Micro-front-end supply chain platform collaboration method and system based on multi-team development

Abstract

The application relates to the development of a synergistic technical field, and relates to a micro-frontend supply chain platform synergy method and system based on multi-team development, which comprises the following steps: performing function analysis on a to-be-developed supply chain scheme to obtain a platform function set, performing function feature matching and development specification on multiple teams according to the platform function set to obtain specification development information; performing multi-team module development and specification review on the platform function set according to the specification development information to obtain a specification code set; performing isolated deployment and micro-application integration on the specification code set to obtain a supply chain front end; obtaining user behavior logs by using the supply chain front end, performing behavior feature extraction and behavior analysis on the user behavior logs to obtain analyzed user behaviors; and performing dynamic synergistic loading on the supply chain front end according to the analyzed user behaviors. The application can improve the efficiency of micro-frontend development synergy.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of collaborative development, in particular to a micro-frontend supply chain platform collaborative method and system based on multi-team development. BACKGROUND

[0002] The supply chain financial platform is a financial service platform aiming to provide financing, settlement, risk management and other services for various participants on the supply chain, the micro-frontend development is a front-end architecture mode aiming to decompose a single front-end application into smaller and more independent components or micro-applications so as to better manage and maintain complex applications, and the micro-frontend supply chain platform is a supply chain financial platform created by using the micro-frontend development technology.

[0003] The existing collaborative methods of the micro-frontend supply chain platform are mostly collaborative methods based on team coordination, which develop the micro-frontend supply chain platform by coordinating multiple teams and uniformly managing the state, and in actual application, the collaborative methods based on team coordination may have problems such as low collaborative efficiency, low platform expansibility and complex state management, which may result in low efficiency of micro-frontend development collaboration. SUMMARY

[0004] The application provides a micro-frontend supply chain platform collaborative method and system based on multi-team development, which mainly aims to solve the problem of low efficiency of micro-frontend development collaboration.

[0005] To achieve the above-mentioned purpose, the application provides a micro-frontend supply chain platform collaborative method based on multi-team development, which comprises the following steps:

[0006] Performing function analysis on the to-be-developed supply chain scheme to obtain a platform function set, and performing function feature matching and development specification on multiple teams according to the platform function set to obtain specification development information;

[0007] Developing multiple team modules and performing specification review on the platform function set according to the specification development information to obtain a specification code set;

[0008] Isolating and deploying the specification code set and integrating micro-applications to obtain a supply chain front end;

[0009] Obtaining user behavior logs by using the supply chain front end, performing behavior feature extraction and behavior analysis on the user behavior logs to obtain analyzed user behaviors;

[0010] Performing dynamic collaborative loading on the supply chain front end according to the analyzed user behaviors.

[0011] Optionally, the function analysis on the to-be-developed supply chain scheme to obtain a platform function set comprises the following steps:

[0012] The supply chain solution to be developed is subjected to text denoising, word segmentation, and word vector encoding to obtain the solution word feature set;

[0013] Topic modeling is performed on the feature set of the proposed scheme to obtain topic word feature groups;

[0014] Functional keywords are extracted from the feature set of the scheme words to obtain keyword feature groups;

[0015] Generate a functional word feature set based on the topic word feature set and the keyword feature set;

[0016] The functional word feature set is subjected to module function clustering to obtain a standard functional word feature class set;

[0017] The platform function set is obtained by performing function mapping on the standard function word feature set.

[0018] Optionally, the step of performing module function clustering on the function word feature set to obtain a standard function word feature class set includes:

[0019] The functional word feature set is weighted by word frequency and attention to obtain a weighted functional word feature set;

[0020] The weighted feature set of functional words is subjected to Gaussian distribution center screening to obtain the primary center word feature group;

[0021] Based on the weighted functional word feature set, the primary central word feature group is divided into neighborhoods to obtain the primary word feature group set;

[0022] Each primary word feature group in the primary word feature group set is selected as the target primary word feature group, and the primary center word feature corresponding to the target primary word feature group in the primary center word feature group is taken as the target primary center word feature.

[0023] Each weighted functional word feature in the weighted functional word feature set is selected as the target weighted functional word feature. The weighted module distance between the target weighted functional word feature and the target primary word feature group is calculated using the following weighted module distance algorithm based on the target primary center word feature:

[0024]

[0025] Where L refers to the weighted module distance, max is the maximum value sign, i is the feature index, N is the total number of features in the target primary word feature group, and q i It is the weight of the i-th weighted functional word feature in the target primary word feature group, α. i β is the i-th weighted functional word feature in the target primary word feature group, and q is the target weighted functional word feature.β is a weight of the target weighted function word feature β, · is an inner product symbol, || is a modulus symbol, γ is the target primary center word feature, q γ is a weight of the target primary center word feature;

[0026] The weighted module distances of all target weighted function word features in the target weighted function word feature set are aggregated into a weighted module distance set, and the target primary word feature group is updated by threshold comparison according to the weighted module distance set, to obtain a target secondary word feature group;

[0027] All target secondary word feature groups are aggregated into a secondary word feature group set, and the secondary word feature group set is iteratively updated to obtain a standard function word feature class set.

[0028] Optionally, the function feature matching and development specification of the multi-team according to the platform function set are obtained, and the specification development information includes:

[0029] The platform function set is word vector encoded and feature matrixed to obtain a function feature matrix set;

[0030] The function feature matrix set is attention encoded to obtain an attention function feature set;

[0031] The attention function feature set is residual connected and layer normalized to obtain a normalized function feature set;

[0032] The normalized function feature set is forward linearly transformed and forward activated to obtain an activated function feature set;

[0033] The normalized function feature set and the activated function feature set are residual connected and layer normalized to obtain an encoded function feature set;

[0034] The encoded function feature set is attention decoded to obtain the specification development information.

[0035] Optionally, the multi-team module development and specification review of the platform function set according to the specification development information are obtained, and the specification code set includes:

[0036] The platform functions in the platform function set are selected one by one as target platform functions, and the target platform functions are team assigned to obtain a target development team;

[0037] The specification development information and the target platform function are sent to the target development team, and the feedback code of the target development team for the specification development information and the target platform function is obtained;

[0038] Compiling the specification development information according to a static rule to obtain a static specification rule;

[0039] Reviewing the feedback code according to the static specification rule to obtain a vulnerability code set and a review log;

[0040] Sending the vulnerability code set and the review log to the target development team to obtain a repair code set of the target development team for the vulnerability code set and the review log;

[0041] Repairing and updating the feedback code according to the repair code set to obtain a specification code, and collecting the specification code of all target platform functions of the platform function set into a specification code set.

[0042] Optionally, the isolated deployment and micro-application integration of the specification code set to obtain a supply chain front end, comprising:

[0043] Functionally packaging the specification code set to obtain a function code package set;

[0044] Container matching the function code package set to obtain a function container set;

[0045] Deploying the function code package set according to the function container set to obtain a function module set;

[0046] Initializing the function module set to obtain a module main application;

[0047] Micro-application registration and application loading of the function module set according to the module main application to obtain a supply chain front end.

[0048] Optionally, the behavior feature extraction and behavior analysis of the user behavior log to obtain an analysis user behavior, comprising:

[0049] Iterative log analysis of the user behavior log to obtain user behavior data;

[0050] Time sequence behavior matching and behavior feature coding of the user behavior data to obtain a behavior feature sequence;

[0051] Difference time sequence gate activation of the behavior feature sequence to obtain a difference time sequence feature;

[0052] Multi-head attention coding of the behavior feature sequence to obtain a behavior attention feature;

[0053] Cyclic time sequence convolution of the behavior feature sequence to obtain a convolution time sequence feature;

[0054] fully connect the difference time sequence feature, the behavior attention feature and the convolution time sequence feature to obtain a fused behavior feature;

[0055] forward feedback propagate the fused behavior feature to obtain an analysis behavior feature;

[0056] perform a reverse normalization operation and a behavior mapping operation on the analysis behavior feature to obtain an analysis user behavior.

[0057] Optionally, the fully connecting the difference time sequence feature, the behavior attention feature and the convolution time sequence feature to obtain a fused behavior feature comprises:

[0058] the difference time sequence feature, the behavior attention feature and the convolution time sequence feature are fully connected by using a full connection algorithm as follows to obtain a fused behavior feature:

[0059] U t = tanh(W g g t +W a a t +W c c t +b)

[0060] wherein U t denotes the fused behavior feature at a t time step, t is a time step index, tanh is an activation function symbol, g t denotes the difference time sequence feature at the t time step, W g denotes a weight matrix of the difference time sequence feature, a t denotes the behavior attention feature at the t time step, W a denotes a weight matrix of the behavior attention feature, c t denotes the convolution time sequence feature at the t time step, W c denotes a weight matrix of the convolution time sequence feature, and b denotes a preset full connection bias vector.

[0061] Optionally, the dynamically coordinating and loading the supply chain front end according to the analysis user behavior comprises:

[0062] performing a module behavior extraction on the analysis user behavior to obtain a functional module sequence;

[0063] performing a timestamp extraction on the functional module sequence to obtain a module switching timestamp;

[0064] performing a coordinated labeling on the functional module sequence according to the module switching timestamp to obtain current functional module data and switching functional module data;

[0065] Timing module uninstalling the supply chain front end according to the current function module data;

[0066] Module preloading the supply chain front end according to the switching function module data.

[0067] To solve the above problems, the application further provides a micro-front-end supply chain platform collaboration system based on multi-team development, which comprises:

[0068] A development specification module is configured to perform function analysis on a to-be-developed supply chain scheme to obtain a platform function set, perform function feature matching on multiple teams according to the platform function set, and develop specifications to obtain specification development information;

[0069] A code review module is configured to perform multi-team module development and specification review on the platform function set according to the specification development information to obtain a specification code set;

[0070] An isolated deployment module is configured to perform isolated deployment and micro-application integration on the specification code set to obtain a supply chain front end;

[0071] A behavior analysis module is configured to obtain user behavior logs by using the supply chain front end, perform behavior feature extraction and behavior analysis on the user behavior logs to obtain analyzed user behaviors;

[0072] A collaborative loading module is configured to perform dynamic collaborative loading on the supply chain front end according to the analyzed user behaviors.

[0073] The embodiments of the application can realize module division and clear definition of platform functions by performing function analysis and development specification, can efficiently analyze corresponding development specifications by combining a self-attention method, thereby improving development efficiency, can realize independent development and corresponding development review of multiple teams under the same standard by performing multi-team module development and specification review, thereby improving development efficiency, can guarantee team independence, reduce coupling between teams, and realize modular independent deployment, thereby improving the flexibility and expansibility of maintenance of the supply chain front end, and improving development efficiency.

[0074] By performing behavior feature extraction and behavior analysis, the use of each function module in the supply chain front end by users in a future time period can be analyzed and predicted, thereby improving the efficiency of collaborative loading, and by performing dynamic collaborative loading, dynamic collaboration of micro-front-end applications can be realized, thereby improving the use efficiency of micro-front-end applications. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 A flowchart illustrating a collaborative method for a micro-frontend supply chain platform developed by multiple teams, provided in an embodiment of the present invention;

[0076] Figure 2 This is a flowchart illustrating the functional analysis process according to an embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram illustrating the process of isolated deployment and micro-application integration provided in an embodiment of the present invention;

[0078] Figure 4 A functional module diagram of a micro-front-end supply chain platform collaborative system based on multi-team development provided in an embodiment of the present invention;

[0079] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0080] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0081] This application provides a collaborative method for a micro-frontend supply chain platform developed by multiple teams. The executing entities of this collaborative method include, but are not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the collaborative method for a micro-frontend supply chain platform developed by multiple teams can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0082] Reference Figure 1 The diagram shown is a flowchart illustrating a collaborative method for a micro-frontend supply chain platform developed by multiple teams, according to an embodiment of the present invention. In this embodiment, the collaborative method for a micro-frontend supply chain platform developed by multiple teams includes:

[0083] S1. Perform functional analysis on the supply chain solution to be developed to obtain the platform function set. Based on the platform function set, perform functional feature matching and development specifications for multiple teams to obtain standardized development information.

[0084] In detail, the supply chain solution refers to a set of tools, systems and strategies designed to manage and optimize each link of the financial supply chain. The financial supply chain involves the entire process from raw material procurement, production and manufacturing, logistics and transportation to the final product delivery to consumers. The supply chain solution can be collected and organized by supply chain personnel after brainstorming.

[0085] Specifically, the various platform functions in the platform function set refer to the functions that the supply chain finance platform to be developed needs to achieve, including supply chain financing, payment and settlement, supply chain management, data analysis, and risk management, etc. The platform functions correspond to the various micro-applications in the micro-frontend.

[0086] In detail, the specification development information includes technical specifications and UI specifications that need to be followed when developing various micro-frontends for the supply chain platform. The technical specifications include code style, naming rules, directory structure, technology stack (such as React, Vue, Angular, etc.) and API interface specifications. The UI specifications include design system, component library and style guide to ensure that the modules developed by various teams are consistent in appearance and interaction.

[0087] In this embodiment of the invention, reference is made to Figure 2 As shown, the functional analysis of the supply chain solution to be developed yields a platform function set, including:

[0088] S21. Perform text denoising, word segmentation, and word vector encoding on the supply chain solution to be developed to obtain the solution word feature set;

[0089] S22. Perform topic modeling on the scheme word feature set to obtain topic word feature groups;

[0090] S23. Extract functional keywords from the scheme word feature set to obtain keyword feature groups;

[0091] S24. Generate a functional word feature set based on the topic word feature set and the keyword feature set;

[0092] S25. Perform module function clustering on the functional word feature set to obtain a standard functional word feature class set;

[0093] S26. Perform functional mapping on the standard functional word feature set to obtain the platform function set.

[0094] In detail, the text denoising and word segmentation refers to removing irrelevant text from the supply chain solution and performing word segmentation on the supply chain solution after removing irrelevant text. The text denoising method can be keyword matching denoising, and the word segmentation method can be word segmentation tools such as jieba.

[0095] Specifically, the word vector encoding method can be the bag-of-words model method, the TF-IDF method, or the Word2Vec, GloVe, or BERT model encoding method. Topic modeling can be performed using the Latent Dirichlet Allocation (LDA) model, which refers to extracting topics from the text, where each topic represents a set of related functions.

[0096] In detail, the step of performing module function clustering on the functional word feature set to obtain a standard functional word feature class set includes:

[0097] The functional word feature set is weighted by word frequency and attention to obtain a weighted functional word feature set;

[0098] The weighted feature set of functional words is subjected to Gaussian distribution center screening to obtain the primary center word feature group;

[0099] Based on the weighted functional word feature set, the primary central word feature group is divided into neighborhoods to obtain the primary word feature group set;

[0100] Each primary word feature group in the primary word feature group set is selected as the target primary word feature group, and the primary center word feature corresponding to the target primary word feature group in the primary center word feature group is taken as the target primary center word feature.

[0101] Each weighted functional word feature in the weighted functional word feature set is selected as the target weighted functional word feature. The weighted module distance between the target weighted functional word feature and the target primary word feature group is calculated using the following weighted module distance algorithm based on the target primary center word feature:

[0102]

[0103] Where L refers to the weighted module distance, max is the maximum value sign, i is the feature index, N is the total number of features in the target primary word feature group, and q i It is the weight of the i-th weighted functional word feature in the target primary word feature group, α. i β is the i-th weighted functional word feature in the target primary word feature group, and q is the target weighted functional word feature. β γ is the weight of the target weighted functional word feature β, · is the inner product symbol, || is the modulo symbol, γ is the target primary center word feature, and q is the weight of the target weighted functional word feature β. γ It is the weight of the primary center word feature of the target;

[0104] The weighted module distances of all target weighted functional word features in the weighted functional word feature set are aggregated into a weighted module distance set. The target primary word feature group is updated by threshold comparison based on the weighted module distance set to obtain the target secondary word feature group.

[0105] All target secondary word feature groups are aggregated into a secondary word feature set, and the secondary word feature set is iteratively updated to obtain a standard functional word feature class set.

[0106] In detail, the word frequency weighting method can be TF-IDF, TextRank, etc., the attention weighting method can be a positional encoding algorithm with self-attention mechanism, and the Gaussian distribution center selection refers to calculating the Gaussian distribution of the weighted functional word feature set, and selecting several non-neighboring weighted functional word features from the weighted functional word feature set according to the Gaussian distribution as primary center word features to form a primary center word feature group.

[0107] Specifically, the neighborhood partitioning refers to the Gaussian distribution of the weighted functional word feature set filtering out feature neighborhoods for each primary central word feature group in the primary central word feature group, and then aggregating the features in the feature neighborhoods as primary word feature groups to form a primary word feature group set.

[0108] In detail, the weighted module distance algorithm can combine the weights of word features and the mean distances between the target weighted functional word features and the target primary word feature group to determine whether the target full-function word features meet the update requirements. The threshold comparison update and the iterative update methods can be density clustering algorithms (OrderingPoints To Identify the Clustering Structure, abbreviated as OPTICS).

[0109] Specifically, the extraction of functional keywords refers to extracting feature words related to functions from the feature set of scheme words and integrating them into a feature set of functional words. The method for extracting functional keywords can be TF-IDF, TextRank, or other methods.

[0110] In detail, generating the functional word feature set refers to deduplicating and merging the topic word feature group and the keyword feature group, and the function mapping method refers to using the reverse mapping scheme of the word variable encoding to map each standard functional word feature class in the standard functional word feature class set to a platform function, and aggregating all platform functions into a platform function set.

[0111] In detail, the process of matching functional characteristics and development specifications of multiple teams based on the platform's functional set to obtain standardized development information includes:

[0112] The platform's functional set is word vectorized and feature matrixed to obtain a functional feature matrix set;

[0113] Attention encoding is performed on the functional feature matrix set to obtain the attention functional feature set;

[0114] The attention function feature set is subjected to residual connection and layer normalization to obtain the normalized function feature set.

[0115] The normalized functional feature set is subjected to a feedforward linear transformation and feedforward activation to obtain the activated functional feature set.

[0116] The normalized functional feature set and the activated functional feature set are subjected to residual connection and layer normalization operations to obtain the encoded functional feature set.

[0117] Attention decoding is performed on the encoded feature set to obtain the standard development information.

[0118] In detail, the attention encoding method can be an encoding method based on the Transformer or BERT model. The residual connection is a connection method that directly skips a certain sub-layer and adds the input and output of the sub-layer to alleviate the gradient vanishing problem during model training. The layer normalization refers to the normalization processing of the data.

[0119] Specifically, the feedforward linear transformation refers to performing a linear transformation using two linear transformation functions of a feedforward neural network (FFN). The feedforward activation refers to activating the network using an activation function. The attention decoding method can be performed using a decoder of a trained Transformer model. The Transformer model can be trained using multiple sets of specification development information determined by personnel and corresponding platform function sets.

[0120] In this embodiment of the invention, by performing functional analysis and development specifications, the platform's functional modules can be clearly defined, and the corresponding development specifications can be efficiently analyzed using the self-attention method, thereby improving development efficiency.

[0121] S2. Based on the specified development information, the platform function set is developed by multiple teams and the specifications are reviewed to obtain the specified code set.

[0122] In detail, each specification code in the specification code set corresponds to the development code of a platform function under the specification development information in the platform function set, and the platform function corresponds to each micro-application in the micro-frontend.

[0123] In this embodiment of the invention, the step of performing multi-team module development and specification review on the platform function set based on the specified development information to obtain a specified code set includes:

[0124] One by one, platform functions from the platform function set are selected as target platform functions, and the target platform functions are assigned to teams to obtain target development teams;

[0125] The specification development information and the target platform functions are sent to the target development team, and feedback code from the target development team regarding the specification development information and the target platform functions is obtained;

[0126] The specification development information is statically compiled to obtain static specification rules;

[0127] The feedback code is reviewed according to the static specification rules to obtain the vulnerability code group and review log;

[0128] Send the vulnerability code set and the review log to the target development team to obtain the fix code set from the target development team for the vulnerability code set and the review log;

[0129] The feedback code is repaired and updated according to the repair code group to obtain the standard code. The standard codes of all target platform functions in the platform function set are then gathered into the standard code group.

[0130] In detail, the team allocation refers to selecting the development team that develops the target platform functions from several independent development teams as the target development team, and the team allocation can be based on the historical development records of each development team.

[0131] Specifically, the feedback code refers to the functional code developed by the target development team for the target platform under the guidance of the specification development information. The static rule compilation method can be rule compilation based on ESLint or Stylelint, and specification review can be performed using Prettier, ESLint or Stylelint, etc.

[0132] In this embodiment of the invention, by conducting multi-team module development and specification review, it is possible to achieve independent development and corresponding development review by multiple teams under the same standard, thereby improving development efficiency.

[0133] S3. The standardized code set is isolated, deployed, and integrated into micro-applications to obtain the supply chain front end.

[0134] In detail, the supply chain front-end is a micro-application front-end that fulfills the requirements of the supply chain finance platform. The micro-application front-end is an architectural pattern that draws on the idea of ​​microservice architecture. It decomposes the front-end application into several independent and autonomous micro-applications. These micro-applications can be independently developed, tested, deployed and maintained by different teams, and then integrated through a main application to form a unified user interface.

[0135] In this embodiment of the invention, reference is made to Figure 3 As shown, obtaining the supply chain front end from the specified code set includes:

[0136] S31. Package the specified code set into functional code packages to obtain a functional code package set;

[0137] S32. Perform container matching on the functional code package set to obtain a functional container set;

[0138] S33. Deploy code from the functional code package set according to the functional container set to obtain a functional module set;

[0139] S34. Initialize the main application of the functional module set to obtain the module main application;

[0140] S35. Based on the main application of the module, perform micro-application registration and application loading on the functional module set to obtain the supply chain front end.

[0141] Specifically, the function packaging refers to packaging the specification code set and the corresponding resource code. Webpack can be used for function packaging. The container matching refers to selecting the corresponding deployment container or server for each function code package in the function code package set. The code deployment method is to specify the corresponding function container in the function container set for each function code package in the function code package set.

[0142] In detail, each functional module in the functional module set is a micro-application module in the micro-frontend. The main application initialization includes initializing the routing address of the main application of the module and generating corresponding navigation configurations based on the routing addresses of each functional module in the functional module set.

[0143] Specifically, the main application of the module is responsible for managing and loading various micro-applications, while providing unified navigation and public resources for the micro-applications. It can utilize the Qiankun framework for micro-application registration and application loading.

[0144] In this embodiment of the invention, by implementing isolated deployment and micro-application integration, the independence of the team can be guaranteed, the coupling between teams can be reduced, and modular independent deployment can be achieved, which improves the flexibility and scalability of the maintenance of the supply chain front end and improves development efficiency.

[0145] S4. Use the supply chain front end to obtain user behavior logs, extract behavioral features and perform behavioral analysis on the user behavior logs to obtain analyzed user behavior.

[0146] In detail, the user behavior log refers to the data entry log obtained when a user uses the supply chain front-end. The user behavior log records information such as the timestamp, frequency, and duration of the user's use of each functional module in the supply chain front-end. The analysis of user behavior refers to the analysis of the user's behavior in using the supply chain front-end in the future.

[0147] In this embodiment of the invention, the step of extracting behavioral features and analyzing behavior from the user behavior logs to obtain analyzed user behavior includes:

[0148] The user behavior logs are traversed and parsed to obtain user behavior data;

[0149] The user behavior data is subjected to time-series behavior matching and behavior feature encoding to obtain a behavior feature sequence;

[0150] Differential temporal gate activation is applied to the behavioral feature sequence to obtain differential temporal features;

[0151] Multi-head attention encoding is performed on the behavioral feature sequence to obtain behavioral attention features;

[0152] Perform cyclic temporal convolution on the behavioral feature sequence to obtain convolutional temporal features;

[0153] The differential temporal features, the behavioral attention features, and the convolutional temporal features are fused using a fully connected process to obtain fused behavioral features.

[0154] The fused behavioral features are propagated forward to obtain the analyzed behavioral features;

[0155] The analyzed user behavior is obtained by performing inverse normalization and behavior mapping operations on the analyzed behavioral features.

[0156] Specifically, regular expressions or log parsing libraries can be used to traverse and parse logs. The time-series behavior matching method refers to matching the behavior data sequence from the user behavior data according to the time sequence using regular expressions or keyword matching. The behavior feature encoding refers to encoding the behavior data sequence with features. Each behavior feature in the behavior feature sequence corresponds to the behavior frequency, the type of functional module used, and the usage duration of each behavior data in the behavior data sequence.

[0157] In detail, the differential temporal gate activation refers to extracting features from the behavioral feature sequence through a gated recurrent unit network in a temporal differential manner; the multi-head attention encoding refers to using a multi-head attention mechanism for feature encoding; and the recurrent temporal convolution refers to using a recurrent temporal neural network model for feature extraction.

[0158] Specifically, the fully connected fusion of the differential temporal features, the behavioral attention features, and the convolutional temporal features to obtain fused behavioral features includes:

[0159] The differential temporal features, the behavioral attention features, and the convolutional temporal features are fused using the following fully connected algorithm to obtain the fused behavioral features:

[0160] U t =tanh(W g g t +W a a t +W c c t +b)

[0161] Among them, U t This refers to the fusion behavior feature at time step t, where t is the time step index, tanh is the activation function symbol, and g... t This refers to the differential time series characteristics at time step t, W g This refers to the weight matrix of the differential time series features, a t W refers to the behavioral attention features at time step t. a This refers to the weight matrix of the behavioral attention features, c t W refers to the convolutional temporal features at time step t. c 'b' refers to the weight matrix of the convolutional temporal features, and 'b' refers to the preset fully connected bias vector.

[0162] In detail, by utilizing the fully connected algorithm, it is possible to achieve the fusion of multiple temporal features and feature standardization operations, thereby improving the accuracy of behavior analysis. Forward feedback propagation can be performed using a behavior analysis model pre-trained from a large number of fused behavior features and corresponding behavior features. The behavior mapping operation is the inverse operation of the behavior feature encoding.

[0163] In this embodiment of the invention, by extracting behavioral features and performing behavioral analysis, it is possible to analyze and predict how users will use various functional modules in the front end of the supply chain in the future, thereby improving the efficiency of collaborative loading.

[0164] S5. Dynamically and collaboratively load the supply chain front-end based on the analyzed user behavior.

[0165] In this embodiment of the invention, the step of dynamically and collaboratively loading the supply chain front-end based on the analyzed user behavior includes:

[0166] The analyzed user behavior is used to extract module behavior, resulting in a sequence of functional modules;

[0167] The timestamps of the functional module sequence are extracted to obtain the module switching timestamps;

[0168] Based on the module switching timestamp, the functional module sequence is collaboratively labeled to obtain the current functional module data and the switched functional module data;

[0169] Based on the current functional module data, the supply chain front-end module is periodically unloaded;

[0170] The supply chain front end is preloaded based on the switching function module data.

[0171] In detail, the module behavior extraction refers to extracting the usage time data of each functional module from the analyzed user behavior. The module switching timestamp includes the start time and stop time of each functional module in the functional module sequence.

[0172] Specifically, the collaborative annotation refers to selecting the currently used functional module and its corresponding shutdown time from the module switching timestamp based on the current time as the current functional module data, and the functional module that may be used in the future and its corresponding start time as the switching functional module data. The unmount() function can be used for timed module unloading, and the mount() function can be used for module preloading.

[0173] In this embodiment of the invention, dynamic collaborative loading enables dynamic collaboration of micro-frontend applications, thereby improving the efficiency of using micro-frontend applications.

[0174] This invention, through functional analysis and development specifications, enables the modular division and clarification of platform functions. It also utilizes self-attention methods to efficiently analyze corresponding development specifications, thereby improving development efficiency. Furthermore, multi-team module development and specification review enable independent development and corresponding reviews by multiple teams under the same standard, further enhancing development efficiency. Isolation deployment and micro-application integration ensure team independence, reduce coupling between teams, and achieve modular independent deployment, improving the flexibility and scalability of supply chain front-end maintenance, and ultimately increasing development efficiency.

[0175] By extracting and analyzing behavioral features, it is possible to analyze and predict user usage patterns for various functional modules in the supply chain front-end over future time periods, thereby improving the efficiency of collaborative loading. Dynamic collaborative loading enables dynamic collaboration of micro-front-end applications, further enhancing their usability. Therefore, the collaborative method for a micro-front-end supply chain platform based on multi-team development proposed in this invention can solve the problem of low efficiency in collaborative micro-front-end development.

[0176] like Figure 4 The diagram shown is a functional module diagram of a micro-front-end supply chain platform collaborative system based on multi-team development, provided by an embodiment of the present invention.

[0177] The micro-frontend supply chain platform collaborative system 100 developed by multiple teams according to this invention can be installed in an electronic device. Depending on the functions implemented, the micro-frontend supply chain platform collaborative system 100 may include a development specification module 101, a code review module 102, an isolation deployment module 103, a behavior analysis module 104, and a collaborative loading module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0178] In this embodiment, the functions of each module / unit are as follows:

[0179] The development specification module 101 is used to perform functional analysis on the supply chain solution to be developed, obtain the platform function set, and perform functional feature matching and development specifications for multiple teams based on the platform function set to obtain specification development information.

[0180] The code review module 102 is used to perform multi-team module development and specification review on the platform function set according to the specification development information to obtain the specification code set;

[0181] The isolation deployment module 103 is used to isolate and deploy the standard code set and integrate micro-applications to obtain the supply chain front end;

[0182] The behavior analysis module 104 is used to obtain user behavior logs using the supply chain front end, extract behavioral features and perform behavior analysis on the user behavior logs to obtain analyzed user behavior.

[0183] The collaborative loading module 105 is used to dynamically collaboratively load the supply chain front end based on the analyzed user behavior.

[0184] In detail, the modules in the micro-frontend supply chain platform collaborative system 100 developed by multiple teams described in this embodiment of the invention adopt the same approach as described above when in use. Figures 1 to 3 The collaborative method of the micro-frontend supply chain platform developed by multiple teams described herein uses the same technical means and can produce the same technical effect, so it will not be elaborated here.

[0185] In the several embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0186] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0188] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0189] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0190] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems described in the system embodiments may also be implemented by a single unit or system through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative method for a micro-frontend supply chain platform developed by multiple teams, characterized in that, The method includes: The supply chain solution to be developed is subjected to text denoising, word segmentation, and word vector encoding to obtain a solution word feature set. The solution word feature set is then subjected to topic modeling to obtain a topic word feature group. Functional keywords are extracted from the solution word feature set to obtain a keyword feature group. A functional word feature set is generated based on the topic word feature group and the keyword feature group. The functional word feature set is then subjected to module function clustering to obtain a standard functional word feature class set. Finally, the standard functional word feature class set is subjected to function mapping to obtain a platform function set. Based on the platform's functional set, functional characteristics and development specifications are matched among multiple teams to obtain standardized development information; Based on the specified development information, the platform function set is developed by multiple teams and the specifications are reviewed to obtain a specified code set. The aforementioned code set is isolated, deployed, and integrated into micro-applications to obtain the supply chain front-end; By utilizing the aforementioned supply chain front-end to obtain user behavior logs, and performing behavioral feature extraction and behavioral analysis on the user behavior logs, the analyzed user behavior is obtained. Dynamically and collaboratively load the supply chain front-end based on the analyzed user behavior. The step of developing and reviewing the platform's functional set using multiple teams based on the specified development information to obtain a specified code set includes: One by one, platform functions from the platform function set are selected as target platform functions, and the target platform functions are assigned to teams to obtain target development teams; The specification development information and the target platform functions are sent to the target development team, and feedback code from the target development team regarding the specification development information and the target platform functions is obtained; The specification development information is statically compiled to obtain static specification rules; The feedback code is reviewed according to the static specification rules to obtain the vulnerability code group and review log; Send the vulnerability code set and the review log to the target development team to obtain the fix code set from the target development team for the vulnerability code set and the review log; The feedback code is repaired and updated according to the repair code group to obtain the standard code. The standard codes of all target platform functions in the platform function set are then gathered into the standard code group.

2. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 1, characterized in that, The step of performing module function clustering on the functional word feature set to obtain a standard functional word feature class set includes: The functional word feature set is weighted by word frequency and attention to obtain a weighted functional word feature set; The weighted feature set of functional words is subjected to Gaussian distribution center screening to obtain the primary center word feature group; Based on the weighted functional word feature set, the primary central word feature group is divided into neighborhoods to obtain the primary word feature group set; Each primary word feature group in the primary word feature group set is selected as the target primary word feature group, and the primary center word feature corresponding to the target primary word feature group in the primary center word feature group is taken as the target primary center word feature. Each weighted functional word feature in the weighted functional word feature set is selected as the target weighted functional word feature. The weighted module distance between the target weighted functional word feature and the target primary word feature group is calculated using the following weighted module distance algorithm based on the target primary center word feature: in, This refers to the distance between the weighted modules. It is the sign of the maximum value. It is the feature number. It is the total number of features of the target primary word feature group. It is the first in the target primary word feature group The weights of each weighted functional word feature, It is the first in the target primary word feature group Weighted functional word features, These are the target weighted functional word features. The target weighted functional word features The weight, It is the inner product symbol. It is the modulo symbol. These are the primary central word features of the target. It is the weight of the primary center word feature of the target; The weighted module distances of all target weighted functional word features in the weighted functional word feature set are aggregated into a weighted module distance set. The target primary word feature group is updated by threshold comparison based on the weighted module distance set to obtain the target secondary word feature group. All target secondary word feature groups are aggregated into a secondary word feature set, and the secondary word feature set is iteratively updated to obtain a standard functional word feature class set.

3. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 1, characterized in that, The process of matching functional characteristics and development specifications of multiple teams based on the platform's functional set to obtain standardized development information includes: The platform's functional set is word vectorized and feature matrixed to obtain a functional feature matrix set; Attention encoding is performed on the functional feature matrix set to obtain the attention functional feature set; The attention function feature set is subjected to residual connection and layer normalization to obtain the normalized function feature set. The normalized functional feature set is subjected to a feedforward linear transformation and feedforward activation to obtain the activated functional feature set. The normalized functional feature set and the activated functional feature set are subjected to residual connection and layer normalization operations to obtain the encoded functional feature set. Attention decoding is performed on the encoded feature set to obtain the standard development information.

4. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 1, characterized in that, The isolation and deployment of the specified code set, along with the integration of micro-applications, yields the supply chain front-end, including: The specified code set is functionally packaged to obtain a functional code package set; Perform container matching on the set of functional code packages to obtain a set of functional containers; Based on the functional container set, the functional code package set is deployed to obtain a functional module set; The main application is initialized by performing main application initialization on the set of functional modules to obtain the module main application; Based on the main application of the module, the functional module set is registered with micro-applications and loaded with applications to obtain the supply chain front end.

5. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 1, characterized in that, The step of extracting behavioral features and analyzing behavior from the user behavior logs to obtain analyzed user behavior includes: The user behavior logs are traversed and parsed to obtain user behavior data; The user behavior data is subjected to time-series behavior matching and behavior feature encoding to obtain a behavior feature sequence; Differential temporal gate activation is applied to the behavioral feature sequence to obtain differential temporal features; Multi-head attention encoding is performed on the behavioral feature sequence to obtain behavioral attention features; Perform cyclic temporal convolution on the behavioral feature sequence to obtain convolutional temporal features; The differential temporal features, the behavioral attention features, and the convolutional temporal features are fused using a fully connected process to obtain fused behavioral features. The fused behavioral features are propagated forward to obtain the analyzed behavioral features; The analyzed user behavior is obtained by performing inverse normalization and behavior mapping operations on the analyzed behavioral features.

6. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 5, characterized in that, The step of performing a fully connected fusion of the differential temporal features, the behavioral attention features, and the convolutional temporal features to obtain fused behavioral features includes: The differential temporal features, the behavioral attention features, and the convolutional temporal features are fused using the following fully connected algorithm to obtain the fused behavioral features: in, It means The fusion behavior characteristics of the time step, It is a time step index. It is the activation function symbol. It means The differential timing features of the time step, This refers to the weight matrix of the differential time series features. It means The behavioral attention features of the time step, This refers to the weight matrix of the behavioral attention features. It means The convolutional temporal features at each time step, This refers to the weight matrix of the convolutional temporal features. This refers to the preset fully connected bias vector.

7. The collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in claim 1, characterized in that, The dynamic collaborative loading of the supply chain front-end based on the analyzed user behavior includes: The analyzed user behavior is used to extract module behavior, resulting in a sequence of functional modules; The timestamps of the functional module sequence are extracted to obtain the module switching timestamps; Based on the module switching timestamp, the functional module sequence is collaboratively labeled to obtain the current functional module data and the switched functional module data; Based on the current functional module data, the supply chain front-end module is periodically unloaded; The supply chain front end is preloaded based on the switching function module data.

8. A collaborative system for a micro-frontend supply chain platform developed by multiple teams, used to implement the collaborative method for a micro-frontend supply chain platform developed by multiple teams as described in any one of claims 1-7, characterized in that, The system includes: The development specification module is used to perform functional analysis on the supply chain solution to be developed, obtain the platform function set, and perform functional feature matching and development specifications for multiple teams based on the platform function set to obtain standardized development information. The code review module is used to conduct multi-team module development and specification review of the platform function set based on the specified development information, and to obtain a specified code set. The isolation deployment module is used to isolate and deploy the specified code set and integrate micro-applications to obtain the supply chain front end; The behavior analysis module is used to obtain user behavior logs from the supply chain front end, extract behavioral features from the user behavior logs and perform behavior analysis to obtain analyzed user behavior. The collaborative loading module is used to dynamically and collaboratively load the front end of the supply chain based on the analyzed user behavior.

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