A cross-departmental resource sharing method and system

Abstract

The application particularly relates to a cross-department resource sharing method and system. The cross-department resource sharing method and system provides an integrated capability integration hub platform, through providing a data integration engine DAE, a business integration engine AGE, a message integration engine MDE and a device integration engine DIE, respectively responsible for adapting and accessing different data, realizing access and analysis of various messages and data, realizing process arrangement around various application scenarios based on a business arrangement engine AMT, and opening and sharing the business and data of various departments through an asset operation and resource portal. The cross-department resource sharing method and system breaks the barriers between departments in terms of data, realizes open sharing of applications, business and data, and cross-department capability reuse, effectively improves the high cooperation of various departments, avoids repetitive investment, and realizes rapid iteration and upgrading of innovative applications.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, and in particular to a cross-departmental resource sharing method and system. Background Technology

[0002] Currently, various departments operate numerous online application systems, which generally suffer from inconsistent construction standards, complex application architectures, inconsistent data standards, difficulties in business interconnection, and data interoperability. This situation not only leads to fragmented applications, scattered data, and complex business processes, requiring staff to operate multiple business systems to complete a single data retrieval, but also hinders closed-loop collaboration and process optimization between departments. Furthermore, it causes an imbalance in system usage: some systems, due to limited data and simple functionality, are idle and unwilling to be used by staff; while others, due to large application volumes, rapid data accumulation, and numerous users, are overloaded, resulting in increasingly longer response times, making them unable to adapt to the ever-growing data application demands in the big data environment, and hindering elastic system expansion and rapid deployment of new features.

[0003] The complexity and diversity of data across industries and departments make it difficult to effectively collect data from the source. While each department and city possesses certain data resources, departmental, industry, and organizational barriers have not been fully broken down. Data resources that should be aggregated have not been aggregated, and the phenomenon of "data silos" has long existed. This has resulted in limited sources, narrow coverage, inconsistent standards, insufficient efforts, and low efficiency in big data integration.

[0004] Given the current challenges faced by various industries in sharing common resources such as business processes, data, and messaging, it is necessary to create a green, shared, open, and innovative resource open platform. This platform will break down information and business barriers between different departments and industries, promoting the large-scale integration, high-level sharing, and in-depth application of information resources. It will enable the effective integration of information and data to promote deep resource fusion, drive business innovation through technological innovation, and ensure the security and integrity of applications, business processes, and data across departments. This will break down data barriers between departments and industries, fully realize the open sharing of applications, business processes, and data, effectively enhance inter-departmental cooperation, avoid redundant investment, and achieve rapid iterative upgrades for innovative business applications.

[0005] Based on this, the invention proposes a cross-departmental resource sharing method and system. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention provides a simple and efficient cross-departmental resource sharing method and system.

[0007] This invention is achieved through the following technical solution:

[0008] A cross-departmental resource sharing method and system is characterized by: providing an integrated capability integration hub platform, which provides a data integration engine (DAE), a business integration engine (AGE), a message integration engine (MDE), and a device integration engine (DIE) to adapt to and access different data, realize the access and parsing of various messages and data, realize process orchestration around various application scenarios based on the business orchestration engine (AMT), and openly share the business and data of various departments through asset operation and resource portals, so as to meet the cross-departmental capability reuse, improve the high degree of cooperation among departments, avoid repetitive investment, and realize the rapid iterative upgrade of business innovation applications.

[0009] The Data Integration Engine (DAE) is responsible for integrating, cleaning, processing, and handling data from various departments.

[0010] The business integration engine AGE is responsible for grouping and defining common business APIs (Application Program Interfaces), registering APIs, configuring policies, and implementing rate limiting and circuit breaking.

[0011] The message integration engine MDE is responsible for defining topics, integrating and distributing messages in the message management system RabbitMQ and the distributed message queue Kafka.

[0012] The Device Integration Engine (DIE) is responsible for accessing various IoT sensing devices via the MQTT standard protocol and for opening up and sharing the monitoring and alarm data collected by the devices.

[0013] The business orchestration engine AMT is responsible for quickly building integrated workflows that combine business, data, messages, and devices through a drag-and-drop shortcut mode to meet the automated integration, processing, and use of specific application scenarios.

[0014] The asset operation is responsible for the access, listing / delisting, and approval of various types of assets;

[0015] The asset portal enables access to various resources, providing users with online application and management capabilities.

[0016] The specific steps are as follows:

[0017] Step S1: Build the Data Integration Engine (DAE)

[0018] Based on distributed big data processing technology, a distributed cluster is built using open-source Flink components, serving as a batch and stream integrated data computing and processing framework for the data integration engine. Connectors for different data sources, such as Oracle connector, HBase connector, and MPP connector, are developed through various database JDBC (Java Database Connectivity) drivers and integrated as plugins for the data integration engine DAE. The connectors enable adaptation and access to various databases.

[0019] Step S2: Build the business integration engine AGE

[0020] A distributed API gateway engine is built based on the open-source API gateway component Kong, which meets the adaptation and access requirements of WebService and RESTAPI; it provides API service registration, monitoring and auditing, and provides an API portal that is open to developers for application, use and management; at the same time, a Redis database cluster is built to store API access records and authentication configurations to avoid gateway performance degradation due to frequent API access.

[0021] Step S3: Build the Message Integration Engine (MDE)

[0022] Set up a RocketMQ dual-master dual-slave cluster, consisting of two Nameserver (domain name) nodes and two Broker (broker) nodes;

[0023] The message integration engine MDE schedules RocketMQ resources through an interface to create topics and key information for applications and configure topic access permissions. Applications create topics through the message integration interface and send and receive messages through the message integration SDK (Software Development Kit) and the native SDK.

[0024] Step S4: Build the Device Integration Engine (DIE)

[0025] A device gateway (Gateway) is built to adapt to and access various IoT sensing devices using standard protocols. The unified device gateway integrates, parses, and distributes MQTT, HTTP, and CoAP protocols. Through device object model configuration, monitoring data from IoT sensing devices is acquired, and the rule engine (REG) configures scenario linkage between different devices according to application scenarios. This enables integrated process management of IoT devices from access, monitoring, scheduling, and rules, achieving automatic device early warning and analysis. By collecting and storing device monitoring and alarm data, the data API interface is finally registered and opened through the API gateway.

[0026] Step S5: Build the Business Orchestration Engine (AMT)

[0027] By building a drag-and-drop visual business orchestration engine, instances of data integration, message integration, business integration, and device integration are realized. Combined with characteristic application scenarios, and following the BPM (Business Process Management) process design method, the independent asset data is orchestrated into automated business processes, realizing automatic message and data distribution and automatic event processing.

[0028] Step S6: Build an asset operation and asset portal

[0029] Ultimately, the data, messages, API services, and device interfaces registered and published by each department will be made available to upper-layer applications through the asset portal. Developers can browse the various data sources and API interface details published by each department through the asset portal and apply for approval online, thereby ensuring that each department can access the resources it needs according to its actual business needs, and truly achieve open sharing of resources.

[0030] In step S1, the data cleaning and transformation component is used to extract features, clean and regularize fields, and filter dirty data from the collected data. The cleaned data is then persisted to the data storage module and made available to upper-layer applications via JDBC or API calls.

[0031] In step S2, load balancing, circuit breaking and rate limiting, routing configuration, and blacklist / whitelist are implemented for the API based on the Kong component.

[0032] In step S3, each Broker master node is configured with a Broker slave node, and data is replicated asynchronously between the master and slave to ensure high availability of the MQ cluster as a whole.

[0033] In step S5, based on the Quartz scheduled task component, business processes are automatically triggered at regular intervals by defining Cron expressions, thereby achieving automated processing of business processes.

[0034] The system based on the cross-departmental resource sharing method of the present invention is characterized by including: a data integration engine (DAE), a business integration engine (AGE), a message integration engine (MDE), a device integration engine (DIE), a business orchestration engine (AMT), an asset operation and an asset portal;

[0035] The Data Integration Engine (DAE) is responsible for integrating, cleaning, processing, and handling data from various departments.

[0036] The business integration engine AGE is responsible for grouping and defining common business APIs (Application Program Interfaces), registering APIs, configuring policies, and implementing rate limiting and circuit breaking.

[0037] The message integration engine MDE is responsible for defining topics, integrating and distributing messages in the message management system RabbitMQ and the distributed message queue Kafka.

[0038] The Device Integration Engine (DIE) is responsible for accessing various IoT sensing devices via the MQTT standard protocol and for opening up and sharing the monitoring and alarm data collected by the devices.

[0039] The business orchestration engine AMT is responsible for quickly building integrated workflows that combine business, data, messages, and devices through a drag-and-drop shortcut mode to meet the automated integration, processing, and use of specific application scenarios.

[0040] The asset operation is responsible for the access, listing / delisting, and approval of various types of assets;

[0041] The asset portal enables access to various resources, providing users with online application and management capabilities.

[0042] The beneficial effects of this invention are: the cross-departmental resource sharing method and system, while ensuring the security and integrity of applications, business and data of each department, breaks down the barriers between departments and industries in terms of data, realizes the open sharing of applications, business and data, and the reuse of cross-departmental capabilities, effectively improves the high degree of cooperation among departments, avoids repetitive investment, and realizes rapid iterative upgrades of business innovation applications. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Appendix Figure 1 This is a schematic diagram of the cross-departmental resource sharing method of the present invention.

[0045] Appendix Figure 2 This is a schematic diagram of the cross-departmental resource sharing system architecture of the present invention.

[0046] Appendix Figure 3 This is a schematic diagram of the Data Integration Engine (DAE) of this invention.

[0047] Appendix Figure 4 This is a schematic diagram of the AGE business integration engine of the present invention.

[0048] Appendix Figure 5 This is a schematic diagram of the Message Integration Engine (MDE) of this invention.

[0049] Appendix Figure 6 This is a schematic diagram of the integrated engine DIE of the device of the present invention.

[0050] Appendix Figure 7 This is a schematic diagram of the AMT (Action Management Machine) orchestration engine of this invention.

[0051] Appendix Figure 8 This is a schematic diagram of the asset operation and asset portal of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0053] This cross-departmental resource sharing approach provides an integrated capability integration hub platform. It provides a data integration engine (DAE), a business integration engine (AGE), a message integration engine (MDE), and a device integration engine (DIE) to adapt to and access different data, enabling access and parsing of various messages and data. Based on the business orchestration engine (AMT), it realizes process orchestration around various application scenarios. Through asset operation and resource portals, it opens up and shares the business and data of various departments, meets the needs of cross-departmental capability reuse, improves the high degree of cooperation between departments, avoids repetitive investment, and enables rapid iterative upgrades of innovative business applications.

[0054] The Data Integration Engine (DAE) is responsible for integrating, cleaning, processing, and handling data from various departments.

[0055] The business integration engine AGE is responsible for grouping and defining common business APIs (Application Program Interfaces), registering APIs, configuring policies, and implementing rate limiting and circuit breaking.

[0056] The message integration engine MDE is responsible for defining topics, integrating and distributing messages in the message management system RabbitMQ and the distributed message queue Kafka.

[0057] The Device Integration Engine (DIE) is responsible for accessing various IoT sensing devices via the MQTT standard protocol and for opening up and sharing the monitoring and alarm data collected by the devices.

[0058] The business orchestration engine AMT is responsible for quickly building integrated workflows that combine business, data, messages, and devices through a drag-and-drop shortcut mode to meet the automated integration, processing, and use of specific application scenarios.

[0059] The asset operation is responsible for the access, listing / delisting, and approval of various types of assets;

[0060] The asset portal enables access to various resources, providing users with online application and management capabilities.

[0061] The specific steps are as follows:

[0062] Step S1: Build the Data Integration Engine (DAE)

[0063] Based on distributed big data processing technology, a distributed cluster is built using open-source Flink components, serving as a batch and stream integrated data computing and processing framework for the data integration engine. Connectors for different data sources, such as Oracle connector, HBase connector, and MPP connector, are developed through various database JDBC (Java Database Connectivity) drivers and integrated as plugins for the data integration engine DAE. The connectors enable adaptation and access to various databases.

[0064] Step S2: Build the business integration engine AGE

[0065] A distributed API gateway engine is built based on the open-source API gateway component Kong, which meets the adaptation and access requirements of WebService and RESTAPI; it provides API service registration, monitoring and auditing, and provides an API portal that is open to developers for application, use and management; at the same time, a Redis database cluster is built to store API access records and authentication configurations to avoid gateway performance degradation due to frequent API access.

[0066] Step S3: Build the Message Integration Engine (MDE)

[0067] Set up a RocketMQ dual-master dual-slave cluster, consisting of two Nameserver (domain name) nodes and two Broker (broker) nodes;

[0068] The message integration engine MDE schedules RocketMQ resources through an interface to create topics and key information for applications and configure topic access permissions. Applications create topics through the message integration interface and send and receive messages through the message integration SDK (Software Development Kit) and the native SDK.

[0069] Step S4: Build the Device Integration Engine (DIE)

[0070] A device gateway (Gateway) is built to adapt to and access various IoT sensing devices using standard protocols. The unified device gateway integrates, parses, and distributes MQTT, HTTP, and CoAP protocols. Through device object model configuration, monitoring data from IoT sensing devices is acquired, and the rule engine (REG) configures scenario linkage between different devices according to application scenarios. This enables integrated process management of IoT devices from access, monitoring, scheduling, and rules, achieving automatic device early warning and analysis. By collecting and storing device monitoring and alarm data, the data API interface is finally registered and opened through the API gateway.

[0071] Step S5: Build the Business Orchestration Engine (AMT)

[0072] By building a drag-and-drop visual business orchestration engine, instances of data integration, message integration, business integration, and device integration are realized. Combined with characteristic application scenarios, and following the BPM (Business Process Management) process design method, the independent asset data is orchestrated into automated business processes, realizing automatic message and data distribution and automatic event processing.

[0073] Step S6: Build an asset operation and asset portal

[0074] Ultimately, the data, messages, API services, and device interfaces registered and published by each department will be made available to upper-layer applications through the asset portal. Developers can browse the various data sources and API interface details published by each department through the asset portal and apply for approval online, thereby ensuring that each department can access the resources it needs according to its actual business needs, and truly achieve open sharing of resources.

[0075] In step S1, the data cleaning and transformation component is used to extract features, clean and regularize fields, and filter dirty data from the collected data. The cleaned data is then persisted to the data storage module and made available to upper-layer applications via JDBC or API calls.

[0076] In step S2, load balancing, circuit breaking and rate limiting, routing configuration, and blacklist / whitelist are implemented for the API based on the Kong component.

[0077] In step S3, each Broker master node is configured with a Broker slave node, and data is replicated asynchronously between the master and slave to ensure high availability of the MQ cluster as a whole.

[0078] In step S5, based on the Quartz scheduled task component, business processes are automatically triggered at regular intervals by defining Cron expressions, thereby achieving automated processing of business processes.

[0079] The system based on this cross-departmental resource sharing approach includes a data integration engine (DAE), a business integration engine (AGE), a messaging integration engine (MDE), an equipment integration engine (DIE), a business orchestration engine (AMT), an asset operations engine, and an asset portal.

[0080] The Data Integration Engine (DAE) is responsible for integrating, cleaning, processing, and handling data from various departments.

[0081] The business integration engine AGE is responsible for grouping and defining common business APIs (Application Program Interfaces), registering APIs, configuring policies, and implementing rate limiting and circuit breaking.

[0082] The message integration engine MDE is responsible for defining topics, integrating and distributing messages in the message management system RabbitMQ and the distributed message queue Kafka.

[0083] The Device Integration Engine (DIE) is responsible for accessing various IoT sensing devices via the MQTT standard protocol and for opening up and sharing the monitoring and alarm data collected by the devices.

[0084] The business orchestration engine AMT is responsible for quickly building integrated workflows that combine business, data, messages, and devices through a drag-and-drop shortcut mode to meet the automated integration, processing, and use of specific application scenarios.

[0085] The asset operation is responsible for the access, listing / delisting, and approval of various types of assets;

[0086] The asset portal enables access to various resources, providing users with online application and management capabilities.

[0087] The foregoing has provided a detailed description of a cross-departmental resource sharing method and system according to an example of the present invention. This section uses specific examples to illustrate the principles and implementation methods of the invention. These examples are only for the purpose of helping to understand the core ideas of the present invention. All other embodiments obtained by those skilled in the art without creative effort, without departing from the principles of the present invention, should fall within the scope of protection of the present invention.

Claims

1. A method for cross-departmental resource sharing, the method comprising: An integrated capability integration hub platform is provided, which realizes access and analysis of various messages and data by providing a data integration engine DAE, a business integration engine AGE, a message integration engine MDE and a device integration engine DIE responsible for adapting and accessing different department data respectively, realizes process orchestration around various application scenarios based on a business orchestration engine AMT, and opens and shares the business and data of each department through an asset operation and an asset portal, so as to meet cross-department capability reuse, improve the high degree of cooperation of each department, avoid repetitive investment, and realize rapid iteration and upgrading of innovative applications; The data integration engine DAE is responsible for realizing data integration, cleaning, processing and treatment of each department; The business integration engine AGE is responsible for grouping definition, API registration, strategy configuration and flow limiting and fusing of general business API; The message integration engine MDE is responsible for Topic definition, message integration and distribution of messages in the message management system RabbitMQ and the distributed message queue Kafka; The device integration engine DIE is responsible for accessing various Internet of Things sensing devices through the MQTT standard protocol, and opening and sharing the monitoring and alarm data collected by the devices; The business orchestration engine AMT is responsible for quickly building a process orchestration integrating business, data, messages and devices through the quick mode of pull, so as to meet the automatic integration, processing and use of specific application scenarios; The asset operation is responsible for realizing access, on- / off-shelf and approval processing of various assets; The asset portal realizes opening of various resources and provides online application and management of users in each department; The specific steps are as follows: Step S1: constructing a data integration engine DAE Based on distributed big data processing technology, a distributed cluster is built by using an open source Flink component as a batch-flow integrated data calculation processing framework of the data integration engine; through various database JDBC drivers, a connector Connector of different data sources is developed as a plugin plug-in of the data integration engine DAE for integration, and the connector Connector is used to realize adaptation and access to various databases; Step S2: constructing a business integration engine AGE Based on an open source API gateway component Kong, a distributed API gateway engine is built to meet the adaptation and access of WebService and RestAPI; an API service registration, monitoring and auditing are provided, and an API portal is provided to be opened to developers for application and management; at the same time, a Redis database cluster is built to store API access records and authentication configuration, so as to avoid performance degradation of the gateway due to frequent API access; Step S3: constructing a message integration engine MDE A message middleware RocketMQ double-master double-slave cluster is built, which includes two Nameserver nodes and two Broker nodes; The message integration engine MDE schedules message middleware RocketMQ resources through an interface to create a Topic and key information for an application and configure Topic access authority; The application party creates a topic through a message integration interface, and transmits and receives messages through a message integration SDK and a native SDK; Step S4: constructing a device integration engine DIE A device gateway GateWay is constructed to adapt to and access various types of Internet of Things sensing device standard protocols, and to integrate, analyze and distribute MQTT protocols, HTTP protocols and CoAP protocols through a unified device gateway. Through device model configuration, monitoring data of the Internet of Things sensing device is obtained, and scene linkage configuration between different devices is performed according to an application scenario through a rule engine REG, so as to realize integrated process management of the Internet of Things device from access, monitoring, scheduling and rules, realize automatic early warning and analysis of the device, collect and store device monitoring and alarm data, and finally register and open the data API interface through an API gateway; Step S5: constructing a business orchestration engine AMT A visual business orchestration engine is constructed to realize data integration, message integration, business integration and device integration instances, and to combine an application scenario with a certain characteristic, and to design a BPM process to arrange independent asset data into an automatic processing business process, so as to realize automatic distribution of messages and data and automatic processing of events. Step S6: constructing an asset operation and asset portal Portal Finally, the data, messages, API services and device interfaces registered and published by each department are opened to the upper layer application through an asset portal. Developers browse detailed information of various data sources and API interfaces published by each department through the asset portal, and apply for approval online, so that each department can access the required resources according to their actual business needs, and truly realize resource sharing.

2. The cross-departmental resource sharing method according to claim 1, characterized in that: In step S1, the data collected is subjected to feature extraction, field cleaning and normalization, and dirty data filtering by a data cleaning and conversion component, and the cleaned data is persisted to a data storage module, and the upper layer application is opened through JDBC or API calling.

3. The cross-departmental resource sharing method of claim 1, wherein: In step S2, API load balancing, fuse flow limiting, routing configuration and black and white lists are realized based on a Kong component.

4. The cross-departmental resource sharing method of claim 1, wherein: In step S3, each Broker master node is configured with a Broker slave node, and data is asynchronously replicated between the master and the slave to ensure overall high availability of the MQ cluster.

5. The cross-departmental resource sharing method of claim 1, wherein: In step S5, based on a Quartz timing task component, a business process is automatically triggered at a specified time through a Cron expression, so as to realize automatic processing of the business process.

6. A system based on the cross-departmental resource sharing method according to any one of claims 1 to 5, characterized in that: The system comprises a data integration engine DAE, a business integration engine AGE, a message integration engine MDE, a device integration engine DIE, a business orchestration engine AMT, an asset operation and an asset portal. The data integration engine DAE is responsible for data integration, cleaning, processing and handling of each department. The business integration engine AGE is responsible for grouping definition, API registration, policy configuration and flow limiting and fusing of general business APIs. The message integration engine MDE is responsible for Topic definition, message integration and distribution of messages in the message management system RabbitMQ and the distributed message queue Kafka; The device integration engine DIE is responsible for accessing various Internet of Things sensing devices through the MQTT standard protocol, and opening and sharing the monitoring and alarm data collected by the devices; The business arrangement engine AMT is responsible for quickly constructing process arrangement integrating business, data, messages and devices through the quick mode of pulling, so as to meet the automatic integration, processing and use of specific application scenarios; The asset operation is responsible for realizing the access, on- / off-shelf and approval processing of various assets; The asset portal realizes the opening of various resources and provides online application and management of users in various departments.

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