Risk business determination method and device for real estate business

Abstract

The invention discloses a risk business determination method and device for real estate business, and the method comprises the steps: determining video data corresponding to a target user, and determining the target access time corresponding to the target user according to the video data; determining an associated user corresponding to the target user by using a preset association recognition algorithm; the associated user and the target user correspond to the same real estate service; determining associated access time corresponding to the associated user; determining first access time according to the target access time and the associated access time; determining the earliest reporting time corresponding to the target user and / or the associated user in a preset reporting information base; and when the earliest reporting time is not earlier than the first access time, determining that the real estate service corresponding to the target user is a risk service. According to the method and the device, the'fly order 'behavior is effectively identified and prevented, the identification precision of the risk business is improved, and the transparency and the compliance of the real estate business are enhanced.

Description

Technical Field

[0001] The present application relates to the field of risk management and control, and in particular to a method and device for determining risk business for real estate business. Background Art

[0002] In the current real estate sales model, sales channels play an important role, and many buyers learn about real estate projects through external channel intermediaries. However, due to the incentive mechanism of real estate sales commissions and the difference in internal and external transaction commissions, there are risky businesses in real estate transactions.

[0003] In the process of entrusting agents to sell commercial housing, "flying orders" is a relatively hidden but extremely harmful behavior. Specifically, the intermediary and the developer's salesperson collude with each other to fabricate natural customers as customers who visit through distributor channels, in order to defraud the developer of the commission paid to the distributor. This behavior not only damages the interests of the developer, but also disrupts the normal market order.

[0004] In addition, since real estate transactions usually involve family members or joint purchases, it means that the purchase decision is often made by multiple people. In this case, home buyers may be influenced from multiple directions, increasing the possibility of "flying orders" occurring.

[0005] Application Contents

[0006] The present application provides a method and device for determining risky business for real estate business, so as to overcome the above-mentioned problems or at least partially solve the above-mentioned risky business that may "fly orders" in the real estate business scenario.

[0007] In a first aspect, the present application provides a method for determining risky business for real estate business, comprising:

[0008] Determine the video data corresponding to the target user, and determine the target access time corresponding to the target user based on the video data;

[0009] Determine the associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business;

[0010] Determine the associated access time corresponding to the associated user;

[0011] Determine the first access time based on the target access time and the associated access time;

[0012] Determine the earliest reporting time corresponding to the target user and / or associated user in a preset reporting information database;

[0013] When the earliest reporting time is not earlier than the first access time, the real estate business corresponding to the target user is determined to be a risky business.

[0014] In a second aspect, the present application provides a risk business determination device for real estate business, including:

[0015] A target access time determination module is configured to determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data;

[0016] The associated user determination module is configured to determine the associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business;

[0017] An associated access time determination module, configured to determine an associated access time corresponding to an associated user;

[0018] A first access time determination module, configured to determine the first access time according to the target access time and the associated access time;

[0019] The earliest reporting time determination module is configured to determine the earliest reporting time corresponding to the target user and / or the associated user in a preset reporting information database;

[0020] The risky business determination module is configured to determine that the real estate business corresponding to the target user is a risky business when the earliest reporting time is not earlier than the first access time.

[0021] In a third aspect, the present application provides a readable medium comprising execution instructions. When a processor of an electronic device executes the execution instructions, the electronic device executes any method described in the first aspect.

[0022] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes any method described in the first aspect.

[0023] The present application provides a method and device for determining risky business for real estate business, by determining the video data corresponding to the target user, and determining the target access time corresponding to the target user based on the video data; determining the associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business; determining the associated access time corresponding to the associated user; determining the first access time based on the target access time and the associated access time; determining the earliest reporting time corresponding to the target user and / or associated user in a preset reporting information database; and determining the real estate business corresponding to the target user as a risky business when the earliest reporting time is not earlier than the first access time. The present application realizes the effective identification and prevention of "flying orders" behavior, improves the identification accuracy of risky business, and enhances the transparency and compliance of real estate business.

[0024] The further effects of the above-mentioned non-conventional preferred manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 A flowchart of a method for determining risk business for real estate business provided in one embodiment of the present application;

[0027] Figure 2 A flowchart of another method for determining risk business for real estate business provided in one embodiment of the present application;

[0028] Figure 3 A flowchart of another method for determining risk business for real estate business provided in one embodiment of the present application;

[0029] Figure 4 A flowchart of another method for determining risk business for real estate business provided in one embodiment of the present application;

[0030] Figure 5 It is a structural diagram of a risk business determination device for real estate business provided by an embodiment of the present application;

[0031] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0033] In the modern real estate sales model, the importance of sales channels is self-evident. A large number of homebuyers learn about real estate projects through external channel intermediaries. Especially in a market environment where information is highly dispersed and complex, intermediaries have become a key bridge connecting homebuyers and developers. However, this process is also accompanied by a complex commission incentive mechanism. In order to stimulate sales, intermediaries and developers usually set up different commission systems for internal and external markets. Internal market transactions often refer to customer resources directly obtained by developers, with lower commissions, while customers introduced through external channel intermediaries will pay higher distribution commissions. Due to this difference in commissions, an industry risk behavior known as "flying orders" has emerged.

[0034] "Flying orders" is a relatively hidden and extremely harmful behavior. Its typical manifestation is that the intermediary and the developer's salesperson collude to fabricate customers who visit naturally as customers guided through the distributor channel, so as to defraud the developer of the high commission paid to the distributor. This behavior is highly hidden because on the surface, the customer has indeed completed the house purchase transaction, and the use record of the distribution channel seems to be legal and reasonable, but in fact it is forged through human operation. This fraudulent behavior not only directly damages the economic interests of the developer, causing it to pay commissions that should not be paid, but also destroys the fair competition environment in the market. In the long run, this behavior will lead to the collapse of industry trust and affect the normal cooperation relationship between developers and intermediaries.

[0035] In addition, the complexity of real estate transactions is also reflected in the fact that they often involve multiple family members or joint purchases. This means that the purchase decision is not made by a single individual, but by multiple related parties. Each participant may be exposed to different information channels, which increases the uncertainty and complexity of the transaction. For example, a family member may learn about the project through natural visits, but when making the final purchase decision, choose to complete the transaction through an intermediary channel contacted by another family member. Intermediaries and developer salesmen take advantage of this multi-party decision-making feature to further conceal the true source of natural visit customers and disguise them as customers brought by distributors, thereby achieving "flying orders".

[0036] Therefore, the present application provides a method for determining risky business for real estate business, which aims to accurately identify and analyze the behavior of target users and their associated users through technical means, so as to effectively identify and prevent possible "flying orders" phenomenon.

[0037] This application is highly flexible and can identify and manage risky businesses when real estate business contracts are signed, and can also be launched in real time when a user is detected entering a video shooting area. This dual trigger mechanism ensures the timeliness and comprehensiveness of risk prevention and control.

[0038] In this embodiment, a risk business determination method for real estate business includes:

[0039] Step 101: Determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data.

[0040] The target visit time refers to the time when the target user first visits a specific real estate business. To achieve this goal, it is first necessary to capture and record the relevant video data of the target user through the monitoring system. This video data can come from the monitoring cameras of the sales office, model house or other places related to the real estate project.

[0041] Once the video data is determined, the next step is to use computer vision technology to analyze the video. Computer vision technology includes a variety of methods such as facial recognition, action detection and image processing, which can accurately identify the appearance time and specific behavior of the target user in the video. For example, facial recognition technology can identify the facial features of the target user in the video.

[0042] After extracting the target user's facial features, the system will compare these features with the historical access records stored in the video library. The video library may contain multiple previous access records of the user, all of which carry corresponding timestamps and facial feature data. By comparing with the historical records, the system can determine whether the user's current access is the first access. If the system detects that the target user already exists in the historical records and there is a corresponding facial feature match, then this access is not the first time, and the system will continue to keep the previous first access time record as the target access time.

[0043] If the facial recognition comparison result shows that the user has not appeared in the video library, or the user's facial features are recognized for the first time, the system will record the time of this visit as the target visit time. This time point marks the exact time when the target user first came into contact with the real estate business, which is the core data for subsequent risk analysis.

[0044] By combining these technical means, the system can efficiently and accurately determine the target visit time of the target user. This time point is the basis for subsequent risk analysis because it marks the time when the user first came into contact with the real estate project. Accurate visit time records not only help prevent "flying orders" but also provide developers with valuable data support to optimize sales strategies and improve customer experience.

[0045] Step 102: Determine an associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business.

[0046] Related users usually refer to individuals who participate in the same real estate business as the target user, such as family members, friends, or other people who have an influence on the purchase decision. In order to ensure the transparency of the business and prevent "flying orders", the system needs to use a preset related identification algorithm to accurately identify these related users.

[0047] First, the association recognition algorithm can establish connections between the target user and other potential associated users through a variety of data sources. An important data source is video data. Through computer vision and facial recognition technology, the system can identify individuals who appear at the same time or multiple times with the target user from the video. These individuals may be family members or friends who visit the project with the target user. By identifying these co-occurring individuals, the system can initially establish potential association relationships.

[0048] In addition to video data, the system can also use data such as registration information left by customers at the sales office to further mine related users. Based on this data, the system can build a more comprehensive social graph that shows the relationship strength and interaction pattern between the target user and other related users.

[0049] Ultimately, this method of identifying related users based on multi-source data provides a powerful tool for risk management in the real estate business. It can help developers identify potential "flying orders" behaviors.

[0050] Step 103: Determine the associated access time corresponding to the associated user.

[0051] The associated visit time refers to the time point when an associated user who has an associated relationship with the target user first visits a specific real estate business. In order to achieve this goal, the system needs to capture and analyze the behavior trajectory of the associated user through a variety of technical means.

[0052] First, the system can use the monitoring system to capture relevant video data of associated users. This data usually comes from surveillance cameras in places related to real estate projects, such as sales offices, model houses, and reception centers. The acquisition of video data is the basis of the entire process, and it provides raw material for subsequent analysis.

[0053] Once the video data is successfully captured, the system will then use computer vision technology to conduct an in-depth analysis of the video. Computer vision technology includes methods such as facial recognition, motion detection, and image processing, which can accurately identify the appearance time and behavioral characteristics of the associated user in the video. In particular, facial recognition technology can generate a unique biometric model by analyzing the user's facial features, such as the shape and relative position of the eyes, nose, and mouth. This model is used to identify and track the behavior of the associated user to determine the time of his or her first visit.

[0054] Once the system extracts the associated user's facial features, it will compare these features with the historical access records stored in the video library. The video library may contain multiple access records of the associated user, all of which are accompanied by corresponding timestamps and facial feature data. By comparing with the historical records, the system can determine whether the associated user's current access is the first access. If the system detects that the associated user already exists in the historical records and there is a corresponding facial feature match, then this access is not the first time, and the system will continue to keep the previous first access time record as the associated access time.

[0055] However, if the facial recognition comparison result shows that the associated user has not appeared in the records in the video library, or the user's facial features are recognized for the first time, the system will record the time of this visit as the associated visit time. This time point marks the exact time when the associated user first came into contact with the real estate business, which is an important reference data for subsequent risk analysis.

[0056] Through the combination of these technical means, the system can efficiently and accurately determine the associated access time of associated users. This time point plays an important role in risk analysis because it not only helps to identify the behavior patterns of associated users, but also further reveals the relationship between target users and associated users, thereby providing developers with a more comprehensive customer behavior analysis. In addition, accurate records of associated access time can also prevent "flying orders" and ensure the compliance and transparency of real estate business.

[0057] Step 104: Determine the first access time according to the target access time and the associated access time.

[0058] The first visit time refers to the specific time point when the target user or its associated user first visits a real estate business. This time point is one of the key data for determining whether the business is risky. In order to accurately determine the first visit time, the system first needs to determine the target visit time of the target user and the associated visit time of the associated user respectively. The target visit time is the time when the target user first visits the real estate business, while the associated visit time is the time when the associated user who has a close relationship with the target user first visits the real estate business. Since the visit behavior of both the target user and the associated user may affect the final purchase decision, the first visit time of both needs to be accurately recorded and analyzed by the system.

[0059] Once the target access time and the associated access time are determined, the system will compare the two time points. According to the logic of risk management, the first access time should be the earliest access time point of the target user and the associated user. This means that if the target access time of the target user is earlier than the associated access time of the associated user, the system will record the target access time of the target user as the first access time. Conversely, if the associated access time of the associated user is earlier than the target access time of the target user, the system will record the associated access time of the associated user as the first access time.

[0060] Step 105: Determine the earliest reporting time corresponding to the target user and / or associated users in a preset reporting information database.

[0061] In the risk management process of real estate business, determining the earliest reporting time corresponding to the target user and / or associated user is one of the key steps to identify potential "flying orders". The earliest reporting time refers to the time when the salesperson or intermediary first enters the customer information into the reporting system. This time point plays an important role in the entire sales process because it is directly related to the source channel of the customer and the legitimacy of the commission.

[0062] In order to determine the earliest reporting time, the system needs to first access the preset reporting information database. The reporting information database is a database that stores all customer reporting records, including the customer's basic information, reporting time, reporting channel, and detailed data such as the person or intermediary responsible for sales. When the target user or associated user is first contacted by the salesperson or intermediary and decides to visit the project, the person in charge will enter the customer's information into the system and generate a reporting record. The timestamp of this record is the customer's reporting time.

[0063] By querying the reporting information database, the system can quickly locate all reporting records of the target user and / or associated users. In order to determine the earliest reporting time, the system will sort these records and find the earliest record. Since a customer may be reported multiple times, or an associated user may be reported through different intermediaries or channels, the system needs to ensure that the earliest reporting record is extracted. This step is crucial because it directly affects the subsequent risk judgment.

[0064] Step 106: When the earliest reporting time is not earlier than the first access time, it is determined that the real estate business corresponding to the target user is a risky business.

[0065] When the earliest filing time is not earlier than the first access time, the system will usually mark this business as a potential risk business. This judgment logic is based on the comparative analysis of the filing time and the access time, aiming to identify possible "flying orders" behavior.

[0066] The filing time refers to the time when the salesperson or intermediary first enters the customer information into the system, while the first visit time refers to the time when the target user or its associated user actually visits the real estate business for the first time. In the normal sales process, the filing time is usually earlier than or the same as the first visit time, because the salesperson or intermediary should report the customer information in time when the customer first contacts the real estate business. This process ensures the source of customers and the transparency of the business.

[0067] However, when the system detects that the earliest reporting time is not earlier than the first visit time, it may mean that there is an anomaly in the sales process. Specifically, this may indicate that the salesperson or intermediary made a supplementary report or false report after the customer actually visited, in an attempt to misrepresent the natural visitor as a customer brought in through their channels, thereby defrauding the developer of the commission. This behavior is known as "flying orders" in the industry and is a serious violation that not only damages the interests of the developer, but also disrupts the normal order of the market.

[0068] When the system detects this anomaly, it will automatically mark the real estate business as a risky business. This mark will trigger further review and investigation, and the developer can take corresponding measures accordingly, such as suspending commission payments, re-examining customer sources, or holding relevant sales staff accountable. Through this mechanism, developers can effectively prevent "flying orders" and safeguard their legitimate rights and interests.

[0069] Through the above technical solution, it can be seen that the beneficial effects of this embodiment are:

[0070] The embodiment of the present application develops a method and device for determining risky business for real estate business, by determining the video data corresponding to the target user, and determining the target access time corresponding to the target user based on the video data; using a preset association recognition algorithm to determine the associated user corresponding to the target user; the associated user and the target user correspond to the same real estate business; determining the associated access time corresponding to the associated user; determining the first access time based on the target access time and the associated access time; determining the earliest reporting time corresponding to the target user and / or associated user in a preset reporting information library; when the earliest reporting time is not earlier than the first access time, determining the real estate business corresponding to the target user is a risky business. The embodiment of the present application realizes the effective identification and prevention of "flying orders" behavior, improves the identification accuracy of risky business, and enhances the transparency and compliance of real estate business.

[0071] Figure 1 What is shown is only a basic embodiment of a method for determining risk business for real estate business of the present application. By making certain optimizations and extensions thereon, other preferred embodiments of a method for determining risk business for real estate business can be obtained.

[0072] like Figure 2 As shown, it is another specific embodiment of the risk business determination method for real estate business of the present application.

[0073] In this embodiment, a risk business determination method for real estate business includes the following steps:

[0074] Step 201: Determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data.

[0075] Step 202: Determine an associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business.

[0076] Step 203: Determine the interactive user corresponding to the target user based on the video data.

[0077] In the risk management and user behavior analysis of real estate business, the process of determining the interactive users corresponding to the target users can provide a more comprehensive understanding of the target users' behavior patterns and identify other users related to them, thereby providing basic data for further risk assessment.

[0078] First, video data is a key source for identifying interactive users. This data usually comes from surveillance cameras in real estate business-related places such as sales offices, model houses, and showrooms. The surveillance system automatically captures and records the activities of target users when they enter these places. Video data not only includes the target user's movement trajectory, but also covers his interactions with other users in a specific time period.

[0079] The system uses computer vision technology to analyze these video data. Computer vision technology includes a variety of methods such as facial recognition, motion detection and group behavior analysis, which can accurately identify the specific behavior of the target user in the video and the interaction with other users. For example, through facial recognition technology, the system can identify each user appearing in the video and determine the interactive relationship between them based on their movement trajectories. If the target user has close contact with other users, visits together or communicates with them within a certain period of time, these behaviors can be regarded as interactions.

[0080] After determining the interactive behavior, the system marks those users who interact with the target user as interactive users. Interactive users may be strangers with whom the target user has a brief exchange, relatives, friends, partners, or other potentially related people. Their appearance in the real estate business scenario may be related to the target user's purchase decision. By identifying these interactive users, the system can more deeply analyze the target user's social network and decision-making background, thereby providing a more accurate basis for subsequent identification of related users and risk assessment.

[0081] Step 204: Determine associated users among the interactive users using an association algorithm.

[0082] By using association algorithms to identify related users among interactive users, the system can deeply explore the potential relationships between target users and other users, and thus reveal key figures who may affect the authenticity of real estate business.

[0083] First, interactive users refer to other users who have had direct or indirect interactions with the target user during the target user's visit to the real estate business. These interactions may occur in sales offices, model houses and other places. Through video data analysis, the system has identified a group of interactive users who may be associated with the target user. However, only a part of these interactive users have a real and substantial connection with the target user in the real estate business, so further screening and identification is necessary.

[0084] To this end, the system uses a preset association algorithm to analyze these interactive users. The association algorithm includes a variety of technologies, such as relationship strength calculation, behavior pattern matching, etc. These technologies can analyze the relationship between the target user and the interactive user from multiple dimensions and levels. For example, relationship strength calculation can quantify the interaction frequency and interaction strength between the target user and the interactive user to determine the closeness of the relationship.

[0085] Through the association algorithm, the system can effectively filter out users who interact with the target user by chance or have no substantial association, and accurately identify users who are closely associated with the target user. These associated users may be relatives, friends, partners, etc. of the target user. After identifying these associated users, the system can further analyze their behavior, access time and other information to provide a more accurate basis for identifying risky businesses.

[0086] The association algorithm can be used to determine the associated users among the interactive users, specifically including: analyzing the interaction mode between the target user and the interactive user to build the social graph of the target user; based on the social graph, using the graph analysis algorithm to identify the associated users corresponding to the target user among the interactive users.

[0087] In the risk management process of real estate business, identifying the associated users of the target user is one of the key steps to prevent potential risks such as "flying orders". To achieve this goal, the system uses an association algorithm to screen out the associated users who have a substantial relationship with the target user from the interactive users. The specific method includes analyzing the interaction between the target user and the interactive user, and building the social graph of the target user based on this, and then identifying the associated users from the interactive users through the graph analysis algorithm.

[0088] There are many ways of interaction, such as visiting the sales office together, participating in the model house tour together, and consulting together in front of the sales consultant. These ways of interaction reflect the closeness and frequency of the relationship between the target user and other users. The system identifies these interactive behaviors through in-depth analysis of video data and records the time, location and users involved in each interaction.

[0089] Next, the system will use the data of these interactions to build a social graph of the target user. A social graph is a visual network structure that shows the network of relationships between the target user and the users they interact with. In this graph, nodes represent the target user and the users they interact with, while lines represent the interactive relationships between them. By building a social graph, the system can intuitively display the social network structure of the target user and identify which users they have closer connections with.

[0090] After the social graph is built, the system will use the graph analysis algorithm to further mine the associated users. Specifically, the graph analysis algorithm identifies users who have closer and more frequent interactions with the target user by analyzing the nodes and connections in the social graph. For example, if a certain interactive user has a large number of connections with the target user, a high frequency of interaction, or is in a relatively core position in the graph, then this user is likely to be an associated user of the target user. The graph analysis algorithm can accurately locate these associated users and help the system filter out associated users from many interactive users.

[0091] Furthermore, based on the social graph, using a graph analysis algorithm to identify associated users corresponding to the target user among the interactive users includes: determining the interaction frequency and interaction intensity between the target user and the interactive user in the social graph; based on the interaction frequency and interaction intensity, using a relationship strength algorithm to determine the interaction relationship value between the target user and the interactive user; when the interaction relationship value is not less than a preset relationship strength threshold, determining the interactive user as an associated user.

[0092] As a visualization tool, the social graph shows the network of relationships between the target user and its interactive users. Each line represents an interaction behavior, and the number and strength of these lines reflect the interaction frequency and interaction intensity. Interaction frequency refers to the number of times the target user interacts with an interactive user, while interaction intensity measures the depth and closeness of these interactions. For example, frequent communication, joint participation in important activities, and even long-term co-residence can increase the weight of interaction intensity.

[0093] The system further processes this data through the relationship strength algorithm. The relationship strength algorithm is a calculation tool that comprehensively evaluates the relationship between the target user and the interactive user. It not only takes into account the frequency and intensity of interaction, but also may combine other factors, such as the time interval of interaction, the relevance of interactive content, etc., to generate a quantitative interactive relationship value. The higher this value is, the closer the relationship between the target user and the interactive user is, and the more likely there is some actual connection.

[0094] To ensure the accuracy of identification, the system presets a relationship strength threshold. When the interactive relationship value between an interactive user and a target user reaches or exceeds this threshold, the system identifies it as a related user. This threshold is usually set based on historical data and actual business needs, and is effectively applied in different scenarios through multiple debugging and optimization.

[0095] In addition to the above-mentioned technical means, natural language algorithms can also be used to perform semantic analysis on video data to determine the keywords of the conversation between the target user and the interactive user; based on the keywords, the associated users corresponding to the target user are determined among the interactive users.

[0096] When the target user and the interactive user talk, the video data not only records their behavior, but also captures the content of the conversation. These conversations may contain important information related to the real estate business, such as purchase intentions, decision-making processes, family members or friends involved, etc. In order to extract useful information from these conversations, the system will apply natural language processing technology to convert and analyze the voice content in the video.

[0097] The natural language algorithm converts the speech in the video into text data through speech recognition technology, and then performs semantic analysis on the text data. The focus of semantic analysis is to identify and extract keywords in the conversation, which are usually closely related to the context of home purchase decisions and real estate business. For example, if the target user and the interactive user mention keywords such as "cooperation", "family", and "joint decision" in the conversation, these words may imply that there is a close relationship between the target user and the interactive user, and there may be an intention to cooperate or jointly purchase a house in the real estate business.

[0098] After the keywords are extracted, the system will further screen the interactive users based on these keywords. By analyzing the roles and participation of these interactive users in the conversation, the system can determine which users have substantial connections with the target user and then determine the related users.

[0099] Step 205: Determine the associated access time corresponding to the associated user.

[0100] Step 206: Determine the first access time according to the target access time and the associated access time.

[0101] Step 207: Determine the earliest reporting time corresponding to the target user and / or associated users in a preset reporting information database.

[0102] Step 208: When the earliest reporting time is not earlier than the first access time, it is determined that the real estate business corresponding to the target user is a risky business.

[0103] Through the above technical solution, it can be seen that the beneficial effects of this embodiment are:

[0104] The process of using association algorithms to identify related users among interactive users helps the system accurately locate key people who may have an impact on real estate business in a complex user relationship network. This process not only improves the accuracy of risk identification, but also provides developers with more comprehensive customer analysis data.

[0105] like Figure 3 FIG. 1 is another specific embodiment of a method for determining risk business of real estate business according to the present application. This embodiment is further described on the basis of the above-mentioned embodiment.

[0106] In this embodiment, a risk business determination method for real estate business includes the following steps:

[0107] Step 301: Determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data.

[0108] Step 302: Determine the associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business.

[0109] Step 303: Determine the reserved information of the target user in the user information database.

[0110] The user information database stores all customer-related details. This information is not only used for daily business operations, but also plays an important role in identifying potential risks and managing customer relationships. Before determining the target user's associated users, the target user's reserved information can be extracted from the user information database.

[0111] Reserved information usually includes personal data provided by the target user in various transactions and activities related to real estate, which may include name, contact information, family member information, etc. This information is provided by the target user during the process of house purchase consultation, contract signing, etc. to ensure the smooth progress of the transaction.

[0112] The process of extracting reserved information requires high accuracy and comprehensiveness. The system first screens out all data related to the target user from the user information database, including but not limited to the basic personal information mentioned above. The system obtains the records left by the target user in all business scenarios by querying the target user's identity identifier (such as ID number or customer number). These records may cover multiple interactions between the target user and the developer, past real estate transaction history, and even participation in activities in other real estate projects.

[0113] Once the target user's reserved information is successfully extracted, the system can use this information to identify other users who may be associated with the target user in the next step of analysis. Through the fine operation of this link, the system not only provides a solid data foundation for subsequent association identification, but also lays an important foundation for the precise management and risk control of real estate business.

[0114] Step 304: Determine the associated user based on the reserved information.

[0115] The system will compare and match the reserved information through association algorithms. For example, the system can identify that multiple users share the same contact number or residential address. These common features may indicate that they belong to the same family or a close-knit group. In addition, if two users provide the same emergency contact or similar contact information on different occasions, this may also suggest that there is a relationship between them.

[0116] In addition to basic direct matching, the system will also combine more complex algorithms for multi-dimensional analysis. These algorithms are not limited to simple text comparison, but may also include behavioral analysis, association of historical transaction data, etc. For example, if two users have appeared in the same real estate business multiple times in past transactions, or visited the same sales office in the same time period, these behavioral characteristics can also be used as a basis for judging the association relationship.

[0117] Through these analyses, the system can effectively identify other users who are substantially related to the target user and mark them as related users. This process not only helps identify potential risky businesses, but also helps developers more comprehensively understand the complex relationship network between customer groups, thereby formulating more effective risk management plans.

[0118] Step 305: Determine the associated access time corresponding to the associated user.

[0119] Step 306: Determine the first access time according to the target access time and the associated access time.

[0120] Step 307: Determine the earliest reporting time corresponding to the target user and / or associated users in a preset reporting information database.

[0121] Step 308: When the earliest reporting time is not earlier than the first access time, it is determined that the real estate business corresponding to the target user is a risky business.

[0122] Through the above technical solution, it can be seen that the beneficial effects of this embodiment are:

[0123] Based on the reserved information of the target user, the system determines which users are actually associated with the target user and marks them as associated users. This method not only improves the accuracy of identification, but also provides an important basis for risk management in real estate business.

[0124] like Figure 4 As shown, it is another specific embodiment of the risk business determination method for real estate business of the present application.

[0125] In this embodiment, a risk business determination method for real estate business includes the following steps:

[0126] Step 401: Determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data.

[0127] Step 402: Determine an associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business.

[0128] Step 403: Determine the associated access time corresponding to the associated user.

[0129] Step 404: Determine the first access time according to the target access time and the associated access time.

[0130] Step 405: Determine the earliest reporting time corresponding to the target user and / or associated users in a preset reporting information database.

[0131] Step 406: When the earliest reporting time is not earlier than the first access time, it is determined that the real estate business corresponding to the target user is a risky business.

[0132] Step 407: When the real estate business is determined to be a risky business, a risk management instruction is generated.

[0133] The generation of risk management instructions means that the system has identified business behaviors that may be abnormal or potentially risky through multi-level data analysis and judgment. This instruction is not only a mark of risk, but also a specific guide for subsequent actions, ensuring that the identified risks can be dealt with in a timely and effective manner.

[0134] The process of generating risk management instructions usually relies on pre-set rules and algorithms that cover the characteristics and response strategies of different types of risky businesses. For example, the system may generate different levels of instructions based on the severity of the risk, from the simplest reminder notification to complex multi-step processing solutions. For lower-level risks, the instructions may only require further manual review; for high-risk businesses, the instructions may include measures such as immediately freezing transactions, notifying relevant departments, or launching detailed investigations.

[0135] The generation of this instruction is not only an automated process, but also involves the system's comprehensive assessment of risky businesses. The system will consider multiple factors, such as the target user's access history, the behavior patterns of related users, the time difference of reporting information, etc., and then combine this information to generate the most suitable management instructions for the current situation. The generated risk management instructions will be quickly transmitted to the risk management system or relevant departments to ensure that immediate action can be taken to prevent the spread of risks.

[0136] Step 408: Based on the risk management instruction, the risk business is sent to the risk management system.

[0137] In real estate business, once the system generates a risk management instruction, the next step is to send the identified risk business to the risk management system for processing in a timely manner. This step ensures that the risk management instruction not only stays at the system level, but can also be quickly put into action, thereby effectively preventing and controlling potential risks.

[0138] Sending risk business to the risk management system means that all relevant information, analysis results and corresponding treatment suggestions are centrally transmitted to the back-end system responsible for risk control. The risk management system is a highly integrated back-end platform that is responsible for further review, processing and tracking of received risk information. Through this process, the system can achieve full monitoring of risk business, closed-loop management from risk identification to final processing.

[0139] During this process, the detailed information contained in the risk management instructions will be passed to the risk management system. The information may include: the access records of the target user and the associated users, the time of the first access, the time difference of the reported information, possible abnormal behavior, etc. Based on this data, the risk management system will automatically assess the risk level of the business and take corresponding measures. These measures may include temporarily freezing the transaction, requiring further review, or directly launching internal or external investigation procedures.

[0140] The risk management system not only receives and executes instructions from the preliminary identification system, but also generates subsequent processing plans based on the risk characteristics and severity of the business, and tracks the processing progress in real time. This centralized risk processing model ensures that all potential risk businesses can be uniformly managed and efficiently processed, avoiding the spread of risks caused by information dispersion or untimely processing.

[0141] Through the above technical solution, it can be seen that the beneficial effects of this embodiment are:

[0142] By generating risk management instructions, the system can quickly transform potential risk businesses into actionable management tasks, providing companies with a clear path of action. This not only improves the efficiency of risk management, but also provides developers with more reliable security in a complex real estate business environment, helping companies effectively avoid possible losses.

[0143] like Figure 5 As shown in the figure, a specific embodiment of a risk business determination device for real estate business of the present application is shown. Figures 1 to 4 A physical device of a method for determining risk business for real estate business. Its technical solution is essentially consistent with the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. In this embodiment, a device for determining risk business for real estate business includes:

[0144] The target access time determination module 501 is configured to determine the video data corresponding to the target user, and determine the target access time corresponding to the target user according to the video data;

[0145] The associated user determination module 502 is configured to determine an associated user corresponding to the target user using a preset associated identification algorithm; the associated user and the target user correspond to the same real estate business;

[0146] The associated access time determination module 503 is configured to determine the associated access time corresponding to the associated user;

[0147] A first access time determination module 504, configured to determine a first access time according to a target access time and an associated access time;

[0148] The earliest reporting time determination module 505 is configured to determine the earliest reporting time corresponding to the target user and / or the associated user in a preset reporting information database;

[0149] The risky business determination module 506 is configured to determine that the real estate business corresponding to the target user is a risky business when the earliest reporting time is not earlier than the first access time.

[0150] Figure 6 It is a structural diagram of an electronic device provided in an embodiment of the present application. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0151] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0152] The memory is used to store execution instructions. Specifically, the execution instructions are computer programs that can be executed. The memory may include internal memory and non-volatile memory, and provides execution instructions and data to the processor.

[0153] In a possible implementation, the processor reads the corresponding execution instructions from the non-volatile memory into the memory and then runs them, and can also obtain the corresponding execution instructions from other devices to form a risk business determination device for real estate business at the logical level. The processor executes the execution instructions stored in the memory to implement a risk business determination method for real estate business provided in any embodiment of the present application through the executed execution instructions.

[0154] The above application Figure 5 The method provided in the illustrated embodiment for execution of a risk business determination device for real estate business can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0155] The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0156] The embodiment of the present application also proposes a readable medium, which stores an execution instruction. When the stored execution instruction is executed by a processor of an electronic device, the electronic device can execute a risk business determination method for a real estate business provided in any embodiment of the present application, and is specifically used to execute the following: Figure 1 or Figure 2 or Figure 3 or Figure 4 The method shown.

[0157] The electronic device in each of the aforementioned embodiments may be a computer.

[0158] Those skilled in the art should understand that the embodiments of the present application can be provided as methods or computer program products. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.

[0159] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0160] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0161] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining risky business for real estate business, characterized in that: include: Determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data; Determine the associated user corresponding to the target user by using a preset associated identification algorithm; The associated user and the target user correspond to the same real estate business; Determine the associated access time corresponding to the associated user; Determine a first access time according to the target access time and the associated access time; Determining the earliest reporting time corresponding to the target user and / or the associated user in a preset reporting information database; When the earliest reporting time is not earlier than the first access time, it is determined that the real estate business corresponding to the target user is a risky business.

2. The method according to claim 1, characterized in that The determining the associated user corresponding to the target user by using a preset association algorithm includes: Based on the video data, determining an interactive user corresponding to the target user; The associated users are determined among the interactive users using the associated algorithm.

3. The method according to claim 2, characterized in that Determining the associated user from the interactive users by using the association algorithm includes: Analyzing the interaction mode between the target user and the interactive user to construct a social graph of the target user; Based on the social graph, a graph analysis algorithm is used to identify associated users corresponding to the target user among the interactive users.

4. The method according to claim 3, characterized in that The identifying, based on the social graph, associated users corresponding to the target user from the interactive users using a graph analysis algorithm comprises: Determining the interaction frequency and interaction intensity between the target user and the interactive user in the social graph; Based on the interaction frequency and the interaction strength, determining the interaction relationship value between the target user and the interactive user using a relationship strength algorithm; When the interactive relationship value is not less than a preset relationship strength threshold, the interactive user is determined to be the associated user.

5. The method according to claim 2, characterized in that: The determining the associated user corresponding to the target user by using a preset associated identification algorithm includes: Performing semantic analysis on the video data using a natural language algorithm to determine keywords of the conversation between the target user and the interactive user; Based on the keyword, the associated user corresponding to the target user is determined among the interactive users.

6. The method according to claim 1, characterized in that The determining the associated user corresponding to the target user by using a preset association algorithm includes: Determining the reserved information of the target user in the user information database; The associated user is determined based on the reserved information.

7. The method according to claim 1, characterized in that Also includes: When it is determined that the real estate business is the risky business, generating a risk management instruction; Based on the risk management instruction, the risk business is sent to a risk management system.

8. A risk business determination device for real estate business, characterized in that: include: a target access time determination module, configured to determine video data corresponding to a target user, and determine a target access time corresponding to the target user based on the video data; An associated user determination module is configured to determine an associated user corresponding to the target user using a preset associated identification algorithm; The associated user and the target user correspond to the same real estate business; An associated access time determination module, configured to determine an associated access time corresponding to the associated user; A first access time determination module, configured to determine a first access time according to the target access time and the associated access time; An earliest reporting time determination module is configured to determine the earliest reporting time corresponding to the target user and / or the associated user in a preset reporting information database; The risky business determination module is configured to determine that the real estate business corresponding to the target user is a risky business when the earliest reporting time is not earlier than the first access time.

9. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute the risk business determination method for real estate business as described in any one of claims 1 to 7.

10. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the risk business determination method for real estate business described in any one of claims 1-7.

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