Multi-brand marketing sharing system and method thereof

Abstract

The invention relates to the technical field of multi-brand marketing, in particular to a multi-brand marketing sharing system and method, and the system comprises a safety protection authorization unit, an edge calculation point processing unit, a point account book intelligent contract unit, and a unified interaction unit. According to the invention, the security protection authorization unit guarantees the security of the system and divides permissions through multi-factor identity verification and dynamic risk assessment, the edge calculation point processing unit processes point data in real time by using caching and prefetching technologies, and the point account book intelligent contract unit records point operation based on an alliance block chain. Multi-brand point general exchange is realized through an intelligent contract, the unified interaction unit provides an operation and management interface for consumers and brand parties, the problems that a point system is not universal and the processing efficiency is low in multi-brand marketing are solved, multi-brand point unified management and free exchange are realized, and the consumer experience and the brand collaborative marketing capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-brand marketing, and in particular to a multi-brand marketing sharing system and method thereof. Background Art

[0002] Multi-brand marketing is an important technology. In a comprehensive commercial plaza, there are many different types of brands gathered together. The points that consumers earn from shopping in fashion brand stores cannot be used in catering stores. This limits the value of the points in the hands of consumers and makes it difficult to stimulate their enthusiasm for cross-brand consumption in the plaza.

[0003] The reason why the traditional multi-brand marketing sharing system has such shortcomings is mainly because the points systems of various brands are isolated from each other and lack a unified management and interaction mechanism. From a technical perspective, traditional systems often rely on centralized databases, with poor data security and privacy, and are vulnerable to attacks and data leaks. Moreover, when processing points redemption business, they are inefficient and unable to respond to consumer needs in real time. In order to solve this problem, we provide a multi-brand marketing sharing system and method. Summary of the invention

[0004] The object of the present invention is to provide a multi-brand marketing sharing system and method thereof to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, a multi-brand marketing sharing system is provided, including a security protection authorization unit, an edge computing points processing unit, a points ledger smart contract unit, and a unified interaction unit;

[0006] The security protection authorization unit includes a biometric module and an access control module. The biometric module adopts a multi-factor identity authentication and a dynamic risk assessment algorithm. When consumers and brands access the system, it combines multiple identity authentication methods and adjusts the verification process according to real-time risks. The access control module allocates and dynamically adjusts the operation permissions to divide the permission levels for brands and consumers.

[0007] The edge computing points processing unit deploys edge computing devices at each brand sales terminal and platform node, uses cache and pre-fetch technology to process local points data in real time, and receives points operation requests authenticated by the security protection authorization unit, and after processing, transmits the points data and related evaluation information to the points ledger smart contract unit;

[0008] The points ledger smart contract unit builds a consortium blockchain based on the equity proof consensus mechanism as a points ledger to record various operations of consumer points, and defines and automatically executes multi-brand points redemption rules through smart contracts. Finally, it receives data from the edge computing points processing unit to update the ledger and feeds back the processing results to the unified interaction unit.

[0009] The unified interactive unit provides consumers with a unified points management and redemption operation interface, and provides brands with a management and query interface.

[0010] As a further improvement of the technical solution, in the security protection authorization unit, the specific operation of the biometric recognition module using multi-factor identity authentication and dynamic risk assessment algorithm is as follows:

[0011] First, collect the static identity information of consumers and brands, including username, password and registered email address, which is recorded as the static identity information set, and collect biometric information at the same time;

[0012] When accessing the system, the static identity information is compared with the pre-stored information. If the comparison is successful, the biometric verification phase begins, and the collected biometric information is compared with the pre-stored biometric template for similarity calculation;

[0013] And define a set of risk assessment indicators, assign weights to each risk assessment indicator, calculate the risk score for each visit, and dynamically adjust the verification process based on the risk score;

[0014] Finally, a set of permission levels is defined, and a corresponding set of operation permissions is defined for each level. Based on the identity information, historical access records, and risk assessment score factors of consumers and brands, the initial permission level is assigned to them through the rule engine, and the user's risk assessment score is updated regularly.

[0015] As a further improvement of the technical solution, the edge computing integral processing unit includes a cache pre-fetch module. The specific method of the cache pre-fetch module using cache and pre-fetch technology to process local integral data in real time is as follows:

[0016] The cache space size is defined by the cache replacement algorithm based on the least recently used, the local integral data is sorted according to the integral operation time, and the integral data queue is constructed. When new integral data arrives, if the cache space is full, the least recently used data is removed and the new data is inserted into the queue head;

[0017] It also analyzes historical integral operation patterns, defines a set of integral operation sequence patterns, and monitors the current integral operation in real time. If the current operation meets a pattern in the set of integral operation sequence patterns, it predicts subsequent integral operations based on the pattern and pre-fetches relevant integral data from the local database into the cache in advance.

[0018] As a further improvement of the present technical solution, the edge computing integral processing unit includes an integral data processing module, which receives the integral operation request after authentication by the security protection authorization unit, and the specific algorithm for processing the integral data is as follows:

[0019] The points operation requests are divided into three types: points increase, points decrease, and points query. For the points increase operation request, the points increase amount and user ID in the request are extracted, and the current points of the user in the local cache are queried. If the current points do not exist in the cache, they are obtained from the local database, and the new points value is calculated. At the same time, the points data in the local cache and database are updated;

[0020] For a point reduction operation request, extract the point reduction amount and user ID in the request, query the current points of the user in the local cache, and if the current points are greater than or equal to the point reduction amount, calculate the new point value and update the point data in the local cache and database at the same time. Otherwise, return an error message indicating insufficient points.

[0021] For points query operation requests, the user ID is extracted and the user's points data is queried from the local cache first. If the data exists in the cache, it is returned directly. If not, it is obtained from the local database and the cache is updated.

[0022] As a further improvement of the present technical solution, the points ledger smart contract unit includes a module for selecting equity distribution. When the module for selecting equity distribution constructs a consortium blockchain based on the equity proof consensus mechanism as a points ledger, the specific algorithm for node selection and equity distribution is as follows:

[0023] Define a set of node qualification evaluation indicators, including the node's computing power, storage capacity, and network bandwidth, and assign weights to each indicator. For each candidate node, calculate its qualification score, and then compare it with the preset qualification score threshold. Only nodes with a qualification score greater than the qualification score threshold are eligible to participate in the alliance blockchain.

[0024] The stake value of a node is defined to be proportional to the node's qualification score. In each consensus cycle, the probability of a node obtaining the right to record is determined based on the node's stake value.

[0025] As a further improvement of the technical solution, the points account smart contract unit includes an automatic execution module. The specific algorithm of the automatic execution module for defining and automatically executing the multi-brand points exchange rules through smart contracts is as follows:

[0026] Define a set of multi-brand points redemption rules. Each rule includes the points redemption ratio and the redemption brand combination. For each rule, set the effective time range.

[0027] When a points redemption request is received, the source brand points, target brand and user ID in the request are extracted, the redemption rule set is traversed, and the rule set that contains the target brand and is effective at the current time is filtered out. For each rule in the rule set, the redeemable target brand points are calculated, and the rule with the most redeemable points is selected as the optimal rule. The points redemption operation is executed and the points ledger is updated.

[0028] As a further improvement of this technical solution, when the points ledger smart contract unit receives data from the edge computing points processing unit to update the ledger, the specific algorithm for data consistency verification is as follows:

[0029] For the integral data transmitted by the edge computing integral processing unit, divide it into multiple data blocks, calculate the hash value for each data block, and combine these hash values ​​into a hash list, use the hash list to build a Merkle tree, and calculate the root hash value of the Merkle tree;

[0030] On each node of the alliance blockchain, the root hash value of the Merkle tree when the ledger was last updated is stored. When new points data is received, the root hash value of the Merkle tree is recalculated and compared with the root hash value of the Merkle tree when the ledger was last updated. If the two are the same, the data is consistent and the ledger is updated. Otherwise, the update is rejected and the edge computing points processing unit is required to resend the data.

[0031] As a further improvement of the technical solution, when the points ledger smart contract unit feeds back the processing results to the unified interaction unit, an encrypted transmission and message queue mechanism is adopted. The specific method is as follows:

[0032] The processing result data is encrypted by a symmetric encryption algorithm to generate encrypted data, the key is encrypted by an asymmetric encryption algorithm to generate an encryption key, and the encrypted data and the encryption key are sent together to the unified interaction unit;

[0033] A message queue is established between the points ledger smart contract unit and the unified interaction unit. The points ledger smart contract unit sends the encrypted processing result message to the sending end of the message queue. The unified interaction unit receives the message from the receiving end of the message queue and processes the message according to the first-in-first-out principle. After receiving the message, the private key is first used to decrypt the encrypted key to obtain the key, and then the key is used to decrypt the encrypted data to obtain the processing result data.

[0034] A second object of the present invention is to provide a method for implementing a multi-brand marketing sharing system including any one of the above, comprising the following steps:

[0035] S1. The security protection authorization unit conducts multi-factor identity authentication and dynamic risk assessment on consumers and brands, allocates and adjusts operation permissions based on the principle of least privilege, and allows subsequent points operations after authentication;

[0036] S2. The edge computing points processing unit receives the points operation request that has passed the security authentication, uses the edge computing device to process the local points data in real time, uses the cache and pre-fetching technology to optimize the processing process, and transmits the processed points data and evaluation information to the points ledger smart contract unit;

[0037] S3, the points ledger smart contract unit receives data from the edge computing points processing unit, verifies and executes the points exchange rules through smart contracts, updates the blockchain points ledger, and feeds back the processing results to the unified interaction unit;

[0038] S4. The unified interactive unit displays consumers' points information and redemption results. Brands can manage and query relevant data through this module. After the points redemption request initiated by the consumer is authenticated by the zero-trust security authentication module, it will be processed by the blockchain points ledger and smart contract module and the results will be displayed.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The security protection authorization unit uses multi-factor authentication and dynamic risk assessment algorithms, combined with biometric technology and the principle of least privilege, to ensure system access security, prevent the leakage of points data, and build a solid security line for multi-brand marketing sharing. The edge computing points processing unit deploys equipment at the sales end of each brand, and uses cache and pre-fetch technology to process points data in real time. When consumers consume and earn points in the square store, they can quickly complete the calculation and recording of points, reduce waiting time, and improve the consumer experience. The points account smart contract unit builds a blockchain points account book, and realizes the free exchange of multi-brand points through smart contracts. The points accumulated by consumers from different brands in the square can be exchanged with each other, which increases the value of points and stimulates cross-brand consumption enthusiasm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is an overall block diagram of the present invention;

[0042] Figure 2 It is the overall flow chart of the present invention.

[0043] The meaning of each number in the figure is:

[0044] 1. Security protection authorization unit; 11. Biometric module; 12. Access control module; 2. Edge computing points processing unit; 21. Cache pre-fetch module; 22. Points data processing module; 3. Points ledger smart contract unit; 31. Equity distribution selection module; 32. Automatic execution module; 4. Unified interaction unit. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments 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.

[0046] The present invention provides a multi-brand marketing sharing system, please refer to Figure 1 As shown, it includes a security protection authorization unit 1, an edge computing points processing unit 2, a points ledger smart contract unit 3 and a unified interaction unit 4;

[0047] The security protection authorization unit 1 includes a biometric module 11 and an access control module 12. The biometric module 11 adopts multi-factor identity authentication and dynamic risk assessment algorithm. When consumers and brands access the system, it combines multiple identity authentication methods and adjusts the verification process based on real-time risks. The access control module 12 allocates and dynamically adjusts operating permissions to divide permission levels for brands and consumers.

[0048] The specific operation of the biometric module 11 using the multi-factor identity authentication and dynamic risk assessment algorithm is as follows:

[0049] First, collect the static identity information of consumers and brands, including username, password and registered email address, which is recorded as the static identity information set ,in is the user name, is the password, In order to register an email address, biometric information such as fingerprints, facial features and irises are collected at the same time. Static identity information is the basic information provided by the user when registering the system. It has a certain uniqueness and verifiability. Biometric information has higher uniqueness and security. The comprehensive collection of these two types of information can provide a more comprehensive basis for subsequent multi-factor identity authentication.

[0050] Multi-factor authentication can significantly improve the security of the system, reduce the security risks caused by the cracking of a single factor, enhance the system's ability to accurately identify user identities, and reduce the possibility of illegal users accessing the system.

[0051] When accessing the system, the static identity information is compared with the pre-stored information, that is, for the collection Each element in , and the corresponding information stored in the system For comparison, if If all of them are true, the static identity information comparison is considered successful. The pre-stored information is accurate information that has been verified and stored in the system when the user registered. Through the comparison, it can be preliminarily confirmed whether the user's identity is legal. It is simple and direct, and can quickly screen out obvious illegal access and improve verification efficiency.

[0052] If the static identity information is successfully matched, the biometric verification phase will begin, where the collected biometric information will be similar to the pre-stored biometric template. Biometrics are unique and stable, so even if the static identity information is leaked, it is difficult for illegal users to obtain the biometrics of legitimate users. Biometric verification can further ensure the authenticity of the user's identity, greatly improve the accuracy and security of identity authentication, and effectively prevent identity fraud.

[0053] Define a set of risk assessment indicators, assign weights to each risk assessment indicator, calculate the risk score for each access, and dynamically adjust the verification process based on the risk score. If the risk score is higher than the set high-risk threshold, additional verification steps are required. Different access situations may have different security risks. Relying solely on static identity authentication methods cannot cope with complex and changing security threats. Through dynamic risk assessment, the verification process can be flexibly adjusted according to actual conditions to improve system security and user experience.

[0054] Define a set of permission levels, define a corresponding set of operation permissions for each level, and assign initial permission levels to them through the rule engine based on the identity information, historical access records, and risk assessment score factors of consumers and brands, thereby achieving fine-grained permission management and improving the security and controllability of the system.

[0055] The edge computing points processing unit 2 deploys edge computing devices at each brand sales terminal and platform node, uses cache and pre-fetching technology to process local points data in real time, and receives points operation requests authenticated by the security protection authorization unit 1. After processing, the points data and related evaluation information are passed to the points ledger smart contract unit 3.

[0056] The edge computing integral processing unit 2 includes a cache pre-fetching module 21. The specific method of the cache pre-fetching module 21 for real-time processing of local integral data using cache and pre-fetching technology is as follows:

[0057] The cache size is defined using a cache replacement algorithm based on least recent use. , sort the local integral data according to the integral operation time, and build the integral data queue ,in Expressed as The points data are arranged from the newest to the oldest according to the operation time. When new points data arrives, if the cache space is full, the least recently used data is removed and the new data is inserted into the head of the queue.

[0058] When cache space is limited, a strategy is needed to decide which data should be retained and which data should be replaced. The least recently used cache replacement algorithm is based on the assumption that the least recently used data is less likely to be used in the future, so removing this data can make room for new data while retaining as much data as possible that may be used again in the near future. The algorithm is simple and efficient, and can better adapt to the data access patterns in most application scenarios, making the data in the cache more likely to be used by subsequent operations, thereby improving the cache hit rate.

[0059] Analyze historical integration operation modes and define integration operation sequence mode sets By mining and analyzing a large amount of historical integral operation data, we can find out the common operation sequence patterns. Users' integral operations often have certain regularity and patterns. By analyzing historical operation patterns, we can predict the operations that users may perform in the future, so that we can prepare in advance and improve the response speed of the system. When users perform operations, the system can provide the required integral data more quickly, reducing response delays and enhancing the fluency of the system.

[0060] Real-time monitoring of the current integration operation, if the current operation meets the integration operation sequence mode set A pattern in , then according to the mode Predict subsequent integral operations and pre-fetch relevant integral data from the local database into the cache in advance. Suppose the current integral operation sequence is , can be achieved through pattern matching algorithms To determine whether there is a matching pattern, if , then according to the mode Determine possible subsequent operations and related points data , and Prefetch from local database into cache.

[0061] Prefetching relevant data before the user performs an operation can directly obtain the data from the cache when the user really needs it, avoiding the time of waiting for data to be read from the database, improving the response speed of the system, further reducing the user's waiting time, and improving the real-time and interactivity of the system. At the same time, since the prefetched data may be used in subsequent operations, it also helps to improve the cache hit rate.

[0062] The edge computing integral processing unit 2 includes an integral data processing module 22. The integral data processing module 22 receives the integral operation request authenticated by the security protection authorization unit 1. The specific algorithm for processing the integral data is as follows:

[0063] The points operation request is divided into three types: points increase, points decrease and points query. It means that different types of integration operations have different processing logics. Classifying requests can make the subsequent processing flow clearer and more organized, making it easier to develop specialized processing algorithms for each type.

[0064] Request for points increase operation , the amount of points added in the extraction request and user ID , query the current points of the user in the local cache If it does not exist in the cache, it is obtained from the local database and a new integral value is calculated. , and update the score data in the local cache and database at the same time. First query the current score from the cache to take advantage of the high-speed reading characteristics of the cache to improve the processing speed. If there is no data in the cache, then obtain it from the database to ensure the accuracy of the data. Calculate the new score value and update the cache and database to ensure the consistency and timeliness of the data.

[0065] For point reduction operation request , the amount of points reduction in the extraction request and user ID , query the current points of the user in the local cache ,like , then calculate the new integral value , and update the points data in the local cache and database at the same time; otherwise, return an error message of insufficient points. Before performing the point reduction operation, it is necessary to check whether the user's current points are sufficient to avoid negative points. Querying the current points from the cache first can improve processing efficiency. If the points are sufficient, update the cache and database to ensure data consistency. If the points are insufficient, timely feedback error information to avoid invalid operations.

[0066] For points query operation request , extract the user ID , prioritize querying the user's points data from the local cache. If the data exists in the cache, it will be returned directly. If not, it will be obtained from the local database and updated. The cache reading speed is much faster than the database. Prioritizing querying from the cache can quickly respond to query requests and improve user experience. If there is no data in the cache, it will be obtained from the database and updated. This not only ensures the accuracy of the data, but also enables subsequent queries to be directly obtained from the cache, improving processing efficiency.

[0067] The points ledger smart contract unit 3 builds a consortium blockchain based on the proof-of-stake consensus mechanism as a points ledger to record various operations of consumer points, and defines and automatically executes multi-brand points redemption rules through smart contracts. Finally, it receives data from the edge computing points processing unit 2 to update the ledger and feeds back the processing results to the unified interaction unit 4.

[0068] The points ledger smart contract unit 3 includes a rights and interests distribution module 31. When the rights and interests distribution module 31 is selected to construct a consortium blockchain based on a proof-of-stake consensus mechanism as a points ledger, the specific algorithm for node selection and rights and interests distribution is as follows:

[0069] Define a set of node qualification evaluation indicators ,in is the computing power of the node, is the storage capacity, Computing power, storage capacity, and network bandwidth are important factors that affect the performance of nodes in the alliance blockchain. By defining these indicators and assigning weights, the capabilities of the nodes can be comprehensively evaluated to ensure that the nodes participating in the alliance blockchain have a certain performance level and ensure the stable operation of the blockchain.

[0070] For each candidate node , calculate its qualification score ,in Is a node In the indicator The quantitative value on the With the preset eligibility score threshold For comparison, only Only nodes with sufficient qualifications are eligible to participate in the alliance blockchain. By quantifying the qualification scores of the nodes, the comprehensive capabilities of each candidate node can be objectively evaluated. Setting the qualification score threshold can ensure that the nodes participating in the alliance blockchain meet certain standards and avoid the negative impact of low-performance nodes on the system.

[0071] Define the node's equity value The eligibility score of the node is proportional to ,in is the proportional coefficient. In each consensus cycle, according to the node's equity value Determine the probability of obtaining bookkeeping rights ,in The core idea of ​​the Proof-of-Stake consensus mechanism is to allow nodes with greater stakes to have a higher probability of obtaining bookkeeping rights, and to link the stake value of the node with the qualification score, so that nodes with better performance have a greater say in the consensus process, incentivizing nodes to provide better resources and services, while also ensuring that the distribution of bookkeeping rights is relatively fair.

[0072] The points book smart contract unit 3 includes an automatic execution module 32. The specific algorithm of the automatic execution module 32 for defining and automatically executing the multi-brand points exchange rules through smart contracts is as follows:

[0073] Define a set of rules for redeeming points across multiple brands , each rule Including points redemption ratio and redemption brand combinations , and set the effective time range for each rule There are differences in the value of points and cooperative relationships between different brands. By defining a set of rules, the redemption conditions and restrictions between points of each brand can be clarified, making the redemption of points more standardized.

[0074] When receiving a points redemption request, extract the source brand points in the request Target Brand and user ID This information is the basis for points redemption. Only by accurately obtaining this information can we further select appropriate rules and complete the redemption. , filter out the target brand And at the current time The set of rules in effect ,Right now Not all rules are applicable to the current redemption request. By filtering, the scope of rules can be narrowed, the workload of subsequent calculations can be reduced, and at the same time, the rules used can be ensured to be valid, so as to improve the efficiency of rule matching, avoid the calculation and processing of invalid rules, and save system resources.

[0075] For rule sets Each rule in , calculate the redeemable target brand points By calculating the redeemable target brand points, the redemption benefits under different rules can be intuitively compared, so as to select the optimal redemption plan, so as to provide users with a clear redemption benefit comparison, help users make more favorable decisions, and also ensure that the system can execute the redemption according to the optimal plan.

[0076] Select the rule with the most redeemable points as the optimal rule ,Right now ; Selecting rules based on the principle of the maximum number of redeemable points can protect the interests of users to the greatest extent and increase the enthusiasm of users to participate in the points redemption, so as to reduce the user's decision-making cost and improve the user experience by automatically selecting the optimal rules.

[0077] Perform points redemption operations to reduce the user's original brand points , increase the target brand points , and updating the points ledger at the same time. Completing the actual transfer of points and updating the ledger is the ultimate goal of points redemption, which ensures the accuracy and consistency of points data, ensures the normal operation of the points system, and enables points data to truly reflect the user's points status, providing reliable data support for subsequent points management and marketing activities.

[0078] When the points ledger smart contract unit 3 receives data from the edge computing points processing unit 2 to update the ledger, the specific algorithm for data consistency verification is as follows:

[0079] For the integral data transmitted by the edge computing integral processing unit 2 , divide it into multiple data blocks , for each data block Calculating the hash value , and combine these hash values ​​into a hash list Calculating hash values ​​by dividing data into blocks can reduce the complexity of data processing. The hash function is unidirectional and unique, and can map data of any length to a hash value of fixed length, which is convenient for subsequent verification and comparison, so as to improve the efficiency of data verification and reduce the burden of data transmission and storage. At the same time, the uniqueness of the hash value can effectively detect whether the data has been tampered with.

[0080] Using Hash Lists Building a Merkle Tree The Merkle tree is a binary tree structure that starts with the hash value in the hash list, calculates the hash value of the parent node in pairs, and builds up in sequence until the hash value of the root node is obtained. , the calculation method is: If and If two adjacent hash values ​​are ,in Represents the concatenation operation. Repeat this process continuously to finally get the root hash value of the Merkle tree. The Merkle tree can efficiently verify the integrity of large amounts of data. Through the root hash value, it can quickly determine whether the entire data set has changed without having to compare each data block one by one, greatly reducing the time and amount of computation for data verification and improving verification efficiency. At the same time, the structure of the Merkle tree allows data verification to be performed locally, making it easier to quickly locate the location of data tampering.

[0081] On each node of the consortium blockchain, the root hash value of the Merkle tree when the ledger was last updated is stored , when new points data is received, recalculate the root hash value of the Merkle tree , and For comparison, if = , the data is consistent and the ledger is updated. Otherwise, the update is rejected and the edge computing integral processing unit 2 is required to resend the data. By comparing the root hash value, it can be quickly determined whether the new data is consistent with the last updated data. If the root hash values ​​are the same, it means that the data has not been tampered with; if they are different, it means that the data may have changed during transmission or processing. This verification method is simple and efficient, and can ensure the consistency and integrity of the data without relying on large amounts of data transmission and complex calculations.

[0082] When the points ledger smart contract unit 3 feeds back the processing results to the unified interaction unit 4, it adopts the encrypted transmission and message queue mechanism. The specific method is as follows:

[0083] Use symmetric encryption algorithm to process result data Encrypt and generate encrypted data , let the encryption function of the symmetric encryption algorithm be , the key is ,but , key Encrypt through asymmetric encryption algorithm to generate encryption keys , let the public key of the asymmetric encryption algorithm be , the encryption function is ,but , and finally encrypt the data and encryption keys The data are sent together to the unified interaction unit 4 .

[0084] The processing result data may contain sensitive information, such as user points details, redemption records, etc. Direct transmission is easy to be stolen or tampered with. Symmetric encryption algorithms have fast encryption speed and are suitable for encrypting large amounts of data; asymmetric encryption algorithms have high security and are used to encrypt symmetric keys to ensure the security of key transmission. The combination of the two encryption algorithms can ensure the security of data and keys while ensuring encryption efficiency, thereby improving the security of data transmission and preventing data from being stolen or tampered with during transmission. At the same time, the high efficiency of symmetric encryption and the security of asymmetric encryption are used to achieve a balance between performance and security.

[0085] Establish a message queue between the points ledger smart contract unit 3 and the unified interaction unit 4 ,The message queue is a first-in-first-out data structure used to store and transmit messages.,The generation of the processing results of the point ledger smart contract unit 3 and the processing of the results by the unified interaction unit 4 may be inconsistent in time.,The message queue can be used as an intermediate buffer to decouple the direct dependency between the two units, improve the asynchronous processing capability of the system, and enable the point ledger smart contract unit 3 and the unified interaction unit 4 to run independently, thereby improving the overall performance and reliability of the system.

[0086] Points ledger smart contract unit 3 sends the encrypted processing result message to the message queue The sending end of the unified interaction unit 4 from the message queue The receiving end receives the message and processes the message according to the first-in-first-out principle. The message is sent and received through the message queue, which can realize asynchronous communication between the two units, improve the response speed and processing efficiency of the system, realize asynchronous processing of the system, and improve the concurrent processing capability of the system.

[0087] After receiving the message, the unified interaction unit 4 first uses the private key Decrypting the encryption key Get the key , assuming that the encryption function of the asymmetric encryption algorithm is ,but , and then use the key Decrypting encrypted data Get the processing result data , let the decryption function of the symmetric encryption algorithm be ,but The unified interaction unit 4 needs to obtain the original processing result data before it can perform subsequent display and processing. By decrypting the encryption key with the private key, and then decrypting the encrypted data with the decrypted key, the original processing result data can be restored, ensuring the security of the data during transmission. Only the unified interaction unit 4 with the correct private key can decrypt the data, preventing data leakage.

[0088] The unified interaction unit 4 provides consumers with a unified points management and redemption operation interface, and provides brands with a management and query interface.

[0089] In the present invention, the security protection authorization unit 1 ensures system security and divides permissions through multi-factor identity authentication and dynamic risk assessment, the edge computing points processing unit 2 uses caching and pre-fetching technology to process points data in real time, the points ledger smart contract unit 3 records points operations based on the alliance blockchain, and realizes multi-brand points redemption through smart contracts. The unified interaction unit 4 provides an operation and management interface for consumers and brands, which solves the problems of the non-universality and low processing efficiency of the points system in multi-brand marketing, realizes the unified management and free redemption of multi-brand points, and improves consumer experience and brand collaborative marketing capabilities.

[0090] A second object of the present invention is to provide a method for implementing a multi-brand marketing sharing system including any one of the above, comprising the following steps:

[0091] S1, Security Protection Authorization Unit 1 performs multi-factor identity authentication and dynamic risk assessment on consumers and brands, allocates and adjusts operation permissions based on the principle of least privilege, and allows subsequent points operations after authentication;

[0092] S2, the edge computing points processing unit 2 receives the points operation request that has passed the security authentication, uses the edge computing device to process the local points data in real time, uses the cache and pre-fetching technology to optimize the processing process, and transmits the processed points data and evaluation information to the points ledger smart contract unit 3;

[0093] S3, the points ledger smart contract unit 3 receives the data from the edge computing points processing unit 2, verifies and executes the points exchange rules through the smart contract, updates the blockchain points ledger, and feeds back the processing results to the unified interaction unit 4;

[0094] S4, unified interaction unit 4 displays the consumer's points information and redemption results. The brand can manage and query relevant data through this module. The points redemption request initiated by the consumer is authenticated by the zero-trust security authentication module, and then processed by the blockchain points ledger and smart contract module and the results are displayed.

[0095] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-brand marketing sharing system, characterized in that: It includes a security protection authorization unit (1), an edge computing points processing unit (2), a points ledger smart contract unit (3) and a unified interaction unit (4); The security protection authorization unit (1) comprises a biometric identification module (11) and an access control module (12). The biometric identification module (11) adopts a multi-factor identity authentication and a dynamic risk assessment algorithm. When consumers and brands access the system, the authentication process is adjusted based on a combination of multiple identity authentication methods and real-time risk. The access control module (12) allocates and dynamically adjusts operation permissions, thereby dividing permission levels for brands and consumers. The edge computing points processing unit (2) deploys edge computing devices at each brand sales terminal and platform node, processes local points data in real time using cache and pre-fetching technology, and receives points operation requests authenticated by the security protection authorization unit (1), and after processing, transmits the points data and related evaluation information to the points ledger smart contract unit (3); The points ledger smart contract unit (3) constructs a consortium blockchain based on a proof-of-stake consensus mechanism as a points ledger to record various operations of consumer points, and defines and automatically executes multi-brand points redemption rules through smart contracts. Finally, it receives data from the edge computing points processing unit (2) to update the ledger, and feeds back the processing results to the unified interaction unit (4); The unified interaction unit (4) provides consumers with a unified points management and redemption operation interface, and provides brands with a management and query interface.

2. A multi-brand marketing sharing system according to claim 1, characterized in that: In the security protection authorization unit (1), the specific operation of the biometric recognition module (11) using the multi-factor identity authentication and dynamic risk assessment algorithm is as follows: First, static identity information of consumers and brands is collected, including username, password, and registered email address, which is recorded as a static identity information set. Biometric information is also collected. When accessing the system, the static identity information is compared with the pre-stored information. If the comparison is successful, the biometric verification phase begins, and the collected biometric information is compared with the pre-stored biometric template for similarity calculation; And define a set of risk assessment indicators, assign weights to each risk assessment indicator, calculate the risk score for each visit, and dynamically adjust the verification process based on the risk score; Finally, a set of permission levels is defined, and a corresponding set of operation permissions is defined for each level. Based on the identity information, historical access records, and risk assessment score factors of consumers and brands, the initial permission level is assigned to them through the rule engine, and the user's risk assessment score is updated regularly.

3. A multi-brand marketing sharing system according to claim 2, characterized in that: The edge computing integral processing unit (2) comprises a cache pre-fetching module (21), and the specific method of the cache pre-fetching module (21) using cache and pre-fetching technology to process local integral data in real time is as follows: The cache space size is defined by the cache replacement algorithm based on the least recently used, the local integral data is sorted according to the integral operation time, and the integral data queue is constructed. When new integral data arrives, if the cache space is full, the least recently used data is removed and the new data is inserted into the queue head; It also analyzes historical integral operation patterns, defines a set of integral operation sequence patterns, and monitors the current integral operation in real time. If the current operation meets a pattern in the set of integral operation sequence patterns, it predicts subsequent integral operations based on the pattern and pre-fetches relevant integral data from the local database into the cache in advance.

4. A multi-brand marketing sharing system according to claim 3, characterized in that: The edge computing integral processing unit (2) comprises an integral data processing module (22), wherein the integral data processing module (22) receives an integral operation request authenticated by the security protection authorization unit (1), and a specific algorithm for processing integral data is as follows: The points operation requests are divided into three types: points increase, points decrease, and points query. For the points increase operation request, the points increase amount and user ID in the request are extracted, and the current points of the user in the local cache are queried. If the current points do not exist in the cache, they are obtained from the local database, and the new points value is calculated. At the same time, the points data in the local cache and database are updated; For a point reduction operation request, extract the point reduction amount and user ID in the request, query the current points of the user in the local cache, and if the current points are greater than or equal to the point reduction amount, calculate the new point value and update the point data in the local cache and database at the same time. Otherwise, return an error message indicating insufficient points. For points query operation requests, the user ID is extracted and the user's points data is queried from the local cache first. If the data exists in the cache, it is returned directly. If not, it is obtained from the local database and the cache is updated.

5. A multi-brand marketing sharing system according to claim 4, characterized in that: The points ledger smart contract unit (3) includes a rights and interests distribution module (31). When the rights and interests distribution module (31) constructs a consortium blockchain based on a proof-of-interest consensus mechanism as a points ledger, the specific algorithm for node selection and rights and interests distribution is as follows: Define a set of node qualification evaluation indicators, including the node's computing power, storage capacity, and network bandwidth, and assign weights to each indicator. For each candidate node, calculate its qualification score, and then compare it with the preset qualification score threshold. Only nodes with a qualification score greater than the qualification score threshold are eligible to participate in the alliance blockchain. The stake value of a node is defined to be proportional to the node's qualification score. In each consensus cycle, the probability of a node obtaining the right to record is determined based on the node's stake value.

6. A multi-brand marketing sharing system according to claim 5, characterized in that: The points ledger smart contract unit (3) includes an automatic execution module (32). The specific algorithm of the automatic execution module (32) for defining and automatically executing the multi-brand points exchange rules through the smart contract is as follows: Define a set of multi-brand points redemption rules. Each rule includes the points redemption ratio and the redemption brand combination. For each rule, set the effective time range. When a points redemption request is received, the source brand points, target brand and user ID in the request are extracted, the redemption rule set is traversed, and the rule set that contains the target brand and is effective at the current time is filtered out. For each rule in the rule set, the redeemable target brand points are calculated, and the rule with the most redeemable points is selected as the optimal rule. The points redemption operation is executed and the points ledger is updated.

7. A multi-brand marketing sharing system according to claim 6, characterized in that: When the points ledger smart contract unit (3) receives data from the edge computing points processing unit (2) to update the ledger, the specific algorithm for data consistency verification is as follows: For the integral data transmitted by the edge computing integral processing unit (2), divide it into multiple data blocks, calculate a hash value for each data block, combine these hash values ​​into a hash list, use the hash list to build a Merkle tree, and calculate the root hash value of the Merkle tree; On each node of the alliance blockchain, the root hash value of the Merkle tree when the ledger was last updated is stored. When new points data is received, the root hash value of the Merkle tree is recalculated and compared with the root hash value of the Merkle tree when the ledger was last updated. If the two are the same, the data is consistent and the ledger is updated. Otherwise, the update is rejected and the edge computing points processing unit (2) is required to resend the data.

8. The multi-brand marketing sharing system according to claim 7, characterized in that: When the points ledger smart contract unit (3) feeds back the processing result to the unified interaction unit (4), an encrypted transmission and message queue mechanism is adopted. The specific method is as follows: The processing result data is encrypted using a symmetric encryption algorithm to generate encrypted data, the key is encrypted using an asymmetric encryption algorithm to generate an encryption key, and the encrypted data and the encryption key are sent together to the unified interaction unit (4); A message queue is established between the points ledger smart contract unit (3) and the unified interaction unit (4). The points ledger smart contract unit (3) sends the encrypted processing result message to the sending end of the message queue. The unified interaction unit (4) receives the message from the receiving end of the message queue and processes the message according to the first-in-first-out principle. After receiving the message, the private key is first used to decrypt the encrypted key to obtain the key, and then the key is used to decrypt the encrypted data to obtain the processing result data.

9. A method for implementing a multi-brand marketing sharing system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Security protection authorization unit (1) performs multi-factor identity authentication and dynamic risk assessment on consumers and brands, allocates and adjusts operation permissions based on the principle of least privilege, and allows subsequent points operations after authentication; S2, the edge computing points processing unit (2) receives the points operation request that has passed the security authentication, processes the local points data in real time using the edge computing device, optimizes the processing process using the cache and pre-fetching technology, and transmits the processed points data and evaluation information to the points ledger smart contract unit (3); S3, the points ledger smart contract unit (3) receives data from the edge computing points processing unit (2), verifies and executes the points exchange rules through the smart contract, updates the blockchain points ledger, and feeds back the processing results to the unified interaction unit; S4, unified interaction unit (4) displays the consumer's points information and redemption results. Brands can manage and query relevant data through this module. The points redemption request initiated by the consumer is authenticated by the zero-trust security authentication module and then processed and displayed by the blockchain points ledger and smart contract module.

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