Internet and government affair big data business cooperative processing method based on data fusion

Abstract

The application discloses an internet and government affair big data business cooperative processing method based on data fusion. The method comprises the following steps: for the matter needing cooperative management, structured information element is decomposed to form a matter subject information table; based on the coupling and association of multi-source and multi-type data and matter subject information, a multi-source and multi-type relation table is formed; for the information of the multi-source and multi-type relation table, the correlation degree and the importance degree are sorted to establish a matter primary and secondary element table; based on the matter primary and secondary element table, by analyzing the characteristic points and the difference points of information, a data multi-dimensional cooperative chain is formed by using cross-domain multi-source and multi-dimensional data; the data cooperative chain is matched with a corresponding decision plan for selection by a decision layer; according to the decision scheme selected by the decision layer, the data multi-dimensional cooperative chain is matched with a corresponding executive department in combination with the matter primary and secondary element table, and the decision scheme is transmitted to the executive department. The application promotes the improvement of the government intelligent service level.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and more specifically, to a method for collaborative processing of Internet and government big data services based on data fusion. Background Art

[0002] The ultimate goal of building a smart government is to achieve smart services. With the rapid development of society, government information resources have grown dramatically, and information barriers have gradually become the greatest obstacle to the development of smart services. Only by addressing the challenges posed by massive, heterogeneous, and disparate data and maximizing its value can we achieve a smart government and enhance smart service capabilities.

[0003] Government data is increasing rapidly, and comprehensive government data is a crucial resource in the big data era. This includes data from areas such as industry and commerce, taxation, administration, and human resources and social security. Currently, the establishment of shared platforms for the opening, optimization, and sharing of government big data is often used to promote data sharing from multiple perspectives, accelerate the elimination of data silos, break down departmental barriers, and enable the government use of government data while promoting its civilian and commercial applications.

[0004] However, the existing solutions have not been able to completely break down the information barriers between different fields and dimensions from a technical perspective, and realize multi-dimensional and multi-field data links, and thus cannot effectively empower grassroots governance with departmental data, which ultimately manifests as low effectiveness in the construction of grassroots governance platforms. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a method for collaborative processing of Internet and government big data services based on data fusion. The method includes:

[0006] For matters that require collaborative management, decompose the structured information elements to form a table of the main information of the matter;

[0007] Based on the information coupling association between multi-source and multi-type data and the subject of the matter, a multi-source and multi-type relationship table is formed;

[0008] For the information in the multi-source and multi-category relationship table, sort the information by relevance and importance, and establish a table of primary and secondary elements of matters;

[0009] Based on the table of primary and secondary elements of the matter, by analyzing the characteristics and differences of the information, a multi-dimensional collaborative chain of data is formed using cross-domain multi-source multi-dimensional data;

[0010] Matching the data collaboration chain with corresponding decision plans for the decision-makers to choose from;

[0011] The data multi-dimensional collaborative chain matches the corresponding execution department based on the decision-making plan selected by the decision-making layer and the table of primary and secondary elements of the matter, and transmits the decision-making plan to the execution department.

[0012] Compared with existing technologies, the present invention has the advantage of leveraging government big data technologies and intelligent technologies to share and aggregate data from various government-related business areas, data structures, and data sources, enabling in-depth data analysis. Furthermore, by linking multidimensional data across various business areas, data formats, and data sources, the present invention solves the problem of government information collaboration and promotes the improvement of government smart services.

[0013] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0015] Figure 1 This is a flow chart of a method for collaborative processing of Internet and government big data services based on data fusion according to an embodiment of the present invention;

[0016] Figure 2 It is a process diagram of a collaborative processing method of Internet and government big data services based on data fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0018] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0019] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0020] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0021] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] The solution provided by the present invention is oriented towards standards, technologies, products, applications and industries, and makes scientific decisions on urban operation management based on government big data, realizing the transformation from perceived information to decision execution, and achieving multi-dimensional collaboration from spatial collaboration at the organizational level, field collaboration at the business level, and cognitive collaboration at the strategic level, providing effective support for comprehensively controlling the operation of urban systems, improving urban management levels and emergency decision-making levels.

[0023] Combine Figure 1 and Figure 2 As shown, the collaborative processing method of Internet and government big data services based on data fusion provided by the present invention includes the following steps.

[0024] In step S110, for matters requiring collaborative management, the structured information elements are decomposed to form a matter subject information table.

[0025] Based on the needs of urban operation and management of government big data, for matters that require collaborative management such as social events, business scenarios, and departmental collaboration, public facilities such as social security facilities and social sensing equipment are used to decompose the structured information elements of the matters using semantic analysis, pattern recognition, dynamic tracking and other technologies to form a matter subject information table.

[0026] In one embodiment, the subject of an event, scene, or business is identified by identifying people or objects, for example, through semantic recognition of state or intent. If a person is identified, pattern recognition is combined with intent to determine the subject's intent. Pattern recognition can be achieved through facial recognition, body recognition, video understanding, or visual scene recognition technologies. Object recognition can be achieved through target detection, target recognition, or attribute recognition technologies. Text recognition can be achieved through natural language processing.

[0027] After recognition, the subject's intention information is structured and stored to form a table of subject information. For example, in the table, person information includes basic information obtained after face recognition, including facial information, body structure information, and action information. Object information includes object category information and object structure information. Text information includes the semantic information of the text.

[0028] By constructing an event subject information table, the status / intention of a person or object can be recorded, that is, the event subject information table includes the event subject type (such as a person or object) and the corresponding status or intention information.

[0029] Step S120 , based on the multi-source and multi-type data and the subject of the matter, information coupling association is performed to form a multi-source and multi-type relationship table.

[0030] For example, the subject of an event is primarily the person or object associated with it, supplemented by three-dimensional space and event time. Multi-source and multi-type data is used to couple information with the subject of the event, forming a multi-source and multi-type relationship table. Multi-source and multi-type data includes government data, public data, internet data, IoT data, geographic information data, and industry data (or domain data).

[0031] Combine Figure 2 As shown, multi-source and multi-type data are informationally coupled with the subject of the matter based on business standard processes and event plans. Multi-source information data is used to assist in analysis, and the multi-source information data can come from multiple departments, multiple industries, multiple fields and other related units.

[0032] In one embodiment, based on the status / intention of people or objects recorded in the subject information table, combined with environmental information such as location coordinates and time, a knowledge graph is constructed using key semantic points to obtain a coupled multi-source, multi-category information relationship table. For example, in the process of handling government affairs, by identifying the submitted materials and the subject's application requirements, and combining the key semantics in the requirements with the corresponding government affairs regulations, the main information points about the subject in the submitted materials, such as the service request type and the scope of the service application, are identified.

[0033] Step S130 , sorting the information in the multi-source multi-category relationship table by relevance and importance, and establishing a table of primary and secondary elements of matters.

[0034] Based on the multi-source and multi-category relationship table, and in accordance with relevant policies, systems, laws, regulations, industry norms and other regulations, artificial intelligence technology is used to sort the relevance and importance of the information in the multi-source and multi-category relationship table, and a table of primary and secondary factors of matters is established.

[0035] For example, the table of primary and secondary elements of matters includes the type of matter, the first primary element, the second primary element, the first secondary element and the second secondary element, etc.

[0036] In one embodiment, a trained deep learning model is used to sort by relevance and importance. During the training phase, the model is trained using the information frequency and priority required in historical solutions for handling similar types of businesses and events. During the actual application phase of the model, the frequency and priority of the information required in solutions for handling similar types of businesses and events are obtained, including the frequency and priority of the units, industries, and fields of the information sources. The trained model is used to make a comprehensive judgment, thereby sorting the relevance and importance of the information in the multi-source and multi-category relationship table. Deep learning models can adopt various types, such as convolutional neural networks.

[0037] Step S140 , based on the main and secondary element table of the matter, by analyzing the feature points and the difference points in the information, a multi-dimensional collaborative chain of data is formed using cross-domain multi-source multi-dimensional data.

[0038] Based on the table of primary and secondary elements of matters, we analyze the characteristic points in the table and the differences in the information. By marking the characteristic points, we use heterogeneous information channels to obtain cross-domain, multi-source, and multi-dimensional data from multiple departments, multiple industries, and multiple fields, forming a multi-dimensional data collaborative chain.

[0039] For example, through the information in the main and secondary element table of matters, the subject is associated with multi-source information based on priority, and at the same time, the information in the main and secondary element table is associated based on the events to be processed and the prescribed process of the business, forming a data chain based on the coordination of multi-source information related to the subject information in the business process.

[0040] Step S150: Match the data collaboration chain with corresponding business flows or decision plans for selection by the decision-making layer.

[0041] In response to decision-making needs, business needs, and scenario needs, by matching the data collaboration chain with corresponding business flows or decision plans, comprehensive decision-making solutions such as decision-making suggestions and dynamic data analysis results are provided to decision-makers from the spatial, business, and strategic levels.

[0042] For example, for matters with multiple business and event processing plans, a plan with more information of a high priority in the data multi-dimensional collaborative chain corresponding to the corresponding business plan is extracted as a contingency plan.

[0043] In step S160, the data multi-dimensional collaborative chain matches the corresponding execution department according to the decision-making plan selected by the decision-making layer and the table of primary and secondary elements of the matter, and transmits the decision-making plan to the execution department.

[0044] After the decision-making level completes the selection of the decision-making plan, the data collaboration chain will prioritize matching the corresponding execution departments based on the content of the plan and the table of primary and secondary elements of the matters, and transmit the matters that need to be handled by the corresponding units in the plan to the corresponding departments, and establish a multi-dimensional collaborative intelligent processing system involving multiple departments, multiple industries, and multiple fields.

[0045] Step S170: Based on the execution results fed back to the data collaboration chain by the execution department, a case experience summary is formed and the decision-making plan is optimized.

[0046] After each department completes its plan execution, the results are fed back to the data collaboration chain. Decision-makers collect these feedback, compile case experience summaries, and optimize decision-making plans. The impact of events on multiple domains is recorded, and based on the aggregated data chain information combined with multi-source and multi-category relationship tables, case experience is shared with relevant business units, enhancing the intelligent processing capabilities of urban operations management.

[0047] To further understand the present invention, Figure 2 For further explanation. The provided collaborative processing method of the Internet and government affairs big data business based on data fusion sequentially constructs the subject information table of the matter, the multi-source and multi-category relationship table, the main and secondary element table of the matter, and the multi-dimensional collaborative chain of data as the first main line for obtaining the decision plan for the subject of the matter. On this main line, on the one hand, the multi-source information data and the business standard process and the event plan are combined for correlation coupling. On the other hand, the multi-source information data and the business standard process and the event plan are combined for two sortings. In addition, for the obtained decision plan, a second main line is formed as the execution processing main line through the selection of the decision-making layer, the transmission of the decision plan, and the execution of the decision plan. Furthermore, by feeding back the execution results to the first main line, a closed-loop process is realized, so that more effective decisions can be made by combining historical experience and the actual multi-source information data.

[0048] In summary, the present invention provides scientific decision-making solutions by performing multi-dimensional analysis of the event subject, including collaborative analysis of multiple departments, multiple industries, and multiple fields. Multi-dimensional collaboration uses government big data technology, intelligent technology, and other technologies to share and aggregate data from various business fields, various data structures, and various data sources related to government affairs, thereby achieving in-depth analysis of the data. In addition, through a variety of different business fields, a variety of different data formats, and a variety of different data sources, associating multi-dimensional data becomes the key to solving government information collaboration problems, thereby promoting the improvement of the government's smart service level.

[0049] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0050] A computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0051] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0052] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0053] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0054] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0055] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0056] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and the module, program segment or part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0057] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A collaborative processing method for Internet and government big data services based on data fusion, comprising the following steps: For matters that require collaborative management, decompose the structured information elements to form a table of the main information of the matter; Based on the information coupling association between multi-source and multi-type data and the subject of the matter, a multi-source and multi-type relationship table is formed; For the information in the multi-source and multi-category relationship table, sort the information by relevance and importance, and establish a table of primary and secondary elements of matters; Based on the table of primary and secondary elements of the matter, by analyzing the characteristics and differences of the information, a multi-dimensional collaborative chain of data is formed using cross-domain multi-source multi-dimensional data; Matching the multi-dimensional collaborative chain of data with corresponding decision plans for selection by the decision-makers; The multi-dimensional data collaborative chain matches the corresponding execution department based on the decision-making plan selected by the decision-making layer and the table of primary and secondary elements of the matter, and transmits the decision-making plan to the execution department; The information coupling and association based on multi-source and multi-type data and the subject of the matter includes: coupling and association with people or objects related to the subject of the matter as the main body, and with three-dimensional space and time of the matter as the auxiliary body; The step of sorting the information in the multi-source multi-category relationship table by relevance and importance and establishing a table of primary and secondary elements of matters includes: Training deep learning models, where training samples reflect the types of historical events, the frequency and priority of information required for corresponding decision-making solutions, and the relationship between the importance of information elements; For the information in the multi-source multi-category relationship table, use the deep learning model to sort the relevance and importance, and then establish the primary and secondary element table of the matter; Among them, the matter subject information table contains information about people, information about objects and text information. The information about people includes facial information, body structured information and action information. The information about objects includes category information and structured information of objects. The text information includes semantic information of text.

2. The method according to claim 1, characterized in that Also includes: Based on the execution results fed back to the multi-dimensional collaborative chain of data by the execution department, a case experience summary is formed and the decision-making plan is optimized.

3. The method according to claim 1, characterized in that The matters that require collaborative management include social events, business scenarios, and departmental collaboration.

4. The method according to claim 1, wherein The multi-source and multi-type data include government data, public data, Internet data, Internet of Things data, geographic information data and domain data.

5. The method according to claim 1, wherein The cross-domain multi-source multi-dimensional data is multi-department, multi-industry and multi-field data obtained by using heterogeneous information channels.

6. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer device comprising a memory and a processor, wherein a computer program capable of being run on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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