A model-driven intelligent operation and maintenance portal method

Abstract

The present invention relates to the field of operation and maintenance communication technology, and specifically discloses a model-driven intelligent operation and maintenance middle platform portal method, including: obtaining real-time business data connected to the portal interface and historical business data imported from the business database, and importing the real-time business data and the historical business data into the data middle platform for preprocessing; the data middle platform matches the real-time business data with the business process information to generate a state association value, inputs the state association value and the historical business data into a machine learning model, and outputs a business association coefficient; according to the user end of the portal accessing the business middle platform, identity authentication judgment is performed, and after the identity authentication judgment is passed, the user end login information is recorded and cached; the application end generates business equipment history data based on the different business user end login information obtained by the business middle platform and the business association coefficient obtained by the data middle platform; the business equipment history data is imported into the data query engine server to obtain the query results and visualize them.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance communication technology, and in particular to a model-driven intelligent operation and maintenance middle-end portal method. Background Art

[0002] The integrated application of the data center in the intelligent operation and maintenance system is the core of intelligent operation and maintenance management. In the integrated application process, the data center serves as the central node for data collection and processing. It usually needs to connect with other business interfaces to achieve comprehensive data connection, obtain the business history data corresponding to the interface, and conduct intuitive query and understanding of the business history data.

[0003] Publication No. CN113849755A provides a model-driven intelligent operation and maintenance middle platform portal method and device, which "constructs the contact model file for each scenario according to the contact model specification of each business component of the intelligent operation and maintenance middle platform", that is, by designing the contact model, the middle platform portal and business components are displayed through each contact in the middle platform portal, and the contact is used as the functional entrance for users to operate the business, providing a middle platform portal bound by scenario, realizing the unification of each business terminal user group and portal, so as to quickly adapt to the display requirements of different terminal users for the portal under the middle platform architecture;

[0004] However, the following problems still exist in the above public content:

[0005] First, the contact model file needs to be independently generated based on the scenario, and situations with a large number of business components and complex scenarios will cause a surge in model files, increasing the operation and maintenance burden and cost; second, there is no caching strategy for the contact model during the operation and maintenance process, and the rules and restrictions on end-user access through the portal lead to an increased risk of repeated access loading and user information security leakage. Summary of the Invention

[0006] The purpose of this invention is to provide a model-driven intelligent operation and maintenance platform portal method to solve the following technical problems:

[0007] How to build a data model based on the correlation of business status, realize real-time authentication based on user access information, and avoid repeated data loading.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A model-driven intelligent operation and maintenance middle platform portal method, wherein the intelligent operation and maintenance middle platform portal includes a portal, an application end, a business middle platform, and a data middle platform; the method includes:

[0010] S1. The API interface of the data center obtains real-time business data and historical business data imported from the business database by connecting to the business center, and imports the real-time business data and historical business data into the data center for preprocessing;

[0011] S2. After preprocessing, the data center matches real-time business data with business process information to generate a state association value. The state association value and historical business data are input into the machine learning model to output the business association coefficient.

[0012] S3. Perform identity authentication based on the portal user's access to the business middle platform. After the identity authentication is passed, the user's login information is recorded and cached.

[0013] S4. The application side generates business equipment history data based on the different business user terminal login information obtained by the business middle platform and the business correlation coefficient obtained by the data middle platform;

[0014] S5. Import the business equipment history data into the data query engine server to obtain the query results and visualize them.

[0015] Preferably, the business process information includes:

[0016] Build a business middle-end architecture generation process platform;

[0017] The process platform initiates process design: design process forms based on business scenario requirements and build business applications;

[0018] The business information received is written into the process form through the business application; the process form divides the business process and generates business process information; the business process information is divided into status according to the degree of business completion. The business process information consists of an identifier, business status, and a value range. The business process information is written by calling the data middle platform interface.

[0019] Preferably, the specific process of the data center matching real-time business data with business process information to generate state association values is as follows:

[0020] The data center determines the real-time business data standard: analyze the data items containing business status in the real-time business data, filter the data items containing business status as "1", and the data items not containing business status as "0"; count the ratio of "1" in all data items, and use this ratio as the status association value.

[0021] Preferably, the state association value and historical business data are input into a machine learning model to output a business association coefficient, including:

[0022] S21. Count the number of all characters in the historical business data that contain the business status data item, select the state-related value and the character count of the corresponding business status data item as a positive sample; and select the character count of other business status data items in the historical business data that do not contain the state-related value as a negative sample;

[0023] S22, inputting the positive sample and the negative sample into the pre-trained recurrent neural network model to establish a state association model;

[0024] S23. Input the state association value and the corresponding real-time business data into the state association model to obtain the business association coefficient:

[0025] By formula Calculate the business correlation coefficient ;in, To include business status, Does not include business status; The first The number of characters in the data item; The first The number of characters in the data item; is the state-related value; is the total number of business status data items, The total number of items excluding business status data items.

[0026] Preferably, the process of performing identity authentication based on the portal user accessing the service center includes:

[0027] S31. The business user terminal accesses the business middle platform through the portal;

[0028] S32. Start user management at the business center and obtain the business user authority level :

[0029] ;in, is the user role, It is a mapping function from roles to permissions; this process ensures the security of the system and the protection of data;

[0030] S33. Determine the confirmation request association based on the business user's authority level and determine whether the requested association information is qualified. If so, associate the service catalog item. If not, indicate that there is no authority.

[0031] S34. Retrieve the business user's identity information according to the associated service catalog item and perform processing confirmation; the processing confirmation includes: automatically processing and confirming according to the defined workflow or forwarding to the corresponding approver to confirm and determine the service processing time.

[0032] Preferably, the process of generating business equipment history data is as follows:

[0033] S41, log event stream is connected to Kafka in real time;

[0034] S42, Flink window function correlation coefficient stream;

[0035] S43, regularly cleaning the login information and correlation coefficient data cache according to the service processing time;

[0036] S44. Dynamically update device history to Redis or Elasticsearch.

[0037] Preferably, the process of importing the business equipment history data into the data query engine server to obtain the query results and visualize them is as follows:

[0038] S51. Export business equipment history data from Redis or Elasticsearch;

[0039] S52, transfer files and load real-time synchronization business equipment history data into the query engine;

[0040] S53. Providing a query interface for external calls: calling the query interface through a client tool or programming language to obtain structured results;

[0041] S54. Display the query structured results through visualization tools to obtain equipment correlation coefficient trends and operation records.

[0042] Beneficial effects of the present invention:

[0043] (1) The present invention ensures data interconnection between the data middle station and the business middle station by preprocessing real-time data and historical data through the data middle station; the data middle station matches the preprocessed real-time business data with the business process information to generate a state association value, and inputs the state association value and historical business data into the machine learning model, and outputs the business association coefficient; it realizes the construction of the model based on the business data, explores the correlation of the business status, and can further drive the model according to the data correlation of the business status, reduce the authentication of the user-side access information, and avoid the repeated loading process of data.

[0044] (2) The present invention performs identity authentication based on the user end of the portal accessing the business middle platform, and realizes the user end identification at the login entry confirmation interface of the portal through the business middle platform, realizes the identity authentication of the user end, ensures the user's safe login process, and after the user identity authentication security judgment is passed, the user end login information is recorded and cached; avoids the process of repeated confirmation based on the business information input, and reduces the system operation burden.

[0045] (3) Generate business equipment history data based on the login information of different business users obtained by the business middle platform and the business correlation coefficient obtained by the data middle platform; and based on the history information of the business equipment, the use and processing records of the equipment can be directly judged to realize the real-time update processing of the equipment information; import the business equipment history data into the data query engine server to obtain the query results and visualize them; through the visualization process, the structured display of the equipment query can be realized, and the records of equipment operation and call information can be intuitively presented, which is convenient for further intelligent business operation and maintenance process.

[0046] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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.

[0048] Figure 1 This is a step diagram of a model-driven intelligent operation and maintenance middle platform portal method of the present invention;

[0049] Figure 2 This is the architecture diagram of the intelligent operation and maintenance middle platform portal of the present invention;

[0050] Figure 3 This is a step diagram of the identity authentication process for the user end of the portal of the present invention to access the business middle platform. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] First, the contact model file needs to be independently generated based on the scenario, and situations with a large number of business components and complex scenarios will cause a surge in model files, increasing the operation and maintenance burden and cost; second, there is no caching strategy for the contact model during the operation and maintenance process, and the rules and restrictions on end-user access through the portal lead to an increased risk of repeated access loading and user information security leakage.

[0053] Due to the large number of actual business components and complex operating scenarios, the existing contact model files are unable to cope with the surge in model files. In actual use, the contact model has no corresponding caching strategy, and repeated prompts are required for real-time loaded business data. In addition, there may be loopholes in user binding and permission management, making it difficult to ensure user information security. Therefore, the contact model is not suitable for a wide range of business operation scenarios. In actual use, it is necessary to start from the business information itself, explore the relevance of business status, and set up a model that can digitize the relevance of business status to achieve real-time authentication based on user access information and avoid repeated data loading.

[0054] Based on the above problems and solutions, please refer to Figure 1-2 As shown, the present invention is a model-driven intelligent operation and maintenance platform portal method, the method includes:

[0055] S1. The API interface of the data center obtains real-time business data and historical business data imported from the business database by connecting to the business center, and imports the real-time business data and historical business data into the data center for preprocessing;

[0056] S2. After preprocessing, the data center matches real-time business data with business process information to generate a state association value. The state association value and historical business data are input into the machine learning model to output the business association coefficient.

[0057] S3. Perform identity authentication based on the portal user's access to the business middle platform. After the identity authentication is passed, the user's login information is recorded and cached.

[0058] S4. The application side generates business equipment history data based on the different business user terminal login information obtained by the business middle platform and the business correlation coefficient obtained by the data middle platform;

[0059] S5. Import the business equipment history data into the data query engine server to obtain the query results and visualize them.

[0060] In the above technical solution, for the design of the intelligent operation and maintenance middle platform portal, we designed the portal, application end, business middle platform and data middle platform, among which the portal and application end provide services for the front end; the business middle platform and data middle platform serve as the back end, usually performing data processing and business arrangements. By setting up the division of the business middle platform and the data middle platform, we can further cope with situations with a large number of business components and complex scenarios, and solve problems such as changeable business management processes and no standardized system to meet functional requirements based on various business types. Through the business middle platform, we can quickly meet various business customization needs and effectively improve the business's scalability and reuse capabilities. The data middle platform is mainly used to realize data integration, data processing and data storage processes, and use the data analysis method in the data middle platform to solve data caching problems.

[0061] Specifically:

[0062] First, the API interface of the data middle platform obtains real-time business data and historical business data imported from the business database by connecting to the business middle platform, and imports the real-time business data and historical business data into the data middle platform for preprocessing. The preprocessing of real-time data and historical data ensures the data interconnection and interoperability between the data middle platform and the business middle platform and the extraction of valid data; then, the data middle platform matches the preprocessed real-time business data with the business process information to generate a state association value, and inputs the state association value and historical business data into the machine learning model to output the business association coefficient; this process mainly realizes the construction of the model based on business data, and attempts to explore the correlation of business status, which can further drive the model according to the data correlation of the business status, reduce the authentication of the user-side access information, and avoid the repeated loading of data.

[0063] Furthermore, the portal's user terminal accesses the business middle platform for identity authentication, and the business middle platform recognizes the user terminal at the portal's login confirmation interface to achieve user terminal identity authentication, ensuring a secure login process for the user. After the user's identity authentication security is determined to be passed, the user terminal login information is recorded and cached, thereby avoiding the process of repeated confirmation based on business information input and reducing the burden on system operation.

[0064] Since the front end includes the portal and the application end, the application end is the process of recording and transmitting the relevant equipment information after the business processing obtained by the back end. It mainly generates business equipment history data based on the login information of different business user terminals obtained by the business middle end and the business correlation coefficient obtained by the data middle end; and based on the history information of the business equipment, the use and processing records of the equipment can be directly judged to realize the real-time update processing of the equipment information; finally, the business equipment history data is imported into the data query engine server to obtain the query results and visualize them; through the visualization process, the structured display of equipment queries can be realized, and the records of equipment operations and call information can be intuitively presented, which is convenient for further intelligent business operation and maintenance processes.

[0065] As an embodiment of the present invention, the business process information includes:

[0066] Build a business middle-end architecture generation process platform;

[0067] The process platform initiates process design: design process forms based on business scenario requirements and build business applications;

[0068] The business information received is written into the process form through the business application; the process form divides the business process and generates business process information; the business process information is divided into status according to the degree of business completion. The business process information consists of an identifier, business status, and a value range. The business process information is written by calling the data middle platform interface.

[0069] In the above technical solution, the interpretation of the business process requires understanding the business scenario, and further realizing business application for business scenario requirements through the process platform of the business middle platform; the business application is achieved by writing the process of the accessed business information, and further dividing the business process according to the generated process form, and generating business process information, and then dividing the business status. The business status is divided according to the degree of business completion to ensure that the business completion degree is subsequently matched with real-time data.

[0070] For example, the business process is the order processing process of the e-commerce platform; building a business middle-end architecture requires setting up an order management platform as a process platform to support multiple types of business lines, such as process collaboration of self-operated e-commerce, third-party merchants, and cross-border purchases; technical implementation usually uses the Camunda process engine to build a configurable process platform; process forms are composed of process designers and form engines to divide business processes; business processes include business completion (business status); in actual use, for example, in the order payment process form, for the completion (payment status) of a certain business device (commodity), since the business process information consists of an identifier, business status, and value domain, the business process information is written by calling the data middle-end interface; in the commodity purchase process: the identifier is the commodity code, the business status is the payment completion process; the value domain is the record information of the commodity; see the table below for details:

[0071] In the table: "null" indicates that payment is pending; "299.00" indicates the payment amount; "self_operation" indicates autonomous operation; "WeChat Pay" indicates that the payment method is WeChat Pay; "2023-07-20 14:30" indicates the payment date; "Shipping" indicates that the product status is in the process of being shipped; "YT4001234567" indicates the order number.

[0072] As an embodiment of the present invention, the specific process of the data center matching real-time business data with business process information to generate state association values is as follows:

[0073] The data center determines the real-time business data standard: analyze the data items containing business status in the real-time business data, filter the data items containing business status as "1", and the data items not containing business status as "0"; count the ratio of "1" in all data items, and use this ratio as the status association value.

[0074] In the above technical solution, when the business process information of a business device containing business status in real-time business data is used as a data item, the data item containing business status is 1, and the data item not containing business status is 0, then the generated business status data item sequence is, for example: {1, 0, 0, 1, ..., 0, 1}, and the total number of the sequence is counted as K; the number j of "1" in the data item is recorded; the value of j / K is used as the state association value; and the larger the state association value, the greater the proportion of the state of business processing performed by the business device; the greater the degree of matching between the business device and the business process information in the real-time business data; corresponding to the order processing process of the e-commerce platform, the greater the payment selectivity of the product.

[0075] As an embodiment of the present invention, the state association value and historical business data are input into a machine learning model to output a business association coefficient, including:

[0076] S21. Count the number of all characters in the historical business data that contain the business status data item, select the state-related value and the character count of the corresponding business status data item as a positive sample; and select the character count of other business status data items in the historical business data that do not contain the state-related value as a negative sample;

[0077] S22, inputting the positive sample and the negative sample into the pre-trained recurrent neural network model to establish a state association model;

[0078] S23. Input the state association value and the corresponding real-time business data into the state association model to obtain the business association coefficient:

[0079] By formula Calculate the business correlation coefficient ;in, To include business status, Does not include business status; The first The number of characters in the data item; The first The number of characters in the data item; is the state-related value; is the total number of business status data items, The total number of items excluding business status data items.

[0080] In the above technical solution, a new model is constructed based on the state association value and historical business data as the training input of the machine model, and the corresponding coefficients are output after the new model is built. The specific method is:

[0081] First, by counting all the characters in the historical business data that contain business status data items, where the business status data items include preset keywords or semantic fragments, such as: "product name\to be paid", "product name\paid", "product name\completed"; calculate the number of characters of its keywords; among them, "product name\to be paid", "product name\paid", "product name\completed", the corresponding number of characters output is [6+1+6]; and those that do not contain business status data items, such as: "product name", "product name", "product name", "product name", the corresponding number of characters output is [6]; by filtering the status associated value and the number of characters of its corresponding business status data item as positive samples; filtering the number of characters of other business status data items in the historical business data that do not contain the status associated value as negative samples;

[0082] Then, the positive and negative samples are input into the pre-trained recurrent neural network model to establish a state association model; the state association value and the corresponding real-time business data are input into the state association model to obtain the business association coefficient; the recurrent neural network model is a GRU network, whose input layer includes a character embedding layer and a position encoding layer, and the output layer is a fully connected layer; the state association value represents the weight of the business state item appearing in the historical data item; therefore, the calculation process is through the formula Calculate the business correlation coefficient ;in, To include business status, Does not include business status; The first The number of characters in the data item; The first The number of characters in the data item; is the state-related value; 、 is the total number of data items;

[0083] By building a specific machine model, that is, building a state association model, parallel processing of current business state items can be achieved, the correlation prediction of business data items can be improved, and the accurate generation of business equipment usage information can be promoted.

[0084] As an embodiment of the present invention, please refer to Figure 3 As shown in the figure, the process of identity authentication based on the portal user accessing the business middle platform includes:

[0085] S31. The business user terminal accesses the business middle platform through the portal;

[0086] S32. Start user management at the business center and obtain the business user authority level :

[0087] ;in, is the user role, It is a mapping function from roles to permissions; this process ensures the security of the system and the protection of data;

[0088] S33. Determine the confirmation request association based on the business user's authority level and determine whether the requested association information is qualified. If so, associate the service catalog item. If not, indicate that there is no authority.

[0089] S34. Retrieve the business user's identity information according to the associated service catalog item and perform processing confirmation; the processing confirmation includes: automatically processing and confirming according to the defined workflow or forwarding to the corresponding approver to confirm and determine the service processing time.

[0090] In the above technical solution, the portal is used as the API access point to help the user end access the business middle platform. This process is mainly an identity authentication judgment process. The specific steps are: first, the business user end accesses the business middle platform through the portal, and then the business middle platform starts user management and obtains the business user's permission level This process is to activate the business middle platform to identify the business user information; the calculation formula is ;in, is the user role, It is a mapping function from roles to permissions; this process ensures the security of the system and the protection of data; then, the confirmation request association is determined based on the business user's permission level. This process is to identify the user's permissions and determine whether the user has violated the rules. The judgment method is: determine whether the request association information is qualified. If so, associate the service catalog item. If not, prompt that there is no permission information; the standard for judging eligibility is determined according to the internal protocol of the business middle platform; during use, you only need to know that the security level of the request is defined by a composite function, which needs to take into account the user's role and the role permission set defined by the system. Finally, the business user's identity information is retrieved according to the associated service catalog item and processed and confirmed; processing confirmation is: automatically processing and confirming according to the defined workflow or forwarding it to the corresponding approver to confirm and determine the service processing time; in the service management and application approval link, the system processes service requests through standardized processes. From service application to approval, each request is associated with a specific service catalog item and automatically processed or forwarded to the corresponding approver according to the defined workflow. This process generates service time: ; According to the complexity of the service , the number of pending requests ; This approach optimizes the service delivery process and improves the efficiency and responsiveness of service management.

[0091] As an embodiment of the present invention, the process of generating business equipment history data is as follows:

[0092] S41, log event stream is connected to Kafka in real time;

[0093] S42, Flink window function business correlation coefficient stream;

[0094] S43, regularly cleaning the login information and business correlation coefficient data cache according to the service processing time;

[0095] S44. Dynamically update device history to Redis or Elasticsearch.

[0096] In the above technical solution, first, through Kafka consumer displacement management, it is ensured that automatic offset submission is enabled and failed events are written to the dead letter queue. Then, Flink checkpoint configuration is performed to confirm the business correlation coefficient; and the login information and business correlation coefficient data cache are regularly cleaned according to the service processing time to reduce the data of low-frequency access devices and dynamically process expired events, including: device active cycle and cold data retention period; finally, the device history is dynamically updated to Redis or Elasticsearch to realize the timely generation of business device history data.

[0097] As an embodiment of the present invention, the process of importing business equipment history data into a data query engine server to obtain query results and visualize them is as follows:

[0098] S51. Export business equipment history data from Redis or Elasticsearch;

[0099] S52, transfer files and load real-time synchronization business equipment history data into the query engine;

[0100] S53. Providing a query interface for external calls: calling the query interface through a client tool or programming language to obtain structured results;

[0101] S54. Display the query structured results through visualization tools to obtain equipment correlation coefficient trends and operation records.

[0102] In the above technical solution, the business equipment history data is visualized by importing the business equipment history data into the data query engine server to obtain the query results and visualize them, ensuring that the development trend of the business equipment correlation coefficient is obtained in a timely manner, thereby improving the intelligent data operation and maintenance process of the business equipment.

[0103] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0104] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the scope of protection of the present invention.

Claims

1. A model-driven intelligent operation and maintenance middle platform portal method, wherein the intelligent operation and maintenance middle platform portal includes a portal, an application end, a business middle platform, and a data middle platform; characterized in that: The method comprises: S1. The API interface of the data center obtains real-time business data and historical business data imported from the business database by connecting to the business center, and imports the real-time business data and historical business data into the data center for preprocessing; S2. After preprocessing, the data center matches real-time business data with business process information to generate a state association value. The state association value and historical business data are input into the machine learning model to output the business association coefficient. S3. Perform identity authentication based on the portal user's access to the business middle platform. After the identity authentication is passed, the user's login information is recorded and cached. S4. The application side generates business equipment history data based on the different business user terminal login information obtained by the business middle platform and the business correlation coefficient obtained by the data middle platform; S5. Import the business equipment history data into the data query engine server to obtain the query results and visualize them.

2. A model-driven intelligent operation and maintenance portal method according to claim 1, characterized in that: The business process information includes: Build a business middle-end architecture generation process platform; The process platform initiates process design: design process forms based on business scenario requirements and build business applications; The business information received is written into the process form through the business application; the process form divides the business process and generates business process information; the business process information is divided into status according to the degree of business completion, and the business process information consists of an identifier, business status, and a value domain, and the business process information is written by calling the data middle platform interface.

3. A model-driven intelligent operation and maintenance platform portal method according to claim 2, characterized in that: The specific process of the data center matching real-time business data with business process information to generate state association values is as follows: The data center determines the real-time business data standard: analyze the data items containing business status in the real-time business data, filter the data items containing business status as "1", and the data items not containing business status as "0"; count the ratio of "1" in all data items, and use this ratio as the status association value.

4. A model-driven intelligent operation and maintenance platform portal method according to claim 3, characterized in that: The state association value and historical business data are input into the machine learning model to output the business association coefficient, including: S21. Count the number of all characters in the historical business data that contain the business status data item, select the state-related value and the character count of the corresponding business status data item as a positive sample; and select the character count of other business status data items in the historical business data that do not contain the state-related value as a negative sample; S22, inputting the positive sample and the negative sample into the pre-trained recurrent neural network model to establish a state association model; S23. Input the state association value and the corresponding real-time business data into the state association model to obtain the business association coefficient: By formula Calculate the business correlation coefficient ;in, To include business status, Does not include business status; The first The number of characters in the data item; The first The number of characters in the data item; is the state-related value; is the total number of business status data items, The total number of items excluding business status data items.

5. A model-driven intelligent operation and maintenance platform portal method according to claim 1, characterized in that: The process of performing identity authentication based on the portal user accessing the service center includes: S31. The business user terminal accesses the business middle platform through the portal; S32. The business middle platform starts user management and obtains the business user authority level; S33. Determine the confirmation request association based on the business user's authority level and determine whether the requested association information is qualified. If so, associate the service catalog item. If not, indicate that there is no authority. S34. Retrieve the business user's identity information according to the associated service catalog item and perform processing confirmation; the processing confirmation includes: automatically processing and confirming according to the defined workflow or forwarding to the corresponding approver to confirm and determine the service processing time.

6. A model-driven intelligent operation and maintenance platform portal method according to claim 5, characterized in that: The process of generating the business equipment history data is as follows: S41, log event stream is connected to Kafka in real time; S42, Flink window function correlation coefficient stream; S43, regularly cleaning the login information and correlation coefficient data cache according to the service processing time; S44. Dynamically update device history to Redis or Elasticsearch.

7. A model-driven intelligent operation and maintenance platform portal method according to claim 1, characterized in that: The process of importing the business equipment history data into the data query engine server to obtain the query results and visualize them is as follows: S51. Export business equipment history data from Redis or Elasticsearch; S52, transfer files and load real-time synchronization business equipment history data into the query engine; S53. Providing a query interface for external calls: calling the query interface through a client tool or programming language to obtain structured results; S54. Display the query structured results through visualization tools to obtain equipment correlation coefficient trends and operation records.

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