Public opinion direction confirmation method, system and device based on comment data

Abstract

The invention provides a public opinion direction confirmation method, system and device based on comment data, and the method comprises the steps: obtaining multi-type comment data related to a preset theme in a plurality of online platforms based on the driving of an event, employing an event triggering mechanism, automatically recognizing and capturing data related to a specific event, and determining the direction of the public opinion. Performing multi-source data fusion and data processing on the data, constructing a causal relationship model by combining the processed data with a causal inference strategy, analyzing each specific time, and determining whether the specific event guides the change direction of the public opinion; according to a specific event and in combination with related historical data, if corresponding intervention measures are taken, an anti-fact scene corresponding to the public opinion is generated, and an anti-fact analysis result is generated; public opinion direction prediction based on a specific event is generated in combination with a causal relationship model and an anti-fact analysis result; according to the method, the change of the public opinion based on the multi-source data can be analyzed and predicted, and the prediction of the public opinion direction is effectively realized.

Description

Technical Field

[0001] The present application belongs to the field of data processing and information analysis technology, and in particular relates to a method, system and device for confirming the direction of public opinion based on comment data. Background Art

[0002] With the increasing popularity of the internet and the rapid development of social media, the ways in which the public expresses their opinions have become more diverse, and the importance of public opinion analysis has become increasingly prominent. Traditional public opinion analysis methods primarily rely on sentiment analysis and keyword matching, but these methods often only reveal superficial correlations and fail to provide a deeper understanding of the causal relationships between events. Furthermore, traditional methods lack the ability to evaluate the effectiveness of interventions, making it impossible to provide effective action recommendations for decision makers.

[0003] In recent years, after an event occurs, relevant information and data are disseminated through various channels, and public commentary is diverse and has varying inclinations. These comments range from rational and objective assessments to exaggerated and even subjective statements that incite subversion, potentially forming inflammatory public opinion. To address these public sentiments, we need to more quickly and accurately predict their changing trends and provide scientific evidence for decision-makers.

[0004] Existing methods have limitations in data acquisition, processing and analysis, and are unable to fully utilize the advantages of multi-source data or respond to the rapidly changing public opinion environment in real time.

[0005] Therefore, there is an urgent need for a method that can monitor public opinion changes in real time based on multi-source data and accurately analyze the direction of public opinion, so as to provide decision makers with more accurate predictions of the direction of public opinion. Summary of the Invention

[0006] This application provides a method for confirming the direction of public opinion based on comment data. This solution collects multi-source data based on event-driven methods, and is based on causal inference and counterfactual analysis methods to achieve real-time monitoring of public opinion changes and accurately analyze and predict the direction of public opinion.

[0007] In a first aspect, an embodiment of the present application provides a method for confirming the direction of public opinion based on comment data, the method comprising: obtaining multi-type comment data related to a preset topic from multiple online platforms based on an event drive, automatically identifying and capturing data related to a specific event using an event triggering mechanism to generate a first data set; fusing multi-source data on the first data set to generate a second data set containing comprehensive public opinion information that conforms to the preset topic; preprocessing the second data set, constructing a unified data format for the data in the preprocessed second data set, and generating a third data set with data consistency; based on the data of the third data set and a causal inference strategy, constructing a causal relationship model based on the data related to the specific event, analyzing each specific time to determine whether the specific event guides the direction of change in public opinion; if the specific event guides the direction of change in public opinion, recording the specific event as a public opinion-oriented event; based on the data related to the specific event in the third data set and related historical data, evaluating if corresponding intervention measures are taken for the public opinion-oriented event, generating a counterfactual scenario of the intervention measures on the public opinion, and generating a counterfactual analysis result based on the counterfactual scenario; combining the causal relationship model and the counterfactual analysis result to generate a public opinion direction prediction based on the specific event.

[0008] By adopting the above scheme, the method for confirming the direction of public opinion based on comment data can combine event-driven data collection and multi-source data fusion, and based on causal inference and counterfactual analysis, it can deeply reveal the causal relationship between events, evaluate the effectiveness of intervention measures, and provide decision makers with accurate predictions of the direction of public opinion.

[0009] In some embodiments of the present invention, the generation of the first data set also includes: by setting keywords or event templates according to the specific events, real-time monitoring of the occurrence of specific events in the comment data, automatically triggering the acquisition of the comment data when relevant events are detected, and generating comment data with event type and trigger condition identification.

[0010] In some embodiments of the present invention, the multi-source data fusion includes: extracting non-text key information from the multi-source data, combining the non-text key information with the text data in the multi-source data, and generating multi-source data fusion information.

[0011] In some embodiments of the present invention, the construction of the causal relationship model also includes: extracting events from the third data set, and constructing an event graph based on the causal relationship between the events; based on the event graph, using a graph theory algorithm to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

[0012] In some embodiments of the present invention, before combining the causal relationship model and the counterfactual analysis results to generate a prediction of the direction of public opinion based on a specific event, the method also includes: collecting the actual public opinion situation, comparing the actual public opinion situation with the counterfactual scenario, and evaluating the effectiveness of the intervention measures; adjusting the parameters in the counterfactual scenario based on the effectiveness of the intervention measures, analyzing the sensitivity of the parameters to changes in public opinion, and finding the key driving parameters.

[0013] This solution, by combining event-driven data collection with multi-source data fusion, introduces causal inference and counterfactual analysis techniques. Combining causal models with counterfactual analysis results, it can deeply reveal the causal relationships between events and evaluate the effectiveness of interventions, providing a scientific basis for decision makers to predict the direction of public opinion. Furthermore, this solution supports the fusion of multi-source data, fully utilizing data from different channels to provide a more comprehensive perspective on public opinion. This solution can help users timely grasp public opinion trends and predict their direction.

[0014] In the second aspect, an embodiment of the present application provides a public opinion direction confirmation system based on comment data, the system comprising: an event-driven data acquisition module, the event-driven data acquisition module is used to obtain multi-type comment data related to preset topics in multiple online platforms based on event-driven, and adopts an event trigger mechanism to automatically identify and capture data related to specific events to generate a first data set; a multi-source data fusion module, the multi-source data fusion module is used to perform multi-source data fusion on the first data set to generate a second data set of comprehensive public opinion information that conforms to the preset topic; a preprocessing module, the preprocessing module is used to preprocess the second data set, construct a unified data format for the data in the preprocessed second data set, and generate a third data set with data consistency; a causal relationship modeling module, the causal relationship modeling module The causal relationship modeling module is used to construct a causal relationship model based on the data related to the specific event based on the data of the third data set and the causal inference strategy, analyze each specific time, and determine whether the specific event guides the direction of change of public opinion; if the specific event guides the direction of change of public opinion, then the specific event is recorded as a public opinion-oriented event; the counterfactual analysis module is used to evaluate the corresponding intervention measures if they are taken for the public opinion-oriented event based on the data related to the specific event of the third data set and related historical data, generate counterfactual scenarios of the intervention measures on the public opinion, and generate counterfactual analysis results based on the counterfactual scenarios; the public opinion direction confirmation module is used to combine the causal relationship model and the counterfactual analysis results to generate a public opinion direction prediction based on a specific event.

[0015] In some embodiments of the present invention, the system also includes a real-time event monitoring module, which is used to: monitor the occurrence of specific events in the comment data in real time by setting keywords or event templates according to the specific events, automatically trigger the acquisition of the comment data when relevant events are detected, and generate comment data with event type and trigger condition identification.

[0016] In some embodiments of the present invention, the causal relationship modeling module also includes: extracting events from the third data set, and constructing an event graph based on the causal relationship between the events; based on the event graph, using a graph theory algorithm to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

[0017] In some embodiments of the present invention, the public opinion direction confirmation module also includes, before combining the causal relationship model and the counterfactual analysis results to generate a public opinion direction prediction based on a specific event: collecting the actual public opinion situation, comparing the actual public opinion situation with the counterfactual scenario, and evaluating the effectiveness of the intervention measures; adjusting the parameters in the counterfactual scenario based on the effectiveness of the intervention measures, analyzing the sensitivity of the parameters to changes in public opinion, and finding the key driving parameters.

[0018] The above solution, through an event-driven data collection mechanism, can monitor public opinion changes in real time and quickly respond to the rapidly changing public opinion environment. Through causal inference, the system can analyze the causal relationship between events, not just the correlation, providing more accurate public opinion analysis results. Through counterfactual analysis, the system can evaluate the actual effects of intervention measures, providing decision makers with a scientific basis and optimizing future action strategies. The system supports the integration of multi-source data, fully utilizing data from different channels to provide a more comprehensive perspective on public opinion. The system has strong real-time performance, can quickly respond to the rapidly changing public opinion environment, and can accurately predict the direction of public opinion by combining causal relationship models and counterfactual analysis results.

[0019] On the third aspect, an embodiment of the present application provides a device for confirming the direction of public opinion based on comment data, which includes a computer device, the computer device including a processor and a memory, the memory storing computer instructions, and the processor being used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the method for confirming the direction of public opinion based on comment data.

[0020] Additional advantages, objects, and features of the present invention will be described in part in the following description and will become apparent to those skilled in the art after studying the following or may be learned by practice of the present invention. The objects and other advantages of the present invention may be particularly pointed out and attained in the description and drawings.

[0021] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure, but do not constitute a limitation of the present disclosure.

[0023] In the attached figure:

[0024] Figure 1 This is a schematic diagram of an implementation method of the public opinion direction confirmation method based on comment data;

[0025] Figure 2 This is a schematic diagram of an implementation of the public opinion direction confirmation system based on comment data;

[0026] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0030] Generally speaking, the existing online public opinion supervision system is unable to efficiently and accurately predict complex public opinion trends.

[0031] Therefore, in order to predict the direction of public opinion, the present application provides a method, system and device for confirming the direction of public opinion based on comment data.

[0032] Figure 1 This is a schematic diagram of a method for confirming the direction of public opinion based on comment data provided in an embodiment of the present application.

[0033] First, as Figure 1 As shown, the embodiment of the present application provides a method for confirming the direction of public opinion based on comment data, and the method includes the following steps:

[0034] S1: Based on event-driven acquisition of multi-type comment data related to preset topics from multiple online platforms, an event triggering mechanism is used to automatically identify and capture data related to specific events to generate the first data set.

[0035] S2: The first data set is subjected to multi-source data fusion to generate a second data set of comprehensive public opinion information that conforms to a preset theme.

[0036] S3: Preprocessing the second data set, constructing a unified data format for the data in the preprocessed second data set, and generating a third data set with data consistency.

[0037] S4: Based on the data of the third data set and the causal inference strategy, a causal relationship model based on the data related to the specific event is constructed, and each specific time is analyzed to determine whether the specific event guides the direction of change of public opinion; if the specific event guides the direction of change of public opinion, then the specific event is recorded as a public opinion-oriented event.

[0038] S5: Based on the data related to the specific event in the third data set and the related historical data, evaluate if corresponding intervention measures are taken for the public opinion-oriented event, generate a counterfactual scenario of the intervention measures on the public opinion, and generate a counterfactual analysis result based on the counterfactual scenario.

[0039] S6: Combine the causal relationship model and counterfactual analysis results to generate a prediction of the direction of public opinion based on specific events.

[0040] By adopting the above scheme, the method for confirming the direction of public opinion based on comment data can combine event-driven data collection and multi-source data fusion, and based on causal inference and counterfactual analysis, it can deeply reveal the causal relationship between events, evaluate the effectiveness of intervention measures, and provide decision makers with accurate predictions of the direction of public opinion.

[0041] In some embodiments of the present invention, in step S1, the generation of the first data set also includes: by setting keywords or event templates according to the specific events, real-time monitoring of the occurrence of specific events in the comment data, automatically triggering the acquisition of the comment data when relevant events are detected, and generating comment data with event type and trigger condition identification.

[0042] In some embodiments of the present invention, the multi-source data fusion includes: extracting non-text key information from the multi-source data, combining the non-text key information with text data in the multi-source data, and generating multi-source data fusion information.

[0043] In some embodiments of the present invention, the construction of the causal relationship model also includes: extracting events from the third data set, and constructing an event graph based on the causal relationship between the events; based on the event graph, using a graph theory algorithm to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

[0044] In some embodiments of the present invention, before combining the causal relationship model and the counterfactual analysis results to generate a prediction of the direction of public opinion based on a specific event, the method also includes: collecting the actual public opinion situation, comparing the actual public opinion situation with the counterfactual scenario, and evaluating the effectiveness of the intervention measures; adjusting the parameters in the counterfactual scenario based on the effectiveness of the intervention measures, analyzing the sensitivity of the parameters to changes in public opinion, and finding the key driving parameters.

[0045] During the specific implementation process, multi-source data fusion processing is performed on the collected multi-source data; the causal relationship between events is established based on causal inference theory, and it is assumed that a certain intervention measure has not occurred, its impact on public opinion is simulated, and a public opinion direction evolution map is generated; the causal relationship model and counterfactual analysis results are combined to confirm the direction of public opinion and its changing trend. The above processing can provide more accurate public opinion direction analysis results.

[0046] Figure 2 It is a schematic diagram of an implementation method of the public opinion direction confirmation system based on comment data.

[0047] Second, as Figure 2 As shown, an embodiment of the present application provides a system for confirming the direction of public opinion based on comment data, the system comprising:

[0048] The event-driven data acquisition module 101 is used to obtain multi-type comment data related to preset topics in multiple online platforms based on event-driven, and adopts an event trigger mechanism to automatically identify and capture data related to specific events to generate a first data set.

[0049] The multi-source data fusion module 102 is used to perform multi-source data fusion on the first data set to generate a second data set of comprehensive public opinion information that meets a preset theme.

[0050] The system monitors and obtains data related to designated topics in real time from multiple sources, including multiple online platforms (such as Weibo, WeChat, Twitter, Facebook), news media (such as Xinhua News Agency, People's Daily, BBC, CNN), government announcements (such as the Chinese government website and the official website of the White House), and corporate statements (such as company websites and news release platforms).

[0051] The system also supports other types of public data sources, such as forums, blogs, comment areas, etc., to ensure the diversity and comprehensiveness of the data.

[0052] The system monitors the occurrence of specific events by setting keywords and event templates. For example, when keywords such as "policy release," "emergency," and "major news report" are detected, the system will automatically trigger a data collection task.

[0053] The preprocessing module 103 is used to preprocess the second data set, construct a unified data format for the data in the preprocessed second data set, and generate a third data set with data consistency.

[0054] Specifically, the system converts data in different formats (such as JSON, XML, CSV, and HTML) into a unified intermediate representation for easy subsequent processing. For example, the system can convert JSON data into a Python dictionary, XML data into a DOM tree structure, and CSV data into a Pandas DataFrame.

[0055] The system uses computer vision, speech recognition and other technologies to process non-text data such as images, videos, and audio, extract key information from them, and combine it with text data to provide a more comprehensive public opinion analysis. For example, the system can use image recognition technology to analyze the image content in news reports; use speech recognition technology to convert audio comments into text for further sentiment analysis and topic classification.

[0056] The causal relationship modeling module 104 is used to construct a causal relationship model based on the data of the third data set and the causal inference strategy based on the data related to the specific event, analyze each specific time, and determine whether the specific event guides the direction of change of public opinion; if the specific event guides the direction of change of public opinion, then the specific event is recorded as a public opinion-oriented event.

[0057] Specifically, the system constructs an event graph based on the causal relationships between events. Nodes in the graph represent different events, and edges represent the causal relationships between events. For example, the system can use causal models (such as DAG and Do-Calculus) to analyze whether a policy release has led to changes in public sentiment or whether a sudden event has sparked social concern.

[0058] The system can also use natural language processing technology (such as named entity recognition and relationship extraction) to identify the connections between events and form an event network, helping the system to more accurately determine which events may affect public opinion.

[0059] Furthermore, as new data is added, the system automatically updates the causal relationship model to maintain its timeliness and accuracy. For example, the system can dynamically adjust the causal relationships between events based on the latest comments, news reports, policy documents, and other data to ensure that the model reflects the latest changes in public opinion.

[0060] The counterfactual analysis module 105 is used to evaluate the corresponding intervention measures if they are taken for the public opinion-oriented event based on the data related to the specific event in the third data set and the related historical data, generate a counterfactual scenario of the intervention measures on the public opinion, and generate a counterfactual analysis result based on the counterfactual scenario.

[0061] Based on historical data and current events, the system generates multiple possible counterfactual scenarios, simulating the impact of different interventions on public opinion. For example, the system can simulate the impact on public sentiment if a policy is not released, or the impact on social attention if an emergency does not occur.

[0062] The system can also adjust parameters in counterfactual scenarios through sensitivity analysis, analyze the sensitivity of different factors to changes in public opinion, and identify key driving factors.

[0063] By adjusting parameters in counterfactual scenarios, the system analyzes the sensitivity of different factors to changes in public opinion and identifies key drivers. For example, the system can adjust parameters such as the timing, content, and dissemination channels of policy releases to analyze their impact on public sentiment; it can also adjust parameters such as the scale, location, and time of an emergency to analyze its impact on social attention.

[0064] The system displays public opinion data to users via trend charts, helping them intuitively understand how public opinion changes over time. For example, the system can create trend charts for sentiment ratios to show how public sentiment is changing, and trend charts for topic popularity to show how attention to different topics is changing.

[0065] The public opinion direction confirmation module 106 is used to combine the causal relationship model and the counterfactual analysis results to generate a public opinion direction prediction based on a specific event.

[0066] The system conducts statistical analysis on public opinion data and calculates key indicators (such as sentiment ratio, topic distribution, hot topics, etc.) to help users understand the overall situation of public opinion. For example, the system can count the ratio of positive, negative, and neutral comments to analyze the changing trend of public sentiment; count the attention of different topics and analyze the hot topics of public concern.

[0067] The system can also select representative comments or events for in-depth analysis, helping users understand the specific reasons for changes in public opinion. For example, the system can analyze the reasons behind a popular comment to identify the factors that attracted widespread attention; or analyze the impact of a sudden event to identify its specific contribution to changes in public opinion.

[0068] Based on the analysis results, the system proposes response strategies or improvement measures to help users take effective action. For example, the system can recommend that the government adjust policy content based on changes in public sentiment, or recommend that companies optimize their brand image based on hot topics of public concern.

[0069] In some embodiments of the present invention, the system also includes a real-time event monitoring module, which is used to: monitor the occurrence of specific events in the comment data in real time by setting keywords or event templates according to the specific events, automatically trigger the acquisition of the comment data when relevant events are detected, and generate comment data with event type and trigger condition identification.

[0070] The system can also capture relevant data in real time through APIs or crawler technology, ensuring its timeliness and accuracy. For important events, the system can set a higher collection frequency, such as once every minute or hour; for less common events, the system can set a lower collection frequency, such as once every day or every week. The system also supports adaptive adjustment of the collection frequency, dynamically adjusting the frequency based on the importance of the event and the speed of public opinion changes to ensure the timeliness of the data.

[0071] The system monitors the occurrence of specific events in real time, such as emergencies, recent events, and epidemic announcements. Based on the data collected in real time, the system can analyze the reasons for a popular comment and find out the factors that have attracted widespread attention; analyze the impact of a sudden event and find out its specific contribution to changes in public opinion.

[0072] In some embodiments of the present invention, the causal relationship modeling module also includes: extracting events from the third data set, and constructing an event graph based on the causal relationship between the events; based on the event graph, using a graph theory algorithm to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

[0073] The system uses graph theory algorithms (such as shortest path and critical path analysis) to identify the main paths of event propagation and the scope of influence, helping the system better understand the evolution of public opinion. For example, the system can use the shortest path algorithm to find the fastest propagation path of public sentiment changes after a policy is released; through critical path analysis, it can identify the key events and factors that influence changes in public opinion.

[0074] In some embodiments of the present invention, the public opinion direction confirmation module also includes, before combining the causal relationship model and the counterfactual analysis results to generate a public opinion direction prediction based on a specific event: collecting the actual public opinion situation, comparing the actual public opinion situation with the counterfactual scenario, and evaluating the effectiveness of the intervention measures; adjusting the parameters in the counterfactual scenario based on the effectiveness of the intervention measures, analyzing the sensitivity of the parameters to changes in public opinion, and finding the key driving parameters.

[0075] During implementation, the system compares actual public opinion changes with counterfactual scenarios, assessing the effectiveness of each intervention and helping decision-makers optimize future action strategies. For example, through comparative analysis, the system can assess whether the shift in public sentiment following a policy announcement is in line with expectations, or whether the level of public concern following an emergency exceeds normal levels.

[0076] The above solution can address the limitations of existing methods for predicting the direction of public opinion in data acquisition, processing, and analysis, as well as their inability to fully utilize the advantages of multi-source data and respond to rapidly changing situations in real time. This solution, through an event-driven data collection mechanism, can monitor public opinion changes in real time and quickly respond to rapidly changing public opinion environments. Through causal inference, the system can analyze the causal relationships between events, not just correlations, providing more accurate public opinion analysis results. Through counterfactual analysis, the system can evaluate the actual effects of intervention measures, providing decision makers with a scientific basis and optimizing future action strategies. The system supports the fusion of multi-source data, fully utilizing data from different channels to provide a more comprehensive perspective on public opinion. The system has strong real-time capabilities, can quickly respond to rapidly changing public opinion environments, and can accurately predict the direction of public opinion by combining causal models and counterfactual analysis results.

[0077] In the third aspect, an embodiment of the present application provides a device for confirming the direction of public opinion based on comment data, which includes a computer device, the computer device including a processor and a memory, the memory storing computer instructions, and the processor being used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps implemented by the method for confirming the direction of public opinion based on comment data.

[0078] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the above-mentioned public opinion direction confirmation system based on comment data is implemented.

[0079] Figure 3 It is a structural diagram of an electronic device provided in one embodiment of the present application.

[0080] like Figure 3 As shown, an embodiment of the present application provides an electronic device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the above-mentioned public opinion direction confirmation system based on comment data is implemented.

[0081] The electronic device may include a processor 1201 and a memory 1202 storing computer program instructions.

[0082] Specifically, the processor 1201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0083] The memory 1202 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1202 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 1202 may include removable or non-removable (or fixed) media. Where appropriate, the memory 1202 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 1202 is a non-volatile solid-state memory.

[0084] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0085] The processor 1201 reads and executes computer program instructions stored in the memory 1202 to implement any one of the methods for determining battery thermal runaway parameters in the above embodiments.

[0086] In one example, the electronic device may further include a communication interface 1203 and a bus 1210. Figure 3 As shown, the processor 1201 , the memory 1202 , and the communication interface 1203 are connected via a bus 1210 and communicate with each other.

[0087] The communication interface 1203 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0088] Bus 1210 includes hardware, software or both, couples the parts of electronic equipment to each other.For example, but not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1210 can include one or more buses. Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0089] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0090] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0091] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0092] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0093] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A method for confirming the direction of public opinion based on comment data, characterized in that: The method comprises: Acquire multi-type comment data related to preset topics from multiple online platforms based on event-driven methods, and automatically identify and capture data related to specific events using an event triggering mechanism to generate a first data set; Perform multi-source data fusion on the first dataset to generate a second dataset containing comprehensive public opinion information that meets the preset theme; Preprocessing the second data set, constructing the data in the preprocessed second data set into a unified data format, and generating a third data set with data consistency; Based on the data from the third dataset and the causal inference strategy, a causal relationship model based on the data related to the specific event is constructed. Each specific time is analyzed to determine whether the specific event leads to the direction of change in public opinion. If the specific event leads to the direction of change in public opinion, the specific event is recorded as a public opinion-oriented event. Based on the data related to the specific event in the third dataset and related historical data, evaluating the effects of intervention measures on the public opinion-oriented event, generating counterfactual scenarios of the intervention measures on the public opinion, and generating counterfactual analysis results based on the counterfactual scenarios; Combining the causal relationship model and counterfactual analysis results, a prediction of the direction of public opinion based on specific events is generated.

2. The method according to claim 1, characterized in that Generating the first data set further includes: By setting keywords or event templates according to the specific events, the occurrence of specific events in the comment data is monitored in real time, and when relevant events are detected, the acquisition of the comment data is automatically triggered to generate comment data with event type and trigger condition identification.

3. The method according to claim 1, characterized in that The multi-source data fusion includes: Extract non-text key information from multi-source data, combine the non-text key information with text data in the multi-source data, and generate multi-source data fusion information.

4. The method according to claim 1, wherein The construction of the causal relationship model also includes: Extracting events from the third data set, and constructing an event graph based on causal relationships between the events; Based on the event graph, a graph theory algorithm is used to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

5. The method according to claim 1, wherein Combining the causal model and counterfactual analysis results, Before generating a prediction of the direction of public opinion based on a specific event, the method further includes: Collect actual public opinion, compare it with the counterfactual scenario, and evaluate the effectiveness of the intervention measures; Adjust the parameters in the counterfactual scenario based on the effects of the intervention measures, analyze the sensitivity of the parameters to changes in public opinion, and identify the key driving parameters.

6. A system for confirming the direction of public opinion based on comment data for implementing the method according to any one of claims 1 to 5, characterized in that: The system comprises: An event-driven data collection module, which is used to obtain multi-type comment data related to a preset topic from multiple online platforms based on event-driven methods, automatically identifying and capturing data related to a specific event using an event triggering mechanism to generate a first data set; A multi-source data fusion module, configured to perform multi-source data fusion on the first data set to generate a second data set containing comprehensive public opinion information that conforms to a preset theme; a preprocessing module configured to preprocess the second data set, construct a unified data format for the data in the preprocessed second data set, and generate a third data set with data consistency; A causal relationship modeling module is used to construct a causal relationship model based on the data of the third data set and the causal inference strategy based on the data related to the specific event, analyze each specific time, and determine whether the specific event leads to the direction of change in public opinion; if the specific event leads to the direction of change in public opinion, then record the specific event as a public opinion-oriented event; a counterfactual analysis module, the counterfactual analysis module being configured to evaluate, based on data related to a specific event in a third data set and related historical data, what would have happened if corresponding intervention measures had been taken with respect to the public opinion-oriented event, generate counterfactual scenarios of the intervention measures affecting the public opinion, and generate counterfactual analysis results based on the counterfactual scenarios; The public opinion direction confirmation module is used to combine the causal relationship model and the counterfactual analysis results to generate a public opinion direction prediction based on a specific event.

7. The system according to claim 6, characterized in that It also includes an event real-time monitoring module, which is used to: By setting keywords or event templates according to the specific events, the occurrence of specific events in the comment data is monitored in real time, and when relevant events are detected, the acquisition of the comment data is automatically triggered to generate comment data with event type and trigger condition identification.

8. The system according to claim 6, wherein: The Causal Modeling module also includes: Extracting events from the third data set, and constructing an event graph based on causal relationships between the events; Based on the event graph, a graph theory algorithm is used to identify the propagation path and impact range of the event, and generate an evolution graph of the public opinion direction.

9. The system according to claim 6, wherein: The public opinion direction confirmation module also includes combining the causal relationship model and counterfactual analysis results to generate a public opinion direction prediction based on a specific event: Collect actual public opinion, compare it with the counterfactual scenario, and evaluate the effectiveness of the intervention measures; Adjust the parameters in the counterfactual scenario based on the effects of the intervention measures, analyze the sensitivity of the parameters to changes in public opinion, and identify the key driving parameters.

10. A device for confirming the direction of public opinion based on comment data, characterized in that: The device includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the method for confirming the direction of public opinion based on comment data as described in any one of claims 1-6.

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