Model-driven intelligent operation and maintenance platform portal method

Abstract

The invention relates to the technical field of operation and maintenance communication, and particularly discloses a model-driven intelligent operation and maintenance platform portal method, which comprises the following steps: acquiring real-time business data butted with a portal interface and historical business data imported by a business database, and importing the real-time business data and the historical business data into a data platform for preprocessing; the data medium station matches the real-time business data with the business process information to generate a state association value, inputs the state association value and historical business data into a machine learning model, and outputs a business association coefficient; performing identity authentication judgment according to a user side access service platform of the portal, and recording and caching user side login information after the identity authentication judgment is passed; the application side generates service equipment resume data according to different service user side login information acquired by the service middle station and the service association coefficient acquired by the data middle station; and importing the resume data of the service equipment into a data query engine server to obtain a query result and visualize the query result.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance communication, and particularly relates to a model-driven intelligent operation and maintenance middle platform portal method. Background Art

[0002] The integrated application of the data middle platform in the intelligent operation and maintenance system is the core of realizing intelligent operation and maintenance management; in the process of integrated application, taking the data middle platform as the central node for data collection and processing, it is usually necessary to dock with other business interfaces to achieve comprehensive data docking, obtain the business resume data corresponding to the interfaces, and perform intuitive queries and understandings on the business resume data; Publication No. CN113849755A provides a model-driven intelligent operation and maintenance middle platform portal method and device, which "constructs the contact model files for each scenario according to the contact model specification of the intelligent operation and maintenance middle platform", that is, through the design of 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 function entry for users to operate the business, providing a middle platform portal bound by scenarios, so as to unify each business terminal user group and the portal, so as to quickly adapt to the display requirements of different terminal users for the portal under the middle platform architecture; However, the above disclosed content still has the following problems: Firstly, the contact model files need to be independently generated depending on the scenarios, and in the case of a large number of business components and complex scenarios, it will cause a sharp increase in model files, increasing the operation and maintenance burden and operation and maintenance costs; secondly, there is no caching strategy for the contact model and rule restrictions on access by terminal users through the portal during the operation and maintenance process, resulting in an increased risk of repeated access and loading and leakage of user information security. Summary of the Invention

[0003] The purpose of the present invention is to provide a model-driven intelligent operation and maintenance middle platform portal method to solve the following technical problems: How to construct a data-based model according to the relevance of business states to realize real-time authentication of access information at the user end and avoid the process of repeated data loading.

[0004] The purpose of the present invention can be achieved through the following technical solutions: 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: S1. The API interface of the data middle platform obtains real-time business data and historical business data imported from the business database by docking with the business middle platform, and imports the real-time business data and historical business data into the data middle platform for preprocessing; S2. After preprocessing, the data middle platform matches the real-time business data with the business process information to generate a status correlation value, inputs the status correlation value and the historical business data into a machine learning model, and outputs a business correlation coefficient; S3. According to the user side of the portal accessing the business middle platform for identity authentication judgment, after the identity authentication judgment passes, record the user side login information and cache it; S4. The application side generates business device resume data according to the different business user side login information obtained by the business middle platform and the business correlation coefficient obtained by the data middle platform; S5. Import the business device resume data into the data query engine server to obtain a query result and visualize it.

[0005] Preferably, the business process information includes: Build a business middle platform architecture to generate a process platform; The process platform initiates process design: design a process form according to the business scenario requirements and build a business application; Write the business information accessed through the business application into the process form; the process form divides the business process to generate business process information; the business process information is divided into states according to the business completion degree, and the business process information consists of an identifier, a business state, and a value range, and writes the business process information by calling the data middle platform interface.

[0006] Preferably, the specific process of the data middle platform matching the real-time business data with the business process information to generate a status correlation value is: The data middle platform determines the real-time business data standard: analyze the data items containing business states in the real-time business data, screen the data items containing business states as "1", and the data items not containing business states as "0"; count the ratio of "1" in all data items, and use this ratio as the status correlation value.

[0007] Preferably, inputting the status correlation value and the historical business data into the machine learning model to output the business correlation coefficient includes: S21. Count all the character numbers of the data items containing business state data in the historical business data, screen the character numbers of the status correlation value and its corresponding business state data items as positive example samples; screen the character numbers of other business state data items in the historical business data that do not contain this status correlation value as negative example samples; S22. Input the positive example samples and the negative example samples into a pre-trained recurrent neural network model to establish a status correlation model; S23. Input the status correlation value and the corresponding real-time business data into the status correlation model to obtain the business correlation coefficient: Obtain the business correlation coefficient through the formula ; where, ; among them, represents including business status, represents not including business status; is the character count of the th data item including business status; is the character count of the th data item not including business status; is the status correlation value; is the total number of data items including business status, is the total number of data items not including business status.

[0008] Preferably, the process of identity authentication judgment according to the access of the user side of the portal to the business middle platform includes: S31. The business user side accesses the business middle platform through the portal; S32. The business middle platform starts user management and obtains the business user permission level : ; where is the user role, is the mapping function from role to permission; this process ensures the security of the system and the protection of data; S33. Determine the confirmation request association according to the size of the business user permission level, and judge whether the request association information is qualified. If so, associate the service catalog item. If not, prompt the lack of permission information; S34. Retrieve the business user side identity information according to the associated service catalog item and perform processing confirmation; the processing confirmation is: automatically process and confirm according to the defined work process or forward to the corresponding approver to confirm and determine the service processing time.

[0009] Preferably, the process of generating business device resume data is: S41. The login event stream is accessed to Kafka in real time; S42. The Flink window function correlates the coefficient stream; S43. Regularly clean the login information and the correlation coefficient data cache according to the service processing time; S44. Dynamically update the device resume to Redis or Elasticsearch.

[0010] Preferably, the process of importing business device resume data into the data query engine server to obtain the query result and perform visualization is: S51. Export the business device resume data from Redis or Elasticsearch; S52. Perform file transfer and real-time synchronization to load the business device resume data into the query engine; S53. Provide a query interface for external calls: Call the query interface through a client tool or programming language to obtain structured results; S54. Display the query structured results through a visualization tool to obtain the device correlation coefficient trend and operation records.

[0011] Advantages of the present invention: (1) Through the preprocessing of real-time data and historical data by the data center in the present invention, the data interconnection between the data center and the business center is ensured; the data center matches the preprocessed real-time business data with the business process information to generate a status correlation value, and inputs the status correlation value and historical business data into a machine learning model to output a business correlation coefficient; realizing the construction of a model based on business data, exploring the relevance of business states, and being able to further drive the model according to the data relevance of business states, reducing the authentication of the access information at the user end, and avoiding the process of repeated data loading.

[0012] (2) In the present invention, the user end of the portal accesses the business center for identity authentication judgment. The business center identifies the user end at the login entry confirmation interface of the portal, realizes the identity authentication of the user end, ensures the safe login process of the user, and records the user end login information and caches it after the judgment of the user identity authentication security passes; avoiding the process of repeated confirmation according to the input of business information, and reducing the system operation burden.

[0013] (3) Generate business device resume data according to the different business user end login information obtained by the business center and the business correlation coefficient obtained by the data center; and the use and processing records of the device can be directly judged according to the resume information of the business device, realizing the real-time update processing of device information; import the business device resume data into the data query engine server to obtain the query result and visualize it; through the visualization process, the structured display of device query can be realized, intuitively presenting records such as device operation and call information, which is convenient for further business intelligent operation and maintenance process.

[0014] Of course, it is not necessary for any product implementing the present invention to achieve all the above-described advantages simultaneously. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other accompanying drawings without creative efforts based on these drawings.

[0016] Figure 1 It is a method step diagram of an intelligent operation and maintenance middle platform portal driven by a model of the present invention; Figure 2 This is the architecture diagram of the intelligent operation and maintenance middle platform portal of the present invention; Figure 3 This is the process step diagram of the identity authentication when the user side of the portal accesses the business middle platform. Specific embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] First, the contact model file needs to be independently generated depending on the scenario, and in the case of many business components and complex scenarios, it will cause a sharp increase in model files, increasing the operation and maintenance burden and operation and maintenance costs; second, during the operation and maintenance process, there is no caching strategy for the contact model, as well as rules restricting access by end users through the portal, resulting in an increased risk of repeated access and loading and leakage of user information security.

[0019] Due to the large number of actual business components and complex operation scenarios, the existing contact model files are difficult to cope with the sharp increase in model files, and in the actual use process, there is no corresponding caching strategy for the contact model. For real-time loaded business data, repeated prompts are required, and there may be loopholes in user binding and permission management, making it difficult to ensure user information security. Therefore, the contact model is not applicable to a wide range of business operation scenarios; in the actual use process, it is necessary to start from the information of the business itself, explore the relevance of business states, and through setting a model that can digitalize the relevance of business states, realize real-time authentication based on the access information of the user side and avoid the process of repeated data loading.

[0020] 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 middle platform portal method, and the method includes: S1. The API interface of the data middle platform obtains real-time business data and historical business data imported from the business database by docking with the business middle platform, and imports the real-time business data and historical business data into the data middle platform for preprocessing; S2. After preprocessing, the data middle platform matches the real-time business data with the business process information to generate a state correlation value, inputs the state correlation value and historical business data into a machine learning model, and outputs a business correlation coefficient; S3. According to the identity authentication judgment of the user side of the portal accessing the business middle platform, after the identity authentication judgment passes, record the user side login information and cache it; S4. The application end generates business device resume data based on the login information of different business user ends obtained from the business middle platform and the business correlation coefficient obtained from the data middle platform. S5. Import the business device resume data into the data query engine server to obtain the query result and perform visualization.

[0021] In the above technical solution, for the design of the intelligent operation and maintenance middle platform portal, we designed the portal, the application end, the business middle platform and the data middle platform. Among them, the portal and the application end provide services for the front end; the business middle platform and the data middle platform are used as the back end, usually for data processing and business arrangement. By setting the division of the business middle platform and the data middle platform, it is possible to further cope with the situation of more business components and complex scenarios, and solve problems such as variable business management processes and lack of standardized systems to meet functional requirements according to various business types. Through the business middle platform, various business customization requirements can be quickly met, and the business expansion ability and reuse ability can be effectively improved. The data middle platform is mainly used to implement the processes of data integration, data processing and data storage, and solve the data caching problem by using the data analysis method in the data middle platform.

[0022] Specifically: First, the API interface of the data middle platform obtains real-time business data and historical business data imported from the business database by docking with the business middle platform, and imports the real-time business data and historical business data into the data middle platform for preprocessing, which is a process to ensure the data interconnection between the data middle platform and the business middle platform and extract effective data through the preprocessing of real-time data and historical data; then, the data middle platform matches the preprocessed real-time business data with the business process information to generate a status correlation value, and inputs the status correlation value and historical business data into the machine learning model to output the business correlation coefficient; this process is mainly to realize the construction of the model based on business data and try to explore the relevance of business states, and can further drive the model according to the data relevance of business states, reduce the authentication of the access information of the user end, and avoid the process of repeated data loading.

[0023] Furthermore, according to the access of the user end of the portal to the business middle platform for identity authentication judgment, the business middle platform identifies the user end at the login entry confirmation interface of the portal to realize the identity authentication of the user end, ensure the secure login process of the user, and record the user end login information and cache it after the judgment of the user identity authentication security passes; avoid the process of repeated confirmation according to the business information entry, and reduce the system operation burden; Since the front end includes the portal and the application end, the application end is a process of recording and transmitting relevant device information after processing the services obtained from the back end. It mainly generates business device resume data based on the login information of different business user ends obtained from the business middle platform and the business correlation coefficients obtained from the data middle platform. And based on the resume information of the business device, the usage and processing records of the device can be directly judged, realizing the real-time update and processing of device information. Finally, the business device resume data is imported into the data query engine server to obtain the query result and visualize it. Through the visualization process, the structured display of device query can be realized, intuitively presenting records such as device operation and call information, which is convenient for further business intelligent operation and maintenance process.

[0024] As an implementation manner of the present invention, the business process information includes: Build a business middle platform architecture to generate a process platform; The process platform initiates process design: design process forms according to business scenario requirements and build business applications; Write the business information accessed through the business application into the process form; the process form divides the business process to generate business process information; the business process information is divided into states according to the business completion degree. The business process information consists of an identifier, a business state, and a value range, and the business process information is written by calling the data middle platform interface.

[0025] In the above technical solution, for the explanation of the business process, it is necessary to understand the business scenario, and further implement the business application for the business scenario requirements through the process platform of the business middle platform. The business application writes the process into the accessed business information, further divides the business process according to the generated process form, and generates business process information, and then divides the business state. The business state is divided according to the business completion degree to ensure the subsequent matching of the business completion degree with real-time data.

[0026] For example, the business process is the order processing process of an e-commerce platform; when building the business middle platform architecture, it is necessary to set the order management platform as the process platform to support the process collaboration of multiple types of business lines, such as self-operated e-commerce, third-party merchants, and cross-border purchases, etc.; the technical implementation usually uses the Camunda process engine to build a configurable process platform; the process form is composed of a process designer and a form engine, which is used to divide the business process; the business process includes the business completion degree (business state); in actual use, for example, in the order payment process form, for the completion degree (payment status) of a certain business device (product), since the business process information consists of an identifier, a business state, and a value range, the business process information is written by calling the data middle platform interface; in the process of purchasing a product: the identifier is the product code, the business state is the payment completion process; the value range is the record information of the product; see the following table for details:

[0027] In the table: "null" indicates that the payment is waiting, "299.00" indicates the payment amount; "self_operation" indicates self-operation; "WeChat Pay" indicates that the payment method is WeChat Pay; "2023-07-20 14:30" indicates the payment date; "Shipping" indicates that the commodity status is in shipping; "YT4001234567" indicates the order number.

[0028] As an implementation manner of the present invention, the specific process of the data middle platform for matching real-time service data and service process information to generate a status association value is as follows: The data middle platform determines the real-time service data standard: analyze the data items containing service status in the real-time service data, screen the data items containing service status as "1", and the data items not containing service status as "0"; count the ratio of "1" in all data items, and use this ratio as the status association value.

[0029] In the above technical solution, when the service process information of a certain service device containing service status in the real-time service data is used as a data item, the data item containing service status is 1, and the data item not containing service status is 0, then the generated sequence of data items of service status is, for example: {1, 0, 0, 1,..., 0, 1}, count the total number of this sequence as K; record the number j of "1" in this data item; use the value of j / K as the status association value; and the larger this status association value is, the greater the proportion of the status of the service device performing service processing is proved; then the matching degree between the service device and the service process information in the real-time service data is also greater; corresponding to the order processing process of the e-commerce platform, that is, the greater the payment selectivity of this commodity is.

[0030] As an implementation manner of the present invention, input the status association value and historical service data into a machine learning model to output a service correlation coefficient, including: S21. Count all the character numbers of the data items containing service status in the historical service data, screen the character numbers of the status association value and its corresponding service status data item as positive example samples; screen the character numbers of other service status data items in the historical service data that do not contain this status association value as negative example samples; S22. Input the positive example samples and negative example samples into a pre-trained recurrent neural network model to establish a status association model; S23. Input the status association value and the corresponding real-time service data into the status association model to obtain the service correlation coefficient: Obtain the service correlation coefficient through the formula ; where contains service status, ​It does not include the business status; is the number of characters of the th data item including the business status; is the number of characters of the th data item not including the business status; is the status correlation value; is the total number of data items including the business status data item, is the total number of data items not including the business status data item.

[0031] In the above technical solution, a new model is constructed by using the status correlation value and historical business data as the training input of the machine model, and the corresponding coefficients are output after building the new model. The specific method is as follows: First, by counting all the characters of the data items including the business status in the historical business data, 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, for "product name\to be paid", "product name\paid", "product name\completed", the corresponding character counts are all [6 + 1 + 6]; while for the data items not including the business status, such as: "product name", "product name", "product name", the corresponding character counts are all [6]; filter the status correlation value and the character counts of its corresponding business status data items as positive example samples; filter the character counts of other business status data items in the historical business data that do not include this status correlation value as negative example samples; Then, input the positive example samples and negative example samples into the pre-trained recurrent neural network model to establish a status correlation model; input the status correlation value and the corresponding real-time business data into the status correlation model to obtain the business correlation coefficient; the recurrent neural network model is a GRU network, its input layer includes a character embedding layer and a position encoding layer, and the output layer is a fully connected layer; the status correlation value represents the weight of the existence of business status items in the historical data item; therefore, the calculation process is through the formula Calculate to obtain the business correlation coefficient ; where, includes the business status, does not include the business status; is the number of characters of the th data item including the business status; is the number of characters of the th data item not including the business status; is the status correlation value; 、 is the total number of data items; By constructing a specific machine model, that is, constructing a state association model, parallel processing of current business state items is achieved, the correlation prediction of business data items is improved, and the accurate generation of business device usage information is promoted.

[0032] As an implementation manner of the present invention, please refer to Figure 3 As shown in the figure, the process of the user side of the portal accessing the business middle platform for identity authentication judgment includes: S31. The business user side accesses the business middle platform through the portal; S32. The business middle platform starts user management and obtains the business user permission level : ; where is the user role, is the mapping function from the role to the permission; this process ensures the security of the system and the protection of data; S33. Determine the confirmation request association according to the size of the business user permission level, and judge whether the request association information is qualified. If so, associate the service catalog item; if not, prompt the lack of permission information; S34. Retrieve the identity information of the business user side according to the associated service catalog item and perform processing confirmation; the processing confirmation is: automatically process the confirmation according to the defined work process or forward it to the corresponding approver to confirm the service processing time.

[0033] In the above technical solution, the portal is used as an API access port to help the user side access the business middle platform. This process is mainly an identity authentication judgment process. The specific steps are: first, the business user side accesses the business middle platform through the portal, and then the business middle platform starts user management and obtains the business user permission level , this process is to activate the recognition of the business user information by the business middle platform; the calculation formula is ; where 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 according to the size of the business user permission level. This process is to identify the user's permissions and judge whether the user has violated the regulations. The judgment method is: judge whether the request association information is qualified. If so, associate the service catalog item. If not, prompt the lack of permission information; the standard for judging qualification is determined according to the internal protocol of the business middle platform; during use, it is only necessary to know that the security level of this request is defined by a composite function, and the user's role and the role permission set defined by the system need to be considered. Finally, the business user client identity information is retrieved according to the associated service catalog item and processed for confirmation; the processing confirmation is: automatically process and confirm according to the defined workflow or forward to the corresponding approver to confirm the service processing time; in the service management and application approval link, the system processes service requests through a standardized process. From service application to approval, each request is associated with a specific service catalog item and is automatically processed or forwarded to the corresponding approver according to the defined workflow. The time when this process generates services: ; according to the service complexity the number of requests to be processed ; this method optimizes the service delivery process and improves the efficiency and response speed of service management.

[0034] As an implementation manner of the present invention, the process of generating business device resume data is as follows: S41. The login event stream is accessed in real time by Kafka; S42. The Flink window function business correlation coefficient stream; S43. Regularly clean the login information and business correlation coefficient data cache according to the service processing time; S44. Dynamically update the device resume to Redis or Elasticsearch.

[0035] In the above technical solution, first, through Kafka consumption offset management, ensure that automatic offset submission is enabled, and write failed events into the dead letter queue. Then, perform Flink checkpoint configuration to confirm the business correlation coefficient; and regularly clean the login information and business correlation coefficient data cache according to the service processing time, reduce the data of devices with low-frequency access, and dynamically process expired events, including: device active cycle and cold data retention period; finally, dynamically update the device resume to Redis or Elasticsearch to realize the timely generation of business device resume data.

[0036] As an implementation manner of the present invention, the process of importing business device resume data into the data query engine server to obtain query results and perform visualization is as follows: S51. Export the business device resume data from Redis or Elasticsearch; S52. Perform file transfer and real-time synchronization to load the business device resume data into the query engine; S53. Provide a query interface for external calls: Call the query interface through a client tool or programming language to obtain a structured result; S54. Display the query structured result through a visualization tool to obtain the device correlation coefficient trend and operation records.

[0037] In the above technical solution, the visualization of the business device resume data is realized by importing the business device resume data into the data query engine server to obtain the query result and perform visualization, ensuring timely obtaining the development trend of the business device correlation coefficient and improving the intelligent data operation and maintenance process of the business device.

[0038] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0039] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology make various modifications, supplements, or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by this application, they should fall within the protection scope of the present invention.

Claims

1. A model-driven intelligent operation and maintenance middle platform portal method, where 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 includes: S1. The API interface of the data middle platform obtains real-time business data and historical business data imported from the business database by docking with the business middle platform, and imports the real-time business data and historical business data into the data middle platform for preprocessing; S2. After preprocessing, the data middle platform matches the real-time business data with the business process information to generate a status association value, inputs the status association value and the historical business data into a machine learning model, and outputs a business correlation coefficient; S3. According to the access of the user side of the portal to the business middle platform for identity authentication judgment, after the identity authentication judgment passes, record the login information of the user side and cache it; S4. The application end generates business device resume data according to the different business user side login information obtained from the business middle platform and the business correlation coefficient obtained from the data middle platform; S5. Import the business device resume data into the data query engine server to obtain a query result and visualize it.

2. The model-driven intelligent operation and maintenance mid-platform portal method according to claim 1, wherein The business process information includes: Build a business middle platform architecture to generate a process platform; The process platform initiates process design: design a process form according to business scenario requirements and build a business application; Write the business information accessed through the business application into the process form; the process form divides the business process to generate business process information; the business process information is divided into states according to the business completion degree, and the business process information consists of an identifier, a business state, and a value range, and writes the business process information by calling the data middle platform interface.

3. A model-driven intelligent operation and maintenance middle platform portal method according to claim 2, characterized in that, The specific process of the data middle platform matching the real-time business data with the business process information to generate a status association value is: The data middle platform determines the real-time business data standard: analyze the data items containing business states in the real-time business data, screen the data items containing business states as "1", and the data items not containing business states 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 mid-platform portal method according to claim 3, characterized in that Inputting the status association value and the historical business data into the machine learning model to output a business correlation coefficient includes: S21. Count all the character counts of the data items containing business state data in the historical business data, screen the character counts of the status association value and its corresponding business state data items as positive example samples; screen the character counts of other business state data items in the historical business data that do not contain this status association value as negative example samples; S22. Input the positive example samples and negative example samples into a pre-trained recurrent neural network model to establish a status association model; S23. Input the status association value and the corresponding real-time business data into the status association model to obtain a business correlation coefficient: Obtained through the formula Calculate the business correlation coefficient ; among them, is the business status included, is the business status not included; is the character count of the th data item including the business status; is the character count of the th data item not including the business status; is the status correlation value; is the total number of data items including the business status, is the total number of data items not including the business status.

5. A model-driven intelligent operation and maintenance mid-office portal method according to claim 1, characterized in that, The process of performing identity authentication judgment according to the access of the user side of the portal to the business middle platform includes: S31. The business user side accesses the business middle platform through the portal; S32. The business middle platform starts user management and obtains the business user permission level; S33. Determine the confirmation request association according to the size of the business user permission level, and judge whether the request association information is qualified. If so, associate the service catalog item. If not, prompt the no-permission information; S34. Retrieve the identity information of the business user terminal according to the associated service catalog item and process for confirmation; the processing confirmation is: automatically process and confirm according to the defined workflow or forward it to the corresponding approver for confirmation to determine the service processing time.

6. The model-driven intelligent operation and maintenance mid-platform portal method according to claim 5, characterized in that The process of generating the business device resume data is as follows: S41. The login event stream is accessed in real time to Kafka. S42. The Flink window function correlates with the coefficient stream. S43. Regularly clean the login information and the associated coefficient data cache according to the service processing time. S44. Dynamically update the device resume to Redis or Elasticsearch.

7. A model-driven intelligent operation and maintenance middle platform portal method according to claim 1, characterized in that, The process of importing the business device resume data into the data query engine server to obtain the query result and perform visualization is as follows: S51. Export the business device resume data from Redis or Elasticsearch. S52. Perform file transfer and real-time synchronization to load the business device resume data into the query engine. S53. Provide a query interface for external calls: call the query interface through the client tool or programming language to obtain the structured result. S54. Display the query structured result through the visualization tool to obtain the device correlation coefficient trend and operation records.

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