Propaganda method and system for countering network fraud, medium and electronic equipment

Abstract

The invention discloses a publicity method and system for countering network fraud, a storage medium and electronic equipment, and relates to the technical field of information. The method comprises the following steps: acquiring a to-be-called list; a current calling user in the to-be-called list is determined, a first fraud strategy is determined based on user basic information of the current calling user, and the first fraud strategy comprises a fraud type, a first fraud level, a first fraud speech and a first fraud tone; obtaining a first feedback result of the current calling user on the first fraud scheme, and calculating an anti-fraud score value of the current calling user based on the first feedback result; when the anti-fraud score value is greater than a score threshold value, adjusting the first fraud strategy to obtain a second fraud strategy; and obtaining a second feedback result of the current calling user for the second fraud scheme, generating an anti-fraud propaganda report based on the first feedback result and the second feedback result, and sending the anti-fraud propaganda report to the current calling user. By implementing the technical scheme provided by the invention, the effectiveness of anti-fraud propaganda can be improved.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a propaganda method, system, medium and electronic equipment for countering network fraud. Background Art

[0002] As telecom network fraud methods continue to innovate, fraudulent rhetoric has become increasingly covert and deceptive, causing a large number of people to suffer property losses. In order to improve citizens' awareness and ability to prevent telecom network fraud, various regions have adopted various forms of anti-fraud publicity and education.

[0003] At present, common anti-fraud publicity and education methods mainly include distributing brochures, playing case videos, holding lectures, etc. Although these publicity methods can let citizens understand the basic routines and prevention points of fraud, due to the lack of actual interactive experience, especially when facing the fraud process of different rhetoric layers and step-by-step pressure, it is often difficult to make correct judgments in time by simply relying on the prevention knowledge mastered in advance, resulting in it is still difficult to accurately identify and effectively respond to real fraud, making the effectiveness of anti-fraud publicity low. Summary of the invention

[0004] The present application provides a propaganda method, system, storage medium and electronic device for countering online fraud, which can improve the effectiveness of anti-fraud propaganda.

[0005] In a first aspect, the present application provides a propaganda method for countering online fraud, the method comprising: Acquire a waiting-to-call list, wherein the waiting-to-call list includes at least one calling user; Determine a current calling user in the waiting-to-call list, and determine a first fraud strategy based on basic user information of the current calling user, wherein the first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone; Obtaining a first feedback result of the current calling user on a first fraud scheme, and calculating an anti-fraud score value of the current calling user based on the first feedback result; When the anti-fraud score is greater than a score threshold, the first fraud strategy is adjusted to obtain a second fraud strategy; Obtain a second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

[0006] By adopting the above technical solution, the first fraud strategy is determined based on the basic information of the current calling user, and the anti-fraud score is calculated based on the first feedback result of the user. When the anti-fraud score is greater than the score threshold, the system can adjust the fraud strategy in time to obtain the second fraud strategy, and simulate the characteristics of layered traps and step-by-step pressure in the real fraud process through dynamic adjustment. At the same time, by analyzing the feedback results of users on different fraud strategies, a personalized anti-fraud publicity report is generated and sent to the user, so that the user can experience the evolution of fraud speech in the actual interaction process, improve the ability to identify and respond to fraud characteristics, and overcome the problem that the existing anti-fraud publicity methods lack practical experience, which makes it difficult for users to identify and respond to fraud in a timely manner, thereby effectively improving the pertinence and effectiveness of anti-fraud publicity.

[0007] In a second aspect of the present application, a propaganda system for countering online fraud is provided, the system comprising: A user information acquisition module, used to acquire a waiting-to-call list, wherein the waiting-to-call list includes at least one calling user; A first strategy determination module, used to determine a current calling user in the waiting-to-call list, and determine a first fraud strategy based on basic user information of the current calling user, wherein the first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone; an anti-fraud score calculation module, configured to obtain a first feedback result of the current calling user on the first fraud scheme, and calculate an anti-fraud score value of the current calling user based on the first feedback result; A second strategy determination module, configured to adjust the first fraud strategy to obtain a second fraud strategy when the anti-fraud score value is greater than a score threshold; An anti-fraud publicity sending module is used to obtain the second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This application determines the first fraud strategy based on the basic information of the current calling user, and calculates the anti-fraud score value based on the user's first feedback result. When the anti-fraud score value is greater than the score threshold, the system can adjust the fraud strategy in time to obtain the second fraud strategy, and simulate the characteristics of layered traps and step-by-step pressure in the real fraud process through dynamic adjustment. At the same time, by analyzing the user's feedback results on different fraud strategies, a personalized anti-fraud publicity report is generated and sent to the user, so that the user can experience the evolution of fraud tactics in the actual interaction process, improve the ability to identify and respond to fraud characteristics, and overcome the problem that the existing anti-fraud publicity methods lack practical experience, which makes it difficult for users to identify and respond to fraud in a timely manner, thereby effectively improving the pertinence and effectiveness of anti-fraud publicity. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of a propaganda method for countering network fraud provided by an embodiment of the present application; Figure 2 This is a module diagram of a propaganda system for countering online fraud provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] Please refer to Figure 1 , a flowchart of a propaganda method for countering network fraud is proposed. The method can be implemented by a computer program, a single-chip microcomputer, or run on a propaganda system for countering network fraud. The computer program can be integrated into a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 50, and the steps are as follows: Step 10: Obtain a waiting-to-call list, where the waiting-to-call list includes at least one calling user.

[0018] In the embodiment of the present application, the waiting-to-call list refers to a list of target users who need to conduct anti-fraud publicity, which includes information of at least one calling user, and each calling user corresponds to basic information used to determine the first fraud strategy, such as age, occupation type, and education level. The waiting-to-call list can be classified and sorted according to factors such as the user's region, age group, and occupational characteristics, so as to facilitate the systematic and orderly development of anti-fraud publicity.

[0019] Specifically, in order to carry out targeted anti-fraud publicity, it is first necessary to obtain a waiting list. The waiting list can be obtained in the following ways: when a user actively signs up to participate in an anti-fraud publicity activity, the user voluntarily fills out a personal information registration form, including basic information such as contact information, age group, occupation type, and education level; or when anti-fraud publicity activities are carried out in communities, enterprises, etc., relevant staff collect the basic information of the participants and compile it into a list. The system imports the collected information into the database to form a waiting list containing at least one calling user. In order to facilitate the subsequent targeted anti-fraud publicity, the system will classify and organize the users in the waiting list, such as grouping them according to age groups, occupation types, etc., so that appropriate fraud strategies can be selected according to the characteristics of different groups. The waiting list obtained in this way not only ensures the legitimacy of the information source, but also lays the foundation for the subsequent personalized anti-fraud publicity, and also facilitates the system to carry out anti-fraud publicity work in an orderly manner according to the basic characteristics of the users.

[0020] Step 20: Determine the current calling user in the waiting-to-be-called list, and determine a first fraud strategy based on the basic user information of the current calling user. The first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone.

[0021] Specifically, after obtaining the waiting list of calls, the system determines the calling user who needs to carry out anti-fraud publicity according to the preset call order. In order to ensure the pertinence and effectiveness of the anti-fraud publicity, the system will analyze the basic information of the current calling user, including age, occupation type and education level. First, the risk coefficient is calculated according to the basic information of the user. For example, for users with older age and lower education level, the risk coefficient is relatively high; while for users with younger age and higher education level, the risk coefficient is relatively low. Based on the calculated risk coefficient, the system determines the corresponding first fraud level. The higher the risk coefficient, the lower the corresponding first fraud level, so as to ensure that the user can accept and generate vigilance. At the same time, the system will select the most confusing fraud type according to the user's occupation type. For example, for corporate employees, business cooperation fraud may be selected; for students, part-time fraud may be selected. After determining the fraud type and the first fraud level, the system retrieves the matching first fraud speech and first fraud tone from the preset speech library. For example, for low-level fraud strategies, simpler and more direct speech and peaceful tone are selected; for high-level fraud strategies, more concealed and circuitous speech and strong tone are selected. Finally, the system integrates the first fraud level, fraud type, first fraud words and first fraud tone into a complete first fraud strategy. The first fraud strategy determined in this way not only takes into account the user's cognition and prevention ability, but also simulates the characteristics of real fraud, making anti-fraud publicity more targeted and effective.

[0022] Based on the above embodiment, as an optional embodiment, the step of determining the first fraud strategy based on the basic user information of the current calling user may further include the following steps: Step 201: Calculate the risk factor based on the basic information of the current calling user, where the basic information includes age, occupation type and education level.

[0023] Specifically, the risk coefficient is calculated using a preset calculation model based on the basic information actively provided by the current calling user. The system first sets weight coefficients and quantitative scores for the user's age, occupation type and education level. For example, the age is divided into multiple intervals and assigned different scores, 5 points for those over 50 years old, 4 points for 35-50 years old, 3 points for 25-35 years old, and 2 points for those under 25 years old; the education level is quantified as 1 point for postgraduates and above, 2 points for undergraduates, 3 points for junior colleges, and 4 points for high school and below; the occupation type is divided into levels according to the sensitivity to fraud, such as 5 points for retirees, 4 points for self-employed persons, 3 points for corporate employees, 2 points for students, and 1 point for public servants. Then, the scores of each item are multiplied by the corresponding weights and summed to obtain the final risk coefficient.

[0024] Step 202: Determine a first fraud level based on the risk factor, and determine a fraud type based on the occupation type.

[0025] Specifically, based on the calculated risk factor, the system divides the fraud level into three levels: primary, intermediate, and advanced. The higher the risk factor, the lower the first fraud level used, to ensure that users can have a preliminary sense of vigilance. At the same time, the system will select the most targeted fraud type based on the user's occupation type, such as business fraud for corporate employees, job-seeking fraud for students, and loan fraud for self-employed business owners. Through this quantitative calculation method based on user characteristics, the system can scientifically evaluate the user's risk tolerance and select the appropriate fraud level and type, making subsequent anti-fraud publicity more targeted and easier for users to accept.

[0026] Step 203: Retrieve the first fraud speech and the first fraud tone that match the fraud type and the first fraud level from the preset speech library.

[0027] Specifically, in order to build a complete first fraud strategy, the system matches the speech and tone based on the pre-established speech library. First, a two-dimensional index table of fraud types and fraud levels is constructed in the speech library, and each index node stores a corresponding speech template set. For example, when the fraud type is business-related and the first fraud level is high-level, the system locates the corresponding speech template set through the index. In this set, each speech template is provided with a keyword label and an applicable scenario label. The system calculates the matching score between the label and the current scenario, and selects the speech template with the highest score as the first fraud speech. At the same time, the system establishes a tone feature mapping table corresponding to the fraud level in the tone parameter library, including speech speed parameters, intonation parameters, and modal particle parameters. The system extracts the corresponding tone parameter combination from the mapping table according to the current first fraud level to generate the first fraud tone.

[0028] Step 203: Integrate the first fraud level, fraud type, first fraud speech, and first fraud tone into a first fraud strategy.

[0029] Specifically, after completing the retrieval of the words and tone, the system integrates the first fraud level, fraud type, first fraud words and first fraud tone according to the preset organizational structure template. During integration, the system first uses the first fraud level and fraud type as the basic attributes of the strategy, then fills the template with the first fraud words, and finally superimposes the parameter configuration of the first fraud tone to form a structured first fraud strategy data package. Through this index-based precise matching and structured integration method, the system can quickly generate fraud strategies that meet actual scenarios and improve the authenticity and persuasiveness of anti-fraud propaganda.

[0030] Step 30: Obtain a first feedback result of the current calling user on the first fraud scheme, and calculate an anti-fraud score value of the current calling user based on the first feedback result.

[0031] Specifically, in order to evaluate the anti-fraud awareness level of the current calling user, the system needs to obtain and analyze the user's feedback on the first fraud scheme. The system records the user's response data during the call through a preset feedback collection template, including the user's voice reply content, reply duration, tone and intonation changes, and other information. The natural language processing technology is used to perform word segmentation and semantic analysis on the user's voice reply content, extract keywords and match them with the preset prevention awareness vocabulary. For example, when the user's reply contains words such as "verify", "check", and "untrustworthy", it indicates that the user has a strong prevention awareness. At the same time, the system will also analyze the user's reply duration. If the user can quickly identify the fraud characteristics and make a vigilant reply, it means that he has a good recognition ability. In addition, the system identifies the user's tone and intonation changes through voice feature analysis technology. For example, when the user shows a doubtful or rejecting tone, it indicates that he has a strong recognition ability. Based on the above multi-dimensional feedback data, the preset scoring model is used to calculate the anti-fraud score. Specifically, the scoring model sets weight coefficients for feedback indicators of different dimensions, and the weighted sum of each score is obtained to obtain the final anti-fraud score. Through this scoring mechanism based on actual user feedback, the system can objectively evaluate users' anti-fraud awareness level and provide targeted improvement basis for subsequent anti-fraud publicity.

[0032] Based on the above embodiment, as an optional embodiment, the step of calculating the anti-fraud score of the current calling user based on the first feedback result may further include the following steps: Step 301: Analyze the first feedback result to obtain call duration, answer keywords and question frequency.

[0033] Specifically, in order to extract the key characteristic indicators of the user's anti-fraud awareness from the first feedback result, the system needs to parse and process the feedback data. First, the first feedback result is separated into two parts: voice data and timestamp data. For timestamp data, the system obtains the call duration by calculating the difference between the call start timestamp and the call end timestamp. For voice data, the system uses voice recognition technology to convert it into text data and uses natural language processing technology for analysis. The system pre-builds a keyword vocabulary containing preventive words such as "investigation", "verification", and "evidence", and obtains the number and distribution of answer keywords through text matching retrieval. At the same time, the system performs intonation feature analysis on the voice data, extracts acoustic feature parameters such as pitch, volume, and speech speed, and when a significant change in voice parameters is detected, such as a sudden increase in volume or a significant increase in speech speed, the system marks it as a question, and obtains the questioning frequency by accumulating the number of markings. Through this multi-dimensional data analysis method, the system can convert unstructured feedback data into quantifiable evaluation indicators, providing an objective basis for subsequent anti-fraud awareness evaluation.

[0034] Step 302: Determine a basic score based on the call duration.

[0035] Specifically, the basic score is set according to the length of the call. The system pre-sets four duration intervals and corresponding basic scores: when the call duration is between 0 and 30 seconds, it indicates that the user is quick to detect fraud, and the basic score is set to 90 points; when the call duration is between 30 seconds and 2 minutes, it indicates that the user is slightly hesitant, and the basic score is set to 80 points; when the call duration is between 2 minutes and 5 minutes, it indicates that the user is not vigilant enough, and the basic score is set to 70 points; when the call duration exceeds 5 minutes, it indicates that the user is extremely likely to be defrauded, and the basic score is set to 60 points.

[0036] Step 303: Determine a vigilance score based on the answer keywords and the question frequency.

[0037] Specifically, after determining the basic score, the system further calculates the vigilance score. The system pre-sets the keyword scoring rules: each time a prevention keyword is identified, such as "verification" and "evidence", 2 points will be added; at the same time, the system adjusts the score according to the frequency of questioning: each time a valid question is detected, such as a sudden change in tone or speed, 3 points will be added. The system adds the keyword score and the questioning score to get the vigilance score. Through this method of determining the basic score based on duration, combined with the vigilance score of keywords and questioning frequency, the system can fully reflect the user's anti-fraud awareness level and provide targeted suggestions for subsequent anti-fraud education.

[0038] Step 304: Perform weighted calculation on the basic score and the vigilance score to obtain an anti-fraud score.

[0039] Specifically, in order to comprehensively evaluate the user's anti-fraud awareness level, the embodiment of the present application fuses the basic score and the vigilance score to obtain the final anti-fraud score. Specifically, the piecewise function calculation method is adopted, and the interval of the basic score is first determined: when the basic score is in the 90-point interval, it indicates that the user has a strong recognition ability, and 80% of the vigilance score is included in the final score; when the basic score is in the 80-point interval, it indicates that the user has a certain awareness of prevention, and 100% of the vigilance score is included in the final score; when the basic score is in the 70-point interval, it indicates that the user is not vigilant enough, and 120% of the vigilance score is included in the final score; when the basic score is in the 60-point interval, it indicates that the user is very easy to be defrauded, and 150% of the vigilance score is included in the final score. This step-by-step calculation method can give the user a greater vigilance score incentive when the user's basic prevention ability is weak. The system obtains the anti-fraud score through this calculation method, which not only reflects the user's basic recognition ability, but also fully considers the vigilance performance shown by the user during the call, making the scoring result more objective and reasonable. For example, when the user's basic score is 70 points and the vigilance score is 15 points, based on the calculation ratio of 120%, the final anti-fraud score is 70+18=88 points, which reflects that the user has improved the overall anti-fraud level through active questioning and prevention.

[0040] Step 40: When the anti-fraud score value is greater than the score threshold, the first fraud strategy is adjusted to obtain a second fraud strategy.

[0041] Specifically, in order to provide more targeted simulation training for users with strong awareness of prevention, the system needs to dynamically adjust the fraud strategy based on the user's anti-fraud score. Among them, the system pre-sets the score threshold to 85 points. When it is detected that the user's anti-fraud score exceeds this threshold, it indicates that the user has a strong anti-fraud awareness. Analyze the key points in the first fraud strategy that are recognized by the user, including suspicious terms, identity information, financial data and other elements. Based on these recognition points, the system extracts more concealed speech templates from the preset fraud strategy library and constructs the second fraud strategy by replacing key information nodes. For example, when the first fraud strategy is recognized by the user using "joint investigation by the public security, procuratorial and judicial organs", the second fraud strategy will adopt the method of "appointment for door-to-door verification"; when the specific amount of funds mentioned in the first fraud strategy is questioned, the second fraud strategy will gradually obtain information in a step-by-step manner. Through this strategy adjustment method based on user prevention performance, the system can simulate the speech upgrade process of real fraudsters, help users experience and identify more confusing fraud methods, and further enhance users' anti-fraud capabilities.

[0042] Based on the above embodiment, as an optional embodiment, when the anti-fraud score value is greater than the score threshold, the step of adjusting the first fraud strategy to obtain the second fraud strategy may also include the following steps: Step 401: When the anti-fraud score value is greater than the score threshold, analyze the warning points in the first feedback result.

[0043] Specifically, in order to make the next round of simulated fraud more targeted and challenging, the system needs to determine a higher level of fraud based on the user's vigilance. When the user's anti-fraud score exceeds the preset threshold of 85 points, the system first conducts an in-depth analysis of the first feedback result to extract the key nodes where the user shows vigilance. The system uses semantic analysis technology to identify the questioning statements and prevention performance in the first feedback result, and classifies them into different types of vigilance points such as identity verification, information verification, and fund confirmation.

[0044] Step 402: Determine a second fraud level based on each of the alert points, the second fraud level being higher than the first fraud level.

[0045] Specifically, for each vigilance point, the system calculates the time location, duration, and number of repeated questioning, and generates a vigilance intensity value. For example, when a user questions the identity information at the beginning of a call and repeatedly verifies it, the intensity value of the vigilance point is higher; when the user is only slightly hesitant when hearing about the fund operation, the intensity value of the vigilance point is lower. The system determines the second fraud level based on the distribution of the intensity values ​​of each vigilance point and the level of the first fraud strategy. Specifically, if the user's vigilance intensity for identity information is the highest, it is upgraded by two levels on the basis of the first fraud level; if the user's vigilance intensity for fund operations is the highest, it is upgraded by three levels. Through this level upgrade method based on vigilance point analysis, the system can specifically increase the difficulty of fraud in the links where users are more vulnerable to fraud, making the training more effective.

[0046] Step 403: Based on the fraud type and the first fraud level, retrieve the second fraud speech and the second fraud tone corresponding to the second fraud level from the preset speech library.

[0047] Specifically, in order to maintain the continuity of the call and improve the concealment of the fraud strategy, the system needs to dynamically adjust the content and tone characteristics of the speech in the current conversation context. The system first retrieves the first speech set from the preset speech library based on the current fraud type (such as impersonating the public security, procuratorial and judicial organs). The first speech set contains standard speech content related to the current case investigation. To ensure the continuity of the speech, the system analyzes the key elements of the first fraud speech (such as case number, amount involved, suspect information, etc.) in the first speech set, and selects the speech content that can be naturally connected and logically echoed to form the second speech set. For example, when the first fraud speech mentions that there are abnormal transactions in the user's bank account, the content selected in the second speech set will conduct in-depth inquiries around the abnormal transaction, or guide the user to verify the relevant transaction records. Subsequently, the system selects the final second fraud speech from the second speech set based on the user's vigilance and the second fraud level. At the same time, the system extracts the second fraud tone that matches the second fraud level from the preset tone parameter library, where the second fraud tone includes speech speed parameters (such as fast speech speed in an emergency, steady speech speed when verifying information), intonation parameters (such as low tone of authoritative tone, gentle tone when soothing emotions), and emotional parameters (such as seriousness, concern, oppression, etc.). The system smoothly transitions the selected tone parameters with the currently used first fraud tone to avoid sudden changes in tone. For example, when a user expresses doubts about identity information, the system maintains the original serious tone and gradually adjusts the speech speed and tone to make the tone transition to a more oppressive and authoritative direction, forming the final second fraud tone. Through this coordinated adjustment of the content of the speech and the tone characteristics, the system can ensure the naturalness and smoothness of the entire call process while enhancing the confusion of the fraud strategy.

[0048] Step 404: Integrate the second fraud level, the second fraud words, and the second fraud tone into a second fraud strategy.

[0049] Specifically, the system integrates the selected second fraud speech template with the tone parameters to generate a second fraud strategy that includes complete speech logic and tone control. Through this precise matching method based on type and level, the system can generate fraud strategies that are more in line with real scenarios and more challenging, allowing users to experience more realistic anti-fraud scenarios during training.

[0050] Step 50: Obtain the second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

[0051] Specifically, in order to help users deeply understand their own performance characteristics in the anti-fraud process and provide targeted improvement suggestions, the system needs to conduct a comprehensive analysis of the feedback results of users in the two rounds of fraud strategies. First, all the reaction information of the user when facing the second fraud strategy is recorded, including the content of the call, voice characteristics, and coping methods, etc., to form the second feedback result. The system compares and analyzes the second feedback result with the first feedback result obtained previously, and extracts the behavioral characteristics of the user in the two rounds of coping process. For example, the system analyzes whether the user can still remain vigilant when facing the upgraded fraud speech, whether he or she is shaken when encountering a more confusing fraud tone, and whether he or she can maintain a sense of prevention under escalating pressure. Based on these analysis results, the system generates an anti-fraud publicity report containing personalized evaluations and improvement suggestions. The report first shows the specific performance of the user in the two rounds of testing, including the identification of vigilance points, the adoption of preventive measures, and the trend of changes in psychological state. Subsequently, the report provides detailed descriptions of the advantages and disadvantages shown by the user, for example, affirming the vigilance points that can be maintained at all times, and proposing improvement suggestions for the links that were neglected in the second round. Finally, the report provides prevention points and practical skills based on the typical characteristics of this type of fraud and the latest fraud methods. The system sends the anti-fraud publicity report to the user through SMS, email and other contact methods reserved by the user, so that the user can timely understand the current status of his or her anti-fraud ability and strengthen the prevention awareness in a targeted manner. Through this two-round feedback-based analysis and reporting mechanism, the system can help users establish a more comprehensive and lasting anti-fraud awareness and improve their ability to deal with real fraud.

[0052] Based on the above embodiment, as an optional embodiment, the step of generating an anti-fraud publicity report based on the first feedback result and the second feedback result may further include the following steps: Step 501: Determine a change value of the alertness level of the current calling user based on the first feedback result and the second feedback result.

[0053] Specifically, in order to accurately evaluate the user's anti-fraud ability and provide personalized anti-fraud guidance, the system needs to conduct a quantitative analysis of the user's performance in the two rounds of fraud response process. The system first calculates the change value of the degree of vigilance based on the first feedback result and the second feedback result. The change value reflects the change in the user's awareness of prevention when facing the upgraded fraud strategy. The system uses a preset calculation model to compare the user's various indicators in the two rounds of feedback (such as the number of suspicious points identified, the timeliness of taking preventive measures, the firmness of resisting inducement, etc.) to obtain a quantitative change value of the degree of vigilance.

[0054] Step 502: Determine the anti-fraud awareness level based on the vigilance level change value.

[0055] Specifically, the system matches the change value of the alertness level in the preset anti-fraud awareness level table to determine the user's anti-fraud awareness level. The level table divides the change value of the alertness level into multiple intervals. For example, when the change value is greater than 0.8, it is determined to be a high level of anti-fraud awareness, when the change value is between 0.3 and 0.8, it is determined to be an intermediate level of anti-fraud awareness, and when the change value is less than 0.3, it is determined to be a primary level of anti-fraud awareness.

[0056] Step 503: Determine an anti-fraud publicity report based on the anti-fraud awareness level and the fraud type. The anti-fraud publicity report includes fraud characteristics and anti-fraud suggestions.

[0057] Specifically, after determining the level of anti-fraud awareness, the system will retrieve the corresponding report template from the preset anti-fraud publicity template library based on the current fraud type. This template contains targeted fraud feature descriptions and anti-fraud suggestions for different anti-fraud awareness levels and fraud types. For example, for users who show basic anti-fraud awareness in frauds impersonating public security, procuratorial and judicial organs, the system will focus on the basic characteristics of this type of fraud (such as fake caller numbers, use of professional terms, etc.) and basic prevention methods in the report; for users who show advanced anti-fraud awareness, it will introduce deeper changes in fraud methods and advanced prevention techniques. Through this personalized report generation mechanism based on quantitative analysis, the system can help users better understand their own anti-fraud capabilities and obtain more targeted improvement suggestions.

[0058] Based on the above embodiment, as an optional embodiment, the step of determining the change value of the alertness level of the current calling user based on the first feedback result and the second feedback result may further include the following steps: Step 5011: extract the key call parameters in the first feedback result and the second feedback result respectively, the key call parameters including call duration, number of inquiries and number of risk point identifications.

[0059] Specifically, from the call records of the first feedback result, the key parameters of the call are extracted through voice recognition and semantic analysis technology. The key parameters of the call include call duration, number of questions and number of risk point identification. Among them, the call duration parameter is obtained by calculating the duration from the user answering the call to hanging up the call, which reflects the user's resistance time during the fraud process; the number of questions parameter is obtained by identifying the frequency of the user's questioning or doubting statements during the call, such as the system detecting questioning words such as "this can't be a fraud" and "why do you do this" and accumulating their occurrence times; the number of risk point identification parameters is obtained by detecting the frequency of users pointing out suspicious information or seeing through fraudulent tricks, such as the number of times users mentioned "formal departments will not allow transfers" and "identity information needs to be verified" and other expressions of prevention awareness. The system uses the same extraction method to obtain the corresponding key parameters of the call from the second feedback result. Through this standardized parameter extraction mechanism, the system can convert the user's performance in two rounds of calls into quantifiable and comparable data indicators, providing an objective basis for the subsequent calculation of the change value of the degree of vigilance. For example, when the system finds that the number of questions raised by the user in the second round of calls has increased significantly, it means that the user's awareness of prevention has improved; when the number of risk point identifications has decreased, it indicates that the user may have become less vigilant when facing upgraded fraud strategies. Through this data-based analysis based on key parameters, the system can more accurately evaluate the changing trend of the user's anti-fraud ability.

[0060] Step 5012: Calculate the change rate of the call duration and the density change rate of the number of inquiries corresponding to the call duration.

[0061] Specifically, in order to accurately evaluate the changes in the user's anti-fraud performance in two rounds of calls, the system needs to perform standardized calculations and comparative analysis on the extracted key call parameters. First, the change rate of call duration is calculated, that is, the ratio of the second round of call duration minus the first round of call duration to the first round of call duration. This change rate reflects the change in the user's response time when facing upgraded fraud strategies. For example, when the first round of call duration is 10 minutes and the second round is 15 minutes, the call duration change rate is 0.5, indicating that the user has extended the prevention time in the second round. Subsequently, the system calculates the density change rate of the number of questions, that is, the number of questions in the two rounds of calls is divided by the corresponding call duration to obtain the questioning density, and then calculates the change ratio of the two rounds of questioning density. This density calculation method can eliminate the impact of the difference in call duration and more accurately reflect the actual changes in the user's questioning frequency. For example, when there are 5 questions in the first round of 10-minute calls (density 0.5 times / minute) and 12 questions in the second round of 15-minute calls (density 0.8 times / minute), the questioning density change rate is 0.6, indicating that the user has increased the questioning frequency in the second round. Similarly, the system calculates the growth rate of the number of risk point identifications, that is, the ratio of the number of risk point identifications in the second round to the call duration minus the corresponding ratio in the first round, and then divides it by the ratio in the first round. This calculation method can accurately reflect the degree of improvement or decline in the user's ability to identify risks. For example, when 0.3 risk points were identified per minute in the first round and increased to 0.5 risk points per minute in the second round, the growth rate was 0.67, indicating that the user's risk identification ability has significantly improved. Through this standardized rate of change calculation mechanism, the system can objectively evaluate the changes in users' anti-fraud performance in calls of different durations, and provide reliable data support for subsequent vigilance assessments.

[0062] Step 5013: Count the growth rate of the number of risk point identifications in the corresponding call duration.

[0063] Specifically, the growth rate of the number of risk point identifications in the corresponding call duration is calculated, that is, the number of risk point identifications in the second round of calls is divided by the call duration to obtain the identification frequency per unit time, and then compared with the identification frequency per unit time of the first round of calls. For example, when 3 risk points are identified in the first round of 10-minute calls (frequency 0.3 times / minute) and 8 risk points are identified in the second round of 15-minute calls (frequency 0.53 times / minute), the system calculates (0.53-0.3) / 0.3 to obtain a growth rate of 0.77, indicating that the user's risk identification ability has significantly improved.

[0064] Step 5014: Calculate the alertness level change value based on the call duration change rate, density change rate and growth rate.

[0065] Specifically, in order to more accurately evaluate the change in the user's vigilance level, the embodiment of the present application adopts a composite calculation method that considers the correlation between indicators and nonlinear characteristics. Specifically, the system uses the following calculation formula to determine the change in vigilance level: Change in vigilance level = arctanh(α×R) × [1 + β×(1-e (-γ×D)) ] × sqrt(1 +δ×L 2 ), where R is the call duration change rate, D is the question density change rate, and L is the risk point identification growth rate; α, β, γ, and δ are adjustment coefficients, which are 0.8, 0.6, 0.9, and 0.7, respectively. In this formula, the arctanh function is used to deal with the saturation effect of call duration changes, that is, when the call duration changes too much, its impact on the degree of vigilance will tend to stabilize; the exponential decay term (1-e (-γ×D) ) reflects the nonlinear effect of the change in questioning density on the degree of vigilance, reflecting the marginal decreasing effect of the improvement of questioning ability; the square term sqrt(1 +δ×L 2 ) emphasizes the importance of risk point identification ability, and when the identification ability is significantly improved, it will make a greater contribution. For example, when R=0.5, D=0.6, I=0.77, the system first calculates arctanh(0.8×0.5)=0.549, and then calculates [1 + 0.6×(1-e (-0.9×0.6) )] =1.296, and finally calculate sqrt(1 +δ×L 2 )=1.336, and the final change in vigilance is 0.952. This calculation formula can more accurately describe the changing characteristics of users' anti-fraud ability, taking into account the interaction and nonlinear effects between various indicators, and provides a more scientific quantitative basis for the subsequent anti-fraud awareness level determination.

[0066] See also Figure 2 , is a schematic diagram of a module of a propaganda system for countering network fraud provided in an embodiment of the present application, wherein the system includes: A user information acquisition module, used to acquire a waiting-to-call list, wherein the waiting-to-call list includes at least one calling user; A first strategy determination module, used to determine a current calling user in the waiting-to-call list, and determine a first fraud strategy based on basic user information of the current calling user, wherein the first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone; an anti-fraud score calculation module, configured to obtain a first feedback result of the current calling user on the first fraud scheme, and calculate an anti-fraud score value of the current calling user based on the first feedback result; A second strategy determination module, configured to adjust the first fraud strategy to obtain a second fraud strategy when the anti-fraud score value is greater than a score threshold; An anti-fraud publicity sending module is used to obtain the second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

[0067] Optionally, the first strategy determination module is further used to calculate the risk factor based on the basic information of the current calling user, the basic information including age, occupation type and education level; Determining a first fraud level based on the risk factor, and determining a fraud type based on the occupation type; Retrieving a first fraudulent speech and a first fraudulent tone that match the fraud type and the first fraud level from a preset speech library; The first fraud level, fraud type, first fraud speech and first fraud tone are integrated into the first fraud strategy.

[0068] Optionally, the anti-fraud score calculation module is further used to analyze the first feedback result to obtain the call duration, answer keywords and questioning frequency; Determine a basic score based on the call duration; Determining a vigilance score based on the answer keywords and the questioning frequency; The basic score and the vigilance score are weighted to obtain the fraud prevention score.

[0069] Optionally, the second strategy determination module is further configured to analyze the alert points in the first feedback result when the anti-fraud score value is greater than a score threshold; Determining a second fraud level based on each of the vigilance points, wherein the second fraud level is higher than the first fraud level; Based on the fraud type and the first fraud level, retrieve a second fraud speech and a second fraud tone corresponding to the second fraud level from a preset speech library; The second fraud level, the second fraud words and the second fraud tone are integrated into the second fraud strategy.

[0070] Optionally, the second strategy determination module is further configured to retrieve a first speech set corresponding to the fraud type from a preset speech library, and filter out a second speech set related to the first fraud speech from the first speech set; Based on the vigilance point, the second fraud speech and the second fraud tone corresponding to the second fraud level are screened out from the second speech set.

[0071] Optionally, the anti-fraud publicity sending module is further used to determine a change value of the vigilance level of the current calling user based on the first feedback result and the second feedback result; Determining the anti-fraud awareness level based on the vigilance level change value; An anti-fraud publicity report is determined based on the anti-fraud awareness level and the fraud type, and the anti-fraud publicity report includes fraud characteristics and anti-fraud suggestions.

[0072] Optionally, the anti-fraud publicity sending module is further used to extract key call parameters in the first feedback result and the second feedback result respectively, and the key call parameters include call duration, number of questions and number of risk point identifications; Calculate the change rate of the call duration and the density change rate of the number of inquiries corresponding to the call duration; Counting the growth rate of the number of risk point identifications in the corresponding call duration; The alertness level change value is calculated based on the change rate of the call duration, the density change rate and the growth rate.

[0073] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0074] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a propaganda method for countering online fraud in the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0075] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0076] The communication bus 302 is used to realize the connection and communication between these components.

[0077] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0078] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0079] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0080] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for a method of promoting a countermeasure against network fraud.

[0081] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method of promoting a method of countering network fraud. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0082] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0087] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0088] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A propaganda method for countering online fraud, characterized in that: The method comprises: Acquire a waiting-to-call list, wherein the waiting-to-call list includes at least one calling user; Determine a current calling user in the waiting-to-call list, and determine a first fraud strategy based on basic user information of the current calling user, wherein the first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone; Obtaining a first feedback result of the current calling user on a first fraud scheme, and calculating an anti-fraud score value of the current calling user based on the first feedback result; When the anti-fraud score is greater than a score threshold, the first fraud strategy is adjusted to obtain a second fraud strategy; Obtain a second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

2. The propaganda method for countering online fraud according to claim 1 is characterized in that: The determining of the first fraud strategy based on the basic user information of the current calling user includes: Calculating a risk factor based on basic information of the current calling user, the basic information including age, occupation type and education level; Determining a first fraud level based on the risk factor, and determining a fraud type based on the occupation type; Retrieving a first fraudulent speech and a first fraudulent tone that match the fraud type and the first fraud level from a preset speech library; The first fraud level, fraud type, first fraud speech and first fraud tone are integrated into the first fraud strategy.

3. The propaganda method for countering online fraud according to claim 1 is characterized in that: The calculating the anti-fraud score value of the current calling user based on the first feedback result includes: Analyze the first feedback result to obtain call duration, answer keywords and question frequency; Determine a basic score based on the call duration; Determining a vigilance score based on the answer keywords and the question frequency; The basic score and the vigilance score are weighted and calculated to obtain an anti-fraud score.

4. The propaganda method for countering online fraud according to claim 1 is characterized in that: When the anti-fraud score value is greater than a score threshold, adjusting the first fraud strategy to obtain a second fraud strategy includes: When the anti-fraud score is greater than the score threshold, analyzing the warning points in the first feedback result; Determine a second fraud level based on each of the vigilance points, wherein the second fraud level is higher than the first fraud level; Based on the fraud type and the first fraud level, retrieve a second fraud speech and a second fraud tone corresponding to the second fraud level from a preset speech library; The second fraud level, the second fraud words and the second fraud tone are integrated into the second fraud strategy.

5. The propaganda method for countering online fraud according to claim 4 is characterized in that: The method of retrieving a second fraud speech and a second fraud tone corresponding to the second fraud level from a preset speech library based on the fraud type and the first fraud level includes: Retrieving a first speech set corresponding to the fraud type from a preset speech library, and selecting a second speech set related to the first fraud speech from the first speech set; Based on the vigilance point, the second fraud speech and the second fraud tone corresponding to the second fraud level are screened out from the second speech set.

6. The propaganda method for countering online fraud according to claim 1 is characterized in that: The generating an anti-fraud publicity report based on the first feedback result and the second feedback result includes: Determine a change value of the alertness level of the current calling user based on the first feedback result and the second feedback result; Determining the anti-fraud awareness level based on the vigilance level change value; An anti-fraud publicity report is determined based on the anti-fraud awareness level and the fraud type, and the anti-fraud publicity report includes fraud characteristics and anti-fraud suggestions.

7. The propaganda method for countering online fraud according to claim 6, characterized in that: The determining the change value of the alertness level of the current calling user based on the first feedback result and the second feedback result includes: Extracting key call parameters from the first feedback result and the second feedback result respectively, wherein the key call parameters include call duration, number of inquiries, and number of risk point identifications; Calculate the change rate of the call duration and the density change rate of the number of inquiries corresponding to the call duration; Counting the growth rate of the number of risk point identifications in the corresponding call duration; The alertness level change value is calculated based on the change rate of the call duration, the density change rate and the growth rate.

8. A propaganda system for countering online fraud, characterized in that: The system comprises: A user information acquisition module, used to acquire a waiting-to-call list, wherein the waiting-to-call list includes at least one calling user; A first strategy determination module, used to determine a current calling user in the waiting-to-call list, and determine a first fraud strategy based on basic user information of the current calling user, wherein the first fraud strategy includes a fraud type, a first fraud level, a first fraud speech, and a first fraud tone; an anti-fraud score calculation module, configured to obtain a first feedback result of the current calling user on the first fraud scheme, and calculate an anti-fraud score value of the current calling user based on the first feedback result; A second strategy determination module, configured to adjust the first fraud strategy to obtain a second fraud strategy when the anti-fraud score value is greater than a score threshold; An anti-fraud publicity sending module is used to obtain the second feedback result of the current calling user on the second fraud scheme, generate an anti-fraud publicity report based on the first feedback result and the second feedback result, and send the anti-fraud publicity report to the current calling user.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

Images