CN112163838B

CN112163838B - Information processing method based on big data and cloud computing and electronic commerce collaboration platform

Info

Publication number: CN112163838B
Application number: CN202011064292.7A
Authority: CN
Inventors: 曹春华
Original assignee: Chain Rui Information Service Nantong Co ltd
Current assignee: Chain Rui Information Service (Nantong) Co.,Ltd.
Priority date: 2020-10-01
Filing date: 2020-10-01
Publication date: 2021-05-25
Anticipated expiration: 2040-10-01
Also published as: CN112163838A; CN112884456A; CN112884457A

Abstract

The embodiment of the application provides an information processing method based on big data and cloud computing and an electronic commerce collaboration platform. In this way, the updating change condition of the domain object of the collaborative information flow node associated with the collaborative processing plan is considered, so that the subsequent collaborative processing cloud computing service can be updated by combining the target domain session service information reflecting the dynamic change of the business, and the coverage range of the subsequent collaborative updating is further expanded.

Description

Information processing method based on big data and cloud computing and electronic commerce collaboration platform

Technical Field

The application relates to the technical field of big data and cloud computing, in particular to an information processing method and an electronic commerce collaboration platform based on big data and cloud computing.

Background

Collaboration, refers to the process or ability of coordinating two or more different resources or individuals to achieve a goal in concert, and all software that facilitates collaboration may be referred to as collaborative management software. Collaborative management software is computer software that is intended to help people participate in a common task to achieve their goals. It usually does not allow individuals to work physically in the same location, but rather work together through an internet connection. It also includes remotely accessing the storage system to obtain shared data files so that they can be accessed, modified, and retrieved by members of the distributed workgroup.

In the related art, the generated cooperative processing plans can reflect the progress process of the cooperative information flow nodes of the user, so that the cooperative processing plans can be analyzed, and the cooperative processing cloud computing service can be updated in advance, so that the increased response time of the subsequent independent adaptive update of the cooperative processing cloud computing service can be reduced. However, the inventor researches and discovers that in the conventional design, only a single collaborative processing plan is usually analyzed, and the method cannot reflect the actual collaborative scene more accurately, so that the coverage of collaborative updating is smaller.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide an information processing method and an electronic commerce collaboration platform based on big data and cloud computing, in which domain session service information in a domain object of a first collaboration information stream node covered by a collaboration processing plan is determined, a first session material set is determined, then a domain object of a second collaboration information stream node and a corresponding second session material set are determined, a target session material matched with a session material in the first session material set is selected from the second session material set, and then target domain session service information matched with domain session service information of the domain object of the first collaboration information stream node in the domain object of the second collaboration information stream node is determined according to the obtained target session material set and then suggested display is performed. In this way, the updating change condition of the domain object of the collaborative information flow node associated with the collaborative processing plan is considered, so that the subsequent collaborative processing cloud computing service can be updated by combining the target domain session service information reflecting the dynamic change of the business, and the coverage range of the subsequent collaborative updating is further expanded.

In a first aspect, the present application provides an information processing method based on big data and cloud computing, which is applied to an e-commerce collaboration platform, where the e-commerce collaboration platform is in communication connection with a plurality of e-commerce collaboration terminals, and the method includes:

in a state that any one collaborative processing plan of the e-commerce collaborative terminal is initiated, determining domain session service information in a domain object of a first collaborative information flow node covered by the collaborative processing plan from a corresponding block chain, and determining a first session material set capable of representing the domain session service information, wherein the domain session service information corresponds to a domain session service;

determining a domain object of a second collaborative information flow node from the block chain to obtain a second session material set capable of representing the domain object of the second collaborative information flow node, wherein the domain object of the second collaborative information flow node is the domain object of the collaborative information flow node associated with the domain object of the first collaborative information flow node;

matching the second session material set with the first session material set, and selecting a target session material matched with the session material in the first session material set from the second session material set based on a matching result to obtain a target session material set;

determining target domain session service information matched with the domain session service information of the domain object of the first collaborative information stream node in the domain objects of the second collaborative information stream node based on the target session material set, wherein the target domain session service information corresponds to domain session service matched with the domain session service of the domain session service information in the domain object of the first collaborative information stream node;

and updating the cooperative processing cloud computing service of the electronic commerce cooperative terminal according to big data configuration information corresponding to target domain session service information corresponding to all initiated cooperative processing plans of the electronic commerce cooperative terminal.

In a possible implementation manner of the first aspect, the method further includes:

determining a domain object mapping feature of a collaborative information flow node that maps from a domain object of the first collaborative information flow node to a domain object of the second collaborative information flow node;

based on the domain object mapping characteristics of the collaborative information flow nodes, target session materials matched with the session materials in the first session material set are estimated from the domain objects of the second collaborative information flow nodes, and a first estimated target session material set is obtained;

correspondingly, the step of selecting the target session material matched with the session material in the first session material set from the second session material set based on the matching result to obtain a target session material set includes:

selecting target session materials matched with the session materials in the first session material set from the second session material set based on the matching result to obtain a second pre-estimated target session material set;

and obtaining a target session material set based on the first pre-estimation target session material set and the second pre-estimation target session material set.

In a possible implementation manner of the first aspect, the matching the second set of session materials with the first set of session materials, and selecting target session materials from the second set of session materials that match the session materials in the first set of session materials based on a matching result includes:

determining session material service characteristics between second session materials in the second session material set and first session materials in the first session material set;

and selecting the target session material of which the service characteristics of the session material meet the preset business rule from the second session material set.

In a possible implementation manner of the first aspect, the determining, based on the target session material set, target domain session service information that is matched with the domain session service information of the domain object of the first collaborative information stream node in the domain objects of the second collaborative information stream node includes:

determining key session materials based on the first session materials in the first session material set and the target session materials corresponding to the first session materials in the target session material set to obtain a key session material set;

selecting target key session materials meeting preset rules from the key session material set;

and determining target domain session service information in the domain object of the second collaborative information stream node based on the first session material set and the target key session material, wherein the target key session material is located in the key session running service of the target domain session service information.

In a possible implementation manner of the first aspect, the updating, according to big data configuration information corresponding to target domain session service information corresponding to all initiated collaborative processing plans of the e-commerce collaborative terminal, a collaborative processing cloud computing service of the e-commerce collaborative terminal includes:

target domain session service information corresponding to all initiated collaborative processing plans of the electronic commerce collaborative terminal is input into a collaborative processing cloud computing service subscribed by the electronic commerce collaborative terminal, and a collaborative interaction update record obtained by identifying the target domain session service information is obtained according to a collaborative configuration mode corresponding to the collaborative processing cloud computing service;

acquiring a plurality of kinds of collaborative linkage event analysis information obtained by performing collaborative linkage event analysis on the collaborative interaction update record, wherein each kind of collaborative linkage event analysis information consists of a collaborative linkage event label of the collaborative interaction update record;

determining target collaborative linkage event analysis information meeting target rules from the multiple pieces of collaborative linkage event analysis information, and searching a target collaborative linkage event tag in collaborative linkage event tags of the target collaborative linkage event analysis information in a first knowledge graph index area, wherein the first knowledge graph index area is used for storing the collaborative linkage event tags with knowledge graph information, and the knowledge graph information is used for indicating knowledge graph content to which the collaborative linkage event tags with the knowledge graph information belong;

determining the target collaborative linkage event label with target collaborative linkage event information in the first knowledge graph index area as an update optimization label of the collaborative interaction update record under the condition that the target collaborative linkage event label is found in the first knowledge graph index area, wherein the knowledge graph information comprises the target knowledge graph information, and the target knowledge graph information is used for indicating the knowledge graph content to which the target collaborative linkage event label belongs;

determining a target knowledge graph updating node of the updated optimization label according to the target knowledge graph information, and determining a knowledge graph updating node of the collaborative interaction updating record according to the target knowledge graph updating node of the updated optimization label;

and updating the cooperative processing cloud computing service of the electronic commerce cooperative terminal according to the knowledge graph updating node of the cooperative interaction updating record.

In a possible implementation manner of the first aspect, the step of determining, from among the plurality of types of collaborative linkage event analysis information, target collaborative linkage event analysis information that satisfies a target rule includes:

acquiring, in the multiple kinds of collaborative linkage event analysis information, derived entity feature information of collaborative work derived entities of all collaborative linkage event tags in each kind of collaborative linkage event analysis information, and determining that the derived entity feature information of the collaborative work derived entities of all collaborative linkage event tags conforms to first collaborative linkage event analysis information of a collaborative configuration mode corresponding to the collaborative processing cloud computing service, where a collaborative linkage business interval of the first collaborative linkage event analysis information is a first collaborative linkage business interval;

determining the first collaborative linkage event analysis information as the target collaborative linkage event analysis information meeting the target rule under the condition that the first collaborative linkage business interval is a business interval of a preset service characteristic label;

under the condition that the first collaborative linkage business interval is not a business interval with a preset service characteristic label, acquiring election collaborative work deduction entities of all collaborative linkage event labels in each type of first collaborative linkage event analysis information in the first collaborative linkage event analysis information of the first collaborative linkage business interval, and determining second collaborative linkage event analysis information with the largest business coverage range of the election collaborative work deduction entities of all collaborative linkage event labels, wherein the collaborative linkage business interval of the second collaborative linkage event analysis information is a second collaborative linkage business interval which is smaller than or equal to the first collaborative linkage business interval;

determining the second collaborative linkage event analysis information as the target collaborative linkage event analysis information meeting the target rule under the condition that the second collaborative linkage business interval is a business interval of a preset service characteristic label;

under the condition that the second collaborative linkage business interval is not a business interval with a preset service characteristic label, acquiring the variation amplitude of the keyword classification level of all collaborative linkage event labels in each second collaborative linkage event analysis information in the second collaborative linkage event analysis information of the second collaborative linkage business interval, and determining third collaborative linkage event analysis information with the minimum variation amplitude of the keyword classification level of all collaborative linkage event labels, wherein the collaborative linkage business interval of the third collaborative linkage event analysis information is a third collaborative linkage business interval which is less than or equal to the second collaborative linkage business interval; determining the third collaborative linkage event analysis information as the target collaborative linkage event analysis information meeting the target rule under the condition that the third collaborative linkage business interval is a business interval of a preset service characteristic label;

under the condition that the third collaborative linkage business interval is not a business interval with a preset service feature tag, obtaining update planning parameters of all collaborative linkage event tags in each third collaborative linkage event analysis information in the third collaborative linkage business interval, and determining fourth collaborative linkage event analysis information with the highest update planning parameter of all collaborative linkage event tags, wherein the collaborative linkage business interval of the fourth collaborative linkage event analysis information is a fourth collaborative linkage business interval, the fourth collaborative linkage business interval is smaller than or equal to the third collaborative linkage business interval, and the update planning parameters are used for indicating the collaborative linkage event tags and updatable objects to form parameters of new collaborative linkage event tags;

and determining the fourth collaborative linkage event analysis information as the target collaborative linkage event analysis information meeting the target rule under the condition that the fourth collaborative linkage business interval is a business interval of a preset service characteristic label.

In a possible implementation manner of the first aspect, the updating, by the knowledge graph update node according to the collaborative interaction update record, the collaborative processing cloud computing service of the e-commerce collaboration terminal includes:

acquiring a knowledge graph updating rule network corresponding to the knowledge graph updating node of the collaborative interaction updating record, and acquiring a target configuration source element sequence of a main configuration source corresponding to a service updating main body of the currently selected knowledge graph updating node of the collaborative interaction updating record based on the knowledge graph updating rule network;

sequentially acquiring target configuration source elements of a main configuration source for indicating global configuration characteristics in a target configuration source element sequence of a main configuration source according to the hierarchical relationship of the target configuration source elements of each main configuration source in the target configuration source element sequence of the main configuration source;

calling a target configuration source element of the main body configuration source to acquire a first rule service index target of a rule service of the knowledge graph updating rule network by adopting an index interval of the target configuration source element of the main body configuration source;

marking a target configuration source element of a demand side main body configuration source of a demand side service carried by a knowledge graph update node of the acquired collaborative interaction update record in a second rule service index target associated with the first rule service index target in the rule service, wherein the demand side service is a demand side service matched according to the global configuration characteristics;

extracting candidate main body configuration source units of the demand side service in target configuration source elements of the demand side main body configuration source, and splicing the candidate main body configuration source units to generate target main body configuration source units of the demand side service;

randomly selecting a first updating rule entity and a second updating rule entity from reference updating rule entities of the knowledge graph updating rule network, and establishing updating rule entity distribution based on the first updating rule entity and the second updating rule entity;

acquiring a third updating rule entity corresponding to the first updating rule entity and a fourth updating rule entity corresponding to the second updating rule entity in the target subject configuration source unit;

acquiring hotspot relationship data of the first updating rule entity and the second updating rule entity as well as the third updating rule entity and the fourth updating rule entity, and mapping the target main configuration source unit to the updating rule entity distribution according to the hotspot relationship data, wherein the updating rule entity distribution is a distribution map established by a reference updating rule entity based on global configuration characteristics in target configuration source elements of the main configuration source;

acquiring the association parameters between the target main body configuration source unit and the reference updating rule entity in the updating rule entity distribution, and generating a matching result of the target configuration source element of the main body configuration source on the demand side and the target configuration source element of the main body configuration source according to the association parameters;

and acquiring update service information of a target configuration source element aiming at the main configuration source in the knowledge graph update rule network based on the matching result, so as to update the cooperative processing cloud computing service of the electronic commerce cooperative terminal.

In a possible implementation manner of the first aspect, the acquiring, in the update rule entity distribution, a correlation parameter between the target subject configuration source unit and the reference update rule entity, and generating a matching result between a target configuration source element of the demand-side subject configuration source and a target configuration source element of the subject configuration source according to the correlation parameter includes:

acquiring configuration source mapping parameters of corresponding nodes between the target main body configuration source unit and the reference updating rule entity in the distribution of the updating rule entity;

when the configuration source mapping parameters meet preset mapping rules, determining that the target configuration source elements of the main body configuration source on the demand side are successfully matched with the target configuration source elements of the main body configuration source; or

Acquiring an entity distribution object of a corresponding node between the target main body configuration source unit and the reference update rule entity in the update rule entity distribution, and acquiring a configuration source mapping parameter corresponding to the entity distribution object of the corresponding node;

and when the configuration source mapping parameters meet the preset mapping rule, determining that the target configuration source element of the demand side main body configuration source is successfully matched with the target configuration source element of the main body configuration source.

distributing the cooperative processing cloud computing service to the e-commerce cooperative terminal, acquiring cooperative plan execution information of a cooperative response object of the e-commerce cooperative terminal aiming at the cooperative processing cloud computing service, and generating a cooperative operation item parameter of the cooperative response object;

acquiring cooperative flow distribution information and cooperative sharing configuration information of the cooperative response object according to the cooperative operation item parameter of the cooperative response object, wherein the cooperative flow distribution information comprises a flow distribution object and a flow distribution coverage service, and the cooperative sharing configuration information comprises a sharing configuration object and a sharing configuration coverage service;

constructing a cooperative service relationship between the process distribution object and the shared configuration object according to the process distribution coverage service and the shared configuration coverage service, and extracting features based on the process distribution object, the shared configuration object and the cooperative service relationship to obtain candidate process distribution features and candidate shared configuration features of the cooperative response object;

and generating suggested cooperative service information of the cooperative response object based on the candidate process distribution characteristics and the candidate sharing configuration characteristics, and displaying the suggested cooperative service information.

In a second aspect, an embodiment of the present application further provides an information processing apparatus based on big data and cloud computing, which is applied to an e-commerce collaboration platform, where the e-commerce collaboration platform is in communication connection with a plurality of e-commerce collaboration terminals, and the apparatus includes:

a first determining module, configured to determine, from a corresponding blockchain in a state where any one of the collaborative processing plans of the e-commerce collaborative terminal is initiated, domain session service information in a domain object of a first collaborative information flow node covered by the collaborative processing plan, and determine a first session material set capable of characterizing the domain session service information, where the domain session service information corresponds to a domain session service;

a second determining module, configured to determine, from the block chain, a domain object of a second collaborative information flow node, to obtain a second session material set capable of characterizing the domain object of the second collaborative information flow node, where the domain object of the second collaborative information flow node is a domain object of a collaborative information flow node associated with the domain object of the first collaborative information flow node;

the matching module is used for matching the second session material set with the first session material set, and selecting a target session material matched with the session material in the first session material set from the second session material set based on a matching result to obtain a target session material set;

a third determining module, configured to determine, based on the target session material set, target domain session service information that is matched with domain session service information of a domain object of the first collaborative information stream node in a domain object of the second collaborative information stream node, where the target domain session service information corresponds to a domain session service that is matched with a domain session service of the domain session service information in the domain object of the first collaborative information stream node;

and the updating module is used for updating the cooperative processing cloud computing service of the electronic commerce cooperative terminal according to the big data configuration information corresponding to the target domain session service information corresponding to all the initiated cooperative processing plans of the electronic commerce cooperative terminal.

In a third aspect, an embodiment of the present application further provides an information processing system based on big data and cloud computing, where the information processing system based on big data and cloud computing includes an e-commerce collaboration platform and a plurality of e-commerce collaboration terminals communicatively connected to the e-commerce collaboration platform;

the electronic commerce collaboration platform is used for:

In a fourth aspect, an embodiment of the present application further provides an electronic commerce collaboration platform, where the electronic commerce collaboration platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is used for being communicatively connected to at least one electronic commerce collaboration terminal, the machine-readable storage medium is used for storing programs, instructions, or codes, and the processor is used for executing the programs, instructions, or codes in the machine-readable storage medium to perform the big data and cloud computing based information processing method in the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer is caused to execute the big data and cloud computing-based information processing method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, the domain session service information in the domain object of the first collaborative information stream node covered by the collaborative processing plan is determined, the first session material set capable of representing the domain session service information is determined, then the domain object of the second collaborative information stream node is determined, the second session material set capable of representing the domain object of the second collaborative information stream node is obtained, so that the target session material matched with the session material in the first session material set is selected from the second session material set to obtain the target session material set, and then the target domain session service information matched with the domain session service information of the domain object of the first collaborative information stream node in the domain object of the second collaborative information stream node is determined according to the target session material set, and then suggestion display is performed. In this way, the updating change condition of the domain object of the collaborative information flow node associated with the collaborative processing plan is considered, so that the subsequent collaborative processing cloud computing service can be updated by combining the target domain session service information reflecting the dynamic change of the business, and the coverage range of the subsequent collaborative updating is further expanded.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an information processing system based on big data and cloud computing according to an embodiment of the present application;

fig. 2 is a schematic flowchart of an information processing method based on big data and cloud computing according to an embodiment of the present application;

fig. 3 is a schematic functional module diagram of an information processing apparatus based on big data and cloud computing according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of an e-commerce collaboration platform for implementing the above-described big data and cloud computing-based information processing method according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interaction diagram of an information processing system 10 based on big data and cloud computing according to an embodiment of the present application. The big data and cloud computing based information processing system 10 may include an e-commerce collaboration platform 100 and an e-commerce collaboration terminal 200 communicatively connected to the e-commerce collaboration platform 100. The big data and cloud computing based information processing system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the big data and cloud computing based information processing system 10 may also include only a portion of the components shown in fig. 1 or may also include other components.

In this embodiment, the e-commerce collaboration terminal 200 may include a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the mobile device may include an internet of things device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the internet of things device may include a control device of a smart appliance device, a smart monitoring device, a smart television, a smart camera, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, virtual reality devices and augmented reality devices may include various virtual reality products and the like.

In this embodiment, the electronic commerce collaboration platform 100 and the electronic commerce collaboration terminal 200 in the big data and cloud computing based information processing system 10 may cooperatively execute the big data and cloud computing based information processing method described in the following method embodiment, and the detailed description of the method embodiment may be referred to for the specific steps executed by the electronic commerce collaboration platform 100 and the electronic commerce collaboration terminal 200.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information processing method based on big data and cloud computing according to an embodiment of the present application, and the information processing method based on big data and cloud computing according to the present embodiment may be executed by the e-commerce collaboration platform 100 shown in fig. 1, and the information processing method based on big data and cloud computing is described in detail below.

Step S110, in a state where any one of the collaborative processing plans of the e-commerce collaborative terminal 200 is initiated, determining, from the corresponding block chain, domain session service information in the domain object of the first collaborative information flow node covered by the collaborative processing plan, and determining a first session material set capable of representing the domain session service information.

For example, the domain session service information in the domain object of the first collaborative information stream node corresponding to the collaborative plan execution node included in the collaborative processing plan may be determined from the corresponding blockchain, and the first set of session materials capable of representing the domain session service information may be determined.

Step S120, determining a domain object of the second collaborative information stream node from the blockchain, and obtaining a second session material set capable of representing the domain object of the second collaborative information stream node.

And the domain object of the second collaborative information flow node is the domain object of the collaborative information flow node associated with the domain object of the first collaborative information flow node. For example, the domain object of the collaborative information flow node associated with the domain object of the first collaborative information flow node may refer to a domain object of a collaborative information flow node in which there is a same kind of collaborative processing plan follow-up behavior.

Step S130, matching the second session material set with the first session material set, and selecting a target session material matched with the session material in the first session material set from the second session material set based on a matching result to obtain a target session material set.

Step S140, determining target domain session service information matched with the domain session service information of the domain object of the first collaborative information stream node in the domain objects of the second collaborative information stream node based on the target session material set.

The target domain session service information corresponds to a domain session service matched with the domain session service of the domain session service information in the domain object of the first collaborative information flow node.

Step S150, updating the cooperative processing cloud computing service of the e-commerce cooperative terminal 200 according to the big data configuration information corresponding to the target domain session service information corresponding to all the initiated cooperative processing plans of the e-commerce cooperative terminal 200.

In this embodiment, the collaborative information flow node may refer to a node through which the collaborative processing plan content is related during the execution process of the collaborative processing plan, for example, an information flow node through a collaborative site.

In this embodiment, the domain session service information corresponds to a domain session service, and the domain session service may refer to a service object that initiates a working domain session in the execution process of the collaborative plan, for example, a service object of a certain collaborative conference working domain, a service object of a certain collaborative orchestration working domain, and the like.

In this embodiment, the session material may refer to session material information used for reflecting the content of the call material of the domain session service information, for example, but not limited to, the session material information of the audio/video call service, the session material information of the online office call service, and the like.

In this embodiment, the cooperative processing plan may refer to a plan from an initial originating node to an ending node when initiating a working domain session in the execution process of the cooperative plan, and the definitions of the initial originating node and the ending node may be flexibly set, which is not limited herein in detail.

In this embodiment, the cooperative processing cloud computing service subscribed by the e-commerce cooperative terminal 200 may refer to a cooperative operation service that is registered in advance by the e-commerce cooperative terminal 200 and may be used for performing cloud computing processing (for example, information distribution, information coordination, and the like), and may specifically be operated in the form of a software service.

Based on the above steps, after determining the domain session service information in the domain object of the first collaborative information flow node covered by the collaborative processing plan and then determining the target domain session service information in the domain object of the second collaborative information flow node that matches the domain session service information of the domain object of the first collaborative information flow node, the collaborative processing cloud computing service of the e-commerce collaborative terminal 200 is updated. In this way, the updating change condition of the domain object of the collaborative information flow node associated with the collaborative processing plan is considered, so that the subsequent collaborative processing cloud computing service can be updated by combining the target domain session service information reflecting the dynamic change of the business, and the coverage range of the subsequent collaborative updating is further expanded.

In a possible implementation manner, before step S130, the present embodiment may determine a domain object mapping feature of a collaborative information flow node that maps from a domain object of a first collaborative information flow node to a domain object of a second collaborative information flow node. And then, based on the domain object mapping characteristics of the collaborative information flow nodes, predicting a target session material matched with the session material in the first session material set from the domain object of the second collaborative information flow node to obtain a first predicted target session material set.

Correspondingly, for step S130, a target session material matched with the session material in the first session material set may be selected from the second session material set based on the matching result to obtain a second pre-estimated target session material set, and then, a target session material set may be obtained based on the first pre-estimated target session material set and the second pre-estimated target session material set.

For another example, in another possible implementation manner, for step S130, session material service characteristics between the second session materials in the second session material set and the first session materials in the first session material set may be specifically determined. And then, selecting the target session materials of which the service characteristics of the session materials meet the preset business rules from the second session material set.

In a possible implementation manner, for step S140, the embodiment may determine the key session material based on the first session material in the first session material set and the target session material corresponding to the first session material in the target session material set, so as to obtain the key session material set. On the basis, the target key session materials meeting the preset rules can be selected from the key session material set. For example, the predetermined rule may indicate that the feature importance level of the conversation material satisfies the predetermined importance level.

Therefore, the target domain session service information in the domain object of the second collaborative information stream node can be determined based on the first session material set and the target key session material, wherein the target key session material is located in the key session running service of the target domain session service information. For example, the target domain session service information in the domain object of the second collaborative information stream node may be determined based on the first set of session materials and the target domain session services characterized by the target key session materials.

In one possible implementation, step S150 may be implemented by the following exemplary substeps, which are described in detail below.

In the substep S151, target domain session service information corresponding to all initiated collaborative processing plans of the e-commerce collaborative terminal 200 is input into the collaborative processing cloud computing service subscribed by the e-commerce collaborative terminal 200, and a collaborative interaction update record obtained by identifying the target domain session service information is obtained according to a collaborative configuration mode corresponding to the collaborative processing cloud computing service.

For example, the collaborative configuration mode corresponding to the collaborative processing cloud computing service may include the recognition matching rule features of the multiple push samples, so that the collaborative interaction update record obtained by recognizing the target domain session service information may be obtained based on the recognition matching rule features of the multiple push samples.

And a substep S152 of obtaining a plurality of collaborative linkage event analysis information obtained by performing collaborative linkage event analysis on the collaborative interaction update record.

For example, each kind of collaborative linkage event analysis information is composed of collaborative linkage event tags of collaborative interaction update records.

And a substep S153, determining target collaborative linkage event analysis information meeting the target rule in the multiple collaborative linkage event analysis information, and searching a target collaborative linkage event label in the collaborative linkage event labels of the target collaborative linkage event analysis information in the first knowledge map index area.

For example, the first knowledge-graph index area is used for storing the collaborative linkage event tags with knowledge-graph information, and the knowledge-graph information is used for indicating the knowledge-graph content to which the collaborative linkage event tags with knowledge-graph information belong.

And a substep S155, determining the target collaborative linkage event label with the target knowledge graph information in the first knowledge graph index area as an update optimization label of the collaborative interaction update record under the condition that the target collaborative linkage event label is found in the first knowledge graph index area.

For example, the knowledge-graph information includes target knowledge-graph information that may be used to indicate knowledge-graph content to which the target collaborative linkage event label belongs.

And a substep S156, determining a target knowledge graph updating node for updating the optimization label according to the target knowledge graph information, and determining a knowledge graph updating node for updating the record in the cooperative interaction according to the target knowledge graph updating node for updating the optimization label.

And a substep S157 of generating a cooperative processing cloud computing service of the e-commerce cooperative terminal 200 according to the knowledge graph update node of the cooperative interaction update record.

Based on the sub-steps, the updated optimized labels of the collaborative interaction updated records are provided with the knowledge graph information used for labeling the knowledge graph content to which the updated optimized labels belong, and the knowledge graph updated nodes of the collaborative interaction updated records are determined, so that the correct identification of the knowledge graph updated nodes of the collaborative interaction updated records is ensured, the problem that the knowledge graph content collaborative linkage event labels are not completely covered due to a full-quantity word bank in the related technology is solved, and the efficiency of identifying the knowledge graph updated nodes is improved.

In one possible implementation, in the sub-step S153, it can be implemented by the following exemplary embodiments, which are described in detail below.

(1) And obtaining, in the multiple kinds of collaborative linkage event analysis information, derived entity characteristic information of the collaborative work derived entities of all collaborative linkage event tags in each kind of collaborative linkage event analysis information, and determining that the derived entity characteristic information of the collaborative work derived entities of all collaborative linkage event tags conforms to the first collaborative linkage event analysis information of the collaborative configuration mode corresponding to the collaborative processing cloud computing service.

For example, the collaborative linkage business section of the first collaborative linkage event analysis information is the first collaborative linkage business section.

(2) And under the condition that the first collaborative linkage business interval is a business interval of a preset service characteristic label, determining the first collaborative linkage event analysis information as target collaborative linkage event analysis information meeting a target rule.

(3) Under the condition that the first collaborative linkage business interval is not a business interval with a preset service characteristic label, electing collaborative work deduction entities of all collaborative linkage event labels in each type of first collaborative linkage event analysis information are obtained from the first collaborative linkage event analysis information of the first collaborative linkage business interval, and second collaborative linkage event analysis information with the largest business coverage range of the electing collaborative work deduction entities of all collaborative linkage event labels is determined.

For example, the collaborative linkage business interval of the second collaborative linkage event analysis information is a second collaborative linkage business interval, and the second collaborative linkage business interval is smaller than or equal to the first collaborative linkage business interval.

(4) And under the condition that the second collaborative linkage business interval is a business interval of a preset service characteristic label, determining the second collaborative linkage event analysis information as target collaborative linkage event analysis information meeting the target rule.

(5) And under the condition that the second collaborative linkage business interval is not a business interval with a preset service characteristic label, acquiring the variation amplitude of the keyword classification level of all collaborative linkage event labels in each second collaborative linkage event analysis information and determining third collaborative linkage event analysis information with the minimum variation amplitude of the keyword classification level of all collaborative linkage event labels from the second collaborative linkage event analysis information of the second collaborative linkage business interval.

For example, the collaborative linkage business interval of the third collaborative linkage event analysis information is a third collaborative linkage business interval, and the third collaborative linkage business interval is less than or equal to the second collaborative linkage business interval. And under the condition that the third collaborative linkage business interval is a business interval of a preset service characteristic label, determining the third collaborative linkage event analysis information as target collaborative linkage event analysis information meeting the target rule.

(6) And under the condition that the third collaborative linkage business interval is not the business interval of the preset service characteristic label, acquiring the updating planning parameters of all collaborative linkage event labels in each third collaborative linkage event analysis information and determining the fourth collaborative linkage event analysis information with the highest updating planning parameters of all collaborative linkage event labels from the third collaborative linkage event analysis information of the third collaborative linkage business interval.

For example, the collaborative linkage business interval of the fourth collaborative linkage event analysis information is a fourth collaborative linkage business interval, the fourth collaborative linkage business interval is smaller than or equal to the third collaborative linkage business interval, and the update planning parameter is used for indicating that the collaborative linkage event label and the updatable object form a parameter of a new collaborative linkage event label.

(7) And under the condition that the fourth collaborative linkage business interval is a business interval of a preset service characteristic label, determining the fourth collaborative linkage event analysis information as target collaborative linkage event analysis information meeting the target rule.

In one possible implementation, in sub-step S157, it may be implemented by the following exemplary embodiments, which are described in detail below.

(1) And acquiring a knowledge graph updating rule network corresponding to the knowledge graph updating node of the collaborative interaction updating record, and acquiring a target configuration source element sequence of a main configuration source corresponding to a service updating main body of the knowledge graph updating node of the currently selected collaborative interaction updating record based on the knowledge graph updating rule network.

(2) And sequentially acquiring target configuration source elements of the main configuration source for indicating the global configuration characteristics in the target configuration source element sequence of the main configuration source according to the hierarchical relationship of the target configuration source elements of each main configuration source in the target configuration source element sequence of the main configuration source.

(3) And calling a target configuration source element of the main body configuration source to acquire a first rule service index target of the rule service of the knowledge graph updating rule network by adopting the index interval of the target configuration source element of the main body configuration source.

(4) And marking a target configuration source element of a demand side main body configuration source of the demand side service carried by the acquired knowledge graph updating node of the collaborative interaction updating record in a second rule service index target associated with the first rule service index target in the rule service, wherein the demand side service is matched with the demand side service according to the global configuration characteristics.

(5) And extracting candidate main body configuration source units of the demand side service in the target configuration source elements of the demand side main body configuration source, and splicing the candidate main body configuration source units to generate the target main body configuration source units of the demand side service.

(6) And randomly selecting a first updating rule entity and a second updating rule entity from the reference updating rule entities of the knowledge graph updating rule network, and establishing the distribution of the updating rule entities based on the first updating rule entity and the second updating rule entity.

(7) A third update rule entity corresponding to the first update rule entity and a fourth update rule entity corresponding to the second update rule entity are obtained in the target subject configuration source unit.

(8) Acquiring hotspot relationship data of the first updating rule entity and the second updating rule entity as well as the third updating rule entity and the fourth updating rule entity, and mapping the target main body configuration source unit to the distribution of the updating rule entities according to the hotspot relationship data.

For example, the update rule entity distribution is a distribution map established by a benchmark update rule entity based on global configuration features in target configuration source elements of the subject configuration source.

(9) And acquiring the association parameters between the target main body configuration source unit and the reference update rule entity in the update rule entity distribution, and generating a matching result of the target configuration source element of the main body configuration source on the demand side and the target configuration source element of the main body configuration source according to the association parameters.

For example, the configuration source mapping parameters of the corresponding nodes between the target subject configuration source unit and the reference update rule entity may be obtained in the update rule entity distribution. And when the configuration source mapping parameters meet the preset mapping rule, determining that the target configuration source element of the main body configuration source on the demand side is successfully matched with the target configuration source element of the main body configuration source.

For another example, in the update rule entity distribution, an entity distribution object of a corresponding node between the target subject configuration source unit and the reference update rule entity is obtained, and a configuration source mapping parameter corresponding to the entity distribution object of the corresponding node is obtained. And when the configuration source mapping parameters meet the preset mapping rule, determining that the target configuration source element of the main body configuration source on the demand side is successfully matched with the target configuration source element of the main body configuration source.

(10) And acquiring the update service information of the target configuration source element aiming at the main configuration source in the knowledge graph update rule network based on the matching result, thereby generating the cooperative processing cloud computing service of the electronic commerce cooperative terminal 200.

Based on the above embodiments (1) - (10), a demand-side service with the same synchronization index and global configuration feature can be obtained by obtaining a target configuration source element of a main configuration source carrying global configuration features and displaying the target configuration source element in a first rule service index target of a rule service of a knowledge graph update rule network, and a target configuration source element of a demand-side main configuration source carrying the demand-side service obtained by marking a second rule service index target indicated by the rule service is finally matched by using the target configuration source element of the main configuration source, so that an automatic matching process is realized, and meanwhile, the interaction and matching of the demand-side service are performed by using the global configuration features, thereby effectively improving the rich interactivity of the demand side.

In a possible implementation manner, the information processing method based on big data and cloud computing provided by this embodiment may further include the following steps, which are described in detail below.

Step S160, acquiring cooperative plan execution information of the cooperative response object of the e-commerce cooperative terminal 200 for the cooperative processing cloud computing service, and generating a cooperative operation item parameter of the cooperative response object.

Step S170, obtaining cooperative flow allocation information and cooperative sharing configuration information of the cooperative response object according to the cooperative operation item parameter of the cooperative response object, where the cooperative flow allocation information includes a flow allocation object and a flow allocation overlay service, and the cooperative sharing configuration information includes a sharing configuration object and a sharing configuration overlay service.

Step S180, constructing a cooperative service relationship between the process distribution object and the shared configuration object according to the process distribution coverage service and the shared configuration coverage service, and extracting features based on the process distribution object, the shared configuration object and the cooperative service relationship to obtain candidate process distribution features and candidate shared configuration features of the cooperative response object.

And step S190, generating suggested cooperative service information of the cooperative response object based on the candidate process distribution characteristics and the candidate sharing configuration characteristics, and displaying the suggested cooperative service information.

Based on the above steps S160 to S190, by considering the cooperative service relationship between the cooperative flow allocation information of the cooperative response object and the overlay service of the cooperative sharing configuration information, and the interaction relationship between the flow allocation information and the sharing configuration information, the suggested cooperative service information meeting the user requirement can be accurately generated.

For example, in this embodiment, the collaboration plan execution information may be used to represent execution process information, such as to-be-handled task process information, including a number of front nodes, a business name, a business description, a sponsor, a launch time, an arrival time, and the like, generated by a collaboration response object of the e-commerce collaboration terminal 200 during execution of the collaboration processing cloud computing service, and is not limited in this respect. In addition, the cooperative operation item parameter of the cooperative response object may be used to characterize the content of the cooperative operation item, such as the cooperative operation behavior information and the cooperative sharing configuration information, of the cooperative response object performed in the cooperative execution process for the cooperative processing cloud computing service. Therefore, the cooperative operation behavior information and the cooperative sharing configuration information of the cooperative response object can be specifically acquired according to the cooperative operation item parameter of the cooperative response object.

For example, in this embodiment, the cooperative operation behavior information may include, for example, a business approval transaction object and a business approval transaction flow form, and the cooperative shared configuration information may include, for example, a shared configuration object and a shared configuration overlay service.

In some possible implementations, for example, with respect to step S190, the response service feature of the collaborative response object may be extracted to obtain the response service feature of the collaborative response object, and then the response service feature of the collaborative response object, the generated collaborative description feature vector, and the candidate shared configuration feature are feature-fused to obtain a fusion feature, so as to generate the suggested collaborative service information of the collaborative response object according to the fusion feature. For example, the suggested collaborative service information of the collaborative response object may be generated according to the collaborative suggestion tag characterized by each feature vector in the fused features.

Based on the above steps, in this embodiment, the cooperative operation item parameter of the cooperative response object is generated by obtaining the cooperative plan execution information of the cooperative response object of the e-commerce cooperative terminal 200 for the cooperative processing cloud computing service, so that the cooperative service relationship between the cooperative operation behavior information of the cooperative response object in the actual service use process and the coverage service of the cooperative sharing configuration information and the mutual influence relationship between the service approval information and the sharing configuration information are further considered based on the cooperative operation item parameter, and the suggested cooperative service information meeting the user requirements can be accurately generated. Therefore, the suggested collaborative service information meeting the user requirements is accurately generated and then is further suggested and displayed, and compared with the traditional mode of executing global analysis of information aiming at the collaborative plan, the accuracy of suggested and displayed information can be improved.

For example, in a possible implementation manner, in the step S180, in the process of performing feature extraction based on the business approval transaction object, the shared configuration object and the collaborative service relationship to obtain the generated collaborative description feature vector and the candidate shared configuration feature of the collaborative response object, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S181 of extracting the characteristics of the business approval transacted objects according to the collaborative service relationship to obtain unit collaborative description characteristic vectors of the business approval transacted objects.

And a substep S182 of determining a global cooperative description feature vector based on the unit cooperative description feature vector of the business approval transacted object, determining a recent business approval transacted object in the business approval transacted object according to the business approval transacted flow form, and determining the unit cooperative description feature vector corresponding to the recent business approval transacted object.

And a substep S183, fusing the global cooperative description feature vector and the unit cooperative description feature vector corresponding to the recent business approval transaction object to obtain a generated cooperative description feature vector of the cooperative response object.

And a substep S184, extracting the characteristics of the shared configuration object according to the collaborative service relationship to obtain the unit shared configuration characteristics of the shared configuration object.

And a substep S185, determining a global sharing configuration characteristic based on the unit sharing configuration characteristic of the sharing configuration object, determining a recent sharing configuration object in the sharing configuration object according to the sharing configuration coverage service, and determining the unit sharing configuration characteristic corresponding to the recent sharing configuration object.

And a substep S186, fusing the global sharing configuration characteristics and the unit sharing configuration characteristics corresponding to the recent sharing configuration object, and collaboratively responding the candidate sharing configuration characteristics of the object.

For example, in one possible implementation, still referring to step S180, the collaboration service relationship may include a collaboration service connectivity graph including a plurality of collaboration service components and a collaboration master key connecting the two collaboration service components.

In the process of establishing the cooperative service relationship between the business approval transacting object and the shared configuration object according to the business approval transacting flow form and the shared configuration coverage business, the business approval transacting object and the shared configuration object can be used as cooperative service components in the cooperative service communication map, and then a cooperative main key between the cooperative service components in the cooperative service communication map is established according to the business approval transacting flow form and the shared configuration coverage business.

The collaboration service component may include a business approval transacting object element and a shared configuration object element.

Therefore, in the process of establishing a cooperative main key between cooperative service components in a cooperative service communication map according to the service approval process flow form and the shared configuration coverage service, the service approval process objects can be subjected to coverage service sequence ordering according to the service approval process flow form to obtain a service approval process ordering result, then service approval process object elements in the cooperative service communication map are connected pairwise according to the service approval process ordering result, and the shared configuration objects are subjected to coverage service sequence ordering according to the shared configuration coverage service to obtain a shared configuration ordering result.

In this way, the shared configuration object elements in the collaborative service connection map can be connected pairwise according to the shared configuration sequencing result, meanwhile, the nodes in the collaborative service connection map are sequentially sequenced in the covering service mode according to the service approval process flow form and the shared configuration covering service, after the global sequencing result is obtained, the nodes in the collaborative service connection map are connected pairwise according to the global sequencing result, and therefore the collaborative service relationship between the service approval process object and the shared configuration object can be obtained.

Fig. 3 is a schematic diagram of functional modules of an information processing apparatus 300 based on big data and cloud computing according to an embodiment of the present disclosure, in this embodiment, the information processing apparatus 300 based on big data and cloud computing may be divided into the functional modules according to the method embodiment executed by the e-commerce collaboration platform 100, that is, the following functional modules corresponding to the information processing apparatus 300 based on big data and cloud computing may be used to execute each method embodiment executed by the e-commerce collaboration platform 100. The big data and cloud computing based information processing apparatus 300 may include a first determining module 310, a second determining module 320, a matching module 330, a third determining module 340, and an updating module 350, wherein functions of the functional modules of the big data and cloud computing based information processing apparatus 300 are respectively described in detail below.

The first determining module 310 is configured to determine, from a corresponding blockchain, domain session service information in a domain object of a first collaborative information flow node covered by a collaborative processing plan in a state where any one collaborative processing plan of the e-commerce collaborative terminal 200 is initiated, and determine a first session material set capable of characterizing the domain session service information, where the domain session service information corresponds to a domain session service. The first determining module 310 may be configured to perform the step S110, and for a detailed implementation of the first determining module 310, reference may be made to the detailed description of the step S110.

The second determining module 320 is configured to determine, from the blockchain, a domain object of a second collaborative information stream node, to obtain a second session material set capable of characterizing the domain object of the second collaborative information stream node, where the domain object of the second collaborative information stream node is a domain object of a collaborative information stream node associated with the domain object of the first collaborative information stream node. The second determining module 320 may be configured to perform the step S120, and as for a detailed implementation of the second determining module 320, reference may be made to the detailed description of the step S120.

And the matching module 330 is configured to match the second session material set with the first session material set, and select, based on a matching result, a target session material that matches the session material in the first session material set from the second session material set to obtain a target session material set. The matching module 330 may be configured to perform the step S130, and the detailed implementation of the matching module 330 may refer to the detailed description of the step S130.

A third determining module 340, configured to determine, based on the target session material set, target domain session service information that is in a domain object of the second collaborative information stream node and is matched with domain session service information of a domain object of the first collaborative information stream node, where the target domain session service information corresponds to a domain session service that is matched with a domain session service of the domain session service information in the domain object of the first collaborative information stream node. The third determining module 340 may be configured to perform the step S140, and for a detailed implementation of the third determining module 340, reference may be made to the detailed description of the step S140.

An updating module 350, configured to update the cooperative processing cloud computing service of the e-commerce cooperative terminal 200 according to big data configuration information corresponding to target domain session service information corresponding to all initiated cooperative processing plans of the e-commerce cooperative terminal 200. The updating module 350 may be configured to perform the step S150, and the detailed implementation of the updating module 350 may refer to the detailed description of the step S150.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical service subscription operation target, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the first determining module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the first determining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram illustrating a hardware structure of the e-commerce collaboration platform 100 for implementing the control device according to the embodiment of the disclosure, and as shown in fig. 4, the e-commerce collaboration platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the first determining module 310, the second determining module 320, the matching module 330, the third determining module 340, and the updating module 350 included in the big data and cloud computing-based information processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the big data and cloud computing-based information processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control transceiving actions of the transceiver 140, so as to perform data transceiving with the e-commerce collaboration terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the e-commerce collaboration platform 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.

In the embodiment shown in FIG. 4, it should be understood that the Processor may be a global rule superposition matching process (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, an embodiment of the present application further provides a readable storage medium, where a computer executing instruction is stored in the readable storage medium, and when a processor executes the computer executing instruction, the verification processing method based on the block chain offline cooperation is implemented as above.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, or as a stand-alone software package on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or digital financial services terminal. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented through interactive services, they may also be implemented through software-only solutions, such as installing the described system on an existing digital financial services terminal or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An information processing method based on big data and cloud computing is applied to an e-commerce collaboration platform, the e-commerce collaboration platform is in communication connection with a plurality of e-commerce collaboration terminals, and the method comprises the following steps:

updating the cooperative processing cloud computing service of the electronic commerce cooperative terminal according to big data configuration information corresponding to target domain session service information corresponding to all initiated cooperative processing plans of the electronic commerce cooperative terminal;

the step of updating the cooperative processing cloud computing service of the e-commerce cooperative terminal according to the big data configuration information corresponding to the target domain session service information corresponding to all the initiated cooperative processing plans of the e-commerce cooperative terminal includes:

acquiring big data configuration information corresponding to the target domain session service information matched under the cooperative processing project corresponding to each cooperative processing plan based on the target domain session service information corresponding to all initiated cooperative processing plans of the electronic commerce cooperative terminal, and performing cooperative interaction identification on the big data configuration information to obtain a cooperative interaction update record;

updating the cooperative processing cloud computing service of the electronic commerce cooperative terminal according to the knowledge graph updating node of the cooperative interaction updating record;

the determining, based on the target session material set, target domain session service information that is matched with the domain session service information of the domain object of the first collaborative information stream node in the domain objects of the second collaborative information stream node includes:

selecting a target key session material meeting a preset rule from the key session material set, wherein the preset rule means that the characteristic importance degree of the session material meets a preset importance degree;

determining target domain session service information in the domain object of the second collaborative information stream node based on the first session material set and the target key session material, wherein the target key session material is located in a key session running service of the target domain session service information, and specifically determining the target domain session service information in the domain object of the second collaborative information stream node based on the target domain session service represented by the first session material set and the target key session material;

the domain session service information corresponds to domain session service, and the domain session service refers to a service object for initiating a working domain session in the execution process of the collaborative plan;

the session material is session material information for reflecting the calling material content of the domain session service information.

2. The big data and cloud computing based information processing method according to claim 1, wherein the method further comprises:

3. The big data and cloud computing based information processing method according to claim 1, wherein the matching the second set of session materials with the first set of session materials, and selecting target session materials from the second set of session materials that match the session materials in the first set of session materials based on matching results comprises:

4. The big data and cloud computing-based information processing method according to claim 1, wherein the step of determining target collaborative linkage event analysis information that satisfies a target rule among the plurality of kinds of collaborative linkage event analysis information includes:

5. The big data and cloud computing based information processing method according to claim 1, wherein the step of updating the collaborative processing cloud computing service of the e-commerce collaborative terminal according to the knowledge graph update node of the collaborative interaction update record comprises:

6. The big data and cloud computing based information processing method according to any one of claims 1 to 5, wherein the method further comprises:

acquiring cooperative plan execution information of a cooperative response object of the electronic commerce cooperative terminal aiming at the cooperative processing cloud computing service, and generating a cooperative operation item parameter of the cooperative response object;

7. An e-commerce collaboration platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one e-commerce collaboration terminal, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the big data and cloud computing based information processing method according to any one of claims 1 to 6.