Intelligent demand distribution platform

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an intelligent demand distribution platform. The user management module is used for managing user account information and executing identity verification; the searching and screening module is connected with the user management module, and is used for receiving a query condition input by a user and screening orders meeting the condition from the order database based on a preset matching rule; and the intelligent behavior analysis module is connected with the searching and screening module and is used for collecting historical interaction data of the user and generating a user interest tag. By adopting the technical scheme of combining intelligent matching and dynamic behavior analysis, the technical effect of accurately recommending orders is achieved. Compared with a matching mode based on a fixed rule or simple label screening in the prior art, the problem that a traditional matching method is difficult to adapt to short-term interest changes of the user and the recommendation content is impressive is solved, the system can adjust the recommendation result in real time, and the matching flexibility and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent demand allocation platform. Background Art

[0002] With the development of the digital economy, more and more companies and individuals hope to obtain orders through flexible employment or online services. However, the traditional demand allocation method has many limitations, making it difficult for service supply and demand parties to connect efficiently. Existing order matching systems usually rely on keyword searches or preset screening rules, which lack flexibility when facing changes in user interests. At the same time, online transactions involve capital flow and identity authentication. If the security mechanism is not perfect, it is easy to lead to fraud risks. Therefore, the current intelligent demand matching system still has some problems that need to be optimized.

[0003] Most existing intelligent matching systems use rule matching or simple tag-based recommendations. Although they can provide basic matching services, they lack an in-depth understanding of user preferences. This approach often relies on fixed screening conditions, such as industry categories, service types, etc., and cannot fully consider short-term changes in user interests. When user needs fluctuate in a short period of time, traditional systems are difficult to respond quickly, resulting in a mismatch between recommendation results and users' real needs, reducing the efficiency of platform use. The security of online transactions depends on identity authentication and payment risk control. However, many existing platforms only use static identity authentication (such as account password login) or fixed payment security strategies, which are difficult to effectively deal with transaction fraud. For example, once a user account is stolen, hackers can bypass security mechanisms through simple password verification to conduct illegal transactions.

[0004] Traditional order allocation methods mostly use manual review or fixed allocation rules, which can meet the needs when the order volume is small, but the system's responsiveness is obviously insufficient under high concurrency. The order allocation mechanism of many current platforms fails to combine real-time load balancing, resulting in a backlog of orders for some service providers, while some orders remain unanswered for a long time. In the absence of intelligent scheduling, the fairness and execution efficiency of task allocation are difficult to guarantee, further affecting the user experience. In addition, the order status update is not transparent, which makes it impossible for users to accurately grasp the progress of the order, affecting the smoothness of the transaction. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent demand allocation platform, which solves the problems of insufficient order matching flexibility, weak transaction security protection, inefficient task allocation and lagging platform management in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: 1. An intelligent demand allocation platform includes: a user management module for managing user account information and performing identity authentication; A search and screening module, which is connected to the user management module, is used to receive the query conditions input by the user and screen the orders that meet the conditions from the order database based on the preset matching rules; Intelligent behavior analysis module, which is connected to the search and screening module to collect historical user interaction data and generate user interest tags; The intelligent matching module is connected to the intelligent behavior analysis module to receive the user's interest tags and order features to calculate the matching degree and recommend orders that meet the user's preferences; The order management module and the intelligent matching module are used to push matching results, store, update and track order status, and provide order processing progress monitoring function; The secure transaction assurance module is connected to the order management module to provide order transaction information and perform security verification of user identity and payment information to ensure the legitimacy and security of transactions; The platform management module is wirelessly connected to other modules to maintain the platform operation status and provide data statistical analysis and system optimization functions.

[0007] Preferably, the user management module includes: The identity verification unit is used to call the authoritative database to verify the identity information submitted by the user and improve the verification security based on multi-factor authentication; The account permission control unit is used to assign different permissions based on user identity categories and perform authorization review on sensitive operations.

[0008] Preferably, the search and screening module includes: Keyword search unit, used to optimize user query content based on natural language processing technology; The multi-condition screening unit is used to screen the orders that meet the requirements based on multiple screening conditions such as geographic location, service category, order budget, and user rating.

[0009] Preferably, the intelligent behavior analysis module includes: Data collection unit, used to collect users' browsing, searching, clicking and ordering behavior data on the platform; The interest tag generation unit is used to calculate the user's attention to orders of different categories based on the user behavior data, and adjust the attention according to the set weight factor to generate interest tags.

[0010] Preferably, the intelligent matching module includes: The short-term interest adjustment unit is used to dynamically adjust the recommendation weight based on the user's recent browsing and interaction behavior, so that the system can respond more quickly to changes in user interests; Long-term preference learning unit, which is used to build a long-term interest model based on user historical data and combine short-term and long-term interests in recommendation calculations; The matching analysis unit is used to calculate the matching degree based on user interest tags, order categories and historical order completion rates, and to sort the recommended orders based on the matching degree values.

[0011] Preferably, when the matching analysis unit calculates the matching degree, dynamic adjustments are made based on the following factors: The correlation between user historical interaction behavior data, interest tags and order categories; Users’ recent behavior frequency, interaction depth, and order completion status.

[0012] Preferably, the order management module includes: An order status monitoring unit, used to update the order status in real time based on the order processing progress, the status including pending, in progress, completed and cancelled; A dedicated service specialist allocation unit is used to automatically assign appropriate service specialists based on order complexity, service provider load balancing, and historical order processing efficiency; The order anomaly detection unit is used to detect order anomalies and adopt different processing strategies according to the anomaly level, including risk warning, audit intervention and order freezing.

[0013] Preferably, the secure transaction assurance module includes: Payment verification unit, used to check the validity of bank card information provided by users and calculate risk scores based on historical account transaction behavior; Dynamic risk control unit, which is used to analyze transaction risks based on user transaction history, device fingerprint and geographic location data, and trigger additional identity verification processes in high-risk transaction situations.

[0014] Preferably, the platform management module includes: Data statistics and analysis unit, used to analyze the platform's order data, user activity and transaction flow information, and provide decision support; System optimization unit, used to adjust intelligent matching rules based on user feedback and optimize search and recommendation algorithms; The abnormal behavior detection processing unit is used to monitor the abnormal behavior of users on the platform.

[0015] Preferably, the abnormal behaviors in the abnormal behavior detection unit include but are not limited to frequent cancellation of orders in a short period of time, abnormally high-value transactions, multiple changes in payment methods in a short period of time, and abnormal login activities, and trigger processing measures; The processing measures include transaction risk verification, temporary account freezing, restriction of specific transaction operations and triggering of manual review.

[0016] The present invention provides an intelligent demand allocation platform, which has the following beneficial effects: 1. The present invention achieves the technical effect of accurately recommending orders by adopting a technical solution combining intelligent matching with dynamic behavior analysis. Compared with the matching method based on fixed rules or simple label screening in the prior art, it solves the problem that traditional matching methods are difficult to adapt to short-term changes in user interests and stereotyped recommended content, so that the system can adjust the recommendation results in real time and improve the flexibility and accuracy of matching.

[0017] 2. The present invention achieves the technical effect of combining payment verification with dynamic risk control by adopting a multi-level security transaction guarantee mechanism. Compared with the single identity authentication or static payment security mechanism in the prior art, it solves the problem that the traditional transaction security system is insufficient in identifying high-frequency transaction fraud, making the transaction security system more comprehensive, effectively reducing the risks of account theft and abnormal payment, and ensuring the safety of user funds.

[0018] 3. The present invention achieves the technical effect of improving order processing efficiency and reducing the impact of abnormal orders by adopting a technical solution combining real-time monitoring of order status with intelligent task allocation. Compared with the existing technology that relies solely on manual or rule-based static allocation, it solves the problems of opaque order processing progress, unreasonable task allocation, long transaction cycle and poor user experience. By combining order priority calculation and load balancing scheduling, the system can dynamically match the most suitable service provider, monitor order status, respond to abnormal situations in a timely manner, and improve the fluency of overall order processing.

[0019] 4. The present invention achieves a more comprehensive platform operation and maintenance management effect by adopting a management mechanism that combines data statistical analysis, system optimization and abnormal behavior monitoring. Compared with the prior art that only relies on manual monitoring or fixed rules to adjust system parameters, it solves the problem of slow platform management response and difficulty in real-time adaptation to changes in the trading environment. Through data trend analysis, intelligent risk scoring and A / B test optimization strategies, the present invention enables the platform to automatically optimize matching rules, resource scheduling and security strategies to ensure a stable trading environment under high concurrency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system framework diagram of the present invention; Figure 2 It is a schematic diagram of a user management module of the present invention; Figure 3 A schematic diagram of a search and screening module of the present invention; Figure 4 Schematic diagram of the intelligent behavior analysis module of the present invention; Figure 5 It is a schematic diagram of the intelligent matching module of the present invention; Figure 6 It is a schematic diagram of the order management module of the present invention; Figure 7 This is a schematic diagram of a secure transaction protection module of the present invention; Figure 8 It is a schematic diagram of the platform management module of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Please refer to the attached Figure 1-Figure 8 ,The embodiment of the present invention provides an intelligent demand allocation platform, including: User management module, used to manage user account information and perform identity authentication; Specifically, in this embodiment, the user management module not only undertakes the management of user identities and authority control, but also ensures the security of platform transactions and the credibility of user information. In order to achieve these functions, the user management module works closely with the intelligent matching, order management and safe transaction guarantee modules to form a complete identity management and transaction protection mechanism. Generally, when using the platform, users first need to complete identity authentication, and then the system will assign permissions according to different user categories, and continuously monitor account security throughout the transaction process to prevent the occurrence of risky behaviors.

[0023] In this embodiment, the user management module includes an identity authentication unit and an account authority control unit.

[0024] In one possible implementation, the identity verification unit is mainly used to verify the identity information submitted by the user to ensure the authenticity of the user's identity. Specifically, the unit can call the authoritative database to compare the ID number, name, mobile phone number and other information submitted by the user to complete basic real-name authentication. In general, the platform adopts a four-factor verification method, namely: V = f(N, ID, P, B); in: V is the identity verification result; N is the user's name; ID is the user’s ID number or other unique identifier; P is the mobile phone number bound to the user; B is the bank card number provided by the user.

[0025] During the identity authentication process, if the user's four-factor information matches the database successfully, the authentication is passed, otherwise the system will prompt the user to supplement or correct the information. In addition, in some embodiments, the identity authentication unit can be combined with face recognition technology.

[0026] Based on user identity authentication, the system can assign permissions according to different user categories. In this embodiment, the account permission control unit is used to distinguish between ordinary users, merchant users and advanced authentication users, and set access rights and operation restrictions according to different types of users. For example: Ordinary users can browse order information freely and submit order applications according to their own needs; Merchant users can place orders and connect with appropriate service providers; Advanced certified users can conduct high-value transactions on the platform and enjoy higher transaction limits, but they must meet additional identity verification requirements.

[0027] In one possible implementation, the account permission control unit uses an access control list (ACL) mechanism to dynamically adjust permissions based on user identity and historical behavior. For example, the system calculates the user's trust score S to determine his or her permission level. The trust score can be calculated using the following formula; S = W1·T+W2·O+W3·R; in: T is the user's account usage time, O is the number of historical transactions of the user, R is the user's credit score, W1, W2, and W3 are different weight factors, which usually satisfy W1+W2+W3=1.

[0028] When the user's trust score S exceeds the set threshold, the system will automatically increase the user's permissions and allow them to conduct larger transactions. On the contrary, if the user performs multiple abnormal operations in a short period of time, such as frequently changing the payment method or submitting inconsistent identity information, the system will reduce their permission level and may trigger additional identity verification processes.

[0029] Through the above technical solution, the user management module of the present invention can effectively ensure the authenticity of the user identity, the security of the transaction, and perform dynamic authority management according to the user's historical behavior.

[0030] A search and screening module, which is connected to the user management module, is used to receive the query conditions input by the user and screen the orders that meet the conditions from the order database based on the preset matching rules; Specifically, the search and screening module in this embodiment improves the query quality through the search optimization mechanism, and further filters the results using the multi-condition screening unit to ensure that the user can quickly locate the target order. This module can not only improve the accuracy of order search, but also reduce the user's decision-making cost and improve the overall interactive experience.

[0031] In this embodiment, the search and screening module includes a keyword search unit and a multi-condition screening unit, which are used to optimize the query method and perform screening based on multi-dimensional features.

[0032] In a possible implementation, the keyword search unit uses natural language processing (NLP) technology to parse the search terms input by the user, and combines semantic analysis to optimize the search matching rules. Specifically, the unit can build the association relationship of the query terms based on the word vector model and calculate the semantic similarity S sim To optimize the matching results. The calculation method is as follows; in: S sim Represents the query vector With document vector The similarity score between them ranges from [-1,1]. sim The closer it is to 1, the more similar the two vectors are; close to 0 means no correlation; less than 0 means there may be a negative correlation; The vector representation of the user query word, where Q i Represents the weight of the query term in the i-th dimension (such as TF-IDF, word vector value, etc.); The vector representation of the candidate document (such as order title, description, etc.), where D i Represents the weight of the document in the i-th dimension; The dimension of the n vector, that is, the size of the feature space where the query term and document representation are located vector and The dot product of , which measures their similarity in each dimension; and They are and The Euclidean norm (L2 norm) of is used to standardize the similarity calculation to ensure that the similarity value is within a reasonable range.

[0033] Threshold setting T sim , when S sim Greater than the set threshold T sim The query and document are considered to match This order will be considered a match with the query term and included in the search results.

[0034] To avoid the search scope being limited due to overly strict query terms, in some embodiments, the system introduces a query expansion strategy, including: Synonym replacement, e.g., "graphic design" can be expanded to "poster design" and "UI design"; Spelling tolerance, e.g., "copywriting" can match the misspelled "wen an zhuan xie" entered by the user; Word segmentation optimization, e.g., "short video editing" can be split and matched with "video production" and "editing service".

[0035] As an option, based on keyword search, users can use the multi-condition filtering unit to further narrow the search scope. In this embodiment, this unit allows users to filter based on multiple conditions such as geographical location, service category, order budget, and user rating to improve the matching efficiency.

[0036] Specifically, the multi-condition filtering unit re-orders the search results by calculating the weights of the filtering conditions The calculation formula is as follows: Where, R is the comprehensive filtering score of the order, m is the total number of filtering conditions, is the weight of the filtering condition j, F j is the score of the order in the dimension of the filtering condition j.

[0037] Generally, the weight W of the filtering condition f is dynamically adjusted by the user's historical behavior. For example; If the user has preferentially filtered "high-rating" orders in the past 5 searches, the weight of the "user rating" condition will automatically increase; If the user's recent filtering preferences change, the system will gradually adjust the weights to ensure that the matching results meet the user's current needs.

[0038] In some embodiments, the multi-condition filtering unit also supports geographical location optimization filtering, i.e.: If the user enables the location service, the system can preferentially recommend orders that are closer; If the user sets a specific area filter, the system will filter out orders that do not meet the area.

[0039] Through the above technical solution, the search and screening module of the present invention can effectively improve the efficiency of users in finding orders, ensure the accuracy of search results, and take into account the personalized needs of users. Compared with the traditional static matching method, the present invention adopts dynamic semantic matching, weighted screening and flexible optimization strategies, which can more flexibly adapt to changes in user preferences, making the search process more intelligent and efficient.

[0040] Intelligent behavior analysis module, which is connected to the search and screening module to collect historical interaction data of users and generate user interest tags; Specifically, in this embodiment, the intelligent behavior analysis module plays an important role in user preference identification and accurate matching. The module builds a user interest model by performing real-time analysis on the user's browsing, searching, clicking, ordering and other interactive behaviors.

[0041] In this embodiment, the intelligent behavior analysis module includes a data collection unit and an interest tag generation unit, and is combined with a user interest weight calculation model based on behavior analysis to achieve automatic modeling and optimization of user interests.

[0042] In a possible implementation, the data collection unit is used to monitor the user's behavior data on the platform in real time and classify and store it according to preset rules. Specifically, the collected data types include: Browsing behavior: order categories visited by users, page dwelling time, scrolling depth, etc.; Search behavior: search keywords entered by users, frequency of use of filter conditions, etc. Click behavior: the frequency of user interaction with a certain type of order, such as whether the user views the same type of order multiple times; Order behavior: order type, order amount, transaction rate, etc. of orders successfully placed by users.

[0043] In some embodiments, the data collection unit may use an event-driven architecture (EDA) to encapsulate the user's real-time operations as behavioral events and store them in a behavioral log database to ensure the real-time and integrity of the data. For example, when a user searches for "graphic design" and clicks on multiple related orders, the system will record the user's attention to orders in this category and use it as the basic data for subsequent interest analysis.

[0044] As an option, the interest tag generation unit is used to analyze the user behavior data and dynamically generate interest tags based on the user's historical preferences. Generally, the construction of interest tags is based on a weighted calculation model, and the calculation method is as follows: in: I is the user's interest index for a certain type of order; W iis the behavior weight factor; B i is the interaction intensity of the user on behavior type i; n is the total number of behavior types.

[0045] Specifically, the behavior types include browsing, searching, clicking, ordering, etc. The weight factor W of each behavior is i Dynamically adjusted by the system. For example, when a certain type of order is frequently browsed in a short period of time, the weight of this behavior will be increased accordingly, while the weight of less frequent behaviors will be reduced accordingly, to ensure that interest tags can dynamically adapt to the latest needs of users.

[0046] Through the above technical solution, the intelligent behavior analysis module of the present invention can effectively identify user interests and dynamically adjust interest tags to improve the accuracy of order matching. Compared with the traditional static user profiling method, the present invention adopts real-time behavior collection, dynamic interest modeling and weighted matching optimization strategy to ensure the intelligence and adaptability of the recommendation system.

[0047] The intelligent matching module is connected to the intelligent behavior analysis module to receive the user's interest tags and order characteristics to calculate the matching degree and recommend orders that meet the user's preferences; Specifically, the intelligent matching module in this embodiment calculates the matching degree based on the user's interest tags and order features, and recommends orders that meet the user's needs. This module relies on the user interest model constructed by the intelligent behavior analysis module, and combines the user input information obtained by the search and screening module to form an accurate matching mechanism. In general, user needs are somewhat dynamic, short-term interests may be affected by the external environment, and long-term preferences can better reflect the user's real needs. Therefore, a single static matching rule is difficult to adapt to the user's personalized needs, and the present invention adopts a short-term interest adjustment mechanism, a long-term preference learning mechanism and a dynamic matching optimization strategy to improve the accuracy and adaptability of the matching results.

[0048] In this embodiment, the intelligent matching module includes a short-term interest adjustment unit, a long-term preference learning unit, and a matching analysis unit, and forms personalized recommendation results by calculating the matching degree between user interest tags and order features.

[0049] In a possible implementation, the short-term interest adjustment unit is used to respond to the user's latest behavior in real time to ensure that the matching results can quickly adapt to the user's short-term demand changes. Specifically, the unit monitors the user's recent interactive behavior, such as; If a user has searched or browsed orders of a certain category multiple times in the past 24 hours, the system will temporarily increase the matching degree of orders in that category; If a user places an order for a certain type of order and this behavior rarely occurs in the historical records, the system may adjust the weight of this category to further observe the user's potential interest.

[0050] Generally, short-term interest adjustments are based on a weighted time decay model, which is calculated as follows: short =(1-α)I prev +αI new ; in: I short is the current short-term interest value; I prev is the previous interest value; I new Contribute value to the latest behavior; α is the adjustment coefficient of short-term interest, which usually satisfies 0<α<1.

[0051] In some embodiments, the short-term interest adjustment unit is also combined with an interest conflict detection mechanism to prevent short-term interest fluctuations from affecting recommendation stability. For example: If the user frequently switches the order category of interest within a short period of time, the system will suspend the adjustment of short-term interests and perform matching calculations based on long-term preferences.

[0052] As an option, the long-term preference learning unit is used to build a user's long-term interest model to ensure that the matching system can still provide stable recommendation results when user behavior fluctuates. Generally, this unit uses the time-weighted accumulation method to calculate the long-term interest weight, and its calculation formula is as follows; L t =βL t-1 +(1-β)I short ; in: L t is the long-term interest value at the current moment; L t-1 is the long-term interest value at the previous moment; I short is the short-term interest value; β is the time decay factor, which usually satisfies 0<β<1.

[0053] In a possible implementation, the long-term interest learning unit assigns different weights to different types of order categories to ensure that the user's core interests can receive a higher matching priority. For example: If a user has been focusing on "design" orders for a long time, even if he occasionally browses other types of orders in the short term, the system will still give priority to recommending design-related content.

[0054] In some embodiments, the system will adjust the long-term interest weight based on the user's order transaction record. For example: If a user has a high order success rate in a certain category, the system will automatically increase the long-term interest weight of that category; If a user has a large number of cancellation records in a certain type of order, the system may lower the recommendation weight of that category to reduce invalid matches.

[0055] As an option, the matching analysis unit is used to calculate the matching degree between the user interest tag and the order feature, and sort the recommended orders based on the matching degree value. Specifically, the matching degree calculation is based on a weighted scoring model, and the calculation method is as follows; in: P is the matching score between the user and a certain order. W i is the weight of user interest tag i, S i Score the similarity between the interest tag and the order category. n is the total number of user interest tags.

[0056] In general, the system will weight the interest tag W i Dynamic adjustments are made to ensure that the matching calculation can reflect the real needs of users. For example: If a certain interest tag has a large number of interactions in the last 7 days, the weight of the tag will automatically increase; If the activity of an interest tag decreases, the system will gradually reduce its weight to avoid outdated interests affecting the quality of recommendations.

[0057] In another possible implementation, the system will also adjust the matching degree based on the market popularity of the order. For example: If the recent demand for a category of orders is high, the system may appropriately increase the matching degree of orders in that category to increase the user's options.

[0058] Through the above technical solution, the intelligent matching module of the present invention can combine short-term interests, long-term preferences and dynamic matching optimization strategies to ensure the accuracy and adaptability of recommendation results. Compared with the traditional static matching method, the present invention adopts real-time interest adjustment, weighted matching calculation and dynamic recommendation optimization strategy, so that the matching system can flexibly adapt to changes in user needs and improve the efficiency and matching degree of order allocation.

[0059] The order management module and the intelligent matching module are used to push matching results, store, update and track order status, and provide order processing progress monitoring function; Specifically, the main function of the order management module in this embodiment is to ensure the management and tracking of the entire life cycle of the order, from order creation, status change to final completion or cancellation, and to provide a transparent and efficient execution process. This module works closely with the intelligent matching module and the secure transaction guarantee module to ensure the matching accuracy and transaction security of the order, and at the same time combines the order anomaly detection mechanism to improve the reliability of order execution. In general, the order management module optimizes the processing efficiency of orders and ensures the smoothness of the transaction process through functions such as status monitoring, task allocation, anomaly detection and dynamic adjustment.

[0060] In this embodiment, the order management module includes an order status monitoring unit, an exclusive service specialist allocation unit, and an order anomaly detection unit, which are used to track the order throughout the entire process and optimize the order management process in combination with intelligent analysis methods.

[0061] In a possible implementation, the order status monitoring unit is used to automatically manage the life cycle of an order to ensure that orders in different states can be processed in a timely manner. Generally, the order status includes: To be accepted: the order is created successfully and waiting for the response from the service provider; In progress: The service provider has accepted the order and the order has entered the execution stage; Completed: The order task has been completed and the user confirms that the delivery is correct; Cancelled: The order is canceled by the user or by system rules.

[0062] In some embodiments, the status change of an order is based on time constraint rules, namely; If the order is placed at the set time T accept If it is not accepted within 10 days, the system will automatically cancel the order; If the order is in the "In Progress" state and exceeds the maximum execution time T max , the system will send reminders to both parties and intervene if necessary.

[0063] In another possible implementation, the order status monitoring unit combines the order priority sorting mechanism to ensure that high-priority orders can be processed faster. For example, the system can calculate the priority score P of the order. order For dynamic adjustment, the calculation method is as follows: P order =W1·U+W2·T+W3·S; in, P order Rate the order priority, U is the user's reputation score, T is the order creation time. S is the order amount, W1, W2, and W3 are weight factors, which usually satisfy W1+W2+W3=1.

[0064] As an option, the dedicated service specialist allocation unit is used to dynamically allocate services based on the complexity of the order and the capabilities of the service provider. In this embodiment, the system automatically allocates services based on the following conditions: Order type: Different types of orders are suitable for service providers with different skills; Task complexity: High-complexity orders are prioritized to be matched with experienced service providers; Load balancing: prevents a single service provider from receiving too many orders, which would affect order quality.

[0065] In some embodiments, the dedicated service specialist allocation unit adopts a load balancing scheduling algorithm, and its allocation method can be expressed as; in: C is the comprehensive allocation score; T order is the order type matching degree; E provider rate the experience of service providers; L provider Current load W T ,W E ,W L is the weight parameter; ∈ is a constant to prevent the denominator from being zero.

[0066] In general, the system will give priority to matching with service providers with the highest comprehensive score C. If there is no suitable service provider at present, the system will expand the matching strategy and relax some screening conditions to improve the matching success rate.

[0067] The order anomaly detection unit is used to identify abnormal orders and adopt different processing strategies according to the abnormality level. For example, the system will monitor the following abnormal behaviors; Order timeout: The system cancels the order after the set timeout period; Users frequently cancel orders: If the same user cancels an order multiple times in a short period of time, the system may lower the credit score of his account; Service providers fail to deliver on time: When the system detects that an order is overdue and not completed, it will automatically trigger a manual review.

[0068] In some embodiments, the order anomaly detection unit uses a risk scoring model to calculate the order anomaly degree R order , which is calculated as follows; R order =αT delay +βCcancel +γS dispute ; in, R order Score the abnormal risk of the order. T delay Delay time for order, C cancel The number of times the user canceled an order. S dispute For order complaint records, α, β, γ are weight parameters.

[0069] As an option, the system will order Different processing strategies are adopted for the value of If R order If the value is below the preset threshold, the system will only give a prompt; If R order If the warning threshold is exceeded, the system may restrict the user's order creation permissions or require additional verification.

[0070] Through the above technical solution, the order management module of the present invention can realize order status monitoring, dynamic task allocation and anomaly detection optimization, ensuring the efficiency and stability of order execution. Compared with the traditional order management method, the present invention adopts intelligent matching, risk assessment and automated task scheduling strategies to make order management more accurate and transparent, and can adapt to complex and changing trading environments.

[0071] The secure transaction assurance module is connected to the order management module to provide order transaction information and perform security verification of user identity and payment information to ensure the legitimacy and security of transactions; Specifically, the secure transaction protection module in this embodiment is mainly used to ensure the security of the transaction process, prevent malicious behavior, payment fraud and financial losses. This module works closely with the user management module, order management module and abnormal behavior detection mechanism to provide systematic protection in identity authentication, payment security, risk control management and other aspects. In general, users need to undergo strict identity authentication before conducting financial transactions, and during the payment process, the system will monitor transaction behavior in real time, identify abnormal transactions, and take corresponding security measures. The present invention ensures the security and stability of platform transactions through multi-dimensional payment verification, dynamic risk control and intelligent early warning mechanism.

[0072] In this embodiment, the secure transaction assurance module includes a payment verification unit and a dynamic risk control unit, which are used to verify user payment information and execute dynamic security policies based on transaction risk levels.

[0073] The payment verification unit is used to check the compliance of the payment information submitted by the user and ensure the security of the fund transaction process. Specifically, the unit can be based on the four-factor verification of the bank card, namely: Identity information I, including user name, ID number or other official authentication information; Bank account information B, including bank card number, bank account opening and account binding information; Contact information P, including the mobile phone number and registration information submitted by the user; Transaction device information D, including device fingerprint, transaction terminal information and network environment parameters.

[0074] In general, the payment verification unit will perform a consistency comparison on the payment information submitted by the user and calculate the payment verification score V based on the weighted matching model. pay ; V pay =W1·I+W2·B+W3·P+W4·D; in: W1, W2, W3, W4 are weight factors of different verification dimensions, usually satisfying W1+W2+W3+W4=1; V pay Exceeding the set threshold T val If the transaction is verified, the transaction is successful; otherwise, additional identity verification is required.

[0075] In some embodiments, the payment verification unit supports two-factor authentication (2FA), namely: First level authentication: When making a transaction, the user needs to enter the payment password or complete biometric authentication; Second level authentication: Depending on the transaction risk, the system may require additional SMS verification codes or dynamic passwords.

[0076] In another possible implementation, the dynamic risk control unit is used to analyze transaction risks in real time and dynamically adjust trading strategies based on risk levels. Generally, the unit will evaluate the following factors: Transaction history matching, judging whether the current transaction is consistent with the user's previous transaction pattern; Transaction device consistency, detecting whether the transaction comes from the user's commonly used device or trusted environment; Network environment security, analysis of IP addresses, proxy server usage and geographic location changes.

[0077] In some embodiments, the system calculates the risk score R based on the transaction risk scoring model. trans ; R trans =α·T diff +β·L change +γ·Vfreq ; in: T diff The deviation between the current transaction amount and the user's historical transaction amount; L change The number of recent device and geographic location changes for the user; V freq is the transaction frequency per unit time; α, β, γ are weight factors of different factors.

[0078] As an option, the system will be based on the transaction risk score R trans Take different levels of processing strategies: If R trans Below the set safety threshold T low , the transaction goes directly through; If R trans In the warning zone [T low ,T high ], the system will trigger an additional authentication step; If R trans Exceeding the risk threshold T high , the transaction will be temporarily frozen and enter the manual review process.

[0079] In one possible implementation, the dynamic risk control unit uses a behavioral feature analysis algorithm combined with time series data to identify abnormal changes in trading patterns. For example: If a user makes multiple large transactions in a short period of time, the system may reduce their payment limit; If the user's payment device or geographic location changes suddenly or significantly, the system may require additional identity verification.

[0080] The secure transaction guarantee module can monitor payment information in real time, assess transaction risks, and dynamically adjust transaction strategies to ensure the safety of user funds and effectively reduce the possibility of transaction fraud. Compared with traditional transaction security systems, the present invention adopts multi-factor payment verification, dynamic risk control calculation, and secure payment storage strategies to make the transaction process more secure and stable, and can take flexible countermeasures for different types of transaction risks, thereby improving the overall transaction credibility of the platform.

[0081] The platform management module is wirelessly connected to other modules to maintain the platform operation status and provide data statistical analysis and system optimization functions; Specifically, in this embodiment, the platform management module is responsible for overall operation management, data analysis, system optimization and abnormal behavior monitoring to ensure the stable operation of the platform and enhance the user experience. This module works closely with the user management module, order management module, intelligent matching module and safe transaction guarantee module to maintain the accuracy of platform data, the rational allocation of system resources, and the standardized management of user behavior. In general, the operation and maintenance management of the platform involves aspects such as order data statistics, user activity analysis, transaction flow monitoring and platform rule adjustment. It is difficult to meet the high concurrency and variable demand transaction environment by relying solely on a single data management method. Therefore, the present invention provides an efficient, flexible and safe management solution through mechanisms such as data statistical analysis, system optimization and abnormal behavior detection to ensure the long-term stable operation of the platform and dynamically adapt to market changes.

[0082] In this embodiment, the platform management module includes a data statistical analysis unit, a system optimization unit, and an abnormal behavior detection unit, which are used to collect and analyze platform operation data, optimize matching rules, and take appropriate management measures for abnormal behaviors.

[0083] The data statistics and analysis unit is used to collect and process order data, user activity and transaction flow to provide operational decision support. Specifically, the types of data collected by this unit include: Order data: number of orders created, order completion rate, order cancellation rate; User behavior data: user login frequency, order interaction, average order processing time; Transaction flow: daily payment amount, fund settlement delay rate, and number of refund requests.

[0084] Generally, the data statistical analysis unit will use a weighted time series analysis model to calculate order trends and changes in user activity. The calculation formula is as follows: in: T order is the order trend index; O i is the order quantity at time i; W i is the weight factor of time i, usually satisfying ∑W i =1.

[0085] In some embodiments, the data statistical analysis unit can be combined with a machine learning model to predict future order growth trends and generate corresponding operation strategies. For example: If the system detects that the recent order growth rate is relatively fast, the server resources can be expanded in advance to prevent the system from being overloaded; if the order completion rate decreases, the order matching mechanism may need to be adjusted to improve task processing efficiency.

[0086] The system optimization unit is used to dynamically adjust the platform's intelligent matching algorithm, order processing mechanism, and user interaction experience. Generally, this unit optimizes system performance and improves recommendation accuracy based on user feedback data and historical operation data. For example: If users frequently modify filtering conditions before placing an order, the system may need to adjust the search filtering mechanism to provide accurate matches more quickly; if the user's order rate is high within a specific time period, the platform can optimize server resource allocation through load balancing strategies.

[0087] In a possible implementation, the system optimization unit uses a dynamic matching weight adjustment strategy to optimize the matching weight of user interest tags. The calculation method is as follows: W new =λW old +(1-λ)·F; in: W new is the optimized weight; W old is the original weight; F is the user feedback score; λ is the time attenuation coefficient, satisfying 0<λ<1. In another possible implementation, the abnormal behavior detection unit is used to monitor abnormal behaviors that may exist in the user and platform operation process to ensure the fairness and security of the trading environment. Specifically, the unit can identify the following abnormal situations: frequent modification of screening conditions in a short period of time may mean that the user has malicious data crawling behavior; If a user submits low-quality orders repeatedly, it may affect the reputation of the platform and restrictive measures need to be taken; An abnormal surge in transaction volume within a short period of time may involve fund fraud or batch attacks.

[0088] In general, the abnormal behavior detection unit uses a risk scoring model to calculate the abnormal behavior index R abnormal ; R abnormal =αF change +βU cancel +γT peak ; in: F change Modify the frequency for the filter criteria; U cancel The number of times the user's order was cancelled; T peak is the trading volume fluctuation value per unit time; α, β, γ are weight factors of different factors.

[0089] In some embodiments, the abnormal behavior detection unit can combine historical behavior pattern analysis to determine whether the user has repeated violations and dynamically adjust the risk threshold. For example: If a user has triggered risk control warnings multiple times in the past week, the system may lower the user's trust score; If the user does not exhibit abnormal behavior again within a certain period of time, the system can gradually restore his normal permissions.

[0090] The platform management module can realize data statistical analysis, system intelligent optimization and abnormal behavior monitoring, ensure the long-term stable operation of the platform, and dynamically adjust the platform operation strategy according to changes in market demand. Compared with the traditional static management method, the present invention adopts intelligent data analysis, optimized matching weights and dynamic risk control strategies to make platform management more flexible and safe, and can make real-time adjustments according to different user behaviors and market environments, thereby improving the stability and credibility of the overall trading environment.

[0091] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent demand allocation platform, characterized by: include; User management module, used to manage user account information and perform identity authentication; A search and screening module, which is connected to the user management module, is used to receive the query conditions input by the user and screen the orders that meet the conditions from the order database based on the preset matching rules; Intelligent behavior analysis module, which is connected to the search and screening module to collect historical interaction data of users and generate user interest tags; The intelligent matching module is connected to the intelligent behavior analysis module to receive the user's interest tags and order characteristics to calculate the matching degree and recommend orders that meet the user's preferences; The order management module and the intelligent matching module are used to push matching results, store, update and track order status, and provide order processing progress monitoring function; The secure transaction assurance module is connected to the order management module to provide order transaction information and perform security verification of user identity and payment information to ensure the legitimacy and security of transactions; The platform management module is wirelessly connected to other modules to maintain the platform operation status and provide data statistical analysis and system optimization functions.

2. The intelligent demand allocation platform according to claim 1, characterized in that: The user management module includes: The identity verification unit is used to call the authoritative database to verify the identity information submitted by the user and improve the verification security based on multi-factor authentication; The account permission control unit is used to assign different permissions based on user identity categories and perform authorization review on sensitive operations.

3. The intelligent demand allocation platform according to claim 1, characterized in that: The search and screening module includes: Keyword search unit, used to optimize user query content based on natural language processing technology; The multi-condition screening unit is used to screen the orders that meet the requirements based on multiple screening conditions such as geographic location, service category, order budget, and user rating.

4. The intelligent demand allocation platform according to claim 1, characterized in that: The intelligent behavior analysis module includes: Data collection unit, used to collect users' browsing, searching, clicking and ordering behavior data on the platform; The interest tag generation unit is used to calculate the user's attention to orders of different categories based on the user behavior data, and adjust the attention according to the set weight factor to generate interest tags.

5. The intelligent demand allocation platform according to claim 1, characterized in that: The intelligent matching module includes: The short-term interest adjustment unit is used to dynamically adjust the recommendation weight based on the user's recent browsing and interaction behavior, so that the system can respond more quickly to changes in user interests; Long-term preference learning unit, which is used to build a long-term interest model based on user historical data and combine short-term and long-term interests in recommendation calculations; The matching analysis unit is used to calculate the matching degree based on user interest tags, order categories and historical order completion rates, and to sort the recommended orders based on the matching degree values.

6. The intelligent demand allocation platform according to claim 5, characterized in that: When the matching analysis unit calculates the matching degree, dynamic adjustments are made based on the following factors: The correlation between user historical interaction behavior data, interest tags and order categories; Users’ recent behavior frequency, interaction depth, and order completion status.

7. The intelligent demand allocation platform according to claim 1, characterized in that: The order management module includes: An order status monitoring unit, used to update the order status in real time based on the order processing progress, the status including pending, in progress, completed and cancelled; A dedicated service specialist allocation unit is used to automatically assign appropriate service specialists based on order complexity, service provider load balancing, and historical order processing efficiency; The order anomaly detection unit is used to detect order anomalies and adopt different processing strategies according to the anomaly level, including risk warning, audit intervention and order freezing.

8. The intelligent demand allocation platform according to claim 1, characterized in that: The secure transaction assurance module comprises: Payment verification unit, used to check the validity of the bank card information provided by the user and calculate the risk score based on the account's historical transaction behavior; Dynamic risk control unit, which is used to analyze transaction risks based on user transaction history, device fingerprint and geographic location data, and trigger additional identity verification processes in high-risk transaction situations.

9. The intelligent demand allocation platform according to claim 1, characterized in that: The platform management module includes: Data statistics and analysis unit, used to analyze the platform's order data, user activity and transaction flow information, and provide decision support; System optimization unit, used to adjust intelligent matching rules based on user feedback and optimize search and recommendation algorithms; The abnormal behavior detection processing unit is used to monitor the abnormal behavior of users on the platform.

10. The intelligent demand allocation platform according to claim 9, characterized in that: Abnormal behaviors in the abnormal behavior detection unit include but are not limited to frequent cancellation of orders in a short period of time, abnormally high-value transactions, multiple changes in payment methods in a short period of time, and abnormal login activities, and trigger processing measures; The processing measures include transaction risk verification, temporary account freezing, restriction of specific transaction operations and triggering of manual review.

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