Data management method and system based on electronic commerce

Abstract

The invention discloses a data management method and system based on e-commerce, and relates to the technical field of data security management, and the method comprises the steps: carrying out the real-time monitoring of an e-commerce platform, and obtaining to-be-managed data; based on the to-be-managed data, calculating a use frequency score, an importance score and a privacy degree score of each data item; according to the use frequency score, the importance score and the privacy degree score of each data item, performing security level preliminary division on the data item by adopting a comprehensive evaluation algorithm to obtain a security level of the data item; and formulating and executing a corresponding security management strategy according to the security level of each data item. According to the invention, the effect and security of data management can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of data security management, and in particular to a data management method and system based on e-commerce. Background Art

[0002] In today's digital age, e-commerce has become an important part of the business field with its high efficiency and convenience. With the vigorous development of e-commerce, a large amount of data is constantly generated and accumulated. These data cover product information, transaction records, personal information and other aspects. For e-commerce companies, effectively managing these data is not only related to the normal operation of the company, but also involves the privacy protection of users and the security of business secrets.

[0003] At present, the existing e-commerce data management methods are often too simple in the quantitative evaluation of data characteristics. They only consider the basic attributes of the data, while ignoring the key factors of the business value and privacy of the data, resulting in inaccurate and incomplete division of security levels. Since the security level of the data divided by the evaluation is itself inaccurate, the security management strategies formulated and implemented according to its security level are often inaccurate and unreliable, which may cause high-value and high-risk data to not be adequately protected and low-value data to be overprotected, ultimately resulting in poor data management results and low security. Based on this, how to provide a new data management method to improve the effectiveness and security of data management has become a technical problem that needs to be solved in this field. Summary of the invention

[0004] The purpose of this application is to provide a data management method and system based on e-commerce, which can effectively improve the effect and security of data management.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a data management method based on e-commerce, and the data management method based on e-commerce includes the following steps.

[0007] The e-commerce platform is monitored in real time to obtain the data to be managed; the e-commerce platform is a platform for the target enterprise to conduct online transactions, and the data to be managed includes several data items in the e-commerce platform, and the data item is a data related to the online transaction.

[0008] Based on the data to be managed, the usage frequency score, importance score and privacy level score of each data item are calculated; wherein the usage frequency score is used to characterize the usage frequency of the data item, the importance score is used to characterize the impact of the data item on the transaction amount and commodity sales volume, and the privacy level score is used to characterize the privacy level of the sensitive information contained in the data item.

[0009] Based on the usage frequency score, the importance score and the privacy level score of each data item, a comprehensive evaluation algorithm is used to perform a preliminary classification of the security level of the data item to obtain the security level of the data item; the comprehensive evaluation algorithm refers to assigning corresponding weights to the usage frequency, importance and privacy level respectively, and combining the three factors of usage frequency, importance and privacy level to comprehensively evaluate the security level of the data item, and the security level includes high level, medium level and low level.

[0010] According to the security level of each of the data items, a corresponding security management strategy is formulated and implemented.

[0011] Optionally, after the step of formulating and executing a corresponding security management strategy according to the security level of each of the data items, the e-commerce-based data management method further includes the following steps.

[0012] The e-commerce platform is continuously monitored in real time, and the security level of each data item is updated in real time, and the security management strategy is automatically adjusted according to the updated security level of the data item.

[0013] Optionally, based on the data to be managed, a usage frequency score, an importance score and a privacy level score of each data item are calculated, which specifically includes the following steps.

[0014] According to the data access log corresponding to each of the data items in the data to be managed, the number of accesses and the number of updates of the data item within a certain time period are determined.

[0015] The usage frequency score of the data item is calculated based on the number of accesses and updates of the data item within a certain time period.

[0016] According to the transaction records corresponding to each of the data items in the data to be managed, the transaction amount proportion and the impact on commodity sales of the data item are determined; the impact on commodity sales refers to the degree of influence of the data item on commodity sales in the transaction records.

[0017] The importance score of the data item is calculated based on the transaction amount ratio and the product sales impact of the data item.

[0018] According to the user information table corresponding to each data item in the data to be managed, the number of types of sensitive information contained in the data item and the basic score of each type of sensitive information are determined.

[0019] The privacy level score of the data item is calculated based on the number of types of sensitive information included in the data item and the basic score of each type of sensitive information.

[0020] Optionally, the usage frequency score is calculated using the following formula.

[0021] .

[0022] in, represents the frequency score, and Represent the number of visits and updates respectively. and are the weights of the number of visits and the number of updates respectively, and They are the maximum number of accesses and the maximum number of updates among all data items, respectively.

[0023] Optionally, the importance score is calculated using the following formula.

[0024] .

[0025] in, represents the importance score, and They represent the proportion of transaction amount and the impact of product sales respectively. and They are the weight of the transaction amount ratio and the weight of the product sales volume influence, and They are respectively the maximum proportion of transaction amount and the maximum impact of product sales among all data items.

[0026] Optionally, the privacy level score is calculated using the following formula.

[0027] .

[0028] in, represents the privacy score, For the The weight of sensitive information, For the The basic score of sensitive information, The number of types of sensitive information contained in the data item.

[0029] Optionally, based on the usage frequency score, the importance score and the privacy level score of each data item, a comprehensive evaluation algorithm is used to perform a preliminary classification of the security level of the data item to obtain the security level of the data item, which specifically includes the following steps.

[0030] A comprehensive evaluation algorithm is used to calculate the comprehensive security score of the data item based on the usage frequency score, the importance score and the privacy level score of each data item.

[0031] According to the comprehensive security score and the security level threshold of the data item, the data item is preliminarily divided into security levels to obtain the security level of the data item.

[0032] Optionally, the comprehensive safety score is calculated using the following formula.

[0033] .

[0034] in, represents the comprehensive safety score, represents the frequency score, represents the importance score, represents the privacy score, , and They are the weight of usage frequency, the weight of importance and the weight of privacy degree respectively.

[0035] Optionally, the security management policy includes a high-level management policy, a medium-level management policy and a low-level management policy; wherein the high-level management policy is used to manage data items with a high security level, the medium-level management policy is used to manage data items with a medium security level, and the low-level management policy is used to manage data items with a low security level.

[0036] The high-level management strategy is an encryption and multi-level access control strategy, which specifically includes: limiting access rights to management personnel only, and performing regular data backup, archiving and security audits. The security audit includes a detailed review of data access records and modification records, and physical security protection of storage devices used to store data items with high security levels.

[0037] The intermediate-level management strategy is an encryption and access control strategy, which specifically includes: limiting access rights to ordinary staff, and monitoring access behavior and archiving data.

[0038] The low-level management strategy is a non-encryption and non-access control strategy, which specifically includes: not encrypting data items, setting access permissions to free access, and regularly cleaning up data and optimizing storage.

[0039] In a second aspect, the present application provides an e-commerce-based data management system, which includes the following functional modules.

[0040] The data monitoring and acquisition module is used to monitor the e-commerce platform in real time and acquire the data to be managed; the e-commerce platform is a platform for the target enterprise to conduct online transactions, and the data to be managed includes several data items in the e-commerce platform, and the data item is a data related to online transactions.

[0041] A multi-factor score calculation module is used to calculate the usage frequency score, importance score and privacy level score of each data item based on the data to be managed; wherein the usage frequency score is used to characterize the usage frequency of the data item; the importance score is used to characterize the impact of the data item on the transaction amount and commodity sales volume; and the privacy level score is used to characterize the privacy level of the sensitive information contained in the data item.

[0042] The security level assessment module is used to perform a preliminary security level classification of the data items according to the usage frequency score, the importance score and the privacy level score of each data item using a comprehensive assessment algorithm to obtain the security level of the data item; the comprehensive assessment algorithm refers to assigning corresponding weights to the usage frequency, importance and privacy level respectively, and combining the three factors of usage frequency, importance and privacy level to comprehensively assess the security level of the data item, and the security level includes high level, medium level and low level.

[0043] The security management policy formulation and execution module is used to formulate and execute corresponding security management policies according to the security level of each data item.

[0044] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0045] The present application provides a data management method and system based on e-commerce. Starting from the three dimensions of frequency of use, importance and privacy of data, the frequency of use, importance and privacy are combined. First, the frequency score, importance score and privacy score of the data are calculated to determine the scores of the data in terms of frequency of use, importance and privacy. Then, a comprehensive evaluation and security classification are performed based on the frequency score, importance score and privacy score of the data, so as to achieve accurate division of the security level of the data, thereby solving the problem that the existing e-commerce data management method is too simple in quantitative evaluation of data features and the division of security levels is not accurate and comprehensive enough. At the same time, on the basis of achieving accurate security level classification of data, the present application formulates and implements corresponding security management strategies according to the accurate security level of the data, thereby ensuring that high-value and high-risk data are fully protected, while avoiding excessive protection of low-value data and wasting resources, effectively improving the effect and security of data management, and providing a strong guarantee for the stable operation of e-commerce enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A flowchart of a data management method based on e-commerce provided in Example 1 of the present application.

[0048] Figure 2 A structural principle diagram of an e-commerce-based data management system provided in Example 2 of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] Example 1 like Figure 1 As shown, this embodiment proposes a data management method based on e-commerce, and the data management method based on e-commerce specifically includes the following steps.

[0051] Step S1: monitor the e-commerce platform in real time to obtain data to be managed.

[0052] In this embodiment, the e-commerce platform is a platform for the target enterprise to conduct online transactions. The e-commerce platform is provided with a database, and the database stores the data to be managed. The data to be managed includes several data items in the e-commerce platform, and the data item refers to a data item related to online transactions. Specifically, the data to be managed may include data access logs, commodity information, transaction records, user information tables including user personal information, etc. of the e-commerce platform. User personal information may include contact information such as name, address, ID card number, mobile phone number, as well as savings card number, credit card number, and bank account number, etc.

[0053] Step S2: Based on the data to be managed, the frequency score, importance score and privacy score of each data item are calculated. The frequency score is used to characterize the frequency of use of the data item; the importance score is used to characterize the impact of the data item on the transaction amount and product sales; and the privacy score is used to characterize the privacy level of the sensitive information contained in the data item.

[0054] In this embodiment, step S2 calculates the usage frequency score, importance score and privacy level score of each data item based on the data to be managed, and specifically includes the following steps.

[0055] Step S21: Determine the number of accesses and updates of each data item in a certain time period according to the data access log corresponding to each data item in the data to be managed.

[0056] Step S22: Calculate the usage frequency score of the data item according to the number of accesses and updates of the data item within a certain period of time.

[0057] In this embodiment, the usage frequency score is calculated using the following formula.

[0058] .

[0059] in, represents the frequency score, and Represent the number of visits and updates respectively. and are the weights of the number of visits and the number of updates respectively, and They are the maximum number of accesses and the maximum number of updates among all data items, respectively.

[0060] Step S23: Determine the transaction amount proportion and product sales impact of the data item according to the transaction record corresponding to each data item in the data to be managed; the product sales impact refers to the degree of influence of the data item in the transaction record on the product sales.

[0061] Step S24: Calculate the importance score of the data item based on the transaction amount ratio and the product sales impact of the data item.

[0062] In this embodiment, the importance score is calculated using the following formula.

[0063] .

[0064] in, represents the importance score, and They represent the proportion of transaction amount and the impact of product sales respectively. and They are the weight of the transaction amount ratio and the weight of the product sales volume influence, and They are respectively the maximum proportion of transaction amount and the maximum impact of product sales among all data items.

[0065] Step S25: According to the user information table corresponding to each data item in the data to be managed, determine the number of types of sensitive information contained in the data item and the basic score of each type of sensitive information.

[0066] Step S26: Calculate the privacy score of the data item based on the number of types of sensitive information contained in the data item and the basic score of each type of sensitive information.

[0067] In this embodiment, the privacy level score is calculated using the following formula.

[0068] .

[0069] in, represents the privacy score, For the The weight of sensitive information, For the The basic score of sensitive information, The number of types of sensitive information contained in the data item.

[0070] Step S3: Based on the frequency score, importance score and privacy score of each data item, a comprehensive evaluation algorithm is used to preliminarily classify the security level of the data item to obtain the security level of the data item. The comprehensive evaluation algorithm refers to assigning corresponding weights to the frequency of use, importance and privacy level, and comprehensively evaluating the security level of the data item by combining the three factors of frequency of use, importance and privacy level. The security level includes high level, medium level and low level.

[0071] In this embodiment, step S3 uses a comprehensive evaluation algorithm to preliminarily classify the security level of the data items according to the usage frequency score, importance score and privacy score of each data item to obtain the security level of the data item, which specifically includes the following steps.

[0072] Step S31: Based on the usage frequency score, importance score and privacy score of each data item, a comprehensive evaluation algorithm is used to calculate the comprehensive security score of the data item.

[0073] In this embodiment, the expression of the comprehensive evaluation algorithm is as follows.

[0074] .

[0075] in, represents the comprehensive safety score, represents the frequency score, represents the importance score, represents the privacy score, , and They are the weight of usage frequency, the weight of importance and the weight of privacy degree respectively.

[0076] Step S32: Perform a preliminary security level classification on the data item based on the comprehensive security score and security level threshold of the data item to obtain the security level of the data item.

[0077] In this embodiment, the security level threshold includes a high level threshold , medium level threshold and low level threshold ,satisfy , based on the comprehensive security score of the data item Based on the above security level thresholds, the data items are initially divided into security levels, which specifically include the following three situations.

[0078] (1) When , the security level of the data item is classified as high.

[0079] (2) When , the security level of the data item is classified as medium.

[0080] (3) When , the security level of the data item is classified as low.

[0081] Step S4: formulate and implement corresponding security management strategies according to the security level of each data item.

[0082] In this embodiment, the security management policy includes a high-level management policy, a medium-level management policy and a low-level management policy; wherein the high-level management policy is used to manage data items with a high security level, the medium-level management policy is used to manage data items with a medium security level, and the low-level management policy is used to manage data items with a low security level.

[0083] Among them, the high-level management strategy is encryption and multi-level access control strategy, which specifically includes: limiting access rights to management personnel only, and conducting regular data backup, archiving and security audits. Security audits include detailed reviews of data access records and modification records, and physical security protection of storage devices used to store data items with high security levels.

[0084] The mid-level management strategy is encryption and access control strategy, which specifically includes: limiting access rights to ordinary staff, monitoring access behavior, and archiving data, among other management measures.

[0085] The low-level management strategy is a non-encryption and non-access control strategy, which specifically includes: not encrypting data items, setting access permissions to free access, and regularly performing data cleanup and storage optimization and other management measures.

[0086] This embodiment takes into account that the existing e-commerce data management methods lack flexibility and intelligence in the formulation and implementation of security management strategies, and often use fixed strategy templates, which cannot be dynamically adjusted and optimized according to the security level of data and business needs, resulting in poor data management effects. Based on this, in this embodiment, after step 4, the e-commerce-based data management method further includes the following steps.

[0087] Step S5: Continuously monitor the e-commerce platform in real time, and update the security level of each data item in real time, and automatically adjust the security management strategy according to the updated security level of the data item, thereby achieving real-time update and dynamic adjustment of the data security level and security management strategy.

[0088] In this embodiment, by continuously monitoring the changes in the usage frequency, importance and privacy level of each data item, the usage frequency score, importance score and privacy level score of each data item are regularly recalculated, and the comprehensive security score of each data item is recalculated using a comprehensive evaluation algorithm based on the updated usage frequency score, importance score and privacy level score. , thereby achieving real-time update of the comprehensive safety score. Then, compare the current (i.e. updated) comprehensive safety score Compared with the previous (pre-update) comprehensive safety score , and determine the security level adjustment threshold Specifically, when When , it means that the comprehensive safety score has changed significantly, the safety level will be reclassified according to the current comprehensive safety score, and the corresponding safety management strategy will be updated. , it means that the comprehensive safety score has not changed significantly. At this time, there is no need to re-classify the safety level and adjust the safety management strategy.

[0089] This embodiment takes into account that a large amount of data will be generated due to continuous real-time monitoring of the e-commerce platform. Based on this, this embodiment is also provided with a storage optimization mechanism, that is, for data with low level, low sensitivity and low comprehensive security score, expired data is regularly cleaned up to release storage space, and unaccessed data is archived and compressed storage is used.

[0090] This embodiment introduces a continuous data monitoring and dynamic adjustment mechanism, which timely adjusts the data security level and management strategy according to the changes in e-commerce business and new security threats, and effectively responds to various complex situations. At the same time, the scientific and reasonable security level assessment and dynamic adjustment mechanism make data management more intelligent and automated, reduce the cost and risk of manual intervention, and create good conditions for the sustainable development of e-commerce.

[0091] This embodiment realizes dynamic division and intelligent management of data security levels by comprehensively considering multiple dimensional characteristics of data, including frequency of use, importance and privacy, and can more accurately reflect the actual security needs and business value of data, thereby improving the accuracy and intelligence of data management, and improving the effect and security of data management. At the same time, by dynamically adjusting the security management strategy, it can adapt to the ever-changing security needs and business environment, further improving the security of data management, and the data security is higher, the data management effect is better, and it can provide more flexible and effective protection for the data security of e-commerce platforms.

[0092] In order to make the technical solution of this embodiment clearer, the following is a detailed description of the specific implementation steps of applying the data management method based on e-commerce in a large-scale comprehensive e-commerce platform scenario in the form of examples. Through multi-dimensional evaluation and dynamic hierarchical management of data, the security and efficiency of data management are effectively improved. Specifically, the following steps are included.

[0093] The e-commerce platform in this embodiment takes a large-scale comprehensive e-commerce platform as an example to demonstrate the process of applying an e-commerce-based data management method in a large-scale comprehensive e-commerce platform scenario. This method effectively improves the security and efficiency of data management through multi-dimensional evaluation and dynamic hierarchical management of data.

[0094] In the specific implementation, first, large-scale comprehensive e-commerce platforms use advanced data collection technology to monitor various types of data in large-scale comprehensive e-commerce platforms in real time, including product information, transaction record information, and personal information. Intelligent analysis algorithms can be used to conduct in-depth analysis of the monitored data features, such as analyzing the relationship between the update frequency of product data and user access patterns, and extracting relevant feature information of each data item from the database of the e-commerce platform, including data access logs, transaction records, user information tables, etc. For each data item , count the number of visits within time t and update times , calculate the data item Frequency score ,Right now ,in, and are the weights of the number of visits and the number of updates respectively, and They are the maximum number of accesses and the maximum number of updates among all data items, respectively.

[0095] For example, assuming the weight of the number of visits is , the weight of the number of updates , calculate the data item Frequency score of , that is, the number of visits to the transaction data of a popular product within a week The maximum number of accesses among all data items. times, number of updates times, the maximum number of updates among all data items The final calculated usage frequency score is .

[0096] Then, for each data item , determine the impact on the business and calculate the proportion of transaction amount and product sales impact , assuming that the weight of the transaction amount is , the weight of the product sales impact , calculate the data item Importance score ,Right now For example, the transaction amount of a high-value commodity accounts for , the maximum value of the transaction amount ratio among all data items , product sales impact , the maximum value of the product sales impact among all data items , then the final importance score calculated is .

[0097] Then, for each data item , comprehensively consider the importance of data to personal identity identification, privacy protection, and business security, determine the sensitivity level of the sensitive information it contains, and set the basic score of the ID card number to , by considering the degree of privacy and combining historical data and the impact on the overall privacy level. , weight , the base score of the contact is set to , weight , for data items containing ID card numbers and contact information, calculate their privacy scores ,Right now Then, using the frequency score, importance score and privacy score, a comprehensive evaluation algorithm is used to preliminarily classify the security level of each data item. Frequency score , Importance score and privacy score , respectively, the frequency scores , Importance score and privacy score Assign corresponding weights, which are , and , we can also use different weight combinations to divide and manage the data security level according to the characteristics of different data, and compare the data management effects under different combinations to determine the proportion of the initial weights allocated above in the comprehensive assessment of the data security level. Then calculate the data item Overall safety score ,Right now ,For example , importance weight , privacy degree weight , calculate the data item Overall safety score , .

[0098] After calculating the comprehensive security score, this embodiment can perform a preliminary classification of the security level of the data item according to the comprehensive security score and the security level threshold, thereby obtaining the security level of the data item. First, three security level thresholds are defined, namely, the high level threshold , medium level threshold and low level threshold , due to the comprehensive safety score , the security level of the data item is classified as high. For data with high security level, such as the above-mentioned popular commodity transaction data and data containing personal sensitive information of users, encryption and multi-level access control strategies are adopted to encrypt the data, limit access rights to management personnel only, and conduct regular data backup, archiving and security audits. The security audit includes a detailed review of the access records and modification records of the data, and physical security protection is performed on the devices storing data with high security level. For example, the server is placed in a safe computer room environment and equipped with fire prevention, waterproofing, anti-theft and other facilities.

[0099] Finally, we continuously monitor the changes in the usage frequency, importance, and privacy level of each data item. For example, as a product promotion ends, the number of accesses and updates to its transaction data decreases. We periodically recalculate the usage frequency score, importance score, and privacy level score of each data item. Assuming that after a period of time, the number of accesses to the product transaction data increases. times, number of updates times, recalculate the usage frequency score ; The importance score remains unchanged ; The privacy score remains unchanged , recalculate the comprehensive safety score Assume that the security level adjustment threshold , compare the current overall safety score Compared with the previous comprehensive safety score , , then the security level remains unchanged. If the score changes significantly, that is, , the security level will be re-divided according to the current comprehensive security score, and the corresponding security management strategy will be updated. For data with low level, low sensitivity and low comprehensive security score, expired data will be cleaned up regularly to release storage space, and unaccessed data will be archived and compressed storage will be used.

[0100] This embodiment achieves scientific hierarchical management of data by applying an e-commerce-based data management method on a large-scale comprehensive e-commerce platform, reasonably allocates security resources according to the actual situation of the data, improves the security and efficiency of data management, and at the same time, through a dynamic adjustment mechanism, can adapt to changes in platform business, thereby ensuring data security and stable operation of the platform.

[0101] In an exemplary embodiment, the e-commerce platform takes a cross-border e-commerce platform as an example and provides a detailed process of applying an e-commerce-based data management method in a cross-border e-commerce platform scenario. This method effectively solves the complex problems faced by cross-border e-commerce data management through characteristic analysis of different types of data and dynamic security classification management, thereby improving data security and management efficiency.

[0102] In the specific implementation, during the data monitoring and acquisition stage, the data of cross-border e-commerce platforms are diverse and complex, including commodity information, transaction records from different regions, and various data related to key businesses such as customs declaration and cross-border payment. Through advanced data collection technology, these massive data are monitored in real time. For example, the update frequency of commodity information is monitored, including price changes and inventory adjustments; at the same time, transaction records are analyzed in detail, such as transaction time, transaction amount, and transaction location. Through intelligent analysis algorithms, data features are deeply mined, such as analyzing the preference trends of users in different regions for specific commodities, and the changing patterns of transaction activity in different time periods.

[0103] In the multi-factor score calculation stage, in terms of calculating the frequency of use score: considering the business characteristics of cross-border e-commerce platforms, the frequency of use of certain data has obvious regional and business differences. For example, transaction records and related logistics information involving popular cross-border commodities may be used very frequently in specific regions or specific business links, because these data are crucial for order processing and logistics distribution. However, some commodity introduction information for specific niche markets may be used less frequently. The frequency of use score is comprehensively evaluated by statistically analyzing indicators such as the number of visits and updates in different business processes and regions. In terms of calculating the importance score: for cross-border e-commerce data, the importance of its evaluation needs to consider multiple factors. Payment information and customs declaration data for cross-border transactions are undoubtedly crucial because they are directly related to the legality of transactions and financial security. Any errors or leaks in these data may lead to serious consequences. At the same time, for some commodity information with core competitiveness, such as detailed introductions of exclusive brands or featured products, their importance is also high because they play a key role in attracting customers and promoting sales. However, some ordinary commodity descriptions or user browsing history are relatively less important. In terms of calculating the privacy score: cross-border e-commerce involves users in different regions, and privacy management varies greatly. The user's personal information, such as name, address, and contact information, has a high degree of privacy. In particular, credit card numbers and bank account numbers that involve payment information are highly sensitive. For data items containing such sensitive information, the privacy level is assessed to be high, and the calculated privacy score will also be high. However, some general user browsing preference data has a relatively low degree of privacy.

[0104] In the security level classification stage, taking into account factors such as frequency of use, importance and privacy, a reasonable comprehensive evaluation algorithm can be used to preliminarily classify the security level of each data item according to the actual situation. For example, cross-border payment information and customs declaration data are classified as high-level due to their high frequency of use (frequent use in transaction processing), high importance (involving transaction security and compliance) and high privacy (including sensitive personal information, credit card numbers and bank account numbers, etc.). The historical browsing records of some ordinary commodities are classified as low-level due to their low frequency of use, relatively low importance and low privacy. For some data in an intermediate state, such as non-critical attribute information of some popular cross-border commodities (such as optional configuration information such as color and size), they are classified as medium-level according to their comprehensive evaluation.

[0105] During the security management strategy formulation and implementation phase, advanced encryption technology can be used for high-level data, such as cross-border payment information and customs declaration data, to ensure the absolute security of data during transmission and storage. For example, use multi-layer encryption algorithms to perform multiple high-intensity encryption on data to prevent data from being stolen or tampered with during cross-border transmission. Set up strict access control policies and adopt specific personnel access control methods to limit access rights to specific personnel such as senior management and core business department personnel, as well as personnel in the finance department responsible for payment processing and personnel in the customs compliance department. At the same time, real-time monitoring and auditing of access behaviors are carried out to record detailed information such as the time, personnel, and operation content of each access. Once abnormal access behavior is found, the alarm mechanism is immediately triggered and corresponding measures are taken, such as suspending access rights and conducting in-depth investigations. Data is backed up regularly and multiple backup mechanisms are adopted, including local backup and off-site backup, to ensure that data will not be lost in the event of unexpected situations (such as server failures, natural disasters, etc.). At the same time, strict physical security protection is carried out on equipment that stores high-level data, and the server is placed in a computer room environment with a high security level, equipped with complete safety facilities such as fire prevention, waterproofing, anti-theft, and lightning protection. For medium-level data, such as non-critical attribute information of popular cross-border commodities, encryption algorithms of appropriate strength can be used to ensure basic data security. For example, symmetric encryption algorithms can be used to encrypt data. Access rights are limited to ordinary staff of relevant business departments, such as ordinary staff of commodity management departments. Access behavior is monitored to detect abnormal situations in a timely manner and deal with them. For example, if an ordinary staff member is found to frequently access data that is not related to his or her duties, inquiries and investigations are conducted in a timely manner. Regularly perform integrity checks and security assessments on data to ensure data security and availability. For example, regularly check whether the data has been tampered with and evaluate whether the current security management policy is still applicable. For low-level data, such as historical browsing records of ordinary commodities, encryption processing is not performed to save system resources, but access rights are set to free access and access logs are recorded. Access logs record information such as the time and IP address of each user's access to these data so that access behavior can be traced back when necessary. Regularly perform data cleanup, delete expired data, and release storage space. For example, data that has not been accessed for more than a preset time (such as 1 year) is deleted from the storage system. At the same time, the storage system is optimized and technologies such as compressed storage are used to improve storage efficiency.

[0106] During the dynamic adjustment stage of data security level, the business environment of cross-border e-commerce platforms is constantly changing, and the characteristics of data will also change accordingly. For example, as market competition intensifies, some originally less important commodity information may become the focus of competition and be used more frequently. Therefore, it is necessary to continuously monitor the changes in data, and when it is found that the frequency of use, importance or privacy of data has changed significantly, its security level and corresponding security management strategy should be adjusted in time. For example, if the sales data of a certain type of commodity has been used more frequently due to marketing activities, its security level will be adjusted from low to medium after calculation, and accordingly, encryption algorithms will be used to protect it, and access rights will be restricted to personnel in relevant business departments. At the same time, if a region has higher requirements for the protection of user personal information, for data items containing sensitive information of relevant individuals, its security level should be adjusted to a higher level in a timely manner, and security measures should be strengthened, such as adopting higher-level encryption technology and stricter access control strategies.

[0107] In summary, this embodiment applies an e-commerce-based data management method to the cross-border e-commerce platform, taking into full account the diversity and complexity of data and the special needs of cross-border business. Through dynamic security hierarchical management and reasonable allocation of security resources, the high security of key data is ensured, while the overall efficiency of data management is improved, and it can flexibly adapt to changes in the cross-border e-commerce business environment, effectively ensuring the security of data and the stable operation of the platform, and providing strong data management support for cross-border e-commerce enterprises in a complex market environment.

[0108] This embodiment monitors various types of data in the e-commerce platform in real time, conducts in-depth analysis of the monitored data features, determines the specific performance of each data item in terms of usage frequency, importance and privacy, and calculates the usage frequency score, importance score and privacy score respectively, which can achieve precise and efficient data security management and improve the security and effectiveness of data management. By dynamically grading security based on the real-time usage frequency, importance and privacy factors of the data, it ensures that high-value and high-risk data are fully protected, while avoiding excessive protection of low-value data and wasting resources. This not only improves the security and effectiveness of data management, but also improves the utilization efficiency of system resources, providing a strong guarantee for the stable operation of e-commerce enterprises.

[0109] Example 2 like Figure 2 As shown, this embodiment proposes an e-commerce-based data management system, which corresponds to the e-commerce-based data management method in Embodiment 1, and specifically includes the following functional modules.

[0110] The data monitoring and acquisition module is used to monitor the e-commerce platform in real time and obtain the data to be managed; the e-commerce platform is a platform for the target enterprise to conduct online transactions, and the data to be managed includes several data items in the e-commerce platform, and a data item is a data related to online transactions.

[0111] The multi-factor score calculation module is used to calculate the usage frequency score, importance score and privacy score of each data item based on the data to be managed; among them, the usage frequency score is used to characterize the usage frequency of the data item, the importance score is used to characterize the impact of the data item on the transaction amount and commodity sales volume, and the privacy score is used to characterize the privacy level of the sensitive information contained in the data item.

[0112] The security level assessment module is used to perform a preliminary classification of the security levels of data items based on the usage frequency score, importance score and privacy score of each data item, using a comprehensive assessment algorithm to obtain the security level of the data item; the comprehensive assessment algorithm refers to assigning corresponding weights to the usage frequency, importance and privacy level respectively, and combining the three factors of usage frequency, importance and privacy level to comprehensively assess the security level of the data item. The security levels include high, medium and low.

[0113] The security management strategy formulation and execution module is used to formulate and execute corresponding security management strategies according to the security level of each data item.

[0114] The data security level dynamic adjustment module is used to continuously monitor the e-commerce platform in real time, update the security level of each data item in real time, and automatically adjust the security management strategy according to the updated security level of the data item.

[0115] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A data management method based on e-commerce, characterized in that: The data management method based on e-commerce comprises: Performing real-time monitoring on the e-commerce platform to obtain the data to be managed; the e-commerce platform is a platform for the target enterprise to conduct online transactions, and the data to be managed includes several data items in the e-commerce platform, and the data item is a data item related to the online transaction; Based on the data to be managed, the frequency score, importance score and privacy score of each data item are calculated; wherein the frequency score is used to characterize the frequency of use of the data item, the importance score is used to characterize the degree of influence of the data item on the transaction amount and the sales volume of the product, and the privacy score is used to characterize the degree of privacy of the sensitive information contained in the data item; According to the frequency score, importance score and privacy score of each data item, a comprehensive evaluation algorithm is used to preliminarily classify the security level of the data item to obtain the security level of the data item; the comprehensive evaluation algorithm refers to assigning corresponding weights to the frequency of use, importance and privacy level respectively, and comprehensively evaluating the security level of the data item by combining the three factors of frequency of use, importance and privacy level, and the security level includes high level, medium level and low level; According to the security level of each of the data items, a corresponding security management strategy is formulated and implemented.

2. The data management method based on e-commerce according to claim 1, characterized in that: After the step of formulating and executing a corresponding security management strategy according to the security level of each data item, the e-commerce-based data management method further comprises: The e-commerce platform is continuously monitored in real time, and the security level of each data item is updated in real time, and the security management strategy is automatically adjusted according to the updated security level of the data item.

3. The data management method based on e-commerce according to claim 1, characterized in that: Based on the data to be managed, the frequency of use score, importance score and privacy score of each data item are calculated, specifically including: Determine the number of accesses and updates of each data item in the data to be managed within a certain time period according to the data access log corresponding to the data item; Calculate the usage frequency score of the data item according to the number of accesses and updates of the data item within a certain period of time; According to the transaction records corresponding to each of the data items in the data to be managed, determining the transaction amount ratio and the impact on commodity sales of the data item; the impact on commodity sales refers to the degree of influence of the data item on commodity sales in the transaction records; The importance score of the data item is calculated based on the transaction amount ratio and the product sales impact of the data item; Determine the number of types of sensitive information contained in the data item and the basic score of each type of sensitive information according to the user information table corresponding to each data item in the data to be managed; The privacy level score of the data item is calculated based on the number of types of sensitive information included in the data item and the basic score of each type of sensitive information.

4. The data management method based on e-commerce according to claim 3 is characterized in that: The frequency of use score is calculated using the following formula: ； in, represents the frequency score, and Represent the number of visits and updates respectively. and are the weights of the number of visits and the number of updates respectively, and They are the maximum number of accesses and the maximum number of updates among all data items, respectively.

5. The data management method based on e-commerce according to claim 3 is characterized in that: The importance score is calculated using the following formula: ； in, represents the importance score, and They represent the proportion of transaction amount and the impact of product sales respectively. and They are the weight of the transaction amount ratio and the weight of the product sales volume influence, and They are respectively the maximum proportion of transaction amount and the maximum impact of product sales among all data items.

6. The data management method based on e-commerce according to claim 3 is characterized in that: The privacy score is calculated using the following formula: ； in, represents the privacy score, For the The weight of sensitive information, For the The basic score of sensitive information. The number of types of sensitive information contained in the data item.

7. The data management method based on e-commerce according to claim 1, characterized in that: According to the usage frequency score, the importance score and the privacy score of each data item, a comprehensive evaluation algorithm is used to perform a preliminary security level classification on the data item to obtain the security level of the data item, specifically including: According to the usage frequency score, the importance score and the privacy degree score of each data item, a comprehensive evaluation algorithm is used to calculate a comprehensive security score of the data item; According to the comprehensive security score and the security level threshold of the data item, the data item is preliminarily divided into security levels to obtain the security level of the data item.

8. The data management method based on e-commerce according to claim 7, characterized in that: The comprehensive safety score is calculated using the following formula: ； in, represents the comprehensive safety score, represents the frequency score, represents the importance score, represents the privacy score, , and They are the weight of usage frequency, the weight of importance and the weight of privacy degree respectively.

9. The data management method based on e-commerce according to claim 1, characterized in that: The security management policy includes a high-level management policy, a medium-level management policy and a low-level management policy; wherein the high-level management policy is used to manage data items with a high security level, the medium-level management policy is used to manage data items with a medium security level, and the low-level management policy is used to manage data items with a low security level; The high-level management strategy is an encryption and multi-level access control strategy, which specifically includes: restricting access rights to only management personnel, and performing regular data backup, archiving and security audits. The security audit includes a detailed review of data access records and modification records, and physical security protection of storage devices used to store data items with a high security level; The intermediate management strategy is encryption and access control strategy, which specifically includes: limiting access rights to ordinary staff, and monitoring access behavior and archiving data; The low-level management strategy is a non-encryption and non-access control strategy, which specifically includes: not encrypting data items, setting access permissions to free access, and regularly cleaning up data and optimizing storage.

10. A data management system based on e-commerce, characterized in that: The data management system based on e-commerce includes: The data monitoring and acquisition module is used to monitor the e-commerce platform in real time and acquire the data to be managed; the e-commerce platform is a platform for the target enterprise to conduct online transactions, and the data to be managed includes several data items in the e-commerce platform, and the data item is a data item related to the online transaction; A multi-factor score calculation module, used to calculate the usage frequency score, importance score and privacy score of each data item based on the data to be managed; wherein the usage frequency score is used to characterize the usage frequency of the data item, the importance score is used to characterize the degree of influence of the data item on the transaction amount and the sales volume of the commodity, and the privacy score is used to characterize the privacy degree of the sensitive information contained in the data item; A security level assessment module, for performing a preliminary security level classification of the data items according to the frequency score, importance score and privacy score of each data item by using a comprehensive assessment algorithm to obtain a security level of the data item; the comprehensive assessment algorithm is to assign corresponding weights to the frequency of use, importance and privacy level respectively, and to comprehensively assess the security level of the data item by combining the three factors of frequency of use, importance and privacy level, wherein the security level includes high level, medium level and low level; The security management policy formulation and execution module is used to formulate and execute corresponding security management policies according to the security level of each data item.

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