Data supply and demand processing method and device, equipment, medium and product

Abstract

The invention discloses a data supply and demand processing method and device, equipment, a medium and a product, relates to the technical field of artificial intelligence, big data, block chains and deep learning, and can be used in the field of financial science and technology. The method comprises the steps of obtaining a data demand of a data demander and a data supply set of a data provider; constructing at least two supply and demand data pairs according to the data demand and the data supply set; performing multi-dimensional relation analysis on the at least two supply and demand data pairs, and determining a target data pair from the at least two supply and demand data pairs; and delivering the supply data in the target data pair to the data demander. Through the technical scheme, the matching precision of the supply and demand data can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, big data, blockchain and deep learning, and can be used in the field of financial technology, and particularly relates to a data supply and demand processing method and device, equipment, medium and product. BACKGROUND

[0002] With the advent of the digital economy era, data has become a key production factor alongside land, labor, capital and technology. However, the current development of the data factor market still faces many challenges, especially in the matching of data supply and demand. SUMMARY

[0003] The present application provides a data supply and demand processing method, device, equipment, medium and product to improve the matching accuracy of supply and demand data.

[0004] According to an aspect of the present application, a data supply and demand processing method is provided, which comprises:

[0005] obtaining data requirements of a data demander and a data supply set of a data provider;

[0006] constructing at least two supply and demand data pairs according to the data requirements and the data supply set;

[0007] performing multi-dimensional relationship analysis on the at least two supply and demand data pairs to determine a target data pair from the at least two supply and demand data pairs;

[0008] delivering supply data in the target data pair to the data demander.

[0009] According to another aspect of the present application, a data supply and demand processing device is provided, which comprises:

[0010] a supply and demand obtaining module for obtaining data requirements of a data demander and a data supply set of a data provider;

[0011] a supply and demand data pair constructing module for constructing at least two supply and demand data pairs according to the data requirements and the data supply set;

[0012] a target data pair determining module for performing multi-dimensional relationship analysis on the at least two supply and demand data pairs to determine a target data pair from the at least two supply and demand data pairs;

[0013] a data delivery module for delivering supply data in the target data pair to the data demander.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the data supply and demand processing method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data supply and demand processing method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the data supply and demand processing method according to any embodiment of the present invention.

[0020] The technical solution of this invention involves obtaining the data requirements of the data demander and the data supply set of the data provider; constructing at least two supply-demand data pairs based on the data requirements and data supply sets; performing multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine the target data pair; and delivering the supply data from the target data pair to the data demander. This technical solution improves data matching efficiency and accuracy by performing multi-dimensional matching analysis on data requirements and data supply.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a data supply and demand processing method provided by an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of a data supply and demand processing method provided by an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a data supply and demand processing device according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the data supply and demand processing method of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Furthermore, it should be noted that the information collected in the technical solution of this invention is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data all comply with the relevant laws, regulations and standards of relevant countries and regions, necessary confidentiality measures have been taken, and public order and good morals are not violated. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0030] Traditional data trading models rely heavily on human intermediaries, making them ill-suited for large-scale, real-time data trading scenarios. Existing data trading platforms suffer from high levels of human intervention, low matching efficiency, and slow delivery. Data requesters spend significant time searching for suitable data sources, while data providers struggle to effectively reach potential users, resulting in both idle and underutilized data resources.

[0031] Data supply and demand often use different terminology systems and classification standards, leading to ineffective matching even when content is related due to differences in expression. Taking patents as an example, the complexity of patent text features and the significant differences between patent supply and demand texts are key factors affecting patent matching effectiveness. This problem is equally prominent in the data trading field, manifesting as a semantic gap and information asymmetry. Existing technologies, such as keyword-based matching methods, lack a deep semantic understanding of data content and demand, failing to effectively capture potential connections between supply and demand, resulting in limited matching accuracy. Improving the automation level of patent knowledge graph construction and achieving the integration of knowledge services and transaction services is crucial to solving the supply-demand matching problem.

[0032] As a special factor of production, data circulation and use involve stringent requirements such as privacy protection, data security, and compliance. Data circulation needs to be conducted within a clear contractual framework to ensure that data use complies with the agreed scope. However, existing data trading platforms often lack granular compliance control mechanisms and security measures, causing data providers to be unwilling to share data due to concerns about compliance risks, further exacerbating the inactivity of the data market.

[0033] Furthermore, traditional data trading platforms typically employ a centralized architecture, which presents risks of single points of failure and insufficient transparency. Inspur Technology's Trusted Data Space technology attempts to address this issue through a distributed connector architecture, but how to improve matching efficiency while ensuring security still requires further exploration.

[0034] Figure 1 This is a flowchart of a data supply and demand processing method according to Embodiment 1 of the present invention. This embodiment is applicable to the matching and interaction between data demanders and data providers in a data trading market. The method can be executed by a data supply and demand processing device, which can be implemented in hardware and / or software. This device can be configured in an electronic device that carries data supply and demand processing functions, such as a supply and demand processing system in a server. Figure 1 As shown, the method includes:

[0035] S110. Obtain the data requirements of data demanders and the data supply set of data providers.

[0036] In this embodiment, the data demander refers to a party with a data need. Data need refers to a specific intention to require data. The data provider refers to a party that provides data; optionally, there can be one or more data providers. The data supply set refers to the collection of data provided by the data providers. It should be noted that the data provider uploads data resources through the interaction module, providing both non-standardized descriptions (such as natural language descriptions) and standardized structured descriptions (such as metadata, data patterns, etc.). The system automatically processes the uploaded data resources, including: extracting key features, generating word vector representations, constructing resource description files, generating supply data, and storing it in the data resource repository. The data marketplace platform stores publicly available descriptions of data resources for potential demanders to browse and retrieve.

[0037] An alternative approach is to directly obtain the data requirements of the data requester through an interactive interface, while simultaneously obtaining the data supply set of the data provider through an interface.

[0038] An optional approach to obtaining data requirements from data requesters includes: performing word segmentation, entity recognition, and relation extraction on the original requirements of the requesters to identify key elements and constraints in the requirements; performing semantic expansion and deep analysis on the key elements based on domain ontology and knowledge graph to determine the potential intent and contextual meaning of the original requirements; and determining data requirements based on the potential intent and contextual meaning.

[0039] Here, "original demand" refers to the demand directly and originally output by the demander. "Key elements" refer to key elements extracted from the original demand, such as keywords. "Constraints" refer to constraints involved in the original demand, such as data query condition constraints. "Domain ontology" is a type of artificial intelligence ontology, belonging to the knowledge engineering field of computer science and technology. It is used to formally describe concepts, attributes, relationships, and constraints within a specific discipline, possessing domain-wide knowledge sharing capabilities. "Knowledge graph" refers to a knowledge graph specific to a particular application scenario.

[0040] Specifically, natural language processing technology can be used to segment, identify entities, and extract relationships from the original requirements to obtain the key elements and constraints in the original requirements. Then, based on domain ontology and knowledge graph, semantic expansion and in-depth analysis can be performed on the key elements to determine the potential intent and contextual meaning of the original requirements. The potential intent and contextual meaning can then be used as the data requirements of the data requester.

[0041] Understandably, by conducting in-depth and semantic analysis of requirements, we can identify the technical needs and business scenarios implied in the requirement text, obtain more accurate requirements, and thus lay the foundation for supply and demand matching.

[0042] S120. Based on the data demand and data supply sets, construct at least two supply and demand data pairs.

[0043] In this embodiment, the supply and demand data pair consists of data demand and supply data.

[0044] Specifically, each supply data in the data demand and data supply set is grouped into multiple supply and demand data pairs.

[0045] S130. Perform multidimensional relationship analysis on at least two supply and demand data pairs to determine the target data pair from the at least two supply and demand data pairs.

[0046] In this embodiment, multidimensional relationship analysis refers to matching analysis from different dimensions, including but not limited to causal, temporal, and spatial dimensions. The target data pair refers to the supply and demand data pair that best matches the data requirements.

[0047] Specifically, for each supply and demand data pair, the data demand and supply data in the supply and demand data pair are matched in terms of causality, time, and space to obtain the matching degree. Based on the matching pair, the supply and demand data pair with the high matching degree is selected from at least two supply and demand data pairs as the target data pair.

[0048] S140, Deliver the target data to the data demander.

[0049] One alternative is to directly deliver the target data to the data demander.

[0050] Another option is to deliver the supply data of the target data pair to the data demander, including: establishing a secure data flow path from the data provider to the data demander in a trusted data space; and delivering the supply data of the target data pair to the data demander based on the secure data flow path.

[0051] Specifically, a trusted data space architecture is adopted, in which data providers and data suppliers access the trusted data space through their respective distributed connector nodes, forming an end-to-end secure data flow path. Based on the secure data flow path, the data supplier delivers the supplied data to the data demander.

[0052] It is understandable that by transmitting data between data demanders and data suppliers in a trusted data space, the confidentiality and integrity of the data during transmission can be ensured.

[0053] At the same time, the system incorporates privacy-preserving computation technology to achieve a secure exchange mode where "data is usable but not visible" when necessary. The data matching privacy-preserving computation model can generate a matching assessment report for data transaction needs while protecting the privacy of the original data, thus promoting the circulation of data value while ensuring data security.

[0054] Another optional approach, before establishing a secure data flow path from the data provider to the data demander in a trusted data space, includes: automatically generating a data usage agreement based on the target data pair's data demand and supply data; the data usage agreement includes the scope of data use, usage period, authorization method, fee terms, and compliance requirements; feeding the data usage agreement back to the data provider and data demander for review and confirmation through a visual interactive module; and obtaining confirmation from both the data provider and data demander respectively.

[0055] Among them, the data usage agreement refers to the smart contract used to regulate the flow of data and ensure that data usage does not exceed the agreed scope.

[0056] Specifically, smart contract technology automatically generates a data usage agreement based on the target data and the data demand and supply data. The data usage agreement is then presented to the data provider and the data requester through a visual interactive module, allowing both parties to review and confirm its content. Upon receiving confirmation from both the data provider and the data requester, the data delivery process is triggered.

[0057] Understandably, by generating a data usage agreement before data delivery and having both the supply and demand parties confirm it, it is possible to ensure that the constraints stipulated in the data usage agreement are enforced during the data circulation process, thereby preventing the risk of data abuse and misuse.

[0058] The technical solution of this invention involves obtaining the data requirements of the data demander and the data supply set of the data provider; constructing at least two supply-demand data pairs based on the data requirements and data supply sets; performing multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine the target data pair; and delivering the supply data from the target data pair to the data demander. This technical solution improves data matching efficiency and accuracy by performing multi-dimensional matching analysis on data requirements and data supply.

[0059] Figure 2 This is a flowchart of a data supply and demand processing method according to Embodiment 2 of the present invention; based on the above embodiments, this embodiment further optimizes the step of "performing multidimensional relationship analysis on at least two supply and demand data pairs and determining the target data pair from at least two supply and demand data pairs", providing an optional implementation scheme. Figure 2 As shown, the method includes:

[0060] S210. Obtain the data requirements of data demanders and the data supply set of data providers.

[0061] S220. Based on the data demand and data supply sets, construct at least two supply and demand data pairs.

[0062] S230. Perform multidimensional relationship analysis on at least two supply and demand data pairs to determine the target data pair from the at least two supply and demand data pairs.

[0063] S240, Deliver the target data to the data demander.

[0064] An optional approach involves performing multidimensional relationship analysis on at least two supply and demand data pairs to determine a target data pair from the at least two supply and demand data pairs, including: extracting features from the supply and demand data pairs to obtain supply and demand feature pairs; wherein the supply and demand feature pairs include demand features and supply features; determining the similarity between the data demand and supply data in the supply and demand data pairs based on the supply and demand feature pairs; comparing the similarity based on a similarity threshold; if the similarity is less than the similarity threshold, determining at least one candidate path corresponding to the supply and demand data pairs from a domain knowledge graph based on the supply and demand data pairs; and determining the target data pair from the at least two supply and demand data pairs based on the candidate path and the similarity.

[0065] Demand features characterize the features of data demand and are represented in matrix or vector form. Supply features characterize the features of supplied data and can also be represented in matrix or vector form. A domain knowledge graph refers to a pre-constructed knowledge graph specific to a particular scenario.

[0066] Specifically, supply and demand data pairs are mapped to a feature space to obtain supply and demand feature pairs. The cosine similarity of these pairs can be calculated based on the features, and this cosine similarity is used as the similarity between the data demand and supply data in the pair. Alternatively, the spatiotemporal and / or structural similarity of the supply and demand data pairs can be determined based on the feature pairs. The cosine similarity and the spatiotemporal and / or structural similarity are then weighted and combined to obtain the similarity between the data demand and supply data in the pair. The similarity is compared to a similarity threshold. If the similarity is greater than the threshold, a direct match is successful, and this supply and demand data pair is selected as the target data pair. If the similarity is less than the threshold, a first-order match fails, and further multi-order relationship analysis between the data demand and supply data is performed. This involves determining at least one candidate path corresponding to the supply and demand data pair from the domain knowledge graph based on the data pairs; and then determining the target data pair from at least two supply and demand data pairs based on the candidate paths and similarities.

[0067] It is understandable that by first performing direct similarity matching (i.e., first-order relationship matching) on ​​supply and demand data pairs, and then performing multi-order relationship analysis to determine the target data pair after the matching fails, the accuracy of supply and demand matching can be improved.

[0068] For example, determining a target data pair from at least two supply and demand data pairs based on candidate paths and similarity includes: determining the path weight of a candidate path based on the edge weights on the candidate path and the path length of the candidate path; summing the path weights of at least one candidate path to obtain a multi-path score; performing a weighted summation of the similarity and multi-path scores to determine a comprehensive score; determining a multi-dimensional value score for the supply and demand data pair; multiplying the comprehensive score and the multi-dimensional value score to obtain a final score; and determining the target data pair from at least two supply and demand data pairs based on the final score.

[0069] Candidate paths refer to paths from demand nodes to supply nodes in the domain knowledge graph. These paths include demand nodes, supply nodes, and concept nodes, as well as direct similarity edges and concept association edges. Direct similarity edges are those between demand and supply nodes, while concept association edges are those between demand nodes and concept nodes, and between supply nodes and concept nodes. Concept nodes are those extracted from the domain knowledge graph. Each edge has an edge weight, which represents the strength of the relationship.

[0070] Specifically, for each candidate path of each supply and demand data pair, the edge weights on the candidate path are multiplied together, and then multiplied by the decay value to obtain the path weight of the candidate path. For example, it can be determined by the following formula:

[0071] ;

[0072] in, The path weight represents the candidate path. Let γ represent the edge weight, and γ∈(0,1] represent the decay factor. Indicates the path length of the candidate path.

[0073] Then the supply and demand data is... The multipath score is obtained by summing the path weights of at least one candidate path. ;in, Let p represent the set of paths consisting of at least one candidate path. Then, a weighted sum of the similarity and multi-path scores is calculated to determine the comprehensive score, i.e.,

[0074] ;

[0075] in, This indicates that the supply and demand data are combined. Indicates similarity; and It is a weighting coefficient, and + =1.

[0076] Next, the multidimensional value score of the supply and demand data pair is determined, specifically by considering other value dimensions such as economic value and strategic value. Let there be a set of value dimensions D = {d1, d2, ..., dh}, each dimension having an evaluation function. and weight The multidimensional value score is then:

[0077] ;

[0078] Next, the overall score and the multidimensional value score are multiplied to obtain the final score, i.e.,

[0079] .

[0080] Finally, based on the final score, the supply and demand data pair with the highest final score is selected as the target data pair from at least two supply and demand data pairs.

[0081] Understandably, determining the target data pair from multiple supply and demand data pairs by setting a final score based on the similarity of supply and demand data pairs, the weight of candidate paths, and other dimensional scores can improve the accuracy of supply and demand data matching.

[0082] The technical solution of this invention involves obtaining the data requirements of the data demander and the data supply set of the data provider; constructing at least two supply-demand data pairs based on the data requirements and data supply sets; performing multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine the target data pair; and delivering the supply data from the target data pair to the data demander. This technical solution improves data matching efficiency and accuracy by performing multi-dimensional matching analysis on data requirements and data supply.

[0083] Figure 3 This is a schematic diagram of a data supply and demand processing device according to Embodiment 3 of the present invention. This embodiment is applicable to situations where data demanders and data providers interact and match in a data trading market. The data supply and demand processing device can be implemented in hardware and / or software, and can be configured in an electronic device that carries data supply and demand processing functions, such as a supply and demand processing system in a server. Figure 3 As shown, the device includes:

[0084] The supply and demand acquisition module 310 is used to acquire the data demand of the data demanders and the data supply set of the data providers;

[0085] Supply and demand data pair construction module 320 is used to construct at least two supply and demand data pairs based on the data demand and data supply sets;

[0086] The target data pair determination module 330 is used to perform multidimensional relationship analysis on at least two supply and demand data pairs to determine the target data pair from the at least two supply and demand data pairs;

[0087] The data delivery module 340 is used to deliver the target data to the supply data to the data demander.

[0088] The technical solution of this invention involves obtaining the data requirements of the data demander and the data supply set of the data provider; constructing at least two supply-demand data pairs based on the data requirements and data supply sets; performing multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine the target data pair; and delivering the supply data from the target data pair to the data demander. This technical solution improves data matching efficiency and accuracy by performing multi-dimensional matching analysis on data requirements and data supply.

[0089] Optionally, the target data pair determination module 330 is used for:

[0090] Feature extraction is performed on supply and demand data pairs to obtain supply and demand feature pairs; where supply and demand feature pairs include demand features and supply features;

[0091] Based on supply and demand characteristics, determine the similarity between data demand and supply data in the supply and demand data pair;

[0092] The comparison is performed based on similarity and a similarity threshold;

[0093] If the similarity is less than the similarity threshold, then at least one candidate path corresponding to the supply and demand data pair is determined from the domain knowledge graph based on the supply and demand data pair.

[0094] Determine candidate paths and similarity to identify target data pairs from at least two supply and demand data pairs.

[0095] Optionally, the target data pair determination module 330 is specifically used for:

[0096] The path weight of a candidate path is determined based on the edge weights on the candidate path and the path length of the candidate path.

[0097] The multi-path score is obtained by summing the path weights of at least one candidate path.

[0098] The similarity score and the multi-path score are weighted and summed to determine the overall score;

[0099] Determine the multidimensional value score of supply and demand data pairs;

[0100] The final score is obtained by multiplying the overall score and the multidimensional value score.

[0101] Based on the final score, the target data pair is determined from at least two supply and demand data pairs.

[0102] Optionally, the supply and demand acquisition module 310 is used for:

[0103] The original requirements from the demand side are segmented into words, entity recognition is performed, and relationship extraction is performed to identify the key elements and constraints in the requirements.

[0104] Based on domain ontology and knowledge graph, semantic expansion and in-depth analysis are performed on key elements to determine the potential intent and contextual meaning of the original requirements.

[0105] Determine data requirements based on underlying intent and contextual meaning.

[0106] Optionally, the device also includes a protocol generation module for:

[0107] In a trusted data space, before establishing a secure data flow path from data provider to data demander, a data usage agreement is automatically generated based on the target data and the data demand and supply data. The data usage agreement includes the scope of data use, usage period, authorization method, fee terms, and compliance requirements.

[0108] The data usage agreement is fed back to the data provider and data requester for review and confirmation through a visual interactive module;

[0109] Obtain confirmation from both the data provider and the data requester.

[0110] Optionally, the data delivery module 340 is used for:

[0111] In a trusted data space, establish a secure data flow path from data providers to data demanders;

[0112] Based on a secure data flow path, the target data is supplied to the data demander.

[0113] The data supply and demand processing apparatus provided in the embodiments of the present invention can execute the data supply and demand processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0114] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0115] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the data supply and demand processing method of the present invention. Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0116] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data supply and demand processing methods.

[0119] In some embodiments, the data supply and demand processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data supply and demand processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data supply and demand processing method by any other suitable means (e.g., by means of firmware).

[0120] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0125] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system. This addresses the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0126] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0127] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data supply-demand processing method characterized by comprising: The method comprises the following steps: obtaining data requirements of a data demander and a data supply set of a data provider; constructing at least two supply-demand data pairs according to the data requirements and the data supply set; performing multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine a target data pair from the at least two supply-demand data pairs; delivering supply data in the target data pair to the data demander.

2. The method of claim 1, wherein, The multi-dimensional relationship analysis on the at least two supply-demand data pairs to determine a target data pair from the at least two supply-demand data pairs comprises: performing feature extraction on the supply-demand data pairs to obtain supply-demand feature pairs; wherein the supply-demand feature pairs comprise demand features and supply features; determining the similarity between data requirements and supply data in the supply-demand data pairs according to the supply-demand feature pairs; comparing the similarity with a similarity threshold value; if the similarity is less than the similarity threshold value, determining at least one candidate path corresponding to the supply-demand data pair from a domain knowledge graph according to the supply-demand data pair; determining the target data pair from the at least two supply-demand data pairs according to the candidate path and the similarity.

3. The method of claim 2, wherein, The determination of the target data pair from the at least two supply-demand data pairs according to the candidate path and the similarity comprises: determining a path weight of the candidate path according to edge weights on the candidate path and a path length of the candidate path; summing the path weights of the at least one candidate path to obtain a multi-path score; performing weighted summation on the similarity and the multi-path score to determine a comprehensive score; determining a multi-dimensional value score of the supply-demand data pair; multiplying the comprehensive score and the multi-dimensional value score to obtain a final score; determining the target data pair from the at least two supply-demand data pairs according to the final score.

4. The method of claim 1, wherein, The obtaining of the data requirements of the data demander comprises: performing word segmentation, entity recognition and relationship extraction on original requirements of the demander to identify key elements and constraint conditions in the requirements; performing semantic extension and depth analysis on the key elements based on a domain ontology and a knowledge graph to determine potential intentions and contextual meanings of the original requirements; determining the data requirements according to the potential intentions and the contextual meanings.

5. The method of claim 1, wherein, Before establishing a secure data flow path from the data provider to the data demander in a trusted data space, the method further comprises: automatically generating a data use agreement according to data requirements and supply data in the target data pair; the data use agreement comprises data use scope, use period, authorization mode, fee terms and compliance requirements, etc.; feeding back the data use agreement to the data provider and the data demander through a visual interaction module for review and confirmation; respectively obtaining confirmation operations of the data provider and the data demander.

6. The method of claim 1, wherein, The delivery of the supply data in the target data pair to the data demander comprises: establishing a secure data flow path from the data provider to the data demander in a trusted data space; delivering the supply data in the target data pair to the data demander based on the secure data flow path.

7. A data supply-demand processing apparatus characterized by comprising: The method comprises the following steps: The supply and demand obtaining module is configured to obtain a data demand of a data demander and a data supply set of a data provider; The supply and demand data pair constructing module is configured to construct at least two supply and demand data pairs according to the data demand and the data supply set; The target data pair determining module is configured to perform multi-dimensional relationship analysis on the at least two supply and demand data pairs, and determine a target data pair from the at least two supply and demand data pairs; The data delivery module is configured to deliver supply data in the target data pair to the data demander.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data supply and demand processing method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the data supply and demand processing method of any one of claims 1-6 when executed.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program implements the data supply and demand processing method according to any one of claims 1-6 when executed by the processor.

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