A method and system for finding potential associated addresses based on communication data

Abstract

The application discloses a method and system for finding potential associated addresses based on communication data, which comprises the following steps: obtaining full-amount communication data of a target object; constructing an explicit associated address set of known addresses; setting a night period, extracting candidate residence points based on the rule that the last call of the day and the first call of the next day access the same base station and do not fall into the explicit associated address set; calculating the spherical distance between the candidate residence points and the explicit associated address set for secondary filtering; performing spatial matching between the remaining residence points and a real estate address entity library containing asset valuation information to generate a potential associated address list; and counting the occurrence frequency, calculating the confidence score by using an exponential decay model and generating an early warning level. The application solves the problem of sparse data by using the rule that the first and last communication data access the same base station, effectively filters noise and known address interference by combining the exclusion mechanism and multi-dimensional value evaluation, and realizes high-precision automatic mining of potential associated addresses and quantitative risk early warning.

Description

Technical Field

[0001] This invention relates to the field of electronic data forensics, and mainly to a method and system for finding potential associated addresses based on communication data. Background Technology

[0002] In scenarios such as asset verification, risk control, and compliance auditing, the verification of potentially related addresses is a crucial task. Because the property registration system typically operates on a "voluntary declaration + property search by person" model, a large number of properties may be registered under the names of relatives, shell companies, or nominees, rendering the traditional search method based on "retrieving files from the real estate registration center" ineffective.

[0003] To address these issues, existing technologies are beginning to incorporate big data methods, utilizing multi-source data such as fund transfer records, travel trajectories, and communication data for cross-referencing analysis. Among these, communication data, due to its wide coverage, real-time nature, and relatively low barrier to entry, has become a crucial data source for asset investigations.

[0004] However, existing location identification schemes based on communication data still have many shortcomings: Coarse granularity: Traditional algorithms typically output large sectors based on the base station coverage radius (200-500 meters), making it difficult to accurately locate specific real estate entities.

[0005] High false alarm rate: Simple time-period statistical models (such as the percentage of stays between 0:00 and 6:00 at night) are easily affected by temporary stays and lack a mechanism to exclude explicitly associated addresses, resulting in a large number of irrelevant addresses being misjudged as potential associated addresses.

[0006] Lack of quantitative assessment: Existing solutions typically only output "suspected locations" without a quantitative assessment of the strength of asset association and asset value, making it difficult for investigators to determine the priority of investigations and resulting in low efficiency.

[0007] Therefore, there is an urgent need for a method to find potential related addresses that can effectively filter noise, accurately locate the physical property, and provide quantifiable confidence assessment. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for finding potential associated addresses based on communication data, so as to solve the problems of low positioning accuracy, high false alarm rate and lack of effective value assessment in the prior art.

[0009] To achieve the above objectives, the present invention provides a method for finding potential associated addresses based on communication data, comprising the following steps: S1. Obtain the communication data of the target object. The communication data shall include at least the International Mobile Subscriber Identity, timestamp, base station number and base station latitude and longitude. S2. Construct an explicit set of associated addresses, importing the known associated addresses of the target object into a GeoJSON polygon list; S3. Set a nighttime period. For each day's communication data, retrieve the last call record of the day and the first call record of the next day within the nighttime period. If the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then the base station is determined as a candidate camping point. S4. Calculate the spherical distance between the candidate dwell point and the nearest address in the explicit associated address set; if the spherical distance is less than the preset spatial redundancy threshold, mark the corresponding candidate dwell point as a redundant point and remove it; S5. Traverse the preset real estate address entity database and calculate the distance between the remaining candidate residence points and the center of each real estate address entity in the database; if the distance between the candidate residence point and the center of each real estate address entity in the database is less than the preset relevant threshold, add the corresponding real estate address entity to the potential associated address list; the real estate address entity database contains the address entity name and center latitude and longitude. S6. Count the number of times the same potentially related address appears within a preset time period, calculate the confidence score based on the number of times the same potentially related address appears within the preset time period, and generate an early warning level based on the confidence score.

[0010] By using the above technical solutions, the rule of using the first and last communication records of the night as the same base station is replaced by the traditional simple time period ratio statistics. This can effectively utilize the sparsity characteristics of communication data to capture real overnight behavior, filter out noise such as passing by and short stays at night, and improve the purity of the dwell point. At the same time, the introduction of an explicit associated address set mechanism can accurately remove known explicit associated addresses and other interference items, and significantly reduce the false alarm rate.

[0011] Furthermore, step S1 also includes cleaning abnormal location data in the communication data; wherein, abnormal location data includes data in which two communication data appear within the same second, and the distance between the base station locations corresponding to the two communication data is greater than a preset distance threshold.

[0012] By using the above technical solutions, and by setting a distance threshold (such as 10km) to clean abnormal location data, invalid records caused by signal abnormalities or base station switching can be removed, ensuring the quality of the data source for subsequent analysis.

[0013] Furthermore, the spherical distance in step S4 is calculated using the following formula: ; in, The distance is spherical. For the Earth's radius, and The latitudes of the two points are respectively. Due to latitude difference, This is due to the difference in longitude.

[0014] By using the above technical solutions and the spherical distance formula to calculate the distance between candidate points and excluded addresses, we can more accurately handle geospatial location relationships. Combined with a preset spatial redundancy threshold (such as 500 meters), we can filter out pseudo-resident points that, although not directly within the excluded polygon, actually belong to the edge of the known address signal coverage.

[0015] Furthermore, step S6 includes: The number of times the same potentially associated address appears within a preset time period is counted. Potentially associated addresses whose number of occurrences within the preset time period exceeds a preset threshold are marked. A confidence score is calculated based on the number of times the same potentially associated address appears within the preset time period after the marked potential associated addresses.

[0016] Furthermore, the formula for calculating the confidence score in step S6 is as follows: ; in, The confidence score is... It is a natural constant. This refers to the number of times the same potentially associated address appears within a preset time period. This is the preset convergence constant.

[0017] By employing the above technical solutions, the discrete occurrence frequency is transformed into a sortable confidence score between 0 and 1 using an exponential decay model, thereby achieving a quantitative evaluation of potential associated addresses.

[0018] Furthermore, step S5, the real estate address entity database, also includes building attribute information and asset valuation information; step S6, generating the early warning level, specifically includes: Obtain asset valuation information and building attribute information for potential related addresses; A multi-dimensional assessment model is constructed based on confidence scores, asset valuation information, and building attribute information. A high-risk red alert is generated when the confidence score is greater than the preset confidence threshold and the asset valuation information is higher than the average housing price level in the city.

[0019] The above technical solutions incorporate asset valuation information and building attribute information, achieving a leap from "location positioning" to "asset value assessment." Through a multi-dimensional assessment model, high-value, highly active potential related addresses are prioritized for early warning.

[0020] Secondly, the present invention provides a system for finding potential associated addresses based on communication data, comprising: The data acquisition module is configured to acquire the communication data of the target object. The communication data includes at least the International Mobile Subscriber Identity, timestamp, base station number, and base station latitude and longitude. The explicit associated address set building module is configured to build explicit associated address sets by importing the known associated addresses of the target object into a GeoJSON polygon list. The candidate call site extraction module is configured to set a nighttime period and, for each day's communication data, retrieve the last call record of the day and the first call record of the next day within the nighttime period; if the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then the base station is determined as a candidate call site. The secondary filtering module is configured to calculate the spherical distance between the candidate dwell point and the nearest address in the explicit associated address set; if the spherical distance is less than the preset spatial redundancy threshold, the corresponding candidate dwell point is marked as a redundant point and removed. The real estate address entity matching module is configured to traverse a pre-set real estate address entity database, calculate the distance between the remaining candidate points and the center of each real estate address entity in the database; if the distance between the candidate point and the center of each real estate address entity in the database is less than a preset threshold, the corresponding real estate address entity is added to the potential associated address list; the real estate address entity database contains the address entity name and center latitude and longitude. The confidence quantification scoring module is configured to count the number of times the same potentially associated address appears within a preset time period, calculate a confidence score based on the number of times the same potentially associated address appears within the preset time period, and generate an early warning level based on the confidence score.

[0021] Furthermore, the system also includes: The visualization application module is configured to display potentially related addresses on the map interface and output a checklist that includes warning levels.

[0022] Through the above technical solutions, the visualization module can intuitively display the geographical distribution of potentially related addresses and output verification reports in the form of corresponding warning lists, which makes it easier for investigators to identify key targets and conduct subsequent offline verification.

[0023] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the above-described method.

[0024] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0025] Compared with the prior art, the beneficial effects of the present invention are: This application replaces the traditional simple time period proportion statistics with the rule of using the first and last communication records of the night as the same base station. It can effectively use the sparsity characteristics of communication data to capture real overnight behavior and filter out noise data such as passing by and short stays at night. By constructing an explicit associated address set and combining it with GeoJSON polygons and spherical distance for secondary filtering, it can accurately remove known explicit associated address interference items and solve the problem of not being able to effectively distinguish between potential associated addresses and explicit associated addresses. By using an exponential decay model to convert discrete occurrence frequencies into sortable confidence scores and warning levels, it realizes the quantitative classification of the probability of potential associated addresses, which is convenient for prioritizing the investigation of high-risk targets. Attached Figure Description

[0026] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Elements in the drawings are not necessarily to scale. The same reference numerals refer to corresponding similar parts.

[0027] Figure 1 This is a flowchart of a method for finding potential associated addresses based on communication data according to an embodiment of the present invention; Figure 2 This is a framework diagram of a system for finding potential associated addresses based on communication data according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device suitable for implementing electronic devices according to embodiments of the present invention. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0029] Firstly, reference Figure 1 This embodiment provides a method for finding potential associated addresses based on communication data.

[0030] S1. Obtain the communication data of the target object. The communication data fields include at least: International Mobile Subscriber Identity (IMSI), timestamp, cell ID, cell coordinates, and Location Area Code (LAC). After obtaining the data, perform abnormal location data cleaning. Due to signal interference and other reasons, location data drift may exist in the communication data. This embodiment sets a preset distance threshold (e.g., 10km). If two communication data entries appear within the same second, and the distance between the base station locations corresponding to these two records is greater than 10km, they are determined to be invalid drift data and discarded.

[0031] S2. Construct an explicit set of associated addresses. To accurately identify potential associated addresses, it is necessary to exclude known explicit associated addresses of the target object. Import the target object's declared property addresses, registered addresses, work unit addresses, historical business registration locations, and frequently used hotels into the system and convert them into a GeoJSON polygon list. It should be noted that the user information such as 'registered address', 'work unit address', 'historical business registration location', 'frequently used hotel', and 'communication data' involved in this embodiment are all anonymized data obtained with the authorization of the target object or after legal approval by relevant institutions. Base station records falling within any polygon range are excluded and not included in subsequent calculations.

[0032] S3. Define the nighttime period as 20:00 to 10:00 the next day. For each day's communication data, retrieve the last call record of the day and the first call record of the next day within this nighttime period. The judgment logic is as follows: if the last call of the day and the first call of the next day access the same base station, and the base station does not fall within the explicit associated address set constructed in step S2, then the base station is determined to be a candidate camping point.

[0033] S4. Calculate the spherical distance between the candidate dwell points obtained in step S3 and the nearest address in the explicit associated address set. Set a spatial redundancy threshold. (For example, 500 meters). If This indicates that although the dwell point does not fall directly within the exclusion polygon, it is extremely close to the known address and is highly likely to be an extension of the signal coverage of the known address. Therefore, it is discarded to further reduce the false alarm rate. Among these, spherical distance... The calculation formula is: ; In the formula, Take the Earth's radius (approximately 6371 km). Latitude Longitude and The latitudes of the two points are respectively. Due to latitude difference, The distance between candidate points and excluded addresses is calculated using the spherical distance formula, which can more accurately handle geospatial relationships. Combined with a preset spatial redundancy threshold (such as 500 meters), it can filter out pseudo-resident points that, although not directly within the excluded polygon, are actually located on the edge of the known address signal coverage.

[0034] S5. Traverse the pre-set real estate address entity database. This database contains the names, center latitude and longitude, building attribute information, and asset valuation information of all address entities in the region. Calculate the distances between the remaining candidate locations and the centers of each real estate address entity in the database. Set relevant thresholds. (For example, 200 meters). If Then, the corresponding real estate address entity will be added to the "Potential Related Address List". If If the correlation is weak, it can be marked as "weak correlation" and is for human reference only.

[0035] S6. Count the number of times the same potentially associated address appears within a preset time period (e.g., 6 months). The confidence score was calculated using the exponential decay model. ; in, The confidence score is... This is a preset convergence constant (e.g., a value of 5). The formula converts discrete occurrence counts into probability scores between 0 and 1. To assess asset value, this embodiment also constructs a multi-dimensional assessment model. It obtains asset valuation information and building attribute information for the corresponding real estate address entity. When the confidence score... When the value exceeds a preset threshold (e.g., 0.63, corresponding to 5 or more occurrences), and the asset valuation of the corresponding real estate address is higher than the average housing price in the area, the system generates a high-risk red alert. By introducing asset valuation information and building attribute information, a leap from "location positioning" to "asset value assessment" is achieved. Using a multi-dimensional assessment model, high-value, highly active potential related addresses are given priority for alerts.

[0036] The beneficial effects of this invention are as follows: This application replaces the traditional simple time period proportion statistics with the rule of using the first and last communication records of the night as the same base station. It can effectively use the sparsity characteristics of communication data to capture real overnight behavior and filter out noise data such as passing by and short stays at night. By constructing an explicit associated address set and combining it with GeoJSON polygons and spherical distance for secondary filtering, it can accurately remove known explicit associated address interference items and solve the problem of not being able to effectively distinguish between potential associated addresses and explicit associated addresses. By using an exponential decay model to convert discrete occurrence frequencies into sortable confidence scores and warning levels, it realizes the quantitative classification of the probability of potential associated addresses, which is convenient for prioritizing the investigation of high-risk targets.

[0037] Secondly, this embodiment provides a system for finding potential associated addresses based on communication data, including: The data acquisition module 100 is configured to acquire the communication data of the target object. The communication data includes at least the International Mobile Subscriber Identity, timestamp, base station number and base station latitude and longitude. The explicit association address set construction module 200 is configured to construct an explicit association address set by importing the known association addresses of the target object into a GeoJSON polygon list. The candidate camping point extraction module 300 is configured to set a nighttime period and, for each day's communication data, retrieve the last call record of the day and the first call record of the next day within the nighttime period; if the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then the base station is determined as a candidate camping point. The secondary filtering module 400 is configured to calculate the spherical distance between the candidate dwell point and the nearest address in the explicit associated address set; if the spherical distance is less than the preset spatial redundancy threshold, the corresponding candidate dwell point is marked as a redundant point and removed. The real estate address entity matching module 500 is configured to traverse a preset real estate address entity database, calculate the distance between the remaining candidate residence points and the center of each real estate address entity in the database; if the distance between the candidate residence point and the center of each real estate address entity in the database is less than a preset relevant threshold, the corresponding real estate address entity is added to the potential associated address list; the real estate address entity database contains the address entity name and center latitude and longitude. The confidence quantification scoring module 600 is configured to count the number of times the same potentially associated address appears within a preset time period, calculate a confidence score based on the number of times the same potentially associated address appears within the preset time period, and generate an early warning level based on the confidence score.

[0038] The visualization application module 700 is configured to display potentially related addresses on a map interface and output a verification list including warning levels. The visualization module can intuitively display the geographical distribution of potentially related addresses and output verification reports in the form of corresponding warning lists, facilitating investigators to identify key targets and conduct subsequent offline verification.

[0039] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system suitable for implementing an electronic device according to embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0040] like Figure 3 As shown, the computer system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the system 300. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0041] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a liquid crystal display (LCD) and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0042] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of the present invention. It should be noted that the computer-readable storage medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0043] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0044] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0045] The modules described in the embodiments of the present invention can be implemented in software or in hardware.

[0046] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the following: acquiring communication data of a target object, the communication data including at least an International Mobile Subscriber Identity (IMSI), timestamp, base station number, and base station latitude and longitude; constructing an explicit associated address set, importing the known associated addresses of the target object into a GeoJSON polygon list; setting a nighttime period, and for each day's communication data, retrieving the last call record of the day and the first call record of the next day within the nighttime period; if the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then determining the base station as a candidate camping point; calculating the candidate camping point and the explicit associated address. The system calculates the spherical distance to the nearest address. If the spherical distance is less than a preset spatial redundancy threshold, the corresponding candidate residency point is marked as a redundant point and removed. It then iterates through a preset real estate address entity database, calculating the distance between the remaining candidate residency points and the center of each real estate address entity in the database. If the distance between the candidate residency point and the center of each real estate address entity in the database is less than a preset relevant threshold, the corresponding real estate address entity is added to a potential associated address list. The real estate address entity database includes the address entity name and its center latitude and longitude. The system counts the number of times the same potential associated address appears within a preset time period, calculates a confidence score based on the number of times the same potential associated address appears within the preset time period, and generates a warning level based on the confidence score.

[0047] The specific embodiments of the present invention have been described above, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0048] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The simple fact that certain measures are recited in mutually different dependent claims does not indicate that combinations of these measures cannot be used for improvement. Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A method for finding potential associated addresses based on communication data, characterized in that, Includes the following steps: S1. Obtain the communication data of the target object, wherein the communication data includes at least the International Mobile Subscriber Identity, timestamp, base station number and base station latitude and longitude; S2. Construct an explicit set of associated addresses, importing the known associated addresses of the target object into a GeoJSON polygon list; S3. Set a nighttime period, and for each day's communication data, retrieve the last call record of the day and the first call record of the next day within the nighttime period; if the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then determine the base station as a candidate camping point; S4. Calculate the spherical distance between the candidate dwell point and the nearest address in the explicit associated address set; if the spherical distance is less than a preset spatial redundancy threshold, mark the corresponding candidate dwell point as a redundant point and remove it; S5. Traverse the preset real estate address entity database and calculate the distance between the remaining candidate residence points and the center of each real estate address entity in the real estate address entity database. If the distance between the candidate residence point and the center of each real estate address entity in the real estate address entity database is less than a preset relevant threshold, then the corresponding real estate address entity is added to the potential associated address list; the real estate address entity database includes the address entity name and center latitude and longitude. S6. Count the number of times the same potential associated address appears within a preset time period, calculate the confidence score based on the number of times the same potential associated address appears within the preset time period, and generate an early warning level based on the confidence score.

2. The method according to claim 1, characterized in that, Step S1 further includes cleaning abnormal location data in the communication data; wherein, the abnormal location data includes data in which two communication data appear within the same second, and the distance between the base station locations corresponding to the two communication data is greater than a preset distance threshold.

3. The method according to claim 1, characterized in that, The spherical distance mentioned in step S4 is calculated according to the following formula: ； in, The spherical distance is... For the Earth's radius, and The latitudes of the two points are respectively. Due to latitude difference, This is due to the difference in longitude.

4. The method according to claim 1, characterized in that, Step S6 includes: The number of times the same potentially associated address appears within a preset time period is counted. Potentially associated addresses whose number of occurrences within the preset time period is greater than a preset threshold are marked. A confidence score is calculated based on the number of occurrences of the same potentially associated address within the preset time period after the marked potential associated addresses.

5. The method according to claim 1, characterized in that, The formula for calculating the confidence score in step S6 is as follows: ； in, The confidence score is... It is a natural constant. The number of times the same potentially associated address appears within the preset time period. This is the preset convergence constant.

6. The method according to claim 1, characterized in that, The real estate address entity database mentioned in step S5 also includes building attribute information and asset valuation information; the generation of early warning levels mentioned in step S6 specifically includes: Obtain asset valuation information and building attribute information for the potential associated addresses; A multidimensional assessment model is constructed based on the confidence score, the asset valuation information, and the building attribute information. A high-risk red alert is generated when the confidence score is greater than a preset confidence threshold and the asset valuation information is higher than the average housing price level in the city.

7. A system for finding potential associated addresses based on communication data, characterized in that, include: The data acquisition module is configured to acquire communication data of the target object, the communication data including at least the International Mobile Subscriber Identity, timestamp, base station number and base station latitude and longitude. The explicit associated address set building module is configured to build explicit associated address sets by importing the known associated addresses of the target object into a GeoJSON polygon list. The candidate call site extraction module is configured to set a nighttime period and, for each day's communication data, retrieve the last call record of the day and the first call record of the next day within the nighttime period; if the base station accessed by the last call record of the day and the first call record of the next day within the nighttime period is the same base station, then the base station is determined as a candidate call site; The secondary filtering module is configured to calculate the spherical distance between the candidate dwell point and the nearest address in the explicit associated address set; if the spherical distance is less than a preset spatial redundancy threshold, the corresponding candidate dwell point is marked as a redundant point and removed. The real estate address entity matching module is configured to traverse a preset real estate address entity database and calculate the distance between the remaining candidate residence points and the center of each real estate address entity in the real estate address entity database. If the distance between the candidate residence point and the center of each real estate address entity in the real estate address entity database is less than a preset relevant threshold, then the corresponding real estate address entity is added to the potential associated address list; the real estate address entity database includes the address entity name and center latitude and longitude. The confidence quantification scoring module is configured to count the number of times the same potentially associated address appears within a preset time period, calculate a confidence score based on the number of times the same potentially associated address appears within the preset time period, and generate an early warning level based on the confidence score.

8. The system according to claim 7, characterized in that, The system also includes: The visualization application module is configured to display the potential associated addresses on a map interface and output a checklist containing the warning level.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-6.

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