Cross-border e-commerce enterprise portrait data management method, system, device and medium

Abstract

The invention discloses a cross-border e-commerce enterprise portrait data management method, system and device and a medium. The method comprises the following steps: carrying out periodic state capture on a business system, and obtaining business data of an enterprise in different regions and different periods; performing real-time calculation on the business data, extracting change trends of key business indexes of different periods in different areas, and detecting similarity; and classifying the regions and periods which reach the preset similar change trend into a cross-region collaborative optimization group, and triggering a cross-region strategy collaboration mechanism to perform frequent co-occurrence point mining on service data in the cross-region collaborative optimization group so as to perform cross-region frequent co-occurrence point service sharing. According to the cross-regional co-occurrence point business information sharing method, the key data of the cross-border business can be obtained by capturing the operation states of the cross-border enterprise in different countries and regions, the dynamic enterprise portrait is formed, and the cross-regional collaborative optimization group is constructed based on the similar change trend of the key business indexes, so that the cross-regional co-occurrence point business information sharing among the regions is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, device and medium for managing cross-border e-commerce enterprise portrait data. Background Art

[0002] When cross-border e-commerce companies conduct global operations, they face the technical challenge of how to fully and in real time grasp the business operation status of different countries and regions. Due to the large differences in user preferences, consumption habits, and cultural backgrounds in various markets, companies need to adjust their operation strategies accordingly. However, traditional business data collection and analysis methods are difficult to meet the needs of cross-border e-commerce companies. The timeliness and accuracy of business data cannot be guaranteed, making it difficult for companies to build their own corporate portraits.

[0003] Cross-border e-commerce companies urgently need a technical means to capture and dynamically present business operation status in real time. This technology needs to have cross-platform and cross-terminal data collection capabilities, automatically identify and extract key business data, and process and analyze the data in real time to form a dynamic corporate portrait, thereby providing real-time support for the company's business operations in different regions. Summary of the invention

[0004] The purpose of the present invention is to provide a cross-border e-commerce enterprise portrait data management method, system, equipment and medium, aiming to solve the problem of how to accurately analyze the business data of cross-border e-commerce and construct and manage enterprise portraits.

[0005] In a first aspect, an embodiment of the present invention provides a method for managing cross-border e-commerce enterprise portrait data, including: Capture the business system's status periodically to obtain the business data of enterprises in different regions and periods; Classify the business data, and dynamically present and store them according to the classification; Based on the time series analysis algorithm, the business data is calculated in real time to extract the change trends of key business indicators in different regions and periods and generate corresponding dynamic business status reports; Detect the similarity of the change trends of key business indicators in dynamic business status reports of different regions and periods; Based on the similarity detection results, the regions and periods that reach the preset similar change trends are identified and classified into the same cross-region collaborative optimization group, and the cross-region strategy collaboration mechanism is triggered to automatically generate collaborative tasks; The association rule mining algorithm is used to mine the frequent co-occurrence points of the business data in the same cross-regional collaborative optimization group, so as to share the frequent co-occurrence points across regions.

[0006] In a second aspect, an embodiment of the present invention provides a cross-border e-commerce enterprise portrait data management system, including: The data acquisition unit is used to periodically capture the status of the business system and obtain the business data of the enterprise in different regions and periods; A data classification unit, used to classify the business data, and dynamically present and store it according to the classification; A report generation unit, used to perform real-time calculations on the business data based on a time series analysis algorithm, extract the change trends of key business indicators in different regions and periods, and generate corresponding dynamic business status reports; A similarity calculation unit, used to detect the similarity of the change trends of key business indicators in the dynamic business status reports of different regions and different periods; The collaborative optimization unit is used to identify the regions and periods that have reached a preset similar change trend based on the similarity detection results and classify them into the same cross-region collaborative optimization group, and trigger the cross-region strategy collaborative mechanism to automatically generate collaborative tasks; The co-occurrence point mining unit is used to mine frequent co-occurrence points of business data in the same cross-region collaborative optimization group using an association rule mining algorithm, so as to share frequent co-occurrence point business across regions.

[0007] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the cross-border e-commerce enterprise portrait data management method described in the first aspect is implemented.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor enables the processor to execute the cross-border e-commerce enterprise portrait data management method described in the first aspect above.

[0009] The beneficial effects of the embodiments of the present invention are: The present invention can periodically capture the operating status of cross-border enterprises in different countries and regions, quickly obtain key data of cross-border business, and form a dynamic corporate portrait, which is helpful to understand the core competitiveness and market performance of the enterprise and provide a reference for investment decisions, cooperation choices or market analysis.

[0010] The present invention constructs a cross-regional collaborative optimization group based on similar change trends of key business indicators in different regions and different periods, so as to realize cross-regional business information sharing between regions with similar key businesses, thereby optimizing resource allocation, improving efficiency, and reducing operating costs on a global scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of a process for managing cross-border e-commerce enterprise portrait data provided by an embodiment of the present invention; Figure 2 A schematic diagram of a sub-process of step S101 provided in an embodiment of the present invention; Figure 3 A schematic diagram of a sub-process of step S102 provided in an embodiment of the present invention; Figure 4 A schematic diagram of a sub-process of step S103 provided in an embodiment of the present invention; Figure 5 A schematic diagram of a sub-process of step S104 provided in an embodiment of the present invention; Figure 6 A schematic diagram of a sub-process of step S105 provided in an embodiment of the present invention; Figure 7 A schematic diagram of a sub-process of step S106 provided in an embodiment of the present invention; Figure 8 A schematic block diagram of a cross-border e-commerce enterprise portrait data management system provided by an embodiment of the present invention; Fig. 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] 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 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.

[0014] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0016] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0017] See also Figure 1 , Figure 1 A schematic diagram of a process for managing cross-border e-commerce enterprise portrait data provided by an embodiment of the present invention; like Figure 1 As shown, the method includes steps S101-S106.

[0018] S101. Capture the business system's status periodically to obtain the business data of the enterprise in different regions and periods; This step aims to collect and organize the business data generated in different regions and periods, so as to obtain the comprehensive business development status of cross-border e-commerce companies in the world.

[0019] S102, classify and process the business data, and dynamically present and store it according to the classification; In this step, the business data is carefully classified to ensure that each data type can be managed in a targeted manner according to its characteristics and needs. Then, based on these classifications, the data is dynamically displayed so that users can quickly find the required information based on different business scenarios and needs. In addition, the classified data needs to be stored effectively to ensure data security and accessibility, while facilitating future retrieval and analysis.

[0020] S103, performing real-time calculations on business data based on a time series analysis algorithm, extracting the changing trends of key business indicators in different regions and periods, and generating corresponding dynamic business status reports; In this step, the time series analysis algorithm is used to deeply calculate the changing trends of key business indicators in different regions and different periods, and a detailed business status report can be dynamically generated to reflect the current status of the business in a timely manner.

[0021] S104, detecting similarities of change trends of key business indicators in dynamic business status reports of different regions and different periods; S105. According to the similarity detection result, the regions and periods reaching the preset similar change trend are identified and classified into the same cross-region collaborative optimization group, and the cross-region strategy collaborative mechanism is triggered to automatically generate collaborative tasks; S106, using an association rule mining algorithm to mine frequent co-occurrence points of business data in the same cross-region collaborative optimization group, so as to share cross-region frequent co-occurrence point business; Steps S104-S106 are intended to find out different regions and different periods with similar change trends of key business indicators, and classify them into the same cross-region collaborative optimization group for cross-region collaborative processing to achieve more efficient business management and optimization.

[0022] In this embodiment, by periodically capturing the operating status of cross-border enterprises in different countries and regions, key data of cross-border business can be quickly obtained to form a dynamic corporate portrait, which helps to understand the core competitiveness and market performance of the enterprise and provide a reference for investment decisions, cooperation choices or market analysis.

[0023] In this embodiment, a cross-regional collaborative optimization group is constructed based on similar change trends of key business indicators in different regions and different periods to achieve cross-regional business information sharing between regions with similar key businesses, thereby optimizing resource allocation, improving efficiency, and reducing operating costs on a global scale.

[0024] In one embodiment, if Figure 1 As shown, step S101 includes: S201, connecting with the business system through a pre-configured data collection interface; S202. Regularly query the global data of each region from the business system according to the preset collection tasks, use SQL statements to perform preliminary filtering and extract corresponding fields including at least business model, product category, market positioning, operation strategy, revenue and profit, and business rules; S203. Clean and standardize the corresponding extracted fields to obtain the business model, product types, market positioning, operation strategy, revenue and profit, and business rules of the enterprise in different regions and different periods.

[0025] In this embodiment, the data acquisition interface is a bridge connecting the business system and the data analysis platform. The data acquisition interface pre-configured by ETL tools such as Kettle or DataStage can be used to connect with the business system, and the required data can be extracted from the business system, and SQL statements can be introduced to filter the corresponding business data, so as to extract the corresponding fields of the required business. The required business at least includes but is not limited to important business data such as business model, product category, market positioning, operation strategy, revenue and profit, and business rules. Then, the extracted business data is cleaned and data standardized, such as converting the text description of "market positioning" into quantifiable indicators, such as converting the qualitative description of different values ​​of different "product categories" into different scoring values, etc., so as to convert the business data into comparable multi-dimensional data for subsequent analysis.

[0026] In one embodiment, if Figure 3 As shown, step S102 includes: S301. Using a rule-based classification algorithm, perform multi-dimensional classification processing on business data according to importance and sensitivity to obtain visualized data and non-visualized data; S302, dynamically displaying the visualized data from multiple angles and dimensions through a data visualization platform; S303: Use encrypted storage and access control measures to store non-visual data.

[0027] In this embodiment, the rules of the classification algorithm are predefined, that is, the importance and sensitivity of each business data are defined, and the business data with low importance and low sensitivity are classified as visual data, and the business data with high importance and high sensitivity are classified as non-visual data. For example, take the above-mentioned business model, product category, market positioning, operation strategy, revenue and profit and business rules as examples; for example, the operation strategy may involve clear strategies (public or internal consensus) and unclear strategies (that is, not yet determined, with high sensitivity and importance, and the core business information needs to be protected at this time), so the clear strategy can be classified as visual data, and the unclear strategy can be classified as non-visual data. The total revenue and profit margin involved in revenue and profit, the total revenue can be classified as visual data, and the profit margin can be classified as non-visual data. It should be understood that the specific classification rules can be pre-defined according to the business environment of the enterprise, laws and regulations, and the business philosophy of the merchant itself.

[0028] After processing the data classification, the part that has been classified as visual data is dynamically displayed from multiple angles and dimensions using the data visualization platform, making the data presentation more intuitive and easy to understand; for the part classified as non-visual data, encrypted storage and access control measures are taken to ensure the security and confidentiality of this data during the storage process. Through these measures, non-visual data can be effectively protected from unauthorized access.

[0029] In one embodiment, if Figure 4 As shown, step S103 includes: S401, constructing business data of different regions and different periods into a time series data set according to time series; S402, using a time series decomposition algorithm to decompose the time series data set into a trend item subsequence, a seasonal item subsequence, and a random item subsequence, and respectively extracting the variation characteristics of each subsequence at different time scales; S403, constructing an ARMA model using the variation characteristics of the trend item subsequence to fit the linear relationship of the sequence; S404, constructing a SARIMA model using the variation characteristics of the seasonal term subsequence to fit the periodicity of the sequence; S405. Based on the ARMA model and the SARIMA model, the variation characteristics of the random item subsequence are incorporated into the model as external factors to construct a VAR model. S406, using the ARMA model, the SARIMA model and the VAR model, perform multi-step forward forecasting on the key business indicators of each region and each period to obtain forecast results; S407. Compare the forecast results of different regions and different periods, quantify the change trend of key business indicators of each region and each period, and generate a dynamic business status report.

[0030] In this embodiment, the time series decomposition algorithm of step S402 can adopt the STL decomposition algorithm; the change characteristics under different time scales can refer to the mean, variance, autocorrelation coefficient and other characteristics of each subsequence of statistics under daily, weekly, monthly and so on.

[0031] In this embodiment, the ARMA model (autoregressive moving average model) in step S403 can be used to fit the linear relationship of the sequence by utilizing the variation characteristics of the trend term subsequence, thereby reflecting the overall development trend of the sequence.

[0032] In this embodiment, the SARIMA model (Seasonal Autoregressive Moving Average Model) of step S404 is used to fit the periodicity of the sequence using the variation characteristics of the seasonal term subsequence, thereby reflecting the periodic fluctuation of the sequence.

[0033] In this embodiment, the VAR model (vector autoregression model) in step S405 is used to remove the residual after trend and seasonality by utilizing the variation characteristics of the random term subsequence.

[0034] In this embodiment, based on the process of steps S401-S407, a dynamic business status report can be obtained, including information such as the current status of the business in each region and each period, future trend forecasts, key influencing factor analysis, and potential risks and opportunities prompts, so as to dynamically update the enterprise portrait, and can be presented in a combination of charts and text descriptions to provide intuitive data support for decision-making.

[0035] In one embodiment, if Figure 5 As shown, step S104 includes: S501, extracting key business indicator data of each region and each period according to each business status report; S502, calculating the similarity matrix of key business indicator data of each region and each period; S503: Perform cluster analysis on the similarity matrix to identify the similarity of the change trends of the key business indicators in each region and each period.

[0036] In this embodiment, the key business indicator data in step S501 may be business data that has a dominant influence on the operation status of each region in each period, and can reflect the business performance of each region in each period.

[0037] In this embodiment, step S502 may use a cosine similarity algorithm or a Euclidean distance algorithm to calculate a similarity matrix of key business indicator data of each region and each period.

[0038] In this embodiment, step S503 may use a k-means clustering algorithm to classify regions and periods with high similarity into the same category, so as to facilitate subsequent identification of regions and periods with similar trend change patterns of key business indicators.

[0039] In one embodiment, if Figure 6 As shown, step S105 includes: S601. Compare the similarity of the change trend of the key business indicators of each region and each period with the preset value, select the regions and periods exceeding the preset value and classify them into the same cross-region collaborative optimization group; S602: For the divided cross-region collaborative optimization groups, the generation of collaborative optimization tasks is automatically triggered through a preset cross-region strategy collaborative mechanism.

[0040] In this embodiment, based on the process of steps S601-S602, regions and periods with similar trend change patterns of key business indicators can be identified. For example, if the adjustment of the business model and product types in region A in May is similar to the business model and product types that region B has already carried out in March of the same year, and the similarity of the change trends of the key business indicators of the two exceeds the preset value, then the two can be classified into the same cross-regional collaborative optimization group, and the generation of collaborative optimization tasks can be triggered, that is, the operation of the business model and product types of region A in May and later can be based on the business data that has been carried out and produced results in region B in March and later, thereby realizing business collaboration within the cross-regional collaborative optimization group.

[0041] In one embodiment, if Figure 7 As shown, step S106 includes: S701, determining the support and confidence thresholds of the association rule mining algorithm according to the business characteristics of the cross-regional collaborative optimization group; S702, using the Apriori algorithm to mine frequent item sets on the business data, obtaining frequent item sets that meet the support threshold, and generating association rules according to the confidence threshold to identify frequent co-occurrence points in the business data; S703: Generate a cross-regional business sharing plan based on the frequent co-occurrence point mining results to achieve business collaboration within the collaborative optimization group.

[0042] In this embodiment, when processing the business data of the cross-regional collaborative optimization group, it is necessary to set specific support and confidence thresholds to determine the association rules. Support refers to the proportion of businesses that contain all items in a rule among all the business data of the cross-regional collaborative optimization group; confidence refers to the conditional probability of the occurrence of the latter item under the condition that the former item occurs. The determination of these two thresholds needs to be adjusted according to the business characteristics of each region in the cross-regional collaborative optimization group to ensure that the mined rules are relevant between regions.

[0043] In this embodiment, the function of the Apriori algorithm is to find out the item sets that appear frequently in the data. These item sets are called frequent item sets. By identifying these frequent item sets, frequent co-occurrence points between regions (such as the sales, evaluation or after-sales situation of a certain product or similar products) can be identified. According to the frequent co-occurrence point mining results, a cross-regional business sharing plan is generated to achieve cross-regional business information sharing within the collaborative optimization group, thereby optimizing resource allocation on a global scale, improving efficiency, and reducing operating costs.

[0044] To facilitate understanding of the effect of this embodiment, a specific example is used for illustration: for example, if there are similar frequent co-occurrence points for product A in region A in May and product A in region B in March, then region A and region B can be classified into the same cross-regional collaborative optimization group, and then the business data of region B on product A in March and thereafter can be shared in the cross-regional collaborative optimization group for reference by region A for business operations of product A in May and thereafter. For example, when the frequent co-occurrence point is the sales of product A, the mutual influence relationship between the total sales, promotion strategy, user group, order volume, and inventory turnover rate of product A can be referenced, so as to provide subsequent improvements and adjustments for region A. Through this collaborative optimization, not only the resource utilization rate between regions is improved, but also the response speed and competitiveness of each regional market are further enhanced. Enterprises can better grasp market dynamics and maximize benefits.

[0045] The embodiment of the present invention also provides a cross-border e-commerce enterprise portrait data management system, which is used to execute any embodiment of the cross-border e-commerce enterprise portrait data management method described above. Figure 8 , Figure 8 It is a schematic block diagram of the cross-border e-commerce enterprise portrait data management system provided by an embodiment of the present invention.

[0046] like Figure 8 As shown, the cross-border e-commerce enterprise portrait data management system 800 includes: a data acquisition unit 801, a data classification unit 802, a report generation unit 803, a similarity calculation unit 804, a collaborative optimization unit 805 and a co-occurrence point mining unit 806.

[0047] The data acquisition unit 801 is used to periodically capture the status of the business system and obtain the business data of the enterprise in different regions and different periods; The data classification unit 802 is used to classify the business data and dynamically present and store it according to the classification; A report generation unit 803 is used to perform real-time calculations on business data based on a time series analysis algorithm, extract the change trends of key business indicators in different regions and periods, and generate corresponding dynamic business status reports; A similarity calculation unit 804 is used to detect the similarity of the change trend of the key business indicators in the dynamic business status reports of different regions and different periods; The collaborative optimization unit 805 is used to identify the regions and periods that have reached a preset similar change trend according to the similarity detection result and classify them into the same cross-region collaborative optimization group, and trigger the cross-region strategy collaborative mechanism to automatically generate collaborative tasks; The co-occurrence point mining unit 806 is used to mine frequent co-occurrence points on the business data in the same cross-region collaborative optimization group using an association rule mining algorithm, so as to share the frequent co-occurrence point business across regions.

[0048] By periodically capturing the operating status of cross-border enterprises in different countries and regions, the system can quickly obtain key data of cross-border business and form a dynamic corporate portrait, which helps to understand the core competitiveness and market performance of enterprises and provide reference for investment decisions, cooperation selection or market analysis. The system builds a cross-regional collaborative optimization group based on similar change trends of key business indicators in different regions and different periods to achieve cross-regional business information sharing between regions with similar key businesses, thereby optimizing resource allocation, improving efficiency, and reducing operating costs on a global scale.

[0049] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0050] The cross-border e-commerce enterprise portrait data management system can be implemented in the form of a computer program. Fig. 9 Runs on the computer device shown.

[0051] See also Fig. 9 , Fig. 9 900 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device 900 is a server, which may be an independent server or a server cluster composed of multiple servers.

[0052] See also Fig. 9 The computer device 900 includes a processor 902 , a memory and a network interface 905 connected via a system bus 901 , wherein the memory may include a non-volatile storage medium 903 and an internal memory 904 .

[0053] The non-volatile storage medium 903 can store an operating system 9031 and a computer program 9032. When the computer program 9032 is executed, the processor 902 can execute a cross-border e-commerce enterprise portrait data management method.

[0054] The processor 902 is used to provide computing and control capabilities to support the operation of the entire computer device 900 .

[0055] The internal memory 904 provides an environment for the operation of the computer program 9032 in the non-volatile storage medium 903. When the computer program 9032 is executed by the processor 902, the processor 902 can execute the cross-border e-commerce enterprise portrait data management method.

[0056] The network interface 905 is used for network communication, such as providing data information transmission, etc. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 900 to which the solution of the present invention is applied. The specific computer device 900 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0057] Those skilled in the art will understand that Fig. 9 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Fig. 9 The embodiments shown are consistent and will not be described again here.

[0058] It should be understood that in the embodiment of the present invention, the processor 902 may be a central processing unit (CPU), and the processor 902 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0059] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, a cross-border e-commerce enterprise portrait data management method of an embodiment of the present invention is implemented.

[0060] The storage medium is a physical, non-transient storage medium, for example, it can be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, etc., which can store program codes.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0062] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A cross-border e-commerce enterprise portrait data management method, characterized in that: include: Capture the business system's status periodically to obtain the business data of enterprises in different regions and periods; Classify the business data, and dynamically present and store them according to the classification; Based on the time series analysis algorithm, the business data is calculated in real time to extract the change trends of key business indicators in different regions and periods and generate corresponding dynamic business status reports; Detect the similarity of the change trends of key business indicators in dynamic business status reports of different regions and periods; Based on the similarity detection results, the regions and periods that reach the preset similar change trends are identified and classified into the same cross-region collaborative optimization group, and the cross-region strategy collaboration mechanism is triggered to automatically generate collaborative tasks; The association rule mining algorithm is used to mine the frequent co-occurrence points of the business data in the same cross-regional collaborative optimization group, so as to share the frequent co-occurrence points across regions.

2. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: The periodic state capture of the business system to obtain the business data of the enterprise in different regions and different periods includes: Connect with business systems through pre-configured data collection interfaces; According to the preset collection tasks, the global data of each region is regularly queried from the business system, and SQL statements are used for preliminary filtering and extraction of corresponding fields including at least business model, product category, market positioning, operation strategy, revenue and profit, and business rules; The extracted corresponding fields are cleaned and data standardized to obtain the business model, product types, market positioning, operation strategy, revenue and profit, and business rules of the enterprise in different regions and different periods.

3. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: The classifying and processing the business data, and dynamically presenting and storing the business data according to the classification, includes: Using a rule-based classification algorithm, the business data is classified in multiple dimensions according to importance and sensitivity to obtain visual data and non-visual data; Dynamically display the visualized data in multiple angles and dimensions through a data visualization platform; The non-visual data is stored using encrypted storage and access control measures.

4. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: The time series analysis algorithm is used to perform real-time calculations on the business data, extract the change trends of key business indicators in different regions and periods, and generate corresponding dynamic business status reports, including: The business data of different regions and periods are constructed into time series data sets according to time series; The time series data set is decomposed into a trend item subsequence, a seasonal item subsequence and a random item subsequence by using a time series decomposition algorithm, and the variation characteristics of each subsequence at different time scales are extracted respectively; An ARMA model is constructed using the variation characteristics of the trend item subsequence to fit the linear relationship of the sequence; The SARIMA model is constructed by using the variation characteristics of the seasonal term subsequence to fit the periodicity of the sequence; On the basis of the ARMA model and the SARIMA model, the variation characteristics of the random item subsequence are incorporated into the model as external factors to construct a VAR model; Using the ARMA model, SARIMA model and VAR model, multi-step forward forecasting is performed on key business indicators of each region and each period to obtain forecast results; Compare the forecast results of different regions and different periods, quantify the change trends of key business indicators in each region and each period, and generate dynamic business status reports.

5. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: The detecting of the similarity of the change trends of the key business indicators in the dynamic business status reports of different regions and different periods includes: Extract key business indicator data for each region and period according to each business status report; Calculate the similarity matrix of key business indicator data for each region and period; Cluster analysis is performed on the similarity matrix to identify the similarities of the change trends of the key business indicators in each region and each period.

6. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: According to the similarity detection results, the regions and periods reaching the preset similar change trends are identified and classified into the same cross-region collaborative optimization group, and the cross-region strategy collaborative mechanism is triggered to automatically generate collaborative tasks, including: Compare the similarity of the change trends of key business indicators in each region and each period with the preset values, select the regions and periods that exceed the preset values ​​and classify them into the same cross-region collaborative optimization group; For the divided cross-region collaborative optimization groups, the generation of collaborative optimization tasks is automatically triggered through the preset cross-region strategy collaboration mechanism.

7. The cross-border e-commerce enterprise portrait data management method according to claim 1 is characterized in that: The method of using an association rule mining algorithm to mine frequent co-occurrence points of business data in the same cross-region collaborative optimization group to share frequent co-occurrence points across regions includes: Determine the support and confidence thresholds of the association rule mining algorithm based on the business characteristics of the cross-regional collaborative optimization group; The Apriori algorithm is used to mine frequent itemsets in business data to obtain frequent itemsets that meet the support threshold, and association rules are generated based on the confidence threshold to identify frequent co-occurrence points in business data. Generate a cross-regional business sharing plan based on the results of frequent co-occurrence point mining to achieve business collaboration within the collaborative optimization group.

8. A cross-border e-commerce enterprise portrait data management system, characterized in that: include: The data acquisition unit is used to periodically capture the status of the business system and obtain the business data of the enterprise in different regions and periods; A data classification unit, used to classify the business data, and dynamically present and store it according to the classification; A report generation unit, used to perform real-time calculations on the business data based on a time series analysis algorithm, extract the change trends of key business indicators in different regions and periods, and generate corresponding dynamic business status reports; A similarity calculation unit, used to detect the similarity of the change trends of key business indicators in the dynamic business status reports of different regions and different periods; The collaborative optimization unit is used to identify the regions and periods that have reached a preset similar change trend based on the similarity detection results and classify them into the same cross-region collaborative optimization group, and trigger the cross-region strategy collaborative mechanism to automatically generate collaborative tasks; The co-occurrence point mining unit is used to mine frequent co-occurrence points of business data in the same cross-region collaborative optimization group using an association rule mining algorithm, so as to share frequent co-occurrence point business across regions.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the cross-border e-commerce enterprise portrait data management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the cross-border e-commerce enterprise portrait data management method as described in any one of claims 1 to 7.

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