Tenant rapid construction method based on intelligent recommendation drive

Abstract

The invention discloses a tenant rapid construction method based on intelligent recommendation driving, and relates to the technical field of data processing, and the method comprises the steps: building a digital construction middle table; obtaining a target construction demand; performing traversal matching on the target scene demand in a digital scene platform to obtain a target scene group; performing traversal matching on a plurality of role sub-channels in the digital role sub-channels to obtain target role sub-channels; extracting a target role demand in the target construction demand, and matching the target role demand in a plurality of role groups of the target role dividers to obtain a target role group; obtaining a first arbitrary configuration strategy of the target role group, and comparing the first arbitrary configuration strategy with the target permission demand to obtain a first arbitrary comparison result; and generating a target recommendation list based on the first arbitrary comparison result, and performing account construction of the target tenant. The technical problems of low configuration efficiency and poor stability in tenant construction in the prior art are solved, and the technical effect of improving the tenant construction efficiency and accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for quickly building tenants based on intelligent recommendation driving. Background Art

[0002] With the rapid development of enterprise informatization and digitalization, tenant systems have been widely used in many fields. However, with the continuous expansion of enterprise scale and the complexity of business processes, the construction and management of tenant systems are also facing increasing challenges. Traditional tenant construction methods mainly rely on manual configuration and expert experience, which is not only time-consuming and labor-intensive, but also prone to errors, resulting in inconsistent and unstable system configuration. In addition, since the business needs of each tenant are different, experts need to spend a lot of time and energy to understand and meet these needs, resulting in slow response and difficulty in quickly adapting to business changes. Summary of the invention

[0003] The embodiment of the present application provides a method for quickly building tenants based on intelligent recommendation drive, which solves the technical problems of low configuration efficiency and poor stability in tenant construction in the prior art.

[0004] In view of the above problems, an embodiment of the present application provides a tenant rapid construction method driven by intelligent recommendation.

[0005] A first aspect of an embodiment of the present application provides a method for quickly building tenants based on intelligent recommendation drive, the method comprising:

[0006] Analyze historical tenant construction records and establish a digital construction middle platform, wherein the digital construction middle platform includes a digital scene middle platform and a digital role middle platform; obtain target construction requirements, wherein the target construction requirements refer to the requirements for account construction of the target tenant; traverse and match the target scene requirements in the target construction requirements in the digital scene middle platform to obtain a target scene group; traverse and match multiple role sub-stations of the digital role middle platform based on the target scene group to obtain a target role sub-station; extract the target role requirements in the target construction requirements, and match the target role requirements in multiple role groups of the target role sub-station to obtain a target role group; obtain a first arbitrary configuration policy of the target role group, and compare the first arbitrary configuration policy with the target permission requirements in the target construction requirements to obtain a first arbitrary comparison result; generate a target recommendation list based on the first arbitrary comparison result, and construct the account of the target tenant according to the target recommendation list.

[0007] The second aspect of the present application provides a tenant rapid construction system based on intelligent recommendation drive, the system comprising:

[0008] A digital construction platform establishment module, the digital construction platform establishment module analyzes historical tenant construction records and establishes a digital construction platform, wherein the digital construction platform includes a digital scene platform and a digital role platform; a target construction requirement acquisition module, the target construction requirement acquisition module acquires the target construction requirement, and the target construction requirement refers to the requirement for account construction of the target tenant; a first traversal matching module, the first traversal matching module traverses and matches the target scene requirements in the target construction requirements in the digital scene platform to obtain a target scene group; a second traversal matching module, the second traversal matching module traverses and matches multiple role platforms of the digital role platform based on the target scene group A row traversal match is performed to obtain a target role platform; a role requirement extraction and matching module is used, wherein the role requirement extraction and matching module extracts the target role requirement in the target construction requirement, and matches the target role requirement in multiple role groups of the target role platform to obtain a target role group; a comparison module is used, wherein the comparison module obtains a first arbitrary configuration strategy of the target role group, and compares the first arbitrary configuration strategy with the target permission requirement in the target construction requirement to obtain a first arbitrary comparison result; an account construction module is used, wherein the account construction module generates a target recommendation list based on the first arbitrary comparison result, and constructs an account of the target tenant according to the target recommendation list.

[0009] A third aspect of an embodiment of the present application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the intelligent recommendation-driven tenant rapid construction method provided in the present application.

[0010] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent recommendation-driven tenant rapid construction method provided in the present application.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The present application analyzes the historical tenant construction records and establishes a digital construction middle station, wherein the digital construction middle station includes a digital scene middle station and a digital role middle station; obtains the target construction requirements, which refers to the requirements for the account construction of the target tenant; traverses and matches the target scene requirements in the target construction requirements in the digital scene middle station to obtain the target scene group; traverses and matches the multiple role sub-stations of the digital role middle station based on the target scene group to obtain the target role sub-station; extracts the target role requirements in the target construction requirements, and matches the target role requirements in the multiple role groups of the target role sub-station to obtain the target role group; obtains the first arbitrary configuration strategy of the target role group, and compares the first arbitrary configuration strategy with the target permission requirements in the target construction requirements to obtain the first arbitrary comparison result; generates a target recommendation list based on the first arbitrary comparison result, and constructs the account of the target tenant according to the target recommendation list. The present invention solves the technical problems of low configuration efficiency and poor stability in tenant construction in the prior art, establishes a digital middle station by analyzing historical data, matches the target requirements with scenes and roles, generates configuration strategies, and automatically completes account construction, so as to achieve the technical effect of improving the efficiency and accuracy of tenant construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A schematic diagram of a method for quickly building tenants based on intelligent recommendation provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of the structure of a tenant rapid construction system based on intelligent recommendation drive provided in an embodiment of the present application;

[0016] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0017] Explanation of the accompanying drawings: digital construction platform establishment module 11, target construction requirement acquisition module 12, first traversal matching module 13, second traversal matching module 14, role requirement extraction and matching module 15, comparison module 16, account construction module 17, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the technical problems of low configuration efficiency and poor stability in tenant construction in the prior art by providing a tenant rapid construction method driven by intelligent recommendations. By analyzing historical data to establish a digital middle platform, matching target requirements with scenarios and roles, generating configuration strategies, and automatically completing account construction, the technical effect of improving tenant construction efficiency and accuracy is achieved.

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0021] Embodiment 1, as Figure 1 As shown, the present application provides a tenant rapid construction method based on intelligent recommendation drive, the method comprising:

[0022] Step S100: Analyze historical tenant construction records and establish a digital construction middle platform, wherein the digital construction middle platform includes a digital scene middle platform and a digital character middle platform.

[0023] In an embodiment of the present application, first, the construction records of all historical tenants are collected and organized, including each tenant's specific construction requirements, system function usage, configured roles and permissions, and other information. Next, a cluster analysis is performed to find the commonalities and patterns in tenant requirements. Specifically, find out which scenario requirements and role requirements are similar or repetitive between different tenants. According to the results of the cluster analysis, similar scenario requirements are classified to form scenario clusters, and each cluster represents a common business scenario requirement. For example, order management, inventory management, customer service, etc. A digital scenario middle platform is then established to manage these scenario groups in a systematic manner. Similar to scenario requirements, role requirements are also clustered to form role clusters. Each role cluster represents a common role requirement and corresponding permission configuration, such as order administrators, logistics coordinators, customer service representatives, etc. Based on these clustering results, a digital role middle platform is established.

[0024] Finally, the digital scene middle platform and the digital role middle platform are integrated to complete the establishment of the digital construction middle platform.

[0025] Furthermore, the method provided in the application embodiment also includes:

[0026] Extract the first construction information of the first historical tenant in the historical tenant construction record, wherein the first construction information includes the first construction requirement of the first historical tenant; perform cluster analysis on the first scene requirement in the first construction requirement to obtain a scene clustering result, and construct the digital scene middle platform according to the multiple scene clustering clusters in the scene clustering result; perform cluster analysis on the first role requirement in the first construction requirement to obtain a role clustering result, and construct the digital role middle platform according to the multiple role clustering clusters in the role clustering result; the digital scene middle platform and the digital role middle platform constitute the digital construction middle platform.

[0027] In an embodiment of the present application, firstly, the first construction information of the first historical tenant is extracted from the construction records of the historical tenants through data collection and preprocessing technology. This information includes data such as specific construction requirements, system function usage, and role and permission configuration. Then, the first scenario requirement in the extracted first construction requirement is converted into a feature vector using text processing and feature extraction technology. The scenario requirement is segmented to remove stop words and noise words, and then converted into a numerical feature vector using natural language processing technology such as TF-IDF or Word2Vec. Afterwards, cluster analysis technology such as K-means algorithm is applied to analyze these feature vectors, and similar scenario requirements are divided into several clusters to identify common business scenario requirements, such as order management, inventory management, customer service, etc. Based on these clustering results, a digital scene middle platform is established. The digital scene middle platform defines different scene groups, creates standardized scene templates for each group, and enters these templates into the scene management system.

[0028] The same cluster analysis technique is then used to analyze the first role requirement in the first build requirement, classify similar role requirements into the same group, form role clusters, and identify common role requirements and authority configurations, such as order administrator, logistics coordinator, customer service representative, etc.

[0029] Then, a digital role middle platform is established based on the role clustering results. The digital role middle platform defines different role groups, creates standardized role templates for each group, and enters these templates into the role management system. Finally, the digital scene middle platform and the digital role middle platform are integrated together to form a digital construction middle platform.

[0030] Furthermore, the method provided in the application embodiment also includes:

[0031] Extract the first permission requirement in the first construction requirement; traverse the first role in the first role requirement in the first permission requirement to obtain a first role permission set; generate a first configuration strategy based on the first role permission set, wherein the first configuration strategy corresponds to a first role group; and construct the digital role middle platform based on a first corresponding relationship between the first configuration strategy and the first role group.

[0032] In an embodiment of the present application, firstly, the first construction information of the first historical tenant is extracted from the construction record of the historical tenant through data collection and preprocessing technology. Next, the first permission requirement in the extracted first construction requirement is processed using feature extraction technology. The specific steps include using natural language processing technology, such as a bag-of-words model or TF-IDF, to convert the text description of the permission requirement into a numerical feature vector. Then, the first role in the first role requirement is traversed in the extracted first permission requirement to determine the permission set required for each role. The specific steps include using a loop and matching algorithm to analyze the specific configuration of each role in the permission requirement, thereby generating a permission set for each role, i.e., the first role permission set.

[0033] Based on the first role permission set, a corresponding configuration strategy, namely the first configuration strategy, is generated using the rule engine. The strategy defines the mapping relationship between the role and its permission set to ensure the consistency and accuracy of the role requirements and permission configuration.

[0034] Then, according to the correspondence between the first configuration strategy and the first role group, a digital role middle platform is constructed. Through the database management system and system design technology, multiple first configuration strategies are integrated to form a systematic role group and construct a digital role middle platform. The digital role middle platform is used to manage and match the system with different role requirements and permission configurations.

[0035] Step S200: Obtain target construction requirements, where the target construction requirements refer to requirements for account construction of the target tenant.

[0036] In the embodiment of the present application, firstly, the account construction requirements of the target tenant are collected through the demand collection tools and techniques, for example, through online questionnaire surveys. Next, the collected demand information is parsed using natural language processing technology to convert the target tenant's demand text into a format that the system can understand. Then, feature extraction technology is applied to convert the parsed demand information into a feature vector, and the target construction requirements are obtained through this process.

[0037] Step S300: traverse and match the target scene requirements in the target construction requirements in the digital scene center to obtain a target scene group.

[0038] In an embodiment of the present application, when traversal matching is performed in a digital scene center, a matching algorithm is applied to calculate the similarity between the target scene requirement feature vector and the feature vector of each scene group, for example, similarity calculation is performed through cosine similarity, Euclidean distance, etc., to determine which scene groups are most matched with the target scene requirement.

[0039] After completing the traversal matching, the most suitable target scenario group is determined based on the similarity score. For example, if the target scenario demand feature vector has a high similarity with the scenario groups "order management + inventory management + customer service", "order management + customer service" and "order management", these scenario groups are used as part of the target scenario group. The target scenario groups finally obtained include various combinations, such as "order management + inventory management + customer service", "order management + customer service", "order management", etc. The specific selection depends on the similarity score.

[0040] Through the above steps, the target scene requirements are matched with the scene groups in the digital scene center to determine the target scene group.

[0041] Step S400: Based on the target scene group, multiple role sub-platforms of the digital role center are traversed and matched to obtain the target role sub-platform.

[0042] In an embodiment of the present application, all role sub-stations in the digital role center are first traversed by a traversal algorithm. The digital role center pre-contains multiple role sub-stations, each of which corresponds to a role group. The role group is composed of a number of role requirements, which reflect the roles and permissions required in different scenarios. For example, the scenario "order management" includes role groups such as "order administrator", "logistics coordinator", "customer service representative", etc. Next, a matching algorithm is used to calculate the matching degree between each target scenario group and the role groups in each role sub-station. Specifically, the role groups in each role sub-station are checked one by one, and the matching with the role groups in the role sub-station is determined by analyzing the role requirements required for the target scenario group. Then, a similarity calculation method such as cosine similarity or Euclidean distance is used to calculate the similarity between the feature vector of the target scenario group and the feature vector of each role group. The higher the similarity score, the higher the matching degree between the target scenario group and the role group.

[0043] For example, the target scenario group "order management + inventory management + customer service" may require roles including order administrator, inventory administrator and customer service representative. When traversing the role sub-platform, the matching algorithm is used to calculate the similarity between these role requirements and the role groups in the role sub-platform, such as "order administrator + inventory administrator + customer service representative", "order administrator + customer service representative", "inventory administrator + customer service representative", etc.

[0044] According to the calculation results of the matching algorithm, the character station with the highest matching degree is determined as the target character station.

[0045] Step S500: extracting target role requirements from the target construction requirements, and matching the target role requirements with a plurality of role groups of the target role sub-platform to obtain a target role group.

[0046] In an embodiment of the present application, the target role requirements in the target construction requirements are first processed by feature extraction technology. The target role requirements describe which roles and their corresponding permissions the target tenant needs in a specific scenario. For example, a target tenant may need roles such as "order manager", "inventory manager", and "customer service representative". Use natural language processing technology, such as bag-of-words model, TF-IDF, etc. to convert these role requirements into feature vectors.

[0047] Next, a matching algorithm is used to match multiple role groups in the target role sub-platform. The target role sub-platform contains multiple role groups, each of which corresponds to several role requirements. Specifically, a traversal algorithm is used to check all role groups in the target role sub-platform one by one, and a similarity calculation method, such as cosine similarity or Euclidean distance, is applied to calculate the similarity between the feature vector of the target role requirement and the feature vector of each role group. Through the similarity calculation, it is determined which role groups are most matched with the target role requirement.

[0048] Through the above steps, the target role requirements are matched with the role groups in the target role distribution platform to determine the target role groups.

[0049] Step S600: Acquire a first arbitrary configuration policy of the target role group, and compare the first arbitrary configuration policy with the target permission requirement in the target construction requirement to obtain a first arbitrary comparison result.

[0050] In the embodiment of the present application, the configuration policy of each role is first extracted from the target role group. The configuration policy refers to the specific permissions and settings assigned to each role, and these policies are predefined. Next, a role group is randomly selected from the target role group, and the configuration policy of each role in the group is extracted to obtain the first arbitrary configuration policy.

[0051] Then, the first arbitrary configuration strategy is compared with the target permission requirement in the target construction requirement, and the target permission requirement in the target construction requirement is converted into a feature vector using feature extraction technology, and the first arbitrary configuration strategy is converted into a feature vector. A similarity calculation method, such as cosine similarity or Euclidean distance, is used to compare the similarity between the feature vector of the target permission requirement and the feature vector of the configuration strategy. Through similarity calculation, the matching degree between the configuration strategy and the permission requirement is evaluated to obtain the first arbitrary comparison result.

[0052] Step S700: Generate a target recommendation list based on the first arbitrary comparison result, and construct the account of the target tenant according to the target recommendation list.

[0053] In the embodiment of the present application, the target recommendation list includes all the best configuration strategies that have been screened out, and is sorted from high to low by the degree of match. When generating the target recommendation list, the configuration strategies with higher matching degrees are sorted by similarity scores to generate the target recommendation list. For example, if there are configuration strategies A, B, and C corresponding to similarity scores of 0.95, 0.90, and 0.85, respectively, the target recommendation list is A, B, and C.

[0054] Then, the target tenant's account is constructed according to the target recommendation list. Account construction refers to setting and assigning corresponding accounts and permissions to the target tenant according to the recommended configuration strategy. Specifically, the configuration strategy in the target recommendation list is applied to set accounts and permissions for each role of the target tenant in turn. For example, if the best strategy in the recommendation list is "Order Administrator + Inventory Administrator + Customer Service Representative", the corresponding account is configured for the tenant according to the strategy. Use the account management system to create an account for each role according to the configuration strategy and assign corresponding permissions. For example, create an account for the "Order Administrator" and grant it the permissions to view orders, edit orders, and process orders.

[0055] Through the above steps, a target recommendation list is generated based on the first arbitrary comparison result, and an account is constructed for the target tenant.

[0056] Furthermore, the method provided in the application embodiment also includes:

[0057] Read a predetermined labeling scheme; label the first arbitrary configuration strategy and the target permission requirement in sequence according to the predetermined labeling scheme to obtain a first arbitrary label vector and a target label vector, respectively; compare and analyze the first arbitrary label vector and the target label vector to obtain a first arbitrary vector matching degree; record the first arbitrary vector matching degree as the first arbitrary comparison result.

[0058] Furthermore, the method provided in the application embodiment also includes:

[0059] The predetermined labeling scheme includes a labeling scheme of a predetermined authority indicator, and the predetermined authority indicator includes multiple authority indicators.

[0060] In the embodiment of the present application, a predefined labeling scheme is first read. The labeling scheme is a predefined rule set for converting complex permission requirements into label vectors that are easy to compare. The labeling scheme includes various permission indicators, such as viewing permission, editing permission, and deletion permission.

[0061] Next, the first arbitrary configuration policy and the target permission requirement are labeled in turn according to the labeling scheme. Specifically, a configuration file or database is used to read the predefined labeling scheme, and the first arbitrary configuration policy is labeled using natural language processing technology to convert each permission into a corresponding label. For example, if the configuration policy includes the permission to view and edit orders, these permissions are identified using NLP tools and converted into corresponding positions in the label vector. Similarly, the target permission requirement is labeled and its permission requirement is converted into a label vector. Through labeling, the first arbitrary label vector and the target label vector are obtained respectively.

[0062] Then, the first arbitrary label vector and the target label vector are compared and analyzed, and the similarity between them is calculated. By using a similarity calculation method, such as cosine similarity or Euclidean distance, the similarity between the first arbitrary label vector and the target label vector is compared and analyzed, and the calculated similarity is used as the first arbitrary vector matching degree, which indicates the matching degree between the first arbitrary configuration policy and the target permission requirement.

[0063] Finally, the first arbitrary vector matching degree is recorded as the first arbitrary comparison result.

[0064] Furthermore, the method provided in the application embodiment also includes:

[0065] Extract a first permission indicator from the multiple permission indicators, and obtain a first indicator labeling scheme for the first permission indicator based on the predetermined labeling scheme; according to the first indicator labeling scheme, when the first permission indicator is in an authorized open state, mark the first permission indicator as 1, and when the first permission indicator is in an authorized unopened state, mark the first permission indicator as 0.

[0066] In the embodiment of the present application, first, a first permission indicator is randomly extracted from multiple permission indicators. The permission indicator is a specific description of various permissions, such as viewing permission, editing permission, deletion permission, etc. The first permission indicator is a specific permission among these permissions, such as "view order permission".

[0067] Next, the first permission indicator is labeled based on a predetermined labeling scheme. Specifically, the database query technology is used to extract the status information of the first permission indicator through SQL statements. For example, the status information of "view order permission" is extracted. Next, a configuration file or database is used to read the predefined labeling scheme. The labeling scheme is a set of rules that specifies how to convert the permission status into a numerical label. These rules are stored in JSON or XML format and read using a parser.

[0068] Then, the first permission indicator is processed according to the first indicator labeling scheme, that is, the state of the first permission indicator is converted into a numerical label according to the labeling scheme. For example, if the "view order permission" is in the authorized open state, it is marked as 1 according to the labeling scheme; if it is in the unauthorized open state, it is marked as 0.

[0069] Furthermore, the method provided in the application embodiment also includes:

[0070] The first arbitrary label vector is compared with the target label vector, and the matching degree of the first arbitrary vector is calculated by using the Tanimoto similarity coefficient algorithm; wherein the calculation formula of the matching degree of the first arbitrary vector is as follows:

[0071]

[0072] T(P i ,P j ) refers to the first arbitrary vector matching degree between the first arbitrary label vector and the target label vector, P i refers to the first arbitrary label vector, P j refers to the target label vector, s 00 +s 11 It refers to the number of matching pairs between the first arbitrary label vector and the target label vector, s 00 +s 01 +s 10 +s 11 It refers to the total number of matching pairs between the first arbitrary label vector and the target label vector, that is, the number of authority indicators in the predetermined authority indicator.

[0073] In the embodiment of the present application, the first arbitrary label vector is compared with the target label vector, and the matching degree is calculated using the Tanimoto similarity coefficient algorithm.

[0074] The matching degree between the two label vectors is calculated by the above formula. The matching degree calculated in this way is the first arbitrary comparison result, which is used to evaluate the matching situation between the configuration policy and the permission requirement.

[0075] Furthermore, the method provided in the application embodiment also includes:

[0076] A descending list of multiple configuration strategies in the target role group is obtained by descending the first arbitrary vector matching degree, which is recorded as the target recommendation list; a top configuration strategy in the target recommendation list is extracted, wherein the top configuration strategy has an identifier of a top vector matching degree; whether the top vector matching degree meets a predetermined matching degree constraint is determined; if so, an account is constructed for the target tenant based on the top configuration strategy.

[0077] In an embodiment of the present application, firstly, multiple configuration strategies in the target role group are sorted based on the first arbitrary vector matching degree. The first arbitrary vector matching degree is the matching degree between the configuration strategy and the permission requirement calculated by the Tanimoto similarity coefficient. The first arbitrary vector matching degrees of all configuration strategies are sorted in descending order using a sorting algorithm to generate a list from high to low. These configuration strategies are sorted according to their matching degree with the target permission requirement, with the most matching strategy at the front, and the sorted configuration strategy list is the target recommendation list. For example, if there are configuration strategies A, B, and C, and their matching degrees are 0.95, 0.90, and 0.85 respectively, then the target recommendation list is A, B, and C.

[0078] Next, the top configuration strategy, that is, the configuration strategy with the highest matching degree, is extracted from the target recommendation list and marked as the top configuration strategy. For example, if A is the strategy with the highest matching degree, then A is the top configuration strategy.

[0079] Then determine whether the top vector matching degree meets the predetermined matching degree constraint. The matching degree constraint is a pre-set minimum matching degree standard used to ensure high consistency between the configuration policy and the permission requirement. Check whether the matching degree of the top configuration policy is greater than or equal to the predetermined matching degree constraint. For example, if the predetermined matching degree constraint is 0.90, the matching degree of 0.95 of the top configuration policy A is in compliance. If the top vector matching degree meets the predetermined matching degree constraint, the target tenant is account-built based on the top configuration policy. Account building refers to setting and assigning corresponding accounts and permissions to the target tenant according to the recommended configuration policy. Apply the top configuration policy to set accounts and permissions for each role of the target tenant in turn. For example, if the top policy A is "order administrator + inventory administrator + customer service representative", the corresponding account is configured for the tenant according to the policy. During the account creation process, use the account management system to create an account for each role according to the configuration policy and assign corresponding permissions. For example, create an account for the "order administrator" and grant it the permissions to view orders, edit orders, and process orders. Ensure that each created account has the correct permission settings to meet the business needs of the target tenant.

[0080] Furthermore, the method provided in the application embodiment also includes:

[0081] If not, the top configuration strategy is used as the target initial configuration strategy of the target tenant, and the target initial configuration strategy is manually adjusted.

[0082] In the embodiment of the present application, if the top vector matching degree does not meet the predetermined matching degree constraint, the top configuration strategy is used as the target initial matching strategy for the target tenant. Manual adjustments are then made, and the target initial matching strategy is reviewed and adjusted in detail by an expert team or administrator based on the specific needs of the target tenant. This may involve adding or removing permissions, modifying role configurations, etc., to ensure that the final configuration strategy can fully meet the tenant's business needs.

[0083] Furthermore, the method provided in the application embodiment also includes:

[0084] Extract the second top configuration strategy in the target recommendation list; use the second top configuration strategy as a manual adjustment reference to manually adjust the target initial configuration strategy.

[0085] In an embodiment of the present application, the second-highest matching strategy is selected from the target recommendation list, that is, the strategy with the second highest matching degree, and is used as a reference for manual adjustment. For example, if the second-highest matching strategy is B, then B is selected as the adjustment reference. Next, the home team or administrator reviews and adjusts the target initial matching strategy based on the second-highest matching strategy, and uses the second-highest matching strategy B as a reference to make detailed adjustments to the target initial matching strategy A to ensure that the adjusted strategy can meet the predetermined matching degree requirements and adapt to specific business needs.

[0086] Furthermore, the method provided in the application embodiment also includes:

[0087] The first construction information includes the first account naming information of the first historical tenant; the target scenario requirement and the target role requirement constitute a target feature group; the first scenario requirement and the first role requirement constitute a first feature group; the target feature group is compared with the first feature group to obtain a first similarity; when the first similarity reaches a predetermined similarity threshold, the first account prefix in the first account naming information is used as the target account prefix of the target tenant; a predetermined account suffix scheme is obtained, and a target account suffix of the target tenant is generated based on the predetermined account suffix scheme; and the target tenant's account is named according to the target account prefix and the target account suffix.

[0088] In the embodiment of the present application, firstly, the first construction information is extracted from the historical data, including the first account naming information of the first historical tenant. Using database query technology, this information is extracted from the historical database through SQL statements to ensure that complete and accurate account naming rules are obtained. This information includes the naming rules of the account prefix and suffix.

[0089] Next, the target scenario requirements are combined with the target role requirements to form a target feature group. Specifically, feature extraction technology is used to convert the text description requirements into numerical feature vectors through natural language processing technology, such as the bag-of-words model or the TF-IDF method. Similarly, the first scenario requirements are combined with the first role requirements to form a first feature group, and the same feature extraction method is used to convert them into feature vectors. Then, the target feature group is compared with the first feature group, and the similarity between them is calculated. When calculating the similarity, a similarity calculation method, such as cosine similarity, is used to calculate the similarity between the vectors to measure the similarity between the two groups of requirements. The first similarity is obtained through this process.

[0090] When the first similarity reaches the predetermined similarity threshold, the first account prefix in the first account naming information is used as the target account prefix of the target tenant. The similarity threshold is a standard pre-set by the system, which is used to determine whether two sets of requirements are similar enough. If the similarity is higher than the threshold, it is considered that the target tenant's requirements are similar to those of the historical tenants, and their account naming rules are borrowed. Next, obtain the predetermined account suffix scheme. The account suffix scheme is a pre-defined set of rules that contains methods for generating account suffixes, such as timestamps, serial numbers, etc. According to the predetermined account suffix scheme, the target account suffix of the target tenant is generated. For example, the account suffix can be the current year, month, or serial number to ensure the uniqueness of the account.

[0091] Finally, the target tenant's account is named based on the target account prefix and target account suffix. The generated account prefix and suffix are combined to form a complete account name.

[0092] Through the above process, the target tenant's account naming is completed.

[0093] Furthermore, the method provided in the application embodiment also includes:

[0094] The predetermined account suffix scheme refers to a scheme that uses the tenant account creation time as the account suffix.

[0095] In the embodiment of the present application, the account suffix scheme is a predefined set of rules, including methods for generating account suffixes, such as timestamps, serial numbers, etc. The predetermined account suffix scheme refers to a scheme that uses the tenant account creation time as the account suffix. These rules are stored in a configuration file and are obtained using configuration file reading technology.

[0096] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0097] The present application analyzes the historical tenant construction records and establishes a digital construction middle station, wherein the digital construction middle station includes a digital scene middle station and a digital role middle station; obtains the target construction requirements, which refers to the requirements for the account construction of the target tenant; traverses and matches the target scene requirements in the target construction requirements in the digital scene middle station to obtain the target scene group; traverses and matches the multiple role sub-stations of the digital role middle station based on the target scene group to obtain the target role sub-station; extracts the target role requirements in the target construction requirements, and matches the target role requirements in the multiple role groups of the target role sub-station to obtain the target role group; obtains the first arbitrary configuration strategy of the target role group, and compares the first arbitrary configuration strategy with the target permission requirements in the target construction requirements to obtain the first arbitrary comparison result; generates a target recommendation list based on the first arbitrary comparison result, and constructs the account of the target tenant according to the target recommendation list. The present invention solves the technical problems of low configuration efficiency and poor stability in tenant construction in the prior art, establishes a digital middle station by analyzing historical data, matches the target requirements with scenes and roles, generates configuration strategies, and automatically completes account construction, so as to achieve the technical effect of improving the efficiency and accuracy of tenant construction.

[0098] Embodiment 2

[0099] Based on the same inventive concept as the tenant rapid construction method based on intelligent recommendation drive in the aforementioned embodiment, Figure 2 As shown, the present application provides a tenant rapid construction system based on intelligent recommendation drive, and the system and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the system includes:

[0100] A digital construction platform establishment module 11, the digital construction platform establishment module 11 analyzes the historical tenant construction records and establishes a digital construction platform, wherein the digital construction platform includes a digital scene platform and a digital role platform; a target construction requirement acquisition module 12, the target construction requirement acquisition module 12 acquires the target construction requirement, and the target construction requirement refers to the requirement for the account construction of the target tenant; a first traversal matching module 13, the first traversal matching module 13 traverses and matches the target scene requirement in the target construction requirement in the digital scene platform to obtain a target scene group; a second traversal matching module 14, the second traversal matching module 14 classifies multiple roles of the digital role platform based on the target scene group. The target role platform is traversed and matched to obtain the target role platform; a role requirement extraction and matching module 15, the role requirement extraction and matching module 15 extracts the target role requirement in the target construction requirement, and matches the target role requirement in multiple role groups of the target role platform to obtain the target role group; a comparison module 16, the comparison module 16 obtains a first arbitrary configuration strategy of the target role group, and compares the first arbitrary configuration strategy with the target permission requirement in the target construction requirement to obtain a first arbitrary comparison result; an account construction module 17, the account construction module 17 generates a target recommendation list based on the first arbitrary comparison result, and constructs the account of the target tenant according to the target recommendation list.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] Extract the first construction information of the first historical tenant in the historical tenant construction record, wherein the first construction information includes the first construction requirement of the first historical tenant; perform cluster analysis on the first scene requirement in the first construction requirement to obtain a scene clustering result, and construct the digital scene middle platform according to the multiple scene clustering clusters in the scene clustering result; perform cluster analysis on the first role requirement in the first construction requirement to obtain a role clustering result, and construct the digital role middle platform according to the multiple role clustering clusters in the role clustering result; the digital scene middle platform and the digital role middle platform constitute the digital construction middle platform.

[0103] Furthermore, the system is also used to implement the following functions:

[0104] Extract the first permission requirement in the first construction requirement; traverse the first role in the first role requirement in the first permission requirement to obtain a first role permission set; generate a first configuration strategy based on the first role permission set, wherein the first configuration strategy corresponds to a first role group; and construct the digital role middle platform based on a first corresponding relationship between the first configuration strategy and the first role group.

[0105] Furthermore, the system is also used to implement the following functions:

[0106] Read a predetermined labeling scheme; label the first arbitrary configuration strategy and the target permission requirement in sequence according to the predetermined labeling scheme to obtain a first arbitrary label vector and a target label vector, respectively; compare and analyze the first arbitrary label vector and the target label vector to obtain a first arbitrary vector matching degree; record the first arbitrary vector matching degree as the first arbitrary comparison result.

[0107] Furthermore, the system is also used to implement the following functions:

[0108] The predetermined labeling scheme includes a labeling scheme of a predetermined authority indicator, and the predetermined authority indicator includes multiple authority indicators.

[0109] Furthermore, the system is also used to implement the following functions:

[0110] Extract a first permission indicator from the multiple permission indicators, and obtain a first indicator labeling scheme for the first permission indicator based on the predetermined labeling scheme; according to the first indicator labeling scheme, when the first permission indicator is in an authorized open state, mark the first permission indicator as 1, and when the first permission indicator is in an authorized unopened state, mark the first permission indicator as 0.

[0111] Furthermore, the system is also used to implement the following functions:

[0112] The first arbitrary label vector is compared with the target label vector, and the matching degree of the first arbitrary vector is calculated by using the Tanimoto similarity coefficient algorithm; wherein the calculation formula of the matching degree of the first arbitrary vector is as follows:

[0113]

[0114] T(P i ,P j ) refers to the first arbitrary vector matching degree between the first arbitrary label vector and the target label vector, P i refers to the first arbitrary label vector, P j refers to the target label vector, s 00 +s 11 It refers to the number of matching pairs between the first arbitrary label vector and the target label vector, s 00 +s 01 +s 10 +s 11 It refers to the total number of matching pairs between the first arbitrary label vector and the target label vector, that is, the number of authority indicators in the predetermined authority indicator.

[0115] Furthermore, the system is also used to implement the following functions:

[0116] A descending list of multiple configuration strategies in the target role group is obtained by descending the first arbitrary vector matching degree, which is recorded as the target recommendation list; a top configuration strategy in the target recommendation list is extracted, wherein the top configuration strategy has an identifier of a top vector matching degree; whether the top vector matching degree meets a predetermined matching degree constraint is determined; if so, an account is constructed for the target tenant based on the top configuration strategy.

[0117] Furthermore, the system is also used to implement the following functions:

[0118] If not, the top configuration strategy is used as the target initial configuration strategy of the target tenant, and the target initial configuration strategy is manually adjusted.

[0119] Furthermore, the system is also used to implement the following functions:

[0120] Extract the second top configuration strategy in the target recommendation list; use the second top configuration strategy as a manual adjustment reference to manually adjust the target initial configuration strategy.

[0121] Furthermore, the system is also used to implement the following functions:

[0122] The first construction information includes the first account naming information of the first historical tenant; the target scenario requirement and the target role requirement constitute a target feature group; the first scenario requirement and the first role requirement constitute a first feature group; the target feature group is compared with the first feature group to obtain a first similarity; when the first similarity reaches a predetermined similarity threshold, the first account prefix in the first account naming information is used as the target account prefix of the target tenant; a predetermined account suffix scheme is obtained, and a target account suffix of the target tenant is generated based on the predetermined account suffix scheme; and the target tenant's account is named according to the target account prefix and the target account suffix.

[0123] Furthermore, the system is also used to implement the following functions:

[0124] The predetermined account suffix scheme refers to a scheme that uses the tenant account creation time as the account suffix.

[0125] Embodiment three, based on the inventive concept of the tenant rapid construction method driven by intelligent recommendation in the above-mentioned embodiment, the present application also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of any one of the methods described in the above-mentioned embodiment one.

[0126] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In the embodiment, the bus architecture is represented by bus 300, which may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0127] Embodiment 4, based on the tenant rapid construction method driven by intelligent recommendation in the above embodiment, the same inventive concept, the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program implements the steps of any one of the methods described in the above embodiment 1 when executed.

[0128] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0130] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A tenant rapid construction method based on intelligent recommendation, characterized in that: include: Analyze historical tenant construction records and establish a digital construction platform, wherein the digital construction platform includes a digital scene platform and a digital role platform; Obtain target building requirements, where the target building requirements refer to account building requirements of the target tenant; Traverse and match the target scene requirements in the target construction requirements in the digital scene center to obtain a target scene group; Based on the target scene group, multiple role sub-stations of the digital role middle station are traversed and matched to obtain a target role sub-station; Extracting target role requirements from the target construction requirements, and matching the target role requirements with a plurality of role groups of the target role sub-platform to obtain a target role group; Acquire a first arbitrary configuration strategy of the target role group, and compare the first arbitrary configuration strategy with the target permission requirement in the target construction requirement to obtain a first arbitrary comparison result; A target recommendation list is generated based on the first arbitrary comparison result, and the account of the target tenant is constructed according to the target recommendation list.

2. The method for quickly building tenants based on intelligent recommendation drive according to claim 1 is characterized in that: include: Extracting first construction information of a first historical tenant from the historical tenant construction record, where the first construction information includes a first construction requirement of the first historical tenant; Performing cluster analysis on the first scene requirement in the first construction requirement to obtain a scene clustering result, and constructing the digital scene middle platform according to a plurality of scene clustering clusters in the scene clustering result; Performing cluster analysis on the first role requirement in the first construction requirement to obtain a role clustering result, and constructing the digital role middle platform according to a plurality of role clustering clusters in the role clustering result; The digital scene middle platform and the digital character middle platform constitute the digital construction middle platform.

3. The method for quickly building tenants based on intelligent recommendation drive according to claim 2 is characterized in that: include: Extracting a first permission requirement from the first construction requirement; Traverse the first role in the first role requirement in the first permission requirement to obtain a first role permission set; Generating a first configuration policy according to the first role permission set, wherein the first configuration policy corresponds to the first role group; The digital role middle platform is constructed based on a first corresponding relationship between the first configuration strategy and the first role group.

4. According to claim 1, the tenant rapid construction method based on intelligent recommendation driving is characterized in that: include: Reading a predetermined labeling scheme; Labeling the first arbitrary configuration strategy and the target permission requirement in sequence according to the predetermined labeling scheme to obtain a first arbitrary label vector and a target label vector respectively; Comparing and analyzing the first arbitrary label vector and the target label vector to obtain a first arbitrary vector matching degree; The first arbitrary vector matching degree is recorded as the first arbitrary comparison result.

5. The method for quickly building tenants based on intelligent recommendation drive according to claim 4 is characterized in that: The predetermined labeling scheme includes a labeling scheme of a predetermined authority indicator, and the predetermined authority indicator includes multiple authority indicators.

6. The method for quickly building tenants based on intelligent recommendation drive according to claim 5 is characterized in that: include: Extracting a first authority indicator from the plurality of authority indicators, and obtaining a first indicator labeling scheme for the first authority indicator based on the predetermined labeling scheme; According to the first indicator labeling scheme, when the first permission indicator is in an authorized open state, the first permission indicator is marked as 1, and when the first permission indicator is in an authorized unopened state, the first permission indicator is marked as 0.

7. The method for quickly building tenants based on intelligent recommendation drive according to claim 6 is characterized in that: include: Comparing the first arbitrary label vector with the target label vector, and calculating the matching degree of the first arbitrary vector using the Tanimoto similarity coefficient algorithm; The calculation formula of the first arbitrary vector matching degree is as follows: T(P i ,P j ) refers to the first arbitrary vector matching degree between the first arbitrary label vector and the target label vector, P i refers to the first arbitrary label vector, P j refers to the target label vector, s 00 +s 11 It refers to the number of matching pairs between the first arbitrary label vector and the target label vector, s 00 +s 01 +s 10 +s 11 It refers to the total number of matching pairs between the first arbitrary label vector and the target label vector, that is, the number of authority indicators in the predetermined authority indicator.

8. The method for quickly building tenants based on intelligent recommendation drive according to claim 1 is characterized in that: include: A descending list of multiple configuration strategies in the target role group is obtained by performing descending order of the first arbitrary vector matching degree, which is recorded as the target recommendation list; Extracting a top configuration strategy from the target recommendation list, wherein the top configuration strategy has an identifier of a top vector matching degree; Determining whether the top bit vector matching degree meets a predetermined matching degree constraint; If it is in compliance, an account is constructed for the target tenant based on the top configuration strategy.

9. The method for quickly building tenants based on intelligent recommendation drive according to claim 8 is characterized in that: If not, the top configuration strategy is used as the target initial configuration strategy of the target tenant, and the target initial configuration strategy is manually adjusted.

10. The method for quickly building tenants based on intelligent recommendation drive according to claim 9, characterized in that: include: Extracting the next top configuration strategy in the target recommendation list; The target initial allocation strategy is manually adjusted using the secondary top allocation strategy as a reference for manual adjustment.

11. The method for quickly building tenants based on intelligent recommendation drive according to claim 2, characterized in that: Also includes: The first construction information includes first account naming information of the first historical tenant; The target scene requirements and the target role requirements constitute a target feature group; The first scenario requirement and the first role requirement form a first feature group; Comparing the target feature group with the first feature group to obtain a first similarity; When the first similarity reaches a predetermined similarity threshold, using the first account prefix in the first account naming information as the target account prefix of the target tenant; Obtaining a predetermined account suffix scheme, and generating a target account suffix of the target tenant based on the predetermined account suffix scheme; The target tenant's account is named according to the target account prefix and the target account suffix.

12. The method for quickly building tenants based on intelligent recommendation drive according to claim 11, characterized in that: The predetermined account suffix scheme refers to a scheme that uses the tenant account creation time as the account suffix.

13. A tenant rapid construction system driven by intelligent recommendation, characterized by: The system comprises: A digital construction platform establishment module, wherein the digital construction platform establishment module analyzes historical tenant construction records and establishes a digital construction platform, wherein the digital construction platform includes a digital scene platform and a digital role platform; A target building requirement acquisition module, wherein the target building requirement acquisition module acquires a target building requirement, wherein the target building requirement refers to a requirement for account building of a target tenant; A first traversal matching module, wherein the first traversal matching module traverses and matches the target scene requirements in the target construction requirements in the digital scene center to obtain a target scene group; A second traversal matching module, which performs traversal matching on a plurality of character sub-stations of the digital character mid-station based on the target scene group to obtain a target character sub-station; A role requirement extraction and matching module, wherein the role requirement extraction and matching module extracts target role requirements from the target construction requirements, and matches the target role requirements with a plurality of role groups of the target role sub-platform to obtain a target role group; A comparison module, wherein the comparison module obtains a first arbitrary configuration strategy of the target role group, and compares the first arbitrary configuration strategy with the target permission requirement in the target construction requirement to obtain a first arbitrary comparison result; An account construction module, wherein the account construction module generates a target recommendation list based on the first arbitrary comparison result, and constructs the account of the target tenant according to the target recommendation list.

14. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the intelligent recommendation-driven tenant rapid construction method described in any one of claims 1 to 12 when executing the executable instructions stored in the memory.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for quickly building tenants based on intelligent recommendation drive as described in any one of claims 1 to 12 is implemented.

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