject type belonging to the historical subject, extract the main idea information of the content; based on the main idea information, perform main idea content warning on the data to be processed.

[0141] In some alternative implementation manners of this embodiment, the above-mentioned historical warning unit is configured to: detect whether the main idea information meets the main idea warning conditions; in response to detecting that the main idea information meets the main idea warning conditions, send a main idea warning message.

[0142] In some alternative implementation manners of this embodiment, the above-mentioned apparatus 300 further includes: a subjective and objective warning unit (not shown in the figure), and the subjective and objective warning unit is configured to: in response to detecting that the main idea information does not meet the main idea warning conditions, extract the subjective and objective information of the content; based on the subjective and objective information, perform subjective and objective content warning on the data to be processed.

[0143] In some alternative implementation manners of this embodiment, the above-mentioned subjective and objective information includes: subjective information and objective information, and the subjective and objective warning unit is configured to: in response to detecting that the main idea information does not meet the main idea warning conditions, extract the narrative elements in the content and use the narrative elements as objective information; extract the emotional tendency text in the content and use the emotional tendency text as subjective information.

[0144] In some alternative implementation manners of this embodiment, the above device 300 includes: a bias warning unit (not shown in the figure), and the bias warning unit is configured to: in response to the subject type not belonging to the history subject, extract the opinion bias information of the content; obtain the user characteristics and behavior patterns of the data to be processed; determine the target group of the data to be processed based on the user characteristics and behavior patterns; and perform group bias warning on the data to be processed based on the opinion bias information and the target group.

[0145] In some alternative implementation manners of this embodiment, the above bias warning unit is configured to: in response to detecting that the opinion bias information is negative and the target group is the young group, send a group opinion warning information.

[0146] In some alternative implementation manners of this embodiment, the above classification unit 302 is configured to: preprocess the data to be processed to obtain preprocessed data; detect whether there is a subject element of any one of multiple subject types in the content of the preprocessed data; and in response to the preprocessed data having the subject element of this subject type, determine the subject type of the data to be processed as this subject type.

[0147] In some alternative implementation manners of this disclosure, the above detection unit 303 is configured to: in response to the subject type belonging to the science subject type, perform word segmentation on the data to be processed to obtain a word segmentation result; match the word segmentation result with at least one natural phenomenon information in the natural science subject type; and in response to the word segmentation result matching successfully with at least one natural phenomenon information, determine that the content has natural phenomenon information.

[0148] In some alternative implementation manners of this embodiment, the above acquisition unit 301 is configured to: acquire acquisition data including at least one modality data; convert the acquisition data into text data; and use the text data as the data to be processed.

[0149] The data processing device provided in the embodiment of this disclosure, first, the acquisition unit 301 acquires the data to be processed; second, the classification unit 302 classifies the content of the data to be processed to determine the subject type of the data to be processed; third, the detection unit 303, in response to the subject type belonging to the science subject type, detects whether the content has natural phenomenon information; fourth, the extraction unit 304, in response to detecting that the content does not have natural phenomenon information, extracts the main idea information of the content; and finally, the warning unit 305 performs main idea content warning on the data to be processed based on the main idea information. Thus, on the basis of detecting the subject type of the data to be processed, by detecting the natural law information and the main idea information, the detection reliability of the data to be processed is improved.

[0150] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0151] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0152] Figure 4 A schematic block diagram of an exemplary electronic device 400 that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0153] As Figure 4 shown, the device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0154] Multiple components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the data processing method. For example, in some embodiments, the data processing method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the data processing method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the data processing method by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0160] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an information server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0161] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0163] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A data processing method, the method comprising: Get the data to be processed; Classifying the content of the data to be processed to determine the subject type of the data to be processed; In response to the subject type belonging to the science subject type, detecting whether the content has natural phenomenon information; In response to detecting that the content does not have natural phenomenon information, extracting subject information of the content; Based on the subject information, a subject content warning is issued for the data to be processed.

2. The method according to claim 1, further comprising: In response to detecting that the content has natural phenomenon information, detecting whether the content conforms to natural law conditions; In response to the content not meeting the conditions of the laws of nature, a law warning message is sent.

3. The method according to claim 2, wherein: In response to detecting that the content has natural phenomenon information, detecting whether the content conforms to the law of nature includes: In response to detecting that the content has natural phenomenon information, extracting environmental features of an object having the natural phenomenon information; Detecting whether the environmental characteristics meet the conditions for the formation of the phenomenon; In response to detecting that the environmental feature meets the phenomenon formation condition, determining that the content meets the law of nature condition; In response to detecting that the environmental feature does not meet the phenomenon formation condition, it is determined that the content does not meet the natural law condition.

4. The method according to claim 1, further comprising: In response to the subject type not belonging to the science subject type, detecting whether the subject type belongs to the history subject type; In response to the subject type belonging to the history subject, extracting the subject information of the content; Based on the subject information, a subject content warning is issued for the data to be processed.

5. The method according to any one of claims 1 to 4, wherein: According to the subject information, the subject content warning of the data to be processed includes: Detecting whether the subject information meets the subject warning condition; In response to detecting that the subject information satisfies a subject warning condition, subject warning information is sent.

6. The method according to claim 5, further comprising: In response to detecting that the subject information does not satisfy the subject warning condition, extracting subjective and objective information of the content; Based on the subjective and objective information, a subjective and objective content warning is performed on the data to be processed.

7. The method according to claim 6, wherein: The subjective and objective information includes: subjective information and objective information, and in response to detecting that the subject information does not meet the subject warning condition, extracting the subjective and objective information of the content includes: In response to detecting that the subject information does not satisfy the subject warning condition, extracting narrative elements from the content and using the narrative elements as objective information; The sentiment tendency text in the content is extracted, and the sentiment tendency text is used as the subjective information.

8. The method according to claim 4, further comprising: In response to the subject type not belonging to the history subject, extracting the viewpoint bias information of the content; Acquire user characteristics and behavior patterns of the data to be processed; Determining a target group of the data to be processed based on the user characteristics and the behavior patterns; Based on the opinion bias information and the target group, a group bias warning is provided for the data to be processed.

9. The method according to claim 8, wherein: The performing group bias warning on the data to be processed based on the opinion bias information and the target group includes: In response to detecting that the opinion bias information is negative and the target group is a young group, group opinion warning information is sent.

10. The method according to claim 1, wherein: The classifying the content of the data to be processed and determining the subject type of the data to be processed includes: Preprocessing the data to be processed to obtain preprocessed data; Detecting whether the content of the preprocessed data contains subject elements of any subject type among multiple subject types; In response to the pre-processed data having a subject element of the subject type, the subject type of the data to be processed is determined to be the subject type.

11. The method according to claim 1, wherein: In response to the subject type belonging to the science subject type, detecting whether the content has natural phenomenon information includes: In response to the subject type belonging to the science subject type, performing word segmentation on the data to be processed to obtain a word segmentation result; Matching the word segmentation result with at least one natural phenomenon information in the natural science subject type; In response to the word segmentation result successfully matching at least one kind of natural phenomenon information, it is determined that the content has natural phenomenon information.

12. The method according to claim 1, wherein: The obtaining of data to be processed comprises: acquiring acquisition data including at least one modality data; Converting the acquired data into text data; The text data is used as data to be processed.

13. A data processing device, comprising: An acquisition unit, configured to acquire data to be processed; A classification unit, configured to classify the content of the data to be processed and determine the subject type of the data to be processed; a detection unit configured to detect whether the content belongs to natural law information in response to the subject type belonging to the science subject type; an extraction unit, configured to extract subject information of the content in response to detecting that the content does not belong to natural law information; The early warning unit is configured to issue a subject content early warning to the data to be processed based on the subject information.

14. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 12.

15. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 12.

16. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 12.

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