Location-based public opinion survey service system

Abstract

The present invention relates to a location-based real-time public opinion survey and analysis service system, and more specifically, to a location-based real-time public opinion survey and analysis service system and method that authenticates a user's geographical location in real time to collect opinions only from respondents within a specific area, guarantees the integrity of the collected data, and analyzes it in real time to provide visualized information.

Description

Technology Field

[0001] The present invention relates to a location-based real-time public opinion survey and analysis service system, and more specifically, to a location-based real-time public opinion survey and analysis service system and method that authenticates a user's geographical location in real time to collect opinions only from respondents within a specific area, guarantees the integrity of the collected data, and analyzes it in real time to provide visualized information. Background Technology

[0002] Generally, public opinion polls have been conducted using various methods, such as telephone, interviews, and online surveys. However, telephone and interview surveys are time-consuming and costly, and tend to have low response rates. Furthermore, particularly in surveys targeting residents of specific regions (e.g., electoral districts), there has been a problem in accurately verifying the actual location of respondents.

[0003] While online or mobile surveys offer high convenience for participation, concerns have been raised regarding the reliability of the regional representativeness of the results due to a lack of means to verify whether respondents are actually present in the relevant area. In other words, it has been difficult to effectively prevent behaviors such as respondents from outside the target area participating or a single respondent submitting surveys in multiple regions.

[0004] Furthermore, it was difficult to guarantee the integrity of the results because there was a constant possibility that collected survey data, once stored on a central server, could be tampered with or falsified by external hacking or internal administrators. This was a problem that could lead to controversy regarding the fairness and reliability of the results, particularly in politically sensitive election polls.

[0005] Furthermore, traditional methods required a significant amount of time from data collection to the derivation of analysis results, which limited the ability to timely identify rapidly changing social phenomena or public opinion trends and promptly reflect them in policy decisions or election strategy formulation. Prior art literature

[0006] Korean Registered Patent Publication No. 10-1312421 The problem to be solved

[0007] The present invention has been devised to solve the aforementioned problems, and the objective of the present invention is to enhance the accuracy and representativeness of specific region-based public opinion surveys by reliably verifying the actual geographical location of respondents when conducting mobile surveys.

[0008] We aim to ensure data integrity and the reliability of results by fundamentally preventing tampering with collected response data.

[0009] We aim to maximize the speed and efficiency of identifying public opinion trends by processing, analyzing, and visualizing survey results in real time.

[0010] The problems of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0012] A location-based real-time public opinion survey and analysis service system according to an embodiment of the present invention for solving the above problem

[0013] It may include a mobile survey interface (10) that operates on a mobile terminal and is executed on the mobile terminal to control participation in the survey based on whether the location is within a predefined geographical area, displays multiple survey questions, and receives user responses.

[0014] In one embodiment,

[0015] The mobile survey interface (10) is,

[0016] A step of obtaining location information of a mobile terminal;

[0017] A step of receiving an authentication result indicating whether the acquired location information is located within a predefined geographic area; and

[0018] A step of controlling the progress or submission of a survey based on a received authentication result, allowing it only when the authentication result indicates that the location is within a predefined geographical area;

[0019] It can be characterized by performing.

[0020] In one embodiment,

[0021] Step of receiving location information from a mobile survey interface (10);

[0022] A step of determining whether received location information is included within a geofence by referring to geofence data for a predefined geographic area; and

[0023] A step of transmitting the determination result to the mobile survey interface (10) as an authentication result;

[0024] It may further include a location-based authentication module (20) configured to perform.

[0025] In one embodiment,

[0026] A step of generating a data record containing submitted survey response data or a corresponding hash value when the authentication result indicates that it is located within a predefined geographic area; and

[0027] A step of ensuring the integrity of survey response data by recording generated data records on a blockchain network based on distributed ledger technology;

[0028] It may further include a blockchain management module (30) configured to perform.

[0029] In one embodiment,

[0030] A step of collecting and storing multiple survey response data submitted when the certification result indicates that it is located within a predefined geographical area;

[0031] A step of performing a data cleaning process on stored survey response data, including missing value processing or outlier detection; and

[0032] A step of generating analysis results by performing statistical analysis based on refined survey response data;

[0033] It may further include a data processing module (40) configured to perform.

[0034] In one embodiment,

[0035] A step of receiving an analysis result from a data processing module (40); and

[0036] A step of providing a user interface that displays the received analysis results in a form including at least one of a map-based visualization, a time series graph, a cartogram, or a summary statistical chart;

[0037] It further includes a real-time dashboard module (50) configured to perform,

[0038] The location-based authentication module (20) is,

[0039] It is configured to determine whether location information is included within a geofence, and then perform de-identification processing on the location information, and

[0040] Communication between the mobile survey interface (10) and at least one other module is encrypted, and

[0041] Access to the analysis results displayed by the real-time dashboard module (50) may be characterized as being controlled according to user authority. Effects of the invention

[0043] According to the present invention, through a location-based authentication module (20) linked with a mobile survey interface (10), the location of a respondent's mobile terminal at the time of survey participation is verified in real time whether it actually exists within a predefined specific geographical area (e.g., a specific electoral district, administrative district, etc.). By doing so, participation of respondents from outside the survey target area is fundamentally blocked, thereby having the effect of significantly improving the sample accuracy and representativeness of the region-based public opinion survey.

[0044] Valid survey response data collected by passing location authentication, or the hash value of the data, is recorded and managed in a distributed ledger using a blockchain management module (30). Due to the nature of blockchain, once data is recorded, it is almost impossible to modify or delete it, so data falsification can be effectively prevented and the integrity of the collected information can be strongly guaranteed. This has the effect of significantly contributing to enhancing social trust in the survey results.

[0045] The data processing module (40) processes a large amount of response data collected in real time without delay and performs various statistical analyses. The analyzed results are immediately provided in the form of intuitive visualizations, such as maps, graphs, and charts, through the real-time dashboard module (50). Through this, system users, such as policymakers or election campaign managers, can quickly grasp rapidly changing public opinion trends and make data-driven decisions, thereby maximizing the efficiency and timeliness of the survey.

[0046] Respondents can conveniently participate in the survey through the mobile survey interface (10) without restrictions on time and place, and can participate with peace of mind as privacy protection measures for sensitive information, such as location information, are applied through the security module (60). This has the effect of contributing to an increase in the survey participation rate.

[0047] The effects according to the present invention are not limited to those exemplified above, and a wider variety of effects are included within the present invention. Brief explanation of the drawing

[0049] Figure 1 illustrates an overall relationship diagram according to the present invention. FIGS. 2 and 3 illustrate a flowchart according to the present invention. Specific details for implementing the invention

[0050] Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention.

[0051] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another.

[0052] Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0053] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a basic artificial intelligence model is trained using a number of training data by a learning algorithm, thereby creating predefined rules of operation or artificial intelligence models configured to perform desired characteristics (or objectives). Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0054] An artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple nodes and weight values, and performs neural network operations through calculations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, multiple weights can be updated so that the loss value or cost value obtained by the artificial intelligence model during the learning process is reduced or minimized. Additionally, to minimize the loss value or cost value, multiple weights can be updated in a direction that minimizes the gradient associated with the loss value or cost value. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0055] A network is a network that serves as a transmission path for web pages; it may be a closed network such as a LAN (Local Area Network) or WAN (Wide Area Network), but it is desirable for it to be an open network such as the Internet. The Internet refers to a global open computer network structure that provides the TCP / IP protocol and various services existing at its upper layers, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).

[0056] Terminals can be implemented in various forms. For example, the terminals described in this specification may include mobile terminals such as smartphones, tablet PCs, PDAs, portable multimedia players, and MP3 players, as well as fixed terminals such as smart TVs and desktop computers.

[0057] In this specification, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. When a component is described as being “connected” or “connected” to another component, it should be understood that it may be directly connected to or connected to that other component, or that there may be other components in between. On the other hand, when a component is described as being “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

[0058] Meanwhile, a "module" or "part" for a component as used in this specification performs at least one function or operation. Furthermore, a "module" or "part" may perform a function or operation by hardware, software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts," excluding a "module" or "part" that must be performed on specific hardware or on at least one processor, may be integrated into at least one module. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0059] In addition, power, power transmission, and control therefor for the following assembly configurations and embodiments, including "by control," follow conventional technology including terminals, applications, hardware control modules, etc., so they are omitted to avoid redundancy.

[0060] In addition, the operation embodiments and configurations described in a general manner without being explained in detail below follow the prior art and are omitted in order to focus on describing the purpose of the present invention and the resulting effects.

[0061] Furthermore, in describing the present invention, if it is determined that a detailed description of related known functions or configurations may unnecessarily obscure the essence of the invention, such detailed description is abbreviated or omitted.

[0062] The mobile survey interface (10) is a user interface module that runs on a smart device and includes the functions of generating and displaying survey questions of various formats (e.g., multiple choice single / multiple choice, subjective short answer / descriptive, Likert scale, ranking type, image selection type, etc.), receiving responses from respondents, and verifying whether the respondent is present in a specific area by checking the respondent's real-time location information.

[0063] In addition, the survey is provided to respondents based on survey questions and target area information defined by the survey manager, and location information obtained through the device's location information access permission is transmitted to the location-based authentication module (20) when a response is entered or when the survey is started, and control is provided so that the survey can proceed or a response can be submitted only when authentication is successful, and a responsive UI optimized for various screen sizes and an intuitive user experience are provided.

[0064] In one embodiment, the mobile survey interface (10) communicates in real time with the location-based authentication module (20) to dynamically control the survey flow according to the authentication result and interacts in a manner that securely transmits response data along with the authenticated location information or area code to the data processing module (40).

[0065] In one embodiment, the mobile survey interface (10) has the technical effect of conducting a survey only on actual respondents within a specific region to ensure region-based response accuracy of the public opinion survey, increasing convenience and speed of participation through a mobile environment, and supporting various types of survey questions.

[0066] The location-based authentication module (20) is an authentication processing module that analyzes the user's location information received from the mobile survey interface (10), determines in real time whether the location is included within a specific predefined geographical boundary (e.g., electoral district polygon), and returns the result.

[0067] In addition, multiple positioning technologies such as GPS, communication networks, and beacons are utilized to obtain coordinates and verify whether they satisfy a set accuracy threshold. The latest geofence data obtained through government-provided public data portals or self-built and periodic updates is loaded to determine whether it is included using an algorithm such as Point-in-Polygon. After performing spoofing prevention logic such as detecting a simulated location, checking whether a VPN / proxy is used, and detecting abnormal location change patterns, the determination result is returned to a mobile survey interface (10).

[0068] In one embodiment, the location-based authentication module (20) exchanges information with the mobile survey interface (10) to implement a conditional survey system based on real-time location verification, and upon successful authentication, transmits (de-identified) location information or a region code along with the response data to the data processing module (40), and interacts in such a way that personal identification information of the authentication process is processed according to the policy of the security module (60).

[0069] In one embodiment, the location-based authentication module (20) has the technical effect of improving the representativeness and accuracy of the public opinion survey by enhancing the regional reliability of the respondent, preventing data contamination and attempts at false responses based on location, and providing flexibility for setting various geographical areas.

[0070] The blockchain management module (30) is a data management module that prevents tampering with data and permanently guarantees integrity by recording the hash value of the data or the anonymized original data in the distributed ledger at the time the survey response data is received by the system.

[0071] Additionally, a hash value or de-identified data of response data that has undergone basic verification including a data refinement process (missing value processing, outlier detection, etc.) is generated from the data processing module (40), and this is configured as a transaction along with metadata such as timestamps, and a request is made to transmit and record it to an established permissioned blockchain network (e.g., Hyperledger Fabric). After the consensus process, the recorded block information is stored for future verification and, if necessary, supports the participation of a third verification institution node.

[0072] In one embodiment, the blockchain management module (30) receives data from the data processing module (40) and records it on the blockchain to ensure the integrity of the data to be analyzed, manages access to the recorded data according to the policy of the security module (60), and interacts in a manner that objectively proves whether the data has been tampered with by comparing it with the original record when a request for data verification is made later.

[0073] In one embodiment, the blockchain management module (30) has the technical effect of ensuring data integrity and imperviousness, increasing transparency and audit traceability, and increasing social trust in the results.

[0074] The data processing module (40) is a computational processing module that collects response data transmitted along with location information from the mobile survey interface (10), stores it in a distributed storage or a central server, refines, processes, and analyzes the stored data to derive statistical meaning and generates data for visualization.

[0075] In addition, a real-time response data stream is received and stored, and a refinement pipeline is operated periodically or on an event basis for processing missing values, detecting outliers, and standardizing data. Various statistical analyses, including the mathematical formulas 1, 2, and 3 of the present invention, are performed based on the refined data to calculate indicators such as regional average support (A_p), RPPS, and TOMI. The analysis results are generated in a form that can be utilized in the real-time dashboard module (50), and the hash value of the processed data is transmitted to the blockchain management module (30).

[0076] In one embodiment, the data processing module (40) receives reliable source data from the mobile survey interface (10) and the location-based authentication module (20), ensures data integrity by linking with the blockchain management module (30), immediately provides analysis results to the real-time dashboard module (50), and interacts by applying the mathematical formulas of the present invention to generate unique indicators.

[0077] In one embodiment, the data processing module (40) provides real-time data analysis and insights, improves the accuracy of analysis results through data quality management, provides new insights through the generation of unique indicators, and has technical effects of scalability and flexibility.

[0078] The real-time dashboard module (50) is a user interface module in the form of a web-based or independent application that displays the analysis results received from the data processing module (40) in various visualization forms (maps, graphs, cartograms, charts, summary statistics, etc.) so that the user can intuitively understand and utilize them.

[0079] In addition, it periodically receives the latest analysis result data from the analysis engine to render visualization components such as regional distribution maps, time series graphs, cartograms, and response summary statistics, provides an interface that allows users to filter data and perform drill-down analysis based on specific conditions (region, period, demographic group, etc.), and supports access control and customization features for administrators.

[0080] In one embodiment, the real-time dashboard module (50) interacts in a manner that effectively delivers the analysis results generated by the data processing module (40) to the end user, provides map-based visualization that utilizes the characteristics of location-based data, and visualizes the unique indicators of the present invention (RPPS, TOMI, etc.) to support the user's in-depth decision-making.

[0081] In one embodiment, the real-time dashboard module (50) has technical effects that improve data accessibility and understanding, support rapid decision-making through real-time updates, and provide multidimensional analysis and deep exploration functions.

[0082] The security module (60) is a module of technical and administrative protection measures applied throughout the system, and is configured to minimize the risk of leakage and misuse of personally identifiable information that may occur during the processing of location information and survey response data, and to strengthen system security.

[0083] In addition, the principle of minimal information collection is applied during location-based authentication, personally identifiable information is immediately anonymized after authentication is complete (e.g., replaced with area codes, k-anonymity is applied), all communication channels are encrypted using TLS / SSL, Role-Based Access Control (RBAC) is applied, blockchain data access management policies (e.g., time-locks restricting access to original data until specific analysis results are disclosed) are implemented, and periodic security audits and vulnerability checks are performed.

[0084] In one embodiment, the security module (60) interacts with the location-based authentication module (20) to perform de-identification of sensitive information after authentication, strengthens data access control in conjunction with the blockchain management module (30), and applies a consistent security policy to all components of the system.

[0085] In one embodiment, the security module (60) has technical effects that enhance responder privacy protection, prevent data leakage and misuse, support compliance with relevant laws and regulations, and improve the reliability of the entire system.

[0086] [Mathematical Formula 1]

[0087]

[0088] i is a variable representing the regions subject to analysis. This variable is determined from the list of regions set in the system (e.g., a list of electoral districts). For instance, it is used within a formula to iterate through each region from i=1 to n, calculate the relevant values, and sum them. Additionally, it serves the effect of identifying and distinguishing data for each region.

[0089] Cw (i) is the weight of region i. The above variable is obtained by querying a value that is pre-calculated and stored in the system database, reflecting the population ratio of the region, past voter turnout, or a response data quality index evaluated by the data processing module (40) (e.g., response completion rate, consistency of responses between items, absence of specific pattern responses, etc.). For example, in the first term of the formula, it is multiplied by the average regional support Ap(i) and the distance attenuation term to adjust the degree to which the support of the region contributes to the overall RPPS. In addition, unlike the conventional simple average method, it has the effect of increasing the realism of the score by reflecting the relative importance of each region.

[0090] Ap(i) is the average policy support of region i. The above variable is obtained by querying the system database for the normalized result value calculated through Equation 3 in the data processing module (40). For example, in the first term of the formula, it is multiplied by the regional weight Cw(i) and the distance attenuation term to reflect the average level of policy preference of the residents of the corresponding region. In addition, it serves as a key indicator that quantitatively represents the level of public opinion by region and has the effect of serving as the basis for the calculation of RPPS.

[0091] Fr(i) is the frequency of survey participation (total number of valid responses) of region i. The above variable is obtained by the data processing module (40) by counting the number of valid response records collected in region i during a specific analysis period in the system database. For example, it is converted into the form ln(1+F_{r}(i)) in the second term of the formula and subtracted from the total RPPS score, thereby limiting the influence when the number of responses from a specific region is excessively high. In addition, it has the effect of preventing the phenomenon where the total score is distorted due to a surge in responses from a minority region.

[0092] Dr(i) is the geographical distance from a predefined reference point to a representative point of region i. The above variable is calculated by obtaining the latitude and longitude coordinates of a reference point (e.g., capital, campaign headquarters) set according to the purpose of analysis and the latitude and longitude coordinates of a representative point (e.g., administrative district center coordinates) of each region i, and then using a standard distance calculation algorithm such as the Haversine formula in a data processing module (40) or a separate GIS module. For example, an exponential attenuation effect is applied in the form of e^(-β√(Dr(i))) in the first term of the formula to reduce the influence on RPPS as the region is further from the reference point. In addition, by using the square root of the distance, the pattern of influence reduction as distance increases can be modeled as a specific non-linear pattern different from linear or simple exponential attenuation (e.g., influence decreases relatively sensitively when the distance is close, becomes less sensitive as the distance increases, or vice versa, which can be adjusted together with the β value according to the analyst's intention), thereby reflecting a distance effect that is differentiated from conventional linear or simple exponential attenuation methods. Depending on the setting of reference points, it can be utilized for analyzing the diffusion of policy preferences or correlations centered on specific hubs.

[0093] β is the distance sensitivity coefficient. This variable is a constant value greater than or equal to 0 that is determined by the analyst through experimental setting or optimization via methods such as cross-validation, taking into account the characteristics of the data and the purpose of the analysis. For example, the magnitude of the exponent in the distance attenuation term (e^(-β√(Dr(i))))) within the formula is adjusted to control the strength of the influence of distance on RPPS. Additionally, it has the effect of allowing the importance of the distance factor to be adjusted according to the analysis scenario.

[0094] ln(...) is the natural logarithm function. In the first term, this function prevents the magnitude of the summed value from becoming excessive, and in the second term, it serves to smoothly adjust the penalty increase rate associated with increased participation frequency.

[0095] Mathematical Equation 1 combines the log-sum structure of the first term and the log-frequency penalty subtraction structure of the second term to aggregate region-specific weighted support by attenuating it according to distance from a reference point, and simultaneously suppresses the influence of regions with excessive participation frequency, thereby calculating a region-specific policy preference score (RPPS) that considers complex factors. This has technical features in that it increases the stability of values ​​compared to conventional techniques, reflects distance and frequency factors non-linearly, and attempts to balance the qualitative aspects (support, importance) and quantitative aspects (participation frequency) of public opinion.

[0096] In one embodiment, it is assumed that a nationwide policy preference index (RPPS) for "new welfare policy A" is calculated at a specific point in time (e.g., February 18, 2025). It is assumed that there are 17 cities and provinces nationwide (i=1,...,17), a reference point: the Government Complex Seoul (specific latitude and longitude coordinates), and data: weights for each city and province (Cw), average policy support (Ap), frequency of survey participation (Fr), distance from the Government Complex Seoul (Dr), and distance sensitivity coefficient (β= 0.05).

[0097]

[0098] The data processing module (40) calculates the following for each region i (from 1 to 17): the first term is Cw(i)·Ap(i)·e^(-0.05×√Dr(i)) and the second term is ln(1+Fr(i)). Then, the first terms of all regions are summed, 1 is added, and the natural logarithm is taken to obtain the final first term value.

[0099] Sum the second terms of all regions to obtain the final second term.

[0100] Final RPPS is calculated as the first term value - the second term value.

[0101] For example, assume that the final RPPS value calculated using the above data is -3.2.

[0102] It is difficult to assign absolute meaning (e.g., "support is very low") to the RPPS value of -3.2 itself. Due to the structure of the formula, this value can result in a negative number, and it is a relative indicator that reflects a combination of various factors.

[0103] For relative comparison, if the RPPS for the same policy A was -4.0 yesterday, the increase to -3.2 today suggests that overall preference has improved relatively, taking distance and frequency factors into account.

[0104] If the RPPS for "Alternative Policy B" was calculated as -1.5 at the same point in time, it can be interpreted that Policy B is currently receiving a higher overall preference than Policy A. However, it should be taken into account that the regional support patterns of the two policies may differ.

[0105] Meanwhile, threshold values ​​can be set in advance according to the purpose of the analysis. For example, internal criteria such as "consider policy implementation if RPPS > -2.0, and re-examine policy if RPPS < -5.0" can be established and utilized for decision-making. Currently, -3.2 can be judged as an 'average' level according to the above criteria.

[0106] Additionally, the data processing module (40) can further analyze how each region contributed to the formation of the final RPPS value. For example, Jeju has a high Ap but is far away, so its contribution to the first term is not large, while Seoul has an average Ap but is high in weight and is close, so its contribution to the first term is large, but at the same time, its contribution to the second term (penalty) may also be large because its Fr is high. This analysis helps in establishing strategies for each region.

[0107] [Mathematical Formula 2]

[0108]

[0109] t is a variable representing the current point in time. The above variable is determined as a value that increases sequentially according to the analysis cycle (e.g., hours, days, weeks) of the data processing module (40). For example, it is used to compare the data at the current point in time with the data at the previous point in time (t-1) within a formula. It also has the effect of providing a standard for tracking changes over time.

[0110] i is the region variable for analysis. The above variable is determined from the list of regions for analysis set in the system, identical to Equation 1. For example, it is used to calculate and sum the contribution of volatility for each region within the formula. It also has the effect of identifying and analyzing volatility by region.

[0111] Ge(i,t) is a weight that reflects the importance or reliability of region i at time t. The above variable is obtained by querying a database for a value calculated by the data processing module (40) by combining the number of respondents in region i at time t, the statistical reliability level of the response data (e.g., the reciprocal of the standard error), the size of the regional population, etc. For example, it is multiplied by the squared change in support in the numerator of the formula to adjust the effect so that changes in important or highly reliable regions have a greater impact on the overall TOMI index. In addition, unlike conventional simple average volatility indices, it has the effect of representing a realistic level of volatility by differentially reflecting the influence of changes in each region.

[0112] Ap(i,t) is the average policy support of region i at time t. The above variable is obtained by querying the system database for the result value at time t, which is calculated and normalized through mathematical formula 3 in the data processing module (40). For example, it is used to calculate the amount of change by calculating the difference from the support of the previous time point Ap(i,t-1) within the formula. In addition, it has the effect of serving as a key input value for the change in the level of public opinion by region over time.

[0113] Ap(i,t-1) is the average policy support of region i at the previous time point t-1. The above variable is obtained by querying the system database for the result value of time point t-1, which is calculated and normalized through mathematical formula 3 in the data processing module (40). For example, it is used to calculate the amount of change by calculating the difference from the support Ap(i,t) at the current time point within the formula. It also has the effect of providing a reference point for calculating the amount of change.

[0114] Rs(i,t) is the number of pure survey participations (responses) that occurred in region i during time point t. The above variable is obtained by the data processing module (40) by counting the number of valid response records that occurred in region i during the time interval (e.g., the last hour) corresponding to time point t in the system database. This is distinguished from Ge(i,t) and represents the 'activity' itself at that time point. For example, it is converted into the form 1+ln(1+Rs(i,t)) in the denominator of the formula, and serves to slightly reduce the contribution to volatility when the number of responses in the region is very high. In addition, it has the effect of preventing a temporary surge in responses in a specific region from excessively distorting the overall volatility index.

[0115] ln(...) is the natural logarithm function. This function serves to apply the damping effect of the contribution to volatility with increasing participation frequency to the denominator in a smooth (non-linear) manner.

[0116] The above mathematical formula 2 measures the time-series volatility of public opinion (TOMI) by calculating the square of the change in average support relative to the previous point in time for each region, adjusting it with a regional importance / trust weight (Ge), dividing it by a log decay term based on the frequency of participation (Rs) at that point in time, and summing them all. Compared to conventional techniques, this is characterized by measuring the magnitude of change (square term), reflecting regional importance (weight Ge), and mitigating distortion caused by a surge in response volume (log frequency decay), thereby enabling the identification of a more stable and realistic trend of time-series public opinion fluctuations.

[0117] In one embodiment, it is assumed that the nationwide public opinion volatility index (TOMI) for "new welfare policy A" is calculated based on today (t) compared to yesterday (t-1). The target area is assumed to be 17 cities and provinces nationwide (i=1,...,17), and the data is assumed to be the importance weight (Ge) for each city and province today (t), the average policy support (Ap) between today (t) and yesterday (t-1), and the participation frequency (Rs) today (t).

[0118]

[0119] The data processing module (40) calculates the following for each region i (from 1 to 17):

[0120] Squared change in support: (Ap(i,t) - Ap(i,t-1))^2, numerator: Ge(i,t) x (squared change), denominator: 1 + ln(1 + Rs(i,t)), and regional TOMI contribution: numerator / denominator. Then, the 'TOMI contribution' of all regions is summed to obtain the final TOMI(t) value.

[0121] For example, assume that the final TOMI(t) value calculated using the above data is 12.7.

[0122] The TOMI value of 12.7 represents the total amount of public opinion volatility that occurred nationwide during the day. A higher value indicates greater volatility.

[0123] For relative comparison, if yesterday's TOMI(t-1) value was 8.5, an increase to 12.7 today means that public opinion has become more active or unstable compared to yesterday. Conversely, if the previous value was 15.0, it indicates that volatility has slowed relatively today.

[0124] If the TOMI value for the other "Policy B" during the same period was 5.0, it suggests that public opinion regarding Policy A is changing much more dynamically compared to Policy B.

[0125] In addition, a threshold value for determining the level of volatility can be set depending on the purpose of the analysis. For example, criteria such as "classify as 'Caution' if TOMI > 10, and as 'Warning' if TOMI > 20, and initiate cause analysis" can be applied. Currently, 12.7 may correspond to the 'Caution' state.

[0126] For contribution analysis, the data processing module (40) can analyze how much each region contributed to the final TOMI value. For example, although the change in support itself is not large (-0.03) in Busan, the high Ge value may have an impact on TOMI. On the other hand, Gwangju has a large change in support (+0.05) and a moderate level of Ge, so its contribution to TOMI may be high. Seoul has a change (+0.03) and a high Ge, but the high Rs value increases the denominator, so its contribution may be partially offset. Through this analysis, it is possible to identify which region is driving the change in public opinion.

[0127] [Mathematical Formula 3]

[0128]

[0129] i is a variable representing the region to be analyzed. This variable is determined from the list of regions to be analyzed set in the system, identical to mathematical formulas 1 and 2. For example, it serves as a criterion for aggregating responses within a specific region i. It also has the effect of distinguishing score calculations by region.

[0130] k is a variable of an individual respondent belonging to region i. The above variable is an identifier assigned from 1 to Ni by the data processing module (40) as it iterates through the response records of region i. For example, it is used to access each individual response record within a formula to retrieve related values. It also has the effect of enabling calculations at the individual response level.

[0131] Ni is the total number of valid respondents within region i. The above variable is obtained by the data processing module (40) by counting the number of valid response records corresponding to region i in the system database. For example, it defines the upper boundary of the summation within the formula. It can also be used as the denominator in a subsequent normalization step. Additionally, it has the effect of indicating the response scale of the corresponding region.

[0132] Sind(i,k) is the individual policy support reported by respondent k. The above variable is obtained by the data processing module (40) retrieving the response value of a specific survey question from response record k in the system database and quantifying / standardizing it to 0 or 1, etc., if necessary. For example, it is multiplied by the respondent weight Ws(i,k) in the numerator of the formula to represent the weighted support of the individual response. In addition, it has the effect of directly reflecting the preference level of each respondent.

[0133] Ws(i,k) is a weight for respondent k. The above variable is obtained by the data processing module (40) by calculating it according to a predefined weighting rule (e.g., post-weighting application logic) based on demographic information stored in response record k, response metadata (response time, results of determining dishonest responses (e.g., cases where the total survey response time is excessively short or long, abnormal pattern responses to specific items, consecutive identical selections, etc.), location accuracy information, etc. For example, it is multiplied by the individual support Sind(i,k) in the numerator of the formula to adjust the contribution by reflecting the importance or reliability of the corresponding response. Additionally, it has the effect of correcting for demographic representativeness or considering response quality.

[0134] Ts(i,k) is the time elapsed since respondent k participated in the survey. The above variable is obtained by the data processing module (40) by calculating the difference (e.g., in units of time) between the timestamp at the current analysis time and the timestamp of response record k recorded in the database. For example, it is multiplied by the square root of distance term inside the exponential function in the denominator of the formula, and serves to reduce the contribution of the response as the response gets older. It also has the effect of giving higher weight to the latest public opinion.

[0135] Ds(i,k) is the geographical distance between the actual response location of respondent k and a predefined center point of region i (e.g., the geographical center of the administrative district or the center of the population distribution). The above variable is obtained by the data processing module (40) by calculating the distance using the Haversine formula or the predefined center point coordinates of region i, using the location coordinates recorded in the response record k. For example, the distance is multiplied by the elapsed time term within the exponential function of the denominator in the formula and the square root is taken (√Ds), which serves to reduce the contribution of responses from locations farther from the center point of the region. Additionally, the form of the square root of the distance (√Ds) models the attenuation effect due to increased distance in a specific way (initial gradualness), and can have the effect of emphasizing key opinions of the region or controlling the influence of responses from boundary areas.

[0136] e is the base of the natural logarithm. The above variable is used to exponentially apply time and distance-based damping effects in the denominator.

[0137] The above mathematical formula 3 calculates a weighted sum score for each region that reflects temporal and spatial factors by summing up the product of the respondent's support (Sind) and weight (Ws) for each individual response (k) in region i, and dividing it by a damping term (1+e^(Ts*√Ds)) that considers the time elapsed from the time of the response (Ts) and the distance from the regional center point (Ds). Since the value itself varies depending on the number of respondents and the weight and damping degree of each response, in order to use it as an average support Ap(i) that can be compared between regions, a normalization process must be performed in the data processing module (40), that is, a process of dividing the calculated 'weighted sum score for each region' by the 'sum of damped weights applied to each response'. Here, the 'sum of damped weights applied to each response' can be calculated as ∑{k=1}^{N_i}[Ws(i,k) / (1+e^(Ts(i,k)*√Ds(i,k)))]. This is characterized by aggregating local scores in a manner that emphasizes recency and centrality by simultaneously considering time elapsed and response location factors at the individual response level, compared to conventional techniques. The interaction in the form of the multiplication of time and the square root of distance is a model that reduces the contribution particularly more significantly when both factors are large simultaneously, and it can be designed for specific analytical purposes.

[0138] In one embodiment, the average support Ap(1,t) for "new welfare policy A" in the Seoul region (i=1) today (t) is calculated. The value is subsequently used in the calculation of RPPS and TOMI.

[0139] Target Responses: Valid response data collected in the Seoul area during today (N1=1200 cases), Data: Individual support (Sind), weight (Ws), response elapsed time (Ts), and distance from the Seoul center point (Ds) for each response k (from 1 to 1200) are collected as follows.

[0140]

[0141] In one embodiment, the data processing module (40) calculates the following for each Seoul region response k (from 1 to 1200):

[0142] Damping factor: 1 + e^(Ts(1,k) x √Ds(1,k))(assuming β=1), weighted support: Sind(1,k) x Ws(1,k), individual contribution: weighted support / damping factor, sum the 'individual contributions' of all respondents k to obtain the intermediate sum score.

[0143] To calculate the final average support, the median sum score is normalized: Ap(1,t) = median sum score / N1 = median sum score / 1200.

[0144] For example, if the intermediate sum score is calculated as 780, the final Ap(1,t) = 780 / 1200 = 0.65.

[0145] The calculated Ap(1,t) = 0.65 represents the average level of support for Policy A in the Seoul area today (based on a 0–1 scale). The above value is the result of reflecting the weights of individual respondents and the attenuation of influence over time and response location (distance from the center point).

[0146] The above value (0.65) can be used as the Ap (1) value for the Seoul region when calculating today's RPPS, and as the Ap (1,t-1) value when calculating tomorrow's TOMI.

[0147] If the criterion for policy success is set as "regional average support of 0.7 or higher," the current Seoul region is 0.65, which can be judged as falling slightly short of the standard.

[0148] In addition, analyzing individual contribution data can provide additional information. For example, by comparing the average contribution of responses within the last hour (small Ts) with the average contribution of responses from 10 hours ago (large Ts), or by comparing the average contribution of responses near the center (small Ds) with responses in the outer regions (large Ds), one can more precisely identify temporal and spatial trends in public opinion change. This can also be used to verify how the time / distance attenuation effect of Equation 3 actually works.

[0149] Although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Various modifications are possible by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0150] Mobile survey interface (10) Location-based authentication module (20) Blockchain management module (30) Data processing module (40) Real-time dashboard module (50) security module (60)

Claims

Claim 1 A location-based real-time public opinion survey and analysis service system comprising a mobile survey interface (10) that operates on a mobile terminal and is executed on the mobile terminal to control participation in a survey based on whether the location is within a predefined geographical area, and which displays a plurality of survey questions and receives user responses, wherein the mobile survey interface (10) is characterized by performing the steps of: acquiring location information of the mobile terminal; receiving an authentication result indicating whether the acquired location information is located within a predefined geographical area; and controlling to allow the progress or submission of the survey only when the authentication result indicates that the location is within the predefined geographical area based on the received authentication result; and the step of receiving location information from the mobile survey interface (10); and determining whether the received location information is included within the geofence by referring to geofence data for the predefined geographical area. The method further includes a location-based authentication module (20) configured to perform the step of transmitting the determination result as an authentication result to a mobile survey interface (10); a blockchain management module (30) configured to perform the step of generating a data record including submitted survey response data or a corresponding hash value when the authentication result indicates that the data is located within a predefined geographical area; and a step of recording the generated data record on a blockchain network based on distributed ledger technology to ensure the integrity of the survey response data; a data processing module (40) configured to perform the step of collecting and storing multiple submitted survey response data when the authentication result indicates that the data is located within a predefined geographical area; a step of performing a data cleaning process including missing value processing or outlier detection on the stored survey response data; and a step of generating an analysis result by performing a statistical analysis based on the cleaned survey response data; and a step of receiving an analysis result from the data processing module (40).A step of providing a user interface that displays the received analysis results in a form including at least one of a map-based visualization, a time-series graph, or a summary statistical chart;The system further includes a real-time dashboard module (50) configured to perform [the following], and the location-based authentication module (20) is configured to determine whether the location information is included within a geofence and then perform de-identification processing on the location information, communication between the mobile survey interface (10) and at least one other module is encrypted, and access to the analysis results displayed by the real-time dashboard module (50) is controlled according to user authority, and the data processing module (40) calculates a regional policy preference score (RPPS) for each analysis target region (i) set in the system database, wherein the regional policy preference score is calculated using the weight (Cw(i)) of each region, the average policy support (Ap(i)), and the geographical distance (Dr(i)) from a reference point to a representative point of the region, and is characterized by applying a non-linear distance attenuation effect that reduces influence as it moves further from the reference point to the geographical distance (Dr(i)) using the form of the square root of the distance (√(Dr(i))), and the data processing A location-based real-time public opinion survey and analysis service system. The module (40) is characterized by combining a log frequency penalty subtraction structure based on the survey participation frequency (Fr(i)) of the region when calculating the policy preference score (RPPS) by the region, and applying a natural logarithm function (ln) to subtract from the total score to limit the influence when the number of responses from a specific region is excessively high. The data processing module (40) is characterized by calculating a time-series public opinion volatility index (TOMI) by comparing data from the current time point (t) and the previous time point (t-1), using the square of the change in support for each region (i) ((Ap(i,t) - Ap(i,t-1))^2) as the numerator and the value obtained by taking the natural logarithm of the number of pure survey participations (Rs(i,t)) at the time point (1+ln(1+Rs(i,t))) as the denominator, thereby non-linearly attenuating the contribution to volatility due to an increase in the number of participations. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 delete

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